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SSRN 2025年

作者： Kuetzing, Peter M. Scheven, Ulrich M. Arruda, Ellen M. Department of Mechanical Engineering University of Michigan Ann ArborMI48109 United States Department of Biomedical Engineering University of Michigan Ann ArborMI48109 United States Program in Macromolecular Macromolecular Science and Engineering University of Michigan Ann ArborMI48109 United States

The ability of the Anterior Cruciate Ligament (ACL) to dissipate energy during cyclic loading conditions is crucial for understanding ACL failure. This study employs a novel mechanical test fixture for both shorter duration Digital Image Correlation (DIC) and longer duration Repeated Cycle-Recovery (RCR) ex-vivo experiments. The fixture ensured effective clamping and precise alignment of the Anterior Medial Bone-Ligament-Bone (AM BLB) samples while preserving the integrity and camera view of the region directly above the entheses. Results from the DIC and RCR experiments provide compelling evidence of localized hysteresis and evolving energy dissipation characteristics. The RCR experiments indicate a reduction in the energy absorption capabilities of the AM BLB with progressive cyclic loading, evident by a decrease in normalized hysteresis and diminished viscoelasticity over time. Furthermore, DIC experiments revealed strain concentrations in areas prone to acute ACL injuries. These findings underscore the importance of considering cyclic loading and localized strain behaviors in ACL failure mechanisms and may inform strategies for injury prevention and rehabilitation. © 2025, The Authors. All rights reserved.

关键词： Cyclic loads

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Reinforcement learning for robust dynamic metabolic control

arXiv

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arXiv 2025年

作者： Espinel-Ríos, Sebastián Walser, River Zhang, Dongda Biomedical Manufacturing Program Commonwealth Scientific and Industrial Research Organisation Clayton Australia Basis Independent Brooklyn New York United States Department of Chemical Engineering University of Manchester Manchester United Kingdom

Dynamic metabolic control can enhance bioprocess flexibility and expand the available optimization degrees of freedom via real-time modulation of metabolic enzyme expression. This allows target metabolic fluxes to be dynamically tuned throughout the process. However, identifying optimal dynamic control policies is challenging due to the presence of potential metabolic burden, cytotoxic effects, and the generally high-dimensional solution space, making exhaustive experimentation impractical. Here, we propose an approach based on reinforcement learning to derive optimal dynamic metabolic control policies by allowing an agent or controller to interact with a surrogate dynamic model in silico. To incorporate and test robustness, we apply domain randomization, enabling the controller to generalize across system uncertainties. Our approach provides an alternative to conventional model-based control such as model predictive control, which requires differentiating the models with respect to decision variables;an often impractical task when dealing with complex stochastic, nonlinear, stiff, or piecewise-defined dynamics. In contrast, our approach only requires forward integration, making the task computationally much simpler with off-the-shelf solvers. We demonstrate our approach with a case study on the dynamic control of acetyl-CoA carboxylase in Escherichia coli for fatty acid biosynthesis. The derived dynamic metabolic control policies outperform static control, achieving up to 40 % higher titers while remaining robust under uncertainty. © 2025, CC BY.

关键词： Stochastic systems

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Pilot Study of Distinct Graphs Models in Analysis of Brain Aging in Resting-State Functional Connectivity Networks

Pilot Study of Distinct Graphs Models in Analysis of Brain A...

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18th International Joint Conference on biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Carvalho, M.A.G. Frayne, R. School of Technology University of Campinas Brazil Radiology and Clinical Neurosciences Hotchkiss Brain Institute Univ. of Calgary Canada Seaman Family MR Research Centre Foothills Medical Centre Calgary Canada Biomedical Engineering Graduate Program University of Calgary Canada Calgary Image Processing and Analysis Centre Foothills Medical Centre Calgary Canada

