Bioengineering

47 pages, 2115 KiB

Open AccessReview

Integrating Artificial Intelligence and Precision Therapeutics for Advancing the Diagnosis and Treatment of Age-Related Macular Degeneration

by Mini Han Wang

Bioengineering 2025, 12(5), 548; https://doi.org/10.3390/bioengineering12050548 - 20 May 2025

Abstract

Age-related macular degeneration (AMD) is a multifactorial retinal disease influenced by complex molecular mechanisms, including genetic susceptibility, inflammation, oxidative stress, and metabolic dysregulation. While substantial progress has been made in understanding its pathogenesis, the full molecular underpinnings of AMD remain unclear, impeding the [...] Read more.

Age-related macular degeneration (AMD) is a multifactorial retinal disease influenced by complex molecular mechanisms, including genetic susceptibility, inflammation, oxidative stress, and metabolic dysregulation. While substantial progress has been made in understanding its pathogenesis, the full molecular underpinnings of AMD remain unclear, impeding the effectiveness of current therapeutic strategies. This study provides an in-depth exploration of the molecular interactions involved in AMD progression, particularly focusing on genetic predispositions (such as CFH, ARMS2/HTRA1, and APOE), inflammatory pathways (including complement system dysregulation and cytokine responses), lipid metabolism (e.g., cholesterol homeostasis and drusen formation), and angiogenesis (VEGF signaling). Through a systematic review and bibliometric analysis of literature published between 2015 and 2025, the study identifies emerging research trends, existing gaps, and promising future therapeutic directions. It further investigates innovative precision medicine approaches, including gene editing (CRISPR), RNA therapeutics (siRNA, antisense oligonucleotides), immunomodulatory therapies, and nanotechnology-based drug delivery systems. Additionally, the study examines the role of metabolic disorders such as diabetes and dyslipidemia in AMD progression, highlighting the influence of systemic health factors on disease onset. Finally, the potential of artificial intelligence (AI) in enhancing AMD management through biomarker-based risk stratification, predictive modeling, and personalized treatment optimization is assessed. By mapping the intricate molecular networks underlying AMD and evaluating novel therapeutic strategies, this research aims to contribute to the development of more effective, individualized treatment protocols for patients with AMD. Full article

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

37 pages, 6055 KiB

Open AccessReview

Recycled Sericin Biopolymer in Biotechnology and Bioelectronics

by Davide Vurro, Aris Liboà, Ilenia D’Onofrio, Giuseppe De Giorgio, Zirong Zhou, Vardan Galstyan, Yajie Qin, Xiongchuan Huang, Pasquale D’Angelo and Giuseppe Tarabella

Bioengineering 2025, 12(5), 547; https://doi.org/10.3390/bioengineering12050547 - 20 May 2025

Abstract

In a world characterized by rapid industrialization and a growing population, plastic or polymeric waste handling has undergone significant transformations. Recycling has become a major strategy where silk sericin has great potential among recyclable polymers. This naturally occurring biopolymer is a sustainable and [...] Read more.

In a world characterized by rapid industrialization and a growing population, plastic or polymeric waste handling has undergone significant transformations. Recycling has become a major strategy where silk sericin has great potential among recyclable polymers. This naturally occurring biopolymer is a sustainable and versatile material with a wide range of potential uses in biotechnology and sensing. Furthermore, preparing and studying new environmentally friendly functional polymers with attractive physicochemical properties can open new opportunities for developing next-generation materials and composites. Herein, we provide an overview of the advances in the research studies of silk sericin as a functional and eco-friendly material, considering its biocompatibility and unique physicochemical properties. The structure of silk sericin and the extraction procedures, considering the influence of preparation methods on its properties, are described. Sericin’s intrinsic properties, including its ability to crosslink with other polymers, its antioxidative capacity, and its biocompatibility, render it a versatile material for multifunctional applications across diverse fields. In biotechnology, the ability to blend sericin with other polymers enables the preparation of materials with varied morphologies, such as films and scaffolds, exhibiting enhanced mechanical strength and anti-inflammatory effects. This combination proves particularly advantageous in tissue engineering and wound healing. Furthermore, the augmentation of mechanical strength, coupled with the incorporation of plasticizers, makes sericin films suitable for the development of epidermal electrodes. Simultaneously, by precisely controlling hydration and permeability, the same material can be tailored for applications in packaging and the food industry. This work highlights the multidisciplinary and multifunctional nature of sericin, emphasizing its broad applicability. Full article

(This article belongs to the Special Issue Engineering Biodegradable-Implant Materials, 2nd Edition)

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21 pages, 4985 KiB

Open AccessArticle

Simulation of a Custom-Made Temporomandibular Joint—An Academic View on an Industrial Workflow

by Annchristin Andres, Kerstin Wickert, Elena Gneiting, Franziska Binmoeller, Stefan Diebels and Michael Roland

Bioengineering 2025, 12(5), 545; https://doi.org/10.3390/bioengineering12050545 - 20 May 2025

Abstract

Temporomandibular joint replacement is a critical intervention for severe temporomandibular joint disorders, enhancing pain levels, jaw function and overall quality of life. In this study, we compare two finite element method-based simulation workflows from both academic and industrial perspectives, focusing on a patient-specific [...] Read more.

