Bioengineering, Volume 11, Issue 8 (August 2024) – 76 articles

During adaptive laboratory evolution experiments, any unexpected interruption in data monitoring or control could lead to the loss of valuable experimental data and compromise the integrity of the entire experiment. Most homemade mini-bioreactors are built employing microcontrollers such as Arduino. Although affordable, these platforms lack the robustness of the programmable logic controller (PLC), which enhances the safety and robustness of the control process. Here, we describe the design and validation of a PLC-controlled morbidostat, an innovative automated continuous-culture mini-bioreactor specifically created to study the evolutionary pathways to drug resistance in microorganisms. This morbidostat includes several improvements, both at the hardware and software level, for better online monitoring and a more robust operation. The device was validated employing Escherichia coli, exploring its adaptive evolution in the presence of didecyldimethylammonium chloride (DDAC), a quaternary ammonium compound widely used for its antimicrobial properties. E. coli was subjected to increasing concentrations of DDAC over 3 days. Our results demonstrated a significant increase in DDAC susceptibility, with evolved populations exhibiting substantial changes in their growth after exposure. Full article

Recent advances in deep learning have shown significant potential for accurate cell detection via density map regression using point annotations. However, existing deep learning models often struggle with multi-scale feature extraction and integration in complex histopathological images. Moreover, in multi-class cell detection scenarios, current density map regression methods typically predict each cell type independently, failing to consider the spatial distribution priors of different cell types. To address these challenges, we propose CellRegNet, a novel deep learning model for cell detection using point annotations. CellRegNet integrates a hybrid CNN/Transformer architecture with innovative feature refinement and selection mechanisms, addressing the need for effective multi-scale feature extraction and integration. Additionally, we introduce a contrastive regularization loss that models the mutual exclusiveness prior in multi-class cell detection cases. Extensive experiments on three histopathological image datasets demonstrate that CellRegNet outperforms existing state-of-the-art methods for cell detection using point annotations, with F1-scores of 86.38% on BCData (breast cancer), 85.56% on EndoNuke (endometrial tissue) and 93.90% on MBM (bone marrow cells), respectively. These results highlight CellRegNet’s potential to enhance the accuracy and reliability of cell detection in digital pathology. Full article

The objective of this study is to demonstrate the potential of a multicompartmental mathematical model to simulate the activity of the gastrointestinal system after the intake of drugs, with a limited number of parameters. The gastrointestinal system is divided into five compartments, modeled as both continuous systems with discrete events (stomach and duodenum) and systems with delay (jejunum, ileum, and colon). The dissolution of the drug tablet occurs in the stomach and is described through the Noyes–Whitney equation, with pH dependence expressed through the Henderson–Hasselbach relationship. The boluses resulting from duodenal activity enter the jejunum, ileum, and colon compartments, where drug absorption takes place as blood flows countercurrent. The model includes only three parameters with assigned physiological meanings. It was tested and validated using data from in vivo experiments. Specifically, the model was tested with the concentration profiles of nine different drugs and validated using data from two drugs with varying initial concentrations. Overall, the outputs of the model are in good agreement with experimental data, particularly with regard to the time of peak concentration. The primary sources of discrepancy were identified in the concentration decay. The model’s main strength is its relatively low computational cost, making it a potentially excellent tool for in silico assessment and prediction of drug adsorption in the intestine. Full article

Ultrasound imaging is vital for diagnosing carotid artery vascular lesions, highlighting the importance of accurately segmenting lumens in ultrasound images to prevent, diagnose and treat vascular diseases. However, noise artifacts, blood residue and discontinuous lumens significantly affect segmentation accuracy. To achieve accurate lumen segmentation in low-quality images, we propose a novel segmentation algorithm which is guided by an adaptively generated shape prior. To tackle the above challenges, we introduce a shape-prior-based segmentation method for carotid artery lumen walls. The shape prior in this study is adaptively generated based on the evolutionary trend of vessel growth. Shape priors guide and constrain the active contour, resulting in precise segmentation. The efficacy of the proposed model was confirmed using 247 carotid artery ultrasound images, with experimental results showing an average Dice coefficient of 92.38%, demonstrating superior segmentation performance compared to existing mathematical models. Our method can quickly and effectively perform accurate lumen segmentation on low-quality carotid artery ultrasound images, which is of great significance for the diagnosis of cardiovascular and cerebrovascular diseases. Full article

