Bioengineering

20 pages, 5408 KB

Open AccessReview

Surgical Approaches to Retinal Gene Therapy: 2025 Update

by Milin J. Patel, Sohum Sheth, Jessica Mar, Ninel Z. Gregori and Jesse D. Sengillo

Bioengineering 2025, 12(10), 1122; https://doi.org/10.3390/bioengineering12101122 - 20 Oct 2025

Gene therapy offers a promising new frontier in the treatment of inherited and acquired retinal disease. This review describes the current surgical delivery approaches for gene therapy to the retina—subretinal, suprachoroidal, and intravitreal—and provides an update on the state of the art for [...] Read more.

Gene therapy offers a promising new frontier in the treatment of inherited and acquired retinal disease. This review describes the current surgical delivery approaches for gene therapy to the retina—subretinal, suprachoroidal, and intravitreal—and provides an update on the state of the art for each method in 2025. Full article

(This article belongs to the Special Issue Materials and Methods for Genetic and Cell-Based Therapy for Ocular Tissue)

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23 pages, 9580 KB

Open AccessArticle

Precision Oncology for High-Grade Gliomas: A Tumor Organoid Model for Adjuvant Treatment Selection

by Arushi Tripathy, Sunjong Ji, Habib Serhan, Reka Chakravarthy Raghunathan, Safiulla Syed, Visweswaran Ravijumar, Sunita Shankar, Dah-Luen Huang, Yazen Alomary, Yacoub Haydin, Tiffany Adam, Kelsey Wink, Nathan Clarke, Carl Koschmann, Nathan Merrill, Toshiro Hara, Sofia D. Merajver and Wajd N. Al-Holou

Bioengineering 2025, 12(10), 1121; https://doi.org/10.3390/bioengineering12101121 - 19 Oct 2025

Abstract

High-grade gliomas (HGGs) are aggressive brain tumors with limited treatment options and poor survival outcomes. Variants including isocitrate dehydrogenase (IDH)-wildtype, IDH-mutant, and histone 3 lysine to methionine substitution (H3K27M)-mutant subtypes demonstrate considerable tumor heterogeneity at the genetic, cellular, and microenvironmental levels. This presents [...] Read more.

High-grade gliomas (HGGs) are aggressive brain tumors with limited treatment options and poor survival outcomes. Variants including isocitrate dehydrogenase (IDH)-wildtype, IDH-mutant, and histone 3 lysine to methionine substitution (H3K27M)-mutant subtypes demonstrate considerable tumor heterogeneity at the genetic, cellular, and microenvironmental levels. This presents a major barrier to the development of reliable models that recapitulate tumor heterogeneity, allowing for the development of effective therapies. Glioma tumor organoids (GTOs) have emerged as a promising model, offering a balance between biological relevance and practical scalability for precision medicine. In this study, we present a refined methodology for generating three-dimensional, multiregional, patient-derived GTOs across a spectrum of glioma subtypes (including primary and recurrent tumors) while preserving the transcriptomic and phenotypic heterogeneity of their source tumors. We demonstrate the feasibility of a high-throughput drug-screening platform to nominate multi-drug regimens, finding marked variability in drug response, not only between patients and tumor types, but also across regions within the tumor. These findings underscore the critical impact of spatial heterogeneity on therapeutic sensitivity and suggest that multiregional sampling is critical for adequate glioma model development and drug discovery. Finally, regional differential drug responses suggest that multi-agent drug therapy may provide better comprehensive oncologic control and highlight the potential of multiregional GTOs as a clinically actionable tool for personalized treatment strategies in HGG. Full article

(This article belongs to the Special Issue Advancing Treatment for Brain Tumors)

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24 pages, 4301 KB

Open AccessArticle

Control Deficits and Compensatory Mechanisms in Individuals with Chronic Ankle Instability During Dual-Task Stair-to-Ground Transition

by Yilin Zhong, Xuanzhen Cen, Xiaopan Hu, Datao Xu, Lei Tu, Monèm Jemni, Gusztáv Fekete, Dong Sun and Yang Song

Bioengineering 2025, 12(10), 1120; https://doi.org/10.3390/bioengineering12101120 - 19 Oct 2025

Abstract

(1) Background: Chronic ankle instability (CAI), a common outcome of ankle sprains, involves recurrent sprains, balance deficits, and gait impairments linked to both peripheral and central neuromuscular dysfunction. Dual-task (DT) demands further aggravate postural control, especially during stair descent, a major source of [...] Read more.

