Bioengineering

39 pages, 1240 KB

Open AccessReview

The Therapeutic Scope of Orofacial Mesenchymal Stem Cells

by Bharath Chandra Vaddaram, Akhilesh Kumar Shakya, Brandon R. Zadeh, Diariza M. Lopez, Jon Wagner, Todd Parco and Umadevi Kandalam

Bioengineering 2025, 12(9), 970; https://doi.org/10.3390/bioengineering12090970 - 11 Sep 2025

Abstract

Orofacial Mesenchymal Stem Cells (OMSCs) are an attractive and promising tool for tissue regeneration, with their potential for craniofacial bone repair being a primary focus of research. A key advantage driving their clinical interest is their accessibility from tissues that are often discarded, [...] Read more.

Orofacial Mesenchymal Stem Cells (OMSCs) are an attractive and promising tool for tissue regeneration, with their potential for craniofacial bone repair being a primary focus of research. A key advantage driving their clinical interest is their accessibility from tissues that are often discarded, such as exfoliated deciduous teeth, which circumvents the ethical concerns and donor site morbidity associated with other stem cell sources. The high proliferation ability and multi-differentiation capacity of OMSCs make them a unique resource for tissue engineering. Recently, OMSCs have been explored in the restoration of the heart and skin, treatment of oral mucosal lesions, and regeneration of hard connective tissues such as cartilage. Beyond their direct regenerative capabilities, OMSCs possess potent immunomodulatory functions, enabling them to regulate the immune system in various inflammatory disorders through the secretion of cytokines. This review offers an in-depth update regarding the therapeutic possibilities of OMSCs, highlighting their roles in the regeneration of bone and various tissues, outlining their immunomodulatory capabilities, and examining the essential technologies necessary for their clinical application. Full article

(This article belongs to the Special Issue Recent Progress in Stem Cells and Stem Cell-Derived Biotherapeutics for Regenerative Medicine Applications)

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15 pages, 3677 KB

Open AccessArticle

Ro(a)d to New Functional Materials: Sustainable Isolation of High-Aspect-Ratio β-Chitin Microrods from Marine Algae

by Jan Ludwig, Florian Kauffmann, Sabine Laschat and Ingrid M. Weiss

Bioengineering 2025, 12(9), 969; https://doi.org/10.3390/bioengineering12090969 - 11 Sep 2025

Abstract

High-aspect-ratio rod-shaped chitins such as chitin whiskers or chitin nano- and microfibers are particularly promising for a wide range of applications, including electrorheological suspensions, lightweight reinforcement material for biocomposites, biomedical scaffolds, and food packaging. Here, we report the first mild water-based mechanical extraction [...] Read more.

High-aspect-ratio rod-shaped chitins such as chitin whiskers or chitin nano- and microfibers are particularly promising for a wide range of applications, including electrorheological suspensions, lightweight reinforcement material for biocomposites, biomedical scaffolds, and food packaging. Here, we report the first mild water-based mechanical extraction protocol to isolate β-chitin microrods from the marine algal species Thalassiosira rotula while preserving their structural integrity throughout the process. The resulting microrods could be distributed into two populations based on the fultoportulae from which they are extruded. The rods exhibit typical dimensions of 12.6 ± 4.0 µm in length and 75 ± 21 nm in diameter (outer fultoportulae) or 17.5 ± 4.7 µm in length and 170 ± 39 nm in diameter (central fultoportulae), yielding high aspect ratios of ~168 and ~103 on average, respectively. Due to this environmentally friendly extraction, the high purity of the synthesized chitin, and the renewable algal source, this work introduces a sustainable route to produce pure biogenic β-chitin microrods. Full article

(This article belongs to the Special Issue Engineering Microalgal Systems for a Greener Future)

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21 pages, 1795 KB

Open AccessReview

Nanoparticle-Based Delivery Systems for Synergistic Therapy in Lung Cancers

by Zicheng Deng, Ali Al Siraj, Isabella Lowry, Ellen Ruan, Rohan Patel, Wen Gao, Tanya V. Kalin and Vladimir V. Kalinichenko

Bioengineering 2025, 12(9), 968; https://doi.org/10.3390/bioengineering12090968 - 9 Sep 2025

Abstract

Lung cancer remains the leading cause of cancer-related mortality worldwide, with conventional treatments often limited by systemic toxicity, different tumor sensitivity to the drugs, and the emergence of multidrug resistance. To address these challenges, nanoparticle-based delivery systems have emerged as an innovative strategy, [...] Read more.

Lung cancer remains the leading cause of cancer-related mortality worldwide, with conventional treatments often limited by systemic toxicity, different tumor sensitivity to the drugs, and the emergence of multidrug resistance. To address these challenges, nanoparticle-based delivery systems have emerged as an innovative strategy, enabling the simultaneous transport of multiple agents, including chemotherapeutic drugs and expression vectors, to enhance treatment efficacy and overcome tumor resistance. This review explores various nanocarrier platforms, such as liposomes, solid lipid nanoparticles, polymeric micelles, and inorganic nanoparticles, specifically designed for lung cancer therapy. Synergistic effects and physicochemical properties of therapeutic agents must be carefully considered in the design of nanoparticle-based co-delivery systems for lung cancer therapy. We highlight the applications of these nanoparticle systems in drug–drug, gene–gene, and drug–gene co-delivery approaches. By addressing the limitations of traditional therapies, nanoparticle-based systems offer a promising avenue to improve outcomes in patients with lung cancers. Full article

(This article belongs to the Section Nanobiotechnology and Biofabrication)

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39 pages, 928 KB

Open AccessReview

Intelligence Architectures and Machine Learning Applications in Contemporary Spine Care

by Rahul Kumar, Conor Dougherty, Kyle Sporn, Akshay Khanna, Puja Ravi, Pranay Prabhakar and Nasif Zaman

Bioengineering 2025, 12(9), 967; https://doi.org/10.3390/bioengineering12090967 - 9 Sep 2025

Abstract

The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies has initiated a paradigm shift in contemporary spine care. This narrative review synthesizes advances across imaging-based diagnostics, surgical planning, genomic risk stratification, and post-operative outcome prediction. We critically assess high-performing AI [...] Read more.

The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies has initiated a paradigm shift in contemporary spine care. This narrative review synthesizes advances across imaging-based diagnostics, surgical planning, genomic risk stratification, and post-operative outcome prediction. We critically assess high-performing AI tools, such as convolutional neural networks for vertebral fracture detection, robotic guidance platforms like Mazor X and ExcelsiusGPS, and deep learning-based morphometric analysis systems. In parallel, we examine the emergence of ambient clinical intelligence and precision pharmacogenomics as enablers of personalized spine care. Notably, genome-wide association studies (GWAS) and polygenic risk scores are enabling a shift from reactive to predictive management models in spine surgery. We also highlight multi-omics platforms and federated learning frameworks that support integrative, privacy-preserving analytics at scale. Despite these advances, challenges remain—including algorithmic opacity, regulatory fragmentation, data heterogeneity, and limited generalizability across populations and clinical settings. Through a multidimensional lens, this review outlines not only current capabilities but also future directions to ensure safe, equitable, and high-fidelity AI deployment in spine care delivery. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Spine Research)

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19 pages, 2665 KB

Open AccessArticle

Spectral Analysis of Extrahepatic Bile Ducts During Normothermic Liver Machine Perfusion

by Philipp Zelger, Benjamin Jenewein, Magdalena Sovago, Felix J. Krendl, Andras T. Meszaros, Benno Cardini, Philipp Gehwolf, Johannes D. Pallua, Simone Graf, Stefan Schneeberger, Margot Fodor and Rupert Oberhuber

Bioengineering 2025, 12(9), 966; https://doi.org/10.3390/bioengineering12090966 - 9 Sep 2025

Abstract

Background: Biliary complications (BC) affect 5–32% of liver transplant (LT) patients and include strictures, leaks, stones, and disease recurrence. Their risk increases with extended criteria donor (ECD) livers, contributing to early graft dysfunction. Normothermic liver machine perfusion (NLMP) helps reduce bile duct [...] Read more.

