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Bioengineering, Volume 11, Issue 4 (April 2024) – 112 articles

Cover Story (view full-size image): Addressing the challenge of articular cartilage repair amidst the growing prevalence of osteoarthritis, this study introduces a novel approach utilizing an alginate-based bioink integrated with a human decellularized articular cartilage matrix for 3D bioprinting. The investigation aims to evaluate the bioink's capacity for supporting chondrocyte viability and promoting chondrogenic differentiation in a bid to engineer cartilage tissue effectively. By leveraging the structural and biological advantages of alginate and the physiological relevance of human-derived cartilage matrix, this research contributes to the advancement of tissue engineering methodologies. View this paper
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12 pages, 1226 KiB  
Review
Fabrication of Artificial Nerve Conduits Used in a Long Nerve Gap: Current Reviews and Future Studies
by Ryosuke Kakinoki, Yukiko Hara, Koichi Yoshimoto, Yukitoshi Kaizawa, Kazuhiko Hashimoto, Hiroki Tanaka, Takaya Kobayashi, Kazuhiro Ohtani, Takashi Noguchi, Ryosuke Ikeguchi, Masao Akagi and Koji Goto
Bioengineering 2024, 11(4), 409; https://doi.org/10.3390/bioengineering11040409 - 22 Apr 2024
Viewed by 1504
Abstract
There are many commercially available artificial nerve conduits, used mostly to repair short gaps in sensory nerves. The stages of nerve regeneration in a nerve conduit are fibrin matrix formation between the nerve stumps joined to the conduit, capillary extension and Schwann cell [...] Read more.
There are many commercially available artificial nerve conduits, used mostly to repair short gaps in sensory nerves. The stages of nerve regeneration in a nerve conduit are fibrin matrix formation between the nerve stumps joined to the conduit, capillary extension and Schwann cell migration from both nerve stumps, and, finally, axon extension from the proximal nerve stump. Artificial nerves connecting transected nerve stumps with a long interstump gap should be biodegradable, soft and pliable; have the ability to maintain an intrachamber fibrin matrix structure that allows capillary invasion of the tubular lumen, inhibition of scar tissue invasion and leakage of intratubular neurochemical factors from the chamber; and be able to accommodate cells that produce neurochemical factors that promote nerve regeneration. Here, we describe current progress in the development of artificial nerve conduits and the future studies needed to create nerve conduits, the nerve regeneration of which is compatible with that of an autologous nerve graft transplanted over a long nerve gap. Full article
(This article belongs to the Special Issue Innovations in Nerve Regeneration)
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<p>Commercially available artificial nerves. <b>Left</b>: Renerve<sup>®</sup> (Nipro, Osaka, Japan); <b>right</b>: Nerbridge<sup>®</sup> (Toyobo, Osaka, Japan). The conduit chambers are occupied by collagen materials in both nerve conduits.</p>
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<p>Schematic diagrams of three kinds of nerve conduit tubes: VCT (vessel-containing tube); ET (empty tube); and LVCT (ligated vessel-containing tube). When a vessel-containing tube (VCT) was created, a myocutaneous flap supplied by the sural vessels was elevated from the lower hind limb and turned proximally. The sural vessel pedicle was inserted into the chamber space through a longitudinal slit of the silicone tube, which was sealed with silicone liquid after the pedicle insertion. Each tube was transplanted between the transected sciatic nerve stumps, leaving a 10 mm gap. In LVCTs, the sural vessels inserted into the conduits were ligated.</p>
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<p>Schematic diagrams of the I-group (a sural vessel pedicle was passed through the chamber space of a PGA conduit) and the E-group (a sural vessel pedicle was attached to the outer surface of a PGA conduit). The interstump gap was set at 5 mm.</p>
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<p>Schematic diagrams of a PGA conduit containing two barrels of DABLs seeded with 3 × 10<sup>6</sup> BMSCs (conduit group) and a 20 mm long autologous nerve graft (autograft group). In the conduit group, the sural vessel pedicle was attached to the outer surface of the PGA conduit.</p>
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<p>Creation of an artificial nerve conduit with a long interstump gap.</p>
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<p>In the PGA group, two pieces of 20 mm long DANBLs (created using chemical surfactants) seeded with 3 × 10<sup>6</sup> BMSCs was transplanted in a 23 mm long PGA conduit. A sural vessel pedicle was attached to the outer surface of the conduit. In the silicone group, a 20 mm long DANBL (created using the thermal method) seeded with 1 × 10<sup>7</sup> BMSCs was transplanted in a 23 mm long silicone conduit. A sural vessel pedicle was passed through the conduit lumen. The auto group consisted of a 20 mm long autologous nerve graft model.</p>
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12 pages, 1150 KiB  
Article
How the Effect of Virtual Reality on Cognitive Functioning Is Modulated by Gender Differences
by Stefania Righi, Gioele Gavazzi, Viola Benedetti, Giulia Raineri and Maria Pia Viggiano
Bioengineering 2024, 11(4), 408; https://doi.org/10.3390/bioengineering11040408 - 21 Apr 2024
Cited by 1 | Viewed by 1376
Abstract
Virtual reality (VR) can be a promising tool to simulate reality in various settings but the real impact of this technology on the human mental system is still unclear as to how VR might (if at all) interfere with cognitive functioning. Using a [...] Read more.
Virtual reality (VR) can be a promising tool to simulate reality in various settings but the real impact of this technology on the human mental system is still unclear as to how VR might (if at all) interfere with cognitive functioning. Using a computer, we can concentrate, enter a state of flow, and still maintain control over our surrounding world. Differently, VR is a very immersive experience which could be a challenge for our ability to allocate divided attention to the environment to perform executive functioning tasks. This may also have a different impact on women and men since gender differences in both executive functioning and the immersivity experience have been referred to by the literature. The present study aims to investigate cognitive multitasking performance as a function of (1) virtual reality and computer administration and (2) gender differences. To explore this issue, subjects were asked to perform simultaneous tasks (span forward and backward, logical–arithmetic reasoning, and visuospatial reasoning) in virtual reality via a head-mounted display system (HDMS) and on a personal computer (PC). Our results showed in virtual reality an overall impairment of executive functioning but a better performance of women, compared to men, in visuospatial reasoning. These findings are consistent with previous studies showing a detrimental effect of virtual reality on cognitive functioning. Full article
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<p>Example of an item of the Visual–Spatial Intelligence Test (<a href="https://www.paginainizio.com/test/quiz.php?id=test_intelligenza_visuo_spaziale" target="_blank">https://www.paginainizio.com/test/quiz.php?id=test_intelligenza_visuo_spaziale</a> accessed on 25 April 2023).</p>
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<p>(<b>A</b>) Span (forward) and (<b>B</b>) span inverse (backward) mean across experiment conditions. Error bars represent ±1 standard error of the mean. *** <span class="html-italic">p</span> &lt; 0.001, * <span class="html-italic">p</span> &lt; 0.05. Blue bars represent the PC condition and red bars represent the HDMS condition.</p>
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<p>Arithmetic reasoning accuracy (p) results showing mean across experiment conditions. Error bars represent ±1 standard error of the mean. *** <span class="html-italic">p</span> &lt; 0.001. Blue bars represent the PC condition and red bars represent the HDMS condition.</p>
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<p>(<b>A</b>) Visuospatial reasoning results. Accuracy (p) means across experimental conditions and sex. (<b>B</b>) Immersivity questionnaire score across experimental conditions. Error bars represent ±1 standard error of the mean. *** <span class="html-italic">p</span> &lt; 0.001. Blue bars represent the PC condition and red bars represent the HDMS condition.</p>
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12 pages, 2446 KiB  
Article
Short-Term l-arginine Treatment Mitigates Early Damage of Dermal Collagen Induced by Diabetes
by Irena Miler, Mihailo D. Rabasovic, Sonja Askrabic, Andreas Stylianou, Bato Korac and Aleksandra Korac
Bioengineering 2024, 11(4), 407; https://doi.org/10.3390/bioengineering11040407 - 21 Apr 2024
Viewed by 1437
Abstract
Changes in the structural properties of the skin due to collagen alterations are an important factor in diabetic skin complications. Using a combination of photonic methods as an optic diagnostic tool, we investigated the structural alteration in rat dermal collagen I in diabetes, [...] Read more.
Changes in the structural properties of the skin due to collagen alterations are an important factor in diabetic skin complications. Using a combination of photonic methods as an optic diagnostic tool, we investigated the structural alteration in rat dermal collagen I in diabetes, and after short-term l-arginine treatment. The multiplex approach shows that in the early phase of diabetes, collagen fibers are partially damaged, resulting in the heterogeneity of fibers, e.g., “patchy patterns” of highly ordered/disordered fibers, while l-arginine treatment counteracts to some extent the conformational changes in collagen-induced by diabetes and mitigates the damage. Raman spectroscopy shows intense collagen conformational changes via amides I and II in diabetes, suggesting that diabetes-induced structural changes in collagen originate predominantly from individual collagen molecules rather than supramolecular structures. There is a clear increase in the amounts of newly synthesized proline and hydroxyproline after treatment with l-arginine, reflecting the changed collagen content. This suggests that it might be useful for treating and stopping collagen damage early on in diabetic skin. Our results demonstrate that l-arginine attenuates the early collagen I alteration caused by diabetes and that it could be used to treat and prevent collagen damage in diabetic skin at a very early stage. Full article
(This article belongs to the Section Biomedical Engineering and Biomaterials)
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Graphical abstract

Graphical abstract
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<p>pSHG images of dermal collagen (<b>a</b>) and calculated β values (<b>b</b>) of control (non-diabetic), <span class="html-small-caps">l</span>-arginine (<span class="html-small-caps">l</span>-arginine-treated non-diabetic), diabetic, and diabetic <span class="html-small-caps">l</span>-arginine-treated rats. Different colors in panel (<b>a</b>) indicate the different values of the β-coefficient (as indicated by the color bar in the leftmost panel). Thus, greener images indicate a higher value of the β-coefficient and, therefore, more arranged collagen. The opposite is true for red images. The values represent the mean ± SEM, for each point n = 27, * <span class="html-italic">p</span> &lt; 0.05. Scale bars: 50 µm.</p>
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<p>Mean Raman spectra of dermal collagen I structure of control (non-diabetic), <span class="html-small-caps">l</span>-arginine (<span class="html-small-caps">l</span>-arginine-treated non-diabetic), diabetic, and diabetic <span class="html-small-caps">l</span>-arginine-treated rats. The light gray highlighted areas are magnified and present the most intensive changes described in <a href="#app1-bioengineering-11-00407" class="html-app">Table S1</a>.</p>
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<p>Raman spectra of dermal collagen I of control (non-diabetic), <span class="html-small-caps">l</span>-arginine (<span class="html-small-caps">l</span>-arginine−treated non-diabetic), diabetic, and diabetic <span class="html-small-caps">l</span>-arginine-treated rats.</p>
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<p>Young’s modulus measured by AFM in control (non-diabetic), <span class="html-small-caps">l</span>-arginine (<span class="html-small-caps">l</span>-arginine-treated non-diabetic), diabetic, and diabetic <span class="html-small-caps">l</span>-arginine-treated rats. The values represent the mean ± SEM, * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Micrographs of the rat skin of control (non-diabetic), <span class="html-small-caps">l</span>-arginine (<span class="html-small-caps">l</span>-arginine-treated non-diabetic), diabetic, and diabetic <span class="html-small-caps">l</span>-arginine-treated rats stained with picrosirius red and analyzed without polarizers (<b>left images</b>) and with linear polarizers (<b>right images</b>). Magnification: 10×; original scale bars: 250 µm.</p>
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21 pages, 3240 KiB  
Article
Deep Transfer Learning Using Real-World Image Features for Medical Image Classification, with a Case Study on Pneumonia X-ray Images
by Chanhoe Gu and Minhyeok Lee
Bioengineering 2024, 11(4), 406; https://doi.org/10.3390/bioengineering11040406 - 20 Apr 2024
Cited by 1 | Viewed by 1620
Abstract
Deep learning has profoundly influenced various domains, particularly medical image analysis. Traditional transfer learning approaches in this field rely on models pretrained on domain-specific medical datasets, which limits their generalizability and accessibility. In this study, we propose a novel framework called real-world feature [...] Read more.
Deep learning has profoundly influenced various domains, particularly medical image analysis. Traditional transfer learning approaches in this field rely on models pretrained on domain-specific medical datasets, which limits their generalizability and accessibility. In this study, we propose a novel framework called real-world feature transfer learning, which utilizes backbone models initially trained on large-scale general-purpose datasets such as ImageNet. We evaluate the effectiveness and robustness of this approach compared to models trained from scratch, focusing on the task of classifying pneumonia in X-ray images. Our experiments, which included converting grayscale images to RGB format, demonstrate that real-world-feature transfer learning consistently outperforms conventional training approaches across various performance metrics. This advancement has the potential to accelerate deep learning applications in medical imaging by leveraging the rich feature representations learned from general-purpose pretrained models. The proposed methodology overcomes the limitations of domain-specific pretrained models, thereby enabling accelerated innovation in medical diagnostics and healthcare. From a mathematical perspective, we formalize the concept of real-world feature transfer learning and provide a rigorous mathematical formulation of the problem. Our experimental results provide empirical evidence supporting the effectiveness of this approach, laying the foundation for further theoretical analysis and exploration. This work contributes to the broader understanding of feature transferability across domains and has significant implications for the development of accurate and efficient models for medical image analysis, even in resource-constrained settings. Full article
(This article belongs to the Special Issue Artificial Intelligence in Advanced Medical Imaging - 2nd Edition)
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<p>Overall framework of the proposed real-world feature transfer learning approach for medical image classification.</p>
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<p>Example X-ray images of each class within the Pneumonia X-ray dataset. The images on the left showcase normal X-ray images and the images on the right showcase pneumonia images.</p>
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<p>Graphical representation of the training loss and accuracy achieved by the models over time. The solid lines represent the real-world feature transfer learning models, while the dashed lines represent from-scratch training models. The rapid convergence of the proposed models demonstrates the feasibility and effectiveness of transferring learning from real-world images to medical images.</p>
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<p>Comprehensive comparison between transfer learning models and models trained from scratch. The line graphs illustrate the performance metrics of test accuracy and F1 score for each model. The solid lines represent the real-world feature transfer learning models, while the dashed lines represent from-scratch training models. The comparison emphasizes the superiority of the transfer learning approach for handling medical image classification.</p>
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11 pages, 4904 KiB  
Article
Breaking the Limit of Cardiovascular Regenerative Medicine: Successful 6-Month Goat Implant in World’s First Ascending Aortic Replacement Using Biotube Blood Vessels
by Kazuki Mori, Tadashi Umeno, Takayuki Kawashima, Tomoyuki Wada, Takuro Genda, Masanagi Arakura, Yoshifumi Oda, Takayuki Mizoguchi, Ryosuke Iwai, Tsutomu Tajikawa, Yasuhide Nakayama and Shinji Miyamoto
Bioengineering 2024, 11(4), 405; https://doi.org/10.3390/bioengineering11040405 - 20 Apr 2024
Cited by 2 | Viewed by 1160
Abstract
This study investigated six-month outcomes of first models of ascending aortic replacement. The molds used to produce the Biotube were implanted subcutaneously in goats. After 2–3 months, the molds were explanted to obtain the Biotubes (inner diameter, 12 mm; wall thickness, 1.5 mm). [...] Read more.