Temporomandibular joint replacement is a critical intervention for severe temporomandibular joint disorders, enhancing pain levels, jaw function and overall quality of life. In this study, we compare two finite element method-based simulation workflows from both academic and industrial perspectives, focusing on a patient-specific case involving a custom-made temporomandibular joint prosthesis. Using computed tomography data and computer-aided design data, we generated different 3D models and performed mechanical testing, including wear and static compression tests. Our results indicate that the academic workflow, which is retrospective, purely image-based and applied post-operatively, produced peak stress values within 9–20% of those obtained from the industrial workflow. The industrial workflow is prospective, pre-operative, computer-aided design-based and guided by stringent regulatory standards and approval protocols. Observed differences between workflows were attributed primarily to distinct modelling assumptions, simplifications and constraints inherent in each method. To explicitly quantify these differences, multiple additional models were generated within the academic workflow using partial data from the industrial process, revealing specific sources of variation in stress distribution and implant performance. The findings underscore the potential of patient-specific simulations not only to refine temporomandibular joint prosthesis design and enhance patient outcomes, but also to highlight the interplay between academic research methodologies and industrial standards in the development of medical devices. Full article

(This article belongs to the Special Issue Advances in Additive Manufacturing Technologies in the Clinical, Pharmaceutical and Biomedical Sectors)

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17 pages, 1910 KiB

Open AccessArticle

The Characterization of Serum-Free Media on Human Mesenchymal Stem Cell Fibrochondrogenesis

by Ka Yu Carissa Kwan, Ke Li, Yu Yang Wang, Wai Yi Tse, Chung Yan Tong, Xu Zhang, Dan Michelle Wang and Dai Fei Elmer Ker

Bioengineering 2025, 12(5), 546; https://doi.org/10.3390/bioengineering12050546 - 19 May 2025

Abstract

Developing fibrochondrogenic serum-free media is important for regenerating diseased and injured fibrocartilage but no defined protocols exist. Towards this goal, we characterized the effect of four candidate fibrochondrogenic serum-free media containing transforming growth factor beta-3 (TGF-β3), insulin-like growth factor-1 (IGF-1), and fibroblast growth [...] Read more.

Developing fibrochondrogenic serum-free media is important for regenerating diseased and injured fibrocartilage but no defined protocols exist. Towards this goal, we characterized the effect of four candidate fibrochondrogenic serum-free media containing transforming growth factor beta-3 (TGF-β3), insulin-like growth factor-1 (IGF-1), and fibroblast growth factor-2 (FGF-2) with high/low glucose and with/without dexamethasone on human mesenchymal stem cells (hMSCs) via proliferation and differentiation assays. In Ki67 proliferation assays, serum-free media containing low glucose and dexamethasone exhibited the highest growth. In gene expression assays, serum-free media containing low glucose and commercially available chondrogenic media (COM) induced high fibrochondrogenic transcription factor expression (scleraxis/SCX and SRY-Box Transcription Factor 9/SOX9) and extracellular matrix (ECM) protein levels (aggrecan/ACAN, collagen type I/COL1A1, and collagen type II/COL2A1), respectively. In immunofluorescence staining, serum-free media containing high glucose and COM induced high fibrochondrogenic transcription factor (SCX and SOX9) and ECM protein (COL1A1, COL2A1, and collagen type X/COL10A1) levels, respectively. In cytochemical staining, COM and serum-free media containing dexamethasone showed a high collagen content whereas serum-free media containing high glucose and dexamethasone exhibited high glycosaminoglycan (GAG) levels. Altogether, defined serum-free media containing high glucose exhibited the highest fibrochondrogenic potential. In summary, this work studied conditions conducive for fibrochondrogenesis, which may be further optimized for potential applications in fibrocartilage tissue engineering. Full article

(This article belongs to the Special Issue Tendon/Ligament and Enthesis Injuries: Repair and Regeneration)

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15 pages, 1129 KiB

Open AccessArticle

A Comparison of Machine Learning-Based Models and a Simple Clinical Bedside Tool to Predict Morbidity and Mortality After Gastrointestinal Cancer Surgery in the Elderly

by Barbara Frezza, Mario Cesare Nurchis, Gabriella Teresa Capolupo, Filippo Carannante, Marco De Prizio, Fabio Rondelli, Danilo Alunni Fegatelli, Alessio Gili, Luca Lepre and Gianluca Costa

Bioengineering 2025, 12(5), 544; https://doi.org/10.3390/bioengineering12050544 - 19 May 2025

Abstract

Frailty in the elderly population is associated with increased vulnerability to stressors, including surgical interventions. This study compared machine learning (ML) models with a clinical bedside tool, the Gastrointestinal Surgery Frailty Index (GiS-FI), for predicting mortality and morbidity in elderly patients undergoing gastrointestinal [...] Read more.

