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Bioengineering
Volume 12
Issue 11
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Bioengineering, Volume 12, Issue 11 (November 2025) – 147 articles

Cover Story (view full-size image): Sarcopenia patients often mask balance deficits through compensatory strategies, evading detection by conventional assessments and increasing fall risk. Our study introduces a novel bioengineering framework that decodes center-of-pressure (COP) signals from a clinical force platform. Using multidimensional temporal analysis—including dynamic time warping—we quantify the hidden "compensatory reserve." This approach enables early risk stratification and paves the way for new clinical tools. View this paper
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29 pages, 2537 KB  
Review
Voice-Based Detection of Parkinson’s Disease Using Machine and Deep Learning Approaches: A Systematic Review
by Hadi Sedigh Malekroodi, Byeong-il Lee and Myunggi Yi
Bioengineering 2025, 12(11), 1279; https://doi.org/10.3390/bioengineering12111279 - 20 Nov 2025
Viewed by 817
Abstract
Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms, among which vocal impairment is one of the earliest and most prevalent. In recent years, voice analysis supported by machine learning (ML) and deep learning (DL) has emerged as [...] Read more.
Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms, among which vocal impairment is one of the earliest and most prevalent. In recent years, voice analysis supported by machine learning (ML) and deep learning (DL) has emerged as a promising non-invasive method for early PD detection. We conducted a systematic review searching PubMed, Scopus, IEEE Xplore, and Web of Science databases for studies published between 2020 and September 2025. A total of 69 studies met the inclusion criteria and were analyzed in terms of dataset characteristics, speech tasks, feature extraction techniques, model architectures, validation strategies, and performance outcomes. Classical ML models such as Support Vector Machines (SVMs) and Random Forests (RFs) achieved high accuracy on small, homogeneous datasets, while DL architectures, particularly Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer-based foundation models, demonstrated greater robustness and scalability across languages and recording conditions. Despite these advances, persistent challenges such as dataset heterogeneity, class imbalance, and inconsistent validation practices continue to hinder reproducibility and clinical translation. Overall, the field is transitioning from handcrafted feature-based pipelines toward self-supervised, representation-learning frameworks that promise improved generalizability. Future progress will depend on the development of large, multilingual, and openly accessible datasets, standardized evaluation protocols, and interpretable AI frameworks to ensure clinically reliable and equitable voice-based PD diagnostics. Full article
(This article belongs to the Section Biosignal Processing)
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29 pages, 1533 KB  
Article
A Two-Step Variable Selection Strategy for Multiply Imputed Survival Data Using Penalized Cox Models
by Qian Yang, Bin Luo, Chenxi Yu and Susan Halabi
Bioengineering 2025, 12(11), 1278; https://doi.org/10.3390/bioengineering12111278 - 20 Nov 2025
Viewed by 380
Abstract
Multiple imputation (MI) is widely used for handling missing data. However, applying penalized methods after MI can be challenging because variable selection may be inconsistent across imputations. We propose a two-step variable selection method for multiply imputed datasets with survival outcomes: apply LASSO [...] Read more.
Multiple imputation (MI) is widely used for handling missing data. However, applying penalized methods after MI can be challenging because variable selection may be inconsistent across imputations. We propose a two-step variable selection method for multiply imputed datasets with survival outcomes: apply LASSO or ALASSO to each MI dataset, followed by ridge regression, and combine estimates using variable selected in any or d% (d = 50, 70, 90, 100) of the MI datasets. For comparison, we also fit stacked MI datasets with weighted penalized regression and a group LASSO approach that enforces consistent selection across imputations. Simulations with Cox models evaluated tuning by AIC, BIC, cross-validation at the minimum error, and the 1SE rule. Across scenarios, performance differed by both the penalization and the selection rule. More conservative choices such as ALASSO with BIC and a 50% inclusion frequency tended to control false positive and gave more stable calibration. The grouped approach achieved comparable selection with modestly higher estimation error. Overall, no single method consistently outperformed others across all scenarios. Our findings suggest that practitioners should weigh trade-offs between selection stability, estimation accuracy, and calibration when applying penalized methods to multiply imputed survival data. Full article
(This article belongs to the Section Biosignal Processing)
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20 pages, 1516 KB  
Article
AI-Assisted Response Surface Methodology for Growth Optimization and Industrial Applicability Evaluation of the Diatom Gedaniella flavovirens GFTA21
by Eun Song Kim, Soo Jeong Lee, Jung A Lee, Sung Min An, Hyun-Ju Hwang, Bum Soo Park, Hae-Won Lee, Cheol-Ho Pan, Daekyung Kim and Kichul Cho
Bioengineering 2025, 12(11), 1277; https://doi.org/10.3390/bioengineering12111277 - 20 Nov 2025
Viewed by 371
Abstract
Although AI-mediated approaches provide promising support for bioengineering using training datasets, their application in microalgal research remains limited. In this study, ChatGPT-4.0, an easily accessible AI model, was employed to optimize culture conditions and evaluate the industrial potential of the isolated diatom Gedaniella [...] Read more.
Although AI-mediated approaches provide promising support for bioengineering using training datasets, their application in microalgal research remains limited. In this study, ChatGPT-4.0, an easily accessible AI model, was employed to optimize culture conditions and evaluate the industrial potential of the isolated diatom Gedaniella flavovirens. Culture optimization was conducted using response surface methodology, in which pH, temperature, and salinity were selected as independent variables. ChatGPT assisted in determining the design and suggested a face-centered central composite design. The optimal conditions for biomass production were determined to be pH 8.30, 23 °C, and 34.24 psu. Analysis of variance revealed significant quadratic effects (p < 0.05), indicating curvature in the response surface. Fatty acid profiling showed high levels of palmitoleic acid, palmitic acid, and eicosapentaenoic acid. Pigment analysis further indicated a high abundance of fucoxanthin, diadinoxanthin, and diatoxanthin. Based on the analyzed compounds, ChatGPT suggested potential applications of the algal strain across various industrial sectors. The most relevant application was identified as aquafeed, as the strain contains metabolites known to enhance pigmentation, growth, and immune responses in aquaculture species. Overall, this study demonstrates ChatGPT-mediated bioengineering as a practical strategy for optimizing culture conditions and evaluating the industrial potential of novel microalgal strains. Full article
(This article belongs to the Special Issue Microalgae Biotechnology and Microbiology: Prospects and Applications)
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19 pages, 3603 KB  
Article
Explainable Machine Learning for Heat-Related Illness Prediction: An XGBoost–SHAP Approach Using Korean Meteorological Data
by Chaeyeong Im, Wonji Kim and Heesoo Kim
Bioengineering 2025, 12(11), 1276; https://doi.org/10.3390/bioengineering12111276 - 20 Nov 2025
Viewed by 703
Abstract
The rising frequency of heat-related illnesses (HRIs) under climate change presents urgent public health challenges, particularly in urban environments. This study develops an explainable machine learning (ML) model to predict HRI risk using metrological data from seven major South Korean metropolitan cities between [...] Read more.