(1) Background: Chronic ankle instability (CAI), a common outcome of ankle sprains, involves recurrent sprains, balance deficits, and gait impairments linked to both peripheral and central neuromuscular dysfunction. Dual-task (DT) demands further aggravate postural control, especially during stair descent, a major source of fall-related injuries. Yet the biomechanical mechanisms of stair-to-ground transition in CAI under dual-task conditions remain poorly understood. (2) Methods: Sixty individuals with CAI and age- and sex-matched controls performed stair-to-ground transitions under single- and dual-task conditions. Spatiotemporal gait parameters, center of pressure (COP) metrics, ankle inversion angle, and relative joint work contributions (Ankle%, Knee%, Hip%) were obtained using 3D motion capture, a force plate, and musculoskeletal modeling. Correlation and regression analyses assessed the relationships between ankle contributions, postural stability, and proximal joint compensations. (3) Results: Compared with the controls, the CAI group demonstrated marked control deficits during the single task (ST), characterized by reduced gait speed, increased step width, elevated mediolateral COP root mean square (COP-ml RMS), and abnormal ankle inversion and joint kinematics; these impairments were exacerbated under DT conditions. Individuals with CAI exhibited a significantly reduced ankle plantarflexion moment and energy contribution (Ankle%), accompanied by compensatory increases in knee and hip contributions. Regression analyses indicated that Ankle% significantly predicted COP-ml RMS and gait speed (GS), highlighting the pivotal role of ankle function in maintaining dynamic stability. Furthermore, CAI participants adopted a “posture-first” strategy under DT, with concurrent deterioration in gait and cognitive performance, reflecting strong reliance on attentional resources. (4) Conclusions: CAI involves global control deficits, including distal insufficiency, proximal compensation, and an inefficient energy distribution, which intensify under dual-task conditions. As the ankle is central to lower-limb kinetics, its dysfunction induces widespread instability. Rehabilitation should therefore target coordinated lower-limb training and progressive dual-task integration to improve motor control and dynamic stability. Full article

(This article belongs to the Special Issue Advancements in Biomechanical Gait Analysis: Implications for Footwear Biomechanics and Sports Injury Prevention)

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16 pages, 6154 KB

Open AccessArticle

Design and Performance Assessment of a High-Resolution Small-Animal PET System

by Wei Liu, Peng Xi, Jiguo Liu, Xilong Xu, Zhaoheng Xie, Yanye Lu, Xiangxi Meng and Qiushi Ren

Bioengineering 2025, 12(10), 1119; https://doi.org/10.3390/bioengineering12101119 - 19 Oct 2025

Abstract

This work reports the performance evaluation of a newly developed small-animal positron emission tomography (PET) system based on lutetium-yttrium oxyorthosilicate (LYSO) crystals and multi-pixel photon counter (MPPC). Performance was evaluated, including spatial resolution, system sensitivity, energy resolution, scatter fraction (SF), noise–equivalent count rate [...] Read more.

This work reports the performance evaluation of a newly developed small-animal positron emission tomography (PET) system based on lutetium-yttrium oxyorthosilicate (LYSO) crystals and multi-pixel photon counter (MPPC). Performance was evaluated, including spatial resolution, system sensitivity, energy resolution, scatter fraction (SF), noise–equivalent count rate (NECR), micro-Derenzo phantom imaging, and in vivo imaging of mice and rats. The system achieved a tangential spatial resolution of 0.9 mm in the axial direction at a quarter axial offset using the three-dimensional ordered-subsets expectation maximization (3D OSEM) reconstruction algorithm. The peak sensitivity was 8.74% within a 200–750 keV energy window, with an average energy resolution of 12.5%. Scatter fractions were 12.9% and 30.0% for mouse- and rat-like phantoms, respectively. The NECR reached 878.7 kcps at 57.6 MBq for the mouse phantom and 421.4 kcps at 63.2 MBq for the rat phantom. High-resolution phantom and in vivo images confirmed the system’s capability for quantitative, high-sensitivity small-animal imaging, demonstrating its potential for preclinical molecular imaging studies. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Oncologic PET Imaging)

