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Bioengineering

Bioengineering is an international, scientific, peer-reviewed, open access journal on the science and technology of bioengineering, published monthly online by MDPI.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, PMC, CAPlus / SciFinder, Inspec, and other databases.
Journal Rank: JCR - Q2 (Engineering, Biomedical)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.4 days after submission; acceptance to publication is undertaken in 3.5 days (median values for papers published in this journal in the second half of 2024).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 3.8 (2023)

Imprint Information Journal Flyer Open Access ISSN: 2306-5354

Latest Articles

19 pages, 3604 KiB

Open AccessReview

Susceptibility-Weighted Imaging (SWI): Technical Aspects and Applications in Brain MRI for Neurodegenerative Disorders

by Federica Vaccarino, Carlo Cosimo Quattrocchi and Marco Parillo

Bioengineering 2025, 12(5), 473; https://doi.org/10.3390/bioengineering12050473 (registering DOI) - 29 Apr 2025

Susceptibility-weighted imaging (SWI) is a magnetic resonance imaging (MRI) sequence sensitive to substances that alter the local magnetic field, such as calcium and iron, allowing phase information to distinguish between them. SWI is a 3D gradient–echo sequence with high spatial resolution that leverages [...] Read more.

Susceptibility-weighted imaging (SWI) is a magnetic resonance imaging (MRI) sequence sensitive to substances that alter the local magnetic field, such as calcium and iron, allowing phase information to distinguish between them. SWI is a 3D gradient–echo sequence with high spatial resolution that leverages both phase and magnitude effects. The interaction of paramagnetic (such as hemosiderin and deoxyhemoglobin), diamagnetic (including calcifications and minerals), and ferromagnetic substances with the local magnetic field distorts it, leading to signal changes. Neurodegenerative diseases are typically characterized by the progressive loss of neurons and their supporting cells within the neurovascular unit. This cellular decline is associated with a corresponding deterioration of both cognitive and motor abilities. Many neurodegenerative disorders are associated with increased iron accumulation or microhemorrhages in various brain regions, making SWI a valuable diagnostic tool in clinical practice. Suggestive SWI findings are known in Parkinson’s disease, Lewy body dementia, atypical parkinsonian syndromes, multiple sclerosis, cerebral amyloid angiopathy, amyotrophic lateral sclerosis, hereditary ataxias, Huntington’s disease, neurodegeneration with brain iron accumulation, and chronic traumatic encephalopathy. This review will assist radiologists in understanding the technical framework of SWI sequences for a correct interpretation of currently established MRI findings and for its potential future clinical applications. Full article

(This article belongs to the Special Issue Modern Medical Imaging in Disease Diagnosis Applications)

40 pages, 1441 KiB

Open AccessArticle

Enhanced and Interpretable Prediction of Multiple Cancer Types Using a Stacking Ensemble Approach with SHAP Analysis

by Shahid Mohammad Ganie, Pijush Kanti Dutta Pramanik and Zhongming Zhao

Bioengineering 2025, 12(5), 472; https://doi.org/10.3390/bioengineering12050472 (registering DOI) - 29 Apr 2025

Background: Cancer is a leading cause of death worldwide, and its early detection is crucial for improving patient outcomes. This study aimed to develop and evaluate ensemble learning models, specifically stacking, for the accurate prediction of lung, breast, and cervical cancers using lifestyle [...] Read more.

Background: Cancer is a leading cause of death worldwide, and its early detection is crucial for improving patient outcomes. This study aimed to develop and evaluate ensemble learning models, specifically stacking, for the accurate prediction of lung, breast, and cervical cancers using lifestyle and clinical data. Methods: 12 base learners were trained on datasets for lung, breast, and cervical cancer. Stacking ensemble models were then developed using these base learners. The models were evaluated for accuracy, precision, recall, F1-score, AUC-ROC, MCC, and kappa. An explainable AI technique, SHAP, was used to interpret model predictions. Results: The stacking ensemble model outperformed individual base learners across all three cancer types. On average, for three cancer datasets, it achieved 99.28% accuracy, 99.55% precision, 97.56% recall, and 98.49% F1-score. A similar high performance was observed in terms of AUC, Kappa, and MCC. The SHAP analysis revealed the most influential features for each cancer type, e.g., fatigue and alcohol consumption for lung cancer, worst concave points, mean concave points, and worst perimeter for breast cancer and Schiller test for cervical cancer. Conclusions: The stacking-based multi-cancer prediction model demonstrated superior accuracy and interpretability compared with traditional models. Combining diverse base learners with explainable AI offers predictive power and transparency in clinical applications. Key demographic and clinical features driving cancer risk were also identified. Further research should validate the model on more diverse populations and cancer types. Full article

(This article belongs to the Section Biosignal Processing)

28 pages, 4919 KiB

Open AccessSystematic Review

Electrophysiological Approaches to Understanding Brain–Muscle Interactions During Gait: A Systematic Review

by Maura Seynaeve, Dante Mantini and Toon T. de Beukelaar

Bioengineering 2025, 12(5), 471; https://doi.org/10.3390/bioengineering12050471 - 29 Apr 2025

This study systematically reviews the role of the cortex in gait control by analyzing connectivity between electroencephalography (EEG) and electromyography (EMG) signals, i.e., neuromuscular connectivity (NMC) during walking. We aim to answer the following questions: (i) Is there significant NMC during gait in [...] Read more.