Frailty in the elderly population is associated with increased vulnerability to stressors, including surgical interventions. This study compared machine learning (ML) models with a clinical bedside tool, the Gastrointestinal Surgery Frailty Index (GiS-FI), for predicting mortality and morbidity in elderly patients undergoing gastrointestinal cancer surgery. In a multicenter analysis of 937 patients aged ≥65 years, the performance of various predictive models including Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), Stepwise Regression, K-Nearest Neighbors, Neural Network, and Support Vector Machine algorithms were evaluated. The overall 30-day mortality and morbidity rates were 6.1% and 35.7%, respectively. For mortality prediction, the RF model demonstrated superior performance with an AUC of 0.822 (95% CI 0.714–0.931), outperforming the GiS-FI score (AUC = 0.772, 95% CI 0.675–0.868). For morbidity prediction, all models showed more modest discrimination, with stepwise regression and LASSO regression achieving the highest performance (AUCs of 0.652 and 0.647, respectively). Our findings suggest that ML approaches, particularly RF algorithm, offer enhanced predictive accuracy compared to traditional clinical scores for mortality risk assessment in elderly cancer patients undergoing gastrointestinal surgery. These advanced analytical tools could provide valuable decision support for surgical risk stratification in this vulnerable population. Full article

(This article belongs to the Special Issue Medical Artificial Intelligence and Data Analysis)

14 pages, 1208 KiB

Open AccessReview

Zirconia in Dental Implantology: A Review of the Literature with Recent Updates

by Sami Aldhuwayhi

Bioengineering 2025, 12(5), 543; https://doi.org/10.3390/bioengineering12050543 - 19 May 2025

Abstract

Zirconia dental implants have emerged as a transformative material in implantology, offering a biocompatible, esthetic, and durable alternative to traditional titanium implants. This comprehensive review explores the key properties of zirconia, including high fracture resistance, esthetic superiority, and low bacterial affinity. The ability [...] Read more.

Zirconia dental implants have emerged as a transformative material in implantology, offering a biocompatible, esthetic, and durable alternative to traditional titanium implants. This comprehensive review explores the key properties of zirconia, including high fracture resistance, esthetic superiority, and low bacterial affinity. The ability of zirconia to integrate with bone through osseointegration, coupled with its resistance to plaque and inflammation, results in a product that is particularly suitable for patients with metal sensitivities or high esthetic demands. However, challenges such as brittleness and complex manufacturing processes persist. Advances in surface modification techniques and material optimization are poised to address these limitations, paving the way for broader applications. The purpose of this descriptive review was to emphasize the mechanical, antibacterial, osteointegration and survival rates of zirconia implants. This paper also summarizes findings from recent empirical studies, highlighting zirconia’s clinical performance, biological responses, and future potential as a mainstream implant material. Full article

(This article belongs to the Special Issue Translational Advances in Dental Implants)

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18 pages, 4189 KiB

Open AccessArticle

Exhale-Focused Thermal Image Segmentation Using Optical Flow-Based Frame Filtering and Transformer-Aided Deep Networks

by Do-Kyeong Lee, Jae-Sung Shin, Jae-Sung Choi, Min-Hyung Choi and Min Hong

Bioengineering 2025, 12(5), 542; https://doi.org/10.3390/bioengineering12050542 - 18 May 2025

Abstract

Since the COVID-19 pandemic, interest in non-contact diagnostic technologies has grown, leading to increased research into remote biosignal monitoring. The respiratory rate, widely used in previous studies, offers limited insight into pulmonary volume. To redress this, we propose a thermal imaging-based framework for [...] Read more.

Since the COVID-19 pandemic, interest in non-contact diagnostic technologies has grown, leading to increased research into remote biosignal monitoring. The respiratory rate, widely used in previous studies, offers limited insight into pulmonary volume. To redress this, we propose a thermal imaging-based framework for respiratory segmentation aimed at estimating non-invasive pulmonary function. The proposed method uses an optical flow magnitude-based thresholding technique to automatically extract exhalation frames and segment them into frame sequences. A TransUNet-based network, combining a Convolutional Neural Network (CNN) encoder–decoder architecture with a Transformer module in the bottleneck, is trained on these sequences. The model’s Accuracy, Precision, Recall, IoU, Dice, and F1-score were 0.9832, 0.9833, 0.9830, 0.9651, 0.9822, and 0.9831, respectively, which results demonstrate high segmentation performance. The method enables the respiratory volume to be estimated by detecting exhalation behavior, suggesting its potential as a non-contact tool to monitor pulmonary function and estimate lung volume. Furthermore, research on thermal imaging-based respiratory volume analysis remains limited. This study expands upon conventional respiratory rate-based approaches to provide a new direction for respiratory analysis using vision-based techniques. Full article

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

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18 pages, 6737 KiB

Open AccessArticle

An Evaluation Model for Brain Ischemia Protection in Mice by Low-Intensity Pulsed Ultrasound Stimulation Based on Functional Cortico-Muscular Coupling

by Ziqiang Jin, Xiaoling Chen, Zechuan Du, Yi Yuan, Xiaoli Li and Ping Xie

Bioengineering 2025, 12(5), 541; https://doi.org/10.3390/bioengineering12050541 - 17 May 2025

Abstract

(1) Background: Ischemic stroke is a major global public-health concern with complex pathogenesis. Current treatment strategies face challenges. Low-intensity pulsed ultrasound stimulation (LIPUS), a non-invasive neuromodulation technology, shows promise in treating ischemic stroke, yet its underlying mechanisms lack in-depth investigation, especially in quantitative [...] Read more.

