Bioengineering

4 pages, 869 KiB

Open AccessEditorial

Liver Cancer: Artificial Intelligence (AI)-Based Integrated Therapeutic Approaches

by Mythileeswari Lakshmikanthan, Sakthivel Muthu, John T. D. Caleb, Yuvaraj Maria Francis and Indra Neel Pulidindi

Bioengineering 2025, 12(8), 837; https://doi.org/10.3390/bioengineering12080837 (registering DOI) - 1 Aug 2025

Abstract

The advent of artificial intelligence and machine leaning techniques has revolutionized the diagnosis and therapy of diseases such as cancer [...] Full article

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

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13 pages, 1454 KiB

Open AccessArticle

Lower Limb Inter-Joint Coordination and End-Point Control During Gait in Adolescents with Early Treated Unilateral Developmental Dysplasia of the Hip

by Chu-Fen Chang, Tung-Wu Lu, Chia-Han Hu, Kuan-Wen Wu, Chien-Chung Kuo and Ting-Ming Wang

Bioengineering 2025, 12(8), 836; https://doi.org/10.3390/bioengineering12080836 (registering DOI) - 31 Jul 2025

Abstract

Background: Residual deficits after early treatment of developmental dysplasia of the hip (DDH) using osteotomy often led to asymmetrical gait deviations with increased repetitive rates of ground reaction force (GRF) in both hips, resulting in a higher risk of early osteoarthritis. This [...] Read more.

Background: Residual deficits after early treatment of developmental dysplasia of the hip (DDH) using osteotomy often led to asymmetrical gait deviations with increased repetitive rates of ground reaction force (GRF) in both hips, resulting in a higher risk of early osteoarthritis. This study investigated lower limb inter-joint coordination and swing foot control during level walking in adolescents with early-treated unilateral DDH. Methods: Eleven female adolescents treated early for DDH using Pemberton osteotomy were compared with 11 age-matched healthy controls. The joint angles and angular velocities of the hip, knee, and ankle were measured, and the corresponding phase angles and continuous relative phase (CRP) for hip–knee and knee–ankle coordination were obtained. The variability of inter-joint coordination was quantified using the deviation phase values obtained as the time-averaged standard deviations of the CRP curves over multiple trials. Results: The DDH group exhibited a flexed posture with increased variability in knee–ankle coordination of the affected limb throughout the gait cycle compared to the control group. In contrast, the unaffected limb compensated for the kinematic alterations of the affected limb with reduced peak angular velocities but increased knee–ankle CRP over double-limb support and trajectory variability over the swing phase. Conclusions: The identified changes in inter-joint coordination in adolescents with early treated DDH provide a plausible explanation for the previously reported increased GRF loading rates in the unaffected limb, a risk factor of premature OA. Full article

(This article belongs to the Special Issue Biomechanics and Motion Analysis)

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12 pages, 1346 KiB

Open AccessArticle

A Language Vision Model Approach for Automated Tumor Contouring in Radiation Oncology

by Yi Luo, Hamed Hooshangnejad, Xue Feng, Gaofeng Huang, Xiaojian Chen, Rui Zhang, Quan Chen, Wil Ngwa and Kai Ding

Bioengineering 2025, 12(8), 835; https://doi.org/10.3390/bioengineering12080835 (registering DOI) - 31 Jul 2025

Abstract

Background: Lung cancer ranks as the leading cause of cancer-related mortality worldwide. The complexity of tumor delineation, crucial for radiation therapy, requires expertise often unavailable in resource-limited settings. Artificial Intelligence (AI), particularly with advancements in deep learning (DL) and natural language processing (NLP), [...] Read more.

Background: Lung cancer ranks as the leading cause of cancer-related mortality worldwide. The complexity of tumor delineation, crucial for radiation therapy, requires expertise often unavailable in resource-limited settings. Artificial Intelligence (AI), particularly with advancements in deep learning (DL) and natural language processing (NLP), offers potential solutions yet is challenged by high false positive rates. Purpose: The Oncology Contouring Copilot (OCC) system is developed to leverage oncologist expertise for precise tumor contouring using textual descriptions, aiming to increase the efficiency of oncological workflows by combining the strengths of AI with human oversight. Methods: Our OCC system initially identifies nodule candidates from CT scans. Employing Language Vision Models (LVMs) like GPT-4V, OCC then effectively reduces false positives with clinical descriptive texts, merging textual and visual data to automate tumor delineation, designed to elevate the quality of oncology care by incorporating knowledge from experienced domain experts. Results: The deployment of the OCC system resulted in a 35.0% reduction in the false discovery rate, a 72.4% decrease in false positives per scan, and an F1-score of 0.652 across our dataset for unbiased evaluation. Conclusions: OCC represents a significant advance in oncology care, particularly through the use of the latest LVMs, improving contouring results by (1) streamlining oncology treatment workflows by optimizing tumor delineation and reducing manual processes; (2) offering a scalable and intuitive framework to reduce false positives in radiotherapy planning using LVMs; (3) introducing novel medical language vision prompt techniques to minimize LVM hallucinations with ablation study; and (4) conducting a comparative analysis of LVMs, highlighting their potential in addressing medical language vision challenges. Full article

