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Bioengineering, Volume 12, Issue 10 (October 2025) – 110 articles

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25 pages, 767 KB  
Review
Enhancing Anaerobic Digestion of Agricultural By-Products: Insights and Future Directions in Microaeration
by Ellie B. Froelich and Neslihan Akdeniz
Bioengineering 2025, 12(10), 1117; https://doi.org/10.3390/bioengineering12101117 (registering DOI) - 18 Oct 2025
Abstract
Anaerobic digestion of manures, crop residues, food waste, and sludge frequently yields biogas with elevated hydrogen sulfide concentrations, which accelerate corrosion and reduce biogas quality. Microaeration, defined as the controlled addition of oxygen at 1 to 5% of the biogas production rate, has [...] Read more.
Anaerobic digestion of manures, crop residues, food waste, and sludge frequently yields biogas with elevated hydrogen sulfide concentrations, which accelerate corrosion and reduce biogas quality. Microaeration, defined as the controlled addition of oxygen at 1 to 5% of the biogas production rate, has been investigated as a low-cost desulfurization strategy. This review synthesizes studies from 2015 to 2025 spanning laboratory, pilot, and full-scale anaerobic digester systems. Continuous sludge digesters supplied with ambient air at 0.28–14 m3 h−1 routinely achieved 90 to 99% H2S removal, while a full-scale dairy manure system reported a 68% reduction at 20 m3 air d−1. Pure oxygen dosing at 0.2–0.25 m3 O2 (standard conditions) per m3 reactor volume resulted in greater than 99% removal. Reported methane yield improvements ranged from 5 to 20%, depending on substrate characteristics, operating temperature, and aeration control. Excessive oxygen, however, reduced methane yields in some cases by inhibiting methanogens or diverting carbon to CO2. Documented benefits of microaeration include accelerated hydrolysis of lignocellulosic substrates, mitigation of sulfide inhibition, and stimulation of sulfur-oxidizing bacteria that convert sulfide to elemental sulfur or sulfate. Optimal redox conditions were generally maintained between −300 and −150 mV, though monitoring was limited by low-resolution oxygen sensors. Recent extensions of the Anaerobic Digestion Model No. 1 (ADM1), a mathematical framework developed by the International Water Association, incorporate oxygen transfer and sulfur pathways, enhancing its ability to predict gas quality and process stability under microaeration. Economic analyses estimate microaeration costs at 0.0015–0.0045 USD m−3 biogas, substantially lower than chemical scrubbing. Future research should focus on refining oxygen transfer models, quantifying microbial shifts under long-term operation, assessing effects on digestate quality and nitrogen emissions, and developing adaptive control strategies that enable reliable application across diverse substrates and reactor configurations. Full article
(This article belongs to the Section Biochemical Engineering)
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15 pages, 2160 KB  
Article
Evaluation of Parkinson’s Disease Motor Symptoms via Wearable Inertial Measurements Units and Surface Electromyography Sensors
by Xiangliang Zhang, Wenhao Pan, Zhuoneng Wu, Xiangzhi Liu, Yiping Sun, Bingfei Fan, Miao Cai, Tong Li and Tao Liu
Bioengineering 2025, 12(10), 1116; https://doi.org/10.3390/bioengineering12101116 (registering DOI) - 18 Oct 2025
Abstract
Parkinson’s disease (PD) is one of the fastest-growing neurodegenerative disorders; its cardinal motor signs—tremor, bradykinesia, and rigidity—substantially impair quality of life. Conventional clinician-rated scales can be subjective and exhibit limited interrater reliability, underscoring the need for objective and reliable quantification. We present an [...] Read more.
Parkinson’s disease (PD) is one of the fastest-growing neurodegenerative disorders; its cardinal motor signs—tremor, bradykinesia, and rigidity—substantially impair quality of life. Conventional clinician-rated scales can be subjective and exhibit limited interrater reliability, underscoring the need for objective and reliable quantification. We present an integrated evaluation framework that leverages surface electromyography (sEMG) with multimodal sensing. For representation learning, we combine time–frequency descriptors with Mini-ROCKET features. Grading is performed by an sEMG-based Unified Parkinson’s Disease Rating Scale (UPDRS) model (LDA-SV) that produces per-segment probabilities for ordinal scores (0–3) and aggregates them via soft voting to assign item-level ratings. Participants completed a standardized protocol spanning gait, seated rest, and upper-limb tasks (forearm pronation–supination, finger-to-nose, fist clench, and thumb–index pinch). Using the aforementioned dataset, we report task-wise performance with 95% confidence intervals and compare the proposed model against CNN, LSTM, and InceptionTime using McNemar tests and log-odds ratios. The results indicate that the proposed model outperforms the three baseline models overall. These findings demonstrate the effectiveness and feasibility of the proposed approach, suggesting a viable pathway for the objective quantification of PD motor symptoms and facilitating broader clinical adoption of sEMG in diagnosis and treatment. Full article
(This article belongs to the Special Issue Advanced Wearable Sensors for Human Gait Analysis)
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17 pages, 2060 KB  
Article
Continuous Optical Biosensing of IL-8 Cancer Biomarker Using a Multimodal Platform
by A. L. Hernandez, K. Mandal, B. Santamaria, S. Quintero, M. R. Dokmeci, V. Jucaud and M. Holgado
Bioengineering 2025, 12(10), 1115; https://doi.org/10.3390/bioengineering12101115 - 17 Oct 2025
Abstract
In this work, we used a label-free biosensor that provides optical readouts to perform continuous detection of human interleukin 8 (IL-8), which is especially overexpressed in certain cancers and, thus, could be an effective biomarker for cancer prognosis estimation and therapy evaluation. For [...] Read more.
In this work, we used a label-free biosensor that provides optical readouts to perform continuous detection of human interleukin 8 (IL-8), which is especially overexpressed in certain cancers and, thus, could be an effective biomarker for cancer prognosis estimation and therapy evaluation. For this purpose, we engineered a compact, portable, and easy-to-assemble biosensing module device. It combines a fluidic chip for reagent flow, a biosensing chip for signal transduction, and an optical readout head based on fiber optics in a single module. The biosensing chip is based on independent arrays of resonant nanopillar transducer (RNP) networks. We integrated the biosensing chip with the RNPs facing down in a simple and rapidly fabricated polydimethyl siloxane (PDMS) microfluidic chip, with inlet and outlet channels for the sample flowing through the RNPs. The RNPs were vertically oriented from the backside through an optical fiber mounted on a holder head fabricated ad hoc on polytetrafluoroethylene (PTFE). The optical fiber was connected to a visible spectrometer for optical response analysis and consecutive biomolecule detection. We obtained a sensogram showing anti-IL-8 immobilization and the specific recognition of IL-8. This unique portable and easy-to-handle module can be used for biomolecule detection within minutes and is particularly suitable for in-line sensing of physiological and biomimetic organ-on-a-chip systems. Cancer biomarkers’ continuous monitoring arises as an efficient and non-invasive alternative to classical tools (imaging, immunohistology) for determining clinical prognostic factors and therapeutic responses to anticancer drugs. In addition, the multiplexed layout of the optical transducers and the simplicity of the monolithic sensing module yield potential high-throughput screening of a combination of different biomarkers, which, together with other medical exams (such as imaging and/or patient history), could become a cutting-edge technology for further and more accurate diagnosis and prediction of cancer and similar diseases. Full article
(This article belongs to the Section Biosignal Processing)
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4 pages, 149 KB  
Editorial
Biomechanics and Motion Analysis: From Human Performance to Clinical Practice
by Chen He, Hong Fu and Christina Zong-Hao Ma
Bioengineering 2025, 12(10), 1114; https://doi.org/10.3390/bioengineering12101114 - 17 Oct 2025
Abstract
Research in biomechanics and motion analysis quantifies motion, forces, and control strategies, bridging the gap between fundamental science and practical applications [...] Full article
(This article belongs to the Special Issue Biomechanics and Motion Analysis)
31 pages, 3812 KB  
Review
Generative Adversarial Networks in Dermatology: A Narrative Review of Current Applications, Challenges, and Future Perspectives
by Rosa Maria Izu-Belloso, Rafael Ibarrola-Altuna and Alex Rodriguez-Alonso
Bioengineering 2025, 12(10), 1113; https://doi.org/10.3390/bioengineering12101113 - 16 Oct 2025
Abstract
Generative Adversarial Networks (GANs) have emerged as powerful tools in artificial intelligence (AI) with growing relevance in medical imaging. In dermatology, GANs are revolutionizing image analysis, enabling synthetic image generation, data augmentation, color standardization, and improved diagnostic model training. This narrative review explores [...] Read more.