This study systematically reviews the role of the cortex in gait control by analyzing connectivity between electroencephalography (EEG) and electromyography (EMG) signals, i.e., neuromuscular connectivity (NMC) during walking. We aim to answer the following questions: (i) Is there significant NMC during gait in a healthy population? (ii) Is NMC modulated by gait task specifications (e.g., speed, surface, and additional task demands)? (iii) Is NMC altered in the elderly or a population affected by a neuromuscular or neurologic disorder? Following PRISMA guidelines, a systematic search of seven scientific databases was conducted up to September 2023. Out of 1308 identified papers, 27 studies met the eligibility criteria. Despite large variability in methodology, significant NMC was detected in most of the studies. NMC was able to discriminate between a healthy population and a population affected by a neuromuscular or neurologic disorder. Tasks requiring higher sensorimotor control resulted in an elevated level of NMC. While NMC holds promise as a metric for advancing our comprehension of brain–muscle interactions during gait, aligning methodologies across studies is imperative. Analysis of NMC provides valuable insights for the understanding of neural control of movement and development of gait retraining programs and contributes to advancements in neurotechnology. Full article

(This article belongs to the Special Issue 10th Anniversary of Bioengineering: Biosignal Processing)

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18 pages, 4564 KiB

Open AccessArticle

Enhancing Gas Fermentation Efficiency via Bioaugmentation with Megasphaera sueciensis and Clostridium carboxidivorans

by Clemens Hiebl, Dominik Pinner, Hannes Konegger, Franziska Steger, Dina Mohamed and Werner Fuchs

Bioengineering 2025, 12(5), 470; https://doi.org/10.3390/bioengineering12050470 - 29 Apr 2025

Gas fermentation aims to fix CO₂ into higher-value compounds, such as short or medium-chain fatty acids or alcohols. In this context, the use of mixed microbial consortia presents numerous advantages, including increased resilience and adaptability. The current study aimed to improve the [...] Read more.

Gas fermentation aims to fix CO₂ into higher-value compounds, such as short or medium-chain fatty acids or alcohols. In this context, the use of mixed microbial consortia presents numerous advantages, including increased resilience and adaptability. The current study aimed to improve the performance of an enriched mixed microbial population via bioaugmentation with Megasphaera sueciensis and Clostridium carboxidivorans to improve the metabolite spectrum. The initial fermentation in trickle-bed reactors mainly yielded acetate, a low-value compound. Introducing M. sueciensis, which converts acetate into higher-chain fatty acids, shifted production toward butyrate (up to 3.2 g/L) and caproate (1.1 g/L). The presence of M. sueciensis was maintained even after several media swaps, showing its ability to establish itself as a permanent part of the microbial community. Metataxonomic analysis confirmed the successful integration of M. sueciensis into the mixed culture, with it becoming a dominant member of the Veillonellaceae family. In contrast, bioaugmentation with C. carboxidivorans was unsuccessful. Although this strain is known for producing alcohols, such as butanol and hexanol, it did not significantly enhance alcohol production, as attempts to establish it within the microbial consortium were unsuccessful. Despite these mixed results, bioaugmentation with complementary microbial capabilities remains a promising strategy to improve gas fermentation efficiency. This approach may enhance the economic feasibility of industrial-scale renewable chemical production. Full article

(This article belongs to the Special Issue Strategies for the Efficient Development of Microbial Bioprocesses)

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48 pages, 3924 KiB

Open AccessReview

Bacteriophages as Targeted Therapeutic Vehicles: Challenges and Opportunities

by Srividhya Venkataraman, Mehdi Shahgolzari, Afagh Yavari and Kathleen Hefferon

Bioengineering 2025, 12(5), 469; https://doi.org/10.3390/bioengineering12050469 - 29 Apr 2025

Bacteriophages, with their distinctive ability to selectively target host bacteria, stand out as a compelling tool in the realm of drug and gene delivery. Their assembly from proteins and nucleic acids, coupled with their modifiable and biologically unique properties, enables them to serve [...] Read more.