(This article belongs to the Special Issue Novel Imaging Techniques in Radiotherapy)

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16 pages, 5224 KiB

Open AccessArticle

The Effects of Calcium Phosphate Bone Cement Preparation Parameters on Injectability and Compressive Strength for Minimally Invasive Surgery

by Qinfeng Qiao, Qianbin Zhao, Jinwen Wang, Mingjun Li, Huan Zhou and Lei Yang

Bioengineering 2025, 12(8), 834; https://doi.org/10.3390/bioengineering12080834 (registering DOI) - 31 Jul 2025

Abstract

Compared with biocompatibility, osteoconductivity, and mechanical properties, the poor injectability of calcium phosphate bone cements (CPCs) is always ignored, which actually hinders the development of CPC clinical transfer in minimally invasive orthopedic surgeries. Moreover, currently, CPC preparation in the clinic is labor-intensive and [...] Read more.

Compared with biocompatibility, osteoconductivity, and mechanical properties, the poor injectability of calcium phosphate bone cements (CPCs) is always ignored, which actually hinders the development of CPC clinical transfer in minimally invasive orthopedic surgeries. Moreover, currently, CPC preparation in the clinic is labor-intensive and requires well-trained technicists, which might also result in the unstable quality of CPCs. In this work, we focused on three research objectives: (i) introducing a standardized preparation method for CPCs; (ii) studying the effects of preparation parameters on CPC injectability and compressive strength; and (iii) studying the injecting condition effects on CPC injectability, aiming to overcome CPCs’ disadvantages in minimally invasive surgeries. Firstly, two strategies, named “variable mixing barrel control (VMBC)” and the “nested blade–baffle stirring rod (NBBSR)”, were proposed in this study to solve the problems in the preparation of CPCs, which involved blending CPC powder and an agent to generate a paste, by enhancing the mixing performance and mimicking human manual stirring actions. Secondly, although the grinding parameter could significantly generate differences in the microstructure of CPCs, the compressive strength remained relatively stable. However, it was found to significantly affect the injectability of CPCs, leading to the inefficient injection of CPCs. Finally, the effects of syringe design, dimensions, and injecting conditions on CPC injectability were studied, and the results showed that the optimization of these factors enables the injection of CPCs, which has otherwise always been infeasible to implement in minimally invasive orthopedic surgeries. Full article

(This article belongs to the Special Issue Advances in Bone Regeneration with Multipotential Stromal Cells and Bioactive Materials)

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19 pages, 3328 KiB

Open AccessArticle

Enhancing Trauma Care: Machine Learning-Based Photoplethysmography Analysis for Estimating Blood Volume During Hemorrhage and Resuscitation

by Jose M. Gonzalez, Lawrence Holland, Sofia I. Hernandez Torres, John G. Arrington, Tina M. Rodgers and Eric J. Snider

Bioengineering 2025, 12(8), 833; https://doi.org/10.3390/bioengineering12080833 (registering DOI) - 31 Jul 2025

Abstract

Hemorrhage is the leading cause of preventable death in trauma care, requiring rapid and accurate detection to guide effective interventions. Hemorrhagic shock can be masked by underlying compensatory mechanisms, which may lead to delayed decision-making that can compromise casualty care. In this proof-of-concept [...] Read more.

Hemorrhage is the leading cause of preventable death in trauma care, requiring rapid and accurate detection to guide effective interventions. Hemorrhagic shock can be masked by underlying compensatory mechanisms, which may lead to delayed decision-making that can compromise casualty care. In this proof-of-concept study, we aimed to develop and evaluate machine learning models to predict Percent Estimated Blood Loss from a photoplethysmography waveform, offering non-invasive, field deployable solutions. Different model types were tuned and optimized using data captured during a hemorrhage and resuscitation swine study. Through this optimization process, we evaluated different time-lengths of prediction windows, machine learning model architectures, and data normalization approaches. Models were successful at predicting Percent Estimated Blood Loss in blind swine subjects with coefficient of determination values exceeding 0.8. This provides evidence that Percent Estimated Blood Loss can be accurately derived from non-invasive signals, improving its utility for trauma care and casualty triage in the pre-hospital and emergency medicine environment. Full article

(This article belongs to the Special Issue Biomedical Engineering Approaches for Non-Invasive Monitoring in Hypovolemia and Hypotension)

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13 pages, 1879 KiB

Open AccessArticle

Dynamic Graph Convolutional Network with Dilated Convolution for Epilepsy Seizure Detection

by Xiaoxiao Zhang, Chenyun Dai and Yao Guo

Bioengineering 2025, 12(8), 832; https://doi.org/10.3390/bioengineering12080832 (registering DOI) - 31 Jul 2025

Abstract

The electroencephalogram (EEG), widely used for measuring the brain’s electrophysiological activity, has been extensively applied in the automatic detection of epileptic seizures. However, several challenges remain unaddressed in prior studies on automated seizure detection: (1) Methods based on CNN and LSTM assume that [...] Read more.