Background: Biliary complications (BC) affect 5–32% of liver transplant (LT) patients and include strictures, leaks, stones, and disease recurrence. Their risk increases with extended criteria donor (ECD) livers, contributing to early graft dysfunction. Normothermic liver machine perfusion (NLMP) helps reduce bile duct (BD) damage overall, but anastomotic region issues persist. This study assessed hyperspectral imaging (HSI) as a non-invasive method to evaluate BD viability during NLMP. Methods: Eleven donor livers underwent NLMP with HSI at the start and end. Seven were transplanted; four were discarded. HSI measured tissue oxygenation, perfusion, and composition. The spectral data were analyzed using ANOVA, post hoc t-tests, and multifactorial ANOVA to assess spectral changes related to BD position, transplant status, and occurrence of BC. Results: Significant spectral changes were found in the BD region during NLMP. Transplanted livers that developed BC showed changes between 525 and 850 nm, while discarded ones had changes between 625 and 725 nm. Specific spectral bands (500–575 nm, 775–1000 nm) were linked to transplant outcomes and BC. Conclusions: HSI shows promise as a non-invasive tool to assess BD viability during NLMP and may help predict post-transplant BC. Full article

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

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10 pages, 299 KB

Open AccessSystematic Review

Clinical Evidence of Wear Occurrence in CFR-PEEK and Metallic Osteosynthesis Implants: A Systematic Literature Review

by Remco Doodkorte, Rachèl Kuske and Jacobus Arts

Bioengineering 2025, 12(9), 965; https://doi.org/10.3390/bioengineering12090965 - 8 Sep 2025

Abstract

Carbon fiber-reinforced polyetheretherketone (CFR-PEEK) as an alternative to metallics in orthopedic implants offers biomechanical and radiological advantages. However, the extent of wear particle generation and its clinical impact are unclear. This systematic review evaluates clinical evidence of wear in fracture fixation devices. A [...] Read more.

Carbon fiber-reinforced polyetheretherketone (CFR-PEEK) as an alternative to metallics in orthopedic implants offers biomechanical and radiological advantages. However, the extent of wear particle generation and its clinical impact are unclear. This systematic review evaluates clinical evidence of wear in fracture fixation devices. A systematic search was conducted to identify clinical studies reporting wear of metallic and CFR-PEEK implants used in extremities. Nineteen studies were included: three prospective cohorts, eight retrospective cohorts, one case series, and six case reports. Among 208 fixation plates, 43 were CFR-PEEK and all 93 intramedullary nails were metallic. Risk of bias ranged from low to serious, mainly due to selection bias. Wear-related complications were reported for both materials. Metallic implants showed elevated serum ion levels, metallic debris in tissues, and, in some cases, metallosis. CFR-PEEK implants showed limited evidence of carbon fiber fragments near implants. One comparative study reported higher inflammatory responses in CFR-PEEK explants, though no direct link between debris and implant removal was found. Both metallic and CFR-PEEK fracture fixation devices generate wear particles, which may induce biological responses. However, wear-related complications appear rare, especially with validated implant designs, and clinical significance of wear debris remains limited. Full article

(This article belongs to the Special Issue Advanced Technology for Implant Polymers in Orthopaedics, Spine, and Trauma)

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17 pages, 2861 KB

Open AccessArticle

Cross-Instrument Data Utilization Based on Laser-Induced Breakdown Spectroscopy (LIBS) for the Identification of Akebia Species

by Yuge Liu, Qianqian Wang, Tianzhong Luo, Zhifang Zhao, Leifu Wang, Shuai Xu, Hao Zhou, Jiquan Zhao, Zixiao Zhou and Geer Teng

Bioengineering 2025, 12(9), 964; https://doi.org/10.3390/bioengineering12090964 - 8 Sep 2025

Viewed by 33

Abstract

New technologies and equipment for medicine analysis and diagnostics have always been critical in clinical medication and pharmaceutical production. Especially in the field of traditional Chinese medicine (TCM) where the chemical composition is not fully clear, cross-device analysis and identification using the same [...] Read more.

New technologies and equipment for medicine analysis and diagnostics have always been critical in clinical medication and pharmaceutical production. Especially in the field of traditional Chinese medicine (TCM) where the chemical composition is not fully clear, cross-device analysis and identification using the same technology can sometimes even lead to misjudgments. Akebia species, capable of inducing heat clearing, diuresis, and anti-inflammatory effects, show great potential in clinical applications. However, the three commonly used species differ in pharmacological effects and therefore should not be used interchangeably. We proposed a method combining LIBS with random forest for species identification and established a modeling and verification scheme across device platforms. Spectra of three Akebia species were collected using two LIBS systems equipped with spectrometers of different resolutions. The data acquired from the low-resolution spectrometer were used for model training, while the data from the high-resolution spectrometers were used for testing. A spectral correction and feature selection (SCFS) method was proposed, in which spectral data were first corrected using a standard lamp, followed by feature selection via analysis of variance (ANOVA) to determine the optimal number of discriminative features. The highest classification accuracy of 80.61% was achieved when 28 features were used. Finally, a post-processing (PP) strategy was applied, where abnormal spectra in the test set were removed using density-based spatial clustering of applications with noise (DBSCAN), resulting in a final classification accuracy of 85.50%. These results demonstrate that the proposed “SCFS-PP” framework effectively enhances the reliability of cross-instrument data utilization and expands the applicability of LIBS in the field of TCM. Full article

(This article belongs to the Section Biochemical Engineering)

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23 pages, 6875 KB

Open AccessArticle

Precision-Controlled Bionic Lung Simulator for Dynamic Respiration Simulation

by Rong-Heng Zhao, Shuai Ren, Yan Shi, Mao-Lin Cai, Tao Wang and Zu-Jin Luo

Bioengineering 2025, 12(9), 963; https://doi.org/10.3390/bioengineering12090963 - 7 Sep 2025

Viewed by 944

Abstract

Mechanical ventilation is indispensable for patients with severe respiratory conditions, and high-fidelity lung simulators play a pivotal role in ventilator testing, clinical training, and respiratory research. However, most existing simulators are passive, single-lung models with limited and discrete control over respiratory mechanics, which [...] Read more.

Mechanical ventilation is indispensable for patients with severe respiratory conditions, and high-fidelity lung simulators play a pivotal role in ventilator testing, clinical training, and respiratory research. However, most existing simulators are passive, single-lung models with limited and discrete control over respiratory mechanics, which constrains their ability to reproduce realistic breathing dynamics. To overcome these limitations, this study presents a dual-chamber lung simulator that can operate in both active and passive modes. The system integrates a sliding mode controller enhanced by a linear extended state observer, enabling the accurate replication of complex respiratory patterns. In active mode, the simulator allows for the precise tuning of respiratory muscle force profiles, lung compliance, and airway resistance to generate physiologically accurate flow and pressure waveforms. Notably, it can effectively simulate pathological conditions such as acute respiratory distress syndrome (ARDS) and chronic obstructive pulmonary disease (COPD) by adjusting key parameters to mimic the characteristic respiratory mechanics of these disorders. Experimental results show that the absolute flow error remains within ±3L/min, and the response time is under 200ms, ensuring rapid and reliable performance. In passive mode, the simulator emulates ventilator-dependent conditions, providing continuous adjustability of lung compliance from 30 to 100mL/cmH

_{2}

O and airway resistance from 2.01 to 14.67

\sqrt{{cmH}_{2} O} / (L / s)

, with compliance deviations limited to ±5%. This design facilitates fine, continuous modulation of key respiratory parameters, making the system well-suited for evaluating ventilator performance, conducting human–machine interaction studies, and simulating pathological respiratory states. Full article

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

21 pages, 1774 KB

Open AccessReview

Current Topics in OCT Applications in Vitreoretinal Surgery

by Shintaro Horie, Takeshi Yoshida and Kyoko Ohno-Matsui

Bioengineering 2025, 12(9), 962; https://doi.org/10.3390/bioengineering12090962 - 7 Sep 2025

Viewed by 222

Abstract

Optical coherence tomography (OCT) is an indispensable tool in modern ophthalmology, where it is used in prior examinations, among various instruments, to assess macular or vitreoretinal diseases. Pathological macular/retinal conditions are almost always examined and evaluated with OCT before and after treatment. Vitreoretinal [...] Read more.