Generative Adversarial Networks (GANs) have emerged as powerful tools in artificial intelligence (AI) with growing relevance in medical imaging. In dermatology, GANs are revolutionizing image analysis, enabling synthetic image generation, data augmentation, color standardization, and improved diagnostic model training. This narrative review explores the landscape of GAN applications in dermatology, systematically analyzing 27 key studies and identifying 11 main clinical use cases. These range from the synthesis of under-represented skin phenotypes to segmentation, denoising, and super-resolution imaging. The review also examines the commercial implementations of GAN-based solutions relevant to practicing dermatologists. We present a comparative summary of GAN architectures, including DCGAN, cGAN, StyleGAN, CycleGAN, and advanced hybrids. We analyze technical metrics used to evaluate performance—such as Fréchet Inception Distance (FID), SSIM, Inception Score, and Dice Coefficient—and discuss challenges like data imbalance, overfitting, and the lack of clinical validation. Additionally, we review ethical concerns and regulatory limitations. Our findings highlight the transformative potential of GANs in dermatology while emphasizing the need for standardized protocols and rigorous validation. While early results are promising, few models have yet reached real-world clinical integration. The democratization of AI tools and open-access datasets are pivotal to ensure equitable dermatologic care across diverse populations. This review serves as a comprehensive resource for dermatologists, researchers, and developers interested in applying GANs in dermatological practice and research. Future directions include multimodal integration, clinical trials, and explainable GANs to facilitate adoption in daily clinical workflows. Full article
(This article belongs to the Special Issue AI-Driven Imaging and Analysis for Biomedical Applications)
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21 pages, 1706 KB  
Article
Spatiotemporal Feature Learning for Daily-Life Cough Detection Using FMCW Radar
by Saihu Lu, Yuhan Liu, Guangqiang He, Zhongrui Bai, Zhenfeng Li, Pang Wu, Xianxiang Chen, Lidong Du, Peng Wang and Zhen Fang
Bioengineering 2025, 12(10), 1112; https://doi.org/10.3390/bioengineering12101112 - 15 Oct 2025
Viewed by 327
Abstract
Cough is a key symptom reflecting respiratory health, with its frequency and pattern providing valuable insights into disease progression and clinical management. Objective and reliable cough detection systems are therefore of broad significance for healthcare and remote monitoring. However, existing algorithms often struggle [...] Read more.
Cough is a key symptom reflecting respiratory health, with its frequency and pattern providing valuable insights into disease progression and clinical management. Objective and reliable cough detection systems are therefore of broad significance for healthcare and remote monitoring. However, existing algorithms often struggle to jointly model spatial and temporal information, limiting their robustness in real-world applications. To address this issue, we propose a cough recognition framework based on frequency-modulated continuous-wave (FMCW) radar, integrating a deep convolutional neural network (CNN) with a Self-Attention mechanism. The CNN extracts spatial features from range-Doppler maps, while Self-Attention captures temporal dependencies, and effective data augmentation strategies enhance generalization by simulating position variations and masking local dependencies. To rigorously evaluate practicality, we collected a large-scale radar dataset covering diverse positions, orientations, and activities. Experimental results demonstrate that, under subject-independent five-fold cross-validation, the proposed model achieved a mean F1-score of 0.974±0.016 and an accuracy of 99.05±0.55 %, further supported by high precision of 98.77±1.05 %, recall of 96.07±2.16 %, and specificity of 99.73±0.23 %. These results confirm that our method is not only robust in realistic scenarios but also provides a practical pathway toward continuous, non-invasive, and privacy-preserving respiratory health monitoring in both clinical and telehealth applications. Full article
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20 pages, 987 KB  
Article
Optimization of the Parameters of a Minimal Coagulation Model
by Carolin Link, Gábor Janiga and Dominique Thévenin
Bioengineering 2025, 12(10), 1111; https://doi.org/10.3390/bioengineering12101111 - 15 Oct 2025
Viewed by 223
Abstract
The formation of a blood clot within a vessel can result in its complete blockage. This phenomenon, known as thrombosis, can have severe consequences. In contrary, thrombosis can be sometimes desirable. Intra-aneurysmal thrombosis is the primary objective of an endovascular treatment aimed at [...] Read more.
The formation of a blood clot within a vessel can result in its complete blockage. This phenomenon, known as thrombosis, can have severe consequences. In contrary, thrombosis can be sometimes desirable. Intra-aneurysmal thrombosis is the primary objective of an endovascular treatment aimed at occluding the aneurysm sac. The proper modeling of the coagulation system is, therefore, important for the prediction, early recognition, and prevention of these tendencies. In silico investigations based on computational fluid dynamics (CFD) extended by thrombosis models provide a valuable tool for a detailed analysis. Minimal models are particularly useful for practical purposes to reduce computational efforts. This work proposes an approach to adapt the parameters of a minimal model to reproduce the behavior obtained with a comprehensive description of the coagulation cascade. The objective is to obtain the same thrombin generation curves while reducing strongly computational costs. For this purpose, machine learning—based here on an evolutionary algorithm—is used to optimize the obtained agreement. By adapting the reaction rate coefficients, a significant improvement can be achieved. The obtained results pave the way for future applications of the improved model in complex configurations such as for planning personalized interventions. Notably, the minimal model will be used for CFD in future studies to take advantage of its low computational cost. Full article
(This article belongs to the Section Biomedical Engineering and Biomaterials)
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35 pages, 1845 KB  
Review
Review of Artificial Intelligence Techniques for Breast Cancer Detection with Different Modalities: Mammography, Ultrasound, and Thermography Images
by Aigerim Mashekova, Michael Yong Zhao, Vasilios Zarikas, Olzhas Mukhmetov, Nurduman Aidossov, Eddie Yin Kwee Ng, Dongming Wei and Madina Shapatova
Bioengineering 2025, 12(10), 1110; https://doi.org/10.3390/bioengineering12101110 - 15 Oct 2025
Viewed by 154
Abstract
Breast cancer remains one of the most prevalent cancers worldwide, necessitating reliable, efficient, and precise diagnostic methods. Meanwhile, the rapid development of artificial intelligence (AI) presents significant opportunities for integration into various fields, including healthcare, by enabling the processing of medical data and [...] Read more.