Graphs have been used successfully to represent and analyze brain networks for many decades. Such structural and functional studies are important for revealing interactions between distinct areas of the brain that, for ex ample, are associated with the performance of a specific task or the onset of a cognitive disorder like dementia. In this pilot study, resting-state functional magnetic resonance imaging data were acquired in a sex-balanced sample of 10 young (20.1±2.1 years) and 10 old (65.6±0.4 years), presumed healthy, adults. We examined the effects of age on whole-brain resting-state functional connectivity (RSFC) networks. We examined two main graph modeling approaches to analyze RSFC networks. These approaches employ different strategies or graph models for thresholding over the complete network or examining changes in graph density. We com puted and compared one graph metric, the modularity, that was derived from the RSFC network graph models. Considering the need for a model that must preserve the network’s connectivity, strategies that use spanning trees as seeds to gradually increase the graph’s density seem more appropriate to represent brain networks. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Magnetic resonance imaging

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First Results on Graph Similarity Search in Resting-State Functional Connectivity Networks Using Spectral and Graph Edit Distances

First Results on Graph Similarity Search in Resting-State Fu...

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18th International Joint Conference on biomedical engineering Systems and Technologies, BIOSTEC 2025

The application of graph theory in the modeling and analysis of brain networks has generated both new op portunities as well as new challenges in neuroscience. Resting state functional connectivity (RSFC) networks studied with graphs is an important field of investigation because of the potential benefits in understanding function in healthy individuals and identifying evidence of brain diseases and injury in patients. This work is unique because it applies information retrieval techniques to create ranked lists from RSFC graph theory derived networks. In our analysis, we used a sample of whole-brain resting-state functional magnetic reso nance imaging (rs-fMRI) data obtained from Young (n = 10, age: 20.1±2.1) and Old (n = 10, 65.6±0.4) sex-balanced groups drawn from a healthy, i.e., neurotypical, cohort. We estimated two well-known distance metrics (graph edit distance and graph spectral distance) and by using information-retrieval graph ranking methods achieved precision measures at the top-5 positions of ranked lists of up to 80%. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Graph neural networks

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Enhanced Performance and Data Privacy in Lung Nodule Classification via Federated Deep Learning Approach 24

Enhanced Performance and Data Privacy in Lung Nodule Classif...

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2024 7th International Conference on Healthcare Service Management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, Ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Synergistic Polydopamine/Graphene Oxide Hybrids: Elevating Electrochemical Performance

ACS Electrochemistry

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ACS Electrochemistry 2025年第4期1卷 453-464页

作者： Busra Ozlu Saimon Moraes Silva Simon E. Moulton Bong Sup Shim Program in Biomedical Science & Engineering Inha University 100 Inha-ro Michuhol-gu Incheon 22212 Republic of Korea Department of Biochemistry and Chemistry La Trobe Institute for Molecular Science The Biomedical and Environmental Sensor Technology (BEST) Research Centre La Trobe University Melbourne Victoria 3086 Australia ARC Centre of Excellence for Electromaterials Science Faculty of Science Engineering and Technology Swinburne University of Technology Hawthorn Victoria 3122 Australia；Aikenhead Centre for Medical Discovery St Vincent’s Hospital Melbourne Melbourne Victoria 3065 Australia；Iverson Health Innovation Research Institute Swinburne University of Technology Melbourne Victoria 3122 Australia Program in Biomedical Science & Engineering Inha University 100 Inha-ro Michuhol-gu Incheon 22212 Republic of Korea；Department of Chemical Engineering Inha University 100 Inha-ro Michuhol-gu Incheon 22212 Republic of Korea

Melanin, a natural biopolymer with a conjugated backbone, stands out as a promising candidate for bioelectronics owing to its redox activity, metal ion chelation, hydration-dependent electrical response, wet adhesion, potential electronic transport, anti-inflammatory properties, and excellent thermal stability. In this study, we explore the impact of hybridizing liquid crystalline graphene oxide (LCGO) into the conductive melanin-like polymer polydopamine (PDA) on the morphology, stability, and synergistic electrochemical performance. Our findings reveal that the gold electrode coated with PDA:LCGO composites exhibit more than two orders of magnitude lower impedance and more than 100 times higher charge storage capacity compared to both bare gold electrodes and those coated with pure PDA. Furthermore, we demonstrate that the morphology and the electrode performance could be tuned by adjusting the synthesis parameters in the PDA:LCGO electrochemical deposition. This PDA:LCGO composite, with its superior electrochemical performance, holds promise for diverse bioelectronics applications ranging from biosensors to implantable bionic interfaces.

