Bioengineering

13 pages, 1146 KiB

Open AccessArticle

Predictive Value of Optical Coherence Tomography Biomarkers in Patients with Persistent Diabetic Macular Edema Undergoing Cataract Surgery Combined with a Dexamethasone Intravitreal Implant

by Giuseppe Fasolino, Maryam Lazaar, Domenico Giovanni Della Rocca, Silke Oellerich and Sorcha Ní Dhubhghaill

Bioengineering 2025, 12(5), 556; https://doi.org/10.3390/bioengineering12050556 - 21 May 2025

Viewed by 34

Abstract

Background: Diabetic macular edema (DME) is the most common cause of vision loss among diabetic patients. The first-line treatments for DME are anti-vascular endothelial growth factor (VEGF)-drugs, while intravitreal steroids are generally reserved for second-line treatment. Limited data exist on the role of [...] Read more.

Background: Diabetic macular edema (DME) is the most common cause of vision loss among diabetic patients. The first-line treatments for DME are anti-vascular endothelial growth factor (VEGF)-drugs, while intravitreal steroids are generally reserved for second-line treatment. Limited data exist on the role of optical coherence tomography (OCT) biomarkers as predictors of success in non-responders to anti-VEGF treatment undergoing simultaneous cataract surgery and dexamethasone intravitreal implant (DEX-I). Methods: This study was designed as a retrospective analysis of patients with DME who were refractory to anti-VEGF treatment but underwent cataract surgery and received a DEX-I at the time of surgery. All procedures were performed between May 2021 and February 2024. The best-corrected visual acuity (BCVA) and central subfoveal thickness (CST) were recorded at baseline and at 1 week, 1 month, and 3 months. The following OCT-based biomarkers were also collected: ellipsoid zone (EZ) integrity, disorganization of the retinal inner layers (DRIL), CST, and hyperreflective foci (HRF). Correlations between the baseline biomarkers and the anatomical outcome were analyzed using linear mixed models (LMMs). Results: Eleven patients (eighteen eyes) met the inclusion criteria. The mean CST decreased significantly from 469.4 ± 53.8 µm at baseline, to 373.1 ± 34.7 µm at 1 week (p = 0.002) and 354.4 ± 24.1 µm at 1 month (p = 0.011). The mean BCVA improved significantly from 0.47 LogMAR to 0.33 LogMAR at 1 week (p = 0.001), 0.23 LogMAR at 1 month (p < 0.001), and 0.25 LogMAR at 3 months (p < 0.001). Baseline predictors significantly influencing CST included the presence of DRIL, a disrupted/absent EZ, and a higher CST. Conclusions: The administration of DEX-I for DME refractory to anti-VEGF treatment in patients undergoing cataract surgery promoted functional improvements persisting longer than the anatomical ones. Patients presenting with DRIL, disrupted EZ, and higher CST at baseline may be better candidates for the combination of DEX-I and cataract surgery. Full article

(This article belongs to the Special Issue Biomedical Applications of Optical Coherence Tomography, Second Edition)

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31 pages, 8144 KiB

Open AccessArticle

In Vitro and In Silico Analysis of Entrainment Characterization in Injection Jet-Assisted Fontan Circulation

by Arka Das, Ray O. Prather, Anthony Damon, Michael Farias, Alain Kassab, Eduardo Divo and William DeCampli

Bioengineering 2025, 12(5), 555; https://doi.org/10.3390/bioengineering12050555 - 21 May 2025

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Abstract

Fontan circulation is a fragile system in which imperfections at any of multiple levels may compromise the quality of life, produce secondary pathophysiology, and shorten life span. Increased inferior vena caval pressure itself may play a role in “Fontan failure”. This study describes [...] Read more.