The electroencephalogram (EEG), widely used for measuring the brain’s electrophysiological activity, has been extensively applied in the automatic detection of epileptic seizures. However, several challenges remain unaddressed in prior studies on automated seizure detection: (1) Methods based on CNN and LSTM assume that EEG signals follow a Euclidean structure; (2) Algorithms leveraging graph convolutional networks rely on adjacency matrices constructed with fixed edge weights or predefined connection rules. To address these limitations, we propose a novel algorithm: Dynamic Graph Convolutional Network with Dilated Convolution (DGDCN). By leveraging a spatiotemporal attention mechanism, the proposed model dynamically constructs a task-specific adjacency matrix, which guides the graph convolutional network (GCN) in capturing localized spatial and temporal dependencies among adjacent nodes. Furthermore, a dilated convolutional module is incorporated to expand the receptive field, thereby enabling the model to capture long-range temporal dependencies more effectively. The proposed seizure detection system is evaluated on the TUSZ dataset, achieving AUC values of 88.7% and 90.4% on 12-s and 60-s segments, respectively, demonstrating competitive performance compared to current state-of-the-art methods. Full article

(This article belongs to the Section Biosignal Processing)

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15 pages, 2220 KiB

Open AccessArticle

Radiologic Assessment of Periprostatic Fat as an Indicator of Prostate Cancer Risk on Multiparametric MRI

by Roxana Iacob, Emil Radu Iacob, Emil Robert Stoicescu, Diana Manolescu, Laura Andreea Ghenciu, Radu Căprariu, Amalia Constantinescu, Iulia Ciobanu, Răzvan Bardan and Alin Cumpănaș

Bioengineering 2025, 12(8), 831; https://doi.org/10.3390/bioengineering12080831 (registering DOI) - 31 Jul 2025

Abstract

Prostate cancer remains one of the most prevalent malignancies among men, and emerging evidence proposed a potential role for periprostatic adipose tissue (PPAT) in tumor progression. However, its relationship with imaging-based risk stratification systems such as PI-RADS remains uncertain. This retrospective observational study [...] Read more.

Prostate cancer remains one of the most prevalent malignancies among men, and emerging evidence proposed a potential role for periprostatic adipose tissue (PPAT) in tumor progression. However, its relationship with imaging-based risk stratification systems such as PI-RADS remains uncertain. This retrospective observational study aimed to evaluate whether periprostatic and subcutaneous fat thickness are associated with PI-RADS scores or PSA levels in biopsy-naïve patients. We retrospectively reviewed 104 prostate MRI scans performed between January 2020 and January 2024. Fat thickness was measured on axial T2-weighted images, and statistical analyses were conducted using Spearman’s correlation and multiple linear regression. In addition to linear measurements, we also assessed periprostatic fat volume and posterior fat thickness derived from imaging data. No significant correlations were observed between fat thickness (either periprostatic or subcutaneous) and PI-RADS score or PSA values. Similarly, periprostatic fat volume showed only a weak, non-significant correlation with PI-RADS, while posterior fat thickness demonstrated a weak but statistically significant positive association. Additionally, subgroup comparisons between low-risk (PI-RADS < 4) and high-risk (PI-RADS ≥ 4) patients showed no meaningful differences in fat measurements. These findings suggest that simple linear fat thickness measurements may not enhance imaging-based risk assessment in prostate cancer, though regional and volumetric assessments could offer modest added value. Full article

(This article belongs to the Special Issue Label-Free Cancer Detection)

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20 pages, 5322 KiB

Open AccessArticle

Regulation of Tetraspanin CD63 in Chronic Myeloid Leukemia (CML): Single-Cell Analysis of Asymmetric Hematopoietic Stem Cell Division Genes

by Christophe Desterke, Annelise Bennaceur-Griscelli and Ali G. Turhan

Bioengineering 2025, 12(8), 830; https://doi.org/10.3390/bioengineering12080830 (registering DOI) - 31 Jul 2025

Abstract

(1) Background: Chronic myeloid leukemia (CML) is a myeloproliferative disorder driven by the BCR::ABL oncoprotein. During the chronic phase, Philadelphia chromosome-positive hematopoietic stem cells generate proliferative myeloid cells with various stages of maturation. Despite this expansion, leukemic stem cells (LSCs) retain self-renewal capacity [...] Read more.

