Bioengineering

17 pages, 40755 KiB

Open AccessArticle

Data-Driven Clustering of Plantar Thermal Patterns in Healthy Individuals: An Insole-Based Approach to Foot Health Monitoring

by Mark Borg, Stephen Mizzi, Robert Farrugia, Tiziana Mifsud, Anabelle Mizzi, Josef Bajada and Owen Falzon

Bioengineering 2025, 12(2), 143; https://doi.org/10.3390/bioengineering12020143 - 1 Feb 2025

Abstract

Monitoring plantar foot temperatures is essential for assessing foot health, particularly in individuals with diabetes at increased risk of complications. Traditional thermographic imaging measures foot temperatures in unshod individuals lying down, which may not reflect thermal characteristics of feet in shod, active, real-world [...] Read more.

Monitoring plantar foot temperatures is essential for assessing foot health, particularly in individuals with diabetes at increased risk of complications. Traditional thermographic imaging measures foot temperatures in unshod individuals lying down, which may not reflect thermal characteristics of feet in shod, active, real-world conditions. These controlled settings limit understanding of dynamic foot temperatures during daily activities. Recent advancements in wearable technology, such as insole-based sensors, overcome these limitations by enabling continuous temperature monitoring. This study leverages a data-driven clustering approach, independent of pre-selected foot regions or models like the angiosome concept, to explore normative thermal patterns in shod feet with insole-based sensors. Data were collected from 27 healthy participants using insoles embedded with 21 temperature sensors. The data were analysed using clustering algorithms, including k-means, fuzzy c-means, OPTICS, and hierarchical clustering. The clustering algorithms showed a high degree of similarity, with variations primarily influenced by clustering granularity. Six primary thermal patterns were identified, with the “butterfly pattern” (elevated medial arch temperatures) predominant, representing 51.5% of the dataset, aligning with findings in thermographic studies. Other patterns, like the “medial arch + metatarsal area” pattern, were also observed, highlighting diverse yet consistent thermal distributions. This study shows that while normative thermal patterns observed in thermographic imaging are reflected in insole data, the temperature distribution within the shoe may better represent foot behaviour during everyday activities, particularly when enclosed in a shoe. Unlike thermal imaging, the proposed in-shoe system offers the potential to capture dynamic thermal variations during ambulatory activities, enabling richer insights into foot health in real-world conditions. Full article

(This article belongs to the Special Issue Body-Worn Sensors for Biomedical Applications)

10 pages, 1064 KiB

Open AccessArticle

Sex-Specific Analysis of Carotid Artery Through Bilateral 3D Modeling via MRI and DICOM Processing

by Pedro Martinez, Jose Roberto Torres, Daniel Conde, Manuel Gomez and Alvaro N. Gurovich

Bioengineering 2025, 12(2), 142; https://doi.org/10.3390/bioengineering12020142 - 1 Feb 2025

Abstract

The present study explores the anatomical differences between sexes of the carotid artery using non-invasive magnetic resonance imaging (MRI) and a DICOM processing protocol. Bilateral three-dimensional models of the carotid artery were constructed for 20 healthy young adults, 10 males and 10 females, [...] Read more.

The present study explores the anatomical differences between sexes of the carotid artery using non-invasive magnetic resonance imaging (MRI) and a DICOM processing protocol. Bilateral three-dimensional models of the carotid artery were constructed for 20 healthy young adults, 10 males and 10 females, in order to evaluate key anatomical landmarks; these include the bifurcation diameter and angle, as well as the internal and external carotid arteries (ICA and ECA) for both sides (left and right). The results show that males exhibit larger bifurcation and ECA diameters, which could indicate reduced endothelial shear stress (ESS). However, as there is no previously observed sex difference in ESS between sexes, compensatory factors might be in play, such as blood pressure. This underscores the interaction between vascular geometry and stroke risk disparities; future research is encouraged to analyze diverse demographics and employ flow modeling techniques to further asses the connection between anatomical differences within a given population and vascular outcomes. Full article

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

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16 pages, 8610 KiB

Open AccessArticle

Characterization of Normal and Degenerative Discovertebral Complexes Using Qualitative and Quantitative Magnetic Resonance Imaging at 4.7T: Longitudinal Evaluation of Immature and Mature Rats

by Benjamin Dallaudière, Emeline J. Ribot, Aurélien J. Trotier, Laurence Dallet, Olivier Thibaudeau, Sylvain Miraux and Olivier Hauger

Bioengineering 2025, 12(2), 141; https://doi.org/10.3390/bioengineering12020141 - 31 Jan 2025

Abstract

Purpose: We assessed the feasibility of qualitative, semiquantitative, and multiparametric quantitative magnetic resonance imaging (MRI) using a three-dimensional (3D) ultrashort echo time (3D-UTE) sequence together with 2D-T2 and 3D-T1 mapping sequences to evaluate normal and pathological discovertebral complexes (DVCs). We assessed the disc [...] Read more.

