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18 pages, 9438 KiB  
Article
High-Throughput and Accurate 3D Scanning of Cattle Using Time-of-Flight Sensors and Deep Learning
by Gbenga Omotara, Seyed Mohamad Ali Tousi, Jared Decker, Derek Brake and G. N. DeSouza
Sensors 2024, 24(16), 5275; https://doi.org/10.3390/s24165275 (registering DOI) - 14 Aug 2024
Abstract
We introduce a high-throughput 3D scanning system designed to accurately measure cattle phenotypes. This scanner employs an array of depth sensors, i.e., time-of-flight (ToF) sensors, each controlled by dedicated embedded devices. The sensors generate high-fidelity 3D point clouds, which are automatically stitched using [...] Read more.
We introduce a high-throughput 3D scanning system designed to accurately measure cattle phenotypes. This scanner employs an array of depth sensors, i.e., time-of-flight (ToF) sensors, each controlled by dedicated embedded devices. The sensors generate high-fidelity 3D point clouds, which are automatically stitched using a point could segmentation approach through deep learning. The deep learner combines raw RGB and depth data to identify correspondences between the multiple 3D point clouds, thus creating a single and accurate mesh that reconstructs the cattle geometry on the fly. In order to evaluate the performance of our system, we implemented a two-fold validation process. Initially, we quantitatively tested the scanner for its ability to determine accurate volume and surface area measurements in a controlled environment featuring known objects. Next, we explored the impact and need for multi-device synchronization when scanning moving targets (cattle). Finally, we performed qualitative and quantitative measurements on cattle. The experimental results demonstrate that the proposed system is capable of producing high-quality meshes of untamed cattle with accurate volume and surface area measurements for livestock studies. Full article
(This article belongs to the Section Physical Sensors)
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Figure 1

Figure 1
<p>(<b>a</b>) A schematic representation of the scanning system. (<b>b</b>) Real-life figure of the camera frame and the system components.</p>
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<p>Overview of the software pipeline: The pipeline begins with the data acquisition of RGBD data, which undergo a segmentation and filtering step to eliminate the background pixels and noise in both depth and RGB space. The filtered data are subsequently backprojected into 3D space and then stitched to form a unified 3D model. A mesh is then constructed over the 3D point cloud. Finally, we measure our traits of interest, volume, and surface area.</p>
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<p>Schematic layout of Server–Client: In this configuration, the Client sends a capture request to 10 Server programs. Each Server program performs the image acquisition request from the Client and the captured data are transmitted to a storage device.</p>
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<p><b>Mask R-CNN Architecture</b> [<a href="#B9-sensors-24-05275" class="html-bibr">9</a>]: Mask R-CNN builds upon two existing Faster R-CNN heads as detailed in [<a href="#B10-sensors-24-05275" class="html-bibr">10</a>,<a href="#B11-sensors-24-05275" class="html-bibr">11</a>]. The left and right panels illustrate the heads for the ResNet C4 and FPN backbones, respectively, with an added mask branch. Spatial resolution and channels are indicated by the numbers, while arrows represent conv, deconv, or FC layers, inferred from the context (conv layers maintain spatial dimensions, whereas deconv layers increase them). All conv layers are 3 × 3, except for the output conv which is 1 × 1. Deconv layers are 2 × 2 with a stride of 2, and ReLU [<a href="#B12-sensors-24-05275" class="html-bibr">12</a>] is used in hidden layers. On the left, ‘res5’ refers to the fifth stage of ResNet, which has been modified so that the first conv layer operates on a 7 × 7 RoI with a stride of 1 (instead of 14 × 14 with a stride of 2 as in [<a href="#B10-sensors-24-05275" class="html-bibr">10</a>]). On the right, ‘×4’ indicates a stack of four consecutive conv layers.</p>
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<p>Multi-view point cloud registration: (<b>a</b>) Given N = 6 point clouds, we perform a simple pairwise registration of point cloud fragments of the scanned cattle. (<b>b</b>) We use the Colored ICP algorithm to solve for the coordinate transformation from camera coordinate frame j to camera coordinate frame i (denoted as <math display="inline"><semantics> <mrow> <msup> <mo> </mo> <mi>i</mi> </msup> <msub> <mi>H</mi> <mi>j</mi> </msub> </mrow> </semantics></math>). Each view is aligned into the coordinate frame of its adjacent camera. We fix the coordinate frame of Camera 1 (<math display="inline"><semantics> <msub> <mi>V</mi> <mn>1</mn> </msub> </semantics></math>) as the world coordinate frame and then align all views with respect to coordinate frame 1. (<b>c</b>) This results in a well-aligned point cloud of the scanned cattle.</p>
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<p>Comparison of 3D point cloud capture quality with and without synchronization using a large box with known dimensions. The left image displays the results without synchronization (0 μs), capturing a total of 17,098 points. The right image shows the same box captured with synchronization (160 μs) with all other settings the same, resulting in a total of 38,631 points, illustrating the significant improvement in data acquisition quality. (<b>a</b>) Large box, 0 s delay, <span class="html-italic">n</span> = 17,098. (<b>b</b>) Large box, 160 μs delay, <span class="html-italic">n</span> = 38,631.</p>
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<p>Results of scanning a cylindrical object in multiple orientations, highlighting the scanner’s accuracy across diverse poses. The horizontal axis displays the predicted volumes and surface areas obtained in each test. Given that the same object was used throughout, the ground truth volume and surface area remain constant. This plot demonstrates the scanner’s precision, as evidenced by the close alignment of the predicted values with the consistent ground truths, illustrating the system’s reliability in varying orientations. (<b>a</b>) Surface area calculation results. (<b>b</b>) Volume calculation results.</p>
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<p>Regression analysis of predicted versus known surface area and volume for multiple static objects. The plots displays the correlation between the scanner’s predicted values and the actual measurements for a cylinder, small box, medium box, and large box, all placed in the same pose across 10 consecutive scans. The high <math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math> values of 0.997 for surface area and 0.999 for volume demonstrate the scanner’s accuracy and consistency in various object dimensions and shapes under controlled conditions. (<b>a</b>) Surface area calculation results. (<b>b</b>) Volume calculation results.</p>
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<p>Performance of the scanner under direct sunlight, using a standard box to simulate outdoor livestock scanning conditions. The graphs show the mean and standard deviation of volume and surface area measurements across 10 consecutive scans. The results here illustrate the slight impact of sunlight on the scanner’s infrared sensors, affecting measurement accuracy. (<b>a</b>) Surface area calculation results from data collected in sunlight. (<b>b</b>) Volume calculation results from data collected in sunlight.</p>
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<p>Segmentation of cattle using combined RGB and depth models via Mask R-CNN: The figure shows an RGBD image of cattle segmented using both RGB and depth data. Results from each model are integrated using a voting arbitrator, resulting in a well-defined segmentation in both modalities.</p>
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<p>Poisson reconstructed meshes of cattle from which we compute the surface area and volume estimates.</p>
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18 pages, 3432 KiB  
Article
Deep Neural Network Integrated into Network-Based Stratification (D3NS): A Method to Uncover Cancer Subtypes from Somatic Mutations
by Matteo Valerio, Alessandro Inno, Alberto Zambelli, Laura Cortesi, Domenica Lorusso, Valeria Viassolo, Matteo Verzè, Fabrizio Nicolis and Stefania Gori
Cancers 2024, 16(16), 2845; https://doi.org/10.3390/cancers16162845 (registering DOI) - 14 Aug 2024
Abstract
(1) Background: The identification of tumor subtypes is fundamental in precision medicine for accurate diagnoses and personalized therapies. Cancer development is often driven by the accumulation of somatic mutations that can cause alterations in tissue functions and morphologies. In this work, a method [...] Read more.