This study investigated six-month outcomes of first models of ascending aortic replacement. The molds used to produce the Biotube were implanted subcutaneously in goats. After 2–3 months, the molds were explanted to obtain the Biotubes (inner diameter, 12 mm; wall thickness, 1.5 mm). Next, we performed ascending aortic replacement using the Biotube in five allogenic goats. At 6 months, the animals underwent computed tomography (CT) and histologic evaluation. As a comparison, we performed similar surgeries using glutaraldehyde-fixed autologous pericardial rolls or pig-derived heterogenous Biotubes. At 6 months, CT revealed no aneurysmalization of the Biotube or pseudoaneurysm formation. The histologic evaluation showed development of endothelial cells, smooth muscle cells, and elastic fibers along the Biotube. In the autologous pericardium group, there was no evidence of new cell development, but there was calcification. The histologic changes observed in the heterologous Biotube group were similar to those in the allogenic Biotube group. However, there was inflammatory cell infiltration in some heterologous Biotubes. Based on the above, we could successfully create the world’s first Biotube-based ascending aortic replacement models. The present results indicate that the Biotube may serve as a scaffold for aortic tissue regeneration. Full article
(This article belongs to the Section Regenerative Engineering)
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<p>(<b>a</b>) Mold appearance. (<b>b</b>) Molds were implanted subcutaneously in animals for 2–3 months. (<b>c</b>) Biotube after removal from the mold. (<b>d</b>) Fibroblasts penetrating the gap between the outer cylinder and inner rod as shown in the mold schema. The tissue-engineered graft is formed by collagen according to gap shape.</p>
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<p>(<b>a</b>) Mold appearance. (<b>b</b>) Molds were implanted subcutaneously in animals for 2–3 months. (<b>c</b>) Biotube after removal from the mold. (<b>d</b>) Fibroblasts penetrating the gap between the outer cylinder and inner rod as shown in the mold schema. The tissue-engineered graft is formed by collagen according to gap shape.</p>
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<p>Intraoperative images of aortic replacement. Prereplacement picture (<b>a</b>) with Biotube (<b>b</b>) and autologous pericardial roll (<b>c</b>). The aorta was resected between the blue lines shown in (<b>a</b>). The length of replacement was approximately 20 mm.</p>
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<p>Contrast-enhanced CT image at 6 months postoperatively. Allogenic Biotube (<b>a</b>,<b>d</b>), heterologous Biotube (<b>b</b>,<b>e</b>), and autologous pericardial graft (<b>c</b>,<b>f</b>). The section indicated by the yellow arrow is the replacement area. There was no evidence of calcification, aneurysmalization, rupture, or pseudoaneurysm in any group.</p>
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<p>Biotubes harvested after 6 months. In the allogenic Biotube group (<b>a</b>), the luminal surface of the graft was smooth, and there was no evidence of thrombi. In the heterogeneous Biotube group (<b>b</b>), there was ulcer-like damage over the whole luminal surface but no evidence of thrombus. In the autologous pericardium group (<b>c</b>), most of the luminal surface of the graft was smooth, but the luminal surface of the roll’s suture line (yellow arrow) was rough. However, there was no evidence of thrombus.</p>
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<p>Histologic findings in the allogenic Biotube and autologous pericardium groups. (<b>a</b>) HE-stained images showing the neoplastic cell layer on the luminal side in both groups. No inflammatory cells were observed around the graft. (<b>b</b>) Masson’s trichrome staining showed the Biotube layer formed by collagen was preserved. Cells on the luminal surface were stained with CD31 immunostaining in both groups. The neoplastic cell layer was stained with α-SMA immunostaining in both groups. The Biotube layer is surrounded by yellow dashed lines. Elastica van Gieson staining showed the development of elastic fibers in the neoplastic cell layer. The Biotube group had a higher elastic fiber density. (<b>c</b>) von Kossa staining showed calcification around the suture lines in the autologous pericardium group. No calcification was observed in the Biotube group.</p>
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<p>Histologic findings in the allogenic and heterogeneous groups. No immune cell infiltration in the allogenic Biotube group was observed. However, immune cell infiltration into the Biotube layer was observed in the heterogeneous Biotube group (arrows). Development of neoplastic cell layers positive for α-SMA was observed in both groups. Masson’s trichrome staining showed destruction of the Biotube layer due to inflammatory cells in the heterogeneous Biotube group.</p>
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<p>A neoplastic cell layer was observed along the Biotube surface in the allogenic Biotube group (<b>a</b>). There were more neoplastic cells around the anastomosis. This was also observed in the heterogeneous Biotube group. (<b>b</b>) Neoplastic cells, mainly smooth muscle cells, are expected to develop from the native aorta along the Biotube.</p>
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<p>A neoplastic cell layer was observed along the Biotube surface in the allogenic Biotube group (<b>a</b>). There were more neoplastic cells around the anastomosis. This was also observed in the heterogeneous Biotube group. (<b>b</b>) Neoplastic cells, mainly smooth muscle cells, are expected to develop from the native aorta along the Biotube.</p>
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10 pages, 1864 KiB  
Article
An Investigation of Running Kinematics with Recovered Anterior Cruciate Ligament Reconstruction on a Treadmill and In-Field Using Inertial Measurement Units: A Preliminary Study
by Matteo Hill, Pierre Kiesewetter, Thomas L. Milani and Christian Mitschke
Bioengineering 2024, 11(4), 404; https://doi.org/10.3390/bioengineering11040404 - 19 Apr 2024
Viewed by 1258
Abstract
Anterior cruciate ligament reconstruction (ACLR) may affect movement even years after surgery. The purpose of this study was to determine possible interlimb asymmetries due to ACLR when running on a treadmill and in field conditions, with the aim of contributing to the establishment [...] Read more.
Anterior cruciate ligament reconstruction (ACLR) may affect movement even years after surgery. The purpose of this study was to determine possible interlimb asymmetries due to ACLR when running on a treadmill and in field conditions, with the aim of contributing to the establishment of objective movement assessment in real-world settings; moreover, we aimed to gain knowledge on recovered ACLR as a biomechanical risk factor. Eight subjects with a history of unilateral ACLR 5.4 ± 2.8 years after surgery and eight healthy subjects ran 1 km on a treadmill and 1 km on a concrete track. The ground contact time and triaxial peak tibial accelerations were recorded using inertial measurement units. Interlimb differences within subjects were tested and compared between conditions. There were no significant differences between limbs in the ACLR subjects or in healthy runners for any of the chosen parameters on both running surfaces. However, peak tibial accelerations were higher during field running (p-values < 0.01; Cohen’s d effect sizes > 0.8), independent of health status. To minimize limb loading due to higher impacts during field running, this should be considered when choosing a running surface, especially in rehabilitation or when running with a minor injury or health issues. Full article
(This article belongs to the Special Issue Biomechanics of Human Movement and Its Clinical Applications)
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<p>Full IMU setup. White circles indicate the positions of the placed sensors used for analysis.</p>
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<p>Vertical peak tibial acceleration values for the experimental group, divided by limb and running condition. ACLRL = anterior cruciate ligament reconstructed limb; HL = healthy limb; * <span class="html-italic">p</span> &lt; 0.01; α = 0.025. ● represents outliers in the data sets. An outlier is defined as a value that is more than 1.5 times the interquartile range.</p>
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<p>Medial–lateral peak tibial acceleration values for the experimental group, divided by limb and running condition. ACLRL = anterior cruciate ligament reconstructed limb; HL = healthy limb; * <span class="html-italic">p</span> &lt; 0.01; α = 0.025.</p>
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<p>Anterior–posterior peak tibial acceleration values of the experimental group, divided by limb and running condition. ACLRL = anterior cruciate ligament reconstructed limb; HL = healthy limb; * <span class="html-italic">p</span> &lt; 0.01; α = 0.025. ● represents outliers in the data sets. An outlier is defined as a value that is more than 1.5 times the interquartile range.</p>
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17 pages, 7407 KiB  
Case Report
Biomechanical Considerations in the Orthodontic Treatment of a Patient with Stabilised Stage IV Grade C Generalised Periodontitis: A Case Report
by Fung Hou Kumoi Mineaki Howard Sum, Zhiyi Shan, Yat Him Dave Chan, Ryan Julian Dick Hei Chu, George Pelekos and Tsang Tsang She
Bioengineering 2024, 11(4), 403; https://doi.org/10.3390/bioengineering11040403 - 19 Apr 2024
Viewed by 1375
Abstract
Orthodontic treatment of periodontally compromised patients presents unique challenges, including controlling periodontal inflammation, applying appropriate force, designing an effective dental anchorage, and maintaining treatment results. Deteriorated periodontal support leads to alterations in the biological responses of teeth to mechanical forces, and thus orthodontists [...] Read more.
Orthodontic treatment of periodontally compromised patients presents unique challenges, including controlling periodontal inflammation, applying appropriate force, designing an effective dental anchorage, and maintaining treatment results. Deteriorated periodontal support leads to alterations in the biological responses of teeth to mechanical forces, and thus orthodontists must take greater care when treating patients with periodontal conditions than when treating those with a good periodontal status. In this article, we report the case of a 59-year-old woman with stabilised Stage IV grade C generalised periodontitis characterised by pathological tooth migration (PTM). The assessment, planning, and treatment of this patient with orthodontic fixed appliances is described. Moreover, the anchorage planning and biomechanical considerations are detailed. Specific orthodontic appliances were employed in this case to produce force systems for achieving precise tooth movement, which included a cantilever, mini-screws, and a box loop. Careful application of those appliances resulted in satisfactory aesthetic and functional orthodontic outcomes in the patient. This case highlights the importance of multidisciplinary collaboration in the treatment of patients with severe periodontitis and the potential for tailored biomechanical approaches in orthodontic treatment to furnish good outcomes. Full article
(This article belongs to the Special Issue Application of Bioengineering to Clinical Orthodontics)
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<p>Pre-treatment photographs.</p>
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<p>Baseline periodontal charting taken on 28 August 2019 and periodontal charting taken on 18 April 2020 at patient’s supportive periodontal care appointment before beginning of the orthodontic treatment. The black arrow pointing up means improvement of the CAL of less than 1 mm. The green arrow pointing up means improvement of the CAL of &gt;1 mm. The black arrow pointing down means worsening of the CAL of less than 1 mm. The red arrow pointing down means worsening of the CAL of &gt;1 mm.</p>
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<p>Pre-treatment panoramic radiograph.</p>
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<p>Pre-treatment lateral cephalometric radiograph.</p>
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<p>Visual treatment objectives (VTOs) showing the planned post-treatment anterior–posterior dental positions of the upper and lower incisors. The blue lines indicate the pretreatment positions of the incisors, and the red lines indicate the planned post-treatment positions. The blue horizontal line represents the occlusal plane of the patient.</p>
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<p>Occlusogram illustrating the patient’s upper and lower final arches passing through the planned dental contact points. The mesio-distal width of each tooth is aligned along the constructed arches. The blue dots indicate the planned post-treatment distal contact points of the second premolars. The green dots indicate the planned post-treatment mesial contact points of the second molars, premolars, and lateral incisors. The red dots indicate the planned post-treatment mesial contact points of the canines. The pink dots indicate the planned post-treatment contact points of the central incisors.</p>
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<p>(<b>a</b>). Occlusal view of the cantilever without the β-Ti wire. (<b>b</b>–<b>d</b>). Occlusal view of the cantilever with the 0.036″ β-Ti wire tied onto the tooth 16 palatal cleat to create a one-point contact. The other end of the cantilever was a folded 0.036″ β-Ti wire that was inserted into the palatal sheath of the CoCr plate of teeth 24 and 25 to form a two-point contact. (<b>e</b>,<b>f</b>) demonstrate the activation force system (blue arrows) and deactivation force system (red arrows) of the cantilever, respectively. The deactivated shape of the cantilever is indicated by the dotted line, and the cantilever was activated by pushing the right side up (until it reached the solid line) to engage the β-Ti wire on the tooth 16 palatal cleat, as shown (<b>e</b>). During deactivation, the cantilever generated an extrusive force on tooth 16, an intrusive force on teeth 24 and 25, and a moment that rotated the teeth 24 and 25 crowns palatally (<b>f</b>).</p>
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<p>(<b>a</b>). Photographs of the patient before uprighting of teeth 24 and 25. (<b>b</b>). Photographs of the patient after uprighting of teeth 24 and 25.</p>
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<p>The maxillary premolar and premolars were tied as one segment using a powerchain, and the mandibular premolars and premolars were tied as one segment using another powerchain. The maxillary and mandibular arches were retracted by applying a retractive force from the upper and lower canines to a mini-screw inserted into the mandibular buccal shelf on both sides (purple circles). The upper arch was retracted using Class I elastics (3/16″, 3.5 oz) linking the upper canines to the corresponding lower mini-screws on each side. Similarly, the lower arch was retracted using powerchains from the lower canines to the mini-screws on the same sides. (The lines of action are indicated by red dotted lines.) The application of such mechanics meant that the deactivation force system at the Cres of the upper (yellow-starred) and lower (green-starred) arches consisted of distal forces (small red arrows) on both arches, with a clockwise moment on the upper arch and an anticlockwise moment on the lower arch (curved red arrows). The deactivation force system at the mini-screw consisted of a mesial force (large red arrow). As each mini-screw was fixed, only the upper and lower teeth were retracted and tipped distally.</p>
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<p>Teeth 34 and 35 were in Class III geometry in both the occlusal and the buccal view. (<b>a</b>,<b>b</b>) From the occlusal view, with the box loop, there were moments that rotated teeth 34 and 35 mesio-buccally (red arrows). In contrast, the powerchain on teeth 34–45 created a mesio-lingual moment on tooth 34 (yellow arrow) that cancelled out the mesio-buccal moment created by the box loop. (<b>a</b>) In the buccal view, there were moments that tipped the crowns of teeth 34 and 35 mesially (red arrow). (<b>b</b>) A schematic illustration of the six geometries is shown in (<b>c</b>).</p>
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<p>Post-orthodontic treatment photographs.</p>
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<p>Near-end-of-treatment panoramic radiograph.</p>
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<p>Near-end-of-treatment lateral cephalometric radiograph.</p>
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<p>Superimposition of the pre-treatment and post-treatment lateral cephalometric radiographs.</p>
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<p>Follow-up review photographs after prosthetic replacement of tooth 11 with a temporary composite bridge from teeth 12–21 and with composite veneers on upper and lower premolars, canines, and incisors.</p>
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<p>Left side: pre-treatment upper and lower occlusal contacts. Right side: post-treatment upper and lower occlusal contacts. Red indicates heavy occlusal contacts, whereas blue and green indicate light occlusal contacts. Orthodontic treatment increased the evenness of the distribution of the occlusal contacts.</p>
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<p>Timeline of the orthodontic treatment.</p>
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19 pages, 10631 KiB  
Article
Dimensionality Matters: Exploiting UV-Photopatterned 2D and Two-Photon-Printed 2.5D Contact Guidance Cues to Control Corneal Fibroblast Behavior and Collagen Deposition
by Cas van der Putten, Gozde Sahin, Rhiannon Grant, Mirko D’Urso, Stefan Giselbrecht, Carlijn V. C. Bouten and Nicholas A. Kurniawan
Bioengineering 2024, 11(4), 402; https://doi.org/10.3390/bioengineering11040402 - 19 Apr 2024
Cited by 1 | Viewed by 1784
Abstract
In the event of disease or injury, restoration of the native organization of cells and extracellular matrix is crucial for regaining tissue functionality. In the cornea, a highly organized collagenous tissue, keratocytes can align along the anisotropy of the physical microenvironment, providing a [...] Read more.
In the event of disease or injury, restoration of the native organization of cells and extracellular matrix is crucial for regaining tissue functionality. In the cornea, a highly organized collagenous tissue, keratocytes can align along the anisotropy of the physical microenvironment, providing a blueprint for guiding the organization of the collagenous matrix. Inspired by this physiological process, anisotropic contact guidance cues have been employed to steer the alignment of keratocytes as a first step to engineer in vitro cornea-like tissues. Despite promising results, two major hurdles must still be overcome to advance the field. First, there is an enormous design space to be explored in optimizing cellular contact guidance in three dimensions. Second, the role of contact guidance cues in directing the long-term deposition and organization of extracellular matrix proteins remains unknown. To address these challenges, here we combined two microengineering strategies—UV-based protein patterning (2D) and two-photon polymerization of topographies (2.5D)—to create a library of anisotropic contact guidance cues with systematically varying height (H, 0 µm ≤ H ≤ 20 µm) and width (W, 5 µm ≤ W ≤ 100 µm). With this unique approach, we found that, in the short term (24 h), the orientation and morphology of primary human fibroblastic keratocytes were critically determined not only by the pattern width, but also by the height of the contact guidance cues. Upon extended 7-day cultures, keratocytes were shown to produce a dense, fibrous collagen network along the direction of the contact guidance cues. Moreover, increasing the heights also increased the aligned fraction of deposited collagen and the contact guidance response of cells, all whilst the cells maintained the fibroblastic keratocyte phenotype. Our study thus reveals the importance of dimensionality of the physical microenvironment in steering both cellular organization and the formation of aligned, collagenous tissues. Full article
(This article belongs to the Special Issue Bioengineering and the Eye—2nd Edition)
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<p>(<b>A</b>) The simplification of 3D corneal stromal architecture into line patterns, followed by the fabrication of cell culture substrates with 2.5D and 2D contact guidance cues using 2PP and LIMAP, respectively. All 2.5D cues contained pattern widths of 5, 10, 20, 50, and 100 μm combined on a single substrate. An example of a false colored scanning electron micrograph shows the top-view of a substrate with 20 µm tall ridges and a width of 5 μm (blue), 10 μm (orange), 20 μm (pink), 50 μm (green), and 100 μm (purple). The height of the topographies was systematically decreased from 20 μm to 10, 5, and 2.5 µm on separate cell culture substrates. For 2D contact guidance cues, the height of the patterns was further reduced to 0 μm, whereas the width was identical to that of 2.5D contact guidance cues. (<b>B</b>) Characterization of contact guidance cues using a laser scanning profilometer shows dimensional accuracy. The 3D profile of patterned cell culture substrates H20, H10, H5, H2.5, and H0 with pattern heights of 20, 10, 5, 2.5, and 0 μm, respectively. (<b>C</b>) Cross-sectional height profile of patterned substrates, with H20 (black), H10 (blue), H5 (red), H2.5 (green), and H0 (yellow).</p>
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<p>Fibroblastic keratocyte orientation is strongly influenced by the height of contact guidance cues. (<b>A</b>) The cells aligned in a 20° range around 0°, with 0° representing perfect alignment along the surface patterns, or a perfect contact guidance response. The fraction of cells aligned on (<b>B</b>) 2D cues lacking any height; 2.5D topographies with a height of (<b>C</b>) 2.5 μm, (<b>D</b>) 5 μm, (<b>E</b>) 10 μm, and (<b>F</b>) 20 μm. For each sample, the data were visualized as condition mean ± standard error of the mean. Complete (180° range) orientation distributions are given in <a href="#app1-bioengineering-11-00402" class="html-app">Figure S2</a>. Results from the statistical analysis are given in <a href="#app1-bioengineering-11-00402" class="html-app">Table S1</a>.</p>
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<p>(<b>A</b>) Heatmap of morphological parameters of fibroblastic keratocytes when cultured on contact guidance cue presenting substrates. Gray scales are normalized for each readout. (<b>B</b>) The projected area, (<b>C</b>) eccentricity, (<b>D</b>) major axis length, and (<b>E</b>) minor axis length of cells on substrates with and without (control, grey) contact guidance cues. All data are presented as mean ± standard error of mean. Statistical differences between conditions are described in <a href="#app1-bioengineering-11-00402" class="html-app">Tables S2–S5</a>.</p>
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<p>Representative maximum-intensity projections scaled to the size of single fibroblastic keratocytes on surface patterns. Visualized are the gelatin cues and coating (green), nuclei (blue), F-Actin (red), and Vinculin (magenta). White arrows indicate cells spreading across multiple cues, red arrows indicate confined cells, yellow arrows indicate cells adhering to the sides of the cues. Scale bars represent 20 μm.</p>
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<p>Quantified gene expression of (<b>A</b>) keratocyte markers ALDH3A1, keratocan, CD34, and lumican, and (<b>B</b>) fibroblast markers α-SMA, CD90, and vimentin with respect to fibroblastic keratocytes on flat substrates. ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Fibroblastic keratocytes and deposited collagen after 7 days of culture on contact guidance cue presenting substrates. Maximum intensity projections show F-actin (red), nuclei (blue), and collagens (white), with (<b>A</b>) 2D; control (no pattern), 5, 10, 20, 50, and 100 μm wide lines, and (<b>B</b>) 2.5D; 2.5, 5, 10, and 20 μm tall and 5, 10, 20, 50, and 100 μm wide contact guidance cues. Scale bars represent 200 μm. (<b>C</b>) Representative 3D visualization of the contact guidance cues (H20W10) with cells and collagen mainly in between the cues. Visualized are F-actin (red), nuclei (blue), gelatin (green), and collagens (white). Scale bar represents 40 μm. (<b>D</b>) Maximum-intensity projection of a zoomed-in region of the cues, showing the location and alignment of newly deposited collagen (white). Scale bar represents 20 μm.</p>
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<p>The orientation distribution of collagen with respect to the contact guidance cues. (<b>A</b>) 0° represents collagen aligning according to the cue direction, whereas −90° represents a counter-clockwise rotation, and 90° represents a clockwise rotation. Orientation distributions of (<b>B</b>) 2D contact guidance cues, and 2.5D topographies with heights of (<b>C</b>) 2.5 μm, (<b>D</b>) 5 μm, (<b>E</b>) 10 μm, (<b>F</b>) 20 μm.</p>
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16 pages, 1642 KiB  
Article
Assessment of Microvascular Hemodynamic Adaptations in Finger Flexors of Climbers
by Blai Ferrer-Uris, Albert Busquets, Faruk Beslija and Turgut Durduran
Bioengineering 2024, 11(4), 401; https://doi.org/10.3390/bioengineering11040401 - 19 Apr 2024
Viewed by 1275
Abstract
Climbing performance is greatly dependent on the endurance of the finger flexors which, in turn, depends on the ability to deliver and use oxygen within the muscle. Near-infrared spectroscopy (NIRS) and diffuse correlation spectroscopy (DCS) have provided new possibilities to explore these phenomena [...] Read more.