(1) Background: Ischemic stroke is a major global public-health concern with complex pathogenesis. Current treatment strategies face challenges. Low-intensity pulsed ultrasound stimulation (LIPUS), a non-invasive neuromodulation technology, shows promise in treating ischemic stroke, yet its underlying mechanisms lack in-depth investigation, especially in quantitative efficacy evaluation. (2) Methods: This study aimed to develop a neuromuscular functional coupling-based dynamic time warping (DTW) model to evaluate LIPUS’s neuroprotective effects in a mouse model of ischemic stroke. A bilateral carotid artery occlusion (BCAO) model in mice was established, and LIPUS treatment was given. Time- and frequency-domain analyses of local field potentials (LFPs) and electromyography (EMG) were conducted, and outcomes were quantified using a percentage-based scoring system. (3) Results: The BCAO+LIPUS group scored significantly higher than the BCAO group. (4) Conclusions: This study demonstrated that LIPUS is neuroprotective in BCAO mice and that the DTW-100 assessment evaluation model can quantify the neuroprotective effects of LIPUS. Full article

(This article belongs to the Section Biosignal Processing)

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17 pages, 4328 KiB

Open AccessArticle

Modelling and Simulation of the Interactions Between the Cardiovascular System and the Combined Use of VA ECMO and IABP: Comparison Between Peripheral and Central Configurations

by Beatrice De Lazzari, Massimo Capoccia, Roberto Badagliacca, Marc O. Maybauer and Claudio De Lazzari

Bioengineering 2025, 12(5), 540; https://doi.org/10.3390/bioengineering12050540 - 17 May 2025

Abstract

Veno-arterial extracorporeal membrane oxygenation (VA ECMO) for the management of refractory cardiogenic shock (CS) has been widely used in recent years. Increased left ventricular (LV) afterload induced by retrograde flow remains a limiting factor, which is particularly evident during peripheral VA ECMO support. [...] Read more.

Veno-arterial extracorporeal membrane oxygenation (VA ECMO) for the management of refractory cardiogenic shock (CS) has been widely used in recent years. Increased left ventricular (LV) afterload induced by retrograde flow remains a limiting factor, which is particularly evident during peripheral VA ECMO support. The concomitant use of the intra-aortic balloon pump (IABP) is an established strategy to achieve LV unloading during VA ECMO support. Nevertheless, there remains controversy about the combined use of IABP during central or peripheral VA ECMO in terms of beneficial effects and outcome. We developed a simulation setting to study left ventricular unloading with IABP during peripheral and central VA ECMO using CARDIOSIM©, an established software simulator of the cardiovascular system. The aim was to quantitatively evaluate potential differences between the two VA ECMO configurations and ascertain the true beneficial effects compared to VA ECMO alone. The combined use of central VA ECMO and IABP decreased left ventricular end systolic volume and left ventricular end diastolic volume by 5–10%; right ventricular end systolic volume and right ventricular end diastolic volume by 10–20%; left atrial end systolic volume and left atrial end diastolic volume by 5–10%. Up to 25% reduction in mean left atrial pressure, up to 15% reduction in pulmonary capillary wedge pressure and up to 25% reduction in mean pulmonary arterial pressure was observed. From an energetic point of view, left ventricular external work decreased by 10–15% whilst up to 40%vreduction in right ventricular external work was observed. The findings make central VA ECMO plus IABP the most appropriate combination for left and right ventricle unloading. Full article

(This article belongs to the Special Issue Numerical Simulation and AI in Biological Systems)

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16 pages, 3988 KiB

Open AccessArticle

An Arthroscopic Robotic System for Meniscoplasty with Autonomous Operation Ability

by Zijun Zhang, Yijun Zhao, Baoliang Zhao, Gang Yu, Peng Zhang, Qiong Wang and Xiaojun Yang

Bioengineering 2025, 12(5), 539; https://doi.org/10.3390/bioengineering12050539 - 17 May 2025

Abstract

Meniscoplasty is a common surgical procedure used to treat meniscus tears. During the operation, there are often key challenges such as a limited visual field, a narrow operating space, and difficulties in controlling the resection range. Therefore, this study developed an arthroscopic robotic [...] Read more.

Meniscoplasty is a common surgical procedure used to treat meniscus tears. During the operation, there are often key challenges such as a limited visual field, a narrow operating space, and difficulties in controlling the resection range. Therefore, this study developed an arthroscopic robotic system with the ability of autonomous meniscus resection to achieve better surgical outcomes. To address the issue of limited visual fields during the operation, this study used the preoperative and intraoperative meniscus point cloud images for surgical navigation and proposed a novel cross-modal point cloud registration framework. After the registration was completed, the robotic system automatically generated a resection path that could maintain the crescent shape of the remaining meniscus based on the improved Rapidly Exploring Random Tree (RRT) path-planning algorithm in this study. Meanwhile, the Remote Center of Motion (RCM) constraint was introduced during the movement of the robot to enhance safety. In this study, the mean squared error of the preoperative–intraoperative meniscus point cloud registration was only 0.1964 mm², which meets the surgical accuracy requirements. We conducted experiments to validate the autonomous operation capabilities of the robot. The robot successfully completed motion-planning and autonomous implementation, thus demonstrating the reliability of the robotic system. Full article