Surface electromyography (sEMG) is commonly used as an interface in human–machine interaction systems due to their high signal-to-noise ratio and easy acquisition. It can intuitively reflect motion intentions of users, thus is widely applied in gesture recognition systems. However, wearable sEMG-based gesture recognition systems are susceptible to changes in environmental noise, electrode placement, and physiological characteristics. This could result in significant performance degradation of the model in inter-session scenarios, bringing a poor experience to users. Currently, for noise from environmental changes and electrode shifting from wearing variety, numerous studies have proposed various data-augmentation methods and highly generalized networks to improve inter-session gesture recognition accuracy. However, few studies have considered the impact of individual physiological states. In this study, we assumed that user exercise could cause changes in muscle conditions, leading to variations in sEMG features and subsequently affecting the recognition accuracy of model. To verify our hypothesis, we collected sEMG data from 12 participants performing the same gesture tasks before and after exercise, and then used Linear Discriminant Analysis (LDA) for gesture classification. For the non-exercise group, the inter-session accuracy declined only by 2.86%, whereas that of the exercise group decreased by 13.53%. This finding proves that exercise is indeed a critical factor contributing to the decline in inter-session model performance. Full article

Background: Despite recent advancements, medical technology has not yet reached its peak. Precision medicine is growing rapidly, thanks to machine learning breakthroughs powered by increased computational capabilities. This article explores a deep learning application for computer-aided diagnosis in dermatology. Methods: Using a custom model based on EfficientNetB3 and deep learning, we propose an approach for skin lesion classification that offers superior results with smaller, cheaper, and faster inference times compared to other models. The skin images dataset used for this research includes 8222 files selected from the authors’ collection and the ISIC 2019 archive, covering six dermatological conditions. Results: The model achieved 95.4% validation accuracy in four categories—melanoma, basal cell carcinoma, benign keratosis-like lesions, and melanocytic nevi—using an average of 1600 images per category. Adding two categories with fewer images (about 700 each)—squamous cell carcinoma and actinic keratoses—reduced the validation accuracy to 88.8%. The model maintained accuracy on new clinical test images taken under the same conditions as the training dataset. Conclusions: The custom model demonstrated excellent performance on the diverse skin lesions dataset, with significant potential for further enhancements. Full article

The addition of intravenous (i.v.) chemotherapy to i.v. immunotherapy for patients with lung cancer results in improved overall survival but is limited by synergistic side effects and an unknown, highly variable final cytotoxic dose within the tumor. The synergy between i.v. chemo- and immunotherapies is hypothesized to occur as a result of cell injury caused by chemotherapy, a mechanism demonstrated to drive antigen presentation within the tumor microenvironment. Intratumoral delivery of chemotherapy may thus be optimized to maximize tumor cell injury. To assess the balance between the damage versus the death of tumor cells, we developed a computational model of intratumoral dynamics within a lung cancer tumor for three different chemotherapy agents following direct injection as a function of location and number of injection sites. We based the model on the morphology of a lung tumor obtained from a thoracic CT scan. We found no meaningful difference in the extent of tumor cell damage between a centrally injected versus peripherally injected agent, but there were significant differences between a single injection versus when the total dose was apportioned between multiple injection sites. Importantly, we also found that the standard chemotherapeutic concentrations used for intravenous administration were effective at causing cell death but were too high to generate significant cell injury. This suggests that to induce maximal tumor cell injury, the optimal concentration should be several orders of magnitude lower than those typically used for intravenous therapy. Full article

Objectives: Clinical molecular genetic testing and molecular imaging dramatically increase the quantity of clinical data. Combined with the extensive application of electronic health records, a medical data ecosystem is forming, which calls for big-data-based medicine models. We tried to use big data analytics to search for similar patients in a cancer cohort, showing how to apply artificial intelligence (AI) algorithms to clinical data processing to obtain clinically significant results, with the ultimate goal of improving healthcare management. Methods: In order to overcome the weaknesses of most data processing algorithms that rely on expert labeling and annotation, we uniformly adopted one-hot encoding for all types of clinical data, calculating the Euclidean distance to measure patient similarity and subgrouping via an unsupervised learning model. Overall survival (OS) was investigated to assess the clinical validity and clinical relevance of the model. Results: We took gastric cancers (GCs) as an example to build a high-dimensional clinical patient similarity network (cPSN). When performing the survival analysis, we found that Cluster_2 had the longest survival rates, while Cluster_5 had the worst prognosis among all the subgroups. As patients in the same subgroup share some clinical characteristics, the clinical feature analysis found that Cluster_2 harbored more lower distal GCs than upper proximal GCs, shedding light on the debates. Conclusion: Overall, we constructed a cancer-specific cPSN with excellent interpretability and clinical significance, which would recapitulate patient similarity in the real-world. The constructed cPSN model is scalable, generalizable, and performs well for various data types. Full article