Climbing performance is greatly dependent on the endurance of the finger flexors which, in turn, depends on the ability to deliver and use oxygen within the muscle. Near-infrared spectroscopy (NIRS) and diffuse correlation spectroscopy (DCS) have provided new possibilities to explore these phenomena in the microvascular environment. The aim of the present study was to explore climbing-related microvascular adaptations through the comparison of the oxygen concentration and hemodynamics of the forearm between climbers and non-climber active individuals during a vascular occlusion test (VOT). Seventeen climbers and fifteen non-climbers joined the study. Through NIRS and DCS, the oxyhemoglobin (O2Hb) and deoxyhemoglobin (HHb) concentrations, tissue saturation index (TSI), and blood flow index (BFI) were obtained from the flexor digitorum profundus during the VOT. During the reactive hyperemia, climbers presented greater blood flow slopes (p = 0.043, d = 0.573), as well as greater O2Hb maximum values (p = 0.001, d = 1.263) and HHb minimum values (p = 0.009, d = 0.998), than non-climbers. The superior hemodynamics presented by climbers could indicate potential training-induced structural and functional adaptations that could enhance oxygen transportation to the muscle, and thus enhance muscle endurance and climbing performance. Full article
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<p>Example from a single participant of the (<b>A</b>) oxyhemoglobin concentration (O<sub>2</sub>Hb), (<b>B</b>) deoxyhemoglobin concentration (HHb), (<b>C</b>) tissue saturation index (TSI), and (<b>D</b>) blood flow index (BFI) changes during the vascular occlusion test (VOT). Vertical lines indicate the inflation and release of the pressure cuff, which were used to divide the test into the baseline phase (BA), occlusion phase (OC), and reactive hyperemia phase (HY). Calculated variables for each signal are schematically represented. Abbreviations: rBF: relative blood flow, tmin: time to minimum value, tmax: time to maximum value, HTR: half-time to recovery.</p>
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<p>Normalized oxyhemoglobin (∆O<sub>2</sub>Hb, (<b>A</b>)), deoxyhemoglobin (∆HHb, (<b>B</b>)), tissue saturation index (∆TSI, (<b>C</b>)), and relative blood flow (rBF, (<b>D</b>)) example traces from a climber (<b>left side</b>) and a non-climber (<b>right side</b>) participant.</p>
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<p>Group means and standard deviations, and individual subject’s values from the maximum delta hemoglobin concentration (HY-∆O<sub>2</sub>Hb<sub>max</sub>, (<b>A</b>)), the minimum delta deoxyhemoglobin concentration (HY-∆HHb<sub>min</sub>, (<b>B</b>)), and the relative blood flow slope (HY-rBF<sub>slope</sub>, (<b>C</b>)) during the hyperemia phase (HY) of the vascular occlusion test. Significant group differences were found for the three parameters (<span class="html-italic">p</span> &lt; 0.05). When DCS-related variables during the HY phase were explored, no main group effect was found (<span class="html-italic">F</span> (1, 30) = 2.046, <span class="html-italic">p</span> = 0.130, <span class="html-italic">η</span><sup>2</sup><span class="html-italic">p</span> = 0.180, power = 0.468), indicating that groups presented similar blood flow dynamics when compared using the compound of DCS variables. Nevertheless, when pairwise comparisons were explored, climbers showed a greater HY-rBF<sub>slope</sub> compared to non-climbers (<span class="html-italic">p</span> = 0.043, <span class="html-italic">d</span> = 0.573), indicating a faster increase in blood flow after the occlusive pressure release (<a href="#bioengineering-11-00401-f003" class="html-fig">Figure 3</a> and as can been seen in <a href="#bioengineering-11-00401-f002" class="html-fig">Figure 2</a> with sample data from each group). Despite this difference, similar HY-rBF<sub>max</sub>, HY-rBF<sub>tmax</sub>, and HY-rBF<sub>HTR</sub> were found between groups.</p>
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<p>Schematic representation summarizing our study methodological approach, main findings, and hypotheses.</p>
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21 pages, 1468 KiB  
Article
Ergonomic Analysis of Dental Work in Different Oral Quadrants: A Motion Capture Preliminary Study among Endodontists
by Sophie Feige, Fabian Holzgreve, Laura Fraeulin, Christian Maurer-Grubinger, Werner Betz, Christina Erbe, Albert Nienhaus, David A. Groneberg and Daniela Ohlendorf
Bioengineering 2024, 11(4), 400; https://doi.org/10.3390/bioengineering11040400 - 19 Apr 2024
Cited by 1 | Viewed by 1530
Abstract
Background: Dentists, including endodontists, frequently experience musculoskeletal disorders due to unfavourable working postures. Several measures are known to reduce the ergonomic risk; however, there are still gaps in the research, particularly in relation to dental work in the different oral regions (Quadrants 1–4). [...] Read more.
Background: Dentists, including endodontists, frequently experience musculoskeletal disorders due to unfavourable working postures. Several measures are known to reduce the ergonomic risk; however, there are still gaps in the research, particularly in relation to dental work in the different oral regions (Quadrants 1–4). Methods: In this study (of a pilot character), a total of 15 dentists (8 male and 7 female) specialising in endodontics were measured while performing root canal treatments on a phantom head. These measurements took place in a laboratory setting using an inertial motion capture system. A slightly modified Rapid Upper Limb Assessment (RULA) coding system was employed for the analysis of kinematic data. The significance level was set at p = 0.05. Results: The ergonomic risk for the entire body was higher in the fourth quadrant than in the first quadrant for 80% of the endodontists and higher than in the second quadrant for 87%. For 87% of the endodontists, the ergonomic risk for the right side of the body was significantly higher in the fourth quadrant compared to the first and second quadrant. The right arm was stressed more in the lower jaw than in the upper jaw, and the neck also showed a greater ergonomic risk in the fourth quadrant compared to the first quadrant. Conclusion: In summary, both the total RULA score and scores for the right- and lefthand sides of the body ranged between 5 and 6 out of a possible 7 points. Considering this considerable burden, heightened attention, especially to the fourth quadrant with a significantly higher ergonomic risk compared to Quadrants 1 and 2, may be warranted. Full article
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<p>Dentist and dental assistant in the Xsens suit during treatment on the dummy head in basic concept 3.</p>
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<p>Dummy head attached to the treatment centre with the prepared tray according to basic concept 4.</p>
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<p>Standardised starting position before the measurements in basic concept 3 (swivel unit) with the prepared tray and dental lamp aligned vertically above the dummy head.</p>
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<p>RULA score “Final overall”.</p>
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<p>RULA score “Left Lower Arm”—Step 2.</p>
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<p>RULA score “Right Upper Arm”—Step 1.</p>
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<p>RULA score “Neck”—Step 9.</p>
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<p>Rel. av. RST “Final overall”.</p>
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<p>Rel. av. RST “Final overall right”.</p>
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<p>Rel. av. RST “Left Upper Arm”—Step 1.</p>
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<p>Rel. av. RST “Left Lower Arm”—Step 2.</p>
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<p>Rel. av. RST “Right Upper Arm”—Step 1.</p>
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<p>Rel. av. RST “Right Lower Arm”—Step 2.</p>
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<p>Rel. av. RST “Neck”—Step 9.</p>
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19 pages, 5200 KiB  
Article
Precision Identification of Locally Advanced Rectal Cancer in Denoised CT Scans Using EfficientNet and Voting System Algorithms
by Chun-Yu Lin, Jacky Chung-Hao Wu, Yen-Ming Kuan, Yi-Chun Liu, Pi-Yi Chang, Jun-Peng Chen, Henry Horng-Shing Lu and Oscar Kuang-Sheng Lee
Bioengineering 2024, 11(4), 399; https://doi.org/10.3390/bioengineering11040399 - 19 Apr 2024
Cited by 1 | Viewed by 1515
Abstract
Background and objective: Local advanced rectal cancer (LARC) poses significant treatment challenges due to its location and high recurrence rates. Accurate early detection is vital for treatment planning. With magnetic resonance imaging (MRI) being resource-intensive, this study explores using artificial intelligence (AI) to [...] Read more.
Background and objective: Local advanced rectal cancer (LARC) poses significant treatment challenges due to its location and high recurrence rates. Accurate early detection is vital for treatment planning. With magnetic resonance imaging (MRI) being resource-intensive, this study explores using artificial intelligence (AI) to interpret computed tomography (CT) scans as an alternative, providing a quicker, more accessible diagnostic tool for LARC. Methods: In this retrospective study, CT images of 1070 T3–4 rectal cancer patients from 2010 to 2022 were analyzed. AI models, trained on 739 cases, were validated using two test sets of 134 and 197 cases. By utilizing techniques such as nonlocal mean filtering, dynamic histogram equalization, and the EfficientNetB0 algorithm, we identified images featuring characteristics of a positive circumferential resection margin (CRM) for the diagnosis of locally advanced rectal cancer (LARC). Importantly, this study employs an innovative approach by using both hard and soft voting systems in the second stage to ascertain the LARC status of cases, thus emphasizing the novelty of the soft voting system for improved case identification accuracy. The local recurrence rates and overall survival of the cases predicted by our model were assessed to underscore its clinical value. Results: The AI model exhibited high accuracy in identifying CRM-positive images, achieving an area under the curve (AUC) of 0.89 in the first test set and 0.86 in the second. In a patient-based analysis, the model reached AUCs of 0.84 and 0.79 using a hard voting system. Employing a soft voting system, the model attained AUCs of 0.93 and 0.88, respectively. Notably, AI-identified LARC cases exhibited a significantly higher five-year local recurrence rate and displayed a trend towards increased mortality across various thresholds. Furthermore, the model’s capability to predict adverse clinical outcomes was superior to those of traditional assessments. Conclusion: AI can precisely identify CRM-positive LARC cases from CT images, signaling an increased local recurrence and mortality rate. Our study presents a swifter and more reliable method for detecting LARC compared to traditional CT or MRI techniques. Full article
(This article belongs to the Special Issue Application of Deep Learning in Medical Diagnosis)
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<p>Diagrammatic representation of study material workflow. This figure outlines the systematic process used in this study, tracing the flow of materials from initial data collection through to their final application within the research framework.</p>
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<p>Characterization and processing of rectal cancer features in CT imaging. (<b>a</b>) Criteria for identifying CRM-threatening features associated with locally advanced rectal cancer in CT scans; (<b>b</b>) illustrative cases of rectal cancer in CT imagery lacking CRM-threatening attributes; (<b>c</b>) comparative illustration of three cropping techniques for rectal cancer image preparation: full image, external pelvic boundary, and internal pelvic boundary.</p>
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<p>AI-based diagnostic framework for detecting the locally advanced rectal cancer architectural blueprint of the AI model for LARC identification. This diagram illustrates the methodological approach and the deep learning architecture utilized in this study for the detection of locally advanced rectal cancer (LARC) from CT scans. The framework encapsulates the AI’s training and predictive process, detailing the progression from image preprocessing to the final LARC classification.</p>
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<p>Optimal diagnostic outcomes using Models 2a and 2b. The green dotted line indicate randomize classifier. (<b>a</b>–<b>c</b>) Performance of Model 2a, amalgamating data from the training set and test set 2, when applied to test set 1. (<b>a</b>) Identification of CRM-threatening features indicative of LARC; (<b>b</b>) determination of LARC status using a hard voting threshold of one-third; (<b>c</b>) assessment of LARC cases employing a soft voting threshold of a half. (<b>d</b>–<b>f</b>) Efficacy of Model 2b, integrating data from the training set and test set 1, utilized on test set 2. (<b>d</b>) Image analysis for CRM-threatening features associated with LARC; (<b>e</b>) LARC case adjudication based on a hard voting threshold of one-fourth; (<b>f</b>) LARC case determination via a soft voting threshold of one-third.</p>
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<p>Five-year local recurrence and overall survival rates for LARC from physician and analysis of different AI methodologies.</p>
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<p>Three visual examples of interpretation results by a physician and AI. The surgery outcome with a positive pathological circumferential margin (pCRM), disease survival of local recurrence, and overall survival time were also recorded. The following abbreviations are used: pCRM+, positive pathological circumferential margin; LR, local recurrence; OS, overall survival.</p>
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13 pages, 1278 KiB  
Article
Effects of Action Observation Plus Motor Imagery Administered by Immersive Virtual Reality on Hand Dexterity in Healthy Subjects
by Paola Adamo, Gianluca Longhi, Federico Temporiti, Giorgia Marino, Emilia Scalona, Maddalena Fabbri-Destro, Pietro Avanzini and Roberto Gatti
Bioengineering 2024, 11(4), 398; https://doi.org/10.3390/bioengineering11040398 - 19 Apr 2024
Viewed by 1188
Abstract
Action observation and motor imagery (AOMI) are commonly delivered through a laptop screen. Immersive virtual reality (VR) may enhance the observer’s embodiment, a factor that may boost AOMI effects. The study aimed to investigate the effects on manual dexterity of AOMI delivered through [...] Read more.
Action observation and motor imagery (AOMI) are commonly delivered through a laptop screen. Immersive virtual reality (VR) may enhance the observer’s embodiment, a factor that may boost AOMI effects. The study aimed to investigate the effects on manual dexterity of AOMI delivered through immersive VR compared to AOMI administered through a laptop. To evaluate whether VR can enhance the effects of AOMI, forty-five young volunteers were enrolled and randomly assigned to the VR-AOMI group, who underwent AOMI through immersive VR, the AOMI group, who underwent AOMI through a laptop screen, or the control group, who observed landscape video clips. All participants underwent a 5-day treatment, consisting of 12 min per day. We investigated between and within-group differences after treatments relative to functional manual dexterity tasks using the Purdue Pegboard Test (PPT). This test included right hand (R), left hand (L), both hands (B), R + L + B, and assembly tasks. Additionally, we analyzed kinematics parameters including total and sub-phase duration, peak and mean velocity, and normalized jerk, during the Nine-Hole Peg Test to examine whether changes in functional scores may also occur through specific kinematic patterns. Participants were assessed at baseline (T0), after the first training session (T1), and at the end of training (T2). A significant time by group interaction and time effects were found for PPT, where both VR-AOMI and AOMI groups improved at the end of training. Larger PPT-L task improvements were found in the VR-AOMI group (d: 0.84, CI95: 0.09–1.58) compared to the AOMI group from T0 to T1. Immersive VR used for the delivery of AOMI speeded up hand dexterity improvements. Full article
(This article belongs to the Special Issue Bioengineering of the Motor System)
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<p>Study participant undergoing the VR-AOMI through the Oculus headset (VR-AOMI), AOMI through a laptop screen (AOMI), and landscape observation through VR (CTRL).</p>
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<p>Study timeline.</p>
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<p>The scores obtained in Purdue Pegboard Test across the three groups are shown. Data are presented as means (dots and triangles) and standard deviation (vertical bars).</p>
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<p>Between-group differences for deltas from T0 to T1 and from T2 to T0 for the PPT-L task. Boxes represent the range between the first and the third quartile, the middle horizontal line is the mean value, and the ends of the vertical line, from top to bottom, are the maximum and minimum values, respectively. Symbols show differences in terms of delta between the VR-AOMI and AOMI groups from T0 to T1 (#) and between the VR-AOMI and AOMI groups with the CTRL group from T0 to T1 and from T0 to T2 (*).</p>
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12 pages, 1521 KiB  
Article
Micro-Computed Tomography Analysis of Peri-Implant Bone Defects Exposed to a Peri-Implantitis Microcosm, with and without Bone Substitute, in a Rabbit Model: A Pilot Study
by Camila Panes, Iván Valdivia-Gandur, Carlos Veuthey, Vanessa Sousa, Mariano del Sol and Víctor Beltrán
Bioengineering 2024, 11(4), 397; https://doi.org/10.3390/bioengineering11040397 - 19 Apr 2024
Viewed by 1288
Abstract
Peri-implantitis is an inflammatory condition characterized by inflammation in the peri-implant connective tissue and a progressive loss of supporting bone; it is commonly associated with the presence of biofilms on the surface of the implant, which is an important factor in the development [...] Read more.