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

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19 pages, 876 KiB

Open AccessArticle

MRMS-CNNFormer: A Novel Framework for Predicting the Biochemical Recurrence of Prostate Cancer on Multi-Sequence MRI

by Tao Lian, Mengting Zhou, Yangyang Shao, Xiaqing Chen, Yinghua Zhao and Qianjin Feng

Bioengineering 2025, 12(5), 538; https://doi.org/10.3390/bioengineering12050538 - 16 May 2025

Abstract

Accurate preoperative prediction of biochemical recurrence (BCR) in prostate cancer (PCa) is essential for treatment optimization, and demands an explicit focus on tumor microenvironment (TME). To address this, we developed MRMS-CNNFormer, an innovative framework integrating 2D multi-region (intratumoral, peritumoral, and [...] Read more.

Accurate preoperative prediction of biochemical recurrence (BCR) in prostate cancer (PCa) is essential for treatment optimization, and demands an explicit focus on tumor microenvironment (TME). To address this, we developed MRMS-CNNFormer, an innovative framework integrating 2D multi-region (intratumoral, peritumoral, and periprostatic) and multi-sequence magnetic resonance imaging (MRI) images (T2-weighted imaging with fat suppression (T2WI-FS) and diffusion-weighted imaging (DWI)) with clinical characteristics. The framework utilizes a CNN-based encoder for imaging feature extraction, followed by a transformer-based encoder for multi-modal feature integration, and ultimately employs a fully connected (FC) layer for final BCR prediction. In this multi-center study (46 BCR-positive cases, 186 BCR-negative cases), patients from centers A and B were allocated to training (n = 146) and validation (n = 36) sets, while center C patients (n = 50) formed the external test set. The multi-region MRI-based model demonstrated superior performance (AUC, 0.825; 95% CI, 0.808–0.852) compared to single-region models. The integration of clinical data further enhanced the model’s predictive capability (AUC 0.835; 95% CI, 0.818–0.869), significantly outperforming the clinical model alone (AUC 0.612; 95% CI, 0.574–0.646). MRMS-CNNFormer provides a robust, non-invasive approach for BCR prediction, offering valuable insights for personalized treatment planning and clinical decision making in PCa management. Full article

(This article belongs to the Section Biosignal Processing)

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18 pages, 2934 KiB

Open AccessArticle

Stabilization of the Bio-Oil Organic Phase via Solvent-Assisted Hydrotreating, Part 1: Investigating the Influence of Various Solvents

by Manqoba Shezi, Manish Sakhakarmy, Sushil Adhikari and Sammy Lewis Kiambi

Bioengineering 2025, 12(5), 537; https://doi.org/10.3390/bioengineering12050537 - 16 May 2025

Abstract

Conventional mild hydrotreatment processes of bio-oil present significant challenges of a high degree of polymerization, a low oil yield, high coke formation, and poor catalyst recovery. To address these challenges, the current study looked into investigating and enhancing the properties of raw bio-oil [...] Read more.

Conventional mild hydrotreatment processes of bio-oil present significant challenges of a high degree of polymerization, a low oil yield, high coke formation, and poor catalyst recovery. To address these challenges, the current study looked into investigating and enhancing the properties of raw bio-oil organic phase samples via a solvent-assisted stabilization approach using methanol (METH), ethanol (ETH), isopropyl alcohol (IPA), and ethyl ether (DME). Solvents like methanol (METH) and ethanol (ETH), which are highly polar, yielded higher oil fractions (64% and 62%, respectively) compared to less polar solvents like ethyl ether (DME) at 59%. Isopropyl alcohol (IPA), with intermediate polarity, achieved a balanced oil yield of 63%, indicating its ability to dissolve both polar and non-polar components. Moisture reduction in stabilized bio-oils followed the order IPA > ETH > METH > DME, with IPA showing the highest reduction due to its structural characteristics facilitating dehydration. Viscosity reduction varied, with IPA > ETH > DME > METH. Carbon recovery in stabilized bio-oils ranged from 65% to 75% for DME, ETH, and METH and was 71% for IPA. The heating values of stabilized bio-oils ranged from 28 to 29 MJ/kg, with IPA-stabilized bio-oil showing the highest value (29.05 ± 0.06 MJ/kg). METH demonstrated high efficiency (74.8%) in stabilizing bio-oil, attributed to its strong hydrogen-donating capability. ETH followed closely at 69.5%, indicating its comparable performance in bio-oil stabilization. With moderate efficiency (69.3%), IPA presents a balanced alternative considering its molecular structure and hydrogen solubility. In contrast, DME exhibited lower efficiency (63.6%) due to its weaker hydrogenation capability and propensity for undesired side reactions. The current study suggests that subcritical conditions up to 200 °C are adequate for METH, ETH, and IPA in bio-oil stabilization, comparable to results obtained under supercritical conditions. Full article

(This article belongs to the Section Biochemical Engineering)

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14 pages, 3217 KiB

Open AccessArticle

Identification of Key Genes and Potential Therapeutic Targets in Sepsis-Associated Acute Kidney Injury Using Transformer and Machine Learning Approaches

by Zhendong Zhai, JunZhe Peng, Wenjun Zhong, Jun Tao, Yaqi Ao, Bailin Niu and Li Zhu

Bioengineering 2025, 12(5), 536; https://doi.org/10.3390/bioengineering12050536 - 16 May 2025

Abstract

Sepsis-associated acute kidney injury (SA-AKI) is a life-threatening complication of sepsis, characterized by high mortality and prolonged hospitalization. Early diagnosis and effective therapy remain difficult despite extensive investigation. To address this, we developed an AI-driven integrative framework that combines a Transformer-based deep learning [...] Read more.