Optical coherence tomography (OCT) is an indispensable tool in modern ophthalmology, where it is used in prior examinations, among various instruments, to assess macular or vitreoretinal diseases. Pathological macular/retinal conditions are almost always examined and evaluated with OCT before and after treatment. Vitreoretinal surgery is one of the most effective treatment options for vitreoretinal diseases. OCT data collected during the treatment of these diseases has accumulated, leading to the reporting of a variety of novel biomarkers and valuable findings related to OCT usage. Recent substantial developments in technology have brought ultra-high-resolution spectral domain/swept source OCT, ultra-widefield OCT, and OCT angiography into the retinal clinic. Here, we review the basic development of the instrument and general applications of OCT in ophthalmology. Subsequently, we provide up-to-date OCT topics based on observations in vitreoretinal surgery. Full article

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

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14 pages, 1276 KB

Open AccessProtocol

Integration of EHR and ECG Data for Predicting Paroxysmal Atrial Fibrillation in Stroke Patients

by Alireza Vafaei Sadr, Manvita Mareboina, Diana Orabueze, Nandini Sarkar, Seyyed Sina Hejazian, Ajith Vemuri, Ravi Shah, Ankit Maheshwari, Ramin Zand and Vida Abedi

Bioengineering 2025, 12(9), 961; https://doi.org/10.3390/bioengineering12090961 - 7 Sep 2025

Viewed by 217

Abstract

Predicting paroxysmal atrial fibrillation (PAF) is challenging due to its transient nature. Existing methods often rely solely on electrocardiogram (ECG) waveforms or Electronic Health Record (EHR)-based clinical risk factors. We hypothesized that explicitly balancing the contributions of these heterogeneous data sources could improve [...] Read more.

Predicting paroxysmal atrial fibrillation (PAF) is challenging due to its transient nature. Existing methods often rely solely on electrocardiogram (ECG) waveforms or Electronic Health Record (EHR)-based clinical risk factors. We hypothesized that explicitly balancing the contributions of these heterogeneous data sources could improve prediction accuracy. We developed a Transformer-based deep learning model that integrates 12-lead ECG signals and 47 structured EHR variables from 189 patients with cryptogenic stroke, including 49 with PAF. By systematically varying the relative contributions of ECG and EHR data, we identified an optimal ratio for prediction. Best performance (accuracy: 0.70, sensitivity: 0.72, specificity: 0.87, Area Under Curve - Receiver Operating Characteristics (AUROC): 0.65, Area Under the Precision-Recall Curve (AUPRC): 0.43) was achieved using a 5-fold cross-validation when EHR data contributed one-third and ECG data two-thirds of the model’s input. This multimodal approach outperformed unimodal models, improving accuracy by 35% over EHR-only and 5% over ECG-only methods. Our results support the value of combining ECG and structured EHR information to improve accuracy and sensitivity in this pilot cohort, motivating validation in larger studies. Full article

(This article belongs to the Special Issue Machine Learning Technology in Predictive Healthcare)

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10 pages, 477 KB

Open AccessArticle

Evaluation of the Validity and Reliability of NeuroSkin’s Wearable Sensor Gait Analysis Device in Healthy Individuals

by Maël Descollonges, Baptiste Moreau, Nicolas Feppon, Oussama Abdoun, Perrine Séguin, Lana Popovic-Maneski, Julie Di Marco and Amine Metani

Bioengineering 2025, 12(9), 960; https://doi.org/10.3390/bioengineering12090960 - 6 Sep 2025

Viewed by 288

Abstract

Gait analysis plays a crucial role in assessing and monitoring the progress of individuals undergoing rehabilitation. This preliminary validation study aims to compare the performance of a new wearable system, NeuroSkin^®, equipped with embedded sensors (inertial measurement unit and pressure sensors), [...] Read more.

Gait analysis plays a crucial role in assessing and monitoring the progress of individuals undergoing rehabilitation. This preliminary validation study aims to compare the performance of a new wearable system, NeuroSkin^®, equipped with embedded sensors (inertial measurement unit and pressure sensors), with the non-wearable gold standard, GAITRite^®, in assessing spatio-temporal parameters during gait. Data was collected from nine healthy participants wearing the NeuroSkin while walking on the GAITRite walkway. Temporal parameters were calculated using the pressure sensors of the NeuroSkin^® to detect heel strike (HS) and toe off (TO) on both sides. Distances were calculated using vertical hip acceleration with an inverted pendulum method. We found that the level of agreement between NeuroSkin^® and GAITRite^® measures was excellent for speed, cadence, as well as length and duration of stride and step (lower bound of intraclass correlation coefficients (ICCs) > 0.95), and moderate to excellent for stance and swing durations (ICC > 0.5). These levels of agreement are comparable to the known test–retest reliability of GAITRite^® measures. These results demonstrate the potential of NeuroSkin^® as an embedded gait assessment system for healthy subjects. As this study was conducted exclusively in healthy adults, the results are not directly generalizable to clinical populations. Thus, future studies are needed to investigate its use in patients. Full article

(This article belongs to the Special Issue Intelligent Systems for Human Action Recognition)

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15 pages, 2654 KB

Open AccessArticle

The Evaluation of a Deep Learning Approach to Automatic Segmentation of Teeth and Shade Guides for Tooth Shade Matching Using the SAM2 Algorithm

by KyeongHwan Han, JaeHyung Lim, Jin-Soo Ahn and Ki-Sun Lee

Bioengineering 2025, 12(9), 959; https://doi.org/10.3390/bioengineering12090959 - 6 Sep 2025

Viewed by 289

Abstract

Accurate shade matching is essential in restorative and prosthetic dentistry yet remains difficult due to subjectivity in visual assessments. We develop and evaluate a deep learning approach for the simultaneous segmentation of natural teeth and shade guides in intraoral photographs using four fine-tuned [...] Read more.

Accurate shade matching is essential in restorative and prosthetic dentistry yet remains difficult due to subjectivity in visual assessments. We develop and evaluate a deep learning approach for the simultaneous segmentation of natural teeth and shade guides in intraoral photographs using four fine-tuned variants of Segment Anything Model 2 (SAM2: tiny, small, base plus, and large) and a UNet baseline trained under the same protocol. The spatial performance was assessed using the Dice Similarity Coefficient (DSC), the Intersection over the Union (IoU), and the 95th-percentile Hausdorff distance normalized by the ground-truth equivalent diameter (HD95). The color consistency within masks was quantified by the coefficient of variation (CV) of the CIELAB components (L*, a*, b*). The perceptual color difference was measured using CIEDE2000 (ΔE00). On a held-out test set, all SAM2 variants achieved a high overlap accuracy; SAM2-large performed best (DSC: 0.987 ± 0.006; IoU: 0.975 ± 0.012; HD95: 1.25 ± 1.80%), followed by SAM2-small (0.987 ± 0.008; 0.974 ± 0.014; 2.96 ± 11.03%), SAM2-base plus (0.985 ± 0.011; 0.971 ± 0.021; 1.71 ± 3.28%), and SAM2-tiny (0.979 ± 0.015; 0.959 ± 0.028; 6.16 ± 11.17%). UNet reached a DSC = 0.972 ± 0.020, an IoU = 0.947 ± 0.035, and an HD95 = 6.54 ± 16.35%. The CV distributions for all of the prediction models closely matched the ground truth (e.g., GT L*: 0.164 ± 0.040; UNet: 0.144 ± 0.028; SAM2-small: 0.164 ± 0.038; SAM2-base plus: 0.162 ± 0.039). The full-mask ΔE00 was low across models, with the summary statistics reported as the median (mean ± SD): UNet: 0.325 (0.487 ± 0.364); SAM2-tiny: 0.162 (0.410 ± 0.665); SAM2-small: 0.078 (0.126 ± 0.166); SAM2-base plus: 0.072 (0.198 ± 0.417); SAM2-large: 0.065 (0.167 ± 0.257). These ΔE00 values lie well below the ≈1 just noticeable difference threshold on average, indicating close chromatic agreement between the predictions and annotations. Within a single dataset and training protocol, fine-tuned SAM2, especially its larger variants, provides robust spatial accuracy, boundary reliability, and color fidelity suitable for clinical shade-matching workflows, while UNet offers a competitive convolutional baseline. These results indicate technical feasibility rather than clinical validation; broader baselines and external, multi-center evaluations are needed to determine its suitability for routine shade-matching workflows. Full article