Peri-implantitis is an inflammatory condition characterized by inflammation in the peri-implant connective tissue and a progressive loss of supporting bone; it is commonly associated with the presence of biofilms on the surface of the implant, which is an important factor in the development and progression of the disease. The objective of this study was to evaluate, using micro-CT, the bone regeneration of surgically created peri-implant defects exposed to a microcosm of peri-implantitis. Twenty-three adult New Zealand white rabbits were included in the study. Bone defects of 7 mm diameter were created in both tibiae, and a cap-shaped titanium device was placed in the center, counter-implanted with a peri-implantitis microcosm. The bone defects received a bone substitute and/or a resorbable synthetic PLGA membrane, according to random distribution. Euthanasia was performed 15 and 30 days postoperatively. Micro-CT was performed on all samples to quantify bone regeneration parameters. Bone regeneration of critical defects occurred in all experimental groups, with a significantly greater increase in cases that received bone graft treatment (p < 0.0001), in all measured parameters, at 15 and 30 days. No significant differences were observed in the different bone neoformation parameters between the groups that did not receive bone grafts (p > 0.05). In this experimental model, the presence of peri-implantitis microcosms was not a determining factor in the bone volume parameter, both in the groups that received regenerative treatment and in those that did not. Full article
(This article belongs to the Special Issue Biomaterials for Bone Repair and Regeneration)
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<p>Schematization of the division of the experimental groups.</p>
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<p>Example of the spatial reorientation applied to each scanned sample using DataViewer v.1.5.6.2 software (Bruker-microCT, Kontich, Bélgica), matching the axial axis and center of the implant. The images oriented in the coronal plane were extracted for subsequent segmentation and analysis of the images. COR: coronal plane; TRA: transverse plane; SAG: sagittal plane.</p>
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<p>Graphs of the parameters obtained by computerized microtomography, 15 days post-surgery: (<b>A</b>) Bone volume (BV) present in the VOI. (<b>B</b>) Percent bone volume (BV/TV). (<b>C</b>) Bone surface (BS). (<b>D</b>) Bone surface/volume ratio (BS/BV). (<b>E</b>) Bone surface density (BS/TV). ** <span class="html-italic">p</span> ≤ 0.001; *** <span class="html-italic">p</span> ≤ 0.0001. PLGA: PLGA membrane; MB: membrane and bone graft; BG: bone graft; CRT: control group; UC: untreated cap.</p>
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<p>Graphs of the parameters obtained by computerized microtomography, 30 days post-surgery: (<b>A</b>) Bone volume (BV) present in the VOI. (<b>B</b>) Percent bone volume (BV/TV). (<b>C</b>) Bone surface (BS). (<b>D</b>) Bone surface/volume ratio (BS/BV). (<b>E</b>) Bone surface density (BS/TV). * <span class="html-italic">p</span> ≤ 0.01; ** <span class="html-italic">p</span> ≤ 0.001; *** <span class="html-italic">p</span> ≤ 0.0001. PLGA: PLGA membrane; MB: membrane and bone graft; BG: bone graft; CRT: control group; UC: untreated cap.</p>
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12 pages, 738 KiB  
Review
A Systematic Review of Bone Bruise Patterns following Acute Anterior Cruciate Ligament Tears: Insights into the Mechanism of Injury
by Sueen Sohn, Saad Mohammed AlShammari, Byung Jun Hwang and Man Soo Kim
Bioengineering 2024, 11(4), 396; https://doi.org/10.3390/bioengineering11040396 - 19 Apr 2024
Viewed by 1182
Abstract
(1) Background: The purpose of this systematic review was to determine the prevalence of bone bruises in patients with anterior cruciate ligament (ACL) injuries and the location of the bruises relative to the tibia and femur. Understanding the relative positions of these bone [...] Read more.
(1) Background: The purpose of this systematic review was to determine the prevalence of bone bruises in patients with anterior cruciate ligament (ACL) injuries and the location of the bruises relative to the tibia and femur. Understanding the relative positions of these bone bruises could enhance our comprehension of the knee loading patterns that occur during an ACL injury. (2) Methods: The MEDLINE, EMBASE, and the Cochrane Library databases were searched for studies that evaluated the presence of bone bruises following ACL injuries. Study selection, data extraction, and a systematic review were performed. (3) Results: Bone bruises were observed in 3207 cases (82.8%) at the lateral tibia plateau (LTP), 1608 cases (41.5%) at the medial tibia plateau (MTP), 2765 cases (71.4%) at the lateral femoral condyle (LFC), and 1257 cases (32.4%) at the medial femoral condyle (MFC). Of the 30 studies, 11 were able to assess the anterior to posterior direction. The posterior LTP and center LFC were the most common areas of bone bruises. Among the 30 studies, 14 documented bone bruises across all four sites (LTP, MTP, LFC, and MFC). The most common pattern was bone bruises appearing at the LTP and LFC. (4) Conclusions: The most frequently observed pattern of bone bruises was restricted to the lateral aspects of both the tibia and femur. In cases where bone bruises were present on both the lateral and medial sides, those on the lateral side exhibited greater severity. The positioning of bone bruises along the front–back axis indicated a forward shift of the tibia in relation to the femur during ACL injuries. Full article
(This article belongs to the Special Issue Biomechanics of Sports Injuries)
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<p>Search strategy for systematic review of bone bruise patterns following anterior cruciate ligament tears.</p>
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<p>Flowchart illustrating the literature search process.</p>
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12 pages, 2260 KiB  
Article
Tensile Yield Strain of Human Cortical Bone from the Femoral Diaphysis Is Constant among Healthy Adults and across the Anatomical Quadrants
by Massimiliano Baleani, Paolo Erani, Alice Acciaioli and Enrico Schileo
Bioengineering 2024, 11(4), 395; https://doi.org/10.3390/bioengineering11040395 - 19 Apr 2024
Viewed by 1242
Abstract
The literature suggests that the yield strain of cortical bone is invariant to its stiffness (elastic modulus) and strength (yield stress). However, data about intra-individual variations, e.g., the influence of different collagen/mineral organisations observed in bone aspects withstanding different habitual loads, are lacking. [...] Read more.
The literature suggests that the yield strain of cortical bone is invariant to its stiffness (elastic modulus) and strength (yield stress). However, data about intra-individual variations, e.g., the influence of different collagen/mineral organisations observed in bone aspects withstanding different habitual loads, are lacking. The hypothesis that the yield strain of human cortical bone tissue, retrieved from femoral diaphyseal quadrants subjected to different habitual loads, is invariant was tested. Four flat dumbbell-shaped specimens were machined from each quadrant of the proximal femoral diaphysis of five adult donors for a total of 80 specimens. Two extensometers attached to the narrow specimen region were used to measure deformation during monotonic tensile testing. The elastic modulus (linear part of the stress–strain curve) and yield strain/stress at a 0.2% offset were obtained. Elastic modulus and yield stress values were, respectively, in the range of 12.2–20.5 GPa and 75.9–136.6 MPa and exhibited a positive linear correlation. All yield strain values were in the narrow range of 0.77–0.87%, regardless of the stiffness and strength of the tissue and the anatomical quadrant. In summary, the results corroborate the hypothesis that tensile yield strain in cortical bone is invariant, irrespective also of the anatomical quadrant. The mean yield strain value found in this study is similar to what was reported by inter-species and evolution studies but slightly higher than previous reports in humans, possibly because of the younger age of our subjects. Further investigations are needed to elucidate a possible dependence of yield strain on age. Full article
(This article belongs to the Section Biomechanics and Sports Medicine)
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Graphical abstract

Graphical abstract
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<p>(<b>I</b>) A 46 mm thick slice is cut from the proximal femoral diaphysis. (<b>II</b>) The distal end of the diaphyseal slice is embedded in acrylic resin. (<b>III</b>) Six cuts are made in the proximal–distal direction, parallel to the frontal plane of the femur. (<b>IV</b>) Six cuts are made in the proximal–distal direction, parallel to the sagittal plane of the femur. (<b>V</b>) A cut is made orthogonal to the axis of the slice at 35 mm from the proximal end. (<b>VI</b>) Sketch showing the dimensions of the cortical slices that were obtained. (<b>VII</b>) The contour of the narrow part of the sample is machined with a diamond-coated tool. The periosteal-side contour of the narrow section was machined 0.7 mm inside the original periosteal surface. (<b>VIII</b>) The surface of the flat dumbbell-shaped specimen is polished by removing 0.05 mm of cortical bone tissue. (<b>IX</b>) Sketch showing the dimensions of the narrow part of flat dumbbell-shaped specimens that were obtained (note: because of the local anatomy of the cortical wall, non-standard (i.e., smaller with a 10:1 ratio of gauge length to thickness and 2.5:1 ratio of gauge length to width) dumbbell specimens were obtained).</p>
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<p>(<b>a</b>) The diaphyseal slice with six cuts in the proximal–distal direction, parallel to the frontal plane of the femur (step III in <a href="#bioengineering-11-00395-f001" class="html-fig">Figure 1</a>). (<b>b</b>) A cortical slice retrieved from the cortical wall (step VI in <a href="#bioengineering-11-00395-f001" class="html-fig">Figure 1</a>). No trabecular bone tissue is present on the endosteal side. (<b>c</b>) The flat dumbbell-shaped specimen obtained at the end of the procedure (step IX in <a href="#bioengineering-11-00395-f001" class="html-fig">Figure 1</a>). The periosteal side (left edge of the specimen) of the gripping part was not machined. Therefore, irregularities in the left contour of the gripping part of the specimen are visible.</p>
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<p>Boxplot of yield strain values (%) by quadrant. The central line in each box indicates the median value, the boxes represent the 25th and 75th percentile, and the whiskers represent the 10th and 90th percentiles.</p>
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<p>Boxplot of yield stress values (MPa) by quadrant. The central line in each box indicates the median value, the boxes represent the 25th and 75th percentile, and the whiskers represent the 10th and 90th percentiles.</p>
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<p>Boxplot of elastic modulus values (GPa) by quadrant. The central line in each box indicates the median value, the boxes represent the 25th and 75th percentile, and the whiskers represent the 10th and 90th percentiles.</p>
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<p>Scatterplot of yield strain versus elastic modulus for all 80 specimens. The line represents the correlation mentioned in the text.</p>
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<p>Scatterplot of yield stress versus elastic modulus for all 80 specimens. The line represents the correlation mentioned in the text.</p>
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12 pages, 6255 KiB  
Article
Finite Element Analysis of Fixed Orthodontic Retainers
by Sebastian Hetzler, Stefan Rues, Andreas Zenthöfer, Peter Rammelsberg, Christopher J. Lux and Christoph J. Roser
Bioengineering 2024, 11(4), 394; https://doi.org/10.3390/bioengineering11040394 - 18 Apr 2024
Viewed by 1425
Abstract
The efficacy of retainers is a pivotal concern in orthodontic care. This study examined the biomechanical behaviour of retainers, particularly the influence of retainer stiffness and tooth resilience on force transmission and stress distribution. To do this, a finite element model was created [...] Read more.
The efficacy of retainers is a pivotal concern in orthodontic care. This study examined the biomechanical behaviour of retainers, particularly the influence of retainer stiffness and tooth resilience on force transmission and stress distribution. To do this, a finite element model was created of the lower jaw from the left to the right canine with a retainer attached on the oral side. Three levels of tooth resilience and variable retainer bending stiffness (influenced by retainer type, retainer diameter, and retainer material) were simulated. Applying axial or oblique (45° tilt) loads on a central incisor, the force transmission increased from 2% to 65% with increasing tooth resilience and retainer stiffness. Additionally, a smaller retainer diameter reduced the uniformity of the stress distribution in the bonding interfaces, causing concentrated stress peaks within a small field of the bonding area. An increase in retainer stiffness and in tooth resilience as well as a more oblique load direction all lead to higher overall stress in the adhesive bonding area associated with a higher risk of retainer bonding failure. Therefore, it might be recommended to avoid the use of retainers that are excessively stiff, especially in cases with high tooth resilience. Full article
(This article belongs to the Special Issue Application of Bioengineering to Clinical Orthodontics)
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<p>(<b>a</b>) Schematic illustration of the three-point bending test setup and (<b>b</b>) close-up of the relevant parts with a triple-stranded stainless steel retainer during testing.</p>
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<p>(<b>a</b>) Cross section of tooth 31 with all its components including the two load cases and (<b>b</b>) close-up of the model with the least and most stiff retainer.</p>
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<p>Force transmission (<span class="html-italic">F<sub>trans</sub></span>) to the adhesive bond of the loaded tooth divided by the applied force (<span class="html-italic">F<sub>res</sub></span>) over bending stiffness (<span class="html-italic">k</span>), normalised to the least stiff retainer (<span class="html-italic">k</span><sub>0</sub>, no. 4). Data points are fitted with a function of the type seen in Formula (3). The area of conventional hand-bent retainers is highlighted and enlarged with a green rectangle.</p>
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<p>Maximum degree of utilised adhesive bond capacity plotted for each tooth for five different retainers. The retainers demonstrate the effects of a variation in the <span class="html-italic">RD</span> configuration. The least stiff retainer (<span class="html-italic">k</span>/<span class="html-italic">k</span><sub>0</sub> = 1) corresponds to the solid green curve. An increase in the <span class="html-italic">RD</span> is displayed with the dashed blue curve while the switch to a solid cross section is displayed with the solid blue curve. A further increase in the <span class="html-italic">RD</span> is displayed with the dashed red curve while the configuration that is different to the dashed blue curve is displayed with the solid red curve. The solid blue and red curves also demonstrate the effect of an increase in the <span class="html-italic">RD</span>. All the displayed models correspond to high TR and LC1.</p>
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<p>Degree of utilised capacity for three different retainers. The values were calculated according to the fracture hypothesis as described in Formula (4) (<span class="html-italic">σ</span>/<span class="html-italic">σ<sub>u</sub></span> + <span class="html-italic">τ</span>/<span class="html-italic">τ<sub>u</sub></span>), where σ is the normal stress, <span class="html-italic">σ<sub>u</sub></span> the tensile bond strength, <span class="html-italic">τ</span> the resulting shear stress, and <span class="html-italic">τ<sub>u</sub></span> the shear bond strength. (<b>a</b>) <span class="html-italic">k</span>/<span class="html-italic">k</span><sub>0</sub> = 1 with <span class="html-italic">RD</span> = 0.38 mm in the multistranded configuration, (<b>b</b>) <span class="html-italic">k</span>/<span class="html-italic">k</span><sub>0</sub> = 12.2 with <span class="html-italic">RD</span> = 0.4 mm and a solid cross section, and (<b>c</b>) <span class="html-italic">k</span>/<span class="html-italic">k</span><sub>0</sub> = 100.1 with <span class="html-italic">RD</span> = 1.20 mm in the multistranded configuration. (<b>a</b>) corresponds to the solid green curve in <a href="#bioengineering-11-00394-f004" class="html-fig">Figure 4</a>, (<b>b</b>) to the solid blue curve, and (<b>c</b>) to the dashed red curve. All displayed models correspond to high TR and LC1.</p>
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<p>Maximum degree of utilised adhesive bond capacity plotted for each tooth for the same retainer but a different load case (dashed line) and a higher TR (blue).</p>
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19 pages, 2977 KiB  
Article
Debulking of the Femoral Stem in a Primary Total Hip Joint Replacement: A Novel Method to Reduce Stress Shielding
by Gulshan Sunavala-Dossabhoy, Brent M. Saba and Kevin J. McCarthy
Bioengineering 2024, 11(4), 393; https://doi.org/10.3390/bioengineering11040393 - 18 Apr 2024
Viewed by 1362
Abstract
In current-generation designs of total primary hip joint replacement, the prostheses are fabricated from alloys. The modulus of elasticity of the alloy is substantially higher than that of the surrounding bone. This discrepancy plays a role in a phenomenon known as stress shielding, [...] Read more.