Breast cancer remains one of the most prevalent cancers worldwide, necessitating reliable, efficient, and precise diagnostic methods. Meanwhile, the rapid development of artificial intelligence (AI) presents significant opportunities for integration into various fields, including healthcare, by enabling the processing of medical data and the early detection of cancer. This review examines the major medical imaging techniques used for breast cancer detection, specifically mammography, ultrasound, and thermography, and identifies widely used publicly available datasets in this domain. It also surveys traditional machine learning and deep learning approaches commonly applied to the analysis of mammographic, ultrasound, and thermographic images, discussing key studies in the field and evaluating the potential of different AI techniques for breast cancer detection. Furthermore, the review highlights the development and integration of explainable artificial intelligence (XAI) to enhance transparency and trust in medical imaging-based diagnoses. Finally, it considers potential future directions, including the application of large language models (LLMs) and multimodal LLMs in breast cancer diagnosis, emphasizing recent research aimed at advancing the precision, accessibility, and reliability of diagnostic systems. Full article
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1 pages, 127 KB  
Correction
Correction: Bräuchle et al. Large-Scale Expansion of Suspension Cells in an Automated Hollow-Fiber Perfusion Bioreactor. Bioengineering 2025, 12, 644
by Eric Bräuchle, Maria Knaub, Laura Weigand, Elisabeth Ehrend, Patricia Manns, Antje Kremer, Hugo Fabre and Halvard Bonig
Bioengineering 2025, 12(10), 1109; https://doi.org/10.3390/bioengineering12101109 - 15 Oct 2025
Viewed by 118
Abstract
In the original publication [...] Full article
(This article belongs to the Section Biomedical Engineering and Biomaterials)
27 pages, 6922 KB  
Article
Real-World Wrist-Derived Digital Mobility Outcomes in People with Multiple Long-Term Conditions: A Comparison of Algorithms
by Dimitrios Megaritis, Lisa Alcock, Kirsty Scott, Hugo Hiden, Andrea Cereatti, Ioannis Vogiatzis and Silvia Del Din
Bioengineering 2025, 12(10), 1108; https://doi.org/10.3390/bioengineering12101108 - 15 Oct 2025
Viewed by 247
Abstract
Digital Mobility outcomes can serve as objective biomarkers of health, but their validation in populations with multiple long-term conditions (MLTCs) based on wrist-worn devices remains unexplored. We refined, improved, and introduced novel algorithms, specifically tailored and adapted for (i) gait sequence detection, (ii) [...] Read more.
Digital Mobility outcomes can serve as objective biomarkers of health, but their validation in populations with multiple long-term conditions (MLTCs) based on wrist-worn devices remains unexplored. We refined, improved, and introduced novel algorithms, specifically tailored and adapted for (i) gait sequence detection, (ii) initial contact identification, and (iii) stride length estimation from a single wrist-worn device. Validation was performed using data from 28 participants with co-occurring MLTCs performing a 2.5 h real-world monitoring session. Reference data from an established multi-sensor system were used to assess algorithm performance across diverse gait patterns of co-occurring MLTCs. Twenty-eight participants (mean age 70.4 years, 43% females) had a median of three co-occurring MLTCs. Among six gait sequence detection methods, improved versions of the Kheirkhahan algorithm performed best (accuracy = 0.92, specificity = 0.96). For initial contact detection (nine methods tested), Shin’s algorithm achieved the highest performance index (0.85) followed by McCamley (0.84). Stride length estimation was most accurate using novel approaches based on the Weinberg method (performance index > 0.70). The proposed fine-tuned algorithms, the newly developed adaptive variants, and the foot-length augmented versions demonstrated robust performance, surpassing many existing methods and addressing the complexity of gait patterns in MLTCs. These findings enable scalable, real-time mobility monitoring in complex clinical populations using accessible wearable technology. Full article
(This article belongs to the Special Issue Advanced Wearable Sensors for Human Gait Analysis)
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19 pages, 4569 KB  
Article
NeuroNet-AD: A Multimodal Deep Learning Framework for Multiclass Alzheimer’s Disease Diagnosis
by Saeka Rahman, Md Motiur Rahman, Smriti Bhatt, Raji Sundararajan and Miad Faezipour
Bioengineering 2025, 12(10), 1107; https://doi.org/10.3390/bioengineering12101107 - 15 Oct 2025
Viewed by 269
Abstract
Alzheimer’s disease (AD) is the most prevalent form of dementia. This disease significantly impacts cognitive functions and daily activities. Early and accurate diagnosis of AD, including the preliminary stage of mild cognitive impairment (MCI), is critical for effective patient care and treatment development. [...] Read more.
Alzheimer’s disease (AD) is the most prevalent form of dementia. This disease significantly impacts cognitive functions and daily activities. Early and accurate diagnosis of AD, including the preliminary stage of mild cognitive impairment (MCI), is critical for effective patient care and treatment development. Although advancements in deep learning (DL) and machine learning (ML) models improve diagnostic precision, the lack of large datasets limits further enhancements, necessitating the use of complementary data. Existing convolutional neural networks (CNNs) effectively process visual features but struggle to fuse multimodal data effectively for AD diagnosis. To address these challenges, we propose NeuroNet-AD, a novel multimodal CNN framework designed to enhance AD classifcation accuracy. NeuroNet-AD integrates Magnetic Resonance Imaging (MRI) images with clinical text-based metadata, including psychological test scores, demographic information, and genetic biomarkers. In NeuroNet-AD, we incorporate Convolutional Block Attention Modules (CBAMs) within the ResNet-18 backbone, enabling the model to focus on the most informative spatial and channel-wise features. We introduce an attention computation and multimodal fusion module, named Meta Guided Cross Attention (MGCA), which facilitates effective cross-modal alignment between images and meta-features through a multi-head attention mechanism. Additionally, we employ an ensemble-based feature selection strategy to identify the most discriminative features from the textual data, improving model generalization and performance. We evaluate NeuroNet-AD on the Alzheimer’s Disease Neuroimaging Initiative (ADNI1) dataset using subject-level 5-fold cross-validation and a held-out test set to ensure robustness. NeuroNet-AD achieved 98.68% accuracy in multiclass classification of normal control (NC), MCI, and AD and 99.13% accuracy in the binary setting (NC vs. AD) on the ADNI dataset, outperforming state-of-the-art models. External validation on the OASIS-3 dataset further confirmed the model’s generalization ability, achieving 94.10% accuracy in the multiclass setting and 98.67% accuracy in the binary setting, despite variations in demographics and acquisition protocols. Further extensive evaluation studies demonstrate the effectiveness of each component of NeuroNet-AD in improving the performance. Full article
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23 pages, 11108 KB  
Article
Generative Modeling for Interpretable Anomaly Detection in Medical Imaging: Applications in Failure Detection and Data Curation
by McKell E. Woodland, Mais Altaie, Caleb S. O’Connor, Austin H. Castelo, Olubunmi C. Lebimoyo, Aashish C. Gupta, Joshua P. Yung, Paul E. Kinahan, Clifton D. Fuller, Eugene J. Koay, Bruno C. Odisio, Ankit B. Patel and Kristy K. Brock
Bioengineering 2025, 12(10), 1106; https://doi.org/10.3390/bioengineering12101106 - 14 Oct 2025
Viewed by 295
Abstract
This work aims to leverage generative modeling-based anomaly detection to enhance interpretability in AI failure detection systems and to aid data curation for large repositories. For failure detection interpretability, this retrospective study utilized 3339 CT scans (525 patients), divided patient-wise into training, baseline [...] Read more.
