Bioengineering

18 pages, 1754 KB

Open AccessArticle

AI-Enhanced Deep Learning Framework for Pulmonary Embolism Detection in CT Angiography

by Nan-Han Lu, Chi-Yuan Wang, Kuo-Ying Liu, Yung-Hui Huang and Tai-Been Chen

Bioengineering 2025, 12(10), 1055; https://doi.org/10.3390/bioengineering12101055 (registering DOI) - 29 Sep 2025

Pulmonary embolism (PE) on CT pulmonary angiography (CTPA) demands rapid, accurate assessment, yet small, low-contrast clots in distal arteries remain challenging. We benchmarked ten fully convolutional network (FCN) backbones and introduced Consensus Intersection-Optimized Fusion (CIOF)—a K-of-M, pixel-wise mask fusion with the voting threshold [...] Read more.

Pulmonary embolism (PE) on CT pulmonary angiography (CTPA) demands rapid, accurate assessment, yet small, low-contrast clots in distal arteries remain challenging. We benchmarked ten fully convolutional network (FCN) backbones and introduced Consensus Intersection-Optimized Fusion (CIOF)—a K-of-M, pixel-wise mask fusion with the voting threshold K* selected on training patients to maximize IoU. Using the FUMPE cohort (35 patients; 12,034 slices) with patient-based random splits (18 train, 17 test), we trained five FCN architectures (each with Adam and SGDM) and evaluated segmentation with IoU, Dice, FNR/FPR, and latency. CIOF achieved the best overall performance (mean IoU 0.569; mean Dice 0.691; FNR 0.262), albeit with a higher runtime (~63.7 s per case) because all ten models are executed and fused; the strongest single backbone was Inception-ResNetV2 + SGDM (IoU 0.530; Dice 0.648). Stratified by embolization ratio, CIOF remained superior across <10⁻⁴, 10⁻⁴–10⁻³, and >10⁻³ clot burdens, with mean IoU/Dice = 0.238/0.328, 0.566/0.698, and 0.739/0.846, respectively—demonstrating gains for tiny, subsegmental emboli. These results position CIOF as an accuracy-oriented, interpretable ensemble for offline or second-reader use, while faster single backbones remain candidates for time-critical triage. Full article

(This article belongs to the Section Biosignal Processing)

14 pages, 1454 KB

Open AccessArticle

Predicting Short-Term Outcome of COVID-19 Pneumonia Using Deep Learning-Based Automatic Detection Algorithm Analysis of Serial Chest Radiographs

by Chae Young Lim, Yoon Ki Cha, Kyeongman Jeon, Subin Park, Kyunga Kim and Myung Jin Chung

Bioengineering 2025, 12(10), 1054; https://doi.org/10.3390/bioengineering12101054 (registering DOI) - 29 Sep 2025

Abstract

This study aimed to evaluate short-term clinical outcomes in COVID-19 pneumonia patients using parameters derived from a commercial deep learning-based automatic detection algorithm (DLAD) applied to serial chest radiographs (CXRs). We analyzed 391 patients with COVID-19 who underwent serial CXRs during isolation at [...] Read more.

This study aimed to evaluate short-term clinical outcomes in COVID-19 pneumonia patients using parameters derived from a commercial deep learning-based automatic detection algorithm (DLAD) applied to serial chest radiographs (CXRs). We analyzed 391 patients with COVID-19 who underwent serial CXRs during isolation at a residential treatment center (median interval: 3.57 days; range: 1.73–5.56 days). Patients were categorized into two groups: the improved group (n = 309), who completed the standard 7-day quarantine, and the deteriorated group (n = 82), who showed worsening symptoms, vital signs, or CXR findings. Using DLAD’s consolidation probability scores and gradient-weighted class activation mapping (Grad-CAM)-based localization maps, we quantified the consolidation area through heatmap segmentation. The weighted area was calculated as the sum of the consolidation regions’ areas, with each area weighted by its corresponding probability score. Change rates (Δ) were defined as per-day differences between consecutive measurements. Prediction models were developed using Cox proportional hazards regression and evaluated daily from day 1 to day 7 after the subsequent CXR acquisition. Among the imaging factors, baseline probability and ΔProbability, ΔArea, and ΔWeighted area were identified as prognostic indicators. The multivariate Cox model incorporating baseline probability and ΔWeighted area demonstrated optimal performance (C-index: 0.75, 95% Confidence Interval: 0.68–0.81; integrated calibration index: 0.03), with time-dependent AUROC (Area Under Receiver Operating Curve) values ranging from 0.74 to 0.78 across daily predictions. These findings suggest that the Δparameters of DLAD can aid in predicting short-term clinical outcomes in patients with COVID-19. Full article