Sepsis-associated acute kidney injury (SA-AKI) is a life-threatening complication of sepsis, characterized by high mortality and prolonged hospitalization. Early diagnosis and effective therapy remain difficult despite extensive investigation. To address this, we developed an AI-driven integrative framework that combines a Transformer-based deep learning model with established machine learning techniques (LASSO, SVM-RFE, Random Forest and neural networks) to uncover complex, nonlinear interactions among gene-expression biomarkers. Analysis of normalized microarray data from GEO (GSE95233 and GSE69063) identified differentially expressed genes (DEGs), and KEGG/GO enrichment via clusterProfiler revealed key pathways in immune response, protein synthesis, and antigen presentation. By integrating multiple transcriptomic cohorts, we pinpointed 617 SA-AKI-associated DEGs—21 of which overlapped between sepsis and AKI datasets. Our Transformer-based classifier ranked five genes (MYL12B, RPL10, PTBP1, PPIA, and TOMM7) as top diagnostic markers, with AUC values ranging from 0.9395 to 0.9996 (MYL12B yielding 0.9996). Drug–gene interaction mining using DGIdb (FDR < 0.05) nominated 19 candidate therapeutics for SA-AKI. Together, these findings demonstrate that melding deep learning with classical machine learning not only sharpens early SA-AKI detection but also systematically uncovers actionable drug targets, laying groundwork for precision intervention in critical care settings. Full article

(This article belongs to the Section Biosignal Processing)

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15 pages, 1804 KiB

Open AccessArticle

Neuromuscular Electrical Stimulation Enhances Lower Limb Muscle Synergies During Jumping in Martial Artists Post-Anterior Cruciate Ligament Reconstruction: A Randomized Crossover Trial

by Xiaoyan Wang, Haojie Li and Jiangang Chen

Bioengineering 2025, 12(5), 535; https://doi.org/10.3390/bioengineering12050535 - 16 May 2025

Abstract

Objective: This study aimed to investigate the effects of neuromuscular electrical stimulation (NMES) on lower limb muscle synergies during the single-leg hop test in martial artists after anterior cruciate ligament (ACL) reconstruction. Methods: Twenty-four martial artists who underwent ACL reconstruction were recruited and [...] Read more.

Objective: This study aimed to investigate the effects of neuromuscular electrical stimulation (NMES) on lower limb muscle synergies during the single-leg hop test in martial artists after anterior cruciate ligament (ACL) reconstruction. Methods: Twenty-four martial artists who underwent ACL reconstruction were recruited and performed a single-leg hop test under two conditions: with NMES (ES) and without NMES (CON). The ES condition involved using Compex SP 8.0 to deliver biphasic symmetrical wave stimulation. Jump performance metrics and electromyographic (EMG) signals were recorded. Muscle synergies of the lower limbs were extracted using non-negative matrix factorization (NMF) to analyze patterns of muscle coordination. Results: Compared with the CON condition, the ES condition significantly reduced the jump time (0.13 ± 0.05 vs. 0.18 ± 0.09; F = 5.660; p = 0.022) and significantly increased the contact time (0.53 ± 0.12 vs. 0.43 ± 0.21; F = 4.013; p = 0.049). Muscle synergy analysis revealed three distinct synergy patterns under both conditions. For synergy pattern 1, compared with the CON condition, the muscle weightings of the rectus femoris and tibialis anterior muscles were significantly increased under the ES condition (p < 0.001). For synergy pattern 2, compared with the CON condition, the muscle weighting of the lateral gastrocnemius muscle was significantly increased under the ES condition (p < 0.001). Additionally, the activation timing of synergy pattern 2 was significantly reduced under the ES condition (p = 0.001). Conclusion: Neuromuscular electrical stimulation enhances jump performance and alters muscle synergy patterns in martial artists after ACL reconstruction. The findings suggest that NMES can promote better lower limb muscle coordination during jumping tasks, potentially aiding in postoperative rehabilitation and performance optimization. Full article

(This article belongs to the Special Issue Electric, Magnetic, and Electromagnetic Fields in Biology and Medicine: From Mechanisms to Biomedical Applications: 3rd Edition)

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8 pages, 781 KiB

Open AccessArticle

Influence of Running Surface Differences on Physiological and Biomechanical Responses During Specific Sports Loading

by Zhiqiang Liang, Qi Shuo, Chuang Gao, Chang-Te Lin and Yufei Fang

Bioengineering 2025, 12(5), 534; https://doi.org/10.3390/bioengineering12050534 - 15 May 2025

Abstract

The surface properties of the running surface have an effect on physiological and biomechanical responses to exercise, but their influence on body composition, blood pressure, and knee joint kinetics during controlled sports loading is less researched. This study compared the effects of treadmill [...] Read more.