This work aims to leverage generative modeling-based anomaly detection to enhance interpretability in AI failure detection systems and to aid data curation for large repositories. For failure detection interpretability, this retrospective study utilized 3339 CT scans (525 patients), divided patient-wise into training, baseline test, and anomaly (having failure-causing attributes—e.g., needles, ascites) test datasets. For data curation, 112,120 ChestX-ray14 radiographs were used for training and 2036 radiographs from the Medical Imaging and Data Resource Center for testing, categorized as baseline or anomalous based on attribute alignment with ChestX-ray14. StyleGAN2 networks modeled the training distributions. Test images were reconstructed with backpropagation and scored using mean squared error (MSE) and Wasserstein distance (WD). Scores should be high for anomalous images, as StyleGAN2 cannot model unseen attributes. Area under the receiver operating characteristic curve (AUROC) evaluated anomaly detection, i.e., baseline and anomaly dataset differentiation. The proportion of highest-scoring patches containing needles or ascites assessed anomaly localization. Permutation tests determined statistical significance. StyleGAN2 did not reconstruct anomalous attributes (e.g., needles, ascites), enabling the unsupervised detection of these attributes: mean (±standard deviation) AUROCs were 0.86 (±0.13) for failure detection and 0.82 (±0.11) for data curation. 81% (±13%) of the needles and ascites were localized. WD outperformed MSE on CT (p < 0.001), while MSE outperformed WD on radiography (p < 0.001). Generative models detected anomalous image attributes, demonstrating promise for model failure detection interpretability and large-scale data curation. Full article
(This article belongs to the Section Biomedical Engineering and Biomaterials)
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14 pages, 1605 KB  
Article
Hamstring Tendon Grafts for Anterior Cruciate Ligament Reconstruction: The Effect of a 180° Twist Angle on Tensile Properties
by Jure Serdar, Ana Pilipović, Giuseppe Filardo, Slavica Knežević, Anita Galić Mihić, Mihovil Plečko, Ozgur Basal and Tomislav Smoljanović
Bioengineering 2025, 12(10), 1105; https://doi.org/10.3390/bioengineering12101105 - 14 Oct 2025
Viewed by 268
Abstract
Background: Evidence regarding the effects of twisting hamstring tendons on graft properties in anterior cruciate ligament (ACL) reconstruction remains controversial. The objective of this study was to evaluate the influence of a 180° twist on the tensile properties of human hamstring tendon grafts [...] Read more.
Background: Evidence regarding the effects of twisting hamstring tendons on graft properties in anterior cruciate ligament (ACL) reconstruction remains controversial. The objective of this study was to evaluate the influence of a 180° twist on the tensile properties of human hamstring tendon grafts (HTGs). Methods: Fourteen human cadavers were included, and hamstring tendons (semitendinosus [ST] and gracilis [GR]) were harvested bilaterally. Matched pairs of tendons were allocated to the ST and GR groups and further subdivided into control (ST-0, GR-0) and experimental (ST-180, GR-180) subgroups. Standard tripled single-tendon grafts were prepared in the control groups, while grafts in the experimental groups were twisted by 180°. All grafts were preconditioned and tested using a universal testing machine (Shimadzu AGS-X, Shimadzu Corporation, Japan) to determine tensile strength, stiffness, tensile modulus, and energy absorption capacity. Results: In the semitendinosus group, the maximum force was 648.72 (±287.71) N for ST-0 and 853.11 (±189.14) N for ST-180, with energy absorption capacities of 9.21 (±4.47) J and 13.48 (±4.95) J, respectively. Although the mean values of the investigated parameters were consistently higher in the ST-180 group, these differences did not reach statistical significance. In the gracilis group, no statistically significant differences were observed between the GR-0 and GR-180 subgroups for any parameter. Conclusions: Twisting hamstring tendons by 180° during graft preparation results in limited alterations of biomechanical properties, without statistically significant improvements. These findings call into question the clinical relevance of this technique in enhancing graft material properties for ACL reconstruction. Full article
(This article belongs to the Special Issue Engineering Biodegradable-Implant Materials, 2nd Edition)
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26 pages, 2235 KB  
Article
AF-DETR: Transformer-Based Object Detection for Precise Atrial Fibrillation Beat Localization in ECG
by Peng Wang, Junxian Song, Pang Wu, Zhenfeng Li, Xianxiang Chen, Lidong Du and Zhen Fang
Bioengineering 2025, 12(10), 1104; https://doi.org/10.3390/bioengineering12101104 - 14 Oct 2025
Viewed by 355
Abstract
Atrial fibrillation (AF) detection in electrocardiograms (ECG) remains challenging, particularly at the heartbeat level. Traditional deep learning methods typically classify ECG segments as a whole, limiting their ability to detect AF at the granularity of individual heartbeats. This paper presents AF-DETR, a novel [...] Read more.
Atrial fibrillation (AF) detection in electrocardiograms (ECG) remains challenging, particularly at the heartbeat level. Traditional deep learning methods typically classify ECG segments as a whole, limiting their ability to detect AF at the granularity of individual heartbeats. This paper presents AF-DETR, a novel transformer-based object detection model for precise AF heartbeat localization and classification. AF-DETR incorporates a CNN backbone and a transformer encoder–decoder architecture, where 2D bounding boxes are used to represent heartbeat positions. Through iterative refinement of these bounding boxes, the model improves both localization and classification accuracy. To further enhance performance, we introduce contrastive denoising training, which accelerates convergence and prevents redundant heartbeat predictions. We evaluate AF-DETR on five publicly available ECG datasets (CPSC2021, AFDB, LTAFDB, MITDB, NSRDB), achieving state-of-the-art performance with F1-scores of 96.77%, 96.20%, 90.55%, and 99.87% for heartbeat-level classification, and segment-level accuracies of 98.27%, 97.55%, 97.30%, and 99.99%, respectively. These results demonstrate the effectiveness of AF-DETR in accurately detecting AF heartbeats and its strong generalization capability across diverse ECG datasets. Full article
(This article belongs to the Section Biosignal Processing)
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18 pages, 4982 KB  
Article
A Novel Multi-Modal Flexible Headband System for Sleep Monitoring
by Zaihao Wang, Yuhao Ding, Hongyu Chen, Chen Chen and Wei Chen
Bioengineering 2025, 12(10), 1103; https://doi.org/10.3390/bioengineering12101103 - 13 Oct 2025
Viewed by 548
Abstract
Sleep monitoring is critical for diagnosing and treating sleep disorders. Although polysomnography (PSG) remains the clinical gold standard, its complexity, discomfort, and lack of portability limit its applicability for long-term and home-based monitoring. To overcome these challenges, this study introduces a novel flexible [...] Read more.