(This article belongs to the Special Issue Artificial Intelligence-Based Medical Imaging Processing)

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17 pages, 1676 KB

Open AccessArticle

Promoting Re-Epithelialization in Diabetic Foot Wounds Using Integrative Therapeutic Approaches

by Lucia Bubulac, Iuliana-Raluca Gheorghe, Elisabeth Ungureanu, Claudia Florina Bogdan-Andreescu, Cristina-Crenguța Albu, Consuela-Mădălina Gheorghe, Ovidiu Mușat, Irina Anca Eremia, Cristina Aura Panea and Alexandru Burcea

Bioengineering 2025, 12(10), 1053; https://doi.org/10.3390/bioengineering12101053 - 29 Sep 2025

Abstract

Background: Diabetes mellitus is a heterogeneous chronic disease with an increasing global prevalence. In Romania, 11.6% of the population is affected, yet only 6.46% receive treatment. Among diabetic patients, 15–25% develop skin lesions that may progress to ulceration and necrosis, significantly impairing [...] Read more.

Background: Diabetes mellitus is a heterogeneous chronic disease with an increasing global prevalence. In Romania, 11.6% of the population is affected, yet only 6.46% receive treatment. Among diabetic patients, 15–25% develop skin lesions that may progress to ulceration and necrosis, significantly impairing quality of life and increasing the risk of complications. Methods: We conducted a prospective study including 28 patients (14 in the control group and 14 in the intervention group) with type I or II diabetes and chronic ulcers of the calf or foot (>4 cm²). The control group received standard therapy with debridement, dressings, antibiotics when indicated, and local and systemic ozone therapy. The intervention group was treated with an Integrative Therapeutic Protocol combining ozone therapy, pulsed electromagnetic field therapy (PEMF), colon hydrotherapy with probiotic supplementation, and an anti-inflammatory alkaline diet. Wound healing (reduction in ulcer surface area) was the primary endpoint; secondary endpoints included changes in glycemia and inflammatory biomarkers. Results: After 8 weeks, the intervention group achieved 86.2% re-epithelialization versus 58.2% in controls (p < 0.01). Significant improvements were also observed in blood glucose level (−38%), HbA1c (−25%), CRP (−26%), and fibrinogen (−28%) relative to baseline, with differences versus controls reaching statistical significance. Conclusions: The Integrative Therapeutic Protocol accelerated wound healing and improved glycemic and inflammatory profiles compared with ozone therapy alone. Although an alkaline diet was recommended, adherence and its specific contribution were not objectively monitored; therefore, this component should be interpreted with caution. Full article

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

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29 pages, 2058 KB

Open AccessArticle

Voice-Based Early Diagnosis of Parkinson’s Disease Using Spectrogram Features and AI Models

by Danish Quamar, V. D. Ambeth Kumar, Muhammad Rizwan, Ovidiu Bagdasar and Manuella Kadar

Bioengineering 2025, 12(10), 1052; https://doi.org/10.3390/bioengineering12101052 - 29 Sep 2025

Abstract

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that significantly affects motor functions, including speech production. Voice analysis offers a less invasive, faster and more cost-effective approach for diagnosing and monitoring PD over time. This research introduces an automated system to distinguish between [...] Read more.

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that significantly affects motor functions, including speech production. Voice analysis offers a less invasive, faster and more cost-effective approach for diagnosing and monitoring PD over time. This research introduces an automated system to distinguish between PD and non-PD individuals based on speech signals using state-of-the-art signal processing and machine learning (ML) methods. A publicly available voice dataset (Dataset 1, 81 samples) containing speech recordings from PD patients and non-PD individuals was used for model training and evaluation. Additionally, a small supplementary dataset (Dataset 2, 15 samples) was created although excluded from experiment, to illustrate potential future extensions of this work. Features such as Mel-frequency cepstral coefficients (MFCCs), spectrograms, Mel spectrograms and waveform representations were extracted to capture key vocal impairments related to PD, including diminished vocal range, weak harmonics, elevated spectral entropy and impaired formant structures. These extracted features were used to train and evaluate several ML models, including support vector machine (SVM), XGBoost and logistic regression, as well as deep learning (DL)architectures such as deep neural networks (DNN), convolutional neural networks (CNN) combined with long short-term memory (LSTM), CNN + gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM). Experimental results show that DL models, particularly BiLSTM, outperform traditional ML models, achieving 97% accuracy and an AUC of 0.95. The comprehensive feature extraction from both datasets enabled robust classification of PD and non-PD speech signals. These findings highlight the potential of integrating acoustic features with DL methods for early diagnosis and monitoring of Parkinson’s Disease. Full article