The rising frequency of heat-related illnesses (HRIs) under climate change presents urgent public health challenges, particularly in urban environments. This study develops an explainable machine learning (ML) model to predict HRI risk using metrological data from seven major South Korean metropolitan cities between May and September 2021–2024. We applied eXtreme Gradient Boosting (XGBoost) to model relationships between daily meteorological variables, including maximum and mean daily temperatures, humidity, solar radiation, wind speed, and precipitation, and HRI occurrence. Model performance was validated using 2025 data and demonstrated strong predictive accuracy, with area under the curve (AUC) values 0.895. To enhance interpretability, Shapley Additive exPlanations (SHAP) analysis identified mean daily temperature, solar radiation, and minimum temperature as the strongest contributors to HRI risk. Time-series comparisons of predicted and actual HRI occurrences further validated the model’s effectiveness in real-world settings. These findings underscore the potential of eXplainable Artificial Intelligence (XAI) for localized health-risk forecasting and support a data-driven basis for developing early warning systems for climate-sensitive diseases to guide proactive public health planning amid escalating urban heat risks. Full article
(This article belongs to the Special Issue Computational Intelligence for Healthcare)
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15 pages, 2839 KB  
Article
Smart e-Textile Singlet Prototype and Concept: Multi Sensor Sensing for Geriatric Monitoring
by Tobias Steinmetzer, Florian Wieczorek, Anselm Naake, Peter Wolf, Alexander Braun and Sven Michel
Bioengineering 2025, 12(11), 1275; https://doi.org/10.3390/bioengineering12111275 - 20 Nov 2025
Viewed by 672
Abstract
This paper explores the development of a Smart e-Textile Singlet designed to enhance geriatric care through continuous monitoring of vital health parameters. The proposed garment integrates various sensors to measure core body temperature, blood oxygen saturation, respiration rate, blood pressure, pulse, electrocardiogram (ECG), [...] Read more.
This paper explores the development of a Smart e-Textile Singlet designed to enhance geriatric care through continuous monitoring of vital health parameters. The proposed garment integrates various sensors to measure core body temperature, blood oxygen saturation, respiration rate, blood pressure, pulse, electrocardiogram (ECG), activity level, and risk of falls. Leveraging advanced technologies such as inertial measurement unit (IMU) sensors, thermoelectric materials, and piezoelectric fibers, the e-textile ensures both functionality and sustainability. Additionally, artificial intelligence algorithms are employed to provide near-real-time feedback and early warnings, significantly improving health management for elderly individuals. This innovative approach not only promotes autonomy and well-being among the elderly but also alleviates the workload of healthcare providers. The Smart e-Textile Singlet represents a multi-sensor solution by offering a holistic monitoring system. Full article
(This article belongs to the Special Issue Wearable, Flexible, and Integrated Microfluidic-Based Biosensors for Point-of-Care Testing Applications)
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23 pages, 7595 KB  
Article
Multiscale Coronary Arterial Network Generation and Hemodynamics Using Patient-Specific Fractional Myocardial Blood Volume
by Mostafa Mahmoudi, Arutyun Pogosyan, Amirhossein Arzani and Kim-Lien Nguyen
Bioengineering 2025, 12(11), 1274; https://doi.org/10.3390/bioengineering12111274 - 20 Nov 2025
Viewed by 500
Abstract
Ischemic heart disease (IHD) is the leading cause of death worldwide. Although 90% of the intramyocardial blood volume resides in the microvasculature, clinical imaging methods cannot visualize the microvascular coronary network in vivo, and non-invasive hemodynamic estimates overlook patient-specific microcirculatory contributions. Herein, we [...] Read more.
Ischemic heart disease (IHD) is the leading cause of death worldwide. Although 90% of the intramyocardial blood volume resides in the microvasculature, clinical imaging methods cannot visualize the microvascular coronary network in vivo, and non-invasive hemodynamic estimates overlook patient-specific microcirculatory contributions. Herein, we present a multiscale framework to extend the epicardial coronary tree and generate 1D microvascular networks in the myocardium based on ferumoxytol-enhanced magnetic resonance coronary imaging and fractional myocardial blood volume (fMBV) maps. Synthetic arterial networks were constructed from MRI data belonging to three swine, four healthy volunteers, and one IHD patient using a modified multistage, adaptive constrained constructive optimization approach. Hemodynamic simulations were performed in synthetic arterial networks. Morphological parameters were compared with empirical models. In 126 arterial networks (n = 6000 terminal segments per subject per seed; six seeds per coronary vessel), the morphometry was strongly correlated with empirical data (r > 0.87), with low variability (CoV < 0.01) across multiple rounds of network simulations. Mixed-effects models and a Dynamic Time Warping analysis confirmed robustness and repeatability. In the IHD patient, simulated arterial networks (n = 15) reproduced tissue-dependent morphological and functional signatures consistent with coronary autoregulation in scar and hypoperfused tissues. The findings establish an early potential for patient-specific microvascular network synthesis and hemodynamic simulations from MRI data. Full article
(This article belongs to the Section Biomedical Engineering and Biomaterials)
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31 pages, 7625 KB  
Article
Phytogenic Silver Nanoparticles Derived from Ricinus communis and Aloe barbadensis: Synthesis, Characterization, and Evaluation of Biomedical Potential
by Anam Ahsan, George F. Gao and Wen-Xia Tian
Bioengineering 2025, 12(11), 1273; https://doi.org/10.3390/bioengineering12111273 - 19 Nov 2025
Viewed by 709
Abstract
The green synthesis of silver nanoparticles (SNPs) using medicinal plants provides a sustainable and eco-friendly approach to nanoparticle production with promising biomedical potential. In this study, Ricinus communis and Aloe barbadensis aqueous leaf extracts were employed as reducing and stabilizing agents to synthesize [...] Read more.
The green synthesis of silver nanoparticles (SNPs) using medicinal plants provides a sustainable and eco-friendly approach to nanoparticle production with promising biomedical potential. In this study, Ricinus communis and Aloe barbadensis aqueous leaf extracts were employed as reducing and stabilizing agents to synthesize R. communis SNPs (RcSNPs) and A. barbadensis SNPs (AbSNPs). The nanoparticles were characterized using ultraviolet–visible spectroscopy, dynamic light scattering, Fourier-transform infrared spectroscopy, scanning electron microscopy, transmission electron microscopy, thermogravimetric analysis, and differential scanning calorimetry to evaluate their physicochemical and thermal properties. RcSNPs and AbSNPs were predominantly spherical, with average sizes of 15–20 nm and 23–28 nm, respectively, and exhibited stability up to ~90 °C. Biological evaluations demonstrated potent antimicrobial, antioxidant, anti-inflammatory, anti-tyrosinase, and cytotoxic activities. Notably, RcSNPs and AbSNPs induced apoptosis through mitochondrial pathway modulation and showed superior cytotoxicity compared to crude plant extracts and several previously reported SNPs. These findings indicate that phytochemical-mediated SNPs not only provide a green route of synthesis but also exhibit multifunctional bioactivities, which may support their potential applications as antimicrobial, antioxidant, depigmenting, and anticancer agents in biomedical and pharmaceutical fields. Full article
(This article belongs to the Special Issue Bioengineering Platforms for Drug Delivery)
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14 pages, 2234 KB  
Article
A Novel Approach for Optimizing Molecularly Imprinted Polymer Composition in Electrochemical Detection of Collagen Peptides
by Naphatsawan Vongmanee, Jindapa Nampeng, Katesirin Rattanapithan, Phuritasinee Sriwichai, Chuchart Pintavirooj and Sarinporn Visitsattapongse
Bioengineering 2025, 12(11), 1272; https://doi.org/10.3390/bioengineering12111272 - 19 Nov 2025
Viewed by 437
Abstract
Collagen peptides are key structural proteins that play an important role in maintaining the integrity and proper function of multiple tissues in the human body. Their breakdown is recognized as an important biomarker for various degenerative conditions, including the loss of muscle mass, [...] Read more.