Sleep monitoring is critical for diagnosing and treating sleep disorders. Although polysomnography (PSG) remains the clinical gold standard, its complexity, discomfort, and lack of portability limit its applicability for long-term and home-based monitoring. To overcome these challenges, this study introduces a novel flexible headband system designed for multi-modal physiological signal acquisition, incorporating dry electrodes, a six-axis inertial measurement unit (IMU), and a temperature sensor. The device supports eight EEG channels and enables wireless data transmission via Bluetooth, ensuring user convenience and reliable long-term monitoring in home environments. To rigorously evaluate the system’s performance, we conducted comprehensive assessments involving 13 subjects over two consecutive nights, comparing its outputs with conventional PSG. Experimental results demonstrate the system’s low power consumption, ultra-low input noise, and robust signal fidelity, confirming its viability for overnight sleep tracking. Further validation was performed using the self-collected HBSleep dataset (over 184 h recordings of the 13 subjects), where state-of-the-art sleep staging models (DeepSleepNet, TinySleepNet, and AttnSleepNet) were applied. The system achieved an overall accuracy exceeding 75%, with AttnSleepNet emerging as the top-performing model, highlighting its compatibility with advanced machine learning frameworks. These results underscore the system’s potential as a reliable, comfortable, and practical solution for accurate sleep monitoring in non-clinical settings. Full article
(This article belongs to the Special Issue Soft and Flexible Sensors for Biomedical Applications)
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31 pages, 1305 KB  
Review
Artificial Intelligence in Cardiac Electrophysiology: A Clinically Oriented Review with Engineering Primers
by Giovanni Canino, Assunta Di Costanzo, Nadia Salerno, Isabella Leo, Mario Cannataro, Pietro Hiram Guzzi, Pierangelo Veltri, Sabato Sorrentino, Salvatore De Rosa and Daniele Torella
Bioengineering 2025, 12(10), 1102; https://doi.org/10.3390/bioengineering12101102 - 13 Oct 2025
Viewed by 552
Abstract
Artificial intelligence (AI) is transforming cardiac electrophysiology across the entire care pathway, from arrhythmia detection on 12-lead electrocardiograms (ECGs) and wearables to the guidance of catheter ablation procedures, through to outcome prediction and therapeutic personalization. End-to-end deep learning (DL) models have achieved cardiologist-level [...] Read more.
Artificial intelligence (AI) is transforming cardiac electrophysiology across the entire care pathway, from arrhythmia detection on 12-lead electrocardiograms (ECGs) and wearables to the guidance of catheter ablation procedures, through to outcome prediction and therapeutic personalization. End-to-end deep learning (DL) models have achieved cardiologist-level performance in rhythm classification and prognostic estimation on standard ECGs, with a reported arrhythmia classification accuracy of ≥95% and an atrial fibrillation detection sensitivity/specificity of ≥96%. The application of AI to wearable devices enables population-scale screening and digital triage pathways. In the electrophysiology (EP) laboratory, AI standardizes the interpretation of intracardiac electrograms (EGMs) and supports target selection, and machine learning (ML)-guided strategies have improved ablation outcomes. In patients with cardiac implantable electronic devices (CIEDs), remote monitoring feeds multiparametric models capable of anticipating heart-failure decompensation and arrhythmic risk. This review outlines the principal modeling paradigms of supervised learning (regression models, support vector machines, neural networks, and random forests) and unsupervised learning (clustering, dimensionality reduction, association rule learning) and examines emerging technologies in electrophysiology (digital twins, physics-informed neural networks, DL for imaging, graph neural networks, and on-device AI). However, major challenges remain for clinical translation, including an external validation rate below 30% and workflow integration below 20%, which represent core obstacles to real-world adoption. A joint clinical engineering roadmap is essential to translate prototypes into reliable, bedside tools. Full article
(This article belongs to the Special Issue Mathematical Models for Medical Diagnosis and Testing)
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17 pages, 1190 KB  
Article
The Effects of Neuromuscular Training on Electromyography, Lower Extremity Kinematics, and Ground Reaction Force During an Unanticipated Side-Cut on Recreational Female Hockey Players
by Tom Johnston, Stephanie Valentin, Susan J. Brown and Konstantinos Kaliarntas
Bioengineering 2025, 12(10), 1101; https://doi.org/10.3390/bioengineering12101101 - 13 Oct 2025
Viewed by 348
Abstract
During an unpredictable side-cut, this study examined how a sport-specific neuromuscular training program (NMTP) influenced electromyography responses in the lower limb posterior muscles, leg movement angles, maximum vertical ground reaction force (vGRF), and the rate of force development of vGRF. Thirty-eight adult female [...] Read more.
During an unpredictable side-cut, this study examined how a sport-specific neuromuscular training program (NMTP) influenced electromyography responses in the lower limb posterior muscles, leg movement angles, maximum vertical ground reaction force (vGRF), and the rate of force development of vGRF. Thirty-eight adult female recreational hockey players were randomly allocated into an intervention group (INT) or a control group (CON). Before beginning training or matches, the INT carried out the NMTP three times per week for eight weeks, whereas the CON performed their routine warm-up. A 45° sidecut (dominant leg only) was performed at baseline and after eight-weeks and recorded with a motion capture system. The effect of group and time, and their interaction, was investigated using a mixed-design ANOVA. After landing, the participants in the INT had greater activation of their gastrocnemius lateralis, gastrocnemius medialis, and gluteus maximus muscles than those in the CON. INT participants showed significantly lower amounts of maximum knee abduction and knee excursion, while there was an increase in these variables for the CON. At week eight, the vGRF RFD decreased for the INT but increased for the CON. Although non-significant, the overall muscle activity showed an increasing trend for the INT when it came to supervised NMTP for eight weeks compared to the effect seen in the CON. This activity caused greater alterations in the motion and forces of the lower body for the INT than the CON. Full article
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22 pages, 253 KB  
Review
Beyond Single Systems: How Multi-Agent AI Is Reshaping Ethics in Radiology
by Sara Salehi, Yashbir Singh, Parnian Habibi and Bradley J. Erickson
Bioengineering 2025, 12(10), 1100; https://doi.org/10.3390/bioengineering12101100 - 13 Oct 2025
Viewed by 396
Abstract
Radiology is undergoing a paradigm shift from traditional single-function AI systems to sophisticated multi-agent networks capable of autonomous reasoning, coordinated decision-making, and adaptive workflow management. These agentic AI systems move beyond simple pattern recognition to encompass complex radiological workflows including image analysis, report [...] Read more.
Radiology is undergoing a paradigm shift from traditional single-function AI systems to sophisticated multi-agent networks capable of autonomous reasoning, coordinated decision-making, and adaptive workflow management. These agentic AI systems move beyond simple pattern recognition to encompass complex radiological workflows including image analysis, report generation, clinical communication, and care coordination. While multi-agent radiological AI promises enhanced diagnostic accuracy, improved workflow efficiency, and reduced physician burden, it simultaneously amplifies the long-standing “black box” problem. Traditional explainable AI methods, which are adequate for understanding isolated diagnostic predictions, fail when applied to multi-step reasoning processes involving multiple specialized agents coordinating across imaging interpretation, clinical correlation, and treatment planning. This paper examines how agentic AI systems in radiology create “compound opacity” layers of inscrutability from agent interactions and distributed decision-making processes. We analyze the autonomy–transparency paradox specific to radiological practice, where increasing AI capability directly conflicts with interpretability requirements essential for clinical trust and regulatory oversight. Through examination of emerging multi-agent radiological workflows, we propose frameworks for responsible implementation that preserve both diagnostic innovation and the fundamental principles of medical transparency and accountability. Full article
13 pages, 602 KB  
Article
Association Between Neck Circumference and Uncontrolled Hyperglycemia Diabetes in Korean Adults: From the Korea National Health and Nutrition Examination Survey, 2019 to 2021
by Hyeonah Seo, Wonju Yoon, Jae Ho Kim, Byung Chul Shin, Hyun Lee Kim, Minkook Son and Youngmin Yoon
Bioengineering 2025, 12(10), 1099; https://doi.org/10.3390/bioengineering12101099 - 13 Oct 2025
Viewed by 291
Abstract
Diabetes mellitus (DM) is a major global health concern, associated with both microvascular and macrovascular complications. Early identification of individuals at risk of hyperglycemia and diabetes progression is crucial for preventing long-term complications and improving patient outcomes. We investigated the association between neck [...] Read more.