Background: The purpose of this study was to establish a standardized structured workflow to compare findings from high-resolution, optimized reconstructions from post-mortem computed tomography (pmCT) with autopsy results in the detection of fractures of the laryngohyoid complex in strangulation victims. Method: Forty-two strangulation cases were selected, and pmCT scans of the laryngohyoid complex were obtained. Both pmCT scans and autopsy reports were analyzed using a structured template and compared using Cohen’s kappa coefficient (κ) and the McNemar test. The study also compared the prevalence of ossa sesamoidea and non-fusion of the major and minor horns of the hyoid bone between both diagnostic methods. Results: The detection of fractures showed a very good correlation between autopsy and pmCT results (κ = 0.905), with the McNemar test showing no statistically significant difference between the two methods. PmCT identified 28 sesamoid bones, 45 non-fusions of the major horns, and 47 non-fusions of the minor horns of the hyoid bone, compared to four, six, and zero, respectively, identified by autopsy (p < 0.0001). Conclusions: Autopsy and pmCT findings correlate well and can be used in a complementary manner. PmCT is superior to autopsy in identifying dislocations and detecting anatomical variations in the laryngohyoid complex, which can lead to misinterpretations during autopsy. Therefore, we do not advocate replacing autopsy with pmCT but propose using a structured workflow, including our standardized reporting template, for evaluating lesions in the laryngohyoid complex. Full article

The Dynamic Gait Event Identifier (DGEI) introduces a pioneering approach for real-time gait event detection that seamlessly aligns with the needs of embedded system design and optimization. DGEI creates a new standard for gait analysis by combining software and hardware co-design with real-time data analysis, using a combination of first-order difference functions and sliding window techniques. The method is specifically designed to accurately separate and analyze key gait events such as heel strike (HS), toe-off (TO), walking start (WS), and walking pause (WP) from a continuous stream of inertial measurement unit (IMU) signals. The core innovation of DGEI is the application of its dynamic feature extraction strategies, including first-order differential integration with positive/negative windows, weighted sleep time analysis, and adaptive thresholding, which together improve its accuracy in gait segmentation. The experimental results show that the accuracy rate of HS event detection is 97.82%, and the accuracy rate of TO event detection is 99.03%, which is suitable for embedded systems. Validation on a comprehensive dataset of 1550 gait instances shows that DGEI achieves near-perfect alignment with human annotations, with a difference of less than one frame in pulse onset times in 99.2% of the cases. Full article

Background: Diffusion-weighted imaging (DWI), a pivotal component of multiparametric magnetic resonance imaging (mpMRI), plays a pivotal role in the detection, diagnosis, and evaluation of gastric cancer. Despite its potential, DWI is often marred by substantial anatomical distortions and sensitivity artifacts, which can hinder its practical utility. Presently, enhancing DWI’s image quality necessitates reliance on cutting-edge hardware and extended scanning durations. The development of a rapid technique that optimally balances shortened acquisition time with improved image quality would have substantial clinical relevance. Objectives: This study aims to construct and evaluate the unsupervised learning framework called attention dual contrast vision transformer cyclegan (ADCVCGAN) for enhancing image quality and reducing scanning time in gastric DWI. Methods: The ADCVCGAN framework, proposed in this study, employs high b-value DWI (b = 1200 ${\mathrm{s}/\mathrm{mm}}^2$) as a reference for generating synthetic b-value DWI (s-DWI) from acquired lower b-value DWI (a-DWI, b = 800 ${\mathrm{s}/\mathrm{mm}}^2$). Specifically, ADCVCGAN incorporates an attention mechanism CBAM module into the CycleGAN generator to enhance feature extraction from the input a-DWI in both the channel and spatial dimensions. Subsequently, a vision transformer module, based on the U-net framework, is introduced to refine detailed features, aiming to produce s-DWI with image quality comparable to that of b-DWI. Finally, images from the source domain are added as negative samples to the discriminator, encouraging the discriminator to steer the generator towards synthesizing images distant from the source domain in the latent space, with the goal of generating more realistic s-DWI. The image quality of the s-DWI is quantitatively assessed using metrics such as the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), mean squared error (MSE), weighted peak signal-to-noise ratio (WPSNR), and weighted mean squared error (WMSE). Subjective evaluations of different DWI images were conducted using the Wilcoxon signed-rank test. The reproducibility and consistency of b-ADC and s-ADC, calculated from b-DWI and s-DWI, respectively, were assessed using the intraclass correlation coefficient (ICC). A statistical significance level of p < 0.05 was considered. Results: The s-DWI generated by the unsupervised learning framework ADCVCGAN scored significantly higher than a-DWI in quantitative metrics such as PSNR, SSIM, FSIM, MSE, WPSNR, and WMSE, with statistical significance (p < 0.001). This performance is comparable to the optimal level achieved by the latest synthetic algorithms. Subjective scores for lesion visibility, image anatomical details, image distortion, and overall image quality were significantly higher for s-DWI and b-DWI compared to a-DWI (p < 0.001). At the same time, there was no significant difference between the scores of s-DWI and b-DWI (p > 0.05). The consistency of b-ADC and s-ADC readings was comparable among different readers (ICC: b-ADC 0.87–0.90; s-ADC 0.88–0.89, respectively). The repeatability of b-ADC and s-ADC readings by the same reader was also comparable (Reader1 ICC: b-ADC 0.85–0.86, s-ADC 0.85–0.93; Reader2 ICC: b-ADC 0.86–0.87, s-ADC 0.89–0.92, respectively). Conclusions: ADCVCGAN shows excellent promise in generating gastric cancer DWI images. It effectively reduces scanning time, improves image quality, and ensures the authenticity of s-DWI images and their s-ADC values, thus providing a basis for assisting clinical decision making. Full article

