Bioengineering

13 pages, 434 KiB

Open AccessArticle

Evaluation of Patient Benefits from the Superficial Circumflex Iliac Artery Perforator Flap in Elderly Patients

by Hongmin Luo, Huining Bian, Zuan Liu, Chuanwei Sun, Hanhua Li, Lianghua Ma, Xiaoyan Wang, Zhifeng Huang, Xu Mu, Shenghua Chen, Yuyang Han, Lin Zhang, Shaoyi Zheng, Zeyang Yao and Wen Lai

Bioengineering 2025, 12(4), 394; https://doi.org/10.3390/bioengineering12040394 (registering DOI) - 7 Apr 2025

Abstract

Background: The superficial circumflex iliac artery perforator (SCIP) flap is widely recognized for its reliability and minimal donor site morbidity in reconstructive surgery. However, its safety and efficacy in elderly patients—a growing demographic with increased comorbidities—remain less understood. This study aims to evaluate [...] Read more.

Background: The superficial circumflex iliac artery perforator (SCIP) flap is widely recognized for its reliability and minimal donor site morbidity in reconstructive surgery. However, its safety and efficacy in elderly patients—a growing demographic with increased comorbidities—remain less understood. This study aims to evaluate the clinical outcomes of the SCIP flap in elderly patients compared to younger patients, focusing on flap survival, complications, and recovery. Methods: In this retrospective cohort study, conducted at Guangdong Provincial People’s Hospital, from 28 August 2019 to 7 June 2024, we included 37 patients who underwent SCIP flap procedures for reconstruction. Patients were divided into two groups: younger (15–59 years) and elderly (≥60 years). Key variables analyzed included demographics, comorbidities, flap characteristics, recipient sites, arterial sources, and surgical outcomes. Univariate analysis and ROC curve analysis were used to explore the impact of age on flap survival and complications. Results: The cohort consisted of 28 younger and 9 elderly patients. Vascular disease was significantly more prevalent in the elderly group (88.9% vs. 21.4%, p = 0.001), and abnormalities in the CTA results indicate that the elderly cohort exhibited a 29-fold increased odds of vascular disease compared to younger patients (OR = 29.17, 95% CI: 4.82–176.40, p = 0.001). However, no significant differences were found between the groups in terms of flap area, recipient sites, or arterial sources. Hospital stay duration and flap survival rates were comparable across both age groups, with no cases of total flap loss reported. While systemic complications were somewhat higher in the elderly group, this difference did not reach statistical significance. The ROC analysis (AUC = 0.52) indicates that age alone is not a significant predictor of flap survival. Conclusions: The SCIP flap is a safe and effective reconstructive option for elderly patients, despite a higher incidence of vascular disease. Flap survival and postoperative recovery were favorable, indicating that the procedure is viable for older patients. These findings support the continued use of SCIP flaps in aging populations, emphasizing the need for individualized surgical approaches to optimize patient outcomes. Full article

(This article belongs to the Special Issue Regenerative Technologies in Plastic and Reconstructive Surgery)

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9 pages, 847 KiB

Open AccessPerspective

Towards a Better Understanding of the Human Health Risk of Per- and Polyfluoroalkyl Substances Using Organoid Models

by Haoan Xu, Jiahui Kang, Xue Gao, Yingying Lan and Minghui Li

Bioengineering 2025, 12(4), 393; https://doi.org/10.3390/bioengineering12040393 (registering DOI) - 7 Apr 2025

Abstract

The ubiquitous presence of per- and polyfluoroalkyl substances (PFAS) in the environment has garnered global public concern. Epidemiological studies have proved that exposure to PFAS is associated with human health risks. Although evidence demonstrated the toxic mechanisms of PFAS based on animal models [...] Read more.

The ubiquitous presence of per- and polyfluoroalkyl substances (PFAS) in the environment has garnered global public concern. Epidemiological studies have proved that exposure to PFAS is associated with human health risks. Although evidence demonstrated the toxic mechanisms of PFAS based on animal models and traditional cell cultures, their limitations in inter-species differences and lack of human-relevant microenvironments hinder the understanding of health risks from PFAS exposure. There is an increasing necessity to explore alternative methodologies that can effectively evaluate human health risks. Human organoids derived from stem cells accurately mimic the sophisticated and multicellular structures of native human organs, providing promising models for toxicology research. Advanced organoids combined with innovative technologies are expected to improve understanding of the breadth and depth of PFAS toxicity. Full article

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

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Graphical abstract

22 pages, 5535 KiB

Open AccessArticle

Computational Modeling of Cardiac Electrophysiology with Human Realistic Heart–Torso Model

by Chen Yang, Yidi Cao, Peilun Li, Yanfei Yang and Min Xiang

Bioengineering 2025, 12(4), 392; https://doi.org/10.3390/bioengineering12040392 (registering DOI) - 6 Apr 2025

Abstract

The electrocardiogram (ECG) has long been considered the non-invasive gold standard in diagnosing heart diseases. However, its connection with the cardiac molecular biology remains somewhat unclear. Therefore, modeling the electrophysiological behavior of the heart provides an important theoretical complement to clinically observable data. [...] Read more.