Purpose: We assessed the feasibility of qualitative, semiquantitative, and multiparametric quantitative magnetic resonance imaging (MRI) using a three-dimensional (3D) ultrashort echo time (3D-UTE) sequence together with 2D-T2 and 3D-T1 mapping sequences to evaluate normal and pathological discovertebral complexes (DVCs). We assessed the disc (nucleus pulposus [NP] and annulus fibrosus [AF]), vertebral endplate (cartilage endplate [CEP] and growth plate [GP]), and subchondral bone (SB) using a rat model of degenerative disc disease (DDD). We also assessed whether this complete MRI cartography can improve the monitoring of DDD. Methods: DDD was induced by percutaneous disc trituration and collagenase injection of the tail. Then, the animals were imaged at 4.7T. The adjacent disc served as the control. The MRI protocol was performed at baseline and each week (W) postoperatively for 2 weeks. Visual analysis and signal intensity measurements from the 3D-UTE images, as well as T2 and T1 measurements, were carried out in all DVC portions. Histological analysis with hematoxylin–eosin and Masson trichrome staining was performed following euthanization of the rats at 2 weeks and the results were compared to the MRI findings. Results: Complete qualitative identification of the normal zonal anatomy of the DVC, including the AF, CEP, and GP, was achieved using the 3D-UTE sequence. Quantitative measurements of the signal-to-noise ratio in the AF and NP enabled healthy DVCs to be distinguished from surgery-induced DDD, based on an increase in these values post-surgery. The 2D-T2 mapping results showed a significant increase in the T2 values of the AF and a decrease in the values of the NP between the baseline and W1 and W2 postoperatively (p < 0.001). In the 3D-T1 mapping, there was a significant decrease in the T1 values of the AF and NP between baseline and W1 and W2 postoperatively in immature rats (p < 0.01). This variation in T1 and T2 over time was consistent with the results of the 3D-UTE sequence. Conclusions: Use of the 3D-UTE sequence enabled a complete, robust, and reproducible visualization of DVC anatomy in both immature and mature rats under both normal and pathological conditions. The findings were supported quantitatively by the T2 and T1 mapping sequences and histologically. This sequence is therefore of prime interest in spinal imaging and should be regularly be performed. Full article

(This article belongs to the Special Issue Recent Advances in Biomedical Imaging: 2nd Edition)

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16 pages, 3090 KiB

Open AccessArticle

MWG-UNet: Hybrid Transformer U-Net Model for Brain Tumor Segmentation in MRI Scans⁺⁺

by Yu Lyu and Xiaolin Tian

Bioengineering 2025, 12(2), 140; https://doi.org/10.3390/bioengineering12020140 - 31 Jan 2025

Abstract

The accurate segmentation of brain tumors from medical images is critical for diagnosis and treatment planning. However, traditional segmentation methods struggle with complex tumor shapes and inconsistent image quality which leads to suboptimal results. To address this challenge, we propose multiple tasking Wasserstein [...] Read more.

The accurate segmentation of brain tumors from medical images is critical for diagnosis and treatment planning. However, traditional segmentation methods struggle with complex tumor shapes and inconsistent image quality which leads to suboptimal results. To address this challenge, we propose multiple tasking Wasserstein Generative Adversarial Network U-shape Network++ (MWG-UNet++) to brain tumor segmentation by integrating a U-Net architecture enhanced with transformer layers which combined with Wasserstein Generative Adversarial Networks (WGAN) for data augmentation. The proposed model called Residual Attention U-shaped Network (RAUNet) for brain tumor segmentation leverages the robust feature extraction capabilities of U-Net and the global context awareness provided by transformers to improve segmentation accuracy. Incorporating WGAN for data augmentation addresses the challenge of limited medical imaging datasets to generate high-quality synthetic images that enhance model training and generalization. Our comprehensive evaluation demonstrates that this hybrid model significantly improves segmentation performance. The RAUNet outperforms compared approaches by capturing long-range dependencies and considering spatial variations. The use of WGANs augments the dataset for resulting in robust training and improved resilience to overfitting. The average evaluation metric for brain tumor segmentation is 0.8965 which outperformed the compared methods. Full article

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

16 pages, 2307 KiB

Open AccessArticle

Deep Learning Approach Predicts Longitudinal Retinal Nerve Fiber Layer Thickness Changes

by Jalil Jalili, Evan Walker, Christopher Bowd, Akram Belghith, Michael H. Goldbaum, Massimo A. Fazio, Christopher A. Girkin, Carlos Gustavo De Moraes, Jeffrey M. Liebmann, Robert N. Weinreb, Linda M. Zangwill and Mark Christopher

Bioengineering 2025, 12(2), 139; https://doi.org/10.3390/bioengineering12020139 - 31 Jan 2025

Abstract

This study aims to develop deep learning (DL) models to predict the retinal nerve fiber layer (RNFL) thickness changes in glaucoma, facilitating the early diagnosis and monitoring of disease progression. Using the longitudinal data from two glaucoma studies (Diagnostic Innovations in Glaucoma Study [...] Read more.