Diabetes mellitus (DM) is a major global health concern, associated with both microvascular and macrovascular complications. Early identification of individuals at risk of hyperglycemia and diabetes progression is crucial for preventing long-term complications and improving patient outcomes. We investigated the association between neck circumference (NC) and hyperglycemia in non-diabetic individuals and in patients with uncontrolled DM, using data from the nationally representative Korea National Health and Nutrition Examination Survey (KNHANES) 2019–2021. Uncontrolled DM was defined as hemoglobin A1c (HbA1c) ≥ 7.0%, while hyperglycemia in non-diabetic individuals was defined as fasting blood glucose ≥ 126 mg/dL or HbA1c ≥ 6.5%. Logistic regression analyses were conducted to evaluate the association between NC and glycemic outcomes. NC was independently associated with hyperglycemia in non-diabetic individuals (Model 1: odds ratio (OR): 1.09; 95% confidence interval (CI): 1.05–1.13; Model 2: OR: 1.09; 95% CI: 1.05–1.13) and patients with uncontrolled DM (Model 1: OR: 1.10; 95% CI: 1.03–1.17; Model 2: OR: 1.11; 95% CI: 1.04–1.18) after adjusting for potential confounders. This study demonstrates that NC is a significant risk factor for hyperglycemia in the general population and for individuals with uncontrolled DM. NC may serve as a simple, non-invasive anthropometric marker to help identify individuals at elevated risk for poor glycemic control. Full article
(This article belongs to the Section Biomedical Engineering and Biomaterials)
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14 pages, 630 KB  
Article
Disease-Specific Prediction of Missense Variant Pathogenicity with DNA Language Models and Graph Neural Networks
by Mohamed Ghadie, Sameer Sardaar and Yannis Trakadis
Bioengineering 2025, 12(10), 1098; https://doi.org/10.3390/bioengineering12101098 - 13 Oct 2025
Viewed by 418
Abstract
Accurate prediction of the impact of genetic variants on human health is of paramount importance to clinical genetics and precision medicine. Recent machine learning (ML) studies have tried to predict variant pathogenicity with different levels of success. However, most missense variants identified on [...] Read more.
Accurate prediction of the impact of genetic variants on human health is of paramount importance to clinical genetics and precision medicine. Recent machine learning (ML) studies have tried to predict variant pathogenicity with different levels of success. However, most missense variants identified on a clinical basis are still classified as variants of uncertain significance (VUS). Our approach allows for the interpretation of a variant for a specific disease and, thus, for the integration of disease-specific domain knowledge. We utilize a comprehensive knowledge graph, with 11 types of interconnected biomedical entities at diverse biomolecular and clinical levels, to classify missense variants from ClinVar. We use BioBERT to generate embeddings of biomedical features for each node in the graph, as well as DNA language models to embed variant features directly from genomic sequence. Next, we train a two-stage architecture consisting of a graph convolutional neural network to encode biological relationships. A neural network is then used as the classifier to predict disease-specific pathogenicity of variants, essentially predicting edges between variant and disease nodes. We compare performance across different versions of our model, obtaining prediction-balanced accuracies as high as 85.6% (sensitivity: 90.5%; NPV: 89.8%) and discuss how our work can inform future studies in this area. Full article
(This article belongs to the Special Issue AI-Driven Approaches to Diseases Detection and Diagnosis)
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57 pages, 1386 KB  
Article
Bidirectional Endothelial Feedback Drives Turing-Vascular Patterning and Drug-Resistance Niches: A Hybrid PDE-Agent-Based Study
by Zonghao Liu, Louis Shuo Wang, Jiguang Yu, Jilin Zhang, Erica Martel and Shijia Li
Bioengineering 2025, 12(10), 1097; https://doi.org/10.3390/bioengineering12101097 - 12 Oct 2025
Viewed by 382
Abstract
We present a hybrid partial differential equation-agent-based model (PDE-ABM). In our framework, tumor cells secrete tumor angiogenic factor (TAF), while endothelial cells chemotactically migrate and branch in response. Reaction–diffusion PDEs for TAF, oxygen, and cytotoxic drug are coupled to discrete stochastic dynamics of [...] Read more.
We present a hybrid partial differential equation-agent-based model (PDE-ABM). In our framework, tumor cells secrete tumor angiogenic factor (TAF), while endothelial cells chemotactically migrate and branch in response. Reaction–diffusion PDEs for TAF, oxygen, and cytotoxic drug are coupled to discrete stochastic dynamics of tumor cells and endothelial tip cells, ensuring multiscale integration. Motivated by observed perfusion heterogeneity in tumors and its pharmacokinetic consequences, we conduct a linear stability analysis for a reduced endothelial–TAF reaction–diffusion subsystem and derive an explicit finite-domain threshold for Turing instability. We demonstrate that bidirectional coupling, where endothelial cells both chemotactically migrate along TAF gradients and secrete TAF, is necessary and sufficient to generate spatially periodic vascular clusters and inter-cluster hypoxic regions. These emergent patterns produce heterogeneous drug penetration and resistant niches. Our results identify TAF clearance, chemotactic sensitivity, and endothelial motility as effective levers to homogenize perfusion. The model is two-dimensional and employs simplified kinetics, and we outline necessary extensions to three dimensions and saturable kinetics required for quantitative calibration. The study links reaction–diffusion mechanisms with clinical principles and suggests actionable strategies to mitigate resistance by targeting endothelial–TAF feedback. Full article
(This article belongs to the Special Issue Applications of Partial Differential Equations in Bioengineering)
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15 pages, 4146 KB  
Article
A Coarse-to-Fine Framework with Curvature Feature Learning for Robust Point Cloud Registration in Spinal Surgical Navigation
by Lijing Zhang, Wei Wang, Tianbao Liu, Jiahui Guo, Bo Wu and Nan Zhang
Bioengineering 2025, 12(10), 1096; https://doi.org/10.3390/bioengineering12101096 - 12 Oct 2025
Viewed by 335
Abstract
In surgical navigation-assisted pedicle screw fixation, cross-source pre- and intra-operative point clouds registration faces challenges like significant initial pose differences and low overlapping ratio. Classical algorithms based on feature descriptor have high computational complexity and are less robust to noise, leading to a [...] Read more.
In surgical navigation-assisted pedicle screw fixation, cross-source pre- and intra-operative point clouds registration faces challenges like significant initial pose differences and low overlapping ratio. Classical algorithms based on feature descriptor have high computational complexity and are less robust to noise, leading to a decrease in accuracy and navigation performance. To address these problems, this paper proposes a coarse-to-fine registration framework. In the coarse registration stage, a Point Matching algorithm based on Curvature Feature Learning (CFL-PM) is proposed. Through CFL-PM and Farthest Point Sampling (FPS), the coarse registration of overlapping regions between the two point clouds is achieved. In the fine registration stage, the Iterative Closest Point (ICP) is used for further optimization. The proposed method effectively addresses the challenges of noise, initial pose and low overlapping ratio. In noise-free point cloud registration experiments, the average rotation and translation errors reached 0.34° and 0.27 mm. Under noisy conditions, the average rotation error of the coarse registration is 7.28°, and the average translation error is 9.08 mm. Experiments on pre- and intra-operative point cloud datasets demonstrate the proposed algorithm outperforms the compared algorithms in registration accuracy, speed, and robustness. Therefore, the proposed method can achieve the precise alignment of the surgical navigation-assisted pedicle screw fixation. Full article
(This article belongs to the Section Biosignal Processing)
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23 pages, 711 KB  
Article
Examining the Acceptance and Use of AI-Based Assistive Technology Among University Students with Visual Disability: The Moderating Role of Physical Self-Esteem
by Sameer M. Alnajdi, Mostafa A. Salem and Ibrahim A. Elshaer
Bioengineering 2025, 12(10), 1095; https://doi.org/10.3390/bioengineering12101095 - 11 Oct 2025
Viewed by 427
Abstract
AI-based assistive technologies (AIATs) are increasingly recognised as essential tools to enhance accessibility, independence, and inclusion for visually impaired students in higher education. However, limited evidence exists regarding the determinants of their acceptance and use, particularly in terms of psychosocial factors. This study [...] Read more.