The electrocardiogram (ECG) has long been considered the non-invasive gold standard in diagnosing heart diseases. However, its connection with the cardiac molecular biology remains somewhat unclear. Therefore, modeling the electrophysiological behavior of the heart provides an important theoretical complement to clinically observable data. This study employed an electrophysiological model, integrating a bidomain model with the Fitzhugh–Nagumo (FHN) model, to compute an ECG and body surface potential maps (BSPMs). Parameters from previous studies were simulated individually for the cardiac domain. A specific set of parameters was selected based on comparisons of the morphology of the 12-lead ECG. The effect of the heart position relative to the torso on the 12-lead ECG was analyzed using a simplified whole-heart model to approximate the realistic heart position within the torso. Significant waveform changes were observed in leads

V I I I

and

a V L

, as compared to other leads. This study employed a realistic heart–torso model, in contrast to earlier studies. External stimuli were incorporated into the original electrophysiological model to account for the electrical isolation between the atria and ventricles. The morphology of the simulated 12-lead ECG closely matched that of clinically observed data. Full article

(This article belongs to the Section Biosignal Processing)

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Graphical abstract

16 pages, 2439 KiB

Open AccessArticle

Ultrasound-Based Deep Learning Radiomics Models for Predicting Primary and Secondary Salivary Gland Malignancies: A Multicenter Retrospective Study

by Zhen Xia, Xiao-Chen Huang, Xin-Yu Xu, Qing Miao, Ming Wang, Meng-Jie Wu, Hao Zhang, Qi Jiang, Jing Zhuang, Qiang Wei and Wei Zhang

Bioengineering 2025, 12(4), 391; https://doi.org/10.3390/bioengineering12040391 (registering DOI) - 5 Apr 2025

Abstract

Background: Primary and secondary salivary gland malignancies differ significantly in treatment and prognosis. However, conventional ultrasonography often struggles to differentiate between these malignancies due to overlapping imaging features. We aimed to develop and evaluate noninvasive diagnostic models based on traditional ultrasound features, radiomics, [...] Read more.

Background: Primary and secondary salivary gland malignancies differ significantly in treatment and prognosis. However, conventional ultrasonography often struggles to differentiate between these malignancies due to overlapping imaging features. We aimed to develop and evaluate noninvasive diagnostic models based on traditional ultrasound features, radiomics, and deep learning—independently or in combination—for distinguishing between primary and secondary salivary gland malignancies. Methods: This retrospective study included a total of 140 patients, comprising 68 with primary and 72 with secondary salivary gland malignancies, all pathologically confirmed, from four medical centers. Ultrasound features of salivary gland tumors were analyzed, and a radiomics model was established. Transfer learning with multiple pre-trained models was used to create deep learning (DL) models from which features were extracted and combined with radiomics features to construct a radiomics-deep learning (RadiomicsDL) model. A combined model was further developed by integrating ultrasound features. Least absolute shrinkage and selection operator (LASSO) regression and various machine learning algorithms were employed for feature selection and modeling. The optimal model was determined based on the area under the receiver operating characteristic curve (AUC), and interpretability was assessed using SHapley Additive exPlanations (SHAP). Results: The RadiomicsDL model, which combines radiomics and deep learning features using the Multi-Layer Perceptron (MLP), demonstrated the best performance on the test set with an AUC of 0.807. This surpassed the performances of the ultrasound (US), radiomics, DL, and combined models, which achieved AUCs of 0.421, 0.636, 0.763, and 0.711, respectively. SHAP analysis revealed that the radiomic feature Wavelet_LHH_glcm_SumEntropy contributed most significantly to the mode. Conclusions: The RadiomicsDL model based on ultrasound images provides an efficient and non-invasive method to differentiate between primary and secondary salivary gland malignancies. Full article

(This article belongs to the Special Issue Diagnostic Imaging and Radiation Therapy in Biomedical Engineering)

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22 pages, 6353 KiB

Open AccessArticle

SwinDAF3D: Pyramid Swin Transformers with Deep Attentive Features for Automated Finger Joint Segmentation in 3D Ultrasound Images for Rheumatoid Arthritis Assessment

by Jianwei Qiu, Grigorios M. Karageorgos, Xiaorui Peng, Soumya Ghose, Zhaoyuan Yang, Aaron Dentinger, Zhanpeng Xu, Janggun Jo, Siddarth Ragupathi, Guan Xu, Nada Abdulaziz, Girish Gandikota, Xueding Wang and David Mills

Bioengineering 2025, 12(4), 390; https://doi.org/10.3390/bioengineering12040390 (registering DOI) - 5 Apr 2025

Abstract

Rheumatoid arthritis (RA) is a chronic autoimmune disease that can cause severe joint damage and functional impairment. Ultrasound imaging has shown promise in providing real-time assessment of synovium inflammation associated with the early stages of RA. Accurate segmentation of the synovium region and [...] Read more.

