Bioengineering

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

Abstract

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

Abstract

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

Abstract

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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Graphical abstract

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

Abstract

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

Viewed by 34

Abstract

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

Viewed by 23

Abstract

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

Viewed by 34

Abstract

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

Viewed by 44

Abstract

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

Viewed by 117

Abstract

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

Viewed by 159

Abstract

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

Viewed by 114

Abstract

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

Viewed by 107

Abstract

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

Viewed by 182

Abstract

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

Viewed by 80

Abstract

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

Viewed by 104

Abstract

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

Viewed by 93

Abstract

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

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Abstract

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

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Abstract

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

Viewed by 157

Abstract

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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16 pages, 2748 KiB

Open AccessArticle

PE-MT: A Perturbation-Enhanced Mean Teacher for Semi-Supervised Image Segmentation

by Wenquan Wang, Zhongwen Li, Xiaoyun Zhang, Gaoqiang Jiang, Yabo Wu, Shuchen Yu, Bihan Tian, Mingzhe Hu, Xiaomin Xu, Wencan Wu, Quanyong Yi and Lei Wang

Bioengineering 2025, 12(5), 453; https://doi.org/10.3390/bioengineering12050453 - 25 Apr 2025

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Abstract

The accurate segmentation of medical images is of great importance in many clinical applications and is generally achieved by training deep learning networks on a large number of labeled images. However, it is very hard to obtain enough labeled images. In this paper, [...] Read more.

The accurate segmentation of medical images is of great importance in many clinical applications and is generally achieved by training deep learning networks on a large number of labeled images. However, it is very hard to obtain enough labeled images. In this paper, we develop a novel semi-supervised segmentation method (called PE-MT) based on the uncertainty-aware mean teacher (UA-MT) framework by introducing a perturbation-enhanced exponential moving average (pEMA) and a residual-guided uncertainty map (RUM) to enhance the performance the student and teacher models. The former is used to alleviate the coupling effect between student and teacher models in the UA-MT by adding different weight perturbations to them, and the latter can accurately locate image regions with high uncertainty via a unique quantitative formula and then highlight these regions effectively in image segmentation. We evaluated the developed method by extracting four different cardiac regions from the public LASC and ACDC datasets. The experimental results showed that our developed method achieved an average Dice similarity coefficient (DSC) of 0.6252 and 0.7836 for four object regions when trained on 5% and 10% labeled images, respectively. It outperformed the UA-MT and can compete with several existing semi-supervised learning methods (e.g., SASSNet and DTC). Full article

(This article belongs to the Section Biosignal Processing)

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19 pages, 5913 KiB

Open AccessArticle

Putative Endoplasmic Reticulum Stress Inducers Enhance Triacylglycerol Accumulation in Chlorella sorokiniana

by Yoomi Roh, Sujeong Je, Naeun Sheen, Chang Hun Shin and Yasuyo Yamaoka

Bioengineering 2025, 12(5), 452; https://doi.org/10.3390/bioengineering12050452 - 25 Apr 2025

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Abstract

Chlorella, recognized for its high lipid and protein content, is increasingly studied for its potential in the food and bio industries. To enhance its production and understand the underlying mechanisms of lipid accumulation, this study investigated the role of endoplasmic reticulum (ER) [...] Read more.

Chlorella, recognized for its high lipid and protein content, is increasingly studied for its potential in the food and bio industries. To enhance its production and understand the underlying mechanisms of lipid accumulation, this study investigated the role of endoplasmic reticulum (ER) stress in modulating lipid metabolism in Chlorella sorokiniana UTEX 2714, using six putative ER stress inducers: 2-deoxy-D-glucose (2-DG), dithiothreitol (DTT), tunicamycin (TM), thapsigargin (TG), brefeldin A (BFA), and monensin (Mon). The results showed that 2-DG, DTT, TM, BFA, and Mon significantly inhibited cell growth in C. sorokiniana. Treatment with 2-DG, DTT, TM, BFA, or Mon resulted in substantial increases in the triacylglycerol (TAG) to total fatty acid (tFA) ratio, with fold changes of 14.8, 7.9, 6.2, 10.1, and 8.9, respectively. Among the tFAs, cells treated with these compounds exhibited higher levels of saturated fatty acids and lower levels of polyunsaturated fatty acids (PUFAs). In contrast, the fatty acid composition of TAGs showed the opposite trend, with relative enrichment in PUFAs. This study enhances our understanding of Chlorella lipid metabolism, providing valuable insights for optimizing lipid production, particularly TAGs enriched with PUFA content, for applications in functional foods, nutraceuticals, and sustainable bioresources. Full article

(This article belongs to the Special Issue Microalgae Biotechnology and Microbiology: Prospects and Applications)