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18 pages, 1670 KB

Open AccessArticle

VNTR Polymorphisms in the SLC6A3 Gene and Their Impact on Time Perception and EEG Activity

by Francisco Victor Costa Marinho, Silmar Silva Teixeira, Giovanny Rebouças Pinto, Thomaz de Oliveira, France Keiko Nascimento Yoshioka, Hygor Fernandes, Aline Miranda, Bruna Brandão Velasques, Alair Pedro Ribeiro de Souza e Silva, Maurício Cagy and Victor Hugo do Vale Bastos

Bioengineering 2025, 12(10), 1118; https://doi.org/10.3390/bioengineering12101118 - 19 Oct 2025

Abstract

Aim: The research examined the relationship between SLC6A3 3′-UTR and intron 8 VNTR polymorphisms and their influence on supra-second time estimation performance and EEG alpha band activity. Material and methods: A total of 178 male participants (aged 18 to 32 years) underwent [...] Read more.

Aim: The research examined the relationship between SLC6A3 3′-UTR and intron 8 VNTR polymorphisms and their influence on supra-second time estimation performance and EEG alpha band activity. Material and methods: A total of 178 male participants (aged 18 to 32 years) underwent genotyping for the SLC6A3 3′-UTR and intron 8 VNTR polymorphisms. An electroencephalographic assessment was conducted targeting the dorsolateral prefrontal cortex (DLPFC), simultaneously with the time estimation task. The 3′-UTR and intron 8 VNTRs polymorphisms were linked to absolute error and ratio in a time estimation task (target duration: 1 s, 4 s, 7 s, and 9 s) neurophysiological variable. Results: Regarding the absolute error and ratio, the combinatory effect of SLC6A3 3′-UTR and intron 8 VNTRs showed a difference in the interpretation of time only for 1 s (p = 0.0002). In the EEG alpha power, the analysis revealed a difference only for the left DLPFC (p = 0.0002). Conclusions: Electrophysiological and behavioral investigation in the time perception associated with the SLC6A3 gene suggests an alternative evaluation of neurobiological aspects inbuilt in timing. The 3′-UTR and intron 8 VNTR polymorphisms modulate dopaminergic neurotransmission during short-temporal scale judgment in the domain of supra seconds and indicate a role in its inputs to the left dorsolateral prefrontal cortex during the voluntary attention processes for visual stimulus. Our findings demonstrate that cognitive resources to capture and store time intervals can be measured based on the EEG power activity pattern. Full article

(This article belongs to the Section Biosignal Processing)

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25 pages, 767 KB

Open AccessReview

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 - 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

Open AccessArticle

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 - 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

Open AccessArticle

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

Open AccessEditorial

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

Open AccessReview

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

Open AccessArticle

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

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 \pm 0.016

and an accuracy of

99.05 \pm 0.55

%, further supported by high precision of

98.77 \pm 1.05

%, recall of

96.07 \pm 2.16

%, and specificity of

99.73 \pm 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

(This article belongs to the Special Issue Artificial Intelligence for Better Healthcare and Precision Medicine, 2nd Edition)

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20 pages, 987 KB

Open AccessArticle

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

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

Open AccessReview

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

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

(This article belongs to the Special Issue Artificial Intelligence for Computer-Aided Detection in Biomedical Applications)

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1 pages, 127 KB

Open AccessCorrection

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

Abstract

In the original publication [...] Full article

(This article belongs to the Section Biomedical Engineering and Biomaterials)

27 pages, 6922 KB

Open AccessArticle

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

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

Open AccessArticle

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

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

(This article belongs to the Special Issue Next-Generation Diagnostic and Therapy Systems for Neurodegenerative Diseases)

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23 pages, 11108 KB

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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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Bioengineering

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