(1) Background: Chronic myeloid leukemia (CML) is a myeloproliferative disorder driven by the BCR::ABL oncoprotein. During the chronic phase, Philadelphia chromosome-positive hematopoietic stem cells generate proliferative myeloid cells with various stages of maturation. Despite this expansion, leukemic stem cells (LSCs) retain self-renewal capacity via asymmetric cell divisions, sustaining the stem cell pool. Quiescent LSCs are known to be resistant to tyrosine kinase inhibitors (TKIs), potentially through BCR::ABL-independent signaling pathways. We hypothesize that dysregulation of genes governing asymmetric division in LSCs contributes to disease progression, and that their expression pattern may serve as a prognostic marker during the chronic phase of CML. (2) Methods: Genes related to asymmetric cell division in the context of hematopoietic stem cells were extracted from the PubMed database with the keyword “asymmetric hematopoietic stem cell”. The collected relative gene set was tested on two independent bulk transcriptome cohorts and the results were confirmed by single-cell RNA sequencing. (3) Results: The expression of genes involved in asymmetric hematopoietic stem cell division was found to discriminate disease phases during CML progression in the two independent transcriptome cohorts. Concordance between cohorts was observed on asymmetric molecules downregulated during blast crisis (BC) as compared to the chronic phase (CP). This downregulation during the BC phase was confirmed at single-cell level for SELL, CD63, NUMB, HK2, and LAMP2 genes. Single-cell analysis during the CP found that CD63 is associated with a poor prognosis phenotype, with the opposite prediction revealed by HK2 and NUMB expression. The single-cell trajectory reconstitution analysis in CP samples showed CD63 regulation highlighting a trajectory cluster implicating HSPB1, PIM2, ANXA5, LAMTOR1, CFL1, CD52, RAD52, MEIS1, and PDIA3, known to be implicated in hematopoietic malignancies. (4) Conclusion: Regulation of CD63, a tetraspanin involved in the asymmetric division of hematopoietic stem cells, was found to be associated with poor prognosis during CML progression and could be a potential new therapeutic target. Full article

(This article belongs to the Special Issue Micro- and Nano-Technologies for Cell Analysis)

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17 pages, 5062 KiB

Open AccessArticle

DropDAE: Denosing Autoencoder with Contrastive Learning for Addressing Dropout Events in scRNA-seq Data

by Wanlin Juan, Kwang Woo Ahn, Yi-Guang Chen and Chien-Wei Lin

Bioengineering 2025, 12(8), 829; https://doi.org/10.3390/bioengineering12080829 (registering DOI) - 31 Jul 2025

Abstract

Single-cell RNA sequencing (scRNA-seq) has revolutionized molecular biology and genomics by enabling the profiling of individual cell types, providing insights into cellular heterogeneity. Deep learning methods have become popular in single cell analysis for tasks such as dimension reduction, cell clustering, and data [...] Read more.

Single-cell RNA sequencing (scRNA-seq) has revolutionized molecular biology and genomics by enabling the profiling of individual cell types, providing insights into cellular heterogeneity. Deep learning methods have become popular in single cell analysis for tasks such as dimension reduction, cell clustering, and data imputation. In this work, we introduce DropDAE, a denoising autoencoder (DAE) model enhanced with contrastive learning, to specifically address the dropout events in scRNA-seq data, where certain genes show very low or even zero expression levels due to technical limitations. DropDAE uses the architecture of a denoising autoencoder to recover the underlying data patterns while leveraging contrastive learning to enhance group separation. Our extensive evaluations across multiple simulation settings based on synthetic data and a real-world dataset demonstrate that DropDAE not only reconstructs data effectively but also further improves clustering performance, outperforming existing methods in terms of accuracy and robustness. Full article

(This article belongs to the Special Issue From Biostatistics to Machine Learning: Modern Approaches in Bioengineering for Modeling and Understanding Biological Complexity)

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17 pages, 920 KiB

Open AccessArticle

Enhancing Early GI Disease Detection with Spectral Visualization and Deep Learning

by Tsung-Jung Tsai, Kun-Hua Lee, Chu-Kuang Chou, Riya Karmakar, Arvind Mukundan, Tsung-Hsien Chen, Devansh Gupta, Gargi Ghosh, Tao-Yuan Liu and Hsiang-Chen Wang

Bioengineering 2025, 12(8), 828; https://doi.org/10.3390/bioengineering12080828 - 30 Jul 2025

Abstract

Timely and accurate diagnosis of gastrointestinal diseases (GIDs) remains a critical bottleneck in clinical endoscopy, particularly due to the limited contrast and sensitivity of conventional white light imaging (WLI) in detecting early-stage mucosal abnormalities. To overcome this, this research presents Spectrum Aided Vision [...] Read more.

Timely and accurate diagnosis of gastrointestinal diseases (GIDs) remains a critical bottleneck in clinical endoscopy, particularly due to the limited contrast and sensitivity of conventional white light imaging (WLI) in detecting early-stage mucosal abnormalities. To overcome this, this research presents Spectrum Aided Vision Enhancer (SAVE), an innovative, software-driven framework that transforms standard WLI into high-fidelity hyperspectral imaging (HSI) and simulated narrow-band imaging (NBI) without any hardware modification. SAVE leverages advanced spectral reconstruction techniques, including Macbeth Color Checker-based calibration, principal component analysis (PCA), and multivariate polynomial regression, achieving a root mean square error (RMSE) of 0.056 and structural similarity index (SSIM) exceeding 90%. Trained and validated on the Kvasir v2 dataset (n = 6490) using deep learning models like ResNet-50, ResNet-101, EfficientNet-B2, both EfficientNet-B5 and EfficientNetV2-B0 were used to assess diagnostic performance across six key GI conditions. Results demonstrated that SAVE enhanced imagery and consistently outperformed raw WLI across precision, recall, and F1-score metrics, with EfficientNet-B2 and EfficientNetV2-B0 achieving the highest classification accuracy. Notably, this performance gain was achieved without the need for specialized imaging hardware. These findings highlight SAVE as a transformative solution for augmenting GI diagnostics, with the potential to significantly improve early detection, streamline clinical workflows, and broaden access to advanced imaging especially in resource constrained settings. Full article