Collagen peptides are key structural proteins that play an important role in maintaining the integrity and proper function of multiple tissues in the human body. Their breakdown is recognized as an important biomarker for various degenerative conditions, including the loss of muscle mass, joint and bone disorders, and compromised skin health. Current analytical approaches for collagen detection, such as ultraviolet spectrometry, enzyme-linked immunosorbent assay (ELISA), high-performance liquid chromatography (HPLC), and histochemical staining, are widely used but often expensive, time-consuming, and reliant on specific laboratory instrumentation, limiting their practicality for routine or rapid diagnostics. This study reports a novel biosensor for collagen peptide detection based on molecularly imprinted polymers (MIPs) integrated with screen-printed electrodes (SPEs). Electrochemical measurements revealed a clear correlation between collagen concentration and current response, confirming effective molecular binding within the imprinted matrix. The optimized MIP-modified electrode exhibited a detection range of 0.1–1000 µg/mL with a limit of detection (LOD) of 1.0106 µg/mL, limit of quantification (LOQ) of 4.46 µg/mL, sensitivity of 8.3816, and correlation coefficient (R2 = 0.9436). These results highlight strong selectivity and sensitivity toward collagen peptides. The proposed MIP-based biosensor provides a rapid, low-cost platform for detecting collagen degradation products and holds potential for early diagnosis and future clinical applications in degenerative disease monitoring. Full article
(This article belongs to the Special Issue Microfluidics and Sensor Technologies in Biomedical Engineering)
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15 pages, 1109 KB  
Article
A Novel Unsupervised You Only Listen Once (YOLO) Machine Learning Platform for Automatic Detection and Characterization of Prominent Bowel Sounds Towards Precision Medicine
by Gayathri Yerrapragada, Jieun Lee, Mohammad Naveed Shariff, Poonguzhali Elangovan, Keerthy Gopalakrishnan, Avneet Kaur, Divyanshi Sood, Swetha Rapolu, Jay Gohri, Gianeshwaree Alias Rachna Panjwani, Rabiah Aslam Ansari, Jahnavi Mikkilineni, Naghmeh Asadimanesh, Thangeswaran Natarajan, Jayarajasekaran Janarthanan, Shiva Sankari Karuppiah, Vivek N. Iyer, Scott A. Helgeson, Venkata S. Akshintala and Shivaram P. Arunachalam
Bioengineering 2025, 12(11), 1271; https://doi.org/10.3390/bioengineering12111271 - 19 Nov 2025
Viewed by 529
Abstract
Phonoenterography (PEG) offers a non-invasive and radiation-free technique to assess gastrointestinal activity through acoustic signal analysis. In this feasibility study, 110 high-resolution PEG recordings (44.1 kHz, 16-bit) were acquired from eight healthy individuals, yielding 6314 prominent bowel sound (PBS) segments through automated segmentation. [...] Read more.
Phonoenterography (PEG) offers a non-invasive and radiation-free technique to assess gastrointestinal activity through acoustic signal analysis. In this feasibility study, 110 high-resolution PEG recordings (44.1 kHz, 16-bit) were acquired from eight healthy individuals, yielding 6314 prominent bowel sound (PBS) segments through automated segmentation. Each event was characterized using a 279-feature acoustic profile comprising Mel-frequency cepstral coefficients (MFCCs), their first-order derivatives (Δ-MFCCs), and six global spectral parameters. After normalization and dimensionality reduction with PCA and UMAP (cosine distance, 35 neighbors, minimum distance = 0.01), five clustering strategies were evaluated. K-Means (k = 5) achieved the most favorable balance between cluster quality (silhouette = 0.60; Calinski–Harabasz = 19,165; Davies–Bouldin = 0.68) and interpretability, consistently identifying five acoustic patterns: single-burst, multiple-burst, harmonic, random-continuous, and multi-modal. Temporal modeling of clustered events further revealed distinct sequential dynamics, with Single-Burst events showing the longest dwell times, random continuous the shortest, and strong diagonal elements in the transition matrix confirming measurable state persistence. Frequent transitions between random continuous and multi-modal states suggested dynamic exchanges between transient and overlapping motility patterns. Together, these findings demonstrate that unsupervised PEG-based analysis can capture both acoustic variability and temporal organization of bowel sounds. This annotation-free approach provides a scalable framework for real-time gastrointestinal monitoring and holds potential for clinical translation in conditions such as postoperative ileus, bowel obstruction, irritable bowel syndrome, and inflammatory bowel disease. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Engineering Applications Towards Clinical Translation)
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22 pages, 5267 KB  
Article
Immunomodulation and Mechanical Characterization of Manuka Honey-Incorporated Near-Field Electrospun Bioresorbable Vascular Grafts
by Alexandra E. Snyder, Evan N. Main and Gary L. Bowlin
Bioengineering 2025, 12(11), 1270; https://doi.org/10.3390/bioengineering12111270 - 19 Nov 2025
Viewed by 829
Abstract
(1) Current synthetic small-diameter vascular grafts fail frequently due to anastomotic hyperplasia and thrombosis caused by mechanical mismatch and incomplete reendothelialization. Polydioxanone near-field electrospun (NFES) vascular templates feature programmable pore sizes to facilitate transmural ingrowth of endothelial cells and show promise in reducing [...] Read more.
(1) Current synthetic small-diameter vascular grafts fail frequently due to anastomotic hyperplasia and thrombosis caused by mechanical mismatch and incomplete reendothelialization. Polydioxanone near-field electrospun (NFES) vascular templates feature programmable pore sizes to facilitate transmural ingrowth of endothelial cells and show promise in reducing mechanical mismatch, but their potential as drug delivery systems remains unexplored. It was hypothesized that Manuka honey incorporation in NFES templates could reduce neutrophil extracellular trap (NET) release but decrease mechanical strength. (2) Templates were fabricated using 90 mg/mL polydioxanone in 1,1,1,3,3,3-hexafluoro-2-propanol (HFP) and Manuka honey concentrations of 0%, 0.1%, 1%, and 10% v/v. Wall thickness (197–236 μm), mechanical properties, Manuka honey elution, and NET release were quantified. (3) The 0.1% and 1% templates best mimicked native vessel mechanics, outperforming the pure HFP template in tensile strength and burst pressure. The 10% templates exhibited significant mechanical strength reductions. Manuka honey elution exhibited a burst release within the first three hours, and all honey was eluted by day three. NET release was elevated in 10% and control groups but was not significantly different from 0.1% and 1%. (4) Overall, low concentrations of Manuka honey maintained mechanical compatibility, but elution must be optimized for immunomodulation, rejecting the initial hypothesis. Full article
(This article belongs to the Section Biomedical Engineering and Biomaterials)
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16 pages, 1258 KB  
Article
Disrupted Corticomuscular Coherence and Force Steadiness During Acute Low Back Pain
by Franciele Parolini, Klaus Becker, Ulysses F. Ervilha, Rubim Santos, João Paulo Vilas-Boas and Márcio Fagundes Goethel
Bioengineering 2025, 12(11), 1269; https://doi.org/10.3390/bioengineering12111269 - 19 Nov 2025
Viewed by 415
Abstract
Background: Acute low back pain can impair motor control, yet its effects on force steadiness and cortical activity remain unclear. Methods: Thirty-three healthy adults (25 men, 8 women) performed a sustained spinal extension task at 20% of their maximum voluntary contraction under pre- [...] Read more.
Background: Acute low back pain can impair motor control, yet its effects on force steadiness and cortical activity remain unclear. Methods: Thirty-three healthy adults (25 men, 8 women) performed a sustained spinal extension task at 20% of their maximum voluntary contraction under pre- and during-pain conditions induced by a hypertonic saline injection, as well as pre- and post-isotonic injection. Electromyography was recorded from the right and left longissimus muscles, and electroencephalography was collected from motor cortical areas. Spectral power in the alpha, beta, and gamma bands, along with corticomuscular and cortico-cortical coherence, was analyzed. Results: Acute pain reduced force steadiness and altered cortical activity, with increased beta and gamma band power in the prefrontal cortex and decreased alpha power in the motor cortex. Localized changes in corticomuscular coherence were observed in the Cz region (beta and gamma bands) during pain, suggesting nociceptive modulation of corticomuscular coupling. Conclusions: Experimentally induced acute low back pain disrupts motor control by reducing force steadiness and modifying cortical activation patterns, highlighting the interplay between pain and neuromuscular regulation. Full article
(This article belongs to the Special Issue Applied Biomechanics in Rehabilitation and Ergonomics)
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20 pages, 4690 KB  
Article
HCTG-Net: A Hybrid CNN–Transformer Network with Gated Fusion for Automatic ECG Arrhythmia Diagnosis
by Ni Xiong, Zibo Wei, Xuehua Wang, Yan Wang and Zhaohui Wang
Bioengineering 2025, 12(11), 1268; https://doi.org/10.3390/bioengineering12111268 - 19 Nov 2025
Viewed by 436
Abstract
Accurate detection of cardiac arrhythmias from electrocardiogram (ECG) signals is essential for the early diagnosis of cardiovascular diseases but remains challenging due to the complex, non-linear nature of ECG waveforms. This study proposes HCTG-Net, a Hybrid CNN–Transformer Network with Gated Fusion, designed to [...] Read more.