The surface properties of the running surface have an effect on physiological and biomechanical responses to exercise, but their influence on body composition, blood pressure, and knee joint kinetics during controlled sports loading is less researched. This study compared the effects of treadmill running (TR) and overground running (OR) on acute physiological and biomechanical adaptation in ten male athletes aged between 23 and 26 years old following a 30 min protocol at 75% VO₂max. Pre- and post-running body composition (fat volume, protein content, and fluid distribution), blood pressure, and knee joint kinetics (total work of muscle extensors—TWMEs) were assessed using bioelectrical impedance analysis, blood pressure monitor, and isokinetic dynamometry. The results indicated that TR led to highly significant reductions in protein content with a considerable accumulation of intracellular fluid. At the same time, TR reduced knee TWME by 27.4%, and OR elevated TWME by 5.6%. No significant differences in blood pressure were observed. These findings highlight surface-specific metabolic stress and biomechanical loading patterns and show that TR augments catabolic responses and knee joint strain despite equivalent external workloads. Full article

(This article belongs to the Special Issue Biomechanics of Sports Injuries)

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19 pages, 12723 KiB

Open AccessArticle

Automated Caries Detection Under Dental Restorations and Braces Using Deep Learning

by Yi-Cheng Mao, Yuan-Jin Lin, Jen-Peng Hu, Zi-Yu Liu, Shih-Lun Chen, Chiung-An Chen, Tsung-Yi Chen, Kuo-Chen Li, Liang-Hung Wang, Wei-Chen Tu and Patricia Angela R. Abu

Bioengineering 2025, 12(5), 533; https://doi.org/10.3390/bioengineering12050533 - 15 May 2025

Abstract

In the dentistry field, dental caries is a common issue affecting all age groups. The presence of dental braces and dental restoration makes the detection of caries more challenging. Traditionally, dentists rely on visual examinations to diagnose caries under restoration and dental braces, [...] Read more.

In the dentistry field, dental caries is a common issue affecting all age groups. The presence of dental braces and dental restoration makes the detection of caries more challenging. Traditionally, dentists rely on visual examinations to diagnose caries under restoration and dental braces, which can be prone to errors and are time-consuming. This study proposes an innovative deep learning and image processing-based approach for automated caries detection under restoration and dental braces, aiming to reduce the clinical burden on dental practitioners. The contributions of this research are summarized as follows: (1) YOLOv8 was employed to detect individual teeth in bitewing radiographs, and a rotation-aware segmentation method was introduced to handle angular variations in BW. The method achieved a sensitivity of 99.40% and a recall of 98.5%. (2) Using the original unprocessed images, AlexNet achieved an accuracy of 95.83% for detecting caries under restoration and dental braces. By incorporating the image processing techniques developed in this study, the accuracy of Inception-v3 improved to a maximum of 99.17%, representing a 3.34% increase over the baseline. (3) In clinical evaluation scenarios, the proposed AlexNet-based model achieved a specificity of 99.94% for non-caries cases and a precision of 99.99% for detecting caries under restoration and dental braces. All datasets used in this study were obtained with IRB approval (certificate number: 02002030B0). A total of 505 bitewing radiographs were collected from Chang Gung Memorial Hospital in Taoyuan, Taiwan. Patients with a history of the human immunodeficiency virus (HIV) were excluded from the dataset. The proposed system effectively identifies caries under restoration and dental braces, strengthens the dentist–patient relationship, and reduces dentist time during clinical consultations. Full article

(This article belongs to the Special Issue New Sight for the Treatment of Dental Diseases: Updates and Direction)

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58 pages, 5907 KiB

Open AccessReview

The Transformation Experiment of Frederick Griffith II: Inclusion of Cellular Heredity for the Creation of Novel Microorganisms

by Günter A. Müller

Bioengineering 2025, 12(5), 532; https://doi.org/10.3390/bioengineering12050532 - 15 May 2025

Abstract

So far, synthetic biology approaches for the construction of artificial microorganisms have fostered the transformation of acceptor cells with genomes from donor cells. However, this strategy seems to be limited to closely related bacterial species only, due to the need for a “fit” [...] Read more.

So far, synthetic biology approaches for the construction of artificial microorganisms have fostered the transformation of acceptor cells with genomes from donor cells. However, this strategy seems to be limited to closely related bacterial species only, due to the need for a “fit” between donor and acceptor proteomes and structures. “Fitting” of cellular regulation of metabolite fluxes and turnover between donor and acceptor cells, i.e. cybernetic heredity, may be even more difficult to achieve. The bacterial transformation experiment design 1.0, as introduced by Frederick Griffith almost one century ago, may support integration of DNA, macromolecular, topological, cybernetic and cellular heredity: (i) attenuation of donor Pneumococci of (S) serotype fosters release of DNA, and hypothetically of non-DNA structures compatible with subsequent transfer to and transformation of acceptor Pneumococci from (R) to (S) serotype; (ii) use of intact donor cells rather than of subcellular or purified fractions may guarantee maximal diversity of the structural and cybernetic matter and information transferred; (iii) “Blending” or mixing and fusion of donor and acceptor Pneumococci may occur under accompanying transfer of metabolites and regulatory circuits. A Griffith transformation experiment design 2.0 is suggested, which may enable efficient exchange of DNA as well as non-DNA structural and cybernetic matter and information, leading to unicellular hybrid microorganisms with large morphological/metabolic phenotypic differences and major features compared to predeceding cells. The prerequisites of horizontal gene and somatic cell nuclear transfer, the molecular mechanism of transformation, the machineries for the biogenesis of bacterial cytoskeleton, micelle-like complexes and membrane landscapes are briefly reviewed on the basis of underlying conceptions, ranging from Darwin’s “gemmules” to “stirps”, cytoplasmic and “plasmon” inheritance, “rhizene agency”, “communicology”, “transdisciplinary membranology” to up to Kirschner’s “facilitated variation”. Full article