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ATP_mCNN: Predicting ATP binding sites through pretrained language models and multi-window neural networks

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Computers in Biology and Medicine 2025年 185卷 109541-109541页

作者： Le, Van-The Malik, Muhammad-Shahid Lin, Yi-Jing Liu, Yu-Chen Chang, Yan-Yun Ou, Yu-Yen Department of Computer Science and Engineering Yuan Ze University Chung-Li32003 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Chung-Li32003 Taiwan Department of Computer Sciences Karakoram International University Gilgit-Baltistan 15100 Pakistan

Adenosine triphosphate plays a vital role in providing energy and enabling key cellular processes through interactions with binding proteins. The increasing amount of protein sequence data necessitates computational methods for identifying binding sites. However, experimental identification of adenosine triphosphate-binding residues remains challenging. To address the challenge, we developed a multi-window convolutional neural network architecture taking pre-trained protein language model embeddings as input features. In particular, multiple parallel convolutional layers scan for motifs localized to different window sizes. Max pooling extracts salient features concatenated across windows into a final multi-scale representation for residue-level classification. On benchmark datasets, our model achieves an area under the ROC curve of 0.95, significantly improving on prior sequence-based models and outperforming convolutional neural network baselines. This demonstrates the utility of pre-trained language models and multi-window convolutional neural networks for advanced sequence-based prediction of adenosine triphosphate-binding residues. Our approach provides a promising new direction for elucidating binding mechanisms and interactions from primary structure. © 2024 Elsevier Ltd

关键词： Convolutional neural networks

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A novel biomechanical model of the proximal mouse forelimb predicts muscle activity in optimal control simulations of reaching movements

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Journal of Neurophysiology 2025年第4期133卷 1266-1278页

作者： Gilmer, Jesse I. Coltman, Susan K. Cuenu, Geraldine Hutchinson, John R. Huber, Daniel Person, Abigail L. Borno, Mazen Al Department of Computer Science and Engineering Computational Bioscience Program University of Colorado Denver j Anschutz Medical Campus Denver CO United States Department of Basic Neuroscience University of Geneva Geneva Switzerland Department of Physiology and Biophysics University of Colorado Denver j Anschutz Medical Campus Denver CO United States Department of Comparative Biomedical Sciences Royal Veterinary College London United Kingdom Department of Kinesiology The Pennsylvania State University University Park PA United States

Mice are key model organisms in neuroscience and motor systems physiology. Fine motor control tasks performed by mice have become widely used in assaying neural and biophysical motor system mechanisms. Although fine motor tasks provide useful insights into behaviors that require complex multi-joint motor control, there is no previously developed physiological biomechanical model of the adult mouse forelimb available for estimating kinematics, muscle activity, or kinetics during behaviors. Here, we developed a musculoskeletal model based on high-resolution imaging of the mouse forelimb that includes muscles spanning the neck, trunk, shoulder, and limbs. Physics-based optimal control simulations of the forelimb model were used to estimate in vivo muscle activity present when constrained to the tracked kinematics during reaching movements. The activity of a subset of muscles was recorded and used to assess the accuracy of the muscle patterning in simulation. We found that the synthesized muscle patterning in the forelimb model had a strong resemblance to empirical muscle patterning, suggesting that our model has utility in providing a realistic set of estimated muscle excitations over time when given a kinematic template. The strength of the similarity between empirical muscle activity and optimal control predictions increases as mice performance improves throughout learning of the reaching task. Our computational tools are available as open-source in the OpenSim physics and modeling platform. Our model can enhance research into limb control across broad research topics and can inform analyses of motor learning, muscle synergies, neural patterning, and behavioral research that would otherwise be inaccessible. NEW & NOTEWORTHY Investigations into motor planning and execution lack an accurate and complete model of the forelimb, which could bolster or expand on findings. We sought to construct such a model using high-detail scans of murine anatomy and prior research into

关键词： biophysical models motor control muscle patterning optimal control reaching behaviors

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Quantifying White Matter Hyperintensities: Predicting Periventricular Fazekas Scores with Uncertainty Estimation

Quantifying White Matter Hyperintensities: Predicting Perive...