(This article belongs to the Special Issue Machine Learning, Artificial Intelligence and Medicine: The Interface of Medicine, Computer Science and Engineering)

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28 pages, 2379 KiB

Open AccessArticle

FADEL: Ensemble Learning Enhanced by Feature Augmentation and Discretization

by Chuan-Sheng Hung, Chun-Hung Richard Lin, Shi-Huang Chen, You-Cheng Zheng, Cheng-Han Yu, Cheng-Wei Hung, Ting-Hsin Huang and Jui-Hsiu Tsai

Bioengineering 2025, 12(8), 827; https://doi.org/10.3390/bioengineering12080827 - 30 Jul 2025

Abstract

In recent years, data augmentation techniques have become the predominant approach for addressing highly imbalanced classification problems in machine learning. Algorithms such as the Synthetic Minority Over-sampling Technique (SMOTE) and Conditional Tabular Generative Adversarial Network (CTGAN) have proven effective in synthesizing minority class [...] Read more.

In recent years, data augmentation techniques have become the predominant approach for addressing highly imbalanced classification problems in machine learning. Algorithms such as the Synthetic Minority Over-sampling Technique (SMOTE) and Conditional Tabular Generative Adversarial Network (CTGAN) have proven effective in synthesizing minority class samples. However, these methods often introduce distributional bias and noise, potentially leading to model overfitting, reduced predictive performance, increased computational costs, and elevated cybersecurity risks. To overcome these limitations, we propose a novel architecture, FADEL, which integrates feature-type awareness with a supervised discretization strategy. FADEL introduces a unique feature augmentation ensemble framework that preserves the original data distribution by concurrently processing continuous and discretized features. It dynamically routes these feature sets to their most compatible base models, thereby improving minority class recognition without the need for data-level balancing or augmentation techniques. Experimental results demonstrate that FADEL, solely leveraging feature augmentation without any data augmentation, achieves a recall of 90.8% and a G-mean of 94.5% on the internal test set from Kaohsiung Chang Gung Memorial Hospital in Taiwan. On the external validation set from Kaohsiung Medical University Chung-Ho Memorial Hospital, it maintains a recall of 91.9% and a G-mean of 86.7%. These results outperform conventional ensemble methods trained on CTGAN-balanced datasets, confirming the superior stability, computational efficiency, and cross-institutional generalizability of the FADEL architecture. Altogether, FADEL uses feature augmentation to offer a robust and practical solution to extreme class imbalance, outperforming mainstream data augmentation-based approaches. Full article

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

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Graphical abstract

23 pages, 4319 KiB

Open AccessArticle

Four-Week Exoskeleton Gait Training on Balance and Mobility in Minimally Impaired Individuals with Multiple Sclerosis: A Pilot Study

by Micaela Schmid, Stefania Sozzi, Bruna Maria Vittoria Guerra, Caterina Cavallo, Matteo Vandoni, Alessandro Marco De Nunzio and Stefano Ramat

Bioengineering 2025, 12(8), 826; https://doi.org/10.3390/bioengineering12080826 (registering DOI) - 30 Jul 2025

Abstract

Multiple Sclerosis (MS) is a chronic neurological disorder affecting the central nervous system that significantly impairs postural control and functional abilities. Robotic-assisted gait training mitigates this functional deterioration. This preliminary study aims to investigate the effects of a four-week gait training with the [...] Read more.

Multiple Sclerosis (MS) is a chronic neurological disorder affecting the central nervous system that significantly impairs postural control and functional abilities. Robotic-assisted gait training mitigates this functional deterioration. This preliminary study aims to investigate the effects of a four-week gait training with the ExoAtlet II exoskeleton on static balance control and functional mobility in five individuals with MS (Expanded Disability Status Scale ≤ 2.5). Before and after the training, they were assessed in quiet standing under Eyes Open (EO) and Eyes Closed (EC) conditions and with the Timed Up and Go (TUG) test. Center of Pressure (CoP) Sway Area, Antero–Posterior (AP) and Medio–Lateral (ML) CoP displacement, Stay Time, and Total Instability Duration were computed. TUG test Total Duration, sit-to-stand, stand-to-sit, and linear walking phase duration were analyzed. To establish target reference values for rehabilitation advancement, the same evaluations were performed on a matched healthy cohort. After the training, an improvement in static balance with EO was observed towards HS values (reduced Sway Area, AP and ML CoP displacement, and Total Instability Duration and increased Stay Time). Enhancements under EC condition were less marked. TUG test performance improved, particularly in the stand-to-sit phase. These preliminary findings suggest functional benefits of exoskeleton gait training for individuals with MS. Full article