Fontan circulation is a fragile system in which imperfections at any of multiple levels may compromise the quality of life, produce secondary pathophysiology, and shorten life span. Increased inferior vena caval pressure itself may play a role in “Fontan failure”. This study describes a mock flow loop model (MFL) designed to quantitatively estimate pulmonary flow entrainment induced by continuous and pulsed flow injections. A patient generic 3D-printed phantom model of the total cavopulmonary connection (TCPC) with average dimensions matching those of a 2–4-year-old patient was inserted in an MFL derived from a reduced lumped parameter model (LPM) representing cardiovascular circulation. The LPM comprises four 2-element Windkessel compartments (compliance and resistance), approximating the upper and lower systemic circulations and the right and left pulmonary circulations. The prescribed cardiac output is about 2.3 L/min for a body surface area of

0.675 m^{2}

. The injections originate from an external pump through a 7–9 fr catheter, following a strict protocol suggested by the clinical team, featuring a variation in injection rate (flow rate), injection volume, and injection modality (continuous or pulsed). The key measurements in this study are the flow rates sampled at the distal pulmonary arteries, as well as at the upper and lower body boundaries. These measurements were then used to calculate effective entrainment as the difference between the measured and expected flow rates, as well as jet relaxation (rise and fall time of injection). The results show that for continuous or pulsed injections, varying the total volume injected has no significant influence on the entrainment rate across all injection rates. On the other hand, for both injection modalities, increasing the injection rate results in a reduction in entrainment that is consistent across all injected volumes. This study demonstrates the effectiveness of a high-speed injection jet entraining a slow co-flow while determining the potential for fluid buildup, which could ultimately cause an increase in caval pressure. To avoid the increase in caval pressure due to mass accumulation, we added a fenestration to our proposed injection jet shunt-assisted Fontan models. It was found that for a set of well-defined parameters, the jet not only can be beneficial to the local flow, but any adverse effect can be obviated by careful tuning. These results were also cross-validated with similar in silico findings. Full article

(This article belongs to the Special Issue Cardiovascular Hemodynamic Characterization: Prospects and Challenges)

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13 pages, 6421 KiB

Open AccessArticle

Advancing Tissue Engineering Through a Portable Perfusion and Incubation System

by Angie Zhu, Emmett Reid, Tilak Jain, Amatullah Mir, Usmaan Siddiqi, Olivia Dunne and Narutoshi Hibino

Bioengineering 2025, 12(5), 554; https://doi.org/10.3390/bioengineering12050554 - 21 May 2025

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Abstract

Perfusion offers unique benefits to tissue-engineered systems, enhancing oxygen and nutrient transport, which improves tissue formation and growth. In this study, we present a novel and integrated portable perfusion system. Weighing < 10 lbs, the system can maintain continuous flow in a standard [...] Read more.

Perfusion offers unique benefits to tissue-engineered systems, enhancing oxygen and nutrient transport, which improves tissue formation and growth. In this study, we present a novel and integrated portable perfusion system. Weighing < 10 lbs, the system can maintain continuous flow in a standard incubation environment (37 °C, 5% CO₂), effectively functioning as a portable perfusion and tissue culturing system. To characterize the perfusion system’s flow parameters, we measured the volumetric flow rate across a range of pressures and found that the system could achieve flow velocities between 1.69 to 4.6

μ

m/s, which is similar to in vivo interstitial flow. Computational fluid dynamics revealed fully developed laminar flow within the sample-containing region of the perfusion system, helping ensure even fluid and nutrient distribution. To study the system’s compatibility with live tissues, bioengineered tissue patches were created and perfused. After 24 h of perfusion, no significant difference in cell viability was observed between the perfused samples and static controls, indicating no adverse effects on cell health. Perfusion also facilitated enhanced spatial organization within tissue patches, reducing the inter-spheroids distance. Furthermore, perfusion strengthened the tissue matrix and reduced the degradation rate of the hydrogel scaffold. Complemented by its ability to provide mobile perfusion and incubation, this novel integrated portable perfusion system holds promise for promoting tissue maturation and advancing tissue bioengineering studies. Full article

(This article belongs to the Special Issue The New Frontiers of Artificial Organs Engineering)

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18 pages, 748 KiB

Open AccessArticle

Analytical Basal-State Model of the Glucose, Insulin, and C-Peptide Systems for Type 2 Diabetes

by Ched C. Chichester, Munekazu Yamakuchi, Kazunori Takenouchi, Teruto Hashiguchi and Drew N. Maywar

Bioengineering 2025, 12(5), 553; https://doi.org/10.3390/bioengineering12050553 - 21 May 2025

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Abstract

We present a mechanistic mathematical model of the basal state for type 2 diabetes mellitus (T2DM) in an analytical form and illustrate its use for in silico basal-state and dynamic studies. At the core of the basal-state model is a quartic equation that [...] Read more.