Bacteriophages, with their distinctive ability to selectively target host bacteria, stand out as a compelling tool in the realm of drug and gene delivery. Their assembly from proteins and nucleic acids, coupled with their modifiable and biologically unique properties, enables them to serve as efficient and safe delivery systems. Unlike conventional nanocarriers, which face limitations such as non-specific targeting, cytotoxicity, and reduced transfection efficiency in vivo, engineered phages exhibit promising potential to overcome these hurdles and improve delivery outcomes. This review highlights the potential of bacteriophage-based systems as innovative and efficient systems for delivering therapeutic agents. It explores strategies for engineering bacteriophage, categorizes the principal types of phages employed for drug and gene delivery, and evaluates their applications in disease therapy. It provides intriguing details of the use of natural and engineered phages in the therapy of diseases such as cancer, bacterial and viral infections, veterinary diseases, and neurological disorders, as well as the use of phage display technology in generating monoclonal antibodies against various human diseases. Additionally, the use of CRISPR-Cas9 technology in generating genetically engineered phages is elucidated. Furthermore, it provides a critical analysis of the challenges and limitations associated with phage-based delivery systems, offering insights for overcoming these obstacles. By showcasing the advancements in phage engineering and their integration into nanotechnology, this study underscores the potential of bacteriophage-based delivery systems to revolutionize therapeutic approaches and inspire future innovations in medicine. Full article

(This article belongs to the Special Issue Disease Diagnosis and Therapy Using Viral Vectors)

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14 pages, 902 KiB

Open AccessArticle

Identification and Patient Benefit Evaluation of Machine Learning Models for Predicting 90-Day Mortality After Endovascular Thrombectomy Based on Routinely Ready Clinical Information

by Andrew Tik Ho Ng and Lawrence Wing Chi Chan

Bioengineering 2025, 12(5), 468; https://doi.org/10.3390/bioengineering12050468 - 28 Apr 2025

Endovascular thrombectomy (EVT) is regarded as the standard of care for acute ischemic stroke (AIS) patients with large vessel occlusion (LVO). However, the mortality rates for these patients remain alarmingly high. Dependable mortality prediction based on timely clinical information is of great importance. [...] Read more.

Endovascular thrombectomy (EVT) is regarded as the standard of care for acute ischemic stroke (AIS) patients with large vessel occlusion (LVO). However, the mortality rates for these patients remain alarmingly high. Dependable mortality prediction based on timely clinical information is of great importance. This study retrospectively reviewed 151 patients who underwent EVT at Pamela Youde Nethersole Eastern Hospital between 1 April 2017, and 31 October 2023. The primary outcome of this study was 90-day mortality after AIS. The models were developed using two feature selection approaches (model I: sequential forward feature selection, model II: sequential forward feature selection after identifying variables through univariate logistic regression) and six algorithms. Model performance was evaluated by using external validation data of 312 cases and compared with three traditional prediction scores. This study identified support vector machine (SVM) using model II as the best algorithm among the various options. Meanwhile, the Houston Intra-Arterial recanalization 2 (HIAT2) score surpassed all algorithms with an AUC of 0.717. However, most algorithms provided a greater net benefit than the traditional prediction scores. Machine learning (ML) algorithms developed with routinely available variables could offer beneficial insights for predicting mortality in AIS patients undergoing EVT. Full article

(This article belongs to the Special Issue Computer Vision and Machine Learning in Medical Applications, 2nd Edition)

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41 pages, 35264 KiB

Open AccessArticle

A New Method and Set of Parameters for Evaluating the Cushioning Effect of Shoe Heels, Revealing the Inadvertent Design of Running Shoes

by Franz Konstantin Fuss, Tizian Scharl and Niko Nagengast

Bioengineering 2025, 12(5), 467; https://doi.org/10.3390/bioengineering12050467 - 28 Apr 2025

According to standards, the heel soles of running shoes are currently tested with an energy absorption of 5 J. This study offers an alternative method to improve the measurement of cushioning properties. The new method uses the ratio of absorbed energy to applied [...] Read more.

According to standards, the heel soles of running shoes are currently tested with an energy absorption of 5 J. This study offers an alternative method to improve the measurement of cushioning properties. The new method uses the ratio of absorbed energy to applied force and determines the maximum of this ratio (optimum or shoulder point) and the associated optimal force, energy, and displacement. This method was applied to 112 shoe models using compression testing. The method was found to be insensitive to strain rates and identified shoes that were over-, well-, or under-designed (running before, at, or after the shoulder point, respectively) relative to the range of the first ground reaction force peak (0.700–2 kN). The optimum ratio was between 0.6 J/kN (barefoot shoes) and 11.2 J/kN (Puma RuleBreaker), the optimal energy was between 0.5 and 40.6 J, the optimal force was between 0.1 and 4.6 kN, and the optimal displacement was between 3 and 23 mm. Participants ran at or near the shoulder point (within the design forgiveness range) unless they were too heavy and ran at their preferred running speed. This study proposes replacing current standards with the new method, allowing consumers to make informed decisions regarding injury prevention while running. Full article

(This article belongs to the Special Issue Advancements in Biomechanical Gait Analysis: Implications for Footwear Biomechanics and Sports Injury Prevention)

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12 pages, 33851 KiB

Open AccessArticle

Development and Validation of a Deep Learning System for the Detection of Nondisplaced Femoral Neck Fractures

by Lianxin Wang, Ce Zhang, Yaozong Wang, Xin Yue, Yunbang Liang and Naikun Sun

Bioengineering 2025, 12(5), 466; https://doi.org/10.3390/bioengineering12050466 - 28 Apr 2025

Hip fractures pose a significant challenge to healthcare systems due to their high costs and associated mortality rates, with femoral neck fractures accounting for nearly half of all hip fractures. This study addresses the challenge of diagnosing nondisplaced femoral neck fractures, which are [...] Read more.