Rheumatoid arthritis (RA) is a chronic autoimmune disease that can cause severe joint damage and functional impairment. Ultrasound imaging has shown promise in providing real-time assessment of synovium inflammation associated with the early stages of RA. Accurate segmentation of the synovium region and quantification of inflammation-specific imaging biomarkers are crucial for assessing and grading RA. However, automatic segmentation of the synovium in 3D ultrasound is challenging due to ambiguous boundaries, variability in synovium shape, and inhomogeneous intensity distribution. In this work, we introduce a novel network architecture, Swin Transformers with Deep Attentive Features for 3D segmentation (SwinDAF3D), which integrates Swin Transformers into a Deep Attentive Features framework. The developed architecture leverages the hierarchical structure and shifted windows of Swin Transformers to capture rich, multi-scale and attentive contextual information, improving the modeling of long-range dependencies and spatial hierarchies in 3D ultrasound images. In a six-fold cross-validation study with 3D ultrasound images of RA patients’ finger joints (n = 72), our SwinDAF3D model achieved the highest performance with a Dice Score (DSC) of 0.838 ± 0.013, an Intersection over Union (IoU) of 0.719 ± 0.019, and Surface Dice Score (SDSC) of 0.852 ± 0.020, compared to 3D UNet (DSC: 0.742 ± 0.025; IoU: 0.589 ± 0.031; SDSC: 0.661 ± 0.029), DAF3D (DSC: 0.813 ± 0.017; IoU: 0.689 ± 0.022; SDSC: 0.817 ± 0.013), Swin UNETR (DSC: 0.808 ± 0.025; IoU: 0.678 ± 0.032; SDSC: 0.822 ± 0.039), UNETR++ (DSC: 0.810 ± 0.014; IoU: 0.684 ± 0.018; SDSC: 0.829 ± 0.027) and TransUNet (DSC: 0.818 ± 0.013; IoU: 0.692 ± 0.017; SDSC: 0.815 ± 0.016) models. This ablation study demonstrates the effectiveness of combining a Swin Transformers feature pyramid with a deep attention mechanism, improving the segmentation accuracy of the synovium in 3D ultrasound. This advancement shows great promise in enabling more efficient and standardized RA screening using ultrasound imaging. Full article

(This article belongs to the Special Issue Artificial Intelligence in Auto-Diagnosis and Clinical Applications, 2nd Edition)

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17 pages, 689 KiB

Open AccessReview

Pathomechanics of Early-Stage Lumbar Intervertebral Disc Degradation Leading to Discogenic Pain—A Narrative Review

by Thomas Hedman and Adam Rogers

Bioengineering 2025, 12(4), 389; https://doi.org/10.3390/bioengineering12040389 (registering DOI) - 5 Apr 2025

Abstract

Although the existence of highly prevalent pain, disability, and work time lost associated with discogenic low back pain is well known, the recognition of the culpability of universally present disc degradation and mechanical insufficiency in the first three decades of life is often [...] Read more.

Although the existence of highly prevalent pain, disability, and work time lost associated with discogenic low back pain is well known, the recognition of the culpability of universally present disc degradation and mechanical insufficiency in the first three decades of life is often overlooked. There is a corresponding “treatment gap” and no current interventions with demonstrated capabilities to address the pain and resist the usual progression of increasing structural failure of spinal tissues with increasing levels of pain and disability. This narrative review summarizes more than forty years of the literature describing the pathomechanics of progressive degradation of lumbar discs, with a focus on studies that implicate an increasing mechanical insufficiency in the etiology of early-stage chronic and recurrent discogenic low back pain. Topics highlighted in this review include the deleterious biological changes that begin soon after birth, stress intensification due to the loss of fluid phase load support, fatigue weakening and damage accumulation in non-regenerative tissue, disc tears, segmental instability, and the timeline for first incidence of chronic low back pain. The review concludes with preferred treatment characteristics and a brief summary of emerging treatment approaches. Full article

(This article belongs to the Section Biomechanics and Sports Medicine)

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22 pages, 2830 KiB

Open AccessArticle

Multimodal Classification of Alzheimer’s Disease Using Longitudinal Data Analysis and Hypergraph Regularized Multi-Task Feature Selection

by Shuaiqun Wang, Huan Zhang and Wei Kong

Bioengineering 2025, 12(4), 388; https://doi.org/10.3390/bioengineering12040388 (registering DOI) - 5 Apr 2025

Abstract

Alzheimer’s disease, an irreversible neurodegenerative disorder, manifests through the progressive deterioration of memory and cognitive functions. While magnetic resonance imaging has become an indispensable neuroimaging modality for Alzheimer’s disease diagnosis and monitoring, current diagnostic paradigms predominantly rely on single-time-point data analysis, neglecting the [...] Read more.