In current-generation designs of total primary hip joint replacement, the prostheses are fabricated from alloys. The modulus of elasticity of the alloy is substantially higher than that of the surrounding bone. This discrepancy plays a role in a phenomenon known as stress shielding, in which the bone bears a reduced proportion of the applied load. Stress shielding has been implicated in aseptic loosening of the implant which, in turn, results in reduction in the in vivo life of the implant. Rigid implants shield surrounding bone from mechanical loading, and the reduction in skeletal stress necessary to maintain bone mass and density results in accelerated bone loss, the forerunner to implant loosening. Femoral stems of various geometries and surface modifications, materials and material distributions, and porous structures have been investigated to achieve mechanical properties of stems closer to those of bone to mitigate stress shielding. For improved load transfer from implant to femur, the proposed study investigated a strategic debulking effort to impart controlled flexibility while retaining sufficient strength and endurance properties. Using an iterative design process, debulked configurations based on an internal skeletal truss framework were evaluated using finite element analysis. The implant models analyzed were solid; hollow, with a proximal hollowed stem; FB-2A, with thin, curved trusses extending from the central spine; and FB-3B and FB-3C, with thick, flat trusses extending from the central spine in a balanced-truss and a hemi-truss configuration, respectively. As outlined in the International Organization for Standardization (ISO) 7206 standards, implants were offset in natural femur for evaluation of load distribution or potted in testing cylinders for fatigue testing. The commonality across all debulked designs was the minimization of proximal stress shielding compared to conventional solid implants. Stem topography can influence performance, and the truss implants with or without the calcar collar were evaluated. Load sharing was equally effective irrespective of the collar; however, the collar was critical to reducing the stresses in the implant. Whether bonded directly to bone or cemented in the femur, the truss stem was effective at limiting stress shielding. However, a localized increase in maximum principal stress at the proximal lateral junction could adversely affect cement integrity. The controlled accommodation of deformation of the implant wall contributes to the load sharing capability of the truss implant, and for a superior biomechanical performance, the collared stem should be implanted in interference fit. Considering the results of all implant designs, the truss implant model FB-3C was the best model. Full article
(This article belongs to the Special Issue Novel and Advanced Technologies for Orthopaedic Implant)
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Graphical abstract
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<p>FEA model mesh and materials of implanted femur. (<b>A</b>) Surface view of 3D model. Two kinematic couplings were used: RP1, between the condyles of the distal femur and RP2, at the center of the femoral head. RP1 was fixed in space, while displacement of RP2 was constrained to the vertical axis of the local coordinate system (red arrow). (<b>B</b>) Vertical section of implanted femur. Tie constraints to bond parts together were used between (1) the caudal and cranial halves of the femur, (2) the acetabular cup and the femoral head and neck, and (3) the reamed femur, cement, and implant and the reamed femur and implant. Surface-to-surface contact was between the calcar collar and mating femur surface. (<b>C</b>) FE mesh model of the entire femur and the direction of displacement from the point of applied load (red arrow).</p>
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<p>Coronal sectional views of implants. External dimensions were adapted from the Zimmer M/L Taper Hip Prosthesis with or without the addition of a collar. Various truss implant designs were created and sectional views of collared implants are shown. (<b>A</b>) Proximal hollowed stem. (<b>B</b>–<b>D</b>) Truss designs, FB-2A, FB-3B, and FB-3C. The distal portion of all stems was solid.</p>
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<p>Stress distribution in natural femur. A small sphere was embedded at the center of the femoral head and the load and directional constraints were applied at the center of the sphere. As defined in ISO 7206:4:2010 [<a href="#B32-bioengineering-11-00393" class="html-bibr">32</a>], a load of 2300 N was applied to the femoral head. (<b>A</b>) von Mises stress intensity distribution. Regions of significant compressive stress occur on the medial aspect of the femur, above and below the lesser trochanter (arrows). (<b>B</b>) Maximum principal (tensile) stress plot. The largest region of tensile stress occurs along the lateral aspect of the diaphysis, while there is also a noticeable region of tensile stress at the femoral neck besides the greater trochanter (block arrows).</p>
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<p>von Mises stress intensity in a femur with collared stems implanted in bone contact. (<b>A</b>) Solid, (<b>B</b>) hollow, (<b>C</b>) FB-2A, (<b>D</b>) FB-3B, and (<b>E</b>) FB-3C. Proximal medial region of stress shielding with solid implant (red frame). Increase in stress returned to the proximal medial bone with FB-3B and FB-3C implants (arrows). The stress plots are set between 0 and 20 MPa for comparative purposes.</p>
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<p>Maximum principal (tensile) stresses in femur with collared stems implanted in interference fit. (<b>A</b>) Solid, (<b>B</b>) hollow, (<b>C</b>) FB-2A, (<b>D</b>) FB-3B, and (<b>E</b>) FB-3C.</p>
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<p>Stress in femur with collarless stems in interference fit. (<b>A</b>,<b>B</b>) von Mises stress and (<b>C</b>,<b>D</b>) maximum principal stress in femur. (<b>A</b>,<b>C</b>) Solid implant and (<b>B</b>,<b>D</b>) FB-3C implant. Region of stress shielding with solid implant (red frame). Noticeably less stress shielding at proximal medial interface irrespective of a collarless FB-3C stem (arrows).</p>
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<p>Maximum principal stresses in collared Ti-6AL-4V implants when in contact with bone. (<b>A</b>) Solid, (<b>B</b>) hollow, (<b>C</b>) FB-2A, (<b>D</b>) FB-3B, and (<b>E</b>) FB-3C. Arrows show regions of peak stress. All stress plots are set between 0 and 100 MPa for comparative purposes.</p>
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<p>von Misses stress intensity in proximal femur. von Misses stress values plotted in cranial-caudal direction at the (<b>A</b>) medial, (<b>B</b>) lateral, (<b>C</b>) anterior, and (<b>D</b>) posterior interference face. Line traces of solid, FB-3B, and FB-3C implants are overlayed in the graphs.</p>
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<p>Stress intensity in a proximal femur implanted with collared and collarless stems of solid and truss configurations. (<b>A</b>) The Gruen reference zones. Nodal stress values were averaged across 5 mm segments in cranial–caudal direction of the reamed bone cavity. (<b>B</b>) Minimum principal stress (compressive) in medial bone (Gruen zone 7), and (<b>C</b>) maximum principal stress (tensile) in proximal lateral bone (Gruen zone 1).</p>
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<p>von Mises stress ratio in proximal femur. The percentage change in stress was calculated by using stress intensity averaged across equally spaced proximal distance at implant interfaces of solid, collared FB-3B, and collared FB-3C implants and, correspondingly, in the intact femur. Stress ratio in (<b>A</b>) proximal medial femur (Gruen zone 7) and (<b>B</b>) proximal lateral femur (Gruen zone 1).</p>
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<p>(<b>A</b>–<b>E</b>) Endurance testing with ISO standard set-up. (<b>A</b>,<b>B</b>) Static load tests of solid and FB-3C implants. Gross yielding of neck region in solid implant and buckling of outer casing below trusses in the FB-3C implant (block arrows). Equivalent plastic strain (PEEQ) is set between 0 and 0.0140. (<b>C</b>,<b>D</b>) Maximum principal stresses in endurance tests of solid and FB-3C implants. Maximum principal stress is set between 0 and 50 MPa. (<b>E</b>) Expanded sectional view of FB-3C implant. Region of peak tensile stress at condylar head-neck junction in solid implant and at lower trusses in FB-3C implant (arrows). (<b>F</b>) Additively manufactured Ti-Al6-4V truss implant, FB-3B, using laser powder bed fusion—external and vertically sectioned views are shown.</p>
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20 pages, 6271 KiB  
Article
Evaluation of Deep Learning Model Architectures for Point-of-Care Ultrasound Diagnostics
by Sofia I. Hernandez Torres, Austin Ruiz, Lawrence Holland, Ryan Ortiz and Eric J. Snider
Bioengineering 2024, 11(4), 392; https://doi.org/10.3390/bioengineering11040392 - 18 Apr 2024
Viewed by 1156
Abstract
Point-of-care ultrasound imaging is a critical tool for patient triage during trauma for diagnosing injuries and prioritizing limited medical evacuation resources. Specifically, an eFAST exam evaluates if there are free fluids in the chest or abdomen but this is only possible if ultrasound [...] Read more.
Point-of-care ultrasound imaging is a critical tool for patient triage during trauma for diagnosing injuries and prioritizing limited medical evacuation resources. Specifically, an eFAST exam evaluates if there are free fluids in the chest or abdomen but this is only possible if ultrasound scans can be accurately interpreted, a challenge in the pre-hospital setting. In this effort, we evaluated the use of artificial intelligent eFAST image interpretation models. Widely used deep learning model architectures were evaluated as well as Bayesian models optimized for six different diagnostic models: pneumothorax (i) B- or (ii) M-mode, hemothorax (iii) B- or (iv) M-mode, (v) pelvic or bladder abdominal hemorrhage and (vi) right upper quadrant abdominal hemorrhage. Models were trained using images captured in 27 swine. Using a leave-one-subject-out training approach, the MobileNetV2 and DarkNet53 models surpassed 85% accuracy for each M-mode scan site. The different B-mode models performed worse with accuracies between 68% and 74% except for the pelvic hemorrhage model, which only reached 62% accuracy for all model architectures. These results highlight which eFAST scan sites can be easily automated with image interpretation models, while other scan sites, such as the bladder hemorrhage model, will require more robust model development or data augmentation to improve performance. With these additional improvements, the skill threshold for ultrasound-based triage can be reduced, thus expanding its utility in the pre-hospital setting. Full article
(This article belongs to the Special Issue Machine-Learning-Driven Medical Image Analysis)
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Figure 1
<p>Representative US images for each scan point. Each column shows US images at the different scan points for negative (<b>top</b>) or positive (<b>bottom</b>) injury state. Please note that the manufacturer logo is present in the top left while the image depth is shown in bottom right corner for B-mode images (RUQ, BLD, PTX_B, and HTX_B). For M-mode images (PTX_M and HTX_M), distance between each tick mark corresponds to 0.2 s.</p>
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<p>Flowchart of architecture optimization pipeline. Sequence of optimization rounds with the parameters that were varied.</p>
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<p>Normalized performance score for the exhaustive optimization for each scan site. Graphical representation of the results for: (<b>A</b>) batch size, (<b>B</b>) optimizer, (<b>C</b>) learning rate, and (<b>D</b>) activation function. Results are normalized to the maximum performing model for each scan site, resulting in the data being gated between 0 and 1 (<span class="html-italic">n</span> = 54 exhaustive optimization model runs).</p>
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<p>Distribution of values across Bayesian optimization for each scan point and hyperparameter. Results are shown as heatmaps for the frequency of Bayesian iterations (<span class="html-italic">n</span> = 100) in which values fell in set bin sizes for (<b>A</b>) CNN layer depth, (<b>B</b>) node size, (<b>C</b>) node size multiplier, (<b>D</b>) filter size, and (<b>E</b>) dropout percentage.</p>
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<p>Prediction results from the LOSO training regimen for the RUQ scan site for different AI architectures. (<b>A</b>–<b>E</b>) Confusion matrices with GradCAM showing AI prediction results for <span class="html-italic">TP</span>, <span class="html-italic">TN</span>, <span class="html-italic">FP</span> and <span class="html-italic">FN</span> for (<b>A</b>) simple CNN architecture, (<b>B</b>) the top optimized CNN model, (<b>C</b>) ShrapML, (<b>D</b>) MobileNetV2, and (<b>E</b>) DarkNet53. Confusion matrix values are shown as the relative amounts to each ground truth category, resulting in the <span class="html-italic">TP</span> and <span class="html-italic">FP</span> rates shown in the first column and the <span class="html-italic">FN</span> and <span class="html-italic">TN</span> rates shown in the second column for blind test image predictions. The GradCAM overlay highlights relevant regions for AI prediction in red/yellow tones. (<b>F</b>) Summary of accuracy metric scores for blind test and split validation datasets for each model. Results are shown as the mean values across LOSO cross-validation runs (<span class="html-italic">n</span> = 5 splits). Error bars denote the standard deviation.</p>
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<p>Prediction results from the LOSO training regimen for the BLD scan site for different AI architectures. (<b>A</b>–<b>E</b>) Confusion matrices with GradCAM showing AI prediction results for <span class="html-italic">TP</span>, <span class="html-italic">TN</span>, <span class="html-italic">FP</span> and <span class="html-italic">FN</span> for (<b>A</b>) simple CNN architecture, (<b>B</b>) the top optimized CNN model, (<b>C</b>) ShrapML, (<b>D</b>) MobileNetV2, and (<b>E</b>) DarkNet53. Confusion matrix values are shown as the relative amounts to each ground truth category, resulting in the <span class="html-italic">TP</span> and <span class="html-italic">FP</span> rates shown in the first column and the <span class="html-italic">FN</span> and <span class="html-italic">TN</span> rates shown in the second column for blind test image predictions. The GradCAM overlay highlights relevant regions for AI prediction in red/yellow tones. (<b>F</b>) Summary of accuracy metric scores for blind test and split validation datasets for each model. Results are shown as the mean values across LOSO cross-validation runs (<span class="html-italic">n</span> = 5 splits). Error bars denote the standard deviation.</p>
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<p>Prediction results from the LOSO training regimen for the PTX_B scan site for different AI architectures. (<b>A</b>–<b>E</b>) Confusion matrices with GradCAM showing AI prediction results for <span class="html-italic">TP</span>, <span class="html-italic">TN</span>, <span class="html-italic">FP</span> and <span class="html-italic">FN</span> for (<b>A</b>) simple CNN architecture, (<b>B</b>) the top optimized CNN model, (<b>C</b>) ShrapML, (<b>D</b>) MobileNetV2, and (<b>E</b>) DarkNet53. Confusion matrix values are shown as the relative amounts to each ground truth category, resulting in the <span class="html-italic">TP</span> and <span class="html-italic">FP</span> rates shown in the first column and the <span class="html-italic">FN</span> and <span class="html-italic">TN</span> rates shown in the second column for blind test image predictions. The GradCAM overlay highlights relevant regions for AI prediction in red/yellow tones. (<b>F</b>) Summary of accuracy scores for blind test and split validation datasets for each model. Results are shown as the mean values across LOSO cross-validation training runs (<span class="html-italic">n</span> = 5 splits). Error bars denote the standard deviation.</p>
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<p>Prediction results from the LOSO training regimen for the PTX_M scan site for different AI architectures. (<b>A</b>–<b>E</b>) Confusion matrices with GradCAM showing AI prediction results for <span class="html-italic">TP</span>, <span class="html-italic">TN</span>, <span class="html-italic">FP</span> and <span class="html-italic">FN</span> for (<b>A</b>) simple CNN architecture, (<b>B</b>) the top optimized CNN model, (<b>C</b>) ShrapML, (<b>D</b>) MobileNetV2, and (<b>E</b>) DarkNet53. Confusion matrix values are shown as the relative amounts to each ground truth category, resulting in the <span class="html-italic">TP</span> and <span class="html-italic">FP</span> rates shown in the first column and the <span class="html-italic">FN</span> and <span class="html-italic">TN</span> rates shown in the second column for blind test image predictions. The GradCAM overlay highlights relevant regions for AI prediction in red/yellow tones. (<b>F</b>) Summary of accuracy metric scores for blind test and split validation datasets for each model. Results are shown as the mean values across LOSO cross-validation training runs (<span class="html-italic">n</span> = 5 splits). Error bars denote the standard deviation.</p>
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<p>Prediction results from the LOSO training regimen for the HTX_B scan site for different AI architectures. (<b>A</b>–<b>E</b>) Confusion matrices with GradCAM showing AI prediction results for <span class="html-italic">TP</span>, <span class="html-italic">TN</span>, <span class="html-italic">FP</span> and <span class="html-italic">FN</span> for (<b>A</b>) simple CNN architecture, (<b>B</b>) the top optimized CNN model, (<b>C</b>) ShrapML, (<b>D</b>) MobileNetV2, and (<b>E</b>) DarkNet53. Confusion matrix values are shown as the relative amounts to each ground truth category, resulting in the <span class="html-italic">TP</span> and <span class="html-italic">FP</span> rates shown in the first column and the <span class="html-italic">FN</span> and <span class="html-italic">TN</span> rates shown in the second column for blind test image predictions. The GradCAM overlay highlights relevant regions for AI prediction in red/yellow tones. (<b>F</b>) Summary of accuracy metric scores for blind test and split validation datasets for each model. Results are shown as the mean values across LOSO cross-validation training runs (<span class="html-italic">n</span> = 5 splits). Error bars denote the standard deviation.</p>
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<p>Prediction results from the LOSO training regimen for the HTX_M scan site for different AI architectures. (<b>A</b>–<b>E</b>) Confusion matrices with GradCAM showing AI prediction results for <span class="html-italic">TP</span>, <span class="html-italic">TN</span>, <span class="html-italic">FP</span> and <span class="html-italic">FN</span> for (<b>A</b>) simple CNN architecture, (<b>B</b>) the top optimized CNN model, (<b>C</b>) ShrapML, (<b>D</b>) MobileNetV2, and (<b>E</b>) DarkNet53. Confusion matrix values are shown as the relative amounts to each ground truth category, resulting in the <span class="html-italic">TP</span> and <span class="html-italic">FP</span> rates shown in the first column and the <span class="html-italic">FN</span> and <span class="html-italic">TN</span> rates shown in the second column for blind test image predictions. The GradCAM overlay highlights relevant regions for AI prediction in red/yellow tones. (<b>F</b>) Summary of accuracy metric scores for blind test and split validation datasets for each model. Results are shown as the mean values across LOSO cross-validation training runs (<span class="html-italic">n</span> = 5 splits). Error bars denote the standard deviation.</p>
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16 pages, 3827 KiB  
Article
Thermal Characterization and Preclinical Feasibility Verification of an Accessible, Carbon Dioxide-Based Cryotherapy System
by Yixin Hu, Naomi Gordon, Katherine Ogg, Dara L. Kraitchman, Nicholas J. Durr and Bailey Surtees
Bioengineering 2024, 11(4), 391; https://doi.org/10.3390/bioengineering11040391 - 18 Apr 2024
Viewed by 1208
Abstract
To investigate the potential of an affordable cryotherapy device for the accessible treatment of breast cancer, the performance of a novel carbon dioxide-based device was evaluated through both benchtop testing and an in vivo canine model. This novel device was quantitatively compared to [...] Read more.