(This article belongs to the Special Issue AI Advancements in Healthcare: Medical Imaging and Sensing Technologies)

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14 pages, 15180 KB

Open AccessArticle

A Neural-Operator Surrogate for Platelet Deformation Across Capillary Numbers

by Marco Laudato

Bioengineering 2025, 12(9), 958; https://doi.org/10.3390/bioengineering12090958 - 6 Sep 2025

Viewed by 278

Abstract

Reliable multiscale models of thrombosis require platelet-scale fidelity at organ-scale cost, a gap that scientific machine learning has the potential to narrow. We trained a DeepONet surrogate on platelet dynamics generated with LAMMPS for platelets spanning ten elastic moduli and capillary numbers (0.07–0.77). [...] Read more.

Reliable multiscale models of thrombosis require platelet-scale fidelity at organ-scale cost, a gap that scientific machine learning has the potential to narrow. We trained a DeepONet surrogate on platelet dynamics generated with LAMMPS for platelets spanning ten elastic moduli and capillary numbers (0.07–0.77). The network takes as input the wall shear stress, bond stiffness, time, and initial particle coordinates and returns the full three-dimensional deformation of the membrane. Mean-squared-error minimization with Adam and adaptive learning-rate decay yields a median displacement error below 1%, a 90th percentile below 3%, and a worst case below 4% over the entire calibrated range while accelerating computation by four to five orders of magnitude. Leave-extremes-out retraining shows acceptable extrapolation: the held-out stiffest and most compliant platelets retain sub-3% median error and an 8% maximum. Error peaks coincide with transient membrane self-contact, suggesting improvements via graph neural trunks and physics-informed torque regularization. These results represent a first demonstration of how the surrogate has the potential for coupling with continuum CFD, enabling future platelet-resolved hemodynamic simulations in patient-specific geometries and opening new avenues for predictive thrombosis modeling. Full article

(This article belongs to the Special Issue Fundamentals and Novel Applications of Fluid Mechanics, Biomechanics, and Acoustics in Biomedical Engineering, 2nd Edition)

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20 pages, 1427 KB

Open AccessArticle

Performance Insights in Speed Climbing: Quantitative and Qualitative Analysis of Key Movement Metrics

by Dominik Pandurević, Paweł Draga, Alexander Sutor and Klaus Hochradel

Bioengineering 2025, 12(9), 957; https://doi.org/10.3390/bioengineering12090957 - 6 Sep 2025

Viewed by 295

Abstract

This study presents a comprehensive analysis of Speed Climbing athletes by examining motion parameters critical to elite performance. As such, several key values are extracted from about 900 competition recordings in order to generate a dataset for the identification of patterns in athletes’ [...] Read more.

This study presents a comprehensive analysis of Speed Climbing athletes by examining motion parameters critical to elite performance. As such, several key values are extracted from about 900 competition recordings in order to generate a dataset for the identification of patterns in athletes’ technique and efficiency. A CNN-based framework is used to automate the detection of human keypoints and features, enabling a large-scale evaluation of climbing dynamics. The results revealed significant variations in performance for single sections of the wall, particularly in relation to start reaction times (with differences of up to 0.27 s) and increased split times the closer the athletes are to the end of the Speed Climbing wall (from 0.39 s to 0.45 s). In addition, a more detailed examination of the movement sequences was carried out by analyzing the velocity trajectories of hands and feet. The results showed that coordinated and harmonic movements, especially of the lower limbs, correlate strongly with the performance outcome. To ensure an individualized view of the data points, a comparison was made between multiple athletes, revealing insights into the influence of individual biomechanics on the efficiency of movements. The findings provide both trainers and athletes with interesting insights in relation to tailoring training methods by including split time benchmarks and limb coordination. Full article

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

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22 pages, 2636 KB

Open AccessArticle

Defining a Simplified Process in Yeast for Production of Enveloped VLP Dengue Vaccine

by Salomé de Sá Magalhães, Stephen A. Morris, Shinta Kusumawardani, Acep Riza Wijayadikusumah, Neni Nurainy and Eli Keshavarz-Moore

Bioengineering 2025, 12(9), 956; https://doi.org/10.3390/bioengineering12090956 - 5 Sep 2025

Viewed by 307

Abstract

Dengue is a rapidly spreading mosquito-borne viral infection, with increasing reports of outbreaks globally. According to the World Health Organization (WHO), by 30 April 2024, over 7.6 million dengue cases were reported, including 3.4 million confirmed cases, more than 16,000 severe cases, and [...] Read more.

Dengue is a rapidly spreading mosquito-borne viral infection, with increasing reports of outbreaks globally. According to the World Health Organization (WHO), by 30 April 2024, over 7.6 million dengue cases were reported, including 3.4 million confirmed cases, more than 16,000 severe cases, and over 3000 deaths. As dengue remains endemic in many regions, there is a critical need for the development of new vaccines and manufacturing processes that are efficient, cost-effective, and capable of meeting growing demand. In this study, we explore an alternative process development pathway for the future manufacturing of a dengue vaccine, utilizing Komagataella phaffii (Pichia pastoris) as the host organism, one of the most promising candidates for the expression of heterologous proteins in vaccine development. It combines the speed and ease of highly efficient prokaryotic platforms with some key capabilities of mammalian systems, making it ideal for scalable and cost-effective production. The key outcomes of our research include (i) demonstrating the versatility of the Komagataella phaffii platform in the production of dengue viral-like particles (VLPs); (ii) optimizing the culture process using Design of Experiments (DoE) approaches in small-scale bioreactors; (iii) developing a novel purification platform for enveloped VLPs (eVLPs), and (iv) establishing alternative biophysical characterization methods for the dengue vaccine prototype. These findings provide a promising foundation for efficient and scalable production of dengue vaccines, addressing both technical and operational challenges in vaccine manufacturing. Full article

(This article belongs to the Section Biochemical Engineering)

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24 pages, 815 KB

Open AccessReview

Porous Structures, Surface Modifications, and Smart Technologies for Total Ankle Arthroplasty: A Narrative Review

by Joshua M. Tennyson, Michael O. Sohn, Arun K. Movva, Kishen Mitra, Conor N. O’Neill, Albert T. Anastasio and Samuel B. Adams

Bioengineering 2025, 12(9), 955; https://doi.org/10.3390/bioengineering12090955 - 5 Sep 2025

Viewed by 416

Abstract

Surface engineering and architectural design represent key frontiers in total ankle arthroplasty (TAA) implant development. This narrative review examines biointegration strategies, focusing on porous structures, surface modification techniques, and emerging smart technologies. Optimal porous architectures with 300–600 µm pore sizes facilitate bone ingrowth [...] Read more.