Alzheimer’s disease, an irreversible neurodegenerative disorder, manifests through the progressive deterioration of memory and cognitive functions. While magnetic resonance imaging has become an indispensable neuroimaging modality for Alzheimer’s disease diagnosis and monitoring, current diagnostic paradigms predominantly rely on single-time-point data analysis, neglecting the inherent longitudinal nature of neuroimaging applications. Therefore, in this paper, we propose a multi-task feature selection algorithm for Alzheimer’s disease classification based on longitudinal imaging and hypergraphs (THM2TFS). Our methodology establishes a multi-task learning framework where feature selection at each temporal interval is treated as an individual task within each imaging modality. To address temporal dependencies, we implement group sparse regularization with two critical components: (1) a hypergraph-induced regularization term that captures high-order structural relationships among subjects through hypergraph Laplacian modeling, and (2) a fused sparse Laplacian regularization term that encodes progressive pathological changes in brain regions across time points. The selected features are subsequently integrated via multi-kernel support vector machines for final classification. We used functional magnetic resonance imaging and structural functional magnetic resonance imaging data from Alzheimer’s Disease Neuroimaging Initiative at four different time points (baseline (T1), 6th month (T2), 12th month (T3), and 24th month (T4)) to evaluate our method. The experimental results show that the accuracy rates of 96.75%, 93.45, and 83.78 for the three groups of classification tasks (AD vs. NC, MCI vs. NC and AD vs. MCI) are obtained, respectively, which indicates that the proposed method can not only capture the relevant information between longitudinal image data well, but also the classification accuracy of Alzheimer’s disease is improved, and it helps to identify the biomarkers associated with Alzheimer’s disease. Full article

(This article belongs to the Special Issue AI in OCT (Optical Coherence Tomography) Image Analysis)

18 pages, 2703 KiB

Open AccessArticle

Altered Effective Connectivity of the Attentional Network in Temporal Lobe Epilepsy with EEG Data

by Xiaojie Wei, Haojun Yang, Ruochen Dang, Bingliang Hu, Li Feng, Yuanyuan Xie and Quan Wang

Bioengineering 2025, 12(4), 387; https://doi.org/10.3390/bioengineering12040387 - 4 Apr 2025

Abstract

Existing studies have shown that the attentional function of epilepsy is prone to be impaired. However, the characterization of brain connectivity behind this impairment remains uncertain. This study investigates attention-related brain connectivity in 92 patients with temporal lobe epilepsy and 78 healthy controls [...] Read more.

Existing studies have shown that the attentional function of epilepsy is prone to be impaired. However, the characterization of brain connectivity behind this impairment remains uncertain. This study investigates attention-related brain connectivity in 92 patients with temporal lobe epilepsy and 78 healthy controls using a 32-channel EEG monitor during an attention network test. Compared to controls, patients showed reduced temporal–occipital connectivity in the alerting and orienting networks, but increased frontal–occipital connectivity in the executive network. Additionally, this study showed that patients and healthy individuals exhibited similar network topologies in the alerting and orienting networks, but the executive networks in patients showed altered topology properties, with a larger clustering coefficient in the theta band and a longer characteristic path length in the delta and theta bands. These findings reveal distinct characteristics of attention network connectivity in patients with temporal lobe epilepsy, offering valuable insights into the underlying mechanisms of epilepsy and providing clinical guidance for long-term monitoring and intervention. Full article

(This article belongs to the Special Issue Multiclass Prediction and Assisted Diagnosis of Mental Disorders Based on Eye Movement and Other Physiological Signals)

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9 pages, 2838 KiB

Open AccessArticle

Enhanced External Counterpulsation Intervention Induces the Variation of Physiological Parameters and Shear Stress Metrics in the Carotid Artery

by Zhenfeng Ren, Zi’an Wu, Yanjing Wang, Israilov Jakhongirkhon, Qianxiang Zhou and Jianhang Du

Bioengineering 2025, 12(4), 386; https://doi.org/10.3390/bioengineering12040386 - 3 Apr 2025

Abstract

Enhanced external counterpulsation (EECP) treatment has been demonstrated to be effectively vasculoprotective and anti-atherosclerotic in clinical observations and controlled trials. The diastolic blood flow augmentation induced by EECP greatly affected the local hemodynamic environment in multiple arterial segments. In this study, a porcine [...] Read more.