(1) Background: The identification of tumor subtypes is fundamental in precision medicine for accurate diagnoses and personalized therapies. Cancer development is often driven by the accumulation of somatic mutations that can cause alterations in tissue functions and morphologies. In this work, a method based on a deep neural network integrated into a network-based stratification framework (D3NS) is proposed to stratify tumors according to somatic mutations. (2) Methods: This approach leverages the power of deep neural networks to detect hidden information in the data by combining the knowledge contained in a network of gene interactions, as typical of network-based stratification methods. D3NS was applied using real-world data from The Cancer Genome Atlas for bladder, ovarian, and kidney cancers. (3) Results: This technique allows for the identification of tumor subtypes characterized by different survival rates and significant associations with several clinical outcomes (tumor stage, grade or response to therapy). (4) Conclusion: D3NS can provide a base model in cancer research and could be considered as a useful tool for tumor stratification, offering potential support in clinical settings. Full article
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Figure 1
<p>Overview of D3NS. (<b>a</b>) Representation of a binary mutation matrix, where for each gene–patient pair, a black point represents a mutated gene corresponding to 1 value. (<b>b</b>) Gene interaction network onto which the mutations are projected. (<b>c</b>) Representation of network-smoothed matrix with continuous values after the network propagation process. (<b>d</b>) Autoencoder’s structure, which receives as input the smoothed mutation profiles of patients and generates their compressed representation, an encoded matrix, with 100 new essential features. (<b>e</b>) Subtypes obtained with K-means consensus clustering after 1000 repetitions and the next evaluation based on clinical data.</p>
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<p>Heatmaps relative to CMs for the different values of k subtypes considered for the stratification, applying some networks to cancer datasets. The blocks in blue correspond to a high consensus value among patient pairs, indicating reliable clusters.</p>
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<p>Analysis for bladder cancer in the 412 patients considered from the TCGA dataset. (<b>a</b>) Significant associations between survival and subtypes obtained for the three networks considered. Dashed line represents the significance threshold: -log10(Log-rank <span class="html-italic">p</span>-value = 0.05). (<b>b</b>) OS Kaplan–Meier curves for the four subtypes (k = 4) obtained using the STRING network.</p>
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<p>Summary of somatic mutations in the 412 patients considered from the bladder cancer TCGA dataset. (<b>a</b>) Distribution of the top 10 mutated genes in the whole population. (<b>b</b>) Distributions of the numbers of mutated genes per patient in each subtype and in the whole population. (<b>c</b>) Distribution of the top 10 mutated genes across the subtypes.</p>
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<p>Analysis for ovarian cancer in the 316 patients considered from the TCGA dataset. (<b>a</b>) Significant associations between survival and subtypes obtained for the three networks considered. Dashed line represents the significance threshold: -log10(Log-rank <span class="html-italic">p</span>-value = 0.05). (<b>b</b>) OS Kaplan–Meier curves for the three subtypes (k = 3) obtained using the HumanNet network.</p>
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<p>Summary of somatic mutations in the 316 patients considered from the ovarian cancer TCGA dataset. (<b>a</b>) Distribution of the top 10 mutated genes in the whole population. (<b>b</b>) Distributions of the numbers of mutated genes per patient in each subtype and in the whole population. (<b>c</b>) Distribution of the top 10 mutated genes across the subtypes.</p>
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<p>Analysis for kidney cancer in the 424 patients considered from the TCGA dataset. (<b>a</b>) Significant associations between survival and subtypes obtained for the three networks considered. Dashed line represents the significance threshold: -log10(Log-rank <span class="html-italic">p</span>-value = 0.05). (<b>b</b>) OS Kaplan–Meier curves for the two subtypes (k = 2) obtained using the Mentha network.</p>
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<p>Summary of somatic mutations in the 424 patients considered from the kidney cancer TCGA dataset. (<b>a</b>) Distribution of the top 10 mutated genes in the whole population. (<b>b</b>) Distributions of the numbers of mutated genes per patient in each subtype and in the whole population. (<b>c</b>) Distribution of the top 10 mutated genes across the subtypes.</p>
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21 pages, 8661 KiB  
Article
Assessing the Peripheral Levels of the Neurotransmitters Noradrenaline, Dopamine and Serotonin and the Oxidant/Antioxidant Equilibrium in Circus Horses
by Raffaella Cocco, Sara Sechi, Maria Rizzo, Federica Arrigo, Claudia Giannetto, Giuseppe Piccione and Francesca Arfuso
Animals 2024, 14(16), 2354; https://doi.org/10.3390/ani14162354 (registering DOI) - 14 Aug 2024
Abstract
Due to the paucity of information on circus management effects on the welfare of horses, this study investigated the plasma concentrations of noradrenaline, dopamine and serotonin, known to be indices of mental status, as well as the reactive oxygen metabolites (d-Roms) and the [...] Read more.
Due to the paucity of information on circus management effects on the welfare of horses, this study investigated the plasma concentrations of noradrenaline, dopamine and serotonin, known to be indices of mental status, as well as the reactive oxygen metabolites (d-Roms) and the biological antioxidant potential (BAP), likely to denote the oxidant/antioxidant equilibrium of organisms, in horses managed in different Italian circuses. For the study, 56 circus horses of different breeds and ages were enrolled and divided into six groups according to the horses’ management (circus management, groups G1–G5; classic riding management representing the control group, CG). From each horse, blood samples were collected in order to assess the concentration of selected parameters. One-way ANOVA showed no differences (p > 0.05) in serotonin, dopamine, noradrenaline, d-Roms and BAP values between circus and control horses. No differences related to the breed of the horses enrolled in the study were found in the values of all investigated parameters (p > 0.05). Furthermore, neurotransmitters showed overlapping levels between the different age classes of investigated horses (p > 0.05); contrariwise, the age of the horse displayed a significant effect on BAP values, with the oldest horses (16–21 age class) exhibiting lower BAP values compared to 4–5, 6–10 and 11–15 age classes (p < 0.05), whereas the d-Roms showed similar values in horses of different age classes (p > 0.05). The results gathered in the present study suggest that the mental status of horses under circus management was not compromised; however, better attention and care in the management of older horses is advocated, as they showed a lower biological antioxidant potential than younger horses; thus, they could be more susceptible to oxidative stress. Full article
(This article belongs to the Section Animal Physiology)
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Figure 1
<p>Normal quantile plot (Q-Q Plot) showing the normal distribution of serotonin, dopamine and noradrenaline values measured in investigated horses when analyzing the group’s effect (G1–G5 vs. CG).</p>
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<p>Normal quantile plot (Q-Q Plot) showing the normal distribution of serotonin, dopamine and noradrenaline values measured in investigated horses when analyzing the breed’s effect (Arabian, Andalusian, Friesian and Pony).</p>
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<p>Normal quantile plot (Q-Q Plot) showing the normal distribution of serotonin, dopamine and noradrenaline values measured in investigated horses when analyzing the effect of age class (4–5, 6–10, 11–15 and 16–21 years).</p>
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<p>Normal quantile plot (Q-Q Plot) showing the normal distribution of the reactive oxygen metabolites (d-Roms) and the biological antioxidant potential (BAP) values measured in investigated horses when analyzing the group’s effect (G1–G5 vs. CG).</p>
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<p>Normal quantile plot (Q-Q Plot) showing the normal distribution of the reactive oxygen metabolites (d-Roms) and the biological antioxidant potential (BAP) values measured in investigated horses when analyzing the breed’s effect (Arabian, Andalusian, Friesian and Pony).</p>
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<p>Normal quantile plot (Q-Q Plot) showing the normal distribution of the reactive oxygen metabolites (d-Roms) and the biological antioxidant potential (BAP) values measured in investigated horses when analyzing the effect of age class (4–5, 6–10, 11–15 and 16–21 years).</p>
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<p>Violin plot showing distributions of serotonin, dopamine and noradrenaline values measured in investigated horses together with the relative statistical significances when analyzing the group’s effect (G1–G5 vs. CG).</p>
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<p>Violin plot showing distributions of the reactive oxygen metabolites (d-Roms) and the biological antioxidant potential (BAP) values measured in investigated horses together with the relative statistical significances when analyzing the group’s effect (G1–G5 vs. CG).</p>
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<p>Mean values ± 95% confidence intervals of serotonin, dopamine and noradrenaline values measured in investigated horses together with the relative statistical significances when analyzing the breed’s effect (Arabian, Andalusian, Friesian and Pony).</p>
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<p>Mean values ± 95% confidence intervals of the reactive oxygen metabolites (d-Roms) and the biological antioxidant potential (BAP) values measured in investigated horses together with the relative statistical significances when analyzing the breed’s effect (Arabian, Andalusian, Friesian and Pony).</p>
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<p>Mean values ± 95% confidence intervals of serotonin, dopamine and noradrenaline values measured in investigated horses together with the relative statistical significances when analyzing the effect of age class (4–5, 6–10, 11–15 and 16–21 years).</p>
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<p>Mean values ± 95% confidence intervals of the reactive oxygen metabolites (d-Roms) and the biological antioxidant potential (BAP) values together with the relative statistical significances when analyzing the effect of age class (4–5, 6–10, 11–15 and 16–21 years).</p>
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24 pages, 3755 KiB  
Article
Artificial Intelligence-Empowered Doppler Weather Profile for Low-Earth-Orbit Satellites
by Ekta Sharma, Ravinesh C. Deo, Christopher P. Davey and Brad D. Carter
Sensors 2024, 24(16), 5271; https://doi.org/10.3390/s24165271 (registering DOI) - 14 Aug 2024
Abstract
Low-Earth-orbit (LEO) satellites are widely acknowledged as a promising infrastructure solution for global Internet of Things (IoT) services. However, the Doppler effect presents a significant challenge in the context of long-range (LoRa) modulation uplink connectivity. This study comprehensively examines the operational efficiency of [...] Read more.