Surface engineering and architectural design represent key frontiers in total ankle arthroplasty (TAA) implant development. This narrative review examines biointegration strategies, focusing on porous structures, surface modification techniques, and emerging smart technologies. Optimal porous architectures with 300–600 µm pore sizes facilitate bone ingrowth and osseointegration, while functionally graded structures address regional biomechanical demands. Surface modification encompasses bioactive treatments (such as calcium phosphate coatings), topographical modifications (including micro/nanotexturing), antimicrobial approaches (utilizing metallic ions or antibiotic incorporation), and wear-resistant technologies (such as diamond-like carbon coatings). Multifunctional approaches combine strategies to simultaneously address infection prevention, enhance osseointegration, and improve wear resistance. Emerging technologies include biodegradable scaffolds, biomimetic surface nanotechnology, and intelligent sensor-based monitoring systems. While many innovations remain in the research stage, they demonstrate the potential to establish TAA as a comprehensive alternative to arthrodesis. Successful implant design requires integrated surface engineering tailored to the ankle joint’s demanding biomechanical and biological environment Full article

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

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23 pages, 1928 KB

Open AccessSystematic Review

Eye Tracking-Enhanced Deep Learning for Medical Image Analysis: A Systematic Review on Data Efficiency, Interpretability, and Multimodal Integration

by Jiangxia Duan, Meiwei Zhang, Minghui Song, Xiaopan Xu and Hongbing Lu

Bioengineering 2025, 12(9), 954; https://doi.org/10.3390/bioengineering12090954 - 5 Sep 2025

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Abstract

Deep learning (DL) has revolutionized medical image analysis (MIA), enabling early anomaly detection, precise lesion segmentation, and automated disease classification. However, its clinical integration faces two major challenges: reliance on limited, narrowly annotated datasets that inadequately capture real-world patient diversity, and the inherent [...] Read more.

Deep learning (DL) has revolutionized medical image analysis (MIA), enabling early anomaly detection, precise lesion segmentation, and automated disease classification. However, its clinical integration faces two major challenges: reliance on limited, narrowly annotated datasets that inadequately capture real-world patient diversity, and the inherent “black-box” nature of DL decision-making, which complicates physician scrutiny and accountability. Eye tracking (ET) technology offers a transformative solution by capturing radiologists’ gaze patterns to generate supervisory signals. These signals enhance DL models through two key mechanisms: providing weak supervision to improve feature recognition and diagnostic accuracy, particularly when labeled data are scarce, and enabling direct comparison between machine and human attention to bridge interpretability gaps and build clinician trust. This approach also extends effectively to multimodal learning models (MLMs) and vision–language models (VLMs), supporting the alignment of machine reasoning with clinical expertise by grounding visual observations in diagnostic context, refining attention mechanisms, and validating complex decision pathways. Conducted in accordance with the PRISMA statement and registered in PROSPERO (ID: CRD42024569630), this review synthesizes state-of-the-art strategies for ET-DL integration. We further propose a unified framework in which ET innovatively serves as a data efficiency optimizer, a model interpretability validator, and a multimodal alignment supervisor. This framework paves the way for clinician-centered AI systems that prioritize verifiable reasoning, seamless workflow integration, and intelligible performance, thereby addressing key implementation barriers and outlining a path for future clinical deployment. Full article

(This article belongs to the Section Biosignal Processing)

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17 pages, 2874 KB

Open AccessArticle

Emulating Hyperspectral and Narrow-Band Imaging for Deep-Learning-Driven Gastrointestinal Disorder Detection in Wireless Capsule Endoscopy

by Chu-Kuang Chou, Kun-Hua Lee, Riya Karmakar, Arvind Mukundan, Pratham Chandraskhar Gade, Devansh Gupta, Chang-Chao Su, Tsung-Hsien Chen, Chou-Yuan Ko and Hsiang-Chen Wang

Bioengineering 2025, 12(9), 953; https://doi.org/10.3390/bioengineering12090953 - 4 Sep 2025

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Abstract

Diagnosing gastrointestinal disorders (GIDs) remains a significant challenge, particularly when relying on wireless capsule endoscopy (WCE), which lacks advanced imaging enhancements like Narrow Band Imaging (NBI). To address this, we propose a novel framework, the Spectrum-Aided Vision Enhancer (SAVE), especially designed to transform [...] Read more.

Diagnosing gastrointestinal disorders (GIDs) remains a significant challenge, particularly when relying on wireless capsule endoscopy (WCE), which lacks advanced imaging enhancements like Narrow Band Imaging (NBI). To address this, we propose a novel framework, the Spectrum-Aided Vision Enhancer (SAVE), especially designed to transform standard white light (WLI) endoscopic images into spectrally enriched representations that emulate both hyperspectral imaging (HSI) and NBI formats. By leveraging color calibration through the Macbeth Color Checker, gamma correction, CIE 1931 XYZ transformation, and principal component analysis (PCA), SAVE reconstructs detailed spectral information from conventional RGB inputs. Performance was evaluated using the Kvasir-v2 dataset, which includes 6490 annotated images spanning eight GI-related categories. Deep learning models like Inception-Net V3, MobileNetV2, MobileNetV3, and AlexNet were trained on both original WLI- and SAVE-enhanced images. Among these, MobileNetV2 achieved an F1-score of 96% for polyp classification using SAVE, and AlexNet saw a notable increase in average accuracy to 84% when applied to enhanced images. Image quality assessment showed high structural similarity (SSIM scores of 93.99% for Olympus endoscopy and 90.68% for WCE), confirming the fidelity of the spectral transformations. Overall, the SAVE framework offers a practical, software-based enhancement strategy that significantly improves diagnostic accuracy in GI imaging, with strong implications for low-cost, non-invasive diagnostics using capsule endoscopy systems. Full article

(This article belongs to the Special Issue Applications of Computational Modeling in Biomedical Image and Signal Processing—2nd Edition)

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21 pages, 2336 KB

Open AccessArticle

Machine and Deep Learning on Radiomic Features from Contrast-Enhanced Mammography and Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Breast Cancer Characterization

by Roberta Fusco, Vincenza Granata, Teresa Petrosino, Paolo Vallone, Maria Assunta Daniela Iasevoli, Mauro Mattace Raso, Sergio Venanzio Setola, Davide Pupo, Gerardo Ferrara, Annarita Fanizzi, Raffaella Massafra, Miria Lafranceschina, Daniele La Forgia, Laura Greco, Francesca Romana Ferranti, Valeria De Soccio, Antonello Vidiri, Francesca Botta, Valeria Dominelli, Enrico Cassano, Charlotte Marguerite Lucille Trombadori, Paolo Belli, Giovanna Trecate, Chiara Tenconi, Maria Carmen De Santis, Luca Boldrini and Antonella Petrillo Show full author list Hide full author list

Bioengineering 2025, 12(9), 952; https://doi.org/10.3390/bioengineering12090952 - 2 Sep 2025

Viewed by 725

Abstract

Objective: The aim of this study was to evaluate the accuracy of machine and deep learning approaches on radiomics features obtained by Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) and contrast enhanced mammography (CEM) in the characterization of breast cancer and in the [...] Read more.

Objective: The aim of this study was to evaluate the accuracy of machine and deep learning approaches on radiomics features obtained by Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) and contrast enhanced mammography (CEM) in the characterization of breast cancer and in the prediction of the tumor molecular profile. Methods: A total of 153 patients with malignant and benign lesions were analyzed and underwent MRI examinations. Considering the histological findings as the ground truth, three different types of findings were used in the analysis: (1) benign versus malignant lesions; (2) G1 + G2 vs. G3 classification; (3) the presence of human epidermal growth factor receptor 2 (HER2+ vs. HER2−). Radiomic features (n = 851) were extracted from manually segmented regions of interest using the PyRadiomics platform, following IBSI-compliant protocols. Highly correlated features were excluded, and the remaining features were standardized using z-score normalization. A feature selection process based on Elastic Net regularization (α = 0.5) was used to reduce dimensionality. Synthetic balancing of the training data was applied using the ROSE method to address class imbalance. Model performance was evaluated using repeated 10-fold cross-validation and AUC-based metrics. Results: Among the 153 patients enrolled in the studies, 113 were malignant lesions. Among the 113 malignant lesions, 32 had high grading (G3) and 66 had the HER2+ receptor. Radiomic features derived from both CEM and DCE-MRI showed strong discriminative performance for malignancy detection, with several features achieving AUCs above 0.80. Gradient Boosting Machine (GBM) achieved the highest accuracy (0.911) and AUC (0.907) in differentiating benign from malignant lesions. For tumor grading, the neural network model attained the best accuracy (0.848), while LASSO yielded the highest sensitivity (0.667) for detecting high-grade tumors. In predicting HER2+ status, the neural network also performed best (AUC = 0.669), with a sensitivity of 0.842. Conclusions: Radiomics-based machine learning models applied to multiparametric CEM and DCE-MRI images offer promising, non-invasive tools for breast cancer characterization. The models effectively distinguished benign from malignant lesions and showed potential in predicting histological grade and HER2 status. These results demonstrate that radiomic features extracted from CEM and DCE-MRI, when analyzed through machine and deep learning models, can support accurate breast cancer characterization. Such models may assist clinicians in early diagnosis, histological grading, and biomarker assessment, potentially enhancing personalized treatment planning and non-invasive decision-making in routine practice. Full article