We present a mechanistic mathematical model of the basal state for type 2 diabetes mellitus (T2DM) in an analytical form and illustrate its use for in silico basal-state and dynamic studies. At the core of the basal-state model is a quartic equation that expresses the basal plasma glucose concentration solely in terms of model parameters. This analytical model avoids a computationally intensive numerical solver and is illustrated by an investigation of how glucose-utilization parameters impact basal glucose, insulin, insulin-dependent utilization, and hepatic extraction, leveraging median parameter values of early-stage T2DM. Furthermore, the presented basal-state model ensures accurate execution of the corresponding dynamic model, which contains basal quantities within its derivative functions; erroneous, unintended dynamics in plasma glucose, insulin, and C-peptide are illustrated using an incorrect basal glucose value. The presented basal model enables efficient and accurate basal-state and dynamic studies, facilitating the understanding of T2DM pathophysiology and the development of T2DM diagnosis, treatment, and management strategies. Full article

(This article belongs to the Section Biosignal Processing)

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22 pages, 3829 KiB

Open AccessArticle

Brain Tumour Segmentation and Grading Using Local and Global Context-Aggregated Attention Network Architecture

by Ahmed Abdulhakim Al-Absi, Rui Fu, Nadhem Ebrahim, Mohammed Abdulhakim Al-Absi and Dae-Ki Kang

Bioengineering 2025, 12(5), 552; https://doi.org/10.3390/bioengineering12050552 - 21 May 2025

Viewed by 12

Abstract

Brain tumours (BTs) are among the most dangerous and life-threatening cancers in humans of all ages, and the early detection of BTs can make a huge difference to their treatment. However, grade recognition is a challenging issue for radiologists involved in automated diagnosis [...] Read more.

Brain tumours (BTs) are among the most dangerous and life-threatening cancers in humans of all ages, and the early detection of BTs can make a huge difference to their treatment. However, grade recognition is a challenging issue for radiologists involved in automated diagnosis and healthcare monitoring. Recent research has been motivated by the search for deep learning-based mechanisms for segmentation and grading to assist radiologists in diagnostic analysis. Segmentation refers to the identification and delineation of tumour regions in medical images, while classification classifies based on tumour characteristics, such as the size, location and enhancement pattern. The main aim of this research is to design and develop an intelligent model that can detect and grade tumours more effectively. This research develops an aggregated architecture called LGCNet, which combines a local context attention network and a global context attention network. LGCNet makes use of information extracted through the task, dimension and scale. Specifically, a global context attention network is developed for capturing multiple-scale features, and a local context attention network is designed for specific tasks. Thereafter, both networks are aggregated, and the learning network is designed to balance all the tasks by combining the loss functions of the classification and segmentation. The main advantage of LGCNet is its dedicated network for a specific task. The proposed model is evaluated by considering the BraTS2019 dataset with different metrics, such as the Dice score, sensitivity, specificity and Hausdorff score. Comparative analysis with the existing model shows marginal improvement and provides scope for further research into BT segmentation and classification. The scope of this study focuses on the BraTS2019 dataset, with future work aiming to extend the applicability of the model to different clinical and imaging environments. Full article

(This article belongs to the Section Biosignal Processing)

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13 pages, 564 KiB

Open AccessArticle

Evidence for the Link Between Non-Motor Symptoms, Kinematic Gait Parameters, and Physical Function in People with Parkinson’s Disease

by Eren Timurtas, Ahmed-Abou Sharkh, Kedar K. V. Mate, Helen Dawes and Nancy E. Mayo

Bioengineering 2025, 12(5), 551; https://doi.org/10.3390/bioengineering12050551 - 21 May 2025

Viewed by 15

Abstract

Background: Parkinson’s disease (PD) affects both motor and non-motor functions, but their interactions are understudied. This study aims to explore the relationships between non-motor and motor effects of PD, focusing on depression, fatigue, gait parameters, concentration, and physical function. Methods: This is a [...] Read more.