Enhanced external counterpulsation (EECP) treatment has been demonstrated to be effectively vasculoprotective and anti-atherosclerotic in clinical observations and controlled trials. The diastolic blood flow augmentation induced by EECP greatly affected the local hemodynamic environment in multiple arterial segments. In this study, a porcine model of hypercholesterolaemia was developed to perform an invasive physiological measurement involving electrocardiogram, blood flow wave, and arterial pressure. Subsequently, a three-dimensional carotid bifurcation model was developed to evaluate the variations in wall shear stress (WSS) and its temporal and spatial oscillations. The results show that, compared to the pre-EECP state, EECP stimulus led to an increase of 28.7% in the common carotid artery (CCA) blood flow volume over a cardiac cycle, as well as an augmentation of 22.73% in the diastolic pressure. Meanwhile, the time-average wall shear stress (TAWSS) over the cardiac cycle increased 25.1%, while the relative residence time (RRT) declined 45.7%. These results may serve to reveal the hemodynamic mechanism of EECP treatment that contributes to its anti-atherosclerotic effects. Full article

(This article belongs to the Special Issue Computational Biofluid Dynamics)

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21 pages, 3171 KiB

Open AccessArticle

U-Net-Based Deep Learning Hybrid Model: Research and Evaluation for Precise Prediction of Spinal Bone Density on Abdominal Radiographs

by Lixiao Zhou, Thongphi Nguyen, Sunghoon Choi and Jonghun Yoon

Bioengineering 2025, 12(4), 385; https://doi.org/10.3390/bioengineering12040385 - 3 Apr 2025

Abstract

Osteoporosis is a metabolic bone disorder characterized by the progressive loss of bone mass, which significantly increases the risk of fractures. While dual-energy X-ray absorptiometry is the standard technique for assessing bone mineral density, its use is limited in high-risk female populations. Additionally, [...] Read more.

Osteoporosis is a metabolic bone disorder characterized by the progressive loss of bone mass, which significantly increases the risk of fractures. While dual-energy X-ray absorptiometry is the standard technique for assessing bone mineral density, its use is limited in high-risk female populations. Additionally, quantitative computed tomography offers three-dimensional evaluations of bone mineral density but is costly and prone to motion artifacts. To overcome these limitations, this study proposes a hybrid model integrating U-Net and artificial neural networks, specifically focusing on abdominal X-ray images in the anteroposterior view for detailed skeletal analysis and improved accuracy in L2 vertebra mineral density measurement. The model targets female patients, who are at a higher risk for spinal disorders and osteoporosis. The U-Net model is employed for image preprocessing to reduce background noise and enhance bone tissue features, followed by analysis with the artificial neural network model to predict bone mineral density through nonlinear regression. The performance of the model, demonstrated by a high correlation coefficient of 0.77 and a low mean absolute error of 0.08 g per square centimeter, highlights its significance and effectiveness, particularly in comparison to dual-energy X-ray absorptiometry. Full article

(This article belongs to the Section Biosignal Processing)

15 pages, 3740 KiB

Open AccessArticle

A Biomechanical Evaluation of a Novel Interspinous Process Device: In Vitro Flexibility Assessment and Finite Element Analysis

by Hangkai Shen, Chuanguang Ju, Tao Gao, Jia Zhu and Weiqiang Liu

Bioengineering 2025, 12(4), 384; https://doi.org/10.3390/bioengineering12040384 (registering DOI) - 3 Apr 2025

Abstract

The interspinous process device (IPD) has emerged as a viable alternative for managing lumbar degenerative pathologies. Nevertheless, limited research exists regarding mechanical failure modes including device failure and spinous process fracture. This study developed a novel IPD (IPD-NEW) and systematically evaluated its biomechanical [...] Read more.

The interspinous process device (IPD) has emerged as a viable alternative for managing lumbar degenerative pathologies. Nevertheless, limited research exists regarding mechanical failure modes including device failure and spinous process fracture. This study developed a novel IPD (IPD-NEW) and systematically evaluated its biomechanical characteristics through finite element (FE) analysis and in vitro cadaveric biomechanical testing. Six human L1–L5 lumbar specimens were subjected to mechanical testing under four experimental conditions: (1) Intact spine (control); (2) L3–L4 implanted with IPD-NEW; (3) L3–L4 implanted with Wallis device; (4) L3–L4 implanted with Coflex device. Segmental range of motion (ROM) was quantified across all test conditions. A validated L1–L5 finite element model was subsequently employed to assess biomechanical responses under both static and vertical vibration loading regimes. Comparative analysis revealed that IPD-NEW demonstrated comparable segmental ROM to the Wallis device while exhibiting lower rigidity than the Coflex implant. The novel design effectively preserved physiological spinal mobility while enhancing load distribution capacity. IPD-NEW demonstrated notable reductions in facet joint forces, device stress concentrations, and spinous process loading compared to conventional implants, particularly under vibrational loading conditions. These findings suggest that IPD-NEW may mitigate risks associated with facetogenic pain, device failure, and spinous process fracture through optimized load redistribution. Full article

(This article belongs to the Special Issue Joint Biomechanics and Implant Design)

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15 pages, 1005 KiB

Open AccessArticle

Decellularization of Human Digits: A Step Towards Off-the-Shelf Composite Allograft Transplantation

by Michelle E. McCarthy, Irina Filz von Reiterdank, Oliver H. Parfitt van Pallandt, McLean S. Taggart, Laura Charlès, Korkut Uygun, Alexandre G. Lellouch, Curtis L. Cetrulo, Jr. and Basak E. Uygun

Bioengineering 2025, 12(4), 383; https://doi.org/10.3390/bioengineering12040383 (registering DOI) - 3 Apr 2025

Abstract

The field of reconstructive surgery faces significant challenges in addressing limb loss and disfigurement, with current organ preservation methods limited by short storage times. Decellularization offers a promising solution for generating engineered alternatives for reconstructive surgery by removing cellular material while preserving the [...] Read more.