To investigate the potential of an affordable cryotherapy device for the accessible treatment of breast cancer, the performance of a novel carbon dioxide-based device was evaluated through both benchtop testing and an in vivo canine model. This novel device was quantitatively compared to a commercial device that utilizes argon gas as the cryogen. The thermal behavior of each device was characterized through calorimetry and by measuring the temperature profiles of iceballs generated in tissue phantoms. A 45 min treatment in a tissue phantom from the carbon dioxide device produced a 1.67 ± 0.06 cm diameter lethal isotherm that was equivalent to a 7 min treatment from the commercial argon-based device, which produced a 1.53 ± 0.15 cm diameter lethal isotherm. An in vivo treatment was performed with the carbon dioxide-based device in one spontaneously occurring canine mammary mass with two standard 10 min freezes. Following cryotherapy, this mass was surgically resected and analyzed for necrosis margins via histopathology. The histopathology margin of necrosis from the in vivo treatment with the carbon dioxide device at 14 days post-cryoablation was 1.57 cm. While carbon dioxide gas has historically been considered an impractical cryogen due to its low working pressure and high boiling point, this study shows that carbon dioxide-based cryotherapy may be equivalent to conventional argon-based cryotherapy in size of the ablation zone in a standard treatment time. The feasibility of the carbon dioxide device demonstrated in this study is an important step towards bringing accessible breast cancer treatment to women in low-resource settings. Full article
(This article belongs to the Special Issue Novel, Low Cost Technologies for Cancer Diagnostics and Therapeutics)
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<p>A comparison of the cryoprobe size, shape, and active freezing zone profiles is shown for the two devices. The CO<sub>2</sub> device has a probe diameter of 4.19 mm, while the benchmark argon device has a probe diameter of 1.50 mm.</p>
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<p>Tissue phantom testing setup. The cryoprobe is placed in a custom fixture containing heated ultrasound gel with four thermocouples at fixed radial distances from the probe surface. The ultrasound gel is surrounded by a 37 °C water bath maintained with temperature monitoring and control, simulating the heat load conditions of a body.</p>
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<p>Positions of the four thermocouples. Left: a comparison of the center distance of the thermocouples for the argon and CO<sub>2</sub> probes. Right: a diagram showing the distance of each thermocouple from the probe surface.</p>
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<p>Diagram of the cryotherapy procedure setup. <b>Left</b>: the device is connected to a standard CO<sub>2</sub> tank. <b>Center</b>: the subject is sterilely draped and prepped, and the probe is inserted into the target mass. <b>Right</b>: the device is turned on and an iceball begins to grow around the probe tip inducing necrosis in the mass.</p>
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<p>Temperature data on tissue phantom during the testing period averaged over 10 trials. <span class="html-italic">r</span> represents the distance of the thermocouple from the probe surface.</p>
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<p>Photograph of iceballs generated during testing in the tissue phantom. <b>Left</b>: CO<sub>2</sub> probe; <b>right</b>: benchmark argon probe.</p>
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<p>Means and 95% confidence intervals of the radial temperature distribution from the surface of the probes at the end of the testing period for each device. The red line plots the logarithmic fit of the mean thermocouple data over 10 trials. The gray area represents the 95% confidence interval of the thermocouple data fitted to a logarithmic equation. The distance of the −20 °C isotherm from the surface of the probe with the 95% confidence interval is 6.3 mm (5.9, 6.6) for the CO<sub>2</sub> device and 6.9 mm (6.1, 7.7) for the benchmark argon device.</p>
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<p>Cross-sectional representation of the probe diameters and the mean diameters of the −20 °C isotherms for each probe during testing, extrapolated from the interpolated isotherm radii, assuming radial symmetry around the probe axis. Iceball size is representative.</p>
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<p>Reconstructed photomicrograph from two pieces of the mass containing annotations that show the cryoprobe orientation (black arrow). The region of necrotic tissue is outlined in green, the region of viable tumor tissue in blue, and the maximum length) and width of necrosis relative to the probe track are identified by the yellow and blue dashed lines, respectively. Length measurements do not include regions without tissue between the two pieces.</p>
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<p>Representative regions of the treated mass showing necrotic and viable tumor tissue at 20× magnification. Region I shows necrotic tissue; region II shows the borderline between necrotic and viable tumor tissue; and region III shows viable tumor tissue. The location of each magnified region is noted on the whole slide image (top left).</p>
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15 pages, 2250 KiB  
Article
DTONet a Lightweight Model for Melanoma Segmentation
by Shengnan Hao, Hongzan Wang, Rui Chen, Qinping Liao, Zhanlin Ji, Tao Lyu and Li Zhao
Bioengineering 2024, 11(4), 390; https://doi.org/10.3390/bioengineering11040390 - 18 Apr 2024
Viewed by 1329
Abstract
With the further development of neural networks, automatic segmentation techniques for melanoma are becoming increasingly mature, especially under the conditions of abundant hardware resources. This allows for the accuracy of segmentation to be improved by increasing the complexity and computational capacity of the [...] Read more.
With the further development of neural networks, automatic segmentation techniques for melanoma are becoming increasingly mature, especially under the conditions of abundant hardware resources. This allows for the accuracy of segmentation to be improved by increasing the complexity and computational capacity of the model. However, a new problem arises when it comes to actual applications, as there may not be the high-end hardware available, especially in hospitals and among the general public, who may have limited computing resources. In response to this situation, this paper proposes a lightweight deep learning network that can achieve high segmentation accuracy with minimal resource consumption. We introduce a network called DTONet (double-tailed octave network), which was specifically designed for this purpose. Its computational parameter count is only 30,859, which is 1/256th of the mainstream UNet model. Despite its reduced complexity, DTONet demonstrates superior performance in terms of accuracy, with an IOU improvement over other similar models. To validate the generalization capability of this model, we conducted tests on the PH2 dataset, and the results still outperformed existing models. Therefore, the proposed DTONet network exhibits excellent generalization ability and is sufficiently outstanding. Full article
(This article belongs to the Special Issue Application of Deep Learning in Medical Diagnosis)
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<p>The DTO-Net network. (This network consists of an encoding phase and two decoding phases. The encoding phase comprises G_O (ghost octave) and ORFB (octave receptive field block) modules, while the decoding phase consists of the G_ECA (ghost efficient channel attention) module).</p>
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<p>The G_O model (this is the specific composition diagram of the corresponding decoder).</p>
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<p>The G_ECA model (this is the specific composition of encoder component module G_ECA, which is improved on the basis of G_O module to make it more suitable for encoders).</p>
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<p>The ORFB model (this is the specific composition of the encoder component module ORFB, which is improved on the basis of the G_O module, which can improve the accuracy very well, but it will produce too many parameters, so it is only used here).</p>
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<p>Overlayed image of segmentation results and ground truth in ISIC2018.</p>
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<p>Overlay image of segmentation results and ground truth in PH2.</p>
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<p>Comparison of segmentation results of each model. The left part (<b>a</b>) is the presentation of results in the ISIC2018 dataset, and the right part (<b>b</b>) is the presentation of segmentation results in the PH2 dataset.</p>
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14 pages, 2488 KiB  
Article
Altered Functional Connectivity of Temporoparietal Lobe in Obstructive Sleep Apnea: A Resting-State fNIRS Study
by Fang Xiao, Minghui Liu, Yalin Wang, Ligang Zhou, Jingchun Luo, Chen Chen and Wei Chen
Bioengineering 2024, 11(4), 389; https://doi.org/10.3390/bioengineering11040389 - 18 Apr 2024
Viewed by 1484
Abstract
Obstructive Sleep Apnea (OSA), a sleep disorder with high prevalence, is normally accompanied by affective, autonomic, and cognitive abnormalities, and is deemed to be linked to functional brain alterations. To investigate alterations in brain functional connectivity properties in patients with OSA, a comparative [...] Read more.
Obstructive Sleep Apnea (OSA), a sleep disorder with high prevalence, is normally accompanied by affective, autonomic, and cognitive abnormalities, and is deemed to be linked to functional brain alterations. To investigate alterations in brain functional connectivity properties in patients with OSA, a comparative analysis of global and local topological properties of brain networks was conducted between patients with OSA and healthy controls (HCs), utilizing functional near-infrared spectroscopy (fNIRS) imaging. A total of 148 patients with OSA and 150 healthy individuals were involved. Firstly, quantitative alterations in blood oxygen concentration, changes in functional connectivity, and variations in graph theory-based network topological characteristics were assessed. Then, with Mann–Whitney statistics, this study compared whether there are significant differences in the above characteristics between patients with OSA and HCs. Lastly, the study further examined the correlation between the altered characteristics and the apnea hypopnea index (AHI) using linear regression. Results revealed a higher mean and standard deviation of hemoglobin concentration in the superior temporal gyrus among patients with OSA compared to HCs. Resting-state functional connectivity (RSFC) exhibited a slight increase between the superior temporal gyrus and other specific areas in patients with OSA. Notably, neither patients with OSA nor HCs demonstrated significant small-world network properties. Patients with OSA displayed an elevated clustering coefficient (p < 0.05) and local efficiency (p < 0.05). Additionally, patients with OSA exhibited a tendency towards increased nodal betweenness centrality (p < 0.05) and degree centrality (p < 0.05) in the right supramarginal gyrus, as well as a trend towards higher betweenness centrality (p < 0.05) in the right precentral gyrus. The results of multiple linear regressions indicate that the influence of the AHI on RSFC between the right precentral gyrus and right superior temporal gyrus (p < 0.05), as well as between the right precentral gyrus and right supramarginal gyrus (p < 0.05), are statistically significant. These findings suggest that OSA may compromise functional brain connectivity and network topological properties in affected individuals, serving as a potential neurological mechanism underlying the observed abnormalities in brain function associated with OSA. Full article
(This article belongs to the Section Biosignal Processing)
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<p>Two-dimensional view of optode placement. Red denotes the source and blue signifies the detector. Seven sources and seven detectors make up a total of 16 channels.</p>
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<p>The altered brain regions in patients with OSA exhibit distinctive characteristics in mean and variance of HbO<sub>2</sub> concentration, with the most prominent changes observed in channel 14, corresponding to the right superior temporal gyrus. Specifically, the mean HbO<sub>2</sub> concentration demonstrates a significant difference, with values of 3.81 ± 1.54 compared to 3.65 ± 1.51 in individuals without OSA. Additionally, there is a notable distinction in the standard deviation (std) of HbO<sub>2</sub> concentration, showing values of 3.73 ± 1.03 for patients with OSA versus 3.56 ± 1.53 for HCs. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Group-averaged correlation matrix plots of temporoparietal regions for individuals with OSA (<b>a</b>) and HCs (<b>b</b>). Each pixel in the correlation matrix plot represents the z-value of the Pearson correlation coefficient after Fisher z-transformation for the corresponding channel pair.</p>
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<p>Abnormal RSFC within the temporoparietal brain network in individuals with OSA compared to HCs.</p>
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<p>The small-world characteristics of the temporoparietal network for both patients with OSA and HCs across the defined wide threshold range. In the figure, it is evident that the average shortest path length ratio (λ) is approximately equal to one, and the small-world index (σ) is greater than one. However, the clustering coefficient ratio (γ) is not significantly greater than one, indicating that neither patients with OSA nor HCs exhibit a typical small-world topology.</p>
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<p>The between-group differences in global network metrics for the temporoparietal network in individuals with OSA and HCs. There is a noticeable trend suggesting a distinction among patients with OSA, with an elevated Cp (<span class="html-italic">p</span> &lt; 0.05) and an increased Eloc (<span class="html-italic">p</span> &lt; 0.05). * <span class="html-italic">p</span> &lt; 0.05.</p>
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22 pages, 4938 KiB  
Article
Functional and Molecular Analysis of Human Osteoarthritic Chondrocytes Treated with Bone Marrow-Derived MSC-EVs
by Annachiara Scalzone, Clara Sanjurjo-Rodríguez, Rolando Berlinguer-Palmini, Anne M. Dickinson, Elena Jones, Xiao-Nong Wang and Rachel E. Crossland
Bioengineering 2024, 11(4), 388; https://doi.org/10.3390/bioengineering11040388 - 17 Apr 2024
Cited by 1 | Viewed by 2284
Abstract
Osteoarthritis (OA) is a degenerative joint disease, causing impaired mobility. There are currently no effective therapies other than palliative treatment. Mesenchymal stromal cells (MSCs) and their secreted extracellular vesicles (MSC-EVs) have shown promise in attenuating OA progression, promoting chondral regeneration, and modulating joint [...] Read more.
Osteoarthritis (OA) is a degenerative joint disease, causing impaired mobility. There are currently no effective therapies other than palliative treatment. Mesenchymal stromal cells (MSCs) and their secreted extracellular vesicles (MSC-EVs) have shown promise in attenuating OA progression, promoting chondral regeneration, and modulating joint inflammation. However, the precise molecular mechanism of action driving their beneficial effects has not been fully elucidated. In this study, we analyzed MSC-EV-treated human OA chondrocytes (OACs) to assess viability, proliferation, migration, cytokine and catabolic protein expression, and microRNA and mRNA profiles. We observed that MSC-EV-treated OACs displayed increased metabolic activity, proliferation, and migration compared to the controls. They produced decreased proinflammatory (Il-8 and IFN-γ) and increased anti-inflammatory (IL-13) cytokines, and lower levels of MMP13 protein coupled with reduced expression of MMP13 mRNA, as well as negative microRNA regulators of chondrogenesis (miR-145-5p and miR-21-5p). In 3D models, MSC-EV-treated OACs exhibited enhanced chondrogenesis-promoting features (elevated sGAG, ACAN, and aggrecan). MSC-EV treatment also reversed the pathological impact of IL-1β on chondrogenic gene expression and extracellular matrix component (ECM) production. Finally, MSC-EV-treated OACs demonstrated the enhanced expression of genes associated with cartilage function, collagen biosynthesis, and ECM organization and exhibited a signature of 24 differentially expressed microRNAs, associated with chondrogenesis-associated pathways and ECM interactions. In conclusion, our data provide new insights on the potential mechanism of action of MSC-EVs as a treatment option for early-stage OA, including transcriptomic analysis of MSC-EV-treated OA, which may pave the way for more targeted novel therapeutics. Full article
(This article belongs to the Section Regenerative Engineering)
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<p>Characteristics of MSCs and MSC-EVs. (<b>A</b>) MSC morphology and tri-lineage potential (adipogenesis, osteogenesis, chondrogenesis) assessment. (<b>B</b>) FC histogram showing MSC surface phenotype profiles for lineage specific markers (CD14, CD19, CD34, CD45), HLA-DR, CD105, CD73, and CD90. (<b>C</b>) TEM image showing EV morphology. (<b>D</b> FC histogram showing positive expression of EV markers CD63, CD81, and CD9. (<b>E</b>) NTA analysis showing EV size distribution. (<b>F</b>) Western blot analysis showing positive expression of EV markers Flotillin-1 and Alix in three independent MSC-EV examples (S1 to S3).</p>
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<p>MSC-EV-treated OACs’ EV uptake, viability, mobility, and migration. (<b>A</b>) Confocal microscopy images to show MSC-EV-treated OACs. OACs internalize MSC-EVs after 1 h of co-incubation. Cut view confirms internalization of MSC-EVs. (<b>B</b>) Analysis of metabolic activity of OACs and EV-treated OACs (OAC + EV) via CellTiter-Glo 2.0 assay after 5 days of culture. Significance was calculated using the Wilcoxon matched pairs signed-rank test. (<b>C</b>) Two-dimensional scratch assay: example images for OAC and OAC + EV samples at 0 and 40 h post-scratch. Significance was calculated using a mixed-effects model, with Sidak’s multiple comparisons test. (<b>D</b>) Cell migration assay: example images are shown for OAC and OAC + EV samples, assessed using a transwell filter system. Total cell count is shown for OAC and OAC ± EV. Significance was calculated using the paired <span class="html-italic">t</span>-test. (<b>B</b>–<b>D</b>) Lines represent mean ± SEM. The significance threshold for <span class="html-italic">p</span>-values was <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>MSC-EV-treated OACs’ cytokine, catabolic protein, gene, and microRNA expression. (<b>A</b>) Secreted cytokine production (IFN-γ, IL-8, IL-13) by MSC-EV-treated OACs by multiplex immunoassay in n = 12 OAC and OAC + EV. (<b>B</b>) Secreted MMP13 protein production by ELISA in n = 15 OAC and OAC + EV, and gene expression assessed by qRT-PCR in 2D monolayer cultures in n = 11 matched OAC controls and OAC + EV. (<b>C</b>) MicroRNA expression assessed by qRT-PCR in 2D monolayer cultures in n = 11 matched OAC controls and OAC + EV. (<b>A</b>–<b>C</b>) Expression differences between groups were calculated using the paired <span class="html-italic">t</span>-test, and lines represent mean ± SEM. The significance threshold for <span class="html-italic">p</span>-values was <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Chondrogenic potential of MSC-EV-treated OACs in a 3D spheroid model. (<b>A</b>) Quantification of sGAG production in OAC and OAC + EV samples. (<b>B</b>) Representative histological analysis of toluidine blue, which stains sGAGs, on tissue slices from three independent experiments (scale bar = 200 µm). Zoomed image is shown in the center and in the peripheral zone of the spheroid slice (scale bar = 125 µm). (<b>C</b>) ACAN relative expression via RT-qPCR, for OAC and OAC + EV, at day 21 with respect to day 2. (<b>D</b>) Representative IHC of aggrecan on tissue slices for OAC and OAC + EV samples (scale bar = 200 µm). Zoomed image is shown in the center and in the peripheral zone of the spheroid slice (scale bar = 125 µm). (<b>A</b>,<b>C</b>) Significance was calculated using the paired <span class="html-italic">t</span>-test and lines represent mean ± SEM. The significance threshold for <span class="html-italic">p</span>-values was <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effect of MSC-EVs and IL-1β on OAC chondrogenic gene expression and ECM component. (<b>A</b>) Relative gene expression of ACAN, SOX9, COL2A1, COL10A1, MMP13, and ADAMTS at day 10 with respect to day 2. (<b>B</b>) Quantification of sGAG production in digested spheroids formed from OAC, OAC + IL, and OAC + IL + EV samples. (<b>C</b>) Representative histological analysis of Toluidine blue staining GAGs from three independent experiments. Scale bars represent 200 µm and 125 µm, respectively, for original and zoomed-in areas, as indicated. (<b>D</b>) Representative IHC of aggrecan staining on tissue sections of OAC, OAC + IL, and OAC + IL + EV samples from three independent experiments. Scale bars represent 200 µm and 125 µm, respectively, for original and zoomed areas, as indicated. (<b>A</b>,<b>B</b>) Significance was calculated using one-way ANOVA and lines represent mean ± SEM. The significance threshold for <span class="html-italic">p</span>-values was <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Gene expression profile of MSC-EV-treated OACs compared to OACs. (<b>A</b>) Heatmap shows unsupervised hierarchical clustering of 54 genes that were significantly differentially expressed between groups: each column represents an individual sample. OAC + EV are depicted in blue, while control OACs are depicted in pink. Patient gender (green, purple) and age (green scale) are also indicated. Relative expression changes are indicated by the color scale (red: high; blue: low). (<b>B</b>) Principal component analysis of MSC-EV-treated OACs vs. control OACs. (<b>C</b>) Reactome pathway analysis for genes significantly differentially expressed between OAC + EV and OAC controls. Both analyses indicate the number of genes implicated in each pathway.</p>
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<p>Differential microRNA expression profiles of OAC + EV and OAC. (<b>A</b>) NanoString microRNA expression profiling: heatmap shows unsupervised hierarchical clustering of significantly differentially expressed microRNAs (<span class="html-italic">p</span> &lt; 0.05, n = 24), based on normalized expression counts, in OAC + EV and controls. Each column represents an individual sample. Relative expression changes are indicated by the color scale (red: high; blue: low). OAC + EV are depicted by black shading, while OAC are depicted by grey shading. (<b>B</b>) KEGG pathways analysis for the 24 differentially expressed microRNAs in OAC + EV vs. control OACs. Pathway analysis was performed using Diana Tools miRPath, based on TarBase v8.0 and the number of target genes and targeting microRNAs are plotted for each pathway (FDR-corrected <span class="html-italic">p</span>-values &lt; 0.05). (<b>C</b>) Validation of selected microRNA expression in an independent cohort of OAC + EV and OAC controls (n = 11) by qRT-PCR. Lines represent mean ± SEM. Significance was calculated using the paired <span class="html-italic">t</span>-test, and the threshold for <span class="html-italic">p</span>-values was <span class="html-italic">p</span> &lt; 0.05. Heatmap to show unsupervised hierarchical clustering of the same microRNAs according to NanoString data (n = 7 matched samples and controls).</p>
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13 pages, 4023 KiB  
Article
Could Dental Material Reuse Play a Significant Role in Preservation of Raw Materials, Water, Energy, and Costs? Microbiological Analysis of New versus Reused Healing Abutments: An In Vitro Study
by Roberto Burioni, Lucia Silvestrini, Bianca D’Orto, Giulia Tetè, Matteo Nagni, Elisabetta Polizzi and Enrico Felice Gherlone
Bioengineering 2024, 11(4), 387; https://doi.org/10.3390/bioengineering11040387 - 16 Apr 2024
Cited by 1 | Viewed by 1070
Abstract
Aim: The objective of this in vitro study was to compare reused and sterilized versus new healing abutments to assess whether a decontamination and sterilization process performed on resued healing abutments was sufficient to remove residual proteins. The two groups were comparable with [...] Read more.