(This article belongs to the Section Biochemical Engineering)

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20 pages, 1912 KiB

Open AccessSystematic Review

Temporary Anchorage Devices in Clear Aligner Therapy: A Systematic Review

by Grazia Marinelli, Angelo Michele Inchingolo, Alessio Danilo Inchingolo, Laura Ferrante, Pasquale Avantario, Merigrazia Campanelli, Andrea Palermo, Francesco Inchingolo and Gianna Dipalma

Bioengineering 2025, 12(5), 531; https://doi.org/10.3390/bioengineering12050531 - 15 May 2025

Abstract

This systematic review analyzed the combined use of aligners and orthodontic temporary anchorage devices (TADs) in orthodontic treatment. The aim was to evaluate the effectiveness, benefits, and potential challenges of integrating the use of miniscrews with aligners. This review was conducted according to [...] Read more.

This systematic review analyzed the combined use of aligners and orthodontic temporary anchorage devices (TADs) in orthodontic treatment. The aim was to evaluate the effectiveness, benefits, and potential challenges of integrating the use of miniscrews with aligners. This review was conducted according to the PRISMA statement, and the protocol was registered at PROSPERO under the ID CRD42024576712. A comprehensive search on PubMed, Scopus, and Web of Science was conducted to identify relevant papers involving patients treated with aligners and TADs, dating from 1 January 2004 to 17 July 2024. The electronic database search identified a total of 458 articles. After eligibility, 14 records were selected for qualitative analysis. The findings suggest that the combination of aligners and miniscrews significantly enhances treatment precision and control, especially in cases requiring complex tooth movements, such as intrusion, extrusion, and distalization. The use of miniscrews allows greater control of movement and stability. The integration of these two techniques presents challenges, such as the need for precise miniscrew placement and potential discomfort during insertion. However, there was high satisfaction due to the aesthetic and comfort benefits of aligners. Further research is desirable to delve deeper into the topic to optimize clinical outcomes. Full article

(This article belongs to the Special Issue Orthodontic Biomechanics)

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30 pages, 2517 KiB

Open AccessArticle

Private Data Incrementalization: Data-Centric Model Development for Clinical Liver Segmentation

by Stephanie Batista, Miguel Couceiro, Ricardo Filipe, Paulo Rachinhas, Jorge Isidoro and Inês Domingues

Bioengineering 2025, 12(5), 530; https://doi.org/10.3390/bioengineering12050530 - 15 May 2025

Abstract

Machine Learning models, more specifically Artificial Neural Networks, are transforming medical imaging by enabling precise liver segmentation, a crucial task for diagnosing and treating liver diseases. However, these models often face challenges in adapting to diverse clinical data sources as differences in dataset [...] Read more.

Machine Learning models, more specifically Artificial Neural Networks, are transforming medical imaging by enabling precise liver segmentation, a crucial task for diagnosing and treating liver diseases. However, these models often face challenges in adapting to diverse clinical data sources as differences in dataset volume, resolution, and origin impact generalization and performance. This study introduces a Private Data Incrementalization, a data-centric approach to enhance the adaptability of Artificial Neural Networks by progressively exposing them to varied clinical data. As the target of this study is not to propose a new image segmentation model, the existing medical imaging segmentation models—including U-Net, ResUNet++, Fully Convolutional Network, and a modified algorithm based on the Conditional Bernoulli Diffusion Model—are used. The study evaluates these four models using a curated private dataset of computed tomography scans from Coimbra University Hospital, supplemented by two public datasets, 3D-IRCADb01 and CHAOS. The Private Data Incrementalization method systematically increases the volume and diversity of training data, simulating real-world conditions where models must handle varied imaging contexts. Pre-processing and post-processing stages, incremental training, and performance evaluations reveal that structured exposure to diverse datasets improves segmentation performance, with ResUNet++ achieving the highest accuracy (0.9972) and Dice Similarity Coefficient (0.9449), and the best Average Symmetric Surface Distance (0.0053 mm), demonstrating the importance of dataset diversity and volume for segmentation models’ robustness and generalization. Private Data Incrementalization thus offers a scalable strategy for building resilient segmentation models, ultimately benefiting clinical workflows, patient care, and healthcare resource management by addressing the variability inherent in clinical imaging data. Full article

(This article belongs to the Section Biosignal Processing)

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