The field of reconstructive surgery faces significant challenges in addressing limb loss and disfigurement, with current organ preservation methods limited by short storage times. Decellularization offers a promising solution for generating engineered alternatives for reconstructive surgery by removing cellular material while preserving the extracellular matrix (ECM) and providing scaffolds for tissue regeneration. In this study, we developed a robust protocol for decellularizing whole digits from long-term freezer storage, achieving the successful removal of cellular material with intact ECM. Digit angiography confirmed the preservation of vascular integrity, facilitating future perfusion for recellularization. Quantitative analysis revealed significantly lower DNA content in decellularized tissues, indicating effective decellularization. Furthermore, extracellular matrix analysis showed the preservation of collagen, elastin, and glycosaminoglycans (GAGs) contents. Histological examination confirmed the reduction in cellularity and maintenance of tissue architecture in decellularized digits. Mechanical strength testing of decellularized digit tendons proved consistent with that of native digits. Our findings highlight the potential of decellularized digits as versatile platforms for tissue engineering and regenerative medicine. Moving forward, further optimization of protocols and collaborative efforts are essential for translating these findings into clinical practice, offering innovative solutions for reconstructive surgery and limb transplantation. Full article

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

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14 pages, 2718 KiB

Open AccessArticle

An Explainable Fusion of ECG and SpO₂-Based Models for Real-Time Sleep Apnea Detection

by Tanmoy Paul, Omiya Hassan, Christina S. McCrae, Syed Kamrul Islam and Abu Saleh Mohammad Mosa

Bioengineering 2025, 12(4), 382; https://doi.org/10.3390/bioengineering12040382 - 3 Apr 2025

Abstract

Obstructive sleep apnea (OSA) is a common disorder characterized by disrupted breathing during sleep, leading to serious health consequences such as daytime fatigue, hypertension, metabolic issues, and cardiovascular disease. Polysomnography (PSG) is the standard diagnostic method but is costly and uncomfortable for patients, [...] Read more.

Obstructive sleep apnea (OSA) is a common disorder characterized by disrupted breathing during sleep, leading to serious health consequences such as daytime fatigue, hypertension, metabolic issues, and cardiovascular disease. Polysomnography (PSG) is the standard diagnostic method but is costly and uncomfortable for patients, which has led to interest in artificial intelligence (AI) for automated OSA detection. To develop an explainable AI model that utilizes electrocardiogram (ECG) and blood oxygen saturation (SpO2) data for real-time apnea detection, providing visual explanations to enhance interpretability and support clinical decisions. It emphasizes giving visual explanations to show how specific segments of the signal contribute to the AI’s conclusions. Furthermore, it explores the combination of individual models to improve detection accuracy. The fusion of individual models demonstrates an enhanced performance in detection accuracy. Visual explanations for AI decisions highlight the importance of certain signal features, making the model’s operations transparent to healthcare providers. The proposed AI model addresses the crucial need for transparent and interpretable AI in healthcare. By providing real-time, explainable OSA detection, this approach represents a significant advancement in the field, potentially improving patient care and aiding in the early identification and management of OSA. Full article

(This article belongs to the Special Issue Wearable Sensors and Measurement Systems for Human Physiology Monitoring)

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15 pages, 3951 KiB

Open AccessArticle

A Lightweight Machine Learning Model for High Precision Gastrointestinal Stromal Tumors Identification

by Xin Sun, Xiwen Mo, Jing Shi, Xinran Zhou, Yanqing Niu, Xiao-Dong Zhang, Man Li and Yonghui Li

Bioengineering 2025, 12(4), 381; https://doi.org/10.3390/bioengineering12040381 - 3 Apr 2025

Abstract

Gastrointestinal stromal tumors (GISTs), which usually develop with a significant malignant potential, are a serious challenge in stromal health. With Endoscopic ultrasound (EUS), GISTs can appear similar to other tumors. This study introduces a lightweight convolutional neural network model optimized for the classification [...] Read more.