(This article belongs to the Special Issue Artificial Intelligence in Neurodegenerative Disorders: Advances in Diagnosis, Prognosis and Treatment)

20 pages, 2545 KB

Open AccessArticle

LG-UNet Based Segmentation and Survival Prediction of Nasopharyngeal Carcinoma Using Multimodal MRI Imaging

by Yuhao Yang, Junhao Wen, Tianyi Wu, Jinrang Dong, Yunfei Xia and Yu Zhang

Bioengineering 2025, 12(10), 1051; https://doi.org/10.3390/bioengineering12101051 - 29 Sep 2025

Abstract

Image segmentation and survival prediction for nasopharyngeal carcinoma (NPC) are crucial for clinical diagnosis and treatment decisions. This study presents an improved 3D-UNet-based model for NPC GTV segmentation, referred to as LG-UNet. The encoder introduces deep strip convolution and channel attention mechanisms to [...] Read more.

Image segmentation and survival prediction for nasopharyngeal carcinoma (NPC) are crucial for clinical diagnosis and treatment decisions. This study presents an improved 3D-UNet-based model for NPC GTV segmentation, referred to as LG-UNet. The encoder introduces deep strip convolution and channel attention mechanisms to enhance feature extraction while avoiding spatial feature loss and anisotropic constraints. The decoder incorporates Dynamic Large Convolutional Kernel (DLCK) and Global Feature Fusion (GFF) modules to capture multi-scale features and integrate global contextual information, enabling precise segmentation of the tumor GTV in NPC MRI images. Risk prediction is performed on the segmented multi-modal MRI images using the Lung-Net model, with output risk factors combined with clinical data in the Cox model to predict metastatic probabilities for NPC lesions. Experimental results on 442 NPC MRI scans from Sun Yat-sen University Cancer Center showed DSC of 0.8223, accuracy of 0.8235, recall of 0.8297, and HD95 of 1.6807 mm. Compared to the baseline model, the DSC improved by 7.73%, accuracy increased by 4.52%, and recall improved by 3.40%. The combined model’s risk prediction showed C-index values of 0.756, with a 5-year AUC value of 0.789. This model can serve as an auxiliary tool for clinical decision-making in NPC. Full article

(This article belongs to the Section Biosignal Processing)

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11 pages, 241 KB

Open AccessReview

Current Concepts in Viscosupplementation: New Classification System and Emerging Frontiers

by Gustavo Constantino de Campos and Alberto Cliquet, Jr.

Bioengineering 2025, 12(10), 1050; https://doi.org/10.3390/bioengineering12101050 - 29 Sep 2025

Abstract

Viscosupplementation with intra-articular hyaluronic acid (HA) is a key therapeutic option for osteoarthritis (OA), yet the field is hampered by clinical controversies and an outdated classification of available products. This comprehensive review critically analyzes the current landscape, moving from a mechanical to a [...] Read more.

Viscosupplementation with intra-articular hyaluronic acid (HA) is a key therapeutic option for osteoarthritis (OA), yet the field is hampered by clinical controversies and an outdated classification of available products. This comprehensive review critically analyzes the current landscape, moving from a mechanical to a biological paradigm of HA’s mechanism of action. We argue that the traditional HA product classification based solely on molecular weight is insufficient, as it conflates chemically distinct products. Therefore, we propose a new, two-tiered classification framework: the primary distinction is based on chemical structure, separating linear (non-modified) HA from cross-linked (chemically modified) HA. Linear HA is then sub-classified by molecular weight (Low, Intermediate, and High), while cross-linked HA is defined as a separate category of hydrogels with a ultra-high effective molecular weight. Within this clearer framework, we analyze the central controversy between formulations, highlighting the pivotal emergence of high-concentration, high-molecular-weight (>2 million Dalton) linear HA. These formulations not only challenge the durability rationale for cross-linking by providing year-long efficacy but also possess a superior biological profile for chondroprotection, preserving chondrocyte viability and function. Furthermore, we explore the expanding frontier of combination therapies, where linear HA serves as the ideal physiological scaffold for agents like corticosteroids, PRP and other injectable orthobiologics such as bone marrow aspirate and stromal vascular fraction. Full article