Skin wounds often form scar tissue during healing. Early intervention with tissue-engineered materials and cell therapies may promote scar-free healing. Exosomes and extracellular vesicles (EV) secreted by mesenchymal stromal cells (MSC) are believed to have high regenerative capacity. EV bioactivity is preserved after lyophilization and storage to enable use in remote and typically resource-constrained environments. We developed a bioprinted bandage containing reconstituted EVs that can be fabricated at the point-of-need. An alginate/carboxymethyl cellulose (CMC) biomaterial ink was prepared, and printability and mechanical properties were assessed with rheology and compression testing. Three-dimensional printed constructs were evaluated for Young’s modulus relative to infill density and crosslinking to yield material with stiffness suitable for use as a wound dressing. We purified EVs from human MSC-conditioned media and characterized them with nanoparticle tracking analysis and mass spectroscopy, which gave a peak size of 118 nm and identification of known EV proteins. Fluorescently labeled EVs were mixed to form bio-ink and bioprinted to characterize EV release. EV bandages were bioprinted on both a commercial laboratory bioprinter and a custom ruggedized 3D printer with bioprinting capabilities, and lyophilized EVs, biomaterial ink, and thermoplastic filament were deployed to an austere Arctic environment and bioprinted. This work demonstrates that EVs can be bioprinted with an alginate/CMC hydrogel and released over time when in contact with a skin-like substitute. The technology is suitable for operational medical applications, notably in resource-limited locations, including large-scale natural disasters, humanitarian crises, and combat zones. Full article

Soft sensors based on deep learning regression models are promising approaches to predict real-time fermentation process quality measurements. However, experimental datasets are generally sparse and may contain outliers or corrupted data. This leads to insufficient model prediction performance. Therefore, datasets with a fully distributed solution space are required that enable effective exploration during model training. In this study, the robustness and predictive capability of the underlying model of a soft sensor was improved by generating synthetic datasets for training. The monitoring of intensified ethanol fermentation is used as a case study. Variational autoencoders were employed to create synthetic datasets, which were then combined with original datasets (experimental) to train neural network regression models. These models were tested on original versus augmented datasets to assess prediction improvements. Using the augmented datasets, the soft sensor predictive capability improved by 34%, and variability was reduced by 82%, based on R² scores. The proposed method offers significant time and cost savings for dataset generation for the deep learning modeling of ethanol fermentation and can be easily adapted to other fermentation processes. This work contributes to the advancement of soft sensor technology, providing practical solutions for enhancing reliability and robustness in large-scale production. Full article

Intermuscular coupling reflects the corticospinal interaction associated with the control of muscles. Nevertheless, the deterioration of intermuscular coupling caused by stroke has not received much attention. The purpose of this study was to investigate the effect of myoelectric-controlled interface (MCI) dimensionality on the intermuscular coupling after stroke. In total, ten age-matched controls and eight stroke patients were recruited and executed elbow tracking tasks within 1D or 2D MCI. Movement performance was quantified using the root mean square error (RMSE). Wavelet coherence was used to analyze the intermuscular coupling in alpha band (8–12 Hz) and beta band (15–35 Hz). The results found that smaller RMSE of antagonist muscles was observed in both groups within 2D MCI compared to 1D MCI. The alpha-band wavelet coherence was significantly lower in the patients compared to the controls during elbow extension. Furthermore, a decreased alpha-band and beta-band wavelet coherence was observed in the controls and stroke patients, as the dimensionality of MCI increased. These results may suggest that stroke-related neural impairments deteriorate the motor performance and intermuscular coordination pattern, and, further, that MCI holds promise as a novel effective tool for rehabilitation through the direct modulation of muscle activation pattern. Full article

In response to the analysis of the functional status of forearm blood vessels, this paper fully considers the orientation of the vascular skeleton and the geometric characteristics of blood vessels and proposes a blood vessel width calculation algorithm based on the radius estimation of the tangent circle (RETC) in forearm near-infrared images. First, the initial infrared image obtained by the infrared camera is preprocessed by image cropping, contrast stretching, denoising, enhancement, and initial segmentation. Second, the Zhang–Suen refinement algorithm is used to extract the vascular skeleton. Third, the Canny edge detection method is used to perform vascular edge detection. Finally, a RETC algorithm is developed to calculate the vessel width. This paper evaluates the accuracy of the proposed RETC algorithm, and experimental results show that the mean absolute error between the vessel width obtained by our algorithm and the reference vessel width is as low as 0.36, with a variance of only 0.10, which can be significantly reduced compared to traditional calculation measurements. Full article