Low-Earth-orbit (LEO) satellites are widely acknowledged as a promising infrastructure solution for global Internet of Things (IoT) services. However, the Doppler effect presents a significant challenge in the context of long-range (LoRa) modulation uplink connectivity. This study comprehensively examines the operational efficiency of LEO satellites concerning the Doppler weather effect, with state-of-the-art artificial intelligence techniques. Two LEO satellite constellations—Globalstar and the International Space Station (ISS)—were detected and tracked using ground radars in Perth and Brisbane, Australia, for 24 h starting 1 January 2024. The study involves modelling the constellation, calculating latency, and frequency offset and designing a hybrid Iterative Input Selection–Long Short-Term Memory Network (IIS-LSTM) integrated model to predict the Doppler weather profile for LEO satellites. The IIS algorithm selects relevant input variables for the model, while the LSTM algorithm learns and predicts patterns. This model is compared with Convolutional Neural Network and Extreme Gradient Boosting (XGBoost) models. The results show that the packet delivery rate is above 91% for the sensitive spread factor 12 with a bandwidth of 11.5 MHz for Globalstar and 145.8 MHz for ISS NAUKA. The carrier frequency for ISS orbiting at 402.3 km is 631 MHz and 500 MHz for Globalstar at 1414 km altitude, aiding in combating packet losses. The ISS-LSTM model achieved an accuracy of 97.51% and a loss of 1.17% with signal-to-noise ratios (SNRs) ranging from 0–30 dB. The XGB model has the fastest testing time, attaining ≈0.0997 s for higher SNRs and an accuracy of 87%. However, in lower SNR, it proves to be computationally expensive. IIS-LSTM attains a better computation time for lower SNRs at ≈0.4651 s, followed by XGB at ≈0.5990 and CNN at ≈0.6120 s. The study calls for further research on LoRa Doppler analysis, considering atmospheric attenuation, and relevant space parameters for future work. Full article
(This article belongs to the Section Remote Sensors)
17 pages, 557 KiB  
Article
Relationship between Markers of Gut Barrier Function and Erythrocyte Membrane PUFAs in Diarrhea-Predominant IBS Patients Undergoing a Low-FODMAP Diet
by Michele Linsalata, Antonia Ignazzi, Benedetta D’Attoma, Giuseppe Riezzo, Domenica Mallardi, Antonella Orlando, Laura Prospero, Maria Notarnicola, Valentina De Nunzio, Giuliano Pinto and Francesco Russo
Nutrients 2024, 16(16), 2706; https://doi.org/10.3390/nu16162706 (registering DOI) - 14 Aug 2024
Abstract
Many patients with irritable bowel syndrome (IBS) have a compromised intestinal barrier associated with low-grade inflammation. Polyunsaturated fatty acids (PUFAs) are potential mediators of inflammation: omega-6 PUFAs are pro-inflammatory, while omega-3 PUFAs are antioxidant and anti-inflammatory. Zonulin is a potential biomarker for small [...] Read more.
Many patients with irritable bowel syndrome (IBS) have a compromised intestinal barrier associated with low-grade inflammation. Polyunsaturated fatty acids (PUFAs) are potential mediators of inflammation: omega-6 PUFAs are pro-inflammatory, while omega-3 PUFAs are antioxidant and anti-inflammatory. Zonulin is a potential biomarker for small intestinal permeability (s-IP). This study investigated the relationship between PUFAs and gastrointestinal (GI) barrier integrity in IBS patients with predominant diarrhea (IBS-D). We evaluated GI barrier function indicators in the urine and bloodstream and erythrocyte membrane PUFA composition in 38 IBS-D patients (5 men, 33 women, 44.11 ± 1.64 years), categorized at baseline by fecal zonulin levels into high (≥107 ng/mL, H-FZ) and normal (<107 ng/mL N-FZ) groups. Evaluations were conducted prior to and following a 12-week diet low in FODMAPs (LFD). At baseline, H-FZ patients had s-IP significantly higher than the reference value, lower n-3 PUFAs levels, and higher n-6/n-3 PUFAs and arachidonic acid (AA) to eicosapentaenoic acid (EPA) ratios than N-FZ. After LFD, H-FZ patients showed significant increases in n-3 PUFAs levels; decreases in n-6 PUFAs, n-6/n-3 PUFAs and AA/EPA ratios; and improved s-IP. The n-6/n-3 PUFAs ratio positively correlated with fecal zonulin levels in all subjects. These findings highlight the relationship between PUFAs and the intestinal barrier, suggesting their role in IBS-D pathophysiology and confirming the positive effects of LFD in managing IBS-D. Full article
(This article belongs to the Section Clinical Nutrition)
15 pages, 1557 KiB  
Article
CastelLact Project: Exploring the Nutritional Status and Dietary Patterns of Pregnant and Lactating Women—A Comprehensive Evaluation of Dietary Adequacy
by Carmen I. Sáez Lleó, Carla Soler, Jose M. Soriano and Nadia San Onofre
Nutrients 2024, 16(16), 2705; https://doi.org/10.3390/nu16162705 (registering DOI) - 14 Aug 2024
Abstract
Promoting optimal nutrition in pregnant and lactating women is crucial for maternal and infant health. This study evaluated their nutritional status and dietary habits, assessing macro and micronutrient intake based on recommendations. A descriptive study with Spanish participants examined social, obstetric, dietary, and [...] Read more.
Promoting optimal nutrition in pregnant and lactating women is crucial for maternal and infant health. This study evaluated their nutritional status and dietary habits, assessing macro and micronutrient intake based on recommendations. A descriptive study with Spanish participants examined social, obstetric, dietary, and anthropometric data using quantitative and qualitative methods. The analysis of fatty acids by gas chromatography revealed significant variability, with notable deviations in specific fatty acids like C:10:0 and C:12:0. Despite some differences, the overall composition aligns with standards. During pregnancy, 53.8% consumed five meals/day. Grilling (92.3%) and baking (76.9%) were common. Food consumption frequency differed from recommendations. Lactating mothers’ mean energy intake was 2575.88 kcal/day ± 730.59 standard deviation (SD), with 45% from carbohydrates and 40% from lipids, including 37.16 g ± 10.43 of saturated fatty acids. Diets during pregnancy lacked fruits, vegetables, legumes, nuts, and cereals. Lactating mothers partially met nutritional objectives, with an energy distribution skewed towards lipids and deficiencies in calcium, iodine, vitamin D, E, and folic acid. Promoting proper nutrition during pregnancy and lactation is essential to safeguard health and prevent chronic diseases. Full article
(This article belongs to the Special Issue Maternal Diet, Body Composition and Offspring Health)
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<p>Gas–liquid chromatogram of fatty acid methyl esters.</p>
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<p>Anthropometric variations of mothers: evolution of body mass index before and after pregnancy.</p>
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<p>Frequency of consumption of the different food groups by mothers during pregnancy according to recommendations [<a href="#B24-nutrients-16-02705" class="html-bibr">24</a>,<a href="#B25-nutrients-16-02705" class="html-bibr">25</a>,<a href="#B26-nutrients-16-02705" class="html-bibr">26</a>,<a href="#B27-nutrients-16-02705" class="html-bibr">27</a>]. R: Ration.</p>
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<p>Oral supplementation during pregnancy and lactation.</p>
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<p>Distribution of the percentage of total caloric value by food group in the diet of nursing mothers.</p>
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18 pages, 2199 KiB  
Systematic Review
Once-Weekly Insulin Icodec in Diabetes Mellitus: A Systematic Review and Meta-Analysis of Randomized Clinical Trials (ONWARDS Clinical Program)
by Giuseppe Lisco, Anna De Tullio, Vincenzo De Geronimo, Vito Angelo Giagulli, Edoardo Guastamacchia, Giuseppina Piazzolla, Olga Eugenia Disoteo and Vincenzo Triggiani
Biomedicines 2024, 12(8), 1852; https://doi.org/10.3390/biomedicines12081852 - 14 Aug 2024
Abstract
Background. One hundred years have passed since the discovery of insulin, which is one of the most relevant events of the 20th century. This period resulted in extraordinary progress in the development of novel molecules to improve glucose control, simplify the insulin regimen, [...] Read more.