Hip fractures pose a significant challenge to healthcare systems due to their high costs and associated mortality rates, with femoral neck fractures accounting for nearly half of all hip fractures. This study addresses the challenge of diagnosing nondisplaced femoral neck fractures, which are often difficult to detect with standard radiographs, especially in elderly patients. This research evaluates a deep learning model that employs a convolutional neural network (CNN) within a ResNet framework, designed to enhance diagnostic accuracy for nondisplaced femoral neck fractures. The model was trained and validated on a dataset of 2032 hip radiographs from two hospitals, with additional external validation performed on datasets from other institutions. The AI model achieved an accuracy of 94.8% and an Area Under Curve of 0.991 on anteroposterior pelvic/hip radiographs, outperforming emergency physicians and delivering results comparable to expert physicians. External validation confirmed the model’s robust accuracy and generalizability across diverse datasets. This study underscores the potential of deep learning models to act as a supplementary tool in clinical settings, potentially reducing diagnostic errors and improving patient outcomes by facilitating a quicker diagnosis and treatment. Full article

(This article belongs to the Special Issue Advanced Engineering Technologies in Orthopaedic Research)

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13 pages, 719 KiB

Open AccessArticle

Impact of Hamstring Graft on Hamstring Peak Torque and Maximum Effective Angle After Anterior Cruciate Ligament Reconstruction: An Exploratory and Preliminary Study

by Ismail Bouzekraoui Alaoui, Ayrton Moiroux-Sahraoui, Jean Mazeas, Georgios Kakavas, Maciej Biały, Maurice Douryang and Florian Forelli

Bioengineering 2025, 12(5), 465; https://doi.org/10.3390/bioengineering12050465 - 28 Apr 2025

Purpose: Anterior cruciate ligament reconstruction (ACLR) using the hamstring graft is commonly performed to restore knee stability; however, it induces significant neuromuscular and biomechanical changes, particularly in the hamstring. This study aimed to evaluate the changes in maximum effective angle, hamstring strength, and [...] Read more.

Purpose: Anterior cruciate ligament reconstruction (ACLR) using the hamstring graft is commonly performed to restore knee stability; however, it induces significant neuromuscular and biomechanical changes, particularly in the hamstring. This study aimed to evaluate the changes in maximum effective angle, hamstring strength, and hamstring-to-quadriceps (H/Q) strength ratio at 3 and 6 months post-ACLR and compare these outcomes to a control group. Methods: This prospective controlled study included 20 ACLR patients and 20 age- and gender-matched controls. Hamstring peak torque, maximum effective angle (MEA), and the H/Q ratio were assessed using isokinetic dynamometry at 60°/s. The ACLR group was evaluated postoperatively at 3 and 6 months, while the control group underwent a single evaluation. Results: At 3 and 6 months, the ACLR group exhibited significantly lower MEA (26.3° ± 8.2 and 28.2° ± 9.4) compared to the control group (36.4° ± 12.0; p < 0.01). Hamstring peak torque and H/Q ratios were also lower in the ACLR group but showed slight improvements over time. The H/Q ratio increased significantly between 3 and 6 months (51% to 56%; p = 0.041). Conclusion: The use of hamstring graft in ACLR leads to persistent MEA and strength deficits despite rehabilitation. Advanced, targeted rehabilitation protocols are essential to address these deficits, optimize recovery, and reduce the risk of reinjury. Full article

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

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19 pages, 12128 KiB

Open AccessArticle

Marker-Less Navigation System for Anterior Cruciate Ligament Reconstruction with 3D Femoral Analysis and Arthroscopic Guidance

by Shuo Wang, Weili Shi, Shuai Yang, Jiahao Cui and Qinwei Guo

Bioengineering 2025, 12(5), 464; https://doi.org/10.3390/bioengineering12050464 - 27 Apr 2025

Accurate femoral tunnel positioning is crucial for successful anterior cruciate ligament reconstruction (ACLR), yet traditional arthroscopic techniques face significant challenges in spatial orientation and precise anatomical localization. This study presents a novel marker-less computer-assisted navigation system that integrates three-dimensional femoral modeling with real-time [...] Read more.