AI-based assistive technologies (AIATs) are increasingly recognised as essential tools to enhance accessibility, independence, and inclusion for visually impaired students in higher education. However, limited evidence exists regarding the determinants of their acceptance and use, particularly in terms of psychosocial factors. This study aimed to extend the Unified Theory of Acceptance and Use of Technology (UTAUT) by incorporating physical self-esteem (PSE) as a moderator and behavioural intention (BI) as a mediator within a single model. Data were collected through a validated questionnaire administered to 395 visually impaired undergraduates across five Saudi universities. Constructs included effort expectancy (EE), performance expectancy (PE), facilitating conditions (FCs), social influence (SI), BI, and PSE. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used for analysis. Results showed that PE and SI significantly predicted both BI and adoption, while EE strongly predicted BI but not AIAT adoption; FC had no significant influence on either outcome. BI positively affected AIAT adoption and mediated the effects of PE, EE, and SI, but not FC. Moderation analysis indicated that PSE strengthened the influence of PE, EE, and SI on BI and adoption. These findings underscore the significance of psychological factors, particularly self-esteem, in promoting the adoption of AIAT and offer guidance for developing inclusive educational strategies. Full article
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11 pages, 717 KB  
Article
Risk of Fall in Patients with Functional Hallux Limitus: A Case–Control Study Using an Inertial Measurement Unit
by Jorge Posada-Ordax, Marta Elena Losa-Iglesias, Ricardo Becerro-de-Bengoa-Vallejo, Eduardo Pérez-Boal, Bibiana Trevissón-Redondo, Israel Casado-Hernández, Vicenta Martínez-Córcoles, Anna Sánchez-Serena and Eva María Martínez-Jiménez
Bioengineering 2025, 12(10), 1094; https://doi.org/10.3390/bioengineering12101094 - 10 Oct 2025
Viewed by 410
Abstract
Functional hallux limitus (FHL) is a biomechanical condition defined by restricted motion of the first metatarsophalangeal joint during walking, which may impair stability and increase fall risk in older adults. This study compared fall risk between patients with asymptomatic FHL and healthy controls [...] Read more.
Functional hallux limitus (FHL) is a biomechanical condition defined by restricted motion of the first metatarsophalangeal joint during walking, which may impair stability and increase fall risk in older adults. This study compared fall risk between patients with asymptomatic FHL and healthy controls using validated assessments. The case–control design included 40 participants over 65 years, divided into 20 with FHL and 20 controls. Mobility was evaluated with the Timed Up and Go Test, postural stability with the Berg Balance Scale, and fear of falling with the Falls Efficacy Scale—International (FES-I). Spatiotemporal gait parameters were measured using an inertial measurement unit (IMU). No significant differences were found between groups in the Timed Up and Go Test (p = 0.694), Berg Balance Scale (p = 0.903), Falls Efficacy Scale—International (p = 0.913), or spatiotemporal parameters. These results suggest that asymptomatic FHL does not significantly affect mobility, stability, or fear of falling in older adults, indicating that it is not a determining factor for fall risk under controlled conditions. Further research is needed in less controlled settings or in patients with painful FHL. Full article
(This article belongs to the Section Biomechanics and Sports Medicine)
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20 pages, 7466 KB  
Article
Feasibility Study of CLIP-Based Key Slice Selection in CT Images and Performance Enhancement via Lesion- and Organ-Aware Fine-Tuning
by Kohei Yamamoto and Tomohiro Kikuchi
Bioengineering 2025, 12(10), 1093; https://doi.org/10.3390/bioengineering12101093 - 10 Oct 2025
Viewed by 483
Abstract
Large-scale medical visual question answering (MedVQA) datasets are critical for training and deploying vision–language models (VLMs) in radiology. Ideally, such datasets should be automatically constructed from routine radiology reports and their corresponding images. However, no existing method directly links free-text findings to the [...] Read more.
Large-scale medical visual question answering (MedVQA) datasets are critical for training and deploying vision–language models (VLMs) in radiology. Ideally, such datasets should be automatically constructed from routine radiology reports and their corresponding images. However, no existing method directly links free-text findings to the most relevant 2D slices in volumetric computed tomography (CT) scans. To address this gap, a contrastive language–image pre-training (CLIP)-based key slice selection framework is proposed, which matches each sentence to its most informative CT slice via text–image similarity. This experiment demonstrates that models pre-trained in the medical domain already achieve competitive slice retrieval accuracy and that fine-tuning them on a small dual-supervised dataset that imparts both lesion- and organ-level awareness yields further gains. In particular, the best-performing model (fine-tuned BiomedCLIP) achieved a Top-1 accuracy of 51.7% for lesion-aware slice retrieval, representing a 20-point improvement over baseline CLIP, and was accepted by radiologists in 56.3% of cases. By automating the report-to-slice alignment, the proposed method facilitates scalable, clinically realistic construction of MedVQA resources. Full article
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35 pages, 19884 KB  
Article
A Monte Carlo-Based 3D Whole Lung Model for Aerosol Deposition Studies: Implementation and Validation
by Georgi Hristov Spasov, Ciro Cottini and Andrea Benassi
Bioengineering 2025, 12(10), 1092; https://doi.org/10.3390/bioengineering12101092 - 10 Oct 2025
Viewed by 390
Abstract
A detailed picture of how an aerosol is transported and deposited in the self-affine bronchial tree structure of patients is fundamental to design and optimize orally inhaled drug products. This work describes a Monte Carlo-based statistical deposition model able to simulate aerosol transport [...] Read more.
A detailed picture of how an aerosol is transported and deposited in the self-affine bronchial tree structure of patients is fundamental to design and optimize orally inhaled drug products. This work describes a Monte Carlo-based statistical deposition model able to simulate aerosol transport and deposition in a 3D human bronchial tree. The model enables working with complex and realistic inhalation maneuvers including breath-holding and exhalation. It can run on fully stochastically generated bronchial trees as well as on those whose proximal airways are extracted from patient chest scans. However, at present, a mechanical breathing model is not explicitly included in our trees; their ventilation can be controlled by means of heuristic airflow splitting rules at bifurcations and by an alveolation index controlling the distal lung volume. Our formulation allows us to introduce different types of pathologies on the trees, both those altering their morphology (e.g., bronchiectasis and chronic obstructive pulmonary disease) and those impairing their function (e.g., interstitial lung diseases and emphysema). In this initial activity we describe deposition and ventilation models as well as the stochastic tree construction algorithm, and we validate them against total, regional, lobar, and sub-lobar deposition for healthy subjects. Full article
(This article belongs to the Section Biomedical Engineering and Biomaterials)
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12 pages, 1349 KB  
Article
Effect of the Ankle–Foot Orthosis Dorsiflexion Angle on Gait Kinematics in Individuals with Hemiparetic Stroke
by Hiroshi Hosokawa, Fumiaki Tamiya, Ren Fujii, Ryu Ishimoto, Masahiko Mukaino and Yohei Otaka
Bioengineering 2025, 12(10), 1091; https://doi.org/10.3390/bioengineering12101091 - 10 Oct 2025
Viewed by 370
Abstract
Ankle-foot orthoses (AFOs) are widely used to improve gait; nonetheless, it remains unclear how specific settings, particularly the dorsiflexion angle, affect gait kinematics in individuals with stroke. This study investigated the effect of different AFO dorsiflexion angles on gait kinematics in ambulatory adults [...] Read more.