This study aims to develop deep learning (DL) models to predict the retinal nerve fiber layer (RNFL) thickness changes in glaucoma, facilitating the early diagnosis and monitoring of disease progression. Using the longitudinal data from two glaucoma studies (Diagnostic Innovations in Glaucoma Study (DIGS) and African Descent and Glaucoma Evaluation Study (ADAGES)), we constructed models using optical coherence tomography (OCT) scans from 251 participants (437 eyes). The models were trained to predict the RNFL thickness at a future visit based on previous scans. We evaluated four models: linear regression (LR), support vector regression (SVR), gradient boosting regression (GBR), and a custom 1D convolutional neural network (CNN). The GBR model achieved the best performance in predicting pointwise RNFL thickness changes (MAE = 5.2 μm, R² = 0.91), while the custom 1D CNN excelled in predicting changes to average global and sectoral RNFL thickness, providing greater resolution and outperforming the traditional models (MAEs from 2.0–4.2 μm, R² from 0.94–0.98). Our custom models used a novel approach that incorporated longitudinal OCT imaging to achieve consistent performance across different demographics and disease severities, offering potential clinical decision support for glaucoma diagnosis. Patient-level data splitting enhances the evaluation robustness, while predicting detailed RNFL thickness provides a comprehensive understanding of the structural changes over time. Full article

(This article belongs to the Special Issue Biomedical Imaging and Analysis of the Eye: Second Edition)

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19 pages, 14248 KiB

Open AccessArticle

Design and Optimization of Stacked Wideband On-Body Antenna with Parasitic Elements and Defected Ground Structure for Biomedical Applications Using SB-SADEA Method

by Mariana Amador, Mobayode O. Akinsolu, Qiang Hua, João Cardoso, Daniel Albuquerque and Pedro Pinho

Bioengineering 2025, 12(2), 138; https://doi.org/10.3390/bioengineering12020138 - 31 Jan 2025

Abstract

The ability to measure vital signs using electromagnetic waves has been extensively investigated as a less intrusive method capable of assessing different biosignal sources while using a single device. On-body antennas, when directly coupled to the human body, offer a comfortable and effective [...] Read more.

The ability to measure vital signs using electromagnetic waves has been extensively investigated as a less intrusive method capable of assessing different biosignal sources while using a single device. On-body antennas, when directly coupled to the human body, offer a comfortable and effective alternative for daily monitoring. Nonetheless, on-body antennas are challenging to design primarily due to the high dielectric constant of body tissues. While the simulation process may often include a body model, a unique model cannot account for inter-individual variability, leading to discrepancies in measured antenna parameters. A potential solution is to increase the antenna’s bandwidth, guaranteeing the antenna’s impedance matching and robustness for all users. This work describes a new on-body microstrip antenna having a stacked structure with parasitic elements, designed and optimized using artificial intelligence (AI). By using an AI-driven design approach, a self-adaptive Bayesian neural network surrogate-model-assisted differential evolution for antenna optimization (SB-SADEA) method to be specific, and a stacked structure having parasitic elements and a defected ground structure with 27 tuneable design parameters, the simulated impedance bandwidth of the on-body antenna was successfully enhanced from 150 MHz to 1.3 GHz, while employing a single and simplified body model in the simulation process. Furthermore, the impact of inter-individual variability on the measured S-parameters was analyzed. The measured results relative to ten subjects revealed that for certain subjects, the SB-SADEA-optimized antenna’s bandwidth reached 1.6 GHz. Full article

(This article belongs to the Special Issue Antennas for Biomedical Applications)

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12 pages, 238 KiB

Open AccessReview

Nanomedicine and Its Role in Surgical Wound Infections: A Practical Approach

by Malak Bentaleb, Mohammed Abdulrahman and Marcelo A. F. Ribeiro Jr

Bioengineering 2025, 12(2), 137; https://doi.org/10.3390/bioengineering12020137 - 31 Jan 2025

Abstract

Surgical wound infections are a major cause of postoperative complications, contributing to surgical morbidity and mortality. With the rise of antibiotic-resistant pathogens, it is crucial to develop new innovative wound materials to manage surgical wound infections using methods that facilitate drug delivery agents [...] Read more.

Surgical wound infections are a major cause of postoperative complications, contributing to surgical morbidity and mortality. With the rise of antibiotic-resistant pathogens, it is crucial to develop new innovative wound materials to manage surgical wound infections using methods that facilitate drug delivery agents and rely on materials other than antimicrobials. Nanoparticles, in particular, have captured researchers’ interest in recent years due to their effectiveness in wound care. They can be classified into three main types: inorganic nanoparticles, lipid-based nanoparticles, and polymeric nanoparticles. Several studies have demonstrated the effectiveness of these new technologies in enhancing wound-healing times and reducing bacterial burden. However, further research is essential to thoroughly evaluate the safety and toxicity of these materials before they can be integrated into routine surgical practice. Full article

(This article belongs to the Special Issue Surgical Wound Infections and Management)

22 pages, 1484 KiB

Open AccessReview

Towards Safety and Regulation Criteria for Clinical Applications of Decellularized Organ-Derived Matrices

by Elena V. A. van Hengel, Luc J. W. van der Laan, Jeroen de Jonge and Monique M. A. Verstegen

Bioengineering 2025, 12(2), 136; https://doi.org/10.3390/bioengineering12020136 - 30 Jan 2025

Abstract

Whole-organ decellularization generates scaffolds containing native extracellular matrix (ECM) components with preserved tissue microarchitecture, providing a promising advancement in tissue engineering and regenerative medicine. Decellularization retains the ECM integrity which is important for supporting cell attachment, growth, differentiation, and biological function. Although there [...] Read more.