Gastrointestinal stromal tumors (GISTs), which usually develop with a significant malignant potential, are a serious challenge in stromal health. With Endoscopic ultrasound (EUS), GISTs can appear similar to other tumors. This study introduces a lightweight convolutional neural network model optimized for the classification of GISTs and leiomyomas using EUS images only. Models are constructed based on a dataset that comprises 13277 augmented grayscale images derived from 703 patients, ensuring a balanced representation between GIST and leiomyoma cases. The optimized model architecture includes seven convolutional units followed by fully connected layers. After being trained and evaluated with a 5-fold cross-validation, the optimized model achieves an average validation accuracy of 96.2%. The model achieved a sensitivity, specificity, positive predictive value, and negative predictive value of 97.7%, 94.7%, 94.6%, and 97.7%, respectively, and significantly outperformed endoscopists’ assessments. The study highlights the model’s robustness and consistency. Our results suggest that instead of using developed deep models with fine-tuning, lightweight models with their simpler designs may grasp the essence and drop speckle noise. A lightweight model as a hypothesis with fewer model parameters is preferable to a deeper model with 10 times the model parameters according to Occam’s razor statement. Full article

(This article belongs to the Section Biosignal Processing)

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17 pages, 1005 KiB

Open AccessReview

The Preservation and Reuse of Lenticules Extracted via Small Incision Lenticule Extraction (SMILE): A Narrative Review

by Yaohua Zhang, Jing Li, Zhiqing Wu, Yong Li, Guoxi Wu and Shengsheng Wei

Bioengineering 2025, 12(4), 380; https://doi.org/10.3390/bioengineering12040380 - 3 Apr 2025

Abstract

Small-incision lenticule extraction (SMILE) is a safe and effective procedure to correct myopia and myopic astigmatism. The corneal stromal lenticules extracted from SMILE surgery have good light transmission, mechanical properties, and biocompatibility, which are suitable for the treatment of a variety of corneal [...] Read more.

Small-incision lenticule extraction (SMILE) is a safe and effective procedure to correct myopia and myopic astigmatism. The corneal stromal lenticules extracted from SMILE surgery have good light transmission, mechanical properties, and biocompatibility, which are suitable for the treatment of a variety of corneal diseases and can solve the problem of donor cornea shortage. At present, no single method of preserving corneal stromal lenticules has been universally accepted as ideal, as the preservation of tissue integrity, optical transmittance, cellular viability, and the potential for long-term storage remain key challenges. Current approaches include short-term preservation methods such as the use of dehydrating agents and Optisol GS, and long-term preservation strategies such as cryopreservation, hydrogel nutrient capsules, and silicone oil. Standardized storage methods can improve the use of SMILE-derived lenticules as a substitute for donor corneal tissue in clinical settings. The reuse of corneal stromal lenticules is a highly regarded research area, especially in hyperopia, presbyopia, keratoconus, and some corneal ulcerative diseases, providing new possibilities for addressing corneal tissue shortage and improving surgical outcomes. Here, we review various preservation methods and clinical applications of SMILE-extracted lenticules, highlighting their potential in addressing corneal tissue shortages and the treatment of a variety of corneal diseases. Full article

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

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12 pages, 8504 KiB

Open AccessArticle

Altered Lactylation Myocardial Tissue May Contribute to a More Severe Energy-Deprived State of the Tissue and Left Ventricular Outflow Tract Obstruction in HOCM

by Ruoxuan Li, Jing Wang, Jia Zhao, Jiao Liu, Yuze Qin, Yue Wang, Yiming Yuan, Nan Kang, Lu Yao, Fan Yang, Ke Feng, Lanlan Zhang, Shengjun Ta, Bo Wang and Liwen Liu

Bioengineering 2025, 12(4), 379; https://doi.org/10.3390/bioengineering12040379 - 3 Apr 2025

Abstract

Hypertrophic cardiomyopathy (HCM) is the most common hereditary cardiovascular disease. In general, obstructive hypertrophic cardiomyopathy (HOCM) is more closely related to severe clinical symptoms and adverse clinical outcomes. Therefore, it is necessary to explore the possible causes of HOCM, which may help physicians [...] Read more.

Hypertrophic cardiomyopathy (HCM) is the most common hereditary cardiovascular disease. In general, obstructive hypertrophic cardiomyopathy (HOCM) is more closely related to severe clinical symptoms and adverse clinical outcomes. Therefore, it is necessary to explore the possible causes of HOCM, which may help physicians better understand the disease and effectively control and manage the progression of the disease. In recent years, the discovery of lactylation has provided scholars with a new direction to explore the occurrence of diseases. In cardiovascular diseases, this post-translational modification can exacerbate cardiac dysfunction, and it can also promote the cardiac repair process after myocardial infarction. In this study, we used the myocardial tissue of mice carrying the Myh7 V878A gene mutation site for protein lactylation detection. Through a further analysis of the enriched pathways using KEGG enrichment, GO enrichment, and Wiki Pathways enrichment, we found that the enriched pathways with lactylation modifications in the HOCM mice mainly included the fatty acid oxidation pathway, the tricarboxylic acid cycle pathway, the adrenergic signaling pathway in cardiomyocytes, and the cardiomyocyte hypertrophy pathway. Among the above pathways, significant changes in lactylation occurred in proteins including Acads, Acaa2, Mdh2, Myl2, and Myl3. We used the COIP experiment to verify the omics results and the ELISA assay to verify the function of the enzymes. We found that a decrease in lactylation modifications also led to a decrease in enzyme function. The abnormalities of these proteins not only lead to abnormalities in energy metabolism in the myocardial tissue of HOCM but also may affect myocardial contractility, resulting in the impaired contractile function of HOCM. The results of this study lay a preliminary theoretical foundation for further exploring the pathogenesis of HOCM. Full article