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3 pages, 130 KiB

Open AccessEditorial

Unlocking Deeper Insights into Medical Images with Machine Learning

by Xiangxue Wang, Cheng Lu and Jun Xu

Bioengineering 2025, 12(5), 451; https://doi.org/10.3390/bioengineering12050451 - 25 Apr 2025

Viewed by 153

Abstract

Recent years have witnessed remarkable progress at the intersection of medical imaging, biochemical assays, image analysis, and machine learning [...] Full article

(This article belongs to the Special Issue Machine-Learning-Driven Medical Image Analysis)

19 pages, 4770 KiB

Open AccessArticle

A Radiomic Model for Gliomas Grade and Patient Survival Prediction

by Ahmad Chaddad, Pingyue Jia, Yan Hu, Yousef Katib, Reem Kateb and Tareef Sahal Daqqaq

Bioengineering 2025, 12(5), 450; https://doi.org/10.3390/bioengineering12050450 - 24 Apr 2025

Viewed by 136

Abstract

Brain tumors are among the most common malignant tumors of the central nervous system, with high mortality and recurrence rates. Radiomics extracts quantitative features from medical images, converting them into predictive biomarkers for tumor diagnosis, prognosis, and survival analysis. Despite the invasiveness and [...] Read more.

Brain tumors are among the most common malignant tumors of the central nervous system, with high mortality and recurrence rates. Radiomics extracts quantitative features from medical images, converting them into predictive biomarkers for tumor diagnosis, prognosis, and survival analysis. Despite the invasiveness and heterogeneity of brain tumors, even with timely treatment, the overall survival time or survival probability is not necessarily favorable. Therefore, accurate prediction of brain tumor grade and survival outcomes is important for personalized treatment. In this study, we propose a radiomic model for the non-invasive prediction of brain tumor grade and patient survival outcomes. We used four magnetic resonance imaging (MRI) sequences from 159 patients with glioma. Four classifiers were employed based on whether feature selection was applied. The features were derived from regions of interest identified and corrected either manually or automatically. The extreme gradient boosting (XGB) model with 3860 radiomic features achieved the highest classification performance, with an AUC of 98.20%, in distinguishing LGG from GBM images using manually corrected labels. Similarly, the Random Forest (RF) model exhibits the best discrimination between short-term and long-term survival groups with a p-value < 0.0003, a hazard ratio (HR) value of 3.24, and a 95% confidence interval (CI) of 1.63–4.43 based on the ICC features. The experimental findings demonstrate strong classification accuracy and effectively predict survival outcomes in glioma patients. Full article

(This article belongs to the Special Issue Biomedical Imaging and Data Analytics for Disease Diagnosis and Treatment, 2nd Edition)

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18 pages, 4305 KiB

Open AccessArticle

Decoding Depression from Different Brain Regions Using Hybrid Machine Learning Methods

by Qi Sang, Chen Chen and Zeguo Shao

Bioengineering 2025, 12(5), 449; https://doi.org/10.3390/bioengineering12050449 - 24 Apr 2025

Viewed by 124

Abstract

Depression has become one of the most common mental illnesses, causing severe physical and mental harm. To clarify the impact of brain region segmentation on the detection accuracy of moderate-to-severe major depressive disorder (MDD) and identify the optimal brain region for detecting MDD [...] Read more.

Depression has become one of the most common mental illnesses, causing severe physical and mental harm. To clarify the impact of brain region segmentation on the detection accuracy of moderate-to-severe major depressive disorder (MDD) and identify the optimal brain region for detecting MDD using electroencephalography (EEG), this study compared eight traditional single-machine learning algorithms with a hybrid machine learning model based on a stacking ensemble technique. The hybrid model employed K-nearest neighbors (KNN), decision tree (DT), and Extreme Gradient Boosting (XGBoost) as base learners and used a DT as the meta-learner. Compared with traditional single methods, the hybrid approach significantly improved detection accuracy by leveraging the strengths of different algorithms. In addition, this study divided the brain regions into the left and right temporal lobes and extracted both linear and nonlinear features to comprehensively capture the complexity and dynamic behavior of EEG signals, enhancing the model’s ability to distinguish features across different brain regions. The experimental results showed that among the eight traditional machine learning methods, the KNN classifier achieved the highest detection accuracy of 96.97% in the left temporal lobe region. In contrast, the stacking hybrid learning model further increased the detection accuracy to 98.07%, significantly outperforming the single models. Moreover, the analysis of the brain region segmentation revealed that the left temporal lobe exhibited higher discriminative power in detecting MDD, highlighting its important role in the neurobiology of depression. This study provides a solid foundation for developing more efficient and portable methods for detecting depression, offering new perspectives and approaches for EEG-based MDD detection, and contributing to the improvement in objectivity and precision in depression diagnosis. Full article

(This article belongs to the Special Issue Artificial Intelligence in Biomedical Imaging, Biosignals and Healthcare)

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23 pages, 6084 KiB

Open AccessArticle

The Multi-Agentization of a Dual-Arm Nursing Robot Based on Large Language Models

by Chuanhong Fang, Xiaotian Yue, Zhendong Zhao and Shijie Guo

Bioengineering 2025, 12(5), 448; https://doi.org/10.3390/bioengineering12050448 - 24 Apr 2025

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Abstract

Nursing robots are designed to serve users, and their ability to interact with humans, as well as to make task-related decisions and decompositions based on such interactions, is a fundamental prerequisite for autonomous execution of nursing tasks. Large language models offer an effective [...] Read more.