The advent of pH-sensitive liposomes (pHLips) has opened new opportunities for the improved and targeted delivery of antitumor drugs as well as gene therapeutics. Comprising fusogenic dioleylphosphatidylethanolamine (DOPE) and cholesteryl hemisuccinate (CHEMS), these nanosystems harness the acidification in the tumor microenvironment and endosomes to deliver drugs effectively. pH-responsive liposomes that are internalized through endocytosis encounter mildly acidic pH in the endosomes and thereafter fuse or destabilize the endosomal membrane, leading to subsequent cargo release into the cytoplasm. The extracellular tumor matrix also presents a slightly acidic environment that can lead to the enhanced drug release and improved targeting capabilities of the nano-delivery system. Recent studies have shown that folic acid (FA) and iRGD-coated nanocarriers, including pH-sensitive liposomes, can preferentially accumulate and deliver drugs to breast tumors that overexpress folate receptors and αvβ3 and αvβ5 integrins. This study focuses on the development and characterization of 5-Fluorouracil (5-FU)-loaded FA and iRGD surface-modified pHLips (FA-iRGD-5-FU-pHLips). The novelty of this research lies in the dual targeting mechanism utilizing FA and iRGD peptides, combined with the pH-sensitive properties of the liposomes, to enhance selective targeting and uptake by cancer cells and effective drug release in the acidic tumor environment. The prepared liposomes were small, with an average diameter of 152 ± 3.27 nm, uniform, and unilamellar, demonstrating efficient 5-FU encapsulation (93.1 ± 2.58%). Despite surface functionalization, the liposomes maintained their pH sensitivity and a neutral zeta potential, which also conferred stability and reduced aggregation. Effective pH responsiveness was demonstrated by the observation of enhanced drug release at pH 5.5 compared to physiological pH 7.4. (84.47% versus 46.41% release at pH 5.5 versus pH 7.4, respectively, in 72 h). The formulations exhibited stability for six months and were stable when subjected to simulated biological settings. Blood compatibility and cytotoxicity studies on MDA-MB-231 and SK-BR3 breast cancer cell lines revealed an enhanced cytotoxicity of the liposomal formulation that was modified with FA and iRGD compared to free 5-FU and minimal hemolysis. Collectively, these findings support the potential of FA and iRGD surface-camouflaged, pH-sensitive liposomes as a promising drug delivery strategy for breast cancer treatment. Full article

Lung cancer, the second most common type of cancer worldwide, presents significant health challenges. Detecting this disease early is essential for improving patient outcomes and simplifying treatment. In this study, we propose a hybrid framework that combines deep learning (DL) with quantum computing to enhance the accuracy of lung cancer detection using chest radiographs (CXR) and computerized tomography (CT) images. Our system utilizes pre-trained models for feature extraction and quantum circuits for classification, achieving state-of-the-art performance in various metrics. Not only does our system achieve an overall accuracy of 92.12%, it also excels in other crucial performance measures, such as sensitivity (94%), specificity (90%), F1-score (93%), and precision (92%). These results demonstrate that our hybrid approach can more accurately identify lung cancer signatures compared to traditional methods. Moreover, the incorporation of quantum computing enhances processing speed and scalability, making our system a promising tool for early lung cancer screening and diagnosis. By leveraging the strengths of quantum computing, our approach surpasses traditional methods in terms of speed, accuracy, and efficiency. This study highlights the potential of hybrid computational technologies to transform early cancer detection, paving the way for wider clinical applications and improved patient care outcomes. Full article

Background: The objective of our study was to biomechanically evaluate the use of kyphoplasty to stabilize post-traumatic segmental instability in incomplete burst fractures of the vertebrae. Methods: The study was performed on 14 osteoporotic spine postmortem samples (Th11–L3). First, acquisition of the native multisegmental kinematics in our robot-based spine tester with three-dimensional motion analysis was set as a baseline for each sample. Then, an incomplete burst fracture was generated in the vertebral body L1 with renewed kinematic testing. After subsequent kyphoplasty was performed on the fractured vertebral body, primary stability was examined again. Results: Initially, a significant increase in the range of motion after incomplete burst fracture generation in all three directions of motion (extension–flexion, lateral tilt, axial rotation) was detected as proof of post-traumatic instability. There were no significant changes to the native state in the adjacent segments. Radiologically, a significant loss of height in the fractured vertebral body was also shown. Traumatic instability was significantly reduced by kyphoplasty. However, native kinematics were not restored. Conclusions: Although post-traumatic segmental instability was significantly reduced by kyphoplasty in our in vitro model, native kinematics could not be reconstructed, and significant instability remained. Full article