Whole-organ decellularization generates scaffolds containing native extracellular matrix (ECM) components with preserved tissue microarchitecture, providing a promising advancement in tissue engineering and regenerative medicine. Decellularization retains the ECM integrity which is important for supporting cell attachment, growth, differentiation, and biological function. Although there are consensus guidelines to standardize decellularization processes and ECM characterization, no specific criteria or standards regarding matrix sterility and biosafety have been established so far. This regulatory gap in safety, sterilization, and regulation criteria has hampered the clinical translation of decellularized scaffolds. In this review, we identify essential criteria for the safe clinical use of decellularized products from both human and animal sources. These include the decellularization efficacy, levels of chemical residue, preservation of ECM composition and physical characteristics, and criteria for the aseptic processing of decellularization to assure sterility. Furthermore, we explore key considerations for advancing decellularized scaffolds into clinical practice, focusing on regulatory frameworks and safety requirements. Addressing these challenges is crucial for minimizing risks of adverse reactions or infection transmission, thereby accelerating the adoption of tissue-engineered products. This review aims to provide a foundation for establishing robust guidelines, supporting the safe and effective integration of decellularized scaffolds into regenerative medicine applications. Full article

(This article belongs to the Section Regenerative Engineering)

31 pages, 705 KiB

Open AccessSystematic Review

Technological Advances for Gait and Balance in Normal Pressure Hydrocephalus: A Systematic Review

by Alessandro Zampogna, Martina Patera, Marco Falletti, Giulia Pinola, Francesco Asci and Antonio Suppa

Bioengineering 2025, 12(2), 135; https://doi.org/10.3390/bioengineering12020135 - 30 Jan 2025

Abstract

Normal pressure hydrocephalus (NPH) is a recognized cause of reversible cognitive and motor decline, with gait and balance impairments often emerging early. Technologies providing gait and balance measures can aid in early detection, diagnosis, and prognosis of the disease. This systematic review comprehensively [...] Read more.

Normal pressure hydrocephalus (NPH) is a recognized cause of reversible cognitive and motor decline, with gait and balance impairments often emerging early. Technologies providing gait and balance measures can aid in early detection, diagnosis, and prognosis of the disease. This systematic review comprehensively discusses previous studies on the instrumental assessment of gait and balance in NPH. A PubMed search following PRISMA guidelines identified studies published between 2000 and 2024 that used laboratory instruments to assess gait and balance in NPH. Studies underwent quality assessment for internal, statistical, and external validity. Methodological details such as motor tasks, instruments, analytical approaches, and main findings were summarized. Overall, this review includes 41 studies on gait and 17 on balance, most of which used observational, cross-sectional designs. These studies employed various tools, such as pressure-sensitive platforms, optoelectronic motion-capture systems, and wearable inertial sensors. Significant differences in kinematic measures of gait and balance have been found in NPH patients compared to healthy controls and individuals with other neurological conditions. Finally, this review explores potential pathophysiological mechanisms underlying the kinematic changes in gait and balance in NPH and emphasizes the absence of longitudinal data, which hinders drawing definitive conclusions for prognostic purposes. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

22 pages, 3265 KiB

Open AccessArticle

Deep Learning-Assisted Diagnostic System: Apices and Odontogenic Sinus Floor Level Analysis in Dental Panoramic Radiographs

by Pei-Yi Wu, Yuan-Jin Lin, Yu-Jen Chang, Sung-Tsun Wei, Chiung-An Chen, Kuo-Chen Li, Wei-Chen Tu and Patricia Angela R. Abu

Bioengineering 2025, 12(2), 134; https://doi.org/10.3390/bioengineering12020134 - 30 Jan 2025

Abstract

Odontogenic sinusitis is a type of sinusitis caused by apical lesions of teeth near the maxillary sinus floor. Its clinical symptoms are highly like other types of sinusitis, often leading to misdiagnosis as general sinusitis by dentists in the early stages. This misdiagnosis [...] Read more.