(This article belongs to the Special Issue Advances in Computational Imaging and Artificial Intelligence for Biomedical and Clinical Applications)

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26 pages, 2735 KB

Open AccessArticle

Time Series Classification of Autism Spectrum Disorder Using the Light-Adapted Electroretinogram

by Sergey Chistiakov, Anton Dolganov, Paul A. Constable, Aleksei Zhdanov, Mikhail Kulyabin, Dorothy A. Thompson, Irene O. Lee, Faisal Albasu, Vasilii Borisov and Mikhail Ronkin

Bioengineering 2025, 12(9), 951; https://doi.org/10.3390/bioengineering12090951 - 2 Sep 2025

Viewed by 769

Abstract

The clinical electroretinogram (ERG) is a non-invasive diagnostic test used to assess the functional state of the retina by recording changes in the bioelectric potential following brief flashes of light. The recorded ERG waveform offers ways for diagnosing both retinal dystrophies and neurological [...] Read more.

The clinical electroretinogram (ERG) is a non-invasive diagnostic test used to assess the functional state of the retina by recording changes in the bioelectric potential following brief flashes of light. The recorded ERG waveform offers ways for diagnosing both retinal dystrophies and neurological disorders such as autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), and Parkinson’s disease. In this study, different time-series-based machine learning methods were used to classify ERG signals from ASD and typically developing individuals with the aim of interpreting the decisions made by the models to understand the classification process made by the models. Among the time-series classification (TSC) algorithms, the Random Convolutional Kernel Transform (ROCKET) algorithm showed the most accurate results with the fewest number of predictive errors. For the interpretation analysis of the model predictions, the SHapley Additive exPlanations (SHAP) algorithm was applied to each of the models’ predictions, with the ROCKET and KNeighborsTimeSeriesClassifier (TS-KNN) algorithms showing more suitability for ASD classification as they provided better-defined explanations by discarding the uninformative non-physiological part of the ERG waveform baseline signal and focused on the time regions incorporating the clinically significant a- and b-waves of the ERG. With the potential broadening scope of practice for visual electrophysiology within neurological disorders, TSC may support the identification of important regions in the ERG time series to support the classification of neurological disorders and potential retinal diseases. Full article

(This article belongs to the Special Issue Retinal Biomarkers: Seeing Diseases in the Eye)

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29 pages, 5939 KB

Open AccessArticle

Structure-Preserving Histopathological Stain Normalization via Attention-Guided Residual Learning

by Nuwan Madusanka, Prathiksha Padmanabha, Kasunika Guruge and Byeong-il Lee

Bioengineering 2025, 12(9), 950; https://doi.org/10.3390/bioengineering12090950 - 1 Sep 2025

Viewed by 463

Abstract

Staining variability in histopathological images compromises automated diagnostic systems by affecting the reliability of computational pathology algorithms. Existing normalization methods prioritize color consistency but often sacrifice critical morphological details essential for accurate diagnosis. This work proposes a novel deep learning framework, integrating enhanced [...] Read more.

Staining variability in histopathological images compromises automated diagnostic systems by affecting the reliability of computational pathology algorithms. Existing normalization methods prioritize color consistency but often sacrifice critical morphological details essential for accurate diagnosis. This work proposes a novel deep learning framework, integrating enhanced residual learning with multi-scale attention mechanisms for structure-preserving stain normalization. The approach decomposes the transformation process into base reconstruction and residual refinement components, incorporating attention-guided skip connections and progressive curriculum learning. The method was evaluated on the MITOS-ATYPIA-14 dataset containing 1420 paired H&E-stained breast cancer images from two scanners. The framework achieved exceptional performance with a structural similarity index (SSIM) of 0.9663 ± 0.0076, representing 4.6% improvement over the best baseline (StainGAN). Peak signal-to-noise ratio (PSNR) reached 24.50 ± 1.57 dB, surpassing all comparison methods. An edge preservation loss of 0.0465 ± 0.0088 demonstrated a 35.6% error reduction compared to the next best method. Color transfer fidelity reached 0.8680 ± 0.0542 while maintaining superior perceptual quality (FID: 32.12, IS: 2.72 ± 0.18). The attention-guided residual learning framework successfully maintains structural integrity during stain normalization, with superior performance across diverse tissue types, making it suitable for clinical deployment in multi-institutional digital pathology workflows. Full article

(This article belongs to the Section Biosignal Processing)

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14 pages, 752 KB

Open AccessArticle

High-Precision Multi-Axis Robotic Printing: Optimized Workflow for Complex Tissue Creation

by Erfan Shojaei Barjuei, Joonhwan Shin, Keekyoung Kim and Jihyun Lee

Bioengineering 2025, 12(9), 949; https://doi.org/10.3390/bioengineering12090949 - 31 Aug 2025

Viewed by 551

Abstract

Three-dimensional bioprinting holds great promise for tissue engineering, but struggles with fabricating complex curved geometries such as vascular networks. Though precise, traditional Cartesian bioprinters are constrained by linear layer-by-layer deposition along fixed axes, resulting in limitations such as the stair-step effect. Multi-axis robotic [...] Read more.

Three-dimensional bioprinting holds great promise for tissue engineering, but struggles with fabricating complex curved geometries such as vascular networks. Though precise, traditional Cartesian bioprinters are constrained by linear layer-by-layer deposition along fixed axes, resulting in limitations such as the stair-step effect. Multi-axis robotic bioprinting addresses these challenges by allowing dynamic nozzle orientation and motion along curvilinear paths, enabling conformal printing on anatomically relevant surfaces. Although robotic arms offer lower mechanical precision than CNC stages, accuracy can be enhanced through methods such as vision-based toolpath correction. This study presents a modular multi-axis robotic embedded bioprinting platform that integrates a six-degrees-of-freedom robotic arm, a pneumatic extrusion system, and a viscoplastic support bath. A streamlined workflow combines CAD modeling, CAM slicing, robotic simulation, and automated execution for efficient fabrication. Two case studies validate the system’s ability to print freeform surfaces and vascular-inspired tubular constructs with high fidelity. The results highlight the platform’s versatility and potential for complex tissue fabrication and future in situ bioprinting applications. Full article

(This article belongs to the Special Issue Insights in Tissue Engineering: Novel Developments, Current Challenges, and Future Perspectives)

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16 pages, 1010 KB

Open AccessReview

Applications of Adipose Tissue Micrografts (ATM) and Dermis Micrografts (DMG) in Wound Healing: A Scoping Review of Clinical Studies

by Konstantinos Zapsalis, Orestis Ioannidis, Elissavet Anestiadou, Maria Pantelidou, Konstantinos Siozos, Christos Xylas, Georgios Gemousakakis, Angeliki Cheva, Chryssa Bekiari, Antonia Loukousia, Savvas Symeonidis, Stefanos Bitsianis, Manousos-Georgios Pramateftakis, Efstathios Kotidis, Ioannis Mantzoros and Stamatios Angelopoulos

Bioengineering 2025, 12(9), 948; https://doi.org/10.3390/bioengineering12090948 - 31 Aug 2025

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Abstract

Adipose tissue micrografts (ATM) and dermis micrografts (DMG) have emerged as promising autologous therapies in regenerative wound care, leveraging mechanically disaggregated cell–matrix constructs to modulate the wound microenvironment and promote tissue repair. This scoping review systematically analyzed clinical studies investigating ATMs and DMGs [...] Read more.