Background: Parkinson’s disease (PD) affects both motor and non-motor functions, but their interactions are understudied. This study aims to explore the relationships between non-motor and motor effects of PD, focusing on depression, fatigue, gait parameters, concentration, and physical function. Methods: This is a secondary analysis of baseline data from a randomized feasibility study using a commercially available Heel2Toe™ sensor, providing auditory feedback for gait quality. The sample included PD patients with gait impairments who walked without aids. Non-motor measures were depression, fatigue, and concentration, while motor measures included gait quality (angular velocity and variability during heel strike, push-off, foot swing) and physical function (6MWT, Mini-BESTest, Neuro-QoL). Path analysis was used to assess direct and indirect effects. Results: Among 27 participants, fatigue impacted heel strike, which affected Neuro-QoL. Mood influenced push-off and Neuro-QoL, with a direct link to 6MWT. Foot swing affected Mini-BESTest and Neuro-QoL directly. Conclusions: Non-motor PD effects directly influenced specific gait parameters and physical function indicators, highlighting potential digital biomarkers of fatigue and mood for targeted interventions. Full article

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

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21 pages, 43908 KiB

Open AccessArticle

WHA-Net: A Low-Complexity Hybrid Model for Accurate Pseudopapilledema Classification in Fundus Images

by Junpeng Pei, Yousong Wang, Mingliang Ge, Jun Li, Yixing Li, Wei Wang and Xiaohong Zhou

Bioengineering 2025, 12(5), 550; https://doi.org/10.3390/bioengineering12050550 - 21 May 2025

Viewed by 14

Abstract

The fundus manifestations of pseudopapilledema closely resemble those of optic disc edema, making their differentiation particularly challenging in certain clinical situations. However, rapid and accurate diagnosis is crucial for alleviating patient anxiety and guiding treatment strategies. This study proposes an efficient low-complexity hybrid [...] Read more.

The fundus manifestations of pseudopapilledema closely resemble those of optic disc edema, making their differentiation particularly challenging in certain clinical situations. However, rapid and accurate diagnosis is crucial for alleviating patient anxiety and guiding treatment strategies. This study proposes an efficient low-complexity hybrid model, WHA-Net, which innovatively integrates three core modules to achieve precise auxiliary diagnosis of pseudopapilledema. First, the wavelet convolution (WTC) block is introduced to enhance the model’s characterization capability for vessel and optic disc edge details in fundus images through 2D wavelet transform and deep convolution. Additionally, the hybrid attention inverted residual (HAIR) block is incorporated to extract critical features such as vascular morphology, hemorrhages, and exudates. Finally, the Agent-MViT module effectively captures the continuity features of optic disc contours and retinal vessels in fundus images while reducing the computational complexity of traditional Transformers. The model was trained and evaluated on a dataset of 1793 rigorously curated fundus images, comprising 895 normal optic discs, 485 optic disc edema (ODE), and 413 pseudopapilledema (PPE) cases. On the test set, the model achieved outstanding performance, with 97.79% accuracy, 95.55% precision, 95.69% recall, and 98.53% specificity. Comparative experiments confirm the superiority of WHA-Net in classification tasks, while ablation studies validate the effectiveness and rationality of each module’s combined design. This research provides a clinically valuable solution for the automated differential diagnosis of pseudopapilledema, with both computational efficiency and diagnostic reliability. Full article

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

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12 pages, 666 KiB

Open AccessArticle

Evaluation of Orthodontic Mini-Implants’ Stability Based on Insertion and Removal Torques: An Experimental Study

by Primavera Sousa-Santos, Sofia Sousa-Santos, Ana Catarina Oliveira, Cíntia Queirós, Joana Mendes, Carlos Aroso and José Manuel Mendes

Bioengineering 2025, 12(5), 549; https://doi.org/10.3390/bioengineering12050549 - 20 May 2025

Viewed by 92

Abstract

Orthodontic mini-implants (MIs) are excellent alternative skeletal anchorage devices. Their stability is important for their survival, requiring appropriate torque application during insertion and removal. Objective: This study aimed to evaluate the influences of the diameter and brand of MIs on their stability by [...] Read more.