Odontogenic sinusitis is a type of sinusitis caused by apical lesions of teeth near the maxillary sinus floor. Its clinical symptoms are highly like other types of sinusitis, often leading to misdiagnosis as general sinusitis by dentists in the early stages. This misdiagnosis delays treatment and may be accompanied by toothache. Therefore, using artificial intelligence to assist dentists in accurately diagnosing odontogenic sinusitis is crucial. This study introduces an innovative odontogenic sinusitis image processing technique, which is fused with common contrast limited adaptive histogram equalization, Min-Max normalization, and the RGB mapping method. Moreover, this study combined various deep learning models to enhance diagnostic accuracy. The YOLO 11n model was used to detect odontogenic sinusitis single tooth position in dental panoramic radiographs and achieved an accuracy of 98.2%. The YOLOv8n-cls model diagnosed odontogenic sinusitis with a final classification accuracy of 96.1%, achieving a 16.9% improvement over non-enhanced methods and outperforming recent studies by at least 4%. Additionally, in clinical applications, the classification accuracy for non-odontogenic sinusitis was 95.8%, while for odontogenic sinusitis it was 97.6%. The detection method developed in this study effectively reduces the radiation dose patients receive during CT imaging and serves as an auxiliary system, providing dentists with reliable support for the precise diagnosis of odontogenic sinusitis. Full article

(This article belongs to the Special Issue Artificial Intelligence Revolution in Biomedical Image and Signal Processing: Innovations and Applications)

18 pages, 2294 KiB

Open AccessReview

Liposomal Nanocarriers to Enhance Skin Delivery of Chemotherapeutics in Cancer Therapy

by Xiangli Liu and Robert A. Falconer

Bioengineering 2025, 12(2), 133; https://doi.org/10.3390/bioengineering12020133 - 30 Jan 2025

Abstract

Cancer chemotherapeutics administered to cancer patients via traditional oral or parenteral routes often encounter poor bioavailability and severe systemic side effects. Skin delivery is a promising alternative route with reduced side effects and improved therapeutic efficacy and has gained significant attention in recent [...] Read more.

Cancer chemotherapeutics administered to cancer patients via traditional oral or parenteral routes often encounter poor bioavailability and severe systemic side effects. Skin delivery is a promising alternative route with reduced side effects and improved therapeutic efficacy and has gained significant attention in recent years. With conventional or deformable liposomal nanocarriers as a skin permeation strategy, cancer chemotherapeutics can be delivered via skin route, offering an option for more efficient therapy. This review summarizes the recent advances in liposome nanocarrier efficacy to enhance the skin delivery of chemotherapeutics with a wide range of physicochemical properties (log P_oct from −0.89 to 5.93, MW from 130 to 1415) in targeting local skin cancer, breast cancer, and tumor metastasis and delivering the drug to systemic circulation to treat distal cancers. The potential mechanisms of skin permeation enhancement by different type of liposomes are also discussed in this review. Full article

(This article belongs to the Section Nanobiotechnology and Biofabrication)

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14 pages, 2583 KiB

Open AccessArticle

Better Cone-Beam CT Artifact Correction via Spatial and Channel Reconstruction Convolution Based on Unsupervised Adversarial Diffusion Models

by Guoya Dong, Yutong He, Xuan Liu, Jingjing Dai, Yaoqin Xie and Xiaokun Liang

Bioengineering 2025, 12(2), 132; https://doi.org/10.3390/bioengineering12020132 - 30 Jan 2025

Abstract

Cone-Beam Computed Tomography (CBCT) holds significant clinical value in image-guided radiotherapy (IGRT). However, CBCT images of low-density soft tissues are often plagued with artifacts and noise, which can lead to missed diagnoses and misdiagnoses. We propose a new unsupervised CBCT image artifact correction [...] Read more.

Cone-Beam Computed Tomography (CBCT) holds significant clinical value in image-guided radiotherapy (IGRT). However, CBCT images of low-density soft tissues are often plagued with artifacts and noise, which can lead to missed diagnoses and misdiagnoses. We propose a new unsupervised CBCT image artifact correction algorithm, named Spatial Convolution Diffusion (ScDiff), based on a conditional diffusion model, which combines the unsupervised learning ability of generative adaptive networks (GAN) with the stable training characteristics of diffusion models. This approach can efficiently and stably achieve CBCT image artifact correction, resulting in clear, realistic CBCT images with complete anatomical structures. The proposed model can effectively improve the image quality of CBCT. The obtained results can reduce artifacts while preserving the anatomical structure of CBCT images. We compared the proposed method with several GAN- and diffusion-based methods. Our method achieved the highest corrected image quality and the best evaluation metrics. Full article

(This article belongs to the Section Biosignal Processing)

30 pages, 1551 KiB

Open AccessArticle

Improved Confidence-Interval Estimations Using Uncertainty Measure and Weighted Feature Decisions for Cuff-Less Blood-Pressure Measurements

by Soojeong Lee, Mugahed A. Al-antari and Gyanendra Prasad Joshi

Bioengineering 2025, 12(2), 131; https://doi.org/10.3390/bioengineering12020131 - 30 Jan 2025

Abstract

This paper presents a method to improve confidence-interval (CI) estimation using individual uncertainty measures and weighted feature decisions for cuff-less blood-pressure (BP) measurement. We obtained uncertainty using Gaussian process regression (GPR). The CI obtained from the GPR model is computed using the distribution [...] Read more.