(This article belongs to the Special Issue Biomaterials for the Repair and Regeneration of Musculoskeletal Tissue)

15 pages, 2400 KB

Open AccessArticle

A Machine Learning Approach for Real-Time Detection of Inadequate Sedation Using Non-EEG Physiological Signals

by Huiquan Wang, Chunliang Jiang, Guanjun Liu, Jing Yuan, Ming Yu, Xin Ma, Chong Liu, Jingyu Xiao and Guang Zhang

Bioengineering 2025, 12(10), 1049; https://doi.org/10.3390/bioengineering12101049 - 29 Sep 2025

Abstract

Sedation is an essential component of the anesthesia process. Inadequate sedation during anesthesia increases the risk of patient discomfort, intraoperative awareness, and psychological trauma. Conventional electroencephalography (EEG) based depth of anesthesia monitoring is often impractical in out-of-hospital settings due to equipment limitations and [...] Read more.

Sedation is an essential component of the anesthesia process. Inadequate sedation during anesthesia increases the risk of patient discomfort, intraoperative awareness, and psychological trauma. Conventional electroencephalography (EEG) based depth of anesthesia monitoring is often impractical in out-of-hospital settings due to equipment limitations and signal artifacts. Alternative non-EEG-based approaches are therefore required. In this study, we developed a machine learning model to detect inadequate sedation using 27 feature parameters, including demographics, vital signs, and heart rate variability metrics, from the open-access VitalDB database. Patient states were defined as inadequate sedation when the bispectral index (BIS) > 60. We systematically evaluated four temporal windows and four algorithms, and assessed model interpretability using Shapley Additive Explanations (SHAP). The Light Gradient Boosting Machine (LGBM) achieved the best performance, with an area under the receiver operating characteristic curve (AUC) of 0.825 and an accuracy (ACC) of 0.741 using a 2 s time window. Extending the time window to 20 s improved both metrics by approximately 0.012. Feature selection identified 12 key parameters that maintained comparable accuracy, confirming robustness with reduced complexity. These findings demonstrate the feasibility of using non-EEG-based physiological data for real-time detection of inadequate sedation. The developed model is interpretable, resource-efficient, scalable, and shows strong potential for integration into portable monitoring systems in prehospital, emergency, and low-resource surgical settings. Full article

(This article belongs to the Special Issue Advancements in Machine Learning for Healthcare: Innovations, Challenges, and Future Directions)

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24 pages, 535 KB

Open AccessArticle

Analysing the Structural Identifiability and Observability of Mechanistic Models of Tumour Growth

by Adriana González Vázquez and Alejandro F. Villaverde

Bioengineering 2025, 12(10), 1048; https://doi.org/10.3390/bioengineering12101048 - 29 Sep 2025

Abstract

Mechanistic cancer models can encapsulate beliefs about the main factors influencing tumour growth. In recent decades, many different types of dynamic models have been used for this purpose. The integration of a model’s differential equations yields a simulation of the behaviour of the [...] Read more.

Mechanistic cancer models can encapsulate beliefs about the main factors influencing tumour growth. In recent decades, many different types of dynamic models have been used for this purpose. The integration of a model’s differential equations yields a simulation of the behaviour of the system over time, thus enabling tumour progression to be predicted. A requisite for the reliability of these quantitative predictions is that the model is structurally identifiable and observable, i.e., that it is theoretically possible to infer the correct values of its parameters and state variables from time course data. In this paper, we show how to analyse these properties of tumour growth models using a well-established methodology, which we implemented previously in an open-source software tool. To this end, we provide an account of 20 published models described by ordinary differential equations, some of which incorporate the effect of interventions including chemotherapy, radiotherapy, and immunotherapy. For each model, we describe its equations and analyse their structural identifiability and observability, discussing how they are affected by the experimental design. We provide computational implementations of these models, which enable readily reproducing results. Our results inform about the possibility of inferring the parameters and state variables of a given model using a specific measurement setup, and, together with the corresponding methodology and implementation, they can be used as a blueprint for analysing other models not included here. Thus, this paper serves as a guide to select the most appropriate model for each application. Full article