Nursing robots are designed to serve users, and their ability to interact with humans, as well as to make task-related decisions and decompositions based on such interactions, is a fundamental prerequisite for autonomous execution of nursing tasks. Large language models offer an effective approach to facilitating human–robot interaction. However, their global perspective can lead to confusion or reduced precision when coordinating the execution of tasks by a dual-arm robot, often generating execution sequences that are inconsistent with real-world conditions. To address this challenge, this study proposes a multi-agent framework, wherein each arm of the nursing robot is conceptualized as an independent agent. Through the application of geometric constraints, these agents maintain appropriate relative positional relationships and achieve coordinated collaboration via a large language model. This approach enhances the task planning capabilities of the robot and improves its efficiency in delivering nursing services. Full article

(This article belongs to the Section Biosignal Processing)

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12 pages, 2215 KiB

Open AccessArticle

A Three-Stage Fusion Neural Network for Predicting the Risk of Root Fracture—A Pilot Study

by Yung-Ming Kuo, Liang-Yin Kuo, Hsun-Yu Huang, Tsen-Yu Sung, Chun-Hung Yang, Wan-Ting Chang and Chien-Shun Lo

Bioengineering 2025, 12(5), 447; https://doi.org/10.3390/bioengineering12050447 - 24 Apr 2025

Viewed by 94

Abstract

Predicting the risk of root fractures following root canal therapy requires diagnosis of the dental history and status of patients. However, dental history is a kind of categorical data type that is not easy to combine with numerical data to obtain good performance [...] Read more.

Predicting the risk of root fractures following root canal therapy requires diagnosis of the dental history and status of patients. However, dental history is a kind of categorical data type that is not easy to combine with numerical data to obtain good performance in deep learning. The accuracy of support vector machine (SVM) and artificial neural networks (ANNs) is 71.7% and 73.1%, respectively. In this study, a three-stage fusion neural network (TSFNN) is proposed to improve the multiple types of clinical data in the dental field based on ANNs. Clinical data were obtained from 145 teeth, comprising 97 fractured teeth and 48 nonfractured teeth. Each dataset contained 17 items, which were divided into 10 categorical items and 7 numerical items. TSFNN combines numerical and categorical NN with batch normalization and embedding layer techniques and can produce the accuracy of 82.1% and a 19.1% improvement in F1-score. It shows impressive performance in predicting the risk of root fracture. Furthermore, due to the limited amount of clinical data, it is believed that such a pilot study can effectively improve the results when the amount of clinical data is insufficient. Full article

(This article belongs to the Special Issue AI-Powered Diagnosis and Treatment Plans in Dentistry and Orofacial Fields)

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12 pages, 5717 KiB

Open AccessArticle

Bilayer Type I Atelocollagen Scaffolds for In Vivo Regeneration of Articular Cartilage Defects

by Sang Hun Woo, Bo Keun Lee, Andrew S. Kwak, Jin Hyo Yang, Seo Yeon Kim, Man Soo Kim and Ji Chul Yoo

Bioengineering 2025, 12(5), 446; https://doi.org/10.3390/bioengineering12050446 - 24 Apr 2025

Viewed by 100

Abstract

Articular cartilage has limited regenerative potential due to its anatomical characteristics, making complete recovery from damage challenging. Microfracture (MFx) is a widely used technique to promote cartilage healing, often enhanced with scaffolds to improve outcomes. In this study, we compared the efficacy of [...] Read more.

Articular cartilage has limited regenerative potential due to its anatomical characteristics, making complete recovery from damage challenging. Microfracture (MFx) is a widely used technique to promote cartilage healing, often enhanced with scaffolds to improve outcomes. In this study, we compared the efficacy of bilayer atelocollagen and standard collagen scaffolds combined with MFx in treating osteochondral defects in a rabbit model. Three articular cartilage defects were created in the femoral condyle of each rabbit and treated with either MFx plus a bilayer atelocollagen scaffold (test group), MFx plus a standard collagen scaffold (positive group), or MFx alone (negative group). Macroscopic and histological assessments were performed at 3, 6, and 12 weeks. By week 12, macroscopic examination showed hyaline-like cartilage restoration in the test group, while the positive group exhibited restoration with some overgrowth, and the negative group showed no restoration. Histological analysis revealed significantly better restoration in the test group than in the negative group, with comparable outcomes between the test and positive groups. These findings suggest that bilayer atelocollagen scaffold implantation following MFx is a promising treatment for articular cartilage defects and may provide a viable therapeutic option for patients with cartilage damage. Full article