This paper presents a method to improve confidence-interval (CI) estimation using individual uncertainty measures and weighted feature decisions for cuff-less blood-pressure (BP) measurement. We obtained uncertainty using Gaussian process regression (GPR). The CI obtained from the GPR model is computed using the distribution of BP estimates, which provides relatively wide CIs. Thus, we proposed a method to obtain improved CIs for individual subjects by applying bootstrap and uncertainty methods using the cuff-less BP estimates of each subject obtained through GPR. This study also introduced a novel method to estimate cuff-less BP with high fidelity by determining highly weighted features using weighted feature decisions. The standard deviation of the proposed method’s mean error is 2.94 mmHg and 1.50 mmHg for systolic blood pressure (SBP) and (DBP), respectively. The mean absolute error results were obtained by weighted feature determination combining GPR and gradient boosting algorithms (GBA) for SBP (1.46 mmHg) and DBP (0.69 mmHg). The study confirmed that the BP estimates were within the CI based on the test samples of almost all subjects. The weighted feature decisions combining GPR and GBA were more accurate and reliable for cuff-less BP estimation. Full article

(This article belongs to the Special Issue Recent Advances in Machine Learning and Explainable Artificial Intelligence in Biomedical Data Mining, and Disease Diagnosis Frameworks)

12 pages, 1551 KiB

Open AccessArticle

Dexamethasone-Functionalized PLLA Membranes: Effects of Layer-by-Layer Coating and Electrospinning on Osteogenesis

by Flavia Gonçalves, Roberta Molisani Letomai, Marjory Muraro Gomes, Maria dos Remédios Aguiar Araújo, Yasmin Silva Muniz, Maria Stella Moreira and Leticia Cidreira Boaro

Bioengineering 2025, 12(2), 130; https://doi.org/10.3390/bioengineering12020130 - 30 Jan 2025

Abstract

The addition of dexamethasone in membranes for guided bone regeneration is promising due to its dual effect: (1) anti-inflammatory action and (2) induction of osteogenesis in host stem cells. Electrospun fiber coating with dexamethasone using the layer-by-layer (LBL) technique offers an interesting alternative [...] Read more.

The addition of dexamethasone in membranes for guided bone regeneration is promising due to its dual effect: (1) anti-inflammatory action and (2) induction of osteogenesis in host stem cells. Electrospun fiber coating with dexamethasone using the layer-by-layer (LBL) technique offers an interesting alternative for the gradual release of the drug, aiming for enhanced osteodifferentiation activity. This study aimed to develop synthetic poly-L-lactide (PLLA) membranes with dexamethasone incorporated into the fibers or coated on their surface, and to evaluate the drug release rate, as well as the material’s ability to promote proliferation, osteoconduction, and osteodifferentiation of human periodontal ligament stem cells (hPDLSCs). PLLA membranes were produced by electrospinning. Dexamethasone was incorporated using three techniques: (A) electrospinning of a co-solution of PLLA with 2.5 w/w% dexamethasone; (B) deposition of four layers on the PLLA membrane using alternating solutions of chitosan and heparin/dexamethasone; (C) deposition of 10 layers on the PLLA membrane using the same solutions. hPDLSC proliferation was measured via CCK-8 at 1, 7, 14, and 21 days. Cellular differentiation was assessed by alkaline phosphatase activity (7 days) and alizarin red staining (21 days) in clonogenic and osteogenic media (ODM). Data were analyzed using one or two-way ANOVA and Tukey test. Electrospun membranes with dexamethasone and those with 4 layers showed immediate drug release within 24 h, whereas 10 layers exhibited gradual release over 14 days. Cumulative drug release was higher for electrospun membranes at 1 and 7 days, similar to 10 layers at 14 and 21 days. The 4 LBL membrane promoted lower hPDLSC proliferation compared to the 10 LBL and electrospun membranes at 21 days but showed increased extracellular matrix mineralization in osteogenic media. No significant differences in alkaline phosphatase expression were observed between materials. Therefore, the addition of dexamethasone in 10 layers, combined with heparin, enables gradual drug release. However, lower drug release in the first 24 h by four LBL membranes improved the material’s osteogenesis properties. None of the materials improved the osteodifferentiation in the clonogenic medium. Full article

(This article belongs to the Special Issue Biomaterials for Cartilage and Bone Tissue Engineering: Second Edition)

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17 pages, 6488 KiB

Open AccessArticle

FDoSR-Net: Frequency-Domain Informed Auto-Encoder Network for Arbitrary-Scale 3D Whole-Heart MRI Super-Resolution

by Corbin Maciel and Qing Zou

Bioengineering 2025, 12(2), 129; https://doi.org/10.3390/bioengineering12020129 - 30 Jan 2025

Abstract

This work aims to develop a three-dimensional (3D) super-resolution (SR) network that would perform arbitrary-scale 3D whole-heart (WH) magnetic resonance imaging (MRI) super-resolution, while maintaining fine image details. One-hundred-twenty 3D WH MR volumes, acquired using four different sequences, are used in this study [...] Read more.