Accurate detection of cardiac arrhythmias from electrocardiogram (ECG) signals is essential for the early diagnosis of cardiovascular diseases but remains challenging due to the complex, non-linear nature of ECG waveforms. This study proposes HCTG-Net, a Hybrid CNN–Transformer Network with Gated Fusion, designed to jointly capture local morphological features and long-range temporal dependencies in ECG data. The model employs a dual-branch architecture, where a residual CNN extracts localized waveform patterns and a Transformer branch models global temporal context. A learnable gated fusion mechanism adaptively balances and integrates features from both branches at the per-dimension level. Experiments conducted on the MIT-BIH Arrhythmia Database demonstrate that HCTG-Net achieves superior performance compared with existing methods, reaching an overall accuracy of 0.9946 and F1-score of 0.9711. Visualization results show well-clustered feature distributions, confirming robust feature learning, while ablation studies verify the complementary roles of the CNN, Transformer, and fusion modules. Overall, HCTG-Net offers a powerful and adaptive framework for automatic ECG-based arrhythmia diagnosis and holds strong potential for real-time clinical and wearable healthcare applications. Full article
(This article belongs to the Section Biosignal Processing)
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14 pages, 1883 KB  
Article
Cone-Beam Computed Tomographic Evaluation of Periapical Lesion Healing After Root Canal Preparation with Different File Systems
by Alaa-Eldeen O. Mais, Amr M. Abdallah, Essam Osman and Hatem A. Alhadainy
Bioengineering 2025, 12(11), 1267; https://doi.org/10.3390/bioengineering12111267 - 19 Nov 2025
Viewed by 458
Abstract
Background: Cone-beam computed tomography (CBCT) was used for a 1-year follow-up of a randomized clinical trial to compare a stainless-steel Tornado file system with OneShape and WaveOne rotary systems for biomechanical canal preparation, as indicated by radiolucency sizes of periapical lesions. Methods [...] Read more.
Background: Cone-beam computed tomography (CBCT) was used for a 1-year follow-up of a randomized clinical trial to compare a stainless-steel Tornado file system with OneShape and WaveOne rotary systems for biomechanical canal preparation, as indicated by radiolucency sizes of periapical lesions. Methods: Lower molars with necrotic pulps and periapical lesions were randomly divided into three groups (n = 20) according to three rotary file systems. After root canal treatment, clinical and assessment of the CBCT periapical index scores were blindly evaluated at one year using pre- and post-instrumentation CBCT images. Statistical analysis was performed to compare the three systems at a p-value of 0.05. Results: The results revealed a significant decrease in the size of apical radiolucency in each group after one-year follow-up, with no statistically significant difference among the three systems (p > 0.05). Conclusions: CBCT is a valuable biomedical imaging modality for assessing periapical lesion healing. Tornado, WaveOne, and OneShape systems can be used with similar efficacy for root canal preparation in teeth with periapical lesions. Clinical Trial Registration: The study was retrospectively registered with ClinicalTrials.gov (NCT06752837). Date of Registration: 30 December 2024. The CONSORT group has identified it as essential. Full article
(This article belongs to the Special Issue New Sights of Machine Learning and Digital Models in Biomedicine)
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23 pages, 2988 KB  
Article
Exploratory Investigation of Motor and Psychophysiological Outcomes Following VR-Based Motor Training with Augmented Sensory Feedback for a Pilot Cohort with Spinal Cord Injury
by Raviraj Nataraj, Mingxiao Liu, Yu Shi, Sophie Dewil and Noam Y. Harel
Bioengineering 2025, 12(11), 1266; https://doi.org/10.3390/bioengineering12111266 - 18 Nov 2025
Viewed by 290
Abstract
Spinal cord injury (SCI) impairs motor function and requires rigorous rehabilitative therapy, motivating the development of approaches that are engaging and customizable. Virtual reality (VR) motor training with augmented sensory feedback (ASF) offers a promising pathway to enhance functional outcomes, yet it remains [...] Read more.
Spinal cord injury (SCI) impairs motor function and requires rigorous rehabilitative therapy, motivating the development of approaches that are engaging and customizable. Virtual reality (VR) motor training with augmented sensory feedback (ASF) offers a promising pathway to enhance functional outcomes, yet it remains unclear how ASF modalities affect performance and underlying psychophysiological states in persons with SCI. Five participants with chronic incomplete cervical-level SCI controlled a virtual robotic arm with semi-isometric upper-body contractions while undergoing ASF training with either visual feedback (VF) or combined visual plus haptic feedback (VHF). Motor performance (pathlength, completion time), psychophysiological measures (EEG, EMG, EDA, HR), and perceptual ratings (agency, motivation, utility) were assessed before and after ASF training. VF significantly reduced pathlength (−12.5%, p = 0.0011) and lowered EMG amplitude (−32.5%, p = 0.0063), suggesting the potential for improved motor performance and neuromuscular efficiency. VHF did not significantly improve performance, but trended toward higher cortical engagement. EEG analyses showed VF significantly decreased alpha and beta activity after training, whereas VHF trended toward mild increases. Regression revealed improved performance was significantly (p < 0.05) associated with changes in alpha power, EMG, EDA, and self-reported motivation. ASF type may differentially shape performance and psychophysiological responses in SCI participants. These preliminary findings suggest VR-based ASF as a potent multidimensional tool for personalizing rehabilitation. Full article
(This article belongs to the Special Issue Innovative Approaches: Harnessing Virtual Reality for Assessment and Rehabilitation in Stroke and Aging-Related Disorders)
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13 pages, 434 KB  
Article
Intra- and Inter-Rater Reliability, Parallel Test Reliability, and Internal Consistency of the Tuning Fork and Monofilament Tests
by Jitka Veldema, Lea Sasse, Jan Straub, Michel Klemm, Leon von Grönheim and Teni Steingräber
Bioengineering 2025, 12(11), 1265; https://doi.org/10.3390/bioengineering12111265 - 18 Nov 2025
Viewed by 248
Abstract
Objectives: Somatosensation is the ability to detect various external and internal stimuli (such as pain, pressure, temperature, or joint position), and its objective and reproducible evaluation is essential for diagnosis, training, and rehabilitation. This study evaluates the methodological quality of two somatosensory assessments [...] Read more.
Objectives: Somatosensation is the ability to detect various external and internal stimuli (such as pain, pressure, temperature, or joint position), and its objective and reproducible evaluation is essential for diagnosis, training, and rehabilitation. This study evaluates the methodological quality of two somatosensory assessments in young healthy adults. Methods: The tuning fork test (administered on five locations of each hemibody) and the monofilament test (administered on 27 locations of each hemibody, and divided into (i) foot and ankle, (ii) leg and thigh, and (iii) trunk subscales) were applied to 58 students by two raters at three different time points (rater 1 test, rater 1 retest, rater 2 test). The intra- and inter-rater reliability, parallel test reliability, and internal consistency were evaluated for each test and subtest. Results: The tuning fork test showed moderate intra- and inter-rater reliability and good internal consistency. The monofilament test showed good to moderate intra- and inter-rater reliability for foot and ankle locations, but poor intra- and inter-rater reliability for leg, thigh, and trunk locations. The total score, left hemibody score, and right hemibody score of the monofilament test showed good or acceptable consistency with leg and thigh subscales, but poor or unacceptable consistency with foot, ankle, and trunk subscales. No acceptable parallel test reliabilities were found between the tuning fork test and the monofilament test. Conclusions: The tuning fork test is a reliable assessment of deep somatosensory function in the lower extremities of healthy young adults. The commercially available monofilament test kits are sufficient to investigate the superficial somatosensitivity of feet and ankles, but are insufficient for an objective evaluation of leg, thigh, and trunk regions. Full article
(This article belongs to the Special Issue Musculoskeletal Function in Health and Disease)
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19 pages, 1503 KB  
Article
Data-Centric AI for EEG-Based Emotion Recognition: Noise Filtering and Augmentation Strategies
by Nadieh Moghadam and Rana Hegazy
Bioengineering 2025, 12(11), 1264; https://doi.org/10.3390/bioengineering12111264 - 18 Nov 2025
Viewed by 436
Abstract
Research in the biomedical field often faces challenges due to the scarcity and high cost of data, which significantly limit the development and application of machine learning models. This paper introduces a data-centric AI framework for EEG-based emotion recognition that emphasizes improving data [...] Read more.
Research in the biomedical field often faces challenges due to the scarcity and high cost of data, which significantly limit the development and application of machine learning models. This paper introduces a data-centric AI framework for EEG-based emotion recognition that emphasizes improving data quality rather than model complexity. Instead of proposing a deep architecture, we demonstrate how participant-guided noise filtering combined with systematic data augmentation can substantially enhance system performance across multiple classification settings: binary (high vs. low arousal), four-quadrant emotions, and seven discrete emotions. Using the SEED-VII dataset, we show that these strategies consistently improve accuracy and F1 scores, achieving competitive or superior performance compared to more sophisticated published models. The findings highlight a practical and reproducible pathway for advancing biomedical AI systems, showing that prioritizing data quality over architectural novelty yields robust and generalizable improvements in emotion recognition. Full article
(This article belongs to the Special Issue Artificial Intelligence for Biomedical Signal Processing, 2nd Edition)
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23 pages, 1175 KB  
Review
3D Photogrammetry-Driven Craniofacial Analysis in Orthodontics: A Scoping Review of Recent Applications
by Pui Ki Hung, Junqi Liu and Zhiyi Shan
Bioengineering 2025, 12(11), 1263; https://doi.org/10.3390/bioengineering12111263 - 18 Nov 2025
Viewed by 473
Abstract
(1) Background: The increasing utilization of three-dimensional (3D) photogrammetry has elevated craniofacial analysis to new dimensions. This scoping review seeks to provide a comprehensive overview of the current applications of 3D photogrammetry-supported craniofacial analysis within orthodontic practice, assess its technical superiority, and explore [...] Read more.
(1) Background: The increasing utilization of three-dimensional (3D) photogrammetry has elevated craniofacial analysis to new dimensions. This scoping review seeks to provide a comprehensive overview of the current applications of 3D photogrammetry-supported craniofacial analysis within orthodontic practice, assess its technical superiority, and explore potential areas for enhancement. (2) Methods: A comprehensive search of the literature was carried across three electronic databases (PubMed, Web of Science, Embase). Two independent reviewers screened the articles and extracted data in accordance with the PRISMA-ScR guideline. The primary findings from the included articles were synthesized and analyzed qualitatively. (3) Results: A total of 479 records were obtained initially, with 53 articles ultimately included after removing duplicates and applying eligibility criteria. The application of 3D photogrammetry in craniofacial analysis has become prevalent in orthodontic practice, encompassing normative facial anthropometry, orthodontic problem finding, orthodontic treatment optimization, and treatment outcome evaluation. (4) Conclusion: 3D photogrammetry offers orthodontists a precise and efficient imaging technique for craniofacial analysis. Full article
(This article belongs to the Section Biomedical Engineering and Biomaterials)
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11 pages, 1463 KB  
Article
Augmented Reality Navigation for Extreme Lateral Interbody Fusion with Posterior Instrumentation: Feasibility, Outcomes, and Surgical Technique
by Gabriel Urreola, Matileen G. Cranick, Jose A. Castillo, Jr., Hania Shahzad, Allan R. Martin, Kee Kim, Safdar Khan and Richard L. Price
Bioengineering 2025, 12(11), 1262; https://doi.org/10.3390/bioengineering12111262 - 18 Nov 2025
Viewed by 349
Abstract
Background: Extreme lateral interbody fusion (XLIF) is a minimally invasive spine procedure that traditionally relies on fluoroscopy and neuromonitoring for safe disc space access and instrumentation. Augmented reality (AR) navigation offers real-time anatomical visualization and may reduce fluoroscopy use. This is the [...] Read more.
Background: Extreme lateral interbody fusion (XLIF) is a minimally invasive spine procedure that traditionally relies on fluoroscopy and neuromonitoring for safe disc space access and instrumentation. Augmented reality (AR) navigation offers real-time anatomical visualization and may reduce fluoroscopy use. This is the first description of applying augmented reality to lateral spine surgery. Methods: We conducted a case series of five patients who underwent AR-guided LLIF between May 2024 and July 2025. Surgery was performed in either lateral decubitus or prone transpsoas (PTP) orientation. AR navigation was performed using the Augmedics xvision Spine System, with intraoperative CT–based registration and optical tool tracking. Clinical and operative data, including operative time, estimated blood loss (EBL), length of stay (LOS), radiation exposure, instrumentation accuracy, and postoperative outcomes, were collected and analyzed. Results: Five patients (4 female, 1 male; age > 65; BMI range 20.7–37.2) underwent AR-guided XLIF across 8 levels (L2–L5). The mean operative time was 5 h 1 min (range: 2 h 8 min–6 h 45 min), and mean EBL was 94 mL. Mean LOS was 5.85 days (range: 2–10). Mean radiation exposure was 21.73 mGy, significantly lower than published averages for fluoroscopy-guided XLIF (108.6 mGy). At follow-up, all patients reported pain reduction, with 4/5 achieving complete symptom resolution. Instrumentation accuracy was confirmed radiographically in all cases. Conclusions: This clinical series demonstrates the first clinical application of AR to lateral lumbar interbody fusion. AR navigation was feasible, safe, and effective, providing accurate disc space access and instrumentation with markedly reduced radiation exposure. These findings support AR as a promising adjunct to improve safety, efficiency, and workflow in lateral spine surgery. Full article
(This article belongs to the Special Issue Augmented Reality, Virtual Reality and Extended Reality Solutions for Biomedical Applications)
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14 pages, 3289 KB  
Article
In Vitro Assessment of Corrosion Rate, Vickers Hardness and SEM Analysis of Glass Ionomer Cements and Calcium Silicate-Based Materials
by Diana Hanu, Sorina Mihaela Solomon, Simona Stoleriu, Alice Murariu, Nicanor Cimpoeșu and Gianina Iovan
Bioengineering 2025, 12(11), 1261; https://doi.org/10.3390/bioengineering12111261 - 18 Nov 2025
Viewed by 459
Abstract
The long-term stability of bioactive dental cements in acidic environments is not yet fully understood, despite their extensive clinical use in restorative and endodontic procedures. The objective of this study is to evaluate the degradation behaviour and mechanical stability of one glass ionomer [...] Read more.
The long-term stability of bioactive dental cements in acidic environments is not yet fully understood, despite their extensive clinical use in restorative and endodontic procedures. The objective of this study is to evaluate the degradation behaviour and mechanical stability of one glass ionomer cement (GC FUJI IX®) and two calcium-silicate-based materials (Biodentine® and Biodentine XP 500®) under simulated acidic oral conditions. A total of 18 samples were prepared and distributed into three groups. The materials were immersed in a solution with a pH of 4.5, and their performance was assessed through a number of different methods. These included mass-loss measurements, corrosion-rate calculations, Vickers microhardness testing, and SEM to characterise the surfaces. Biodentine® exhibited the highest degradation, followed by Bio-Dentine XP 500® and GC FUJI IX®. The data were confirmed by one-way ANOVA and a post hoc Tukey’s test. This indicated a statistically significant superiority (p < 0.05) of Biodentine XP 500® over glass ionomers in terms of surface hardness maintenance under acidic conditions. Biodentine®, a calcium silicate-based material, demonstrated inferior chemical stability compared to GC FUJI IX® and Biodentine XP 500®, likely due to its modified calcium-silicate formulation that limits ionic dissolution. In addition, the study revealed that Biodentine XP 500® exhibited the highest Vickers hardness under acidic conditions. The findings reported in this study offer valuable insights into the material selection process for low-pH clinical scenarios and contribute to a more comprehensive understanding of the chemical–mechanical stability of modern bioactive dental restoratives. Full article
(This article belongs to the Special Issue Advanced Dental Materials for Restorative Dentistry)
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32 pages, 381 KB  
Review
Large Language Models in Bio-Ontology Research: A Review
by Prashanti Manda
Bioengineering 2025, 12(11), 1260; https://doi.org/10.3390/bioengineering12111260 - 18 Nov 2025
Viewed by 704
Abstract
Biomedical ontologies are critical for structuring domain knowledge and enabling integrative analyses in the life sciences. Traditional ontology development is labor-intensive, requiring extensive expert curation. Recent advances in artificial intelligence, particularly large language models (LLMs), have opened new possibilities to automate and enhance [...] Read more.
Biomedical ontologies are critical for structuring domain knowledge and enabling integrative analyses in the life sciences. Traditional ontology development is labor-intensive, requiring extensive expert curation. Recent advances in artificial intelligence, particularly large language models (LLMs), have opened new possibilities to automate and enhance various aspects of bio-ontology research. This review article synthesizes findings from recent studies on LLM-assisted ontology creation, mapping, integration, and semantic search, while addressing challenges such as bias, reliability, and ethical concerns. We also discuss promising future directions and emerging trends that may further transform the way biomedical ontologies are developed, maintained, and used. Full article
(This article belongs to the Section Biosignal Processing)
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35 pages, 1561 KB  
Article
An Integrative Review of Computational Methods Applied to Biomarkers, Psychological Metrics, and Behavioral Signals for Early Cancer Risk Detection
by Lucia Bubulac, Tudor Georgescu, Mirela Zivari, Dana-Maria Popescu-Spineni, Cristina-Crenguţa Albu, Adrian Bobu, Sebastian Tiberiu Nemeth, Claudia-Florina Bogdan-Andreescu, Adriana Gurghean and Alin Adrian Alecu
Bioengineering 2025, 12(11), 1259; https://doi.org/10.3390/bioengineering12111259 - 17 Nov 2025
Viewed by 672
Abstract
The global rise in cancer incidence and mortality represents a major challenge for modern healthcare. Although current screening programs rely mainly on histological or immunological biomarkers, cancer is a multifactorial disease in which biological, psychological, and behavioural determinants interact. Psychological dimensions such as [...] Read more.
The global rise in cancer incidence and mortality represents a major challenge for modern healthcare. Although current screening programs rely mainly on histological or immunological biomarkers, cancer is a multifactorial disease in which biological, psychological, and behavioural determinants interact. Psychological dimensions such as stress, anxiety, and depression may influence vulnerability and disease evolution through neuro-endocrine, immune, and behavioural pathways, especially by affecting adherence to therapeutic recommendations. However, these dimensions remain underexplored in current screening workflows. This review synthesizes current evidence on the integration of biological markers (tumor and inflammatory biomarkers), psychometric profiling (stress, depression, anxiety, personality traits), and behavioural digital phenotyping (facial micro-expressions, vocal tone, gait/posture metrics) for potential early cancer risk evaluation. We examine recent advances in computational sciences and artificial intelligence that could enable multimodal signal harmonization, structured representation, and hybrid data fusion models. We discuss how structured computational information management may improve interpretability and may support future AI-assisted screening paradigms. Finally, we highlight the relevance of digital health infrastructure and telemedical platforms in strengthening accessibility, continuity of monitoring, and population-level screening coverage. Further empirical research is required to determine the true predictive contribution of psychological and behavioural modalities beyond established biological markers. Full article
(This article belongs to the Special Issue Artificial Intelligence for Better Healthcare and Precision Medicine, 2nd Edition)
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31 pages, 3161 KB  
Review
Artificial Intelligence-Assisted Dermatologic Screening: Epidemiology and Clinical Features of Basal Cell Carcinoma, Squamous Cell Carcinoma, Seborrheic Keratosis and Actinic Keratosis
by Teng-Li Lin, Kun-Hua Lee, Riya Karmakar, Arvind Mukundan, Jeevitha Sundarraj, Chun-Te Lu, Shang-Chin Hsieh and Hsiang-Chen Wang
Bioengineering 2025, 12(11), 1258; https://doi.org/10.3390/bioengineering12111258 - 17 Nov 2025
Viewed by 501
Abstract
This literature review synthesizes contemporary evidence regarding the epidemiology, screening guidelines, clinical manifestations, and machine-learning solutions for four prevalent non-melanoma skin lesions: basal cell carcinoma (BCC), squamous cell carcinoma (SCC), seborrheic keratosis (SK), and actinic keratosis (AK). This study presents a summary of [...] Read more.
This literature review synthesizes contemporary evidence regarding the epidemiology, screening guidelines, clinical manifestations, and machine-learning solutions for four prevalent non-melanoma skin lesions: basal cell carcinoma (BCC), squamous cell carcinoma (SCC), seborrheic keratosis (SK), and actinic keratosis (AK). This study presents a summary of common indices and recent screening alternatives, accompanied by a critical assessment of contemporary advancements in artificial intelligence (AI) and machine learning (ML) for the identification and classification of images utilizing standardized benchmark databases. The literature search and selection focused on peer-reviewed studies published from 2018 to December 2024, emphasizing diagnostic performance, datasets, preprocessing methodologies, and assessment metrics. This work compares and contextualizes reported results, highlighting the challenges posed by different study designs and biases in datasets that hinder direct comparisons among studies. The consistency of deep learning classifiers in lesion detection, the significance of sensitivity-oriented thresholding for early detection applications, and challenges associated with class imbalance and the under-representation of darker skin tones in publicly accessible datasets are studied. With practical implications for clinical adoption, emphasizing targeted screening of at-risk populations, the supplementary benefits of dermoscopy and the imperative for multi-center, demographically diverse validation have been concluded. Additionally, future research on standardized reporting, external validation, and interpretable, workflow-compatible AI systems has been proposed. Full article
(This article belongs to the Special Issue Artificial Intelligence for Skin Diseases Classification)
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25 pages, 2256 KB  
Perspective
Immersive Virtual Reality Environments as Psychoanalytic Settings: A Conceptual Framework for Modeling Unconscious Processes Through IoT-Based Bioengineering Interfaces
by Vincenzo Maria Romeo
Bioengineering 2025, 12(11), 1257; https://doi.org/10.3390/bioengineering12111257 - 17 Nov 2025
Viewed by 633
Abstract
Background: Immersive Virtual Reality (IVR) is gaining increasing relevance in the field of mental health as a tool for therapeutic simulation and embodied experience. However, most existing VR applications are grounded in cognitive–behavioral frameworks, leaving unexplored the integration of symbolic, intersubjective, and unconscious [...] Read more.
Background: Immersive Virtual Reality (IVR) is gaining increasing relevance in the field of mental health as a tool for therapeutic simulation and embodied experience. However, most existing VR applications are grounded in cognitive–behavioral frameworks, leaving unexplored the integration of symbolic, intersubjective, and unconscious dimensions. Psychoanalysis—particularly its constructs of setting, rêverie, and transference—offers a unique epistemological basis for designing therapeutic environments that engage implicit emotional processes. Aim: This paper aims to develop a conceptual framework for modeling IVR-based therapeutic settings inspired by psychoanalytic theory and enhanced through IoT-enabled biosensing technologies. Methods/Approach: We propose a three-layer architecture: (1) a somatic layer involving IoT-based real-time physiological monitoring (e.g., heart rate variability, galvanic skin response, eye-tracking, EEG); (2) a symbolic-narrative layer where the VR environment dynamically adapts to the user’s affective state through immersive visual and auditory stimuli; and (3) a relational layer where AI-driven avatars simulate transferential dynamics. The model is theoretically grounded in psychoanalytic literature and informed by current advances in affective computing and bioengineering. Conclusions: By bridging psychoanalytic metapsychology and bioengineering design, this framework proposes a novel approach to therapeutic IVR systems that move beyond explicit cognition to engage the embodied unconscious. The integration of IoT biosignals enables the mapping and modulation of internal states within a structured symbolic space, opening new pathways for the clinical application of digital psychoanalysis. Full article
(This article belongs to the Special Issue IoT Technology in Bioengineering Applications: Second Edition)
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12 pages, 1086 KB  
Article
Impact of Fatigue on Spine Dynamic Stability and Gait Patterns in Runners with Moderate Flatfoot Versus Normal Arch
by Zihang Xu, Zixiang Gao, Zhanyi Zhou, Yucheng Wang, Jianqi Pan, Liangliang Xiang, Yang Song, Dong Sun, Zsolt Radak and Xuanzhen Cen
Bioengineering 2025, 12(11), 1256; https://doi.org/10.3390/bioengineering12111256 - 17 Nov 2025
Viewed by 539
Abstract
Background: Running is a widely practiced physical activity but carries a high risk of injury, with foot structure, particularly the medial arch, playing a vital role in biomechanical performance and injury prevention. As the core of foot support, the arch is essential for [...] Read more.
Background: Running is a widely practiced physical activity but carries a high risk of injury, with foot structure, particularly the medial arch, playing a vital role in biomechanical performance and injury prevention. As the core of foot support, the arch is essential for absorbing impact, transmitting force, and maintaining dynamic stability. This study aims to compare the dynamic stability of runners with moderate flatfoot and those with normal arches in the initial, steady, and fatigue stages in order to elucidate how fatigue differently affects their dynamic postural control. Methods: Twelve male runners were recruited. Using inertial measurement units (IMUs) and a Zebris treadmill system, data on Maximum Lyapunov Exponent(MLE) and plantar center of pressure (COP) trajectories were collected during the initial, steady-state, and fatigued phases. Results: In the fatigue phase, runners with flatfoot showed an increase of 0.05 s−1 in short-term MLE compared to those with normal arches (p < 0.05), indicating significantly lower stability under fatigue. Conclusions: The deterioration of lower-limb dynamic stability in flatfoot runners is dependent on fatigue. Specifically, their overall lower dynamic stability stems primarily from a marked increase in MLE when entering the fatigued phase. Concurrently, fatigue induces alterations in COP trajectory and temporal gait parameters in flatfoot runners; they signify reduced efficiency in gait control. Full article
(This article belongs to the Section Biomechanics and Sports Medicine)
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34 pages, 1138 KB  
Review
The Constrained Disorder Principle: A Paradigm Shift for Accurate Interactome Mapping and Information Analysis in Complex Biological Systems
by Yaron Ilan
Bioengineering 2025, 12(11), 1255; https://doi.org/10.3390/bioengineering12111255 - 16 Nov 2025
Viewed by 405
Abstract
The interactome, which represents the comprehensive network of molecular interactions within biological systems, has become a crucial framework for understanding cellular functions and disease mechanisms. However, current interactome models face significant limitations because they fail to account for the inherent variability and randomness [...] Read more.
The interactome, which represents the comprehensive network of molecular interactions within biological systems, has become a crucial framework for understanding cellular functions and disease mechanisms. However, current interactome models face significant limitations because they fail to account for the inherent variability and randomness of biological systems. The Constrained Disorder Principle (CDP) offers an innovative approach to addressing these limitations by integrating physiological variability and biological noise as essential components rather than viewing them as experimental artifacts. This paper examines how the CDP may enhance the accuracy of interactome models by incorporating the dynamic and variable nature of biological systems while preserving functional constraints. We suggest that incorporating controlled variability into interactome models may significantly improve their predictive power and biological relevance. This shift moves away from static network representations toward dynamic, context-dependent interaction maps that more accurately reflect the reality of living systems. Through a comprehensive analysis of existing clinical data and theoretical frameworks, we propose methodological advances and provide evidence for the functional importance of biological variability at the molecular, cellular, and organ levels. Full article
(This article belongs to the Section Cellular and Molecular Bioengineering)
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18 pages, 616 KB  
Article
Does Resistance Indicate Malposition? A Standardized Comparison of Pedicle Screw Placement
by Sascha Kurz, Benjamin Fischer, Janine Schultze, Florian Metzner, Toni Wendler, Christoph-Eckhard Heyde and Stefan Schleifenbaum
Bioengineering 2025, 12(11), 1254; https://doi.org/10.3390/bioengineering12111254 - 16 Nov 2025
Viewed by 330
Abstract
Pedicle screw malpositioning remains a frequent complication, with reported rates from 2% to 15%, often leading to revision surgeries. Analyzing mechanical resistance and torque encountered during screw insertion has been implicated as a promising approach for real-time detection. Five fresh-frozen human thoracolumbar spine [...] Read more.
Pedicle screw malpositioning remains a frequent complication, with reported rates from 2% to 15%, often leading to revision surgeries. Analyzing mechanical resistance and torque encountered during screw insertion has been implicated as a promising approach for real-time detection. Five fresh-frozen human thoracolumbar spine specimens were utilized in this study. Using 3D-printed templates, correct trajectories were systematically compared against four defined malpositions (medial, lateral, superior, superolateral), with offsets ranging from 2.0 mm to 3.5 mm. Drilling, tapping, and insertion phases were conducted at a constant speed and defined feed force. Contrary to the anticipated behavior, malpositioned trajectories showed no statistically significant difference in peak torque compared to correct trajectories across all phases (e.g., tapping p=0.944, r=0.01; insertion p=0.693, r=0.05). Regional stratification between thoracic and lumbar spine also failed to yield significant differences. The only statistically significant difference was observed between the correct trajectory and the superolateral malposition during drilling (p=0.038). Under the tested standardized conditions, torque-based mechanical resistance during pedicle screw placement is generally not a reliable and consistent real-time indicator of malposition. Full article
(This article belongs to the Special Issue Spine Biomechanics)
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25 pages, 4862 KB  
Review
Comparative Efficacy of Platelet-Rich Fibrin, Freeze-Dried Bone Allograft, or Spontaneous Healing for Alveolar Ridge Preservation: Systematic Review and Meta-Analysis
by Abeer S. Al-Zawawi, Amani M. Basudan, Rand Osama Alkhani, Lamis Khalid Alraddadi, Shikha Fahad Bin-Muhayya, Layan Abdullah Alzuwayyid, Deemah Alsaeed, Eithar Ibrahim Alrosaa, Lana Mohammed Alrasheed, Muneerah Abduaziz Alfahad, Ghadeer Mohammed Almutairi, Jana Alawad, Wasan Saeed Koaban, Munirah Naeem Alsubaie and Sundar Ramalingam
Bioengineering 2025, 12(11), 1253; https://doi.org/10.3390/bioengineering12111253 - 16 Nov 2025
Viewed by 750
Abstract
Alveolar ridge preservation (ARP) is crucial for maintaining bone and soft-tissue integrity after tooth extraction, thereby facilitating future implant placement. Among various biomaterials, platelet-rich fibrin (PRF) and freeze-dried bone allograft (FDBA) are commonly used; however, their comparative effectiveness remains unclear. This systematic review [...] Read more.
Alveolar ridge preservation (ARP) is crucial for maintaining bone and soft-tissue integrity after tooth extraction, thereby facilitating future implant placement. Among various biomaterials, platelet-rich fibrin (PRF) and freeze-dried bone allograft (FDBA) are commonly used; however, their comparative effectiveness remains unclear. This systematic review and meta-analysis aimed to evaluate and compare the outcomes of PRF, FDBA, and spontaneous healing with blood clot in ARP, incorporating recent randomized controlled trials and comparative studies published up to June 2025. Electronic searches were conducted across multiple databases following the PRISMA 2020 guidelines, and the risk of bias was assessed using RoB-2 and ROBINS-I tools. Primary outcomes included changes in alveolar ridge height and width, while secondary outcomes encompassed histological, radiographic, implant-related, and patient-centered measures. Twenty studies were included for qualitative synthesis and sixteen for quantitative analysis. Meta-analyses showed no significant difference between PRF and FDBA in ridge height (SMD = −0.24; 95% CI: −0.56 to 0.08; p = 0.145) or width preservation (SMD = −0.16; 95% CI: −0.73 to 0.42; p = 0.597). PRF significantly reduced ridge height loss compared to spontaneous healing (SMD = −0.79; 95% CI: −1.33 to −0.25; p = 0.004) and enhanced histologic new bone formation (SMD = 1.43; 95% CI: 0.39 to 2.47; p = 0.007), while FDBA showed a non-significant trend toward benefit (SMD = −0.37; 95% CI: −0.86 to 0.11; p = 0.129). Moderate risk-of-bias and heterogeneity were observed among included studies. In conclusion, PRF and FDBA are both effective for alveolar ridge preservation, outperforming spontaneous healing. PRF offers biologically driven benefits in bone quality and soft-tissue healing, whereas FDBA provides greater structural stability. These findings suggest a promising clinical potential for PRF in improving bone quality at the implant site. Moreover, considering cost, preparation complexity, and site-specific needs, PRF may serve as a cost-effective, clinically favorable option for ARP. Future multi-center randomized trials with standardized PRF protocols and long-term follow-up are recommended. Full article
(This article belongs to the Special Issue Periodontics and Implant Dentistry)
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9 pages, 601 KB  
Article
Effects of the Combined Abdominal Draw-In Maneuver and Manual Resistance on Lumbopelvic Muscle Activity and Anterior Pelvic Tilt During Prone Hip Extension
by Dong-Woo Kim and Young-Jun Shin
Bioengineering 2025, 12(11), 1252; https://doi.org/10.3390/bioengineering12111252 - 16 Nov 2025
Viewed by 425
Abstract
This study investigated the effects of applying the abdominal draw-in maneuver (ADIM) and manual resistance (MR), separately and in combination, during prone hip extension (PHE) on muscle activity and anterior pelvic tilt. Twenty-four healthy adult males performed PHE under three randomized conditions: ADIM, [...] Read more.
This study investigated the effects of applying the abdominal draw-in maneuver (ADIM) and manual resistance (MR), separately and in combination, during prone hip extension (PHE) on muscle activity and anterior pelvic tilt. Twenty-four healthy adult males performed PHE under three randomized conditions: ADIM, MR, and ADIM combined with MR. Electromyography was used to measure gluteus maximus (GM), erector spinae (ES), internal oblique (IO), and hamstring activity, while anterior pelvic tilt angle was assessed using a gyroscopic sensor. Repeated-measures ANOVA revealed significant differences across conditions (p < 0.05). Post hoc analysis showed that GM and IO activity were significantly greater in the ADIM combined with MR condition than in either ADIM or MR alone, with MR also producing higher values than ADIM (p < 0.05). ES activity was lowest in the ADIM condition, while ADIM combined with MR produced lower ES activity than MR (p < 0.05). The GM/ES ratio was highest in ADIM combined with MR compared with the other conditions (p < 0.05). Anterior pelvic tilt angle was significantly smaller in both the ADIM and ADIM combined with MR conditions compared with MR (p < 0.05). These findings suggest that combined ADIM with MR induces strong IO contraction and enhances lumbopelvic stability, leading to substantially increased GM activity. Full article
(This article belongs to the Special Issue Physical Therapy and Rehabilitation)
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23 pages, 3035 KB  
Article
Predicting Major Depressive Disorder Using Neural Networks from Spectral Measures of EEG Data
by Igor Kozulin, Ekaterina Merkulova, Vasiliy Savostyanov, Haonan Shi, Xinyi Wang, Andrey Bocharov and Alexander Savostyanov
Bioengineering 2025, 12(11), 1251; https://doi.org/10.3390/bioengineering12111251 - 16 Nov 2025
Viewed by 395
Abstract
Processing electroencephalogram (EEG) data using neural networks is becoming increasingly important in modern medicine. This study introduces the development of a neural network method using a combination of psychological questionnaire data and spectral characteristics of resting-state EEG. The data were collected from 71 [...] Read more.
Processing electroencephalogram (EEG) data using neural networks is becoming increasingly important in modern medicine. This study introduces the development of a neural network method using a combination of psychological questionnaire data and spectral characteristics of resting-state EEG. The data were collected from 71 individuals: 42 healthy and 29 with major depressive disorder (MDD). We evaluated four classes of algorithms—traditional machine learning, deep learning (LSTM), ablation analysis, and feature importance analysis—for two primary tasks: binary classification (healthy vs. MDD) and regression for predicting Beck Depression Inventory scores (BDI). Our results demonstrate that the superiority of a given method is task-dependent. For regression, an LSTM network applied to delta-rhythm EEG data achieved a breakthrough performance of R2 = 0.742 (MAE = 6.114), representing an 86% improvement over traditional Ridge regression. Ablation studies identified delta and alpha rhythms as the most informative neurophysiological biomarkers. Furthermore, feature importance analysis revealed a triad of dominant psychometric predictors: ruminative thinking (31.2%), age (27.9%), and hostility (18.5%), which collectively accounted for 75.2% of the feature importance in predicting severity. LSTM on spectral EEG data provides a superior quantitative assessment of depression severity, while Logistic Regression on psychometric or EEG data offers a highly reliable tool for screening and confirmatory diagnosis. This methodology provides a robust, objective framework for MDD diagnosis that is independent of a patient’s subjective self-assessment, thus facilitating enhanced clinical decision-making and personalized treatment monitoring. Full article
(This article belongs to the Special Issue AI and Machine Learning in Medical Image Processing: Innovations and Applications)
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15 pages, 1444 KB  
Article
Paper-Based Device for the Colorimetric Determination of Glucose in Whole-Blood Samples Using a Smartphone
by Lara B. A. Boga, Katia Gianni, Mariano N. Aleman, Marcos S. Almiron Arroyo and Rossana E. Madrid
Bioengineering 2025, 12(11), 1250; https://doi.org/10.3390/bioengineering12111250 - 15 Nov 2025
Viewed by 669
Abstract
In many clinical settings, there is a great need for rapid, simple, and reliable diagnostic tools for the detection and quantification of various biomarkers. These tools enable early medical decisions, which can significantly influence patient recovery. Paper-based analytical devices (PADs) have become promising [...] Read more.
In many clinical settings, there is a great need for rapid, simple, and reliable diagnostic tools for the detection and quantification of various biomarkers. These tools enable early medical decisions, which can significantly influence patient recovery. Paper-based analytical devices (PADs) have become promising platforms for rapid and low-cost diagnostic testing in recent years. Among the most important biomarkers is glucose, a key metabolite involved in numerous physiological processes, which allows for the diagnosis and control of diabetes, the prevention of serious long-term complications such as cardiovascular disease, and the monitoring of the effect of medication, diet, and exercise on sugar levels in these patients. A fundamental step in detecting this marker in laboratories is the separation of plasma from whole blood. Several studies have demonstrated the successful integration of plasma separation in μPADs. This work presents the development of a paper-based device for the colorimetric detection of glucose in whole-blood samples, allowing plasma separation and using a smartphone to perform quantitative determination. Full article
(This article belongs to the Section Biomedical Engineering and Biomaterials)
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