Background. One hundred years have passed since the discovery of insulin, which is one of the most relevant events of the 20th century. This period resulted in extraordinary progress in the development of novel molecules to improve glucose control, simplify the insulin regimen, and ameliorate the quality of life. In late March 2024, the first once-weekly basal analog Icodec was approved for diabetes mellitus, generating high expectations. Our aim was to systematically review and meta-analyze the efficacy and safety of Icodec compared to once-daily insulin analogs in type 1 (T1D) and type 2 diabetes (T2D). Methods. PubMed/MEDLINE, Cochrane Library, and ClinicalTrials.gov were searched for randomized clinical trials (RCTs). Studies were included for the synthesis according to the following prespecified inclusion criteria: uncontrolled T1D or T2D, age ≥ 18 years, insulin Icodec vs. active comparators (Degludec U100, Glargine U100, Glargine U300, and Detemir), phase 3, multicenter, double-blind or open-label RCTs, and a study duration ≥ 24 weeks. Results. The systematic review included 4347 patients with T1D and T2D inadequately controlled (2172 randomized to Icodec vs. 2175 randomized to once-daily basal analogs). Icodec, compared to once-daily basal analogs, slightly reduced the levels of glycated hemoglobin (HbA1c) with an estimated treatment difference (ETD) of −0.14% [95%CI −0.25; −0.03], p = 0.01, and I2 68%. Patients randomized to Icodec compared to those on once-daily basal analogs had a greater probability to achieve HbA1c < 7% without clinically relevant or severe hypoglycemic events in 12 weeks from randomization with an estimated risk ratio (ERR) of 1.17, [95%CI 1.01, 1.36], p = 0.03, and I2 66%. We did not find a difference in fasting glucose levels, time in range, and time above range between Icodec and comparators. Icodec, compared to once-daily basal analogs, resulted in a slight but statistically significant weight gain of 0.62 kg [95%CI 0.25; 0.99], p = 0.001, and I2 25%. The frequency of hypoglycemic events (ERR 1.16 [95%CI 0.95; 1.41]), adverse events (ERR 1.04 [95%CI 1.00; 1.08]), injection-site reactions (ERR 1.08 [95%CI 0.62; 1.90]), and the discontinuation of treatments were similar between the two groups. Icodec was found to work better when used in a basal-only than basal-bolus regimen with an ETD in HbA1c of −0.22%, a probability of achieving glucose control of +33%, a probability of achieving glucose control without clinically relevant or severe hypoglycemia of +28%, more time spent in target (+4.55%) and less time spent in hyperglycemia (−5.14%). The risk of clinically relevant or severe hypoglycemic events was significantly higher when background glinides and sulfonylureas were added to basal analogs (ERR 1.42 [95%CI 1.05; 1.93]). Conclusion. Insulin Icodec is substantially non-inferior to once-daily insulin analogs in T2D, either insulin-naïve or insulin-treated. However, Icodec works slightly better than competitors when used in a basal-only rather than basal-bolus regimen. Weight gain and hypoglycemic risk are substantially low but not negligible. Patients’ education, adequate lifestyle and pharmacological interventions, and appropriate therapy adjustments are essential to minimize risks. This systematic review is registered as PROSPERO CRD42024568680. Full article
(This article belongs to the Special Issue New Advances in Insulin—100 Years since Its Discovery)
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<p>Timeline summarizing the most relevant discoveries and events in the fields of Diabetology, Biotechnology, and Pharmacology that have characterized the last 100 years.</p>
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<p>Risk of bias of RCTs included in the systematic review and meta-analysis from the ONWARDS clinical program.</p>
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<p>Forest plot of meta-analysis for mean change in Glycated Hemoglobin (ETD, %) from baseline to study completion (intention-to-treat analysis).</p>
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<p>Forest plot of meta-analysis for probability (ERR) to achieve optimal glucose control (i.e., HbA1c &lt; 7%) from baseline to 12 weeks (intention-to-treat analysis).</p>
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<p>Forest plot of meta-analysis for probability (ERR) to achieve optimal glucose control (i.e., HbA1c &lt; 7%) without clinically relevant (level 2) or severe (level 3) hypoglycemic events from baseline to 12 weeks (intention-to-treat analysis).</p>
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<p>Forest plot of meta-analysis for mean change in body weight (ETD, kg) from baseline to study completion (intention-to-treat analysis).</p>
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<p>Forest plot of meta-analysis for probability (ERR, %) of experiencing clinically relevant (level 2) or severe (level 3) hypoglycemic events from baseline study completion (intention-to-treat analysis).</p>
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12 pages, 663 KiB  
Article
The Effect of an Exercise Paddock on Dairy Cow Behavior, Health, and Nutrient Digestion during the Transition from Pregnancy to Lactation
by Amin Cai, Shiwei Wang, Pengtao Li, Kris Descovich, Tong Fu, Hongxia Lian, Tengyun Gao and Clive J. C. Phillips
Animals 2024, 14(16), 2353; https://doi.org/10.3390/ani14162353 - 14 Aug 2024
Abstract
Providing an exercise paddock may improve the behavior and health of cows in their dry period. We compared a control group of cows in a shed with no exercise paddock and an experimental group in the same shed but with access to an [...] Read more.
Providing an exercise paddock may improve the behavior and health of cows in their dry period. We compared a control group of cows in a shed with no exercise paddock and an experimental group in the same shed but with access to an exercise paddock. Both groups had ad libitum total mixed ration (TMR) indoors combined with access to a paddock (Group EX). The other group was just offered TMR indoors (Group IN). Total lying time was longer for cows without the exercise paddock (859 min/d) than for those with the paddock (733 min/d) (p = 0.012). Lying bouts were shorter, there were more allogrooming bouts, and drinking time was longer if an exercise paddock was provided. Cows with the paddock spent on average 76 min/d in paddock activity. Non-esterified fatty acids in the blood were increased by providing the exercise paddock. No significant differences in postpartum milk yield and calf weight of dry cows with or without access to exercise paddock were observed. However, crude protein and neutral detergent fiber digestibility were increased by providing the exercise paddock. The results suggest that providing an exercise paddock for cows in their dry period increased activity, including allogrooming, reduced lying, and improved digestibility of some major nutrients in the feed. Full article
(This article belongs to the Section Animal Nutrition)
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<p>Overhead elevation of the cow shed. Group IN = indoor group with no exercise paddock, and Group EX = Group with an exercise paddock. <span class="html-fig-inline" id="animals-14-02353-i001"><img alt="Animals 14 02353 i001" src="/animals/animals-14-02353/article_deploy/html/images/animals-14-02353-i001.png"/></span>= camera.</p>
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<p>Frequency percentage distribution of cow lying duration for cows in Treatments IN and EX. Group IN = indoor group with no exercise paddock, and Group EX = Group with an exercise paddock.</p>
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26 pages, 31159 KiB  
Article
A Method for Predicting High-Resolution 3D Variations in Temperature and Salinity Fields Using Multi-Source Ocean Data
by Xiaohu Cao, Chang Liu, Shaoqing Zhang and Feng Gao
J. Mar. Sci. Eng. 2024, 12(8), 1396; https://doi.org/10.3390/jmse12081396 - 14 Aug 2024
Abstract
High-resolution three-dimensional (3D) variations in ocean temperature and salinity fields are of great significance for ocean environment monitoring. Currently, AI-based 3D temperature and salinity field predictions rely on expensive 3D data, and as the prediction period increases, the stacking of high-resolution 3D data [...] Read more.