This work aims to develop a three-dimensional (3D) super-resolution (SR) network that would perform arbitrary-scale 3D whole-heart (WH) magnetic resonance imaging (MRI) super-resolution, while maintaining fine image details. One-hundred-twenty 3D WH MR volumes, acquired using four different sequences, are used in this study for training, validation, and testing. The proposed method utilizes a frequency-domain regularization in training to maintain fine image detail along with a 3D autoencoder framework. It is also trained in manner to enable it to perform arbitrary factor SR. The proposed method is compared against multiple super-resolution algorithms including two state-of-the-art deep learning methods referred to here as ACNS and TFC as well as nearest neighbor interpolation. The proposed method was evaluated quantitatively and compared against the competing methods with the mean result of the proposed method and the improvements provided by the proposed method (reported by mean percentage between the proposed method and all other competing methods) were recorded. The metrics of interest used for the quantitative comparison are peak signal-to-noise ratio (PSNR, mean = 34.10, mean percentage of improvement = 4.5%), structural similarity index measure (SSIM, mean = 0.94, mean percentage of improvement = 2.2%), mean squared error (MSE, mean = 0.00094, mean percentage of improvement = 48.2%), and root mean squared error (RMSE, mean = 0.024, mean percentage of improvement = 31.0%). Moreover, qualitative comparison was performed using multiple visual comparisons. The quantitative results achieved demonstrate that the proposed method regularly outperforms all other comparison methods. The visual comparisons demonstrate that the proposed method outperforms current state-of-the-art methods in preserving fine image details, as well as its ability to do so for multiple SR factors. Full article

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

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17 pages, 7347 KiB

Open AccessArticle

After Extraction, Upper Premolars Undergo Programmed Socket Collapse with Development of Cavitations Rather than Complete Socket Healing: A Radiological Study

by Shahram Ghanaati, Joanna Śmieszek-Wilczewska, Sarah Al-Maawi, Anja Heselich and Robert Sader

Bioengineering 2025, 12(2), 128; https://doi.org/10.3390/bioengineering12020128 - 29 Jan 2025

Abstract

The alveolar ridge undergoes a loss in volume and atrophy after tooth extraction. Understanding the wound healing and bone regeneration process after tooth extraction is a key factor in the insertion of dental implants. Therefore, the aim of the present study was to [...] Read more.

The alveolar ridge undergoes a loss in volume and atrophy after tooth extraction. Understanding the wound healing and bone regeneration process after tooth extraction is a key factor in the insertion of dental implants. Therefore, the aim of the present study was to analyze the socket healing process after the extraction of upper premolars based on cone beam computed tomography (CBCT) over six months. Special focus was placed on the morphological changes in the alveolar crest and within the socket. A retrospective analysis of patients in need of tooth extraction in the upper premolar region was performed in this study. All patients received flapless tooth extraction under local anesthesia and CBCT immediately after tooth extraction. Further CBCT analysis was performed after three months for the first group (n = 18) and after six months for the second group (n = 18). The results showed that all sockets underwent an inward movement of the defect walls towards the defect center, resulting in reduced total alveolar ridge volume and defect volume. This result was observed after three months and persisted after six months. The inward movement was quantified as a vertical socket collapse of up to 30.1 ± 9.0% after three months and 34.3 ± 6.7% after six months. The horizontal inward movement was quantified as a buccal socket collapse of 47.7 ± 12.3% after three months and 55.7 ± 29.1% after six months. New bone formation within the socket was evident, especially in the occlusal part of the socket. Additionally, bone formation was primarily observed as bone apposition along the socket walls and did not reach the defect center in most cases. The combination of socket collapse and bone apposition led to the formation of cavitations inside the socket that were mostly localized under the occlusal part. These novel findings with respect to socket collapse and formation of cavitation represent a paradigm shift and call for reconsidering the current understanding of socket healing. Based on the data, socket healing should be understood as a patient-specific process that requires 3D radiographic analysis for planning dental implants. Full article

(This article belongs to the Section Regenerative Engineering)

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13 pages, 560 KiB

Open AccessSystematic Review

Relevance of Leg Rehabilitation to Modulating Neurogenic Lower Urinary Tract Symptoms: A Systematic Review

by Gianluca Ciardi, Donatella Giraudo, Milena Fontana, Chiara Citterio, Paola Gandolfi and Gianfranco Lamberti

Bioengineering 2025, 12(2), 127; https://doi.org/10.3390/bioengineering12020127 - 29 Jan 2025

Abstract

Neurogenic lower urinary tract dysfunction (NLUTD) is a secondary complication of a wide range of neurological disorders, which affects patients’ everyday life and self-efficacy. Some brain imaging studies have shown an overlap between motor activation of the pelvic floor and lower limbs. This [...] Read more.