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

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17 pages, 5905 KiB

Open AccessArticle

An Improved Cole–Cole Model for Characterizing In Vivo Dielectric Properties of Lung Tissue at Different Tide Volumes: An Animal Study

by Yangchun Qin, Liang Zhang, Tixin Han, Yifan Liu, Xuechao Liu, Feng Fu, Hang Wang, Shuoyao Qu, Zhanqi Zhao, Lin Yang and Meng Dai

Bioengineering 2025, 12(5), 445; https://doi.org/10.3390/bioengineering12050445 - 24 Apr 2025

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Abstract

Objective: The air content within the lungs directly influences the dielectric properties of lung tissue; however, previous studies were conducted under ex vivo conditions and without quantitatively controlling air volume. This study aims to develop an improved model using in vivo measurements to [...] Read more.

Objective: The air content within the lungs directly influences the dielectric properties of lung tissue; however, previous studies were conducted under ex vivo conditions and without quantitatively controlling air volume. This study aims to develop an improved model using in vivo measurements to accurately characterize the dielectric properties of rabbit lung tissue across various tidal volumes. Methods: In this study, six sets of different tidal volumes (30, 40, 50, 60, 70, 80 mL) were set in the frequency band of 100 MHz~1 GHz to analyze the trend of the dielectric properties, and the dielectric parameters were systematically constructed under the conditions of different tidal volumes. Results: It was found that the conductivity and permittivity of rabbit lung tissue showed a decreasing trend with increasing tidal volume in the measuring frequency band. The traditional Cole–Cole model has limitations in simulating the dielectric properties of in vivo lung tissues. Therefore, by refining and optimizing the model, this study successfully reduced the average error between the measured data and the model fitting to less than 5%. Conclusions: This study lays the groundwork for investigating the relationship between total air volume within the lungs and their dielectric properties in vivo. Full article

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

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13 pages, 822 KiB

Open AccessArticle

Modification of a Two-Part Cancellous Locking Screw: A Pilot Study on Increasing Resistance to Axial Pullout Strength

by Chia-Hao Hsu, Nin-Chieh Hsu, Sung-Yen Lin, Cheng-Chang Lu, Yin-Chih Fu, Hsuan-Ti Huang, Chung-Hwan Chen and Pei-Hsi Chou

Bioengineering 2025, 12(5), 444; https://doi.org/10.3390/bioengineering12050444 - 23 Apr 2025

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Abstract

Background/Objectives: The pullout failure of conventional locking screws (LSs, screws with a locking mechanism) may occur in patients with osteoporosis, particularly when inserted near joints or across periarticular fractures (e.g., proximal humerus). The two-part locking cancellous screw modification (TP-LCS, screws composed of two [...] Read more.

Background/Objectives: The pullout failure of conventional locking screws (LSs, screws with a locking mechanism) may occur in patients with osteoporosis, particularly when inserted near joints or across periarticular fractures (e.g., proximal humerus). The two-part locking cancellous screw modification (TP-LCS, screws composed of two parts) in metaphyseal cancellous bone is hypothesized to increase bone purchase and holding power. This study aimed to test the hypothesized advantages of TP-LCS over LSs. Methods: An MTS 370 series frame with an axial/torsional load cell was used to test driving torque and axial pullout strength, following ASTM F543-07 standards. The TP-LCS group featured a newly modified screw design made from titanium alloy (Ti6Al4V), while conventional LSs (Synthes) were used for the control group. Statistical significance was assessed for selected comparisons relevant to the research objectives, including driving torque and axial pullout strength. Results: The driving torque test showed that TP-LCS had a significantly higher maximum insertion torque (4.9 ± 0.4 N·cm) compared to LSs (4.2 ± 0.4 N·cm) (p = 0.0269), although no significant difference was found in maximum removal torque (p = 0.1046). The axial pullout test revealed that TP-LCS had significantly higher pullout strength (223.5 ± 12.2 N) compared to LSs (203.5 ± 11.5 N) (p = 0.0284). Failure during the axial pullout test often involved cracking of the test block material around the screw threads, causing the screw to pull out. Conclusions: These results support the hypothesis that TP-LCS may offer improved axial pullout resistance compared to LSs, making it a potentially beneficial modification for LSs in osteoporotic metaphyseal regions or near joints. This study provides biomechanical insights into the advantages of the modified screw design over conventional LSs. Full article

(This article belongs to the Special Issue Medical Devices and Implants, 2nd Edition)

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Bioengineering, Volume 12, Issue 5 (May 2025) – 40 articles

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