(This article belongs to the Special Issue Mathematical and Computational Modeling of Cancer Progression)

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16 pages, 1974 KB

Open AccessArticle

Color Change in Commercial Resin Composites with Different Photoinitiators

by Feng Gao and David W. Berzins

Bioengineering 2025, 12(10), 1047; https://doi.org/10.3390/bioengineering12101047 - 28 Sep 2025

Abstract

The yellowing effect of camphorquinone (CQ) has led manufacturers to add alternative initiators into resin composites (RCs) to reduce the amount of CQ used. The aim of this study was to investigate the color change in commercial RCs with alternative photoinitiators besides CQ. [...] Read more.

The yellowing effect of camphorquinone (CQ) has led manufacturers to add alternative initiators into resin composites (RCs) to reduce the amount of CQ used. The aim of this study was to investigate the color change in commercial RCs with alternative photoinitiators besides CQ. Color change upon polymerization and aging in air and artificial saliva for up to 3 months was tested for seven commercial RCs (traditional and bulk-fill) with either CQ only or CQ and additional photoinitiators (CQ+). Color measurements were obtained with a spectrophotometer. Color change (ΔE) was calculated using the CIELab and CIEDE2000 formulae. ANOVA and a post hoc SNK test were conducted for statistical analysis. Upon polymerization, the ΔE of CQ+ was greater than that of CQ only, except in the case of dual-cure HyperFIL. The storage conditions did not affect the color change within 24 h for either air or artificial saliva, whereas they did have an influence on color stability when RCs were aged for 1 month and 3 months. The color changes in the RCs aged in artificial saliva were considered clinically acceptable for all RCs tested except HyperFIL. Additional photoinitiator systems tended to result in a greater color change upon polymerization but did not affect color change upon aging. During shade selection, especially when additional photoinitiators besides CQ are used, a guide reflecting the color after polymerization should be used. Full article

(This article belongs to the Special Issue Advanced Dental Materials for Restorative Dentistry)

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22 pages, 4893 KB

Open AccessArticle

Ultrawidefield-to-Conventional Fundus Image Translation with Scaled Feature Registration and Distorted Vessel Correction

by JuChan Kim, Junghyun Bum, Duc-Tai Le, Chang-Hwan Son, Eun Jung Lee, Jong Chul Han and Hyunseung Choo

Bioengineering 2025, 12(10), 1046; https://doi.org/10.3390/bioengineering12101046 - 28 Sep 2025

Abstract

Conventional fundus (CF) and ultrawidefield fundus (UF) imaging are two primary modalities widely used in ophthalmology. Despite the complementary use of both imaging modalities in clinical practice, existing research on fundus image translation has yet to reach clinical viability and often lacks the [...] Read more.

Conventional fundus (CF) and ultrawidefield fundus (UF) imaging are two primary modalities widely used in ophthalmology. Despite the complementary use of both imaging modalities in clinical practice, existing research on fundus image translation has yet to reach clinical viability and often lacks the necessary accuracy and detail required for practical medical use. Additionally, collecting paired UFI-CFI data from the same patients presents significant limitations, and unpaired learning-based generative models frequently suffer from distortion phenomena, such as hallucinations. This study introduces an enhanced modality transformation method to improve the diagnostic support capabilities of deep learning models in ophthalmology. The proposed method translates UF images (UFIs) into CF images (CFIs), potentially replacing the dual-imaging approach commonly used in clinical practice. This replacement can significantly reduce financial and temporal burdens on patients. To achieve this, this study leveraged UFI–CFI image pairs obtained from the same patient on the same day. This approach minimizes information distortion and accurately converts the two modalities. Our model employs scaled feature registration and distorted vessel correction methods to align UFI–CFI pairs effectively. The generated CFIs not only enhance image quality and better represent the retinal area compared to existing methods but also effectively preserve disease-related details from UFIs, aiding in accurate diagnosis. Furthermore, compared with existing methods, our model demonstrated a substantial 18.2% reduction in MSE, a 7.2% increase in PSNR, and a 12.7% improvement in SSIM metrics. Notably, our results show that the generated CFIs are nearly indistinguishable from the real CFIs, as confirmed by ophthalmologists. Full article