Adipose tissue micrografts (ATM) and dermis micrografts (DMG) have emerged as promising autologous therapies in regenerative wound care, leveraging mechanically disaggregated cell–matrix constructs to modulate the wound microenvironment and promote tissue repair. This scoping review systematically analyzed clinical studies investigating ATMs and DMGs in acute and chronic wounds. Eight studies, comprising randomized controlled trials, observational studies, and case series, were identified, involving diverse wound types such as burns, ulcers, surgical dehiscence, and posttraumatic defects. All interventions utilized mechanical disaggregation (Rigenera^® system) to produce micrografts, which were applied via perilesional injection, scaffold-assisted delivery, or topical administration. Outcomes consistently demonstrated accelerated re-epithelialization, enhanced angiogenesis, improved scar remodeling, and low complication rates. In select studies, micrografts were combined with platelet-rich fibrin or stromal vascular fraction, suggesting potential synergistic effects. While one randomized trial showed superior healing outcomes with DMGs over collagen scaffolds, others yielded mixed results, likely reflecting heterogeneity in methodology and outcome measures. Overall, the available clinical evidence supports the safety, feasibility, and biological activity of micrograft-based therapies. However, larger, standardized, and mechanistically driven studies are required to validate their efficacy and define optimal protocols across wound etiologies. Full article

(This article belongs to the Special Issue Recent Advancements in Wound Healing and Repair)

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36 pages, 4960 KB

Open AccessSystematic Review

The Effects of Rehabilitation Programs Incorporating Breathing Interventions on Chronic Neck Pain Among Patients with Forward Head Posture: A Systematic Review and Meta-Analysis

by Seri Park, Kihyun Kim and Minbong Kang

Bioengineering 2025, 12(9), 947; https://doi.org/10.3390/bioengineering12090947 - 31 Aug 2025

Viewed by 804

Abstract

The effectiveness of breathing interventions on postural alignment, pain reduction, and functional improvement in patients with forward head posture (FHP) and chronic neck pain remains uncertain. Previously conducted randomized controlled trials (RCTs) that involved breathing interventions were identified through searches of the PubMed, [...] Read more.

The effectiveness of breathing interventions on postural alignment, pain reduction, and functional improvement in patients with forward head posture (FHP) and chronic neck pain remains uncertain. Previously conducted randomized controlled trials (RCTs) that involved breathing interventions were identified through searches of the PubMed, Cochrane Library, Web of Science, and Scopus databases. Studies were included if they applied diaphragmatic breathing, breathing muscle training, or feedback breathing exercises for at least 2 weeks to chronic neck pain (duration ≥ 3 months) and/or forward head posture. The craniovertebral angle (CVA), the visual analog scale (VAS), and the neck disability index (NDI) were the primary outcome measures. The results showed that breathing interventions had a moderate effect size in terms of improving the CVA. Limited effects were observed for pain reduction, and improvements in neck disability approached statistical significance. However, despite these positive findings, the overall evidence was rated as ‘very low certainty’ in the GRADE assessment, primarily due to high heterogeneity among studies, limited sample sizes, and the potential for unit-of-analysis errors in diagnosis-based subgroup analyses. Consequently, their overall effectiveness in chronic neck pain was limited. Future research is needed to explore a multidisciplinary approach to neck pain using standardized protocols and larger samples. Full article

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

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52 pages, 44108 KB

Open AccessArticle

Experimental Validation of Time-Explicit Ultrasound Propagation Models with Sound Diffusivity or Viscous Attenuation in Biological Tissues Using COMSOL Multiphysics

by Nuno A. T. C. Fernandes, Shivam Sharma, Ana Arieira, Betina Hinckel, Filipe Silva, Ana Leal and Óscar Carvalho

Bioengineering 2025, 12(9), 946; https://doi.org/10.3390/bioengineering12090946 - 31 Aug 2025

Viewed by 502

Abstract

Ultrasonic wave attenuation in biological tissues arises from complex interactions between mechanical, structural, and fluidic properties, making it essential to identify dominant mechanisms for accurate simulation and device design. This work introduces a novel integration of experimentally measured tissue parameters into time-explicit nonlinear [...] Read more.

Ultrasonic wave attenuation in biological tissues arises from complex interactions between mechanical, structural, and fluidic properties, making it essential to identify dominant mechanisms for accurate simulation and device design. This work introduces a novel integration of experimentally measured tissue parameters into time-explicit nonlinear acoustic wave simulations, in which the equations are directly solved in the time domain using an explicit solver. This approach captures the full transient waveform without relying on frequency-domain simplifications, offering a more realistic representation of ultrasound propagation in heterogeneous media. The study estimates both sound diffusivity and viscous damping parameters (dynamic and bulk viscosity) for a broad range of ex vivo tissues (skin, adipose tissue, skeletal muscle, trabecular/cortical bone, liver, myocardium, kidney, tendon, ligament, cartilage, and gray/white brain matter). Four regression models (power law, linear, exponential, logarithmic) were applied to characterize their frequency dependence between 0.5 and 5 MHz. Results show that attenuation is more strongly driven by bulk viscosity than dynamic viscosity, particularly in fluid-rich tissues such as liver and myocardium, where compressional damping dominates. The power-law model consistently provided the best fit for all attenuation metrics, revealing a scale-invariant frequency relationship. Tissues such as cartilage and brain showed weaker viscous responses, suggesting the need for alternative modeling approaches. These findings not only advance fundamental understanding of attenuation mechanisms but also provide validated parameters and modeling strategies to improve predictive accuracy in therapeutic ultrasound planning and the design of non-invasive, tissue-specific acoustic devices. Full article

(This article belongs to the Special Issue Recent Advances in the Application of Mathematical and Computational Models in Biomedical Science and Engineering (2nd Edition))

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9 pages, 626 KB

Open AccessArticle

The Influence of a Predegenerated Autological Nerve Graft on the Results of Peripheral Nerve Repair in the Upper Extremities After Injuries

by Krzysztof Suszyński, Bartłomiej Błaszczyk, Dariusz Górka and Stanisław Kwiek

Bioengineering 2025, 12(9), 945; https://doi.org/10.3390/bioengineering12090945 - 31 Aug 2025

Viewed by 501

Abstract

The improvement in peripheral nerve repair is still challenging, with the process being difficult and frequently unsatisfying. Injuries, even minor ones, can lead to limitations, including the loss of important life functions such as fingers, hands, or all limbs. Our previous research on [...] Read more.

The improvement in peripheral nerve repair is still challenging, with the process being difficult and frequently unsatisfying. Injuries, even minor ones, can lead to limitations, including the loss of important life functions such as fingers, hands, or all limbs. Our previous research on animals revealed that the distal part of autologous predegenerated nerve grafts, which were injured and left in place for 7 days, was more capable of supporting reconstructed nerve regeneration. Little is known about the efficacy of using predegenerated autologous grafts in humans. Encouraged by promising results in animal models, we decided to investigate this process in humans. A total of 31 patients were evaluated in the study; 19 predegenerated (injured and left in situ for 7 days) autologous sural nerve implants and 12 fresh sural nerve implants were used, and the period of 2 years after operation was chosen as the time of final clinical assessment. Clinical assessment and motor and sensory nerve conduction velocity were assessed. All data were statistically analyzed using stepwise regression testing and a one-way analysis of variance followed by Tukey’s test for continuous values and the Mann–Whitney U test for ordinal values. Differences were considered statistically significant for p ≤ 0.05. It turns out that autologous, predegenerated sural nerve grafts used for the reconstruction of traumatic peripheral nerves results in better quantitative and qualitative clinical functional outcomes and more adequate nerve conduction parameters. Full article

(This article belongs to the Section Regenerative Engineering)

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19 pages, 1324 KB

Open AccessReview

Smart Microbiomes: How AI Is Revolutionizing Personalized Medicine

by Luana Alexandrescu, Ionut Tiberiu Tofolean, Laura Maria Condur, Doina Ecaterina Tofolean, Alina Doina Nicoara, Lucian Serbanescu, Elena Rusu, Andreea Nelson Twakor, Eugen Dumitru, Andrei Dumitru, Cristina Tocia, Lucian Flavius Herlo, Daria Maria Alexandrescu and Alina Mihaela Stanigut

Bioengineering 2025, 12(9), 944; https://doi.org/10.3390/bioengineering12090944 - 31 Aug 2025

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Abstract

Background: Recent studies have shown that gut microbiota have important roles in different human diseases. There has been an ever-increasing application of high-throughput technologies for the characterization of microbial ecosystems. This led to an explosion of various molecular profiling data, and the analysis [...] Read more.