Aim: The objective of this in vitro study was to compare reused and sterilized versus new healing abutments to assess whether a decontamination and sterilization process performed on resued healing abutments was sufficient to remove residual proteins. The two groups were comparable with respect to patient safety. Materials and methods: During the period from September 2022 to October 2023, healing abutment screws were selected and divided into two groups according to whether they were new or previously used in patients. The samples were subjected to a decontamination and sterilization protocol, and results from sample sterility evaluation and assessment of surface protein levels were recorded. Results: The obtained results revealed a significant difference in the OD562 nm values between new and reused healing abutment samples. The assay demonstrates how treated healing abutments were still contaminated by residual proteins. Conclusion: Within the limitations of the present study, although from an infectious point of view sterilization results in the total eradication of pathogens, surface proteins remain on reused healing abutments. Full article
(This article belongs to the Special Issue Dental Implant Reconstruction and Biomechanical Evaluation)
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<p>Workflow details.</p>
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<p>HAs following the above-described decontamination and sterilization process.</p>
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<p>Colorimetric variation based on reduction of Cu<sup>2+</sup> in the presence of protein and in an alkaline environment and subsequent chelation reaction between BCA and Cu<sup>1+</sup> resulting from the first reaction.</p>
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<p>Sterile tube containing a HA in BCA solution.</p>
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<p>Sterile tubes free of HAs and containing only BCS.</p>
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<p>Aliquots of 150 µL were added to 96-well plates in triplicate. All tubes retain the green coloring as the reaction to test for surface proteins has not yet been performed. The three sterile tubes free of HAs and containing only BCS are placed apart on the left to distinguish them from the sample under examination.</p>
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<p>BSA standard curve.</p>
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<p>Configuration obtained with the MicroBCA assay. Reused healing screws with surface proteins appear violet in color.</p>
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<p>Difference in the OD562 nm values between new and reused HA samples.</p>
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<p>Average residual protein concentration detected in the eluate from TG screws.</p>
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17 pages, 5127 KiB  
Article
Balance Evaluation Based on Walking Experiments with Exoskeleton Interference
by Liping Wang, Xin Li, Yiying Peng, Jianda Han and Juanjuan Zhang
Bioengineering 2024, 11(4), 386; https://doi.org/10.3390/bioengineering11040386 - 16 Apr 2024
Viewed by 1153
Abstract
The impairment of walking balance function seriously affects human health and will lead to a significantly increased risk of falling. It is important to assess and improve the walking balance of humans. However, existing evaluation methods for human walking balance are relatively subjective, [...] Read more.
The impairment of walking balance function seriously affects human health and will lead to a significantly increased risk of falling. It is important to assess and improve the walking balance of humans. However, existing evaluation methods for human walking balance are relatively subjective, and the selected metrics lack effectiveness and comprehensiveness. We present a method to construct a comprehensive evaluation index of human walking balance. We used it to generate personal and general indexes. We first pre-selected some preliminary metrics of walking balance based on theoretical analysis. Seven healthy subjects walked with exoskeleton interference on a treadmill at 1.25 m/s while their ground reaction force information and kinematic data were recorded. One subject with Charcot–Marie–Tooth walked at multiple speeds without the exoskeleton while the same data were collected. Then, we picked a number of effective evaluation metrics based on statistical analysis. We finally constructed the Walking Balance Index (WBI) by combining multiple metrics using principal component analysis. The WBI can distinguish walking balance among different subjects and gait conditions, which verifies the effectiveness of our method in evaluating human walking balance. This method can be used to evaluate and further improve the walking balance of humans in subsequent simulations and experiments. Full article
(This article belongs to the Special Issue Bioengineering of the Motor System)
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<p>Gait experimental platform. The gait experimental platform included a control system, an actuation system, transmissions, a motion capture system, and an ankle exoskeleton.</p>
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<p>Three ankle interference torque profiles. These torques were applied only in the support phase (0–60% of a gait cycle) for safety. (<b>A</b>) Sinusoidal interference torque. It consisted of two continuous sinusoids. (<b>B</b>) Constant interference torque. Its maximum torque remained the same. (<b>C</b>) Random interference torque. The torque profile was generated randomly.</p>
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<p>The changes in the preliminary metrics. (<b>A</b>) The changes in the positions and velocities of the COP. (<b>B</b>) The changes in the accelerations of the COM in three directions and the resultant acceleration. (<b>C</b>) The changes in the positions of the COM. (<b>D</b>) The changes in the relative position between the COP and CMP, MOS, and trunk angular acceleration. The blue curves show the changes for subject2 under the ZT condition. The red curves show the changes for subject2 under the MIX condition. The purple curves show the changes for subject8 with CMT under the NW condition. The shadow areas show the fluctuation range of different gait cycles.</p>
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<p>The values of personal WBI for seven healthy subjects. The blue curves are the WBI of each stride under the ZT condition. The red curves and areas are the mean and fluctuation range of the WBI under the MIX condition.</p>
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<p>The results of WBI for subject2. (<b>A</b>) The WBI under ZT and CON conditions (above). The WBI under ZT and RAN conditions (below). (<b>B</b>) Bars and whiskers are means and standard deviations of the WBI for subject2 under ZT and CON conditions (above) and ZT and RAN conditions (below).</p>
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<p>The values of the WBI for the six healthy subjects using the general index constructed from one subject’s data obtained under ZT and MIX conditions. Bars and whiskers are the means and standard deviations of the WBI for six healthy subjects under different gait conditions.</p>
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<p>WBI of healthy subject1 under ZT and MIX conditions and subject8 with CMT under the NW condition. (<b>A</b>) The results of the WBI constructed from subject8’s NW condition and subject1’s MIX condition. (<b>B</b>) The results of the WBI constructed from subject1’s ZT and MIX conditions. The blue curves show subject1’s WBI for each stride under the ZT condition. The red curves and areas show the mean and fluctuation range of subject1’s WBI under the MIX condition. The purple curves and areas show the mean and fluctuation range of subject8’s WBI under the NW condition.</p>
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11 pages, 1891 KiB  
Article
Application of the Single Source—Detector Separation Algorithm in Wearable Neuroimaging Devices: A Step toward Miniaturized Biosensor for Hypoxia Detection
by Thien Nguyen, Soongho Park, Jinho Park, Asma Sodager, Tony George and Amir Gandjbakhche
Bioengineering 2024, 11(4), 385; https://doi.org/10.3390/bioengineering11040385 - 16 Apr 2024
Viewed by 1513
Abstract
Most currently available wearable devices to noninvasively detect hypoxia use the spatially resolved spectroscopy (SRS) method to calculate cerebral tissue oxygen saturation (StO2). This study applies the single source—detector separation (SSDS) algorithm to calculate StO2. Near-infrared spectroscopy (NIRS) data [...] Read more.
Most currently available wearable devices to noninvasively detect hypoxia use the spatially resolved spectroscopy (SRS) method to calculate cerebral tissue oxygen saturation (StO2). This study applies the single source—detector separation (SSDS) algorithm to calculate StO2. Near-infrared spectroscopy (NIRS) data were collected from 26 healthy adult volunteers during a breath-holding task using a wearable NIRS device, which included two source—detector separations (SDSs). These data were used to derive oxyhemoglobin (HbO) change and StO2. In the group analysis, both HbO change and StO2 exhibited significant change during a breath-holding task. Specifically, they initially decreased to minimums at around 10 s and then steadily increased to maximums, which were significantly greater than baseline levels, at 25–30 s (p-HbO < 0.001 and p-StO2 < 0.05). However, at an individual level, the SRS method failed to detect changes in cerebral StO2 in response to a short breath-holding task. Furthermore, the SSDS algorithm is more robust than the SRS method in quantifying change in cerebral StO2 in response to a breath-holding task. In conclusion, these findings have demonstrated the potential use of the SSDS algorithm in developing a miniaturized wearable biosensor to monitor cerebral StO2 and detect cerebral hypoxia. Full article
(This article belongs to the Special Issue Neuroimaging Techniques for Wearable Devices in Bioengineering)
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<p>Illustration of devices that use the SRS method and SSDS algorithm. SRS-based devices need to have at least 2 source—detector pairs, while SSDS-based devices need only one source—detector pair; hence, they can be made smaller and simpler. In addition, there is an assumption that d has to be significantly larger than ∂d in the SRS method. As a result, SRS-based devices need to have detectors far away from the light source, which reduces the signal to noise ratio and increases the device size.</p>
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<p>Cerebral HbO changes due to the breath-holding task; (<b>a</b>) HbO change from 3 cm SDS; (<b>b</b>) HbO change from 4 cm SDS. Error bars represent standard error. * indicates a <span class="html-italic">p</span> value &lt; 0.05 and ** indicates a <span class="html-italic">p</span> value &lt; 0.001.</p>
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<p>Change in StO<sub>2</sub>-SRS due to the breath-holding task. Error bars represent standard error. * indicates a <span class="html-italic">p</span> value &lt; 0.05. StO<sub>2</sub>-SRS change was obtained from the processed data worksheet.</p>
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<p>Change in StO<sub>2</sub>-SSDS due to the breath-holding task. (<b>a</b>) StO<sub>2</sub>-SSDS calculated from 3 cm SDS; (<b>b</b>) StO<sub>2</sub>-SSDS calculated from 4 cm SDS. Error bars represent standard error. * indicates a <span class="html-italic">p</span> value &lt; 0.05.</p>
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<p>StO<sub>2</sub>-SRS and StO<sub>2</sub>-SSDS before, during, and after a breath-holding task; (<b>a</b>) short task and (<b>b</b>) long task. Vertical lines mark the start and end of the breath-holding task.</p>
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11 pages, 1901 KiB  
Article
A Clinical Trial Evaluating the Efficacy of Deep Learning-Based Facial Recognition for Patient Identification in Diverse Hospital Settings
by Ayako Sadahide, Hideki Itoh, Ken Moritou, Hirofumi Kameyama, Ryoya Oda, Hitoshi Tabuchi and Yoshiaki Kiuchi
Bioengineering 2024, 11(4), 384; https://doi.org/10.3390/bioengineering11040384 - 15 Apr 2024
Viewed by 1556
Abstract
Background: Facial recognition systems utilizing deep learning techniques can improve the accuracy of facial recognition technology. However, it remains unclear whether these systems should be available for patient identification in a hospital setting. Methods: We evaluated a facial recognition system using deep learning [...] Read more.
Background: Facial recognition systems utilizing deep learning techniques can improve the accuracy of facial recognition technology. However, it remains unclear whether these systems should be available for patient identification in a hospital setting. Methods: We evaluated a facial recognition system using deep learning and the built-in camera of an iPad to identify patients. We tested the system under different conditions to assess its authentication scores (AS) and determine its efficacy. Our evaluation included 100 patients in four postures: sitting, supine, and lateral positions, with and without masks, and under nighttime sleeping conditions. Results: Our results show that the unmasked certification rate of 99.7% was significantly higher than the masked rate of 90.8% (p < 0.0001). In addition, we found that the authentication rate exceeded 99% even during nighttime sleeping. Furthermore, the facial recognition system was safe and acceptable for patient identification within a hospital environment. Even for patients wearing masks, we achieved a 100% success rate for authentication regardless of illumination if they were sitting with their eyes open. Conclusions: This is the first systematical study to evaluate facial recognition among hospitalized patients under different situations. The facial recognition system using deep learning for patient identification shows promising results, proving its safety and acceptability, especially in hospital settings where accurate patient identification is crucial. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Biomedicine)
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<p>(<b>A</b>) Standard photography; (<b>B</b>) supine position with low illumination; (<b>C</b>) left lateral position with low illumination.</p>
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<p>Authentication scores (AS) in 16 patterns depending on the combination of masking, eyes, body position, and illumination. The AS is shown in the upper half, and the 16 patterns are in the lower half. The AS of intentionally wrong patients was significantly lower than that of correct patients for each situation (<span class="html-italic">p</span> &lt; 0.0001). In the upper panel, red boxes = scores for correct patients, and black boxes = scores for wrong patients. In the lower panel, solid boxes = conditions of mask, closed eyes, supine, and low illumination.</p>
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<p>False rejection rate (FRR) and false acceptance rate (FAR) under conditions (<b>A</b>) without and (<b>B</b>) with a mask. The upper panels show the total range, and the lower panels extend the crossing ranges between the FRR and the FAR. The threshold is the maximum score of wrong matches among all 158,400 values. The thresholds without and with masks were 0.642 and 0.620, respectively. FRR = red lines, FAR = blue lines.</p>
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<p>Frequency of successful matches in 16 combinations of mask-wearing, eye closure, body position, and illumination. The rate of successful matches can be indicated as [(1-FRR) × 100 (%)]. The frequency of successful matches is shown in the upper part, and the 16 patterns are in the lower part. In the lower panel, solid boxes = conditions with mask, closed eyes, supine, sufficient or low illumination. The exact numbers are provided in the top row of the matching rate bar chart.</p>
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<p>Authentication scores (<b>A</b>) and successful matching rates (<b>B</b>) in the nighttime condition. All images were taken under the low illumination condition. In the lower panel, solid boxes = conditions with mask, closed eyes, supine, and low illumination; R = right lateral position; L = left lateral position. In the upper panel in (<b>A</b>), red boxes = scores for correct patients, and black boxes = scores for wrong patients. The exact numbers are provided in the top row of the matching rate bar chart.</p>
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15 pages, 3732 KiB  
Article
Primary Stability of Implants Inserted into Polyurethane Blocks: Micro-CT and Analysis In Vitro
by Chadi Dura Haddad, Ludovica Andreatti, Igor Zelezetsky, Davide Porrelli, Gianluca Turco, Lorenzo Bevilacqua and Michele Maglione
Bioengineering 2024, 11(4), 383; https://doi.org/10.3390/bioengineering11040383 - 15 Apr 2024
Viewed by 1249
Abstract
The approach employed for the site preparation of the dental implant is a variable factor that affects the implant’s primary stability and its ability to integrate with the surrounding bone. The main objective of this in vitro study is to evaluate the influence [...] Read more.