(This article belongs to the Special Issue Artificial Intelligence-Based Medical Imaging Processing)

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39 pages, 9769 KB

Open AccessReview

Label-Free Cancer Detection Methods Based on Biophysical Cell Phenotypes

by Isabel Calejo, Ana Catarina Azevedo, Raquel L. Monteiro, Francisco Cruz and Raphaël F. Canadas

Bioengineering 2025, 12(10), 1045; https://doi.org/10.3390/bioengineering12101045 - 28 Sep 2025

Abstract

Progress in clinical diagnosis increasingly relies on innovative technologies and advanced disease biomarker detection methods. While cell labeling remains a well-established technique, label-free approaches offer significant advantages, including reduced workload, minimal sample damage, cost-effectiveness, and simplified chip integration. These approaches focus on the [...] Read more.

Progress in clinical diagnosis increasingly relies on innovative technologies and advanced disease biomarker detection methods. While cell labeling remains a well-established technique, label-free approaches offer significant advantages, including reduced workload, minimal sample damage, cost-effectiveness, and simplified chip integration. These approaches focus on the morpho-biophysical properties of cells, eliminating the need for labeling and thus reducing false results while enhancing data reliability and reproducibility. Current label-free methods span conventional and advanced technologies, including phase-contrast microscopy, holographic microscopy, varied cytometries, microfluidics, dynamic light scattering, atomic force microscopy, and electrical impedance spectroscopy. Their integration with artificial intelligence further enhances their utility, enabling rapid, non-invasive cell identification, dynamic cellular interaction monitoring, and electro-mechanical and morphological cue analysis, making them particularly valuable for cancer diagnostics, monitoring, and prognosis. This review compiles recent label-free cancer cell detection developments within clinical and biotechnological laboratory contexts, emphasizing biophysical alterations pertinent to liquid biopsy applications. It highlights interdisciplinary innovations that allow the characterization and potential identification of cancer cells without labeling. Furthermore, a comparative analysis addresses throughput, resolution, and detection capabilities, thereby guiding their effective deployment in biomedical research and clinical oncology settings. Full article

(This article belongs to the Special Issue Label-Free Cancer Detection)

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18 pages, 2313 KB

Open AccessArticle

In Silico and In Vitro Comparison of Seven Closed and Semi-Closed Leaflet Designs for Transcatheter Heart Valve Replacements

by Alexander Breitenstein-Attach, Marvin Steitz, Jordi Modolell, Sugat Ratna Tuladhar, Boris Warnack, Peter Kramer, Frank Edelmann, Felix Berger and Boris Schmitt

Bioengineering 2025, 12(10), 1044; https://doi.org/10.3390/bioengineering12101044 - 28 Sep 2025

Abstract

Purpose: Transcatheter heart valve replacements (TVR) are typically designed in a closed shape with initial leaflet coaptation. However, recent studies suggest a semi-closed geometry without a predefined coaptation zone, relying on diastolic pressure and clinical oversizing of 10–20 % for closure. This approach [...] Read more.

Purpose: Transcatheter heart valve replacements (TVR) are typically designed in a closed shape with initial leaflet coaptation. However, recent studies suggest a semi-closed geometry without a predefined coaptation zone, relying on diastolic pressure and clinical oversizing of 10–20 % for closure. This approach may minimize pinwheeling, a phenomenon linked to early valve degeneration. Method: Seven valve geometries were assessed: one closed design (G0) and six semi-closed variations (G1–G6). The semi-closed designs differed in free edge shape (linear, concave, convex) and opening degree, defined as the relative distance from the leaflet to the valve center in the unloaded state. The opening degree was systematically increased across G1–G6, with G6 exhibiting the highest value. 30 mm valves were fabricated using porcine pericardium and self-expanding nitinol stents. Performance was assessed in a pulse duplicator system, evaluating transvalvular pressure gradient (TPG), effective orifice area (EOA), regurgitation fraction (RF) and a novel pinwheeling index (PI) which was validated by finite element simulations. Results: Finite element simulations demonstrated that semi-closed geometries achieve valve closure at a diameter reduction of >5%. In vitro tests confirmed these findings with more homogeneous coaptation and reduced pinwheeling. With increased opening degree the RF reduced significantly (RF_G0 = 18.54 ± 8.05%; RF_G6 = 8.22 ± 1.27%; p < 0.0001), while valve opening remained comparable (p = 0.4519). Conclusions: A semi-closed leaflet geometry enhances valve closure, reducing regurgitation and pinwheeling while preserving effective opening. With clinical oversizing, a higher opening degree improves coaptation and may enhance durability by mitigating structural deterioration, ultimately improving the long-term performance and lifespan of transcatheter valve replacements. Full article