Background: Recent studies have shown that gut microbiota have important roles in different human diseases. There has been an ever-increasing application of high-throughput technologies for the characterization of microbial ecosystems. This led to an explosion of various molecular profiling data, and the analysis of such data has shown that machine-learning algorithms have been useful in identifying key molecular signatures. Results: In this review, we first analyze how dysbiosis of the intestinal microbiota relates to human disease and how possible modulation of the gut microbial ecosystem may be used for disease intervention. Further, we introduce categories and the workflows of different machine-learning approaches and how they perform integrative analysis of multi-omics data. Last, we review advances of machine learning in gut microbiome applications and discuss challenges it faces. Conclusions: We conclude that machine learning is indeed well suited for analyzing gut microbiome and that these approaches are beneficial for developing gut microbe-targeted therapies, helping in achieving personalized and precision medicine. Full article

(This article belongs to the Section Biosignal Processing)

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25 pages, 4378 KB

Open AccessArticle

Mechanical Properties and Microstructure of Decellularized Brown Seaweed Scaffold for Tissue Engineering

by Svava Kristinsdottir, Ottar Rolfsson, Olafur Eysteinn Sigurjonsson, Sigurður Brynjolfsson and Sigrun Nanna Karlsdottir

Bioengineering 2025, 12(9), 943; https://doi.org/10.3390/bioengineering12090943 - 31 Aug 2025

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Abstract

In response to the growing demand for sustainable biomaterials in tissue engineering, we investigated the potential of structurally intact brown seaweed scaffolds derived from Laminaria digitata (L.D.) and Laminaria saccharina (L.S.), produced by a detergent-free, visible-light decellularization process aimed [...] Read more.

In response to the growing demand for sustainable biomaterials in tissue engineering, we investigated the potential of structurally intact brown seaweed scaffolds derived from Laminaria digitata (L.D.) and Laminaria saccharina (L.S.), produced by a detergent-free, visible-light decellularization process aimed at preserving structural integrity. Blades were submerged in cold flow-through and aerated water with red (620 nm) and blue (470 nm) light exposure for 4 weeks. Histology, scanning electron microscopy (SEM), and micro-computed tomography (micro-CT) analyses demonstrated that the light decellularization process removed cells/debris, maintained essential structural features, and significantly increased scaffold porosity. Mechanical property analysis through tensile testing revealed a substantial increase in tensile strength post decellularization, with L.D. scaffolds increasing from 3.4 MPa to 8.7 MPa and L.S. scaffolds from 2.1 MPa to 6.6 MPa. Chemical analysis indicated notable alterations in polysaccharide and protein composition following decellularization. Additionally, scaffolds retained high swelling and fluid absorption capacities, critical for biomedical uses. These findings underscore that the decellularized L.D. and L.S. scaffolds preserved structural integrity and exhibited enhanced mechanical properties, interconnected porous structures, and significant liquid retention capabilities, establishing them as promising biomaterial candidates for soft-tissue reinforcement, wound care, and broader applications in regenerative medicine. Full article

(This article belongs to the Special Issue Blue Economy: Innovative Strategies for Valorising Micro- and Macroalgae)

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12 pages, 678 KB

Open AccessReview

Superior Capsule Reconstruction Graft Selection: The Influence of Biological Properties of Grafts on Healing and Re-Tearing

by Mingde Cao, Mingguang Bi, Shuai Yuan, Yuhao Wu, Patrick Shu-Hang Yung and Michael Tim-Yun Ong

Bioengineering 2025, 12(9), 942; https://doi.org/10.3390/bioengineering12090942 - 31 Aug 2025

Viewed by 474

Abstract

Arthroscopic Superior Capsular Reconstruction has emerged as a promising surgical intervention for irreparable massive rotator cuff tears, aiming to restore glenohumeral joint stability and improve patient outcomes. A critical determinant of ASCR success is the selection of an appropriate graft material. This review [...] Read more.

Arthroscopic Superior Capsular Reconstruction has emerged as a promising surgical intervention for irreparable massive rotator cuff tears, aiming to restore glenohumeral joint stability and improve patient outcomes. A critical determinant of ASCR success is the selection of an appropriate graft material. This review explores the spectrum of grafts utilized in ASCR, including autografts, allografts, xenografts, and synthetic materials. The primary focus is on how the inherent biological properties of these grafts—such as cellularity, vascularity, immunogenicity, and extracellular matrix composition—profoundly influence the processes of graft healing, integration into host tissues, and ultimately, the rates of re-tearing. Autografts, particularly fascia lata, often demonstrate superior biological incorporation due to their viable cells and non-immunogenic nature, leading to high healing rates. Allografts, while offering advantages like reduced donor site morbidity, present biological challenges related to decellularization processes and slower remodeling, resulting in more variable healing outcomes. Xenografts face significant immunological hurdles, often leading to rejection and poor integration. Synthetic grafts provide an off-the-shelf option but interact with host tissue primarily as a scaffold, without true biological integration. Understanding the nuanced biological characteristics of each graft type is paramount for surgeons aiming to optimize healing environments and minimize re-tear rates, thereby enhancing the long-term efficacy of ASCR. Full article

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

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19 pages, 7295 KB

Open AccessArticle

Performance Comparison of a Neural Network and a Regression Linear Model for Predictive Maintenance in Dialysis Machine Components

by Alessia Nicosia, Nunzio Cancilla, Michele Passerini, Francesca Sau, Ilenia Tinnirello and Andrea Cipollina

Bioengineering 2025, 12(9), 941; https://doi.org/10.3390/bioengineering12090941 - 30 Aug 2025

Viewed by 471

Abstract

Ensuring the reliability of dialysis machines and their components, such as sensors and actuators, is critical for maintaining continuous and safe dialysis treatment for patients with chronic kidney disease. This study investigates the application of Artificial Intelligence for detecting drift in dialysis machine [...] Read more.

Ensuring the reliability of dialysis machines and their components, such as sensors and actuators, is critical for maintaining continuous and safe dialysis treatment for patients with chronic kidney disease. This study investigates the application of Artificial Intelligence for detecting drift in dialysis machine components by comparing a Long Short-Term Memory (LSTM) neural network with a traditional linear regression model. Both models were trained to learn normal patterns from time-dependent signals monitoring the performance of specific components of a dialytic machine, such as a weight loss sensor in the present case, enabling the detection of anomalies related to sensor degradation or failure. Real-world data from multiple clinical cases were used to validate the approach. The LSTM model achieved high reconstruction accuracy on normal signals (most errors < 0.02, maximum ≈ 0.08), and successfully detected anomalies exceeding this threshold in complaint cases, where the model anticipated failures up to five days in advance. On the contrary, the linear regression model was limited to detecting only major deviations. These findings highlighted the advantages of AI-based methods in equipment monitoring, minimizing unplanned downtime, and supporting preventive maintenance strategies within dialysis care. Future work will focus on integrating this model into both clinical and home dialysis settings, aiming to develop a scalable, adaptable, and generalizable solution capable of operating effectively across various conditions. Full article

(This article belongs to the Special Issue Deciphering Medicine: The Role of Explainable Artificial Intelligence in Healthcare Innovations, 2nd Edition)

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Bioengineering, Volume 12, Issue 9 (September 2025) – 73 articles

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