Accurate femoral tunnel positioning is crucial for successful anterior cruciate ligament reconstruction (ACLR), yet traditional arthroscopic techniques face significant challenges in spatial orientation and precise anatomical localization. This study presents a novel marker-less computer-assisted navigation system that integrates three-dimensional femoral modeling with real-time arthroscopic guidance. The system employs advanced image processing techniques for accurate condyle segmentation and implements the Bernard and Hertel (BH) grid system for standardized positioning. A curvature-based feature extraction approach precisely identifies the capsular line reference (CLR) on the lateral condyle surface, forming the foundation for establishing the BH reference grid. The system’s two-stage registration framework, combining SIFT-ICP algorithms, achieves accurate alignment between preoperative models and arthroscopic views. Validation results from expert surgeons demonstrated high precision, with 71.5% of test groups achieving acceptable or excellent performance standards (mean deviation distances: 1.12–1.86 mm). Unlike existing navigation solutions, our system maintains standard surgical workflow without requiring additional surgical instruments or markers, offering an efficient and minimally invasive approach to enhance ACLR precision. This innovation bridges the gap between preoperative planning and intraoperative execution, potentially improving surgical outcomes through standardized tunnel positioning. Full article

(This article belongs to the Special Issue Advances in Medical 3D Vision: Voxels and Beyond)

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16 pages, 680 KiB

Open AccessReview

Revolutionizing Utility of Big Data Analytics in Personalized Cardiovascular Healthcare

by Praneel Sharma, Pratyusha Sharma, Kamal Sharma, Vansh Varma, Vansh Patel, Jeel Sarvaiya, Jonsi Tavethia, Shubh Mehta, Anshul Bhadania, Ishan Patel and Komal Shah

Bioengineering 2025, 12(5), 463; https://doi.org/10.3390/bioengineering12050463 - 27 Apr 2025

The term “big data analytics (BDA)” defines the computational techniques to study complex datasets that are too large for common data processing software, encompassing techniques such as data mining (DM), machine learning (ML), and predictive analytics (PA) to find patterns, correlations, and insights [...] Read more.

The term “big data analytics (BDA)” defines the computational techniques to study complex datasets that are too large for common data processing software, encompassing techniques such as data mining (DM), machine learning (ML), and predictive analytics (PA) to find patterns, correlations, and insights in massive datasets. Cardiovascular diseases (CVDs) are attributed to a combination of various risk factors, including sedentary lifestyle, obesity, diabetes, dyslipidaemia, and hypertension. We searched PubMed and published research using the Google and Cochrane search engines to evaluate existing models of BDA that have been used for CVD prediction models. We critically analyse the pitfalls and advantages of various BDA models using artificial intelligence (AI), machine learning (ML), and artificial neural networks (ANN). BDA with the integration of wide-ranging data sources, such as genomic, proteomic, and lifestyle data, could help understand the complex biological mechanisms behind CVD, including risk stratification in risk-exposed individuals. Predictive modelling is proposed to help in the development of personalized medicines, particularly in pharmacogenomics; understanding genetic variation might help to guide drug selection and dosing, with the consequent improvement in patient outcomes. To summarize, incorporating BDA into cardiovascular research and treatment represents a paradigm shift in our approach to CVD prevention, diagnosis, and management. By leveraging the power of big data, researchers and clinicians can gain deeper insights into disease mechanisms, improve patient care, and ultimately reduce the burden of cardiovascular disease on individuals and healthcare systems. Full article

(This article belongs to the Special Issue Smart Applications and Technology for Cardiovascular Disease Management)

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19 pages, 7692 KiB

Open AccessArticle

Biomechanical Analysis of Rheumatoid Arthritis of the Hand and the Design of Orthotics: A Finite Element Study

by Guiyuan Li, Jie Yang, Pengfei Feng, Xiaona Li and Weiyi Chen

Bioengineering 2025, 12(5), 462; https://doi.org/10.3390/bioengineering12050462 - 27 Apr 2025

Hand orthoses are often recommended as a rehabilitation measure for patients with rheumatoid arthritis (RA). However, existing research on the efficacy of hand orthoses has predominantly focused on 3D-printed devices and post-intervention clinical functional assessments, which tend to be subjective. There is a [...] Read more.

Hand orthoses are often recommended as a rehabilitation measure for patients with rheumatoid arthritis (RA). However, existing research on the efficacy of hand orthoses has predominantly focused on 3D-printed devices and post-intervention clinical functional assessments, which tend to be subjective. There is a notable lack of biomechanical studies evaluating the effects of wearing orthoses. Therefore, in this study, the finite element method was used to analyze the biomechanical properties of an RA hand. A hand orthosis was designed based on the principle of three-point force, and a composite model of the RA hand and orthosis was constructed to verify its effectiveness. The results showed that the peak stress and displacement of the RA hand were 3.22–183.21% and 28.81–124.23% higher than those of the normal hand. Compared with the RA hand under direct force, the peak stress of the RA hand after wearing orthosis was generally reduced by 3.05–55.60%, and the peak displacement was generally reduced by 20.35–71.43%, verifying the effectiveness of the orthosis. Additionally, variations in the magnitude of three-point forces influenced the orthopedic effects. This study proves the effectiveness of hand orthosis and provides some theoretical data for subsequent research and treatment of rheumatoid arthritis. Full article