Orthodontic mini-implants (MIs) are excellent alternative skeletal anchorage devices. Their stability is important for their survival, requiring appropriate torque application during insertion and removal. Objective: This study aimed to evaluate the influences of the diameter and brand of MIs on their stability by measuring the maximum insertion and removal torques after they had been aged in a pH 7 artificial saliva for 4 weeks at 37 °C. Methods: Forty Ti6Al4V alloy MIs of two different brands and diameters were divided into four groups. They were placed in artificial bone blocks using the NSK^® Surgic Pro coupled with a digital torque gauge (Centor Touch Star TH^®) to measure the maximum insertion and removal torques. Results: After ageing, the Fatscrew (Fts) MIs were more stable when removed than the white brand (WB) MIs. The WB MIs lost stability over time, while the Fts MIs—especially the 2.0 mm ones—maintained good stability. Conclusions: The significant differences between the tested groups, especially the stability observed in the 2.0 mm Fts MIs compared to the other groups, highlight the importance of brand and diameter size in the effectiveness of MIs. Full article

(This article belongs to the Special Issue Orthodontic Biomechanics)

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47 pages, 7533 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

Viewed by 85

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)

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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

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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

Viewed by 150

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

Viewed by 264

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, 1280 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

Viewed by 139

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)

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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

Viewed by 228

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

Viewed by 151

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

Viewed by 182

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

Viewed by 142

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

Viewed by 184

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

Viewed by 114

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

Viewed by 95

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

Viewed by 116

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

Viewed by 103

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

Viewed by 169

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

Viewed by 168

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

Viewed by 378

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

Viewed by 168

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

Viewed by 113

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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40 pages, 28853 KiB

Open AccessArticle

Pioneering Soundscapes: Investigating Commercial Fused Deposition Modelling Filament’s Potential for Ultrasound Technology in Bone Tissue Scaffolds

by Hatice Kübra Bilgili and Masahiro Todoh

Bioengineering 2025, 12(5), 529; https://doi.org/10.3390/bioengineering12050529 - 15 May 2025

Viewed by 225

Abstract

Daily exposure to various forces creates defects in the musculoskeletal system, leading to health issues, especially for bones. Bone tissue scaffolds and ultrasound technology are both utilized in research and in clinics to enhance bone tissue regeneration. This study aimed to investigate the [...] Read more.

Daily exposure to various forces creates defects in the musculoskeletal system, leading to health issues, especially for bones. Bone tissue scaffolds and ultrasound technology are both utilized in research and in clinics to enhance bone tissue regeneration. This study aimed to investigate the potential of commercially available fused deposition modeling (FDM) filaments for ultrasound technology using X-ray diffraction (XRD), Raman spectroscopy, nanoindentation, three-point bending, and scanning electron microscopy (SEM) characterization methods. Customized FDM filaments were produced by combining polylactic acid (PLA) FDM filaments with medical-grade polycaprolactone (PCL). Using these, we observed the successful production of complex tissue scaffolds via PLAPCL4060 and PLAPCL5050 FDM filaments. Additionally, the presence of the contrast difference observed via SEM for PLAPCL4060 suggests phase segregation and a material that has both damping and activating characteristics under ultrasound propagation. Mechanical characterization provided hardness and elastic modulus values, while the three-point bending results proved the flexible nature of PLAPCL4060 and PLAPCL5050, which is important for their dynamicity and responsiveness under ultrasound propagation. Accelerated degradation experiments provided crucial information regarding the effect of the porosity and gradients of scaffolds under ultrasound stimulation. Future studies based on this approach will contribute to understanding the true potential of these filaments for bone tissue. Full article