(This article belongs to the Special Issue Advances in Physical Therapy and Rehabilitation)

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19 pages, 4756 KiB

Open AccessArticle

Quasi-3D Mechanistic Model for Predicting Eye Drop Distribution in the Human Tear Film

by Harsha T. Garimella, Carly Norris, Carrie German, Andrzej Przekwas, Ross Walenga, Andrew Babiskin and Ming-Liang Tan

Bioengineering 2025, 12(8), 825; https://doi.org/10.3390/bioengineering12080825 (registering DOI) - 30 Jul 2025

Abstract

Topical drug administration is a common method of delivering medications to the eye to treat various ocular conditions, including glaucoma, dry eye, and inflammation. Drug efficacy following topical administration, including the drug’s distribution within the eye, absorption and elimination rates, and physiological responses [...] Read more.

Topical drug administration is a common method of delivering medications to the eye to treat various ocular conditions, including glaucoma, dry eye, and inflammation. Drug efficacy following topical administration, including the drug’s distribution within the eye, absorption and elimination rates, and physiological responses can be predicted using physiologically based pharmacokinetic (PBPK) modeling. High-resolution computational models of the eye are desirable to improve simulations of drug delivery; however, these approaches can have long run times. In this study, a fast-running computational quasi-3D (Q3D) model of the human tear film was developed to account for absorption, blinking, drainage, and evaporation. Visualization of blinking mechanics and flow distributions throughout the tear film were enabled using this Q3D approach. Average drug absorption throughout the tear film subregions was quantified using a high-resolution compartment model based on a system of ordinary differential equations (ODEs). Simulations were validated by comparing them with experimental data from topical administration of 0.1% dexamethasone suspension in the tear film (R² = 0.76, RMSE = 8.7, AARD = 28.8%). Overall, the Q3D tear film model accounts for critical mechanistic factors (e.g., blinking and drainage) not previously included in fast-running models. Further, this work demonstrated methods toward improved computational efficiency, where central processing unit (CPU) time was decreased while maintaining accuracy. Building upon this work, this Q3D approach applied to the tear film will allow for more seamless integration into full-body models, which will be an extremely valuable tool in the development of treatments for ocular conditions. Full article

(This article belongs to the Special Issue The Power of Models and Simulation Tools in Biomedical and Biochemical Engineering)

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16 pages, 2242 KiB

Open AccessArticle

Comparative Effectiveness of Tunneling vs. Coronally Advanced Flap Techniques for Root Coverage: A 6–12-Month Randomized Clinical Trial

by Luis Chauca-Bajaña, Pedro Samuel Vásquez González, María José Alban Guijarro, Carlos Andrés Guim Martínez, Byron Velásquez Ron, Patricio Proaño Yela, Alejandro Ismael Lorenzo-Pouso, Alba Pérez-Jardón and Andrea Ordoñez Balladares

Bioengineering 2025, 12(8), 824; https://doi.org/10.3390/bioengineering12080824 - 30 Jul 2025

Abstract

Background: Gingival recession is a common condition involving apical displacement of the gingival margin, leading to root surface exposure and associated complications such as dentin hypersensitivity and root caries. Among the most effective treatment options are the tunneling technique (TUN) and the coronally [...] Read more.

Background: Gingival recession is a common condition involving apical displacement of the gingival margin, leading to root surface exposure and associated complications such as dentin hypersensitivity and root caries. Among the most effective treatment options are the tunneling technique (TUN) and the coronally advanced flap (CAF), both combined with connective tissue grafts (CTGs). This study aimed to evaluate and compare the clinical outcomes of TUN + CTG and CAF + CTG in terms of root coverage and keratinized tissue width (KTW) over a 6–12-month follow-up. Methods: A randomized, double-blind clinical trial was conducted following CONSORT guidelines (ClinicalTrials.gov ID: NCT06228534). Participants were randomly assigned to receive either TUN + CTG or CAF + CTG. Clinical parameters, including gingival recession depth (REC) and KTW, were assessed at baseline as well as 6 months and 12 months postoperatively using a calibrated periodontal probe. Statistical analysis was performed using descriptive statistics and linear mixed models to compare outcomes over time, with a significance level set at 5%. Results: Both techniques demonstrated significant clinical improvements. At 6 months, mean root coverage was 100% in CAF + CTG cases and 97% in TUN + CTG cases, while complete root coverage (REC = 0) was observed in 100% and 89% of cases, respectively. At 12 months, root coverage remained stable, at 99% in the CAF + CTG group and 97% in the TUN + CTG group. KTW increased in both groups, with higher values observed in the CAF + CTG group (3.53 mm vs. 3.11 mm in TUN + CTG at 12 months). No significant postoperative complications were reported. Conclusions: Both TUN + CTG and CAF + CTG are safe and effective techniques for treating RT1 and RT2 gingival recession, offering high percentages of root coverage and increased KTW. While CAF + CTG achieved slightly superior coverage and tissue gain, the TUN was associated with better aesthetic outcomes and faster recovery, making it a valuable alternative in clinical practice. Full article