High-resolution three-dimensional (3D) variations in ocean temperature and salinity fields are of great significance for ocean environment monitoring. Currently, AI-based 3D temperature and salinity field predictions rely on expensive 3D data, and as the prediction period increases, the stacking of high-resolution 3D data greatly increases the difficulty of model training. This paper transforms the prediction of 3D temperature and salinity into the prediction of sea surface elements and the inversion of subsurface temperature and salinity using sea surface elements, by leveraging the relationship between sea surface factors and subsurface temperature and salinity. This method comprehensively utilizes multi-source ocean data to avoid the issue of data volume caused by stacking high-resolution historical data. Specifically, the model first utilizes 1/4° low-resolution satellite remote sensing data to construct prediction models for sea surface temperature (SST) and sea level anomaly (SLA), and then uses 1/12° high-resolution temperature and salinity data as labels to build an inversion model of subsurface temperature and salinity based on SST and SLA. The prediction model and inversion model are integrated to obtain the final high-resolution 3D temperature and salinity prediction model. Experimental results show that the 20-day prediction results in the two sea areas of the coastal waters of China and the Northwest Pacific show good performance, accurately predicting ocean temperature and salinity in the vast majority of layers, and demonstrate higher resource utilization efficiency. Full article
(This article belongs to the Section Physical Oceanography)
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<p>Data partitioning for prediction and inversion. Figure (<b>a</b>) shows the division of forecast data, and figure (<b>b</b>) shows the division of inversion data.</p>
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<p>SimVP-gsta model. Figure (<b>a</b>) shows the SimVP–gsta model, and figure (<b>b</b>) shows the gsta module.</p>
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<p>M-ViT model. Figure (<b>a</b>) shows the M–ViT model, and figure (<b>b</b>) shows the Mobile–ViT module.</p>
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<p>Three-dimensional prediction model.</p>
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<p>Bilinear interpolation model.</p>
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<p>Loss function settings in the inversion experiment.</p>
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<p>Visualization of the average SST prediction error over 20 days in the Coastal Waters of China. The MAE chart is on the left, and the RMSE chart is on the right.</p>
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<p>Visualization of the average SST prediction error over 20 days in the Northwest Pacific. The MAE chart is on the left, and the RMSE chart is on the right.</p>
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<p>Visualization of the average SLA prediction error over 20 days in the Coastal Waters of China. The MAE chart is on the left, and the RMSE chart is on the right.</p>
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<p>Visualization of the average SLA prediction error over 20 days in the Northwest Pacific. The MAE chart is on the left, and the RMSE chart is on the right.</p>
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<p>Visualization of errors in 48-layer temperature and salinity inversion in the Coastal Waters of China. Figure (<b>a</b>) represents the temperature error, with the MAE chart on the left and the RMSE chart on the right. Figure (<b>b</b>) represents the salinity error, with the MAE chart on the left and the RMSE chart on the right.</p>
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<p>Visualization of errors in 48-layer temperature and salinity inversion in the Northwest Pacific. Figure (<b>a</b>) represents the temperature error, with the MAE chart on the left and the RMSE chart on the right. Figures (<b>b</b>) represents the salinity error, with the MAE chart on the left and the RMSE chart on the right.</p>
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<p>Three-dimensional sea temperature prediction error curves in the Coastal Waters of China. Figures (<b>a</b>–<b>d</b>) show the average MAE for temperature every 5 days from 1 to 20 days, and figures (<b>e</b>–<b>h</b>) show the average RMSE for temperature.</p>
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<p>Three-dimensional salinity prediction error curves in the Coastal Waters of China. Figures (<b>a</b>–<b>d</b>) show the average MAE for salinity, and figures (<b>e</b>–<b>h</b>) show the average RMSE for salinity.</p>
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<p>Visualization of the 3D temperature and salinity predictions for day 1 in the Coastal Waters of China. (<b>a</b>) Temperature, (<b>b</b>) salinity.</p>
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<p>Visualization of the 3D temperature and salinity predictions for day 20 in the Coastal Waters of China. (<b>a</b>) Temperature, (<b>b</b>) salinity.</p>
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<p>Three-dimensional sea temperature prediction error curves in the Northwest Pacific. Figures (<b>a</b>–<b>d</b>) show the average MAE for temperature every 5 days from 1 to 20 days, and figures (<b>e</b>–<b>h</b>) show the average RMSE for temperature.</p>
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<p>Three-dimensional sea salinity prediction error curves in the Northwest Pacific. Figures (<b>a</b>–<b>d</b>) show the average MAE for salinity, and figures (<b>e</b>–<b>h</b>) show the average RMSE for salinity.</p>
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<p>Visualization of the 3D temperature and salinity predictions for day 1 in the Northwest Pacific. (<b>a</b>) Temperature, (<b>b</b>) salinity.</p>
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<p>Visualization of the 3D temperature and salinity predictions for day 20 in the Northwest Pacific. (<b>a</b>) Temperature, (<b>b</b>) salinity.</p>
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<p>Visualization of temperature and salinity prediction errors. The areas highlighted in the figure are the regions with larger forecast errors in this instance.</p>
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<p>The RMSE curves for temperature with different methods. Figures (<b>a</b>–<b>d</b>) show the errors averaged every 5 days for the 20-day temperature and salinity forecasts. Orange and red box indicate the positions where error changes are noticeably variable over time.</p>
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<p>The RMSE curves for salinity with different methods. Figures (<b>a</b>–<b>d</b>) show the errors averaged every 5 days for the 20-day temperature and salinity forecasts. Orange and red box indicate the positions where error changes are noticeably variable over time.</p>
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12 pages, 445 KiB  
Article
Investigating a New Way to Assess Metabolic Risk in Pregnant Females with Prior RYGB Surgery
by Teresa Gisinger, Birgit Reiter, Karin Preindl, Thomas Stimpfl, Liliana-Imi Gard, Sabina Baumgartner-Parzer, Alexandra Kautzky-Willer and Michael Leutner
Nutrients 2024, 16(16), 2704; https://doi.org/10.3390/nu16162704 - 14 Aug 2024
Abstract
Background: Obesity in pregnancy is linked to adverse clinical outcomes such as gestational diabetes. Recently, a risk score calculated by different ceramide concentrations was recognized as a new way to investigate cardiovascular risk. The aim was to analyze if the ceramide risk score [...] Read more.
Background: Obesity in pregnancy is linked to adverse clinical outcomes such as gestational diabetes. Recently, a risk score calculated by different ceramide concentrations was recognized as a new way to investigate cardiovascular risk. The aim was to analyze if the ceramide risk score and cardiometabolic risk vary between normal-weight, obese, and females with prior Roux-en-Y bypass surgery (RYGB) during pregnancy. Methods: Three cohorts were investigated: first, 25 pregnant females with a history of RYGB; second, 19 with preconception BMI ≥ 35 kg/m2; and third, 19 normal-weight (preconception BMI < 25 kg/m2). Around the 24th to 28th weeks of gestation routine laboratory assessments, 3 h 75 g oral and intravenous glucose tolerance tests were carried out. The correlation of ceramide risk scores and ceramide ratios (Cer(d18:1/18:0)/Cer(d18:1/16:0)) with metabolic parameters was analyzed via Pearson correlation. The cohorts were compared via ANOVA and unpaired t-tests. Results: The RYGB cohort had lower ceramide risk scores and ratios compared to obese pregnant females (7.42 vs.9.34, p = 0.025; 0.33 vs.0.47, p < 0.001). Ceramide risk score and ratio were found to correlate negatively with insulin sensitivity (measured with the Matsuda (r = −0.376, p = 0.031; r = −0.455, p = 0.008) and calculated sensitivity index (r = −0.358, p = 0.044; r = −0.621, p < 0.001)) in females without RYGB. The ceramide risk score correlated positively with body fat in RYGB females (r = 0.650, p = 0.012). Conclusions: We found that females after RYGB have lower ceramide risk scores and ceramide ratios compared to obese pregnant females, possibly indicating lower metabolic risk. Full article
(This article belongs to the Special Issue Nutrition and Supplements during Pregnancy (2nd Edition))
24 pages, 10977 KiB  
Article
Examining the Controls on the Spatial Distribution of Landslides Triggered by the 2008 Wenchuan Ms 8.0 Earthquake, China, Using Methods of Spatial Point Pattern Analysis
by Guangshun Bai, Xuemei Yang, Guangxin Bai, Zhigang Kong, Jieyong Zhu and Shitao Zhang
Sustainability 2024, 16(16), 6974; https://doi.org/10.3390/su16166974 - 14 Aug 2024
Abstract
Landslide risk management contributes to the sustainable development of the region. Understanding the spatial controls on the distribution of landslides triggered by earthquakes (EqTLs) is difficult in terms of the prediction and risk assessment of EqTLs. In this study, landslides are regarded as [...] Read more.