(This article belongs to the Section Regenerative Engineering)

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19 pages, 881 KiB

Open AccessArticle

Cross-Subject Emotion Recognition with CT-ELCAN: Leveraging Cross-Modal Transformer and Enhanced Learning-Classify Adversarial Network

by Ping Li, Ao Li, Xinhui Li and Zhao Lv

Bioengineering 2025, 12(5), 528; https://doi.org/10.3390/bioengineering12050528 - 15 May 2025

Viewed by 112

Abstract

Multimodal physiological emotion recognition is challenged by modality heterogeneity and inter-subject variability, which hinder model generalization and robustness. To address these issues, this paper proposes a new framework, Cross-modal Transformer with Enhanced Learning-Classifying Adversarial Network (CT-ELCAN). The core idea of CT-ELCAN is to [...] Read more.

Multimodal physiological emotion recognition is challenged by modality heterogeneity and inter-subject variability, which hinder model generalization and robustness. To address these issues, this paper proposes a new framework, Cross-modal Transformer with Enhanced Learning-Classifying Adversarial Network (CT-ELCAN). The core idea of CT-ELCAN is to shift the focus from conventional signal fusion to the alignment of modality- and subject-invariant emotional representations. By combining a cross-modal Transformer with ELCAN, a feature alignment module using adversarial training, CT-ELCAN learns modality- and subject-invariant emotional representations. Experimental results on the public datasets DEAP and WESAD demonstrate that CT-ELCAN achieves accuracy improvements of approximately 7% and 5%, respectively, compared to state-of-the-art models, while also exhibiting enhanced robustness. Full article

(This article belongs to the Special Issue Biomedical Applications of Multimodal Imaging Combined with Artificial Intelligence)

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16 pages, 5043 KiB

Open AccessArticle

Transforming Bone Tunnel Evaluation in Anterior Cruciate Ligament Reconstruction: Introducing a Novel Deep Learning System and the TB-Seg Dataset

by Ke Xie, Mingqian Yu, Jeremy Ho-Pak Liu, Qixiang Ma, Limin Zou, Gene Chi-Wai Man, Jiankun Xu, Patrick Shu-Hang Yung, Zheng Li and Michael Tim-Yun Ong

Bioengineering 2025, 12(5), 527; https://doi.org/10.3390/bioengineering12050527 - 15 May 2025

Viewed by 153

Abstract

Evaluating bone tunnels is crucial for assessing functional recovery after anterior cruciate ligament reconstruction. Conventional methods are imprecise, time-consuming, and labor-intensive. This study introduces a novel deep learning-based system for accurate bone tunnel segmentation and assessment. The system has two primary stages. Firstly, [...] Read more.

Evaluating bone tunnels is crucial for assessing functional recovery after anterior cruciate ligament reconstruction. Conventional methods are imprecise, time-consuming, and labor-intensive. This study introduces a novel deep learning-based system for accurate bone tunnel segmentation and assessment. The system has two primary stages. Firstly, the ResNet50-Unet network is employed to capture the bone tunnel area in each slice. Subsequently, in the bone texture analysis, the open-source software 3D Slicer is leveraged to execute three-dimensional reconstruction based on the segmented outcomes from the previous stage. The ResNet50-Unet network was trained and validated using a newly developed dataset named tunnel bone segmentation (TB-Seg). The outcomes reveal commendable performance metrics, with mean intersection over union (mIoU), mean average precision (mAP), precision, and recall on the validation set reaching 76%, 85%, 88%, and 85%, respectively. To assess the robustness of our innovative bone texture system, we conducted tests on a cohort of 24 patients, successfully extracting bone volume/total volume, trabecular thickness, trabecular separation, trabecular number, and volumetric information. The system excels with substantial significance in facilitating the subsequent analysis of the intricate interplay between bone tunnel characteristics and the postoperative recovery trajectory after anterior cruciate ligament reconstruction. Furthermore, in our five randomly selected cases, clinicians utilizing our system completed the entire analytical workflow in a mere 357–429 s, representing a substantial improvement compared to the conventional duration exceeding one hour. Full article

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

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