Background: Total joint arthroplasty (TJA) is an orthopedic procedure commonly used to treat damaged joints. Despite the efficacy of TJA, postoperative complications, including aseptic prosthesis loosening and infections, are common. Moreover, the effects of individual genetic susceptibility and modifiable risk factors on these complications are unclear. This study analyzed these effects to enhance patient prognosis and postoperative management. Methods: We conducted an extensive genome-wide association study (GWAS) and Mendelian randomization (MR) study using UK Biobank data. The cohort included 2964 patients with mechanical complications post-TJA, 957 with periprosthetic joint infection (PJI), and a control group of 398,708 individuals. Genetic loci associated with postoperative complications were identified by a GWAS analysis, and the causal relationships of 11 modifiable risk factors with complications were assessed using MR. Results: The GWAS analysis identified nine loci associated with post-TJA complications. Two loci near the PPP1R3B and RBM26 genes were significantly linked to mechanical complications and PJI, respectively. The MR analysis demonstrated that body mass index was positively associated with the risk of mechanical complications (odds ratio [OR]: 1.42; p < 0.001). Higher educational attainment was associated with a decreased risk of mechanical complications (OR: 0.55; p < 0.001) and PJI (OR: 0.43; p = 0.001). Type 2 diabetes was suggestively associated with mechanical complications (OR, 1.18, p = 0.02), and hypertension was suggestively associated with PJI (OR, 1.41, p = 0.008). Other lifestyle factors, including smoking and alcohol consumption, were not causally related to postoperative complications. Conclusions: The genetic loci near PPP1R3B and RBM26 influenced the risk of post-TJA mechanical complications and infections, respectively. The effects of genetic and modifiable risk factors, including body mass index and educational attainment, underscore the need to perform personalized preoperative assessments and the postoperative management of surgical patients. These results indicate that integrating genetic screening and lifestyle interventions into patient care can improve the outcomes of TJA and patient quality of life. Full article

The accurate segmentation of prostate cancer (PCa) from multiparametric MRI is crucial in clinical practice for guiding biopsy and treatment planning. Existing automated methods often lack the necessary accuracy and robustness in localizing PCa, whereas interactive segmentation methods, although more accurate, require user intervention on each input image, thereby limiting the cost-effectiveness of the segmentation workflow. Our innovative framework addresses the limitations of current methods by combining a coarse segmentation network, a rejection network, and an interactive deep network known as Segment Anything Model (SAM). The coarse segmentation network automatically generates initial segmentation results, which are evaluated by the rejection network to estimate their quality. Low-quality results are flagged for user interaction, with the user providing a region of interest (ROI) enclosing the lesions, whereas for high-quality results, ROIs were cropped from the automatic segmentation. Both manually and automatically defined ROIs are fed into SAM to produce the final fine segmentation. This approach significantly reduces the annotation burden and achieves substantial improvements by flagging approximately 20% of the images with the lowest quality scores for manual annotation. With only half of the images manually annotated, the final segmentation accuracy is statistically indistinguishable from that achieved using full manual annotation. Although this paper focuses on prostate lesion segmentation from multimodality MRI, the framework can be adapted to other medical image segmentation applications to improve segmentation efficiency while maintaining high accuracy standards. Full article

Background: This biomechanical in vitro study compared two kyphoplasty devices for the extent of height reconstruction, load-bearing capacity, cement volume, and adjacent fracture under cyclic loading. Methods: Multisegmental (T11–L3) specimens were mounted into a testing machine and subjected to compression, creating an incomplete burst fracture of L1. Kyphoplasty was performed using a one- or two-compartment device. Then, the testing machine was used for a cyclic loading test of load-bearing capacity to compare the two groups for the amount of applied load until failure and subsequent adjacent fracture. Results: Vertebral body height reconstruction was effective for both groups but not statistically significantly different. After cyclic loading, refracture of vertebrae that had undergone kyphoplasty was not observed in any specimen, but fractures were observed in adjacent vertebrae. The differences between the numbers of cycles and of loads were not statistically significant. An increase in cement volume was strongly correlated with increased risks of adjacent fractures. Conclusion: The two-compartment device was not substantially superior to the one-compartment device. The use of higher cement volume correlated with the occurrence of adjacent fractures. Full article

Although methods for generating human induced pluripotent stem cell (hiPSC)-derived motor nerve organoids are well established, those for sensory nerve organoids are not. Therefore, this study investigated the feasibility of generating sensory nerve organoids composed of hiPSC-derived sensory neurons using a microfluidic approach. Notably, sensory neuronal axons from neurospheres containing 100,000 cells were unidirectionally elongated to form sensory nerve organoids over 6 mm long axon bundles within 14 days using I-shaped microchannels in microfluidic devices composed of polydimethylsiloxane (PDMS) chips and glass substrates. Additionally, the organoids were successfully cultured for more than 60 days by exchanging the culture medium. The percentage of nuclei located in the distal part of the axon bundles (the region 3−6 mm from the entrance of the microchannel) compared to the total number of cells in the neurosphere was 0.005% for live cells and 0.008% for dead cells. Molecular characterization confirmed the presence of the sensory neuron marker ISL LIM homeobox 1 (ISL1) and the capsaicin receptor transient receptor potential vanilloid 1 (TRPV1). Moreover, capsaicin stimulation activated TRPV1 in organoids, as evidenced by significant calcium ion influx. Conclusively, this study demonstrated the feasibility of long-term organoid culture and the potential applications of sensory nerve organoids in bioengineered nociceptive sensors. Full article