(This article belongs to the Special Issue Biomechanics of Orthopaedic Rehabilitation)

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17 pages, 1064 KiB

Open AccessReview

Challenges in Combining EMG, Joint Moments, and GRF from Marker-Less Video-Based Motion Capture Systems

by H. M. Rehan Afzal, Borhen Louhichi and Nashmi H. Alrasheedi

Bioengineering 2025, 12(5), 461; https://doi.org/10.3390/bioengineering12050461 - 27 Apr 2025

The evolution of motion capture technology from marker-based to marker-less systems is a promising field, emphasizing the critical role of combining electromyography (EMG), joint moments, and ground reaction forces (GRF) in advancing biomechanical analysis. This review examines the integration of EMG, joint moments, [...] Read more.

The evolution of motion capture technology from marker-based to marker-less systems is a promising field, emphasizing the critical role of combining electromyography (EMG), joint moments, and ground reaction forces (GRF) in advancing biomechanical analysis. This review examines the integration of EMG, joint moments, and GRF in marker-less video-based motion capture systems, focusing on current approaches, challenges, and future research directions. This paper recognizes the significant challenges of integrating the aforementioned modalities, which include problems of acquiring and synchronizing data and the issue of validating results. Particular challenges in accuracy, reliability, calibration, and environmental influences are also pointed out, together with the issue of the standard protocols of multimodal data fusion. Using a comparative analysis of significant case studies, the review examines existing methodologies’ successes and weaknesses and established best practices. New emerging themes of machine learning techniques, real-time analysis, and advancements in sensing technologies are also addressed to improve data fusion. By highlighting both the limitations and potential advancements, this review provides essential insights and recommendations for future research to optimize marker-less motion capture systems for comprehensive biomechanical assessments. Full article

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

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19 pages, 2089 KiB

Open AccessArticle

Biogas Digestate and Its Electrodialysis Concentrate as Alternative Media Composition for A. platensis Cultivation: A Study on Nutrient Recovery from Dairy Wastewater

by Elena Singer, Sun-Hwa Jung, Vivekanand Vivekanand and Christoph Lindenberger

Bioengineering 2025, 12(5), 460; https://doi.org/10.3390/bioengineering12050460 - 26 Apr 2025

The dairy industry generates substantial nutrient-rich wastewater, posing environmental challenges if discharged untreated. This study explores the potential of using the cyanobacterium Arthrospira platensis for nutrient recovery from dairy wastewater, precisely the liquid biogas digestate (BD). The research investigates the feasibility of utilising [...] Read more.

The dairy industry generates substantial nutrient-rich wastewater, posing environmental challenges if discharged untreated. This study explores the potential of using the cyanobacterium Arthrospira platensis for nutrient recovery from dairy wastewater, precisely the liquid biogas digestate (BD). The research investigates the feasibility of utilising BD and electrodialysis-concentrated BD (BD concentrate) as alternative media for A. platensis cultivation, with a focus on biomass productivity, nutrient uptake, and high-value product formation. Batch and continuous cultivation modes were employed. In batch experiments, biomass productivity was in the ratio of 0 and 0.27 g L⁻¹ d⁻¹, which was 8–100% lower than simulated values for all five tested media compositions. Phosphate fixation was limited with no fixation during batch cultivation and 8–69% during continuous cultivation, likely due to suboptimal N/P ratios, while ammonium removal remained consistently high (>98%). Phycocyanin yield decreased significantly by 92% at high BD concentrate concentrations compared to standard media. Continuous cultivation with 50% BD concentrate improved biomass productivity to 1.02 g L⁻¹ d⁻¹ and pigment yield to 107.9 mg g⁻¹, suggesting a sufficient supply of nutrients. The findings highlight the potential of BD-based media for nutrient recovery but emphasise the need for optimisation strategies, such as nutrient supplementation and microbial adaptation, to enhance performance. Full article

(This article belongs to the Special Issue Biological Wastewater Treatment and Resource Recovery)

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15 pages, 15638 KiB

Open AccessArticle

Comparative Evaluation of Bovine- and Porcine-Deproteinized Grafts for Guided Bone Regeneration: An In Vivo Study

by Blaire V. Slavin, Vasudev Vivekanand Nayak, Marcelo Parra, Robert D. Spielman, Matteo S. Torquati, Nicholas J. Iglesias, Paulo G. Coelho and Lukasz Witek

Bioengineering 2025, 12(5), 459; https://doi.org/10.3390/bioengineering12050459 - 26 Apr 2025

Guided bone regeneration (GBR) procedures have been indicated to enhance bone response, reliably regenerate lost tissue, and create an anatomically pleasing ridge contour for biomechanically favorable and prosthetically driven implant placement. The aim of the current study was to evaluate and compare the [...] Read more.