(This article belongs to the Section Cellular and Molecular Bioengineering)

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2 pages, 213 KiB

Open AccessEditorial

The 10th Anniversary of Bioengineering: Biochemical Engineering

by Christoph Herwig

Bioengineering 2025, 12(4), 378; https://doi.org/10.3390/bioengineering12040378 - 2 Apr 2025

Abstract

Biochemical engineering is a multidisciplinary field that utilizes principles of biology, chemistry, and engineering to develop processes and products involving biological materials [...] Full article

(This article belongs to the Special Issue 10th Anniversary of Bioengineering: Biochemical Engineering)

11 pages, 2946 KiB

Open AccessArticle

Predicting the Evolution of Lung Squamous Cell Carcinoma In Situ Using Computational Pathology

by Alon Vigdorovits, Gheorghe-Emilian Olteanu, Ovidiu Tica, Andrei Pascalau, Monica Boros and Ovidiu Pop

Bioengineering 2025, 12(4), 377; https://doi.org/10.3390/bioengineering12040377 - 2 Apr 2025

Abstract

Lung squamous cell carcinoma in situ (SCIS) is the preinvasive precursor lesion of lung squamous cell carcinoma (SCC). Only around two-thirds of these lesions progress to invasive cancer, while one-third undergo spontaneous regression, which presents a significant clinical challenge due to the risk [...] Read more.

Lung squamous cell carcinoma in situ (SCIS) is the preinvasive precursor lesion of lung squamous cell carcinoma (SCC). Only around two-thirds of these lesions progress to invasive cancer, while one-third undergo spontaneous regression, which presents a significant clinical challenge due to the risk of overtreatment. The ability to predict the evolution of SCIS lesions can significantly impact patient management. Our study explores the use of computational pathology in predicting the evolution of SCIS. We used a dataset consisting of 112 H&E-stained whole slide images (WSIs) that were obtained from the Image Data Resource public repository. The dataset corresponded to tumors of patients who underwent biopsies of SCIS lesions and were subsequently followed up by bronchoscopy and CT scans to monitor for progression to SCC. We used this dataset to train two models: a pathomics-based ridge classifier trained on 80 principal components derived from almost 2000 extracted features and a deep convolutional neural network with a modified ResNet18 architecture. The performance of both approaches in predicting progression was assessed. The pathomics-based ridge classifier model obtained an F1-score of 0.77, precision of 0.80, and recall of 0.77. The deep learning model performance was similar, with a WSI-level F1-score of 0.80, precision of 0.71, and recall of 0.90. These findings highlight the potential of computational pathology approaches in providing insights into the evolution of SCIS. Larger datasets will be required in order to train highly accurate models. In the future, computational pathology could be used in predicting outcomes in other preinvasive lesions. Full article

(This article belongs to the Special Issue Computational Pathology and Artificial Intelligence)

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11 pages, 254 KiB

Open AccessArticle

Randomised Clinical Trial: Effect of AH Plus and Neosealer Flo on Postoperative Pain and Healing of Periapical Lesions

by Juan Algar, Cristian Docampo-Vázquez, Cristina Rico-Romano, Ana Boquete-Castro, Cristina Obispo-Díaz and Juan Manuel Aragoneses

Bioengineering 2025, 12(4), 376; https://doi.org/10.3390/bioengineering12040376 - 2 Apr 2025

Abstract

Apical periodontitis is a common inflammatory condition associated with root canal treatment (RCT) failure. The quality of the three-dimensional root canal seal is critical to the success of the treatment. Bioceramic sealants, such as Neosealer Flo, offer biological advantages such as osteoconduction, biocompatibility [...] Read more.

Apical periodontitis is a common inflammatory condition associated with root canal treatment (RCT) failure. The quality of the three-dimensional root canal seal is critical to the success of the treatment. Bioceramic sealants, such as Neosealer Flo, offer biological advantages such as osteoconduction, biocompatibility and sustained calcium ion release, which may improve apical healing. The aim of this study was to compare AH Plus and Neosealer Flo in terms of postoperative pain, extrusion and periapical healing. A single-blind, randomised clinical trial was conducted with 60 patients divided into AH Plus and Neosealer Flo groups. Post-operative pain was assessed using a visual analogue scale (VAS) at 24 and 48 h and at 7 days. Seal quality and periapical healing were assessed at 6 months using the AAE success criteria by clinical and radiographic evaluation. Neosealer Flo resulted in less postoperative pain at 24 h and 7 days compared to AH Plus. Extrusion did not significantly affect pain or correlate with the type of sealer used. Both materials achieved similar periapical healing rates. Neosealer Flo demonstrated advantages in pain reduction, while both sealants showed comparable efficacy. Full article

(This article belongs to the Special Issue Advanced Biomaterials, Coatings, and Techniques: Applications in Medicine)

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