According to the modular hypothesis for the control of movement, muscles are recruited in synergies, which capture muscle coordination in space, time, or both. In the last two decades, muscle synergy analysis has become a well-established framework in the motor control field and for the characterization of motor impairments in neurological patients. Altered modular control during a locomotion task has been often proposed as a potential quantitative metric for characterizing pathological conditions. Therefore, the purpose of this systematic review is to analyze the recent literature that used a muscle synergy analysis of neurological patients’ locomotion as an indicator of motor rehabilitation therapy effectiveness, encompassing the key methodological elements to date. Searches for the relevant literature were made in Web of Science, PubMed, and Scopus. Most of the 15 full-text articles which were retrieved and included in this review identified an effect of the rehabilitation intervention on muscle synergies. However, the used experimental and methodological approaches varied across studies. Despite the scarcity of studies that investigated the effect of rehabilitation on muscle synergies, this review supports the utility of muscle synergies as a marker of the effectiveness of rehabilitative therapy and highlights the challenges and open issues that future works need to address to introduce the muscle synergies in the clinical practice and decisional process. Full article

Accurate evaluation of retinopathy of prematurity (ROP) severity is vital for screening and proper treatment. Current deep-learning-based automated AI systems for assessing ROP severity do not follow clinical guidelines and are opaque. The aim of this study is to develop an interpretable AI system by mimicking the clinical screening process to determine ROP severity level. A total of 6100 RetCam Ⅲ wide-field digital retinal images were collected from Guangdong Women and Children Hospital at Panyu (PY) and Zhongshan Ophthalmic Center (ZOC). A total of 3330 images of 520 pediatric patients from PY were annotated to train an object detection model to detect lesion type and location. A total of 2770 images of 81 pediatric patients from ZOC were annotated for stage, zone, and the presence of plus disease. Integrating stage, zone, and the presence of plus disease according to clinical guidelines yields ROP severity such that an interpretable AI system was developed to provide the stage from the lesion type, the zone from the lesion location, and the presence of plus disease from a plus disease classification model. The ROP severity was calculated accordingly and compared with the assessment of a human expert. Our method achieved an area under the curve (AUC) of 0.95 (95% confidence interval [CI] 0.90–0.98) in assessing the severity level of ROP. Compared with clinical doctors, our method achieved the highest F1 score value of 0.76 in assessing the severity level of ROP. In conclusion, we developed an interpretable AI system for assessing the severity level of ROP that shows significant potential for use in clinical practice for ROP severity level screening. Full article

Respiratory syncytial virus (RSV) is a common respiratory pathogen that infects the human lungs and respiratory tract, often causing symptoms similar to the common cold. Vaccination is the most effective strategy for managing viral outbreaks. Currently, extensive efforts are focused on developing a vaccine for RSV. Traditional vaccine design typically involves using an attenuated form of the pathogen to elicit an immune response. In contrast, peptide-based vaccines (PBVs) aim to identify and chemically synthesize specific immunodominant peptides (IPs), known as T-cell epitopes (TCEs), to induce a targeted immune response. Despite their potential for enhancing vaccine safety and immunogenicity, PBVs have received comparatively less attention. Identifying IPs for PBV design through conventional wet-lab experiments is challenging, costly, and time-consuming. Machine learning (ML) techniques offer a promising alternative, accurately predicting TCEs and significantly reducing the time and cost of vaccine development. This study proposes the development and evaluation of eight hybrid ML predictive models created through the permutations and combinations of two classification methods, two feature weighting techniques, and two feature selection algorithms, all aimed at predicting the TCEs of RSV. The models were trained using the experimentally determined TCEs and non-TCE sequences acquired from the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) repository. The hybrid model composed of the XGBoost (XGB) classifier, chi-squared (ChST) weighting technique, and backward search (BST) as the optimal feature selection algorithm (ChST−BST–XGB) was identified as the best model, achieving an accuracy, sensitivity, specificity, F1 score, AUC, precision, and MCC of 97.10%, 0.98, 0.97, 0.98, 0.99, 0.99, and 0.96, respectively. Additionally, K-fold cross-validation (KFCV) was performed to ensure the model’s reliability and an average accuracy of 97.21% was recorded for the ChST−BST–XGB model. The results indicate that the hybrid XGBoost model consistently outperforms other hybrid approaches. The epitopes predicted by the proposed model may serve as promising vaccine candidates for RSV, subject to in vitro and in vivo scientific assessments. This model can assist the scientific community in expediting the screening of active TCE candidates for RSV, ultimately saving time and resources in vaccine development. Full article