Guided bone regeneration (GBR) procedures have been indicated to enhance bone response, reliably regenerate lost tissue, and create an anatomically pleasing ridge contour for biomechanically favorable and prosthetically driven implant placement. The aim of the current study was to evaluate and compare the bone regenerative performance of deproteinized bovine bone (DBB) and deproteinized porcine bone (DPB) grafts in a beagle mandibular model for the purposes of GBR. Four bilateral defects of 10 mm × 10 mm were induced through the mandibular thickness in each of the 10 adult beagle dogs being studied. Two of the defects were filled with DPB, while the other two were filled with DBB, after which they were covered with collagen-based membranes to allow compartmentalized healing. Animals were euthanized after 6, 12, 24, or 48 weeks postoperatively. Bone regenerative capacity was evaluated by qualitative histological and quantitative microtomographic analyses. Microcomputed tomography data of the bone (%), graft (%), and space (%) were compared using a mixed model analysis. Qualitatively, no histomorphological differences in healing were observed between the DBB and DPB grafts at any time point. By 48 weeks, the xenografts (DBB and DPB) were observed to have osseointegrated with regenerating spongy bone and a close resemblance to native bone morphology. Quantitatively, a higher amount of bone (%) and a corresponding reduction in empty space (space (%)) were observed in defects treated by DBB and DPB grafts over time. However, no statistically significant differences in bone (%)were observed between DBB (71.04 ± 8.41 at 48 weeks) and DPB grafts (68.38 ± 10.30 at 48 weeks) (p > 0.05). GBR with DBB and DPB showed no signs of adverse immune response and led to similar trends in bone regeneration over 48 weeks of permitted healing. Full article

(This article belongs to the Special Issue Bioengineered Organs for Transplantation: Unveiling the Future of Organ Replacement)

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18 pages, 3417 KiB

Open AccessReview

Biological Acoustic Levitation and Its Potential Application for Microgravity Study

by Taylor Boudreaux, Luke Freyhof, Brandon D. Riehl, Eunju Kim, Ryan M. Pedrigi and Jung Yul Lim

Bioengineering 2025, 12(5), 458; https://doi.org/10.3390/bioengineering12050458 - 25 Apr 2025

The open and contactless environment of acoustic levitation provides a unique condition in experimenting with varying substances while levitated for observation and implementation with other devices, with recent improvements in cost and accessibility. We briefly decipher the theory behind acoustic levitation and describe [...] Read more.

The open and contactless environment of acoustic levitation provides a unique condition in experimenting with varying substances while levitated for observation and implementation with other devices, with recent improvements in cost and accessibility. We briefly decipher the theory behind acoustic levitation and describe currently available levitation platforms. Then, how these platforms have been employed in biological applications is reviewed. Intriguingly, recent researches indicated the viability of acoustic levitation to be utilized as a microgravity simulator. We introduce existing on-ground microgravity platforms, and discuss the potential of acoustic levitation in simulating microgravity. Acoustic levitation could be an alternative to microgravity platforms such as clinostats while allowing for novel microgravity research. On the other hand, the microgravity provided by acoustic levitation may be restricted due to potential limitations in the available levitation volume, relatively larger gravity compared to 10⁻³ g centrifugal acceleration from clinostats, and probable instability due to air perturbations and acoustic streaming. With more knowledge about in-droplet particle rotation and the regulatory factors during levitation, acoustic levitation may provide a new and advanced platform for microgravity simulation via taking advantage of its availability for real-time observation and manipulation of samples via added instrumentation while samples are levitated in a simulated microgravity condition. Full article

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

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17 pages, 5071 KiB

Open AccessArticle

The Effect of Tumor Necrosis Factor-α and Interleu-Kin-1β on the Restorative Properties of Human Oligodendrocyte Precursor Cells In Vitro

by Zhaoyan Wang, Ying He, Qian Wang, Weipeng Liu, Yinxiang Yang, Haipeng Zhou, Xuexia Ma, Caiyan Hu, Zuo Luan and Suqing Qu

Bioengineering 2025, 12(5), 457; https://doi.org/10.3390/bioengineering12050457 - 25 Apr 2025

Premature white matter injury (PWMI) represents the principal form of brain injury in preterm infants, and effective therapies remain elusive. Transplantation of oligodendrocyte precursor cells (OPCs) emerges as a potential treatment for PWMI, yet the injury-induced inflammatory response may impact these cells’ functionality. [...] Read more.