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IEEE International Symposium on biomedical Imaging

作者： Susanne Schmid Amirmohammed Shamaei Roberto Souza Richard Frayne Biomedical Engineering Graduate Program University of Calgary Hotchkiss Brain Institute University of Calgary Seaman Family MR Research Centre Foothills Medical Centre Calgary AB Canada Electrical and Software Engineering University of Calgary Radiology University of Calgary Clinical Neurosciences University of Calgary

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

White matter hyperintensities (WMH) are crucial markers in brain magnetic resonance (MR) images, often quantified using the Fazekas score. This study presents a deep learning model to predict Fazekas scores in the periventricular region from T1-weighted and FLAIR images. Our model achieved a Matthew correlation coefficient of 0.68 and F1 scores of 0.69, 0.88, 0.86, and 1.0 for Fazekas scores from 0 to 3, respectively, surpassing previous methods. We introduce a novel integration of t-distributed stochastic neighbor embedding (t-SNE) with uncertainty analysis using Monte Carlo dropout, offering insights into the model's decision-making. Results show effective class distinction, with increased uncertainty at class transitions indicating ambiguity, and confident misclassified cases suggesting overlapping features or label noise. These findings highlight that simple, well-tuned models, coupled with interpretability techniques, can provide robust predictions of WMH severity.

关键词： Deep learning Uncertainty Monte Carlo methods Noise Stochastic processes Magnetic resonance Estimation Predictive models Brain modeling White matter

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A kidney organoid-based readout to assess disease activity in primary and recurrent focal segmental glomerulosclerosis

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Kidney International 2025年第5期107卷 888-902页

作者： Gupta, Ashwani Kumar Minocha, Ekta Koss, Kyle M. Naved, Bilal A. Safar-Boueri, Luisa Wertheim, Jason A. Gallon, Lorenzo Department of Surgery University of Arizona College of Medicine Tucson AZ United States Bio5 Institute University of Arizona Tucson AZ United States Surgery Service Southern Arizona Veterans Affairs Health Care System Tucson AZ United States Department of Medicine Northwestern University Feinberg School of Medicine Chicago IL United States Department of Neurobiology University of Texas Medical Branch Galveston TX United States Sealy Institute for Drug Discovery University of Texas Medical Branch Galveston TX United States Department of Biomedical Engineering Northwestern University Evanston IL United States Department of Biomedical Engineering University of Arizona Tucson AZ United States Department of Medicine Abdominal Organ Transplant Program University of Illinois Chicago Chicago IL United States

Primary focal segmental glomerulosclerosis (pFSGS) is an acquired kidney disorder that frequently leads to kidney failure and confers an elevated risk of recurrence after kidney transplantation, termed recurrent pFSGS. Unfortunately, there is no diagnostic method to foresee recurrence of pFSGS after kidney transplantation. Progress in developing assays to test disease activity is hampered by few preclinical models to replicate disease and inability of in vitro cultured primary podocytes to remain terminally differentiated. In recent years, advancements in kidney organoid biology have led to the development of kidney tissues with glomeruli and major nephron segments including podocytes. To develop a pFSGS model, we studied the effect of plasma from patients diagnosed with pFSGS on kidney organoids differentiated from human pluripotent stem cells. The pFSGS plasma treatment induced podocytopathy, extracellular matrix protein deposition, fibrosis and apoptosis within organoids, whereas non-recurrent plasma did not affect organoid structure. pFSGS plasma also led to loss of normal expression patterns of podocyte specific proteins, nephrin and podocin within podocytes. Further, cytokine array profiling revealed that pFSGS plasma induced secretion of cytokines associated with inflammation and angiogenesis. Additionally, kidney organoids treated with plasma obtained after therapeutic plasma exchange for recurrent pFSGS led to lower cell death in organoids after sequential exchanges with the final exchange showing the least apoptotic cells without morphological abnormality. Overall, our results demonstrate the potential of kidney organoids in advancing kidney disease modeling. These insights could be applied in clinical settings to assist in gauging FSGS recurrence risk prior to kidney transplantation. © 2025 International Society of Nephrology

关键词： extracellular matrix glomerulus human plasma kidney organoids podocytes primary focal segmental glomerulosclerosis

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