The aim was to objectively assess compliance in patients prescribed headgear and evaluate the impact of monitoring awareness, treatment duration, gender, and age on compliance levels. A total of 22 patients with Class II malocclusion wore the headgear integrated with the force and temperature sensitive Smartgear monitoring system (Smartgear, Swissorthodontics AG, Cham, Switzerland). Patients were instructed to wear the headgear for 13 h daily over a 3-month period. Randomly, 11 patients were informed that they monitored and 11 were not informed. Data were organized using Microsoft Excel and analyzed using R for statistical estimates, graphs, and hypothesis testing. Smartgear recorded an average daily compliance of 6.7 h. No statistically significant differences were found in cooperation between study group and control group over the 3 months of treatment, regardless of gender and age. However, there was slight greater cooperation in the first month than in the other months, and patients ≤10 years of age had almost 2 h more cooperation than their older counterparts. Moreover, the informed group exhibited an average of 1.1 more hours of cooperation per day than the uninformed group, which may carry clinical significance. This cooperation primarily occurred at night and was found to be statistically significant. Compliance among young patients typically remained lower than the prescribed level, regardless of their gender and psychological maturity. Although an awareness of monitoring does not seem to improve compliance, implementing such systems could still offer dentists a valuable means of obtaining objective information about their patients’ adherence. Full article

Nanotechnology plays a pivotal role in the biomedical field, especially in the synthesis and regulation of drug particle size. Reducing drug particles to the micron or nanometer scale can enhance bioavailability. Supercritical fluid technology, as a green drug development strategy, is expected to resolve the challenges of thermal degradation, uneven particle size, and organic solvent residue faced by traditional methods such as milling and crystallization. This paper provides an insight into the application of super-stable homogeneous intermix formulating technology (SHIFT) and super-table pure-nanomedicine formulation technology (SPFT) developed based on supercritical fluids for drug dispersion and micronization. These technologies significantly enhance the solubility and permeability of hydrophobic drugs by controlling the particle size and morphology, and the modified drugs show excellent therapeutic efficacy in the treatment of hepatocellular carcinoma, pathological scarring, and corneal neovascularization, and their performance and efficacy are highlighted when administered through multiple routes of administration. Overall, supercritical fluids have opened a green and efficient pathway for clinical drug development, which is expected to reduce side effects and enhance therapeutic efficacy. Full article

The rising prevalence of mental illness is straining global mental health systems, particularly affecting older adults who often face deteriorating physical health and decreased autonomy and quality of life. Early detection and targeted rehabilitation are crucial in mitigating these challenges. Mindfulness acceptance and commitment therapy (ACT) holds promise for enhancing motivation and well-being among the elderly, although delivering such psychological interventions is hindered by limited access to services, prompting exploration of remote delivery options like mobile applications. In this paper, we introduce the BrainHeart App (v.1.1.8), a mobile application tailored to improve physical and mental well-being in seniors. The app features a 10-day ACT program and other sections promoting healthy lifestyle. In a pilot study involving twenty participants, individuals engaged in daily mental exercises for 10 days using the app. Clinical evaluations, including assessments of psychological flexibility, overall cognitive profile, mindfulness disposition, cognitive fusion, and heart rate collected with Polar H10, were conducted at baseline (T0) and one month post-intervention (T1). Analysis revealed significant improvements in almost all neuropsychological scores, with high usability reported (system usability scale average score: 82.3 ± 9.31). Additionally, a negative correlation was found between usability and experiential avoidance (r = −0.51; p = 0.026), and a notable difference in heart rate was observed between baseline and post-intervention (F-value = 3.06; p-value = 0.09). These findings suggest that mindfulness-ACT exercises delivered via the BrainHeart App can enhance the well-being of elderly individuals, highlighting the potential of remote interventions in addressing mental health needs in this population. Full article

Medical image registration has become pivotal in recent years with the integration of various imaging modalities like X-ray, ultrasound, MRI, and CT scans, enabling comprehensive analysis and diagnosis of biological structures. This paper provides a comprehensive review of registration techniques for medical images, with an in-depth focus on 2D-2D image registration methods. While 3D registration is briefly touched upon, the primary emphasis remains on 2D techniques and their applications. This review covers registration techniques for diverse modalities, including unimodal, multimodal, interpatient, and intra-patient. The paper explores the challenges encountered in medical image registration, including geometric distortion, differences in image properties, outliers, and optimization convergence, and discusses their impact on registration accuracy and reliability. Strategies for addressing these challenges are highlighted, emphasizing the need for continual innovation and refinement of techniques to enhance the accuracy and reliability of medical image registration systems. The paper concludes by emphasizing the importance of accurate medical image registration in improving diagnosis. Full article