Ankle-foot orthoses (AFOs) are widely used to improve gait; nonetheless, it remains unclear how specific settings, particularly the dorsiflexion angle, affect gait kinematics in individuals with stroke. This study investigated the effect of different AFO dorsiflexion angles on gait kinematics in ambulatory adults with hemiparesis. Twenty-six individuals with post-stroke hemiparesis walked on a treadmill while wearing the same type of AFO at four ankle dorsiflexion angles: 0°, 5°, 10°, and 15°. Temporal-spatial variables, joint angles, and toe clearance and its components were quantified using three-dimensional analysis. The double-stance time before the paretic swing shortened significantly with increasing dorsiflexion angle, whereas the mean stride time and length did not significantly change. During the swing phase, increased AFO dorsiflexion was associated with reduced maximal knee flexion, in addition to its direct effect on ankle angles. The absolute toe clearance height was unaffected by the AFO settings; however, the contribution of ankle dorsiflexion to limb shortening increased stepwise from 0° to 15°, and the hip elevation and compensatory movement ratio declined. In conclusion, increasing the AFO dorsiflexion angle significantly altered gait kinematics, with distal ankle mechanics replacing inefficient hip compensation and reducing double-stance time. Full article
(This article belongs to the Section Biomechanics and Sports Medicine)
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19 pages, 3921 KB  
Article
Curcumin-Functionalized Ag and ZnO Nanoparticles: A Nanotherapeutic Approach for Treating Infections in Diabetic Wounds
by Mahboubeh Dolatyari, Parisa Rostami, Mahya Rostami, Ali Rostami and Hamit Mirtagioglu
Bioengineering 2025, 12(10), 1090; https://doi.org/10.3390/bioengineering12101090 - 9 Oct 2025
Viewed by 517
Abstract
Chronic wounds, such as diabetic ulcers, remain a significant clinical challenge due to high infection risk and delayed healing. This study presents a comprehensive evaluation of a novel wound dressing incorporating curcumin-functionalized silver–zinc oxide (Ag-ZnO) nanoparticles. The formulation was rationally designed based on [...] Read more.
Chronic wounds, such as diabetic ulcers, remain a significant clinical challenge due to high infection risk and delayed healing. This study presents a comprehensive evaluation of a novel wound dressing incorporating curcumin-functionalized silver–zinc oxide (Ag-ZnO) nanoparticles. The formulation was rationally designed based on molecular docking simulations that identified curcumin as a high-affinity ligand for Staphylococcus aureus Protein A. The synthesized nanoparticles demonstrated potent, broad-spectrum antibacterial activity, achieving complete inhibition of multidrug-resistant pathogens, including MRSA, within 60 s. A critical comparative assessment, incorporating an unloaded Ag-ZnO nanoparticle control group, was conducted in both a rabbit wound model and a randomized clinical trial (n = 75 patients). This design confirmed that the enhanced wound-healing efficacy is specifically attributable to the synergistic effect of curcumin combined with the nanoparticles. The curcumin-loaded Ag-ZnO treatment group showed a statistically significant reduction in healing time compared to both standard care and unloaded nanoparticle controls (e.g., medium wounds: 19.6 days vs. 90.6, p < 0.001). These findings demonstrate that curcumin-functionalized Ag-ZnO nanoparticles offer a safe and highly effective therapeutic strategy, providing robust antibacterial action and significantly accelerated wound healing. Full article
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16 pages, 1613 KB  
Review
Application of Machine Learning in Predicting Osteogenic Differentiation of Mesenchymal Stem Cells
by Hanyue Mao, Zheng Zhou, Ying Yang, Kunlu Lin, Chuyao Zhou and Xiaoyan Wang
Bioengineering 2025, 12(10), 1089; https://doi.org/10.3390/bioengineering12101089 - 9 Oct 2025
Viewed by 421
Abstract
This article reviews the progress made in applying machine learning to predict the osteogenic differentiation of mesenchymal stem cells. Bone defects pose a significant clinical challenge due to limitations of traditional therapies such as autologous bone graft donor shortages, allograft immune risks and [...] Read more.
This article reviews the progress made in applying machine learning to predict the osteogenic differentiation of mesenchymal stem cells. Bone defects pose a significant clinical challenge due to limitations of traditional therapies such as autologous bone graft donor shortages, allograft immune risks and so on. Mesenchymal stem cells offer a promising solution for bone regeneration due to their osteogenic differentiation potential, but their clinical utility is hindered by unpredictable differentiation efficiency and heterogeneity. Machine learning has emerged as a powerful tool to address these issues by enabling early, non-invasive prediction of osteogenic differentiation and high-throughput analysis of complex data like morphology and omics. This review systematically summarizes the application of ML in three key areas: early prediction using cellular morphology, omics data analysis for biomarker discovery, and drug/biomaterial screening for enhancing osteogenesis. We compare the performance of multiple ML models like ResNet-50, LASSO, and random forests and highlight their advantages and limitations. Additionally, we discuss challenges in data standardization and model interpretability, and propose future directions for translating ML into clinical practice. This review provides a comprehensive overview of how ML can revolutionize MSC-based bone regeneration by improving prediction accuracy and optimizing therapeutic strategies. Full article
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14 pages, 2299 KB  
Article
Innovative Compact Vibrational System with Custom GUI for Modulating Trunk Proprioception Using Individualized Vibration Parameters
by Debdyuti Mandal, John R. Gilliam, Sheri P. Silfies and Sourav Banerjee
Bioengineering 2025, 12(10), 1088; https://doi.org/10.3390/bioengineering12101088 - 7 Oct 2025
Viewed by 450
Abstract
Conventional vibrational systems associated with proprioception are mostly equipped with a single standard frequency and amplitude. This feature often fails to show kinesthetic illusion on different subjects, as different individuals respond to different frequencies and amplitudes. Additionally, different muscle groups may also require [...] Read more.
Conventional vibrational systems associated with proprioception are mostly equipped with a single standard frequency and amplitude. This feature often fails to show kinesthetic illusion on different subjects, as different individuals respond to different frequencies and amplitudes. Additionally, different muscle groups may also require the flexibility of frequencies and amplitudes. We developed a custom vibrational system that is equipped with flexible frequency and amplitude, adapted to a custom graphical user interface (GUI). Based on the user’s criteria, the proposed vibrational system enables a wide range of frequencies and amplitudes that can be swept under a single platform. In addition, the system uses small linear actuators that are wearable and attach to the subject without the need for restrictive straps. The vibrational system was used to model trunk proprioceptive impairment associated with low back pain. Low back pain is the leading cause of disability worldwide. It is mostly associated with impaired postural control of the trunk. For postural control, the somatosensory system transmits proprioceptive (position sense) information from the sensors in the skin, joints, muscles, and tendons. Proprioceptive studies on trunk muscles have been conducted where the application of vibration at a set amplitude and frequency across all participants resulted in altered proprioception and a kinesthetic illusion, but not in all individuals. To assess the feasibility of the system, we manipulated the trunk proprioception of five subjects, demonstrating that the vibrational system is capable of modulating trunk proprioception and the value of customizing parameters of the system to obtain maximal deficits from individual subjects. Full article
(This article belongs to the Special Issue Low-Back Pain: Assessment and Rehabilitation Research)
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