(This article belongs to the Special Issue Recent Advances in Cardiothoracic Assist Devices)

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24 pages, 1403 KB

Open AccessReview

Environmental Impacts and Strategies for Bioremediation of Dye-Containing Wastewater

by Mukesh Kumar, Anshuman Mishra, Suresh Kumar Patel, Jyoti Kushwaha, Sunita Singh, Vinay Mishra, Deepak Singh, Vijay Singh, Balendu Shekher Giri, Reeta Rani Singhania and Dhananjay Singh

Bioengineering 2025, 12(10), 1043; https://doi.org/10.3390/bioengineering12101043 - 28 Sep 2025

Abstract

Rapid industrialization, along with the development of textile and other associated industries, has led to the discharge of dyes, heavy metals, and other carcinogenic and environmentally harmful substances into water bodies. The volume of wastewater containing dyes is increasing day by day. Raised [...] Read more.

Rapid industrialization, along with the development of textile and other associated industries, has led to the discharge of dyes, heavy metals, and other carcinogenic and environmentally harmful substances into water bodies. The volume of wastewater containing dyes is increasing day by day. Raised levels of dyes, along with other contaminants, in wastewater are becoming a global concern, as these affect human health as well as aquatic flora and fauna. Bioremediation is one of the effective, sustainable, eco-friendly and cost-effective approaches for the treatment of wastewater containing dyes. This paper presents a state-of-the-art review of bioremediation techniques used for the removal of dyes from textile wastewater. The usage of various strains, e.g., bacteria, algae, yeast, enzymes, fungi, etc., is discussed in detail. Bioremediation of dyes using bioreactors and microbial fuel cells is also explored in this study. Full article

(This article belongs to the Special Issue Biological Wastewater Treatment and Resource Recovery, 2nd Edition)

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18 pages, 6192 KB

Open AccessArticle

The Impact of Stenosis Severity on Hemodynamic Parameters in the Iliac Artery: A Fluid–Structure Interaction Study

by Nima Rahmati, Hamidreza Pouraliakbar, Arshia Eskandari, Kian Javari, Alireza Jabbarinick, Parham Sadeghipour, Madjid Soltani and Mona Alimohammadi

Bioengineering 2025, 12(10), 1042; https://doi.org/10.3390/bioengineering12101042 - 28 Sep 2025

Abstract

The common iliac artery supplies blood to the lower extremities, and stenosis in this region severely impacts hemodynamics. This study investigates the effects of 25%, 50%, and 75% iliac artery stenosis on key hemodynamic parameters using a fluid–structure interaction (FSI) approach. Semi-idealized geometries [...] Read more.

The common iliac artery supplies blood to the lower extremities, and stenosis in this region severely impacts hemodynamics. This study investigates the effects of 25%, 50%, and 75% iliac artery stenosis on key hemodynamic parameters using a fluid–structure interaction (FSI) approach. Semi-idealized geometries reconstructed from patient-specific data modeled realistic arterial behavior. Parameters such as wall displacement, time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), high oscillatory low shear magnitude (HOLMES) index, and endothelial cell activation potential (ECAP) were evaluated. Results showed peak wall displacement of 2.85 mm in the bifurcation zone under 75% stenosis. TAWSS increased with stenosis severity, peaking in stenotic regions and decreasing significantly downstream. OSI was highest in non-stenosed right branches and bifurcation areas, indicating multidirectional shear forces. HOLMES values were lowest downstream of stenoses, indicating disturbed flow. ECAP exceeded the thrombosis risk threshold (1.4 Pa⁻¹) in post-stenotic zones under 75% stenosis, suggesting a higher risk of clot formation. These results demonstrate that stenosis disrupts local flow and causes hemodynamic changes downstream, emphasizing the need for comprehensive clinical assessment beyond the stenotic site. Regions with elevated ECAP and low HOLMES downstream may be prone to thrombosis, highlighting the importance of careful hemodynamic monitoring for treatment strategies. Full article