Landslide risk management contributes to the sustainable development of the region. Understanding the spatial controls on the distribution of landslides triggered by earthquakes (EqTLs) is difficult in terms of the prediction and risk assessment of EqTLs. In this study, landslides are regarded as a spatial point pattern to test the controls on the spatial distribution of landslides and model the landslide density prediction. Taking more than 190,000 landslides triggered by the 2008 Wenchuan Ms 8.0 earthquake (WcEqTLs) as the research object, the relative density estimation, Kolmogorov–Smirnov testing based on cumulative distribution, receiver operating characteristic curve (ROC) analysis, and Poisson density modeling are comprehensively applied to quantitatively determine and discuss the different control effects of seven factors representing earthquakes, geology, and topography. The distance to the surface ruptures (dSR) and the distance to the epicenter (dEp) show significant and strong control effects, which are far stronger than the other five factors. Using only the dSR, dEp, engineering geological rock group (Eg), and the range, a particularly effective Poisson model of landslide density is constructed, whose area under the ROC (AUC) reaches 0.9244 and whose very high-density (VHD) zones can contain 50% of landslides and only comprise 3.9% of the study areas. This research not only deepens our understanding of the spatial distribution of WcEqTLs but also provides new technical methods for such investigation and analysis. Full article
(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction, 2nd Volume)
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<p>Landslide point locations [<a href="#B33-sustainability-16-06974" class="html-bibr">33</a>], WcEq epicenter location, and surface ruptures [<a href="#B43-sustainability-16-06974" class="html-bibr">43</a>,<a href="#B44-sustainability-16-06974" class="html-bibr">44</a>]. The gray grid lines in the study area display a custom coordinate system, with the epicenter as (0,0), along the surface rupture zone as the X-axis, the main propagation direction of the earthquake as the positive X-axis, and the vertical surface rupture as the Y-axis, with an interval of 10 km. The inset map shows major tectonic features in Longmenshan vicinity [<a href="#B43-sustainability-16-06974" class="html-bibr">43</a>]: The red box in the map indicates the location of the study area. LTB—Longmenshan thrust belt (southwestern China, eastern edge of the Qinghai–Tibet Plateau); ATF—Altyn Tagh fault; HF—Haiyuan fault; JLF—Jiali fault; NCB—North China block; RRF—Red River fault; SCB—South China block; XF—Xianshuihe fault; XJF—Xiaojiang fault; I—Qaidam–Qilian block; II—Bayan Har block; III—Sichuan–Yunnan block. The white arrow indicates the block motion direction.</p>
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<p>Maps of seven covariates.</p>
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<p>The relative distribution estimation of WcEqTLs density on the dSR. A is the curve of the estimation. B is the map of the estimation. The red horizontal dashed line in the (<b>A</b>) and the red circular dashed line in the (<b>B</b>) indicate that the average landslide density in the whole study area is 2.6 landslides/km<sup>2</sup>. The solid black line is the estimation result of this method. The blue dots are results of the discrete method.</p>
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<p>The relative distribution estimation of WcEqTLs density on the dEp. A is the curve of the estimation. B is the map of the estimation. The red horizontal dashed line in (<b>A</b>) and the red circular dashed line in (<b>B</b>) indicate that the average landslide density in the whole study area is 2.6 landslides/km<sup>2</sup>. The blue dots are results of the discrete method.</p>
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<p>The relative distribution estimation of the WcEqTLs density on the Elv, the range, the Slp, and the Asp. The red horizontal dashed lines in (<b>A</b>–<b>D</b>) indicate the average landslide density. The red dots are results of the discrete method.</p>
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<p>The relative distribution estimation of the WcEqTLs density on the Eg. (<b>A</b>) is the density histogram of classified landslides. The blue horizontal dashed line indicates that the average landslide density in the whole study area is 2.6 landslides/km<sup>2</sup>. (<b>B</b>) is the spatial distribution map of landslide density in different engineering geological rock groups.</p>
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<p>The statistical curve of the landslide cumulative probability relative to the dSR (<b>A</b>) and the dEp (<b>B</b>). The solid black line is the observation statistical curve, and the red dashed line is the CSR hypothetical statistical curve.</p>
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<p>The landslide density depends on the topographical and geomorphological factors significance test result chart. (<b>A</b>,<b>B</b>,<b>C</b>,<b>D</b>) are the range, the Slp, the Elv, and the Asp, respectively.</p>
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<p>The ROC chart of WcEqTLs dependents on covariates.</p>
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<p>ROC charts of models. (<b>B</b>) is a partial enlargement of (<b>A</b>), whose range of the X axis is 0.10~0.25 and the range of the Y axis is 0.80~0.95. The red dashed line in the figure (<b>A</b>) is for the CSR.</p>
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<p>A landslide density prediction map and classification map of each model.</p>
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24 pages, 1463 KiB  
Article
Quality and Efficiency of Coupled Iterative Coverage Path Planning for the Inspection of Large Complex 3D Structures
by Xiaodi Liu, Minnan Piao, Haifeng Li, Yaohua Li and Biao Lu
Drones 2024, 8(8), 394; https://doi.org/10.3390/drones8080394 - 14 Aug 2024
Abstract
To enable unmanned aerial vehicles to generate coverage paths that balance inspection quality and efficiency when performing three-dimensional inspection tasks, we propose a quality and efficiency coupled iterative coverage path planning (QECI-CPP) method. First, starting from a cleaned and refined mesh model, this [...] Read more.
To enable unmanned aerial vehicles to generate coverage paths that balance inspection quality and efficiency when performing three-dimensional inspection tasks, we propose a quality and efficiency coupled iterative coverage path planning (QECI-CPP) method. First, starting from a cleaned and refined mesh model, this was segmented into narrow and normal spaces, each with distinct constraint settings. During the initialization phase of viewpoint generation, factors such as image resolution and orthogonality degree were considered to enhance the inspection quality along the path. Then, the optimization objective was designed to simultaneously consider inspection quality and efficiency, with the relative importance of these factors adjustable according to specific task requirements. Through iterative adjustments and optimizations, the coverage path was continuously refined. In numerical simulations, the proposed method was compared with three other classic methods, evaluated across five aspects: image resolution, orthogonality degree, path distance, computation time, and total path cost. The comparative simulation results show that the QECI-CPP achieves maximum image resolution and orthogonality degree while maintaining inspection efficiency within a moderate computation time, demonstrating the effectiveness of the proposed method. Additionally, the flexibility of the planned path is validated by adjusting the weight coefficient in the optimized objective function. Full article
18 pages, 3252 KiB  
Review
5β-Dihydrosteroids: Formation and Properties
by Trevor M. Penning and Douglas F. Covey
Int. J. Mol. Sci. 2024, 25(16), 8857; https://doi.org/10.3390/ijms25168857 - 14 Aug 2024
Abstract
5β-Dihydrosteroids are produced by the reduction of Δ4-3-ketosteroids catalyzed by steroid 5β-reductase (AKR1D1). By analogy with steroid 5α-reductase, genetic deficiency exists in AKR1D1 which leads to errors in newborn metabolism and in this case to bile acid deficiency. Also, like the [...] Read more.
5β-Dihydrosteroids are produced by the reduction of Δ4-3-ketosteroids catalyzed by steroid 5β-reductase (AKR1D1). By analogy with steroid 5α-reductase, genetic deficiency exists in AKR1D1 which leads to errors in newborn metabolism and in this case to bile acid deficiency. Also, like the 5α-dihydrosteroids (e.g., 5α-dihydrotestosterone), the 5β-dihydrosteroids produced by AKR1D1 are not inactive but regulate ligand access to nuclear receptors, can act as ligands for nuclear and membrane-bound receptors, and regulate ion-channel opening. For example, 5β-reduction of cortisol and cortisone yields the corresponding 5β-dihydroglucocorticoids which are inactive on the glucocorticoid receptor (GR) and provides an additional mechanism of pre-receptor regulation of ligands for the GR in liver cells. By contrast, 5β-pregnanes can act as neuroactive steroids at the GABAA and NMDA receptors and at low-voltage-activated calcium channels, act as tocolytic agents, have analgesic activity and act as ligands for PXR, while bile acids act as ligands for FXR and thereby control cholesterol homeostasis. The 5β-androstanes also have potent vasodilatory properties and work through blockade of Ca2+ channels. Thus, a preference for 5β-dihydrosteroids to work at the membrane level exists via a variety of mechanisms. This article reviews the field and identifies gaps in knowledge to be addressed in future research. Full article
(This article belongs to the Special Issue Molecular Insights in Steroid Biosynthesis and Metabolism)
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<p>Bent steroid configuration seen in 5β-dihydrosteroids.</p>
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<p>Metabolism of Δ<sup>4</sup>-3-ketosteroids to tetrahydrosteroids. The sequential role of aldo-keto reductases is illustrated. Reproduced with permission from Endocrine Society [<a href="#B5-ijms-25-08857" class="html-bibr">5</a>].</p>
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<p>AKR1D1 splice variants. Reproduced with permission from <span class="html-italic">Steroids</span> [<a href="#B14-ijms-25-08857" class="html-bibr">14</a>].</p>
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<p>Control of ligand access to the glucocorticoid receptor in liver cells.</p>
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<p>Biosynthesis of 5β-pregnanes from progesterone.</p>
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<p>Bioactive 5β-dihydrosteroids.</p>
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<p>Allopregnanolone, <span class="html-italic">ent</span>-allopregnanolone and pregnanolone, <span class="html-italic">ent</span>-pregnanolone. The plane of the page is the mirror plane with allopregnanolone and pregnanolone behind the plane of the page and the <span class="html-italic">ent</span>-allopregnanolone and <span class="html-italic">ent</span>-pregnanolone in front of the plane of the page. Overlay of the respective enantiomer pairs would superimpose the A and C rings as well as the 18 and 19 methyl groups in each enantiomer pair.</p>
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<p>Properties of <span class="html-italic">ent</span>-steroids. The figure summarizes various effects where the enantiomers of AlloP (allopregnanolone) have been compared, including effects with enantioselectivity (<span class="html-italic">nat &gt; ent</span>), and effects where the enantiomers are equivalent (<span class="html-italic">nat = ent</span>). Reproduced with permission from <span class="html-italic">Neuroscience Biohav. Res</span> [<a href="#B60-ijms-25-08857" class="html-bibr">60</a>].</p>
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29 pages, 11620 KiB  
Article
A New Renieramycin T Right-Half Analog as a Small Molecule Degrader of STAT3
by Preeyaphan Phookphan, Satapat Racha, Masashi Yokoya, Zin Zin Ei, Daiki Hotta, Hongbin Zou and Pithi Chanvorachote
Mar. Drugs 2024, 22(8), 370; https://doi.org/10.3390/md22080370 - 14 Aug 2024
Abstract
Constitutive activation of STAT3 contributes to tumor development and metastasis, making it a promising target for cancer therapy. (1R,4R,5S)-10-hydroxy-9-methoxy-8,11-dimethyl-3-(naphthalen-2-ylmethyl)-1,2,3,4,5,6-hexahydro-1,5-epiminobenzo[d]azocine-4-carbonitrile, DH_31, a new derivative of the marine natural product Renieramycin T, showed potent activity against H292 and H460 cells, with IC50 values of [...] Read more.