Premature white matter injury (PWMI) represents the principal form of brain injury in preterm infants, and effective therapies remain elusive. Transplantation of oligodendrocyte precursor cells (OPCs) emerges as a potential treatment for PWMI, yet the injury-induced inflammatory response may impact these cells’ functionality. To date, no studies have explored the influence of inflammatory factors on the functionality of human (h) OPCs. The predominant inflammatory cytokines identified in PWMI lesions are tumor necrosis factor (TNF)-α and interleukin (IL)-1β. This study investigates the impact of these cytokines on hOPC migration, proliferation, and differentiation using the human adult neural stem cell amplification and differentiation system in vitro. Results indicate that IL-1β significantly impedes hOPC migration, while both TNF-α and IL-1β hinder proliferation and differentiation. In summary, inflammatory factors overexpressed following PWMI impede OPCs from realizing their regenerative potential. These findings underscore the necessity of modulating the post-PWMI inflammatory milieu to enhance the efficacy of transplanted cells concerning migration, proliferation, and differentiation. Full article

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

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28 pages, 2622 KiB

Open AccessArticle

Effects of a Wearable Assistive Device on Postural Control and Stability During Symmetric and Asymmetric Intermittent Trunk Flexion Tasks

by Pranav Madhav Kuber and Ehsan Rashedi

Bioengineering 2025, 12(5), 456; https://doi.org/10.3390/bioengineering12050456 - 25 Apr 2025

Assistive devices, such as Exoskeletons (EXOs) can enhance endurance, but could inadvertently alter body mechanics, compromise balance, and elevate fall risk, particularly under fatigue. We evaluated effects of an EXO on postural stability during standing still and sustained trunk flexion tasks as users [...] Read more.

Assistive devices, such as Exoskeletons (EXOs) can enhance endurance, but could inadvertently alter body mechanics, compromise balance, and elevate fall risk, particularly under fatigue. We evaluated effects of an EXO on postural stability during standing still and sustained trunk flexion tasks as users become fatigued during intermittently performed tasks. As trunk bending is common across many occupational/routine tasks, a repetitive 45° trunk flexion task was selected. In this controlled laboratory study, symmetric and asymmetric trunk flexion tasks were performed by twelve participants with a Back-support EXO until medium-high fatigue level (7/10 on Borg CR10 scale). Outcomes showed that the device increased trunk flexion durations (~16~25%), and upper-body movement beyond intended position. EXO-use improved stability by reducing maximum deviation (~22%) and mean velocity (~57%) of Center of Pressure (COP) co-ordinates. Asymmetric trunk flexion without assistance led to highest mean velocity of COP during fatigued state, but the same remained lower (~67%) with EXO-use, even with fatigue. The device decreased variance of COP during in medial/lateral direction (~44%), but increased the same in anterior/posterior direction by the same amount. Efforts in this study contribute towards understanding implications of using assistive devices for improving human performance across diverse applications. Full article

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

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3 pages, 133 KiB

Open AccessEditorial

New Sight of Intelligent Algorithm Models and Medical Devices in Bioengineering: Updates and Directions

by Luca Mesin

Bioengineering 2025, 12(5), 455; https://doi.org/10.3390/bioengineering12050455 - 25 Apr 2025

The integration of artificial intelligence (AI) models and advanced medical devices has led to significant advancements in healthcare, rehabilitation, and biomedical research [...] Full article

(This article belongs to the Special Issue New Sight of Intelligent Algorithm Model and Medical Device in Bioengineering: Updates and Direction)

11 pages, 1228 KiB

Open AccessArticle

Radiomics Analysis of Whole-Kidney Non-Contrast CT for Early Identification of Chronic Kidney Disease Stages 1–3

by Guirong Zhang, Pan Zhang, Yuwei Xia, Feng Shi, Yuelang Zhang and Dun Ding

Bioengineering 2025, 12(5), 454; https://doi.org/10.3390/bioengineering12050454 - 25 Apr 2025

Background: The early stages of chronic kidney disease (CKD) are often undetectable on traditional non-contrast computed tomography (NCCT) images through visual assessment by radiologists. This study aims to evaluate the potential of radiomics-based quantitative features extracted from NCCT, combined with machine learning techniques, [...] Read more.

Background: The early stages of chronic kidney disease (CKD) are often undetectable on traditional non-contrast computed tomography (NCCT) images through visual assessment by radiologists. This study aims to evaluate the potential of radiomics-based quantitative features extracted from NCCT, combined with machine learning techniques, in differentiating CKD stages 1–3 from healthy controls. Methods: This retrospective study involved 1099 CKD patients (stages 1–3) and 1099 healthy participants who underwent NCCT. Bilateral kidney volumes of interest were automatically segmented using a deep learning-based segmentation approach (VB-net) on CT images. Radiomics models were constructed using the mean values of features extracted from both kidneys. Key features were selected through Relief, MRMR, and LASSO regression algorithms. A machine learning classifier was trained to differentiate CKD from healthy kidneys and compared with the radiologist assessments. Model performance was evaluated using the area under the curve (AUC) of receiver operating characteristic analysis. Results: In the training set, the AUCs for the Gaussian process (GP) classifier model and radiologist assessments were 0.849 and 0.570, respectively. In the testing set, the AUC values were 0.790 for the GP model and 0.575 for radiologist assessments. Conclusions: The NCCT-based radiomics model demonstrates significant clinical utility by enabling non-invasive, early diagnosis of CKD stages 1–3, outperforming radiologist assessments. Full article

(This article belongs to the Section Biosignal Processing)

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