Neurogenic lower urinary tract dysfunction (NLUTD) is a secondary complication of a wide range of neurological disorders, which affects patients’ everyday life and self-efficacy. Some brain imaging studies have shown an overlap between motor activation of the pelvic floor and lower limbs. This systematic review sought to examine the possibility of improving overactive bladder outcomes through a conservative approach based on lower limb training. We conducted a systematic literature review, following the PRISMA guidelines. The following databases were searched: PEDro, PubMed, TRIP, Cochrane Library, EDS base index, Google Scholar, and CINAHL. The PEDro Scale and Cochrane Risk of Bias Assessment Tool were used to assess the overall study quality and sources of bias. A total of 5567 records were retrieved through the systematic search, of which 104 were sought for retrieval; two cohort studies and one randomized controlled trial were finally included. Urodynamics and specific bladder functionality questionnaires showed preliminary evidence of improvement following lower limb stimulation, implemented according to different treatment types (exoskeleton training and weight-suspension walking training). Lower limb-focused exercises showed promising results for improving bladder function, despite the small number of studies and small sample sizes. Future research should confirm this hypothesis using larger samples. Full article

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

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17 pages, 7759 KiB

Open AccessArticle

Identification of Potential Therapeutic Targets for Sensorineural Hearing Loss and Evaluation of Drug Development Potential Using Mendelian Randomization Analysis

by Shun Ding, Qiling Tong, Yixuan Liu, Mengyao Qin and Shan Sun

Bioengineering 2025, 12(2), 126; https://doi.org/10.3390/bioengineering12020126 - 29 Jan 2025

Abstract

Background: Sensorineural hearing loss (SNHL) is a major contributor to hearing impairment, yet effective therapeutic options remain elusive. Mendelian randomization (MR) has proven valuable for drug repurposing and identifying new therapeutic targets. This study aims to pinpoint novel treatment targets for SNHL, exploring [...] Read more.

Background: Sensorineural hearing loss (SNHL) is a major contributor to hearing impairment, yet effective therapeutic options remain elusive. Mendelian randomization (MR) has proven valuable for drug repurposing and identifying new therapeutic targets. This study aims to pinpoint novel treatment targets for SNHL, exploring their pathophysiological roles and potential adverse effects. Methods: This research utilized the UKB-PPP database to access cis-protein quantitative trait locus (cis-pQTL) data, with SNHL data sourced from the FinnGen database as the endpoint for the MR causal analysis of drug targets. Colocalization analysis was employed to determine whether SNHL risk and protein expression share common SNPs. A phenotype-wide association analysis was conducted to assess the potential side effects of these targets. Drug prediction and molecular docking were subsequently used to evaluate the therapeutic potential of the identified targets. Results: Four drug target proteins significantly associated with sensorineural hearing loss (SNHL) were determined by Mendelian randomization (MR) analysis and co-localization analysis. These drug targets include LATS1, TEF, LMNB2, and OGFR and were shown to have fewer potential side effects when acting on these target proteins by phenotype-wide association analysis. Genes associated with sensorineural hearing loss are primarily implicated in the Hippo signaling pathway, cell–cell adhesion, and various binding regulatory activities and are involved in the regulation of cell proliferation and apoptosis. Next, drugs for the treatment of SNHL were screened by the DsigDB database and molecular docking, and the top 10 drugs were selected based on p-value. Among them, atrazine CTD 00005450 was identified as the most likely therapeutic target, followed by ampyrone HL60 DOWN and genistein CTD 00007324. In addition, LMNB2, LATS1, and OGFR could be intervened in by multiple drugs; however, fewer drugs intervened in TEF. Conclusion: This study has successfully identified four promising drug targets for SNHL, which are likely to be effective in clinical trials with minimal side effects. These findings could significantly streamline drug development for SNHL, potentially reducing the costs and time associated with pharmaceutical research and development. Full article

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

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13 pages, 534 KiB

Open AccessReview

Scoping Review of Machine Learning and Patient-Reported Outcomes in Spine Surgery

by Christian Quinones, Deepak Kumbhare, Bharat Guthikonda and Stanley Hoang

Bioengineering 2025, 12(2), 125; https://doi.org/10.3390/bioengineering12020125 - 29 Jan 2025

Abstract

Machine learning is an evolving branch of artificial intelligence that is being applied in neurosurgical research. In spine surgery, machine learning has been used for radiographic characterization of cranial and spinal pathology and in predicting postoperative outcomes such as complications, functional recovery, and [...] Read more.

Machine learning is an evolving branch of artificial intelligence that is being applied in neurosurgical research. In spine surgery, machine learning has been used for radiographic characterization of cranial and spinal pathology and in predicting postoperative outcomes such as complications, functional recovery, and pain relief. A relevant application is the investigation of patient-reported outcome measures (PROMs) after spine surgery. Although a multitude of PROMs have been described and validated, there is currently no consensus regarding which questionnaires should be utilized. Additionally, studies have reported varying degrees of accuracy in predicting patient outcomes based on questionnaire responses. PROMs currently lack standardization, which renders them difficult to compare across studies. The purpose of this manuscript is to identify applications of machine learning to predict PROMs after spine surgery. Full article

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

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Bioengineering

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