Constitutive activation of STAT3 contributes to tumor development and metastasis, making it a promising target for cancer therapy. (1R,4R,5S)-10-hydroxy-9-methoxy-8,11-dimethyl-3-(naphthalen-2-ylmethyl)-1,2,3,4,5,6-hexahydro-1,5-epiminobenzo[d]azocine-4-carbonitrile, DH_31, a new derivative of the marine natural product Renieramycin T, showed potent activity against H292 and H460 cells, with IC50 values of 5.54 ± 1.04 µM and 2.9 ± 0.58 µM, respectively. Structure–activity relationship (SAR) analysis suggests that adding a naphthalene ring with methyl linkers to ring C and a hydroxyl group to ring E enhances the cytotoxic effect of DH_31. At 1–2.5 µM, DH_31 significantly inhibited EMT phenotypes such as migration, and sensitized cells to anoikis. Consistent with the upregulation of ZO1 and the downregulation of Snail, Slug, N-cadherin, and Vimentin at both mRNA and protein levels, in silico prediction identified STAT3 as a target, validated by protein analysis showing that DH_31 significantly decreases STAT3 levels through ubiquitin-proteasomal degradation. Immunofluorescence and Western blot analysis confirmed that DH_31 significantly decreased STAT3 and EMT markers. Additionally, molecular docking suggests a covalent interaction between the cyano group of DH_31 and Cys-468 in the DNA-binding domain of STAT3 (binding affinity = −7.630 kcal/mol), leading to destabilization thereafter. In conclusion, DH_31, a novel RT derivative, demonstrates potential as a STAT3-targeting drug that significantly contribute to understanding of the development of new targeted therapy. Full article
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Figure 1

Figure 1
<p>Derivatives of the RT right-half analog—DH_17, DH_20, DH_23, DH_26, DH_28, DH_30, and DH_31. (<b>A</b>) The structure of Renieramycin T, TM-(−)-18, and the core structure of the RT right-half analog with R. R represents the position of the pyridyl, thiazolyl, or naphthalenyl group in ring C of the RT right-half analog, respectively. (<b>B</b>) Structures of the present RT right-half analogs: DH_17, DH_20, DH_23, DH_26, DH_28, DH_30, and DH_31. (<b>C</b>) Step-by-step synthesis for derivatives of RT right-half analogs.</p>
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<p>The effect of RT right-half analogs on cytotoxicity in NSCLC and human normal lung epithelial (BEAS-2B) cell lines and apoptotic cell death in NSCLC cells. (<b>A</b>) NSCLC H292 and H460 cells were treated with derivatives of RT right-half analogs for 24 h and analyzed using MTT assay to assess cytotoxicity. (<b>B</b>) IC<sub>50</sub> values for H292 and H460 cell lines were calculated. (<b>C</b>) BEAS-2B cells were treated with DH_28, DH_30, DH_31, and TM-(−)-18 for 24 h. The cytotoxic effects were evaluated using an MTT assay, and the IC<sub>50</sub> values for BEAS-2B cells were determined. (<b>D</b>) H292 and H460 cells were seeded and treated with 0–10 μM of DH_28, DH_30, and DH_31 for 24 h. Hoechst 33342 and PI were used to stain the cell nuclei. Images were obtained under a fluorescence microscope. (<b>E</b>) The percentages of cell death were calculated based on the stained images in H292 and H460 cells. Data represent the mean ± SD (<span class="html-italic">n</span> = 3). *, ** and *** indicate a statistically significant difference between the treated and the untreated cells at <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">p</span> &lt; 0.01, and <span class="html-italic">p</span> &lt; 0.001, respectively.</p>
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<p>Putative analysis of NSCLC against DH_31 and the effect of DH_31 on EMT-association proteins. (<b>A</b>) Venn diagram of NSCLC and DH_31 targets and GO enrichment analysis of putative targets was performed to clarify the relevant biologic processes (<span class="html-italic">p</span> &lt; 0.01). The y-axis represents GO terms, and the x-axis indicates the number of genes enriched in that term. The color from blue to red indicates the value of <span class="html-italic">p</span>. The adjust (FDR) is becoming smaller with greater credibility and importance. (<b>B</b>) The expression levels of ZO1, ZEB1, Slug, Snail, N-cadherin, and Vimentin were visualized by fluorescence microscopy. Scale bar, 20 µm. Bar graphs show the relative levels of ZO1, ZEB1, Slug, Snail, N-cadherin, and Vimentin. (<b>C</b>) The protein expression levels of ZO1, Slug, Snail, N-cadherin, Vimentin and β–actin were evaluated by Western blot analysis. The relative protein levels were calculated by densitometry. Data represent the mean ± SD (<span class="html-italic">n</span> = 3). *, **, and *** indicate a statistically significant difference between the treated and untreated cells at <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">p</span> &lt; 0.01, and <span class="html-italic">p</span> &lt; 0.001, respectively.</p>
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<p>The effects of DH_31 on migration and anoikis resistance on NSCLC H460. (<b>A</b>) DH_31 decreased the migration of H460 cells. (<b>B</b>) The relative migration levels of the treated and untreated cells were determined at 24, 48, and 72 h. (<b>C</b>) DH_31 increased the sensitivity to anoikis in H460 cells. (<b>D</b>) The relative viability of cells was determined after culture under detachment conditions for 6, 12, and 24 h. Scale bar, 20 µm. Data represent the mean ± SD (<span class="html-italic">n</span> = 3). ** and *** indicate a statistically significant difference between the treated and the untreated cells at <span class="html-italic">p</span> &lt; 0.01 and <span class="html-italic">p</span> &lt; 0.001, respectively.</p>
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<p>STAT3 is Identified as a Potential Target of DH_31. (<b>A</b>) The top 10 targets among the 64 targets were ranked based on the number of degrees, visualized by the CytoHubba plugin. The degree values of the top 10 targets in the PPI network were ranked, with STAT3 having the highest degree. The intensity of the colors corresponded to the degree values, with purple indicating large values, pink indicating moderate values, and yellow indicating small values. (<b>B</b>) H460 cells treated with DH_31 (0–2.5 μM) for 24 h were stained with anti-STAT3 antibody (red) and examined using confocal laser scanning microscopy. Cell nuclei were stained with Hoechst 33342 (blue). Scale bar, 10 µm. Arrows denote localized STAT3 proteins. (<b>C</b>) The relative levels of STAT3 of H460 were determined by immunofluorescence analysis. (<b>D</b>) The protein expression levels of STAT3 and β–actin was evaluated by Western blot analysis. (<b>E</b>) The relative protein levels were calculated by densitometry. Data represent the mean ± SD (<span class="html-italic">n</span> = 3). * and *** indicate a statistically significant difference between the treated and untreated cells at <span class="html-italic">p</span> &lt; 0.05 and <span class="html-italic">p</span> &lt; 0.001, respectively.</p>
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<p>The effect of DH_31 on enhanced ubiquitin-mediated STAT3 proteasomal degradation in NSCLC H460. H460 cells were treated with DH_31 (0–2.5 μM) for 8 h. (<b>A</b>) The expression levels of STAT3 mRNA were determined by RT-PCR. (<b>B</b>) The ubiquitin–proteasome inhibitor MG132 reversed the inhibitory effect of DH_31 on the expression of the STAT3 protein. After treatment with or without MG132 (10 µM) for 1 h, cells were treated with DH_31 (0–2.5 µM) for 6 h. The STAT3 levels were measured using Western blot analysis and calculated by densitometry. (<b>C</b>) DH_31 induced the ubiquitin–proteasomal degradation of STAT3. After treatment with or without MG132 (10 µM) for 1 h, cells were treated with DH_31 (0 and 2.5 µM) for 6 h. The protein lysates were collected subsequent to STAT3 immunoprecipitation, and the ubiquitinated protein levels were measure by Western blot analysis. Ub-STAT3 levels were calculated by densitometry. Data represent the mean ± SD (<span class="html-italic">n</span> = 3). *, and ** indicate a statistically significant difference between the treated and untreated cells at <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">p</span> &lt; 0.01, and <span class="html-italic">p &lt;</span> 0.001, respectively. # and ## indicate a statistically significant difference from the cells without MG132 at <span class="html-italic">p</span> &lt; 0.05 and <span class="html-italic">p</span> &lt; 0.01, respectively.</p>
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<p>Domain structure of STAT3 and structure of DH_31 with in silico predicted binding configurations. (<b>A</b>) Schematic of the domain structure of STAT3 and the structure of the dimer interface of STAT3 (PDB: 1BG1) illustrating the surface locations of the DNA-binding domain (residues 321–494) (red) and the SH2 domain (residues 584–688) (green), (<b>B</b>) the binding interaction of DH_31 to the SH2 domain of STAT3, (<b>C</b>) the binding interaction of DH_31 to the DNA-binding domain, and (<b>D</b>) the binding interaction of TM-(−)-18 to the DNA-binding domain of STAT3. (<b>E</b>) The binding energy of DH_31 and TM-(−)-18 at the SH2 domain and the DNA-binding domain.</p>
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<p>The effect of DH_31 on the mRNA expression of EMT markers in NSCLC H460. (<b>A</b>) Schematic representation of the of STAT3 transcription factor binding sites in target genes. (<b>B</b>) The mRNA expression of <span class="html-italic">ZO1</span>, <span class="html-italic">Slug</span>, <span class="html-italic">Snail</span>, <span class="html-italic">N-cadherin</span>, and <span class="html-italic">Vimentin</span> in H460 cells treated with DH_31 (0–2.5 µM).</p>
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<p>Synthesis of <b>2e</b>.</p>
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<p>Synthesis of <b>2f</b>: DH_30.</p>
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<p>Synthesis of <b>3a</b>: DH_17.</p>
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<p>Synthesis of <b>3b</b>: DH_20.</p>
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<p>Synthesis of <b>3c</b>: DH_23.</p>
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<p>Synthesis of <b>3d</b>: DH_26.</p>
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<p>Synthesis of <b>3e</b>: DH_28.</p>
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<p>Synthesis of <b>3f</b>: DH_31.</p>
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15 pages, 5966 KiB  
Article
Long-Term Survival and Regeneration Following Transplantation of 3D-Printed Biodegradable PCL Tracheal Grafts in Large-Scale Porcine Models
by Sen-Ei Shai, Yi-Ling Lai, Yi-Wen Hung, Chi-Wei Hsieh, Kuo-Chih Su, Chun-Hsiang Wang, Te-Hsin Chao, Yung-Tsung Chiu, Chia-Ching Wu and Shih-Chieh Hung
Bioengineering 2024, 11(8), 832; https://doi.org/10.3390/bioengineering11080832 - 14 Aug 2024
Abstract
Polycaprolactone (PCL) implants in large animals show great promise for tracheal transplantation. However, the longest survival time achieved to date is only about three weeks. To meet clinical application standards, it is essential to extend the survival time and ensure the complete integration [...] Read more.