The approach employed for the site preparation of the dental implant is a variable factor that affects the implant’s primary stability and its ability to integrate with the surrounding bone. The main objective of this in vitro study is to evaluate the influence of different techniques used to prepare the implant site on the primary stability of the implant in two different densities of artificial bone. Materials and Methods: A total of 150 implant sites were prepared in rigid polyurethane blocks to simulate two distinct bone densities of 15 pounds per cubic foot (PCF) and 30 PCF, with a 1-mm-thick simulated cortex. The implant sites were equally distributed among piezoelectric surgery (PES), traditional drills (TD), and black ruby magnetic mallet inserts (MM). Two methods have been employed to evaluate the implant’s primary stability, Osstell and micro-tomography. Results: In the present study, we observed significant variations in the implant stability quotient (ISQ) values. More precisely, our findings indicate that the ISQ values were generally higher for 30 PCF compared to 15 PCF. In terms of the preparation technique, PES exhibited the greatest ISQ values, followed by MM, and finally TD. These findings corresponded for both bone densities of 30 PCF (PES 75.6 ± 1.73, MM 69.8 ± 1.91, and TD 65.8 ± 1.91) and 15 PCF (PES 72.3 ± 1.63, MM 62.4 ± 1.77, and TD 60.6 ± 1.81). By utilizing Micro-CT scans, we were able to determine the ratio of the implant occupation to the preparation site. Furthermore, we could calculate the maximum distance between the implant and the wall of the preparation site. The findings demonstrated that PES had a higher ratio of implant to preparation site occupation, followed by TD, and then the MM, at a bone density of 30 PCF (PES 96 ± 1.95, TD 94 ± 1.88, and MM 90.3 ± 2.11). Nevertheless, there were no statistically significant differences in the occupation ratio among these three approaches in the bone density of 15 PCF (PES 89.6 ± 1.22, TD 90 ± 1.31, and MM 88.4 ± 1.17). Regarding the maximum gap between the implant and the site preparation, the smallest gaps were seen when TD were used, followed by MM, and finally by PES, either in a bone density 15 PCF (PES 318 ± 21, TD 238 ± 17, and MM 301 ± 20 μm) or in a bone density 30 PCF (PES 299 ± 20, TD 221 ± 16, and MM 281 ± 19 μm). A statistical analysis using ANOVA revealed these differences to be significant, with p-values of < 0.05. Conclusion: The outcomes of this study indicate that employing the PES technique and osteo-densification with MM during implant insertion may enhance the primary stability and increase the possibility of early implant loading. Full article
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<p>Implant preparation in a grid of 1.5-cm-by-1.5-cm squares on the polyurethane blocks.</p>
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<p>(<b>1</b>)—First Scan of the preparation site (pre), (<b>2</b>)—Second Scan of the inserted screw (post), (<b>3</b>)—overlapping of both scans (pre and post) and determination of the maximum distance between the screw and the bone (red line).</p>
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<p>The MM technique (30 PCF).</p>
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<p>The PES technique (30 PCF).</p>
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<p>The TD technique (30 PCF).</p>
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<p>The MM technique (15 PCF).</p>
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<p>The PES technique (15 PCF).</p>
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<p>The TD technique (15PCF).</p>
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15 pages, 6261 KiB  
Article
Limb Volume Measurements: A Comparison of Circumferential Techniques and Optoelectronic Systems against Water Displacement
by Giovanni Farina, Manuela Galli, Leonardo Borsari, Andrea Aliverti, Ioannis Th. Paraskevopoulos and Antonella LoMauro
Bioengineering 2024, 11(4), 382; https://doi.org/10.3390/bioengineering11040382 - 15 Apr 2024
Cited by 1 | Viewed by 1219
Abstract
Background. Accurate measurements of limb volumes are important for clinical reasons. We aimed to assess the reliability and validity of two centimetric and two optoelectronic techniques for limb volume measurements against water volumetry, defined as the gold standard. Methods. Five different measurement methods [...] Read more.
Background. Accurate measurements of limb volumes are important for clinical reasons. We aimed to assess the reliability and validity of two centimetric and two optoelectronic techniques for limb volume measurements against water volumetry, defined as the gold standard. Methods. Five different measurement methods were executed on the same day for each participant, namely water displacement, fixed-height (circumferences measured every 5 (10) cm for the upper (lower limb) centimetric technique, segmental centimetric technique (circumferences measured according to proportional height), optoelectronic plethysmography (OEP, based on a motion analysis system), and IGOODI Gate body scanner technology (which creates an accurate 3D avatar). Results. A population of 22 (15 lower limbs, 11 upper limbs, 8 unilateral upper limb lymphoedema, and 6 unilateral lower limb lymphoedema) participants was selected. Compared to water displacement, the fixed-height centimetric method, the segmental centimetric method, the OEP, and the IGOODI technique resulted in mean errors of 1.2, 0.86, −16.0, and 0.71%, respectively. The corresponding slopes (and regression coefficients) of the linear regression lines were 1.0002 (0.98), 1.0047 (0.99), 0.874 (0.94) and 0.9966 (0.99). Conclusion. The centimetric methods and the IGOODI system are accurate in measuring limb volume with an error of <2%. It is important to evaluate new objective and reliable techniques to improve diagnostic and follow-up possibilities. Full article
(This article belongs to the Special Issue Optical Techniques for Biomedical Engineering)
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<p>Experimental setup of the different measures of the lower limb: water volumetry (<b>top left panel</b>, please notice the patient in need of help to keep the position), circumferential techniques in orthostatism (<b>top middle panel</b>) and clinostatism (<b>top right panel</b>), optoelectronic plethysmography (<b>bottom left panel</b>) and the corresponding 3D marker reconstruction (<b>bottom middle panel</b>), and 3D avatar (i.e., a virtual twin of the patient) from the IGOODI Gate technology (<b>bottom right panel</b>).</p>
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<p>Experimental setup of the different measures of the upper limb: water volumetry (<b>top left panel</b>), circumferential techniques in orthostatism (<b>top middle panel</b>) and clinostatism (<b>top right panel</b>), optoelectronic plethysmography (<b>bottom left panel</b>) and the corresponding 3D marker reconstruction (<b>bottom middle panel</b>), and 3D avatar (i.e., a virtual twin of the patient) created by the IGOODI Gate technology (<b>bottom right panel</b>).</p>
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<p>Lower (<b>left panel</b>) and upper (<b>right panel</b>) limb detection points identified using the segmental technique. The patients signed written informed consent for the publication of their photos.</p>
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<p>(<b>Left</b>) Linear correlation graph, with water displacement on the x-axis and the fixed-height technique on the y-axis. Short dashed line: the interpolating line of the data. (<b>Right</b>) Bland-Altman graph, with the average between the two measurements on the x-axis and the difference between the fixed-height technique and water displacement. Solid line: mean difference. Short-dashed lines: mean difference ±2 standard deviations. Circle: lower limb. Square: upper limb. Red: clinostatism. Cyan: orthostatism.</p>
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<p>(<b>Left</b>) Linear correlation graph, with water displacement on the x-axis and the segmental technique on the y-axis. Short dashed line: the interpolating line of the data. (<b>Right</b>) Bland-Altman graph, with the average between the two measurements on the x-axis and the difference between the segmental technique and water displacement. Solid line: mean difference. Short-dashed lines: mean difference ±2 standard deviations. Circle: lower limb. Square: upper limb. Red: clinostatism. Cyan: orthostatism.</p>
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<p><b>(Left</b>) Linear correlation graph, with the segmental technique on the x-axis and the optoelectronic plethysmography data on the y-axis. Short dashed line: the interpolating line of the data. (<b>Right</b>) Bland-Altman graph, with the average between the two measurements on the x-axis and the difference between optoelectronic plethysmography and the fixed-height technique. Solid line: mean difference. Short-dashed lines: mean difference ±2 standard deviations. Circle: lower limb. Square: upper limb. Red: clinostatism. Cyan: orthostatism.</p>
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<p>(<b>Left</b>) Linear correlation graph, with water displacement on the x-axis and the IGOODI system on the y-axis. Short dashed line: the interpolating line of the data. (<b>Right</b>) Bland-Altman graph, with the average between the two measurements on the x-axis and the difference between the IGOODI system and water displacement. Solid line: mean difference. Short-dashed lines: mean difference ±2 standard deviations. Circle: lower limb. Square: upper limb. Cyan: orthostatism.</p>
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19 pages, 1491 KiB  
Article
Development of a Human B7-H3-Specific Antibody with Activity against Colorectal Cancer Cells through a Synthetic Nanobody Library
by Jingxian Li, Bingjie Zhou, Shiting Wang, Jiayi Ouyang, Xinyi Jiang, Chenglin Wang, Teng Zhou, Ke-wei Zheng, Junqing Wang and Jiaqi Wang
Bioengineering 2024, 11(4), 381; https://doi.org/10.3390/bioengineering11040381 - 15 Apr 2024
Viewed by 1847
Abstract
Nanobodies have emerged as promising tools in biomedicine due to their single-chain structure and inherent stability. They generally have convex paratopes, which potentially prefer different epitope sites in an antigen compared to traditional antibodies. In this study, a synthetic phage display nanobody library [...] Read more.
Nanobodies have emerged as promising tools in biomedicine due to their single-chain structure and inherent stability. They generally have convex paratopes, which potentially prefer different epitope sites in an antigen compared to traditional antibodies. In this study, a synthetic phage display nanobody library was constructed and used to identify nanobodies targeting a tumor-associated antigen, the human B7-H3 protein. Combining next-generation sequencing and single-clone validation, two nanobodies were identified to specifically bind B7-H3 with medium nanomolar affinities. Further characterization revealed that these two clones targeted a different epitope compared to known B7-H3-specific antibodies, which have been explored in clinical trials. Furthermore, one of the clones, dubbed as A6, exhibited potent antibody-dependent cell-mediated cytotoxicity (ADCC) against a colorectal cancer cell line with an EC50 of 0.67 nM, upon conversion to an Fc-enhanced IgG format. These findings underscore a cost-effective strategy that bypasses the lengthy immunization process, offering potential rapid access to nanobodies targeting unexplored antigenic sites. Full article
(This article belongs to the Section Nanobiotechnology and Biofabrication)
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<p>Quality evaluation of synthetic nanobody library by next-generation sequencing. (<b>A</b>) Diagram depicting the synthetic nanobody gene. The framework regions, highlighted in gray, remained constant in sequence. Conversely, sections of the CDR loops were subject to variation, with CDR1, CDR2, and CDR3 depicted in orange, blue, and green, respectively. Partial randomization was accomplished using mixed nucleotides, while highly variable regions were randomized using degenerate codons NNB, denoted by asterisks (*). (<b>B</b>) For demonstration, amino acid distributions in desired positions were shown for sequences with the short CDR3 in the library. The IMGT definition was used to annotate the CDRs using the AbYsis tool (<a href="http://www.abysis.org/abysis/index.html" target="_blank">http://www.abysis.org/abysis/index.html</a>, accessed on 6 June 2023). (<b>C</b>) The observed and expected ratios for 20 amino acid types over fully randomized sites in nanobodies with the short CDR3 loop. (<b>D</b>) Percentage of intact sequences in NGS data for the <span class="html-italic">E. coli</span> library (E1), three batches of independently prepared phage display libraries without anti-Myc tag enrichment (P1, P2, and P3), and the phage library after anti-Myc tag enrichment (P-Myc). (<b>E</b>) Pie charts show percentages of sequences with one, two, three, and more appearances in corresponding libraries as (<b>D</b>).</p>
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<p>Phage display panning process and NGS mining. (<b>A</b>) The panning process is shown schematically with the amount of recombinant proteins used in each round listed. B7-H3-coated wells are denoted by a “+” sign, and skimmed-milk blocked-control wells are denoted by a “−” sign. The solid lines with arrows indicate that the eluted phages from the previous round were amplified prior to the subsequent round. The dashed line with an arrow indicates that unbound phages from the preceding incubation step were transferred directly to the succeeding wells. In round 3, phages eluted from B7-H3 (−) (negative pool) and B7-H3 (+) (positive pool) were amplified and used for NGS analysis. (<b>B</b>) Single-colony phage ELISA identified potential specific clones. Phages produced from randomly picked colonies were tested for the binding to 4IgB7-H3 or skimmed-milk-blocked control wells. The specificity score was calculated by dividing the absorbance in an antigen-coated well by the absorbance in the blocked control well. Clones with absorbance below 0.1 in the control wells and a specificity score higher than five were determined to be specific binders. Gray and red bars indicated non-specific and specific binders, respectively. (<b>C</b>) NGS result of round 3. The ratio of percentages in the positive versus the negative pools is graphed for each unique sequence against its corresponding percentage in the positive pool. In this analysis, only sequences exceeding 0.01% in the positive pool were considered. Each blue open circle or colored solid circle represented a unique sequence.</p>
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<p>Binding validation for selected clones. (<b>A</b>) ELISA showed binding of selected clones in hFc format to 4IgB7-H3-coated wells and blocked control wells. (<b>B</b>) A6-hFc and a13-hFc showed dose-dependent binding to recombinant B7-H3 in indirect ELISA assay. B7-H3-specific MAb 8H9 was used as the positive control. (<b>C</b>) BLI sensorgrams showed A6, a13, and 8H9 bound to a serial dilution (900 nM, 300 nM, 100 nM, and 33 nM) of recombinant B7-H3. Raw data are in blue, cyan, and green, respectively, and the fitting curves are in red.</p>
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<p>Epitope binning analysis. Streptavidin-coated biosensors were coupled with biotinylated (<b>A</b>) 8H9-hFc or (<b>B</b>) A6-hFc, followed by antigen C-2IgB7-H3 and incubated with the sandwiching MAb (MAb2). The labels indicate the MAb2 identity. (<b>C</b>) Sequence alignment of human and murine B7-H3 with residue numbers provided (referring to the N-terminus of human B7-H3). Residues 1-28 are suggested to be the signal sequence and cleaved during protein maturation. (<b>D</b>) Predicted structure of the N-terminal 2Ig of human B7-H3 was extracted from AlphaFold protein structure database, with non-conserved residues between human and murine B7-H3 depicted in spheres of different colors. Residue numbers (in both N- and C-terminal 2Ig) and the corresponding amino acids in human B7-H3 are labeled. The corresponding amino acids in murine B7-H3 are in parentheses.</p>
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<p>Characterization of A6. (<b>A</b>) Western blot analysis of A6 binding to HCT116 and Jurkat cell lysates, using 8H9 as the positive control. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as a loading control. (<b>B</b>) Flow cytometric analysis of A6 binding to HCT116 WT and B7-H3 knockdown (B7-H3 KD) cell pools. A human IgG1 isotype was used as a negative control. (<b>C</b>) Left, one representative flow cytometric experiment is shown for the dose-dependent binding of A6-hFc to HCT116. Right, data from three independent experiments were averaged and fitted with a three parameter non-linear model.</p>
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<p>ADCC activity of A6. HCT116 was used as target cells and Jurkat-Lucia NFAT-CD16 (InvivoGen) as reporter cells. The dose-dependent experiment was conducted by measuring the induced luciferase activity of the assay supernatant after 24 h. An IgG1 isotype served as the negative control. The data represent means ± standard deviation (SD) from three independent experiments.</p>
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14 pages, 3647 KiB  
Article
Characteristics of Far-Infrared Ray Emitted from Functional Loess Bio-Balls and Its Effect on Improving Blood Flow
by Yeon Jin Choi, Woo Cheol Choi, Gye Rok Jeon, Jae Ho Kim, Min Seok Kim and Jae Hyung Kim
Bioengineering 2024, 11(4), 380; https://doi.org/10.3390/bioengineering11040380 - 15 Apr 2024
Viewed by 1086
Abstract
XRD diffraction and IR absorption were investigated for raw loess powder and heat-treated loess powder. Raw loess retains its useful minerals, but loses their beneficial properties when calcined at 850 °C and 1050 °C. To utilize the useful minerals, loess balls were made [...] Read more.
XRD diffraction and IR absorption were investigated for raw loess powder and heat-treated loess powder. Raw loess retains its useful minerals, but loses their beneficial properties when calcined at 850 °C and 1050 °C. To utilize the useful minerals, loess balls were made using a low-temperature wet-drying method. The radiant energy and transmittance were measured for the loess balls. Far-infrared ray (FIR) emitted from loess bio-balls is selectively absorbed as higher vibrational energy by water molecules. FIR can raise the body’s core temperature, thereby improving blood flow through the body’s thermoregulatory mechanism. In an exploratory study with 40 participants, when the set temperature of the loess ball mat was increased from 25 °C to 50 °C, blood flow increased by 39.01%, from 37.48 mL/min to 52.11 mL/min, in the left middle finger; in addition, it increased by 39.62%, from 37.15 mL/min to 51.87 mL/min, in the right middle finger. The FIR emitted from loess balls can be widely applied, in various forms, to diseases related to blood flow, such as cold hands and feet, diabetic foot, muscle pain, and menstrual pain. Full article
(This article belongs to the Section Biomedical Engineering and Biomaterials)
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<p>Flow chart outlining the manufacturing process of functional loess bio-balls.</p>
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<p>Principles of PDF. The frequency of light reflected by tissues in a stationary state does not change, but the light reflected by red blood cells flowing through capillaries changes frequency due to the Doppler effect.</p>
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<p>X-ray diffraction patterns of untreated loess powder and heat-treated loess powder.</p>
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<p>IR absorption spectra of loess powder at various temperatures.</p>
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<p>The loess bio-balls manufactured by the LW method. The diameter of the loess bio-ball is about 1.3 mm.</p>
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<p>(<b>a</b>) Radiant intensity and (<b>b</b>) emissivity of FIR emitted from loess bio-ball at 40 °C (313 K) as a function of wavelength.</p>
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<p>Radiant energy and peak wavelength of loess bio-ball as a function of temperature.</p>
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<p>Transmitted energy and transmittance of FIR emitted from loess bio-ball for five materials. For comparison with values transmitted through materials, the values (<math display="inline"><semantics> <mrow> <mn>3.74</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mn>2</mn> <mo> </mo> </mrow> </msup> </mrow> </semantics></math>W/<math display="inline"><semantics> <mrow> <msup> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> <mo>·</mo> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>, 0.927) of the loess sample represent the radiant energy and emissivity of the FIR emitted from loess bio-ball.</p>
Full article ">Figure 9
<p>Blood flow and epidermal temperature at LMF when using loess bio-ball mat.</p>
Full article ">Figure 10
<p>Blood flow and epidermal temperature at RBT when using a loess bio-ball mat.</p>
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