(This article belongs to the Special Issue Biomaterials and Technology for Oral and Dental Health)

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11 pages, 737 KiB

Open AccessArticle

Generation of an In Vitro Cartilage Aging Model Using Human Sera from Old Donors

by Sophie Hines, Meagan J. Makarczyk, Joseph Garzia and Hang Lin

Bioengineering 2025, 12(8), 823; https://doi.org/10.3390/bioengineering12080823 - 30 Jul 2025

Abstract

Cartilage degradation is a key feature of osteoarthritis (OA), a joint disease that significantly impacts the quality of life of the elderly population. While advanced age is recognized as one of the major risk factors for OA, the underlying mechanisms are not fully [...] Read more.

Cartilage degradation is a key feature of osteoarthritis (OA), a joint disease that significantly impacts the quality of life of the elderly population. While advanced age is recognized as one of the major risk factors for OA, the underlying mechanisms are not fully understood. Research involving cartilage from aged animals has improved our understanding of the changes associated with aging. However, studies with aged animals can be time-consuming and costly. In this study, we investigate the use of human sera from older donors as a stressor to induce aging-like changes in cultured human chondrocytes. First, we assess the expression levels of markers related to chondrogenesis, hypertrophy, fibrosis, and inflammation in human chondrocytes treated with sera from younger or older human donors. Next, we evaluate the regenerative potential of these sera-treated chondrocytes by stimulating them with the anabolic factor transforming growth factor (TGF)-β3. The results show that treatment with sera from older donors induced an aging-like phenotype in chondrocytes and impaired their ability to generate new cartilage. These findings provide insight into the role of systemic factors (serum) in cartilage aging and offer a novel in vitro model for studying age-related changes in chondrocytes. Full article

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

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16 pages, 2078 KiB

Open AccessArticle

Optimizing Yeast Surface-Displayed Unspecific Peroxygenase Production for Sustainable Biocatalysis

by Niklas Teetz, Luc Zuhse and Dirk Holtmann

Bioengineering 2025, 12(8), 822; https://doi.org/10.3390/bioengineering12080822 - 30 Jul 2025

Abstract

Unspecific peroxygenases (UPOs) are promising biocatalysts for oxyfunctionalizations in future sustainable economies and can be efficiently immobilized on the cell surface of their heterologous production yeast. This immobilization has versatile uses, ranging from the mL to m³ scale; but the production of [...] Read more.

Unspecific peroxygenases (UPOs) are promising biocatalysts for oxyfunctionalizations in future sustainable economies and can be efficiently immobilized on the cell surface of their heterologous production yeast. This immobilization has versatile uses, ranging from the mL to m³ scale; but the production of the yeast surface displayed UPOs, and their handling has yet to be optimized to advance sustainable industrial processes in light of the UN’s sustainable development goals. Here, we present optimized production protocols for surface-displayed UPOs for shaken and stirred systems in different scales and describe suitable storage conditions and a sterilization method. We utilized one-factor-at-a-time and design of experiments approaches. We were able to streamline published protocols for shaken flask cultivations to achieve a 60% increase in volumetric activity, using reduced amounts of media. We also show at least a doubling of final activity for bioreactor cultivations by utilizing a different medium than the industry standard. Finally, we present a novel, robust protocol for parallelized methanol-induced enzyme production in Komagataella phaffii in a BioLector XT^® reactor. Enzyme activity did not decrease and even increased by our recommended sterilization method and during storage over 87 days. This study aims to advance the yeast surface display immobilization method by providing methods for efficient production, storage and utilization of this promising biocatalyst. Full article

(This article belongs to the Special Issue Contribution of Bioprocess Engineering to the Achievement of the UN Sustainable Development Goals (SDGs))

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10 pages, 3057 KiB

Open AccessArticle

A Retrospective Time and Value Analysis of Surgical Oncology Cases Using 3D Printing: A Comprehensive Cancer Center Experience

by Sujaya H. Rao, James Harris, Lumarie Santiago, Paige D. Brown, Justin Bird and Karthik Tappa

Bioengineering 2025, 12(8), 821; https://doi.org/10.3390/bioengineering12080821 - 30 Jul 2025

Abstract

Introduction: The use of 3D-printed models in surgical planning has gained traction in light of its potential to improve precision and patient outcomes. The objective of this study was to review data and provide a time and value analysis of the use of [...] Read more.