Polycaprolactone (PCL) implants in large animals show great promise for tracheal transplantation. However, the longest survival time achieved to date is only about three weeks. To meet clinical application standards, it is essential to extend the survival time and ensure the complete integration and functionality of the implant. Our study investigates the use of three-dimensional (3D)-printed, biodegradable, PCL-based tracheal grafts for large-scale porcine tracheal transplantation, assessing the feasibility and early structural integrity crucial for long-term survival experiments. A biodegradable PCL tracheal graft was fabricated using a BIOX bioprinter and transplanted into large-scale porcine models. The grafts, measuring 20 × 20 × 1.5 mm, were implanted following a 2 cm circumferential resection of the porcine trachea. The experiment design was traditionally implanted in eight porcines to replace four-ring tracheal segments, only two of which survived more than three months. Data were collected on the graft construction and clinical outcomes. The 3D-printed biosynthetic grafts replicated the native organ with high fidelity. The implantations were successful, without immediate complications. At two weeks, bronchoscopy revealed significant granulation tissue around the anastomosis, which was managed with laser ablation. The presence of neocartilage, neoglands, and partial epithelialization near the anastomosis was verified in the final pathology findings. Our study demonstrates in situ regenerative tissue growth with intact cartilage following transplantation, marked by neotissue formation on the graft’s exterior. The 90-day survival milestone was achieved due to innovative surgical strategies, reinforced with strap muscle attached to the distal trachea. Further improvements in graft design and granulation tissue management are essential to optimize outcomes. Full article
(This article belongs to the Special Issue Tissue Engineering and Regenerative Medicine in Bioengineering)
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Graphical abstract

Graphical abstract
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<p>A schematic representation of the experimental design of the tracheal transplantation using 3D-printed biodegradable PCL grafts in female LY porcines. Method I, which did not involve fixation of the distal trachea, resulted in exclusions due to various complications. In contrast, Method II, which employed fixation, led to successful inclusions and prolonged survival. The evaluation process included bronchoscopy, laser ablation, gross investigation, and histological analysis with H&amp;E staining to assess neocartilage development and address complications.</p>
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<p>Neotissue growth outside the graft from the proximal to the distal region with gross and histology images following implantation into large-scale porcine models for over 90 days. This figure illustrates the growth of neotissue in two porcine subjects, one with a soft tissue infection and the other without, following the implantation of tracheal grafts for over 90 days. The histological analysis of the neotissue was conducted at the proximal (<b>A</b>,<b>B</b>), middle (<b>C</b>,<b>D</b>), and distal (<b>E</b>,<b>F</b>) regions of the infected Pig1. Red circles indicate areas of heterotopic ossification. Similarly, the histology of the neotissue was examined at the proximal (<b>G</b>,<b>H</b>), middle (<b>I</b>,<b>J</b>), and distal (<b>K</b>,<b>L</b>) regions of the non-infected Pig2. The gross images display intact tracheal rings, while the histology images, stained with H&amp;E, highlight the development of cartilage and areas of heterotopic ossification, indicated by red circles. The neotissue extends from the proximal to the distal region outside the graft. Intact cylindrical neotissue, including cartilage, is visible in sections (<b>A</b>,<b>C</b>,<b>E</b>,<b>I</b>,<b>K</b>), with section (<b>G</b>) displaying only a half-ring of neotissue. The scale bars in the histology images represent 10 mm, providing a reference for the size of the observed features.</p>
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<p>A Comparative Histological Analysis of Tracheal Tissue Regeneration in Two Large-Scale Porcine Models 90 Days Post-Implantation. (<b>A</b>–<b>D</b>) exhibit a regenerated tracheal section with a fully integrated structure, showcasing robust neocartilage formation at the tip (<b>B</b>), dense clusters of newly formed submucosal glands (<b>C</b>), and a smooth epithelial lining (<b>D</b>). The total view (<b>A</b>) highlights the well-organized tissue with clear chondrogenesis and glandular development. (<b>E</b>–<b>H</b>) display a contrasting tracheal section where irregularities are more pronounced. There is evident heterotopic ossification (<b>E</b>), with the central area (<b>F</b>) showing lamellar bone trabeculae amidst fibrotic-like marrow tissue. The submucosal glands are irregularly clustered (<b>G</b>), and the epithelium appears uneven (<b>H</b>). The total view (<b>E</b>) underscores the disparity in tissue organization compared to the left panels.</p>
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<p>Histology at the Proximal Area of Neotissue Features Following Implantation into Large-Scale Porcine for Over 90 Days. (<b>A</b>) Heterotopic ossification; (<b>B</b>) Polyp; (<b>C</b>) Bacterial clump; (<b>D</b>) Congestion in the submucosal area. Yellow, red, blue, and green squares are magnified at each indicated image (<b>B</b>–<b>E</b>) at 100×.</p>
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<p>Features of Perichondrial Papillae (PP), Pre-resorptive Layers (PRL), and Various Stains of Vascular Canals (VCs) in Neocartilage after Graft Implantation for over 19 Days. (<b>A</b>–<b>C</b>) The PP and PRL were detected by H&amp;E, safranin O/fast green, and IHC with αSMA antibodies. “P” is indicated in the perichondrium; black arrows represent PRL. (<b>D</b>–<b>L</b>) The VCs were detected by H&amp;E, safranin O/fast green, alcian blue stains, and IHC with antibodies for type II collagen, Sox9, aggrecan, PCNA, αSMA, and CD31. Protein expression is indicated in brown; safranin O/fast green appears red; alcian blue is shown in blue. These data were magnified at 200×.</p>
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<p>Proposed Four-Stage Chondrogenesis within the Graft Neotissue as Revealed by H&amp;E, Alcian Blue, Safranin O/Fast Green, and IHC (Type II Collagen, Sox9, Aggrecan, and PCNA Antibodies) Stains. (<b>A</b>–<b>D</b>) Different stages of cartilage during chondrogenesis were investigated through H&amp;E staining. (<b>E</b>–<b>H</b>) The histology and GAG content of the different stages of cartilage during chondrogenesis were investigated using Alcian blue stain. The GAGs of the cartilage matrix were detected by Alcian blue and are indicated in blue. (<b>I</b>–<b>L</b>) The histology and GAG content of the different stages of cartilage during chondrogenesis were investigated with safranin O/Fast Green stain. The GAGs of the cartilage matrix were detected with safranin O/Fast Green, indicating a red color. Black arrows: the modulator structure of the vascular canal (VC). Images were at 400× magnification. (<b>M</b>) Chondrocyte numbers quantified for different stages of chondrogenesis of graft neotissue using H&amp;E stain (<span class="html-italic">n</span> = 4 views at 400× magnification for each group). (<b>N</b>–<b>P</b>) Ratios of protein expressions (Sox9, Aggrecan, and PCNA) quantified for different stages of chondrogenesis of graft neotissue based on IHC stainings (<span class="html-italic">n</span> = 4 views at 200× magnification for each group). Data are shown as mean ± SD values, and statistically significant differences across various stages were determined at <span class="html-italic">p</span> &lt; 0.05.</p>
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