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

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22 pages, 3784 KB

Open AccessArticle

Orthodontic Bracket Removal and Enamel Roughness: Comparing the Effects of Sapphire and Metallic Brackets in an In Vitro Study

by Cosmin Bogdan Licsăndroiu, Mihaela Jana Țuculină, Adelina Smaranda Bugălă, Petre Costin Mărășescu, Felicia Ileana Mărășescu, Andreea Gabriela Nicola, Cristian Niky Cumpătă, Cosmin Mihai Mirițoiu, Ovidiu Ioan Gheorghe, Maria Cristina Bezna, Elena Verona Licsăndroiu and Ionela Teodora Dascălu

Bioengineering 2025, 12(10), 1041; https://doi.org/10.3390/bioengineering12101041 - 28 Sep 2025

Abstract

Background: Enamel surface roughness after bracket debonding is an important issue due to its impact on plaque accumulation and the potential development of carious lesions. This in vitro study aimed to assess enamel roughness after the removal of metallic and sapphire brackets and [...] Read more.

Background: Enamel surface roughness after bracket debonding is an important issue due to its impact on plaque accumulation and the potential development of carious lesions. This in vitro study aimed to assess enamel roughness after the removal of metallic and sapphire brackets and the effect of a remineralization treatment. Methods: Two hundred extracted human permanent teeth with healthy enamel were randomly distributed into two groups (n = 100) and bonded with either metallic or sapphire brackets using the same adhesive (3M™ Transbond™ XT (St. Paul, MN, USA), Minnesota Mining and Manufacturing Company, MN, USA). The enamel surface roughness was measured before bonding, after debonding, and after remineralization using SEM and a TR200 roughness (SaluTron GmbH, Frechen, Germany) tester. The parameter Ra was used to quantify the surface roughness. One-way ANOVA, the normality test, variance homogeneity, and the Bonferroni post hoc test were used to analyze the data. Results: Debonding significantly increased the enamel surface roughness in both groups. The sapphire bracket group presented significantly higher mean Ra values post debonding (4.14 ± 0.36 µm) compared to the metallic group (2.56 ± 0.52 µm). Remineralization led to a decrease in surface roughness in both groups, though not to baseline levels. The changes were statistically significant (p < 0.01), with a power of the test of 1.0. Conclusions: The bracket material significantly affects enamel surface roughness after orthodontic debonding. Sapphire brackets produced greater surface irregularities than metallic ones. Remineralization partially reduced roughness in both groups, with the final values in the metallic group being closer to baseline levels. Crucially, these values remained far above the clinical threshold for plaque retention, highlighting the need for improved debonding techniques. Full article

(This article belongs to the Special Issue New Sight for the Treatment of Dental Diseases: Updates and Direction)

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15 pages, 5911 KB

Open AccessArticle

Integrative Bioinformatics-Guided Analysis of Glomerular Transcriptome Implicates Potential Therapeutic Targets and Pathogenesis Mechanisms in IgA Nephropathy

by Tiange Yang, Mengde Dai, Fen Zhang and Weijie Wen

Bioengineering 2025, 12(10), 1040; https://doi.org/10.3390/bioengineering12101040 - 27 Sep 2025

Abstract

(1) Background: IgA nephropathy (IgAN) is a leading cause of chronic kidney disease worldwide. Despite its prevalence, the molecular mechanisms of IgAN remain poorly understood, partly due to limited research scale. Identifying key genes involved in IgAN’s pathogenesis is critical for novel diagnostic [...] Read more.

(1) Background: IgA nephropathy (IgAN) is a leading cause of chronic kidney disease worldwide. Despite its prevalence, the molecular mechanisms of IgAN remain poorly understood, partly due to limited research scale. Identifying key genes involved in IgAN’s pathogenesis is critical for novel diagnostic and therapeutic strategies. (2) Methods: We identified differentially expressed genes (DEGs) by analyzing public datasets from the Gene Expression Omnibus. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to elucidate the biological roles of DEGs. Hub genes were screened using weighted gene co-expression network analysis combined with machine learning algorithms. Immune infiltration analysis was conducted to explore associations between hub genes and immune cell profiles. The hub genes were validated using receiver operating characteristic curves and area under the curve. (3) Results: We identified 165 DEGs associated with IgAN and revealed pathways such as IL-17 signaling and complement and coagulation cascades, and biological processes including response to xenobiotic stimuli. Four hub genes were screened: three downregulated (FOSB, SLC19A2, PER1) and one