Introduction: The use of 3D-printed models in surgical planning has gained traction in light of its potential to improve precision and patient outcomes. The objective of this study was to review data and provide a time and value analysis of the use of 3D printing at a National Cancer Institute (NCI)-designated comprehensive cancer center. The estimated time of surgical procedures for surgical planning was compared with the time required for procedures that did not use 3D printing. Providers who used 3D printing completed surveys, and then the results of said surveys were analyzed to assess the value of 3D printing. Materials and Methods: Electronic health records were reviewed for patients who underwent hemipelvectomies with and without 3D printing. A list of 20 observations involving 3D printing was used as a baseline sample and matched with another 20 observations that did not utilize 3D printing. Electronic health records were reviewed to obtain mean estimates of the procedure time. The data was collected and analyzed between January 2018 and April 2025. Results: The mean surgery time for procedures using 3D printing was 868 min, compared to 993 min for procedures that did not utilize 3D printing. In contrast, the median procedure times were 907.5 min for procedures using 3D printing and 945.0 min for those that did not utilize 3D printing. Most providers (85.7%) felt that using 3D-printed models or guides was important. Similarly, 80% responded that using a 3D-printed model or guide saved them time, and another 73.3% responded that after using the 3D-printed model, they were confident in their treatment plan. Conclusions: Using 3D printing for surgical cases at the comprehensive cancer center saved procedure time and added value for the surgeons. Full article

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

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20 pages, 1125 KiB

Open AccessReview

Brain-Computer Interfaces for Stroke Motor Rehabilitation

by Alessandro Tonin, Marianna Semprini, Pawel Kiper and Dante Mantini

Bioengineering 2025, 12(8), 820; https://doi.org/10.3390/bioengineering12080820 - 30 Jul 2025

Abstract

Brain–computer interface (BCI) technology holds promise for improving motor rehabilitation in stroke patients. This review explores the immediate and long-term effects of BCI training, shedding light on the potential benefits and challenges. Clinical studies have demonstrated that BCIs yield significant immediate improvements in [...] Read more.

Brain–computer interface (BCI) technology holds promise for improving motor rehabilitation in stroke patients. This review explores the immediate and long-term effects of BCI training, shedding light on the potential benefits and challenges. Clinical studies have demonstrated that BCIs yield significant immediate improvements in motor functions following stroke. Patients can engage in BCI training safely, making it a viable option for rehabilitation. Evidence from single-group studies consistently supports the effectiveness of BCIs in enhancing patients’ performance. Despite these promising findings, the evidence regarding long-term effects remains less robust. Further studies are needed to determine whether BCI-induced changes are permanent or only last for short durations. While evaluating the outcomes of BCI, one must consider that different BCI training protocols may influence functional recovery. The characteristics of some of the paradigms that we discuss are motor imagery-based BCIs, movement-attempt-based BCIs, and brain-rhythm-based BCIs. Finally, we examine studies suggesting that integrating BCIs with other devices, such as those used for functional electrical stimulation, has the potential to enhance recovery outcomes. We conclude that, while BCIs offer immediate benefits for stroke rehabilitation, addressing long-term effects and optimizing clinical implementation remain critical areas for further investigation. Full article

(This article belongs to the Special Issue Recent Trends and Future Directions in Biosignal Processing and Applications: State-of-the-Art Reviews)

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20 pages, 6254 KiB

Open AccessArticle

Two-Dimensional Latent Space Manifold of Brain Connectomes Across the Spectrum of Clinical Cognitive Decline

by Güneş Bayır, Demet Yüksel Dal, Emre Harı, Ulaş Ay, Hakan Gurvit, Alkan Kabakçıoğlu and Burak Acar

Bioengineering 2025, 12(8), 819; https://doi.org/10.3390/bioengineering12080819 - 29 Jul 2025

Abstract

Alzheimer’s Disease and Dementia (ADD) progresses along a continuum of cognitive decline, typically from Subjective Cognitive Impairment (SCI) to Mild Cognitive Impairment (MCI) and eventually to dementia. While many studies have focused on classifying these clinical stages, fewer have examined whether brain connectomes [...] Read more.

Alzheimer’s Disease and Dementia (ADD) progresses along a continuum of cognitive decline, typically from Subjective Cognitive Impairment (SCI) to Mild Cognitive Impairment (MCI) and eventually to dementia. While many studies have focused on classifying these clinical stages, fewer have examined whether brain connectomes encode this continuum in a low-dimensional, interpretable form. Motivated by the hypothesis that structural brain connectomes undergo complex yet compact changes across cognitive decline, we propose a Graph Neural Network (GNN)-based framework that embeds these connectomes into a two-dimensional manifold to capture the evolving patterns of structural connectivity associated with cognitive deterioration. Using attention-based graph aggregation and Principal Component Analysis (PCA), we find that MCI subjects consistently occupy an intermediate position between SCI and ADD, and that the observed transitions align with known clinical biomarkers of ADD pathology. This hypothesis-driven analysis is further supported by the model’s robust separation performance, with ROC-AUC scores of 0.93 for ADD vs. SCI and 0.81 for ADD vs. MCI. These findings offer an interpretable and neurologically grounded representation of dementia progression, emphasizing structural connectome alterations as potential markers of cognitive decline. Full article

(This article belongs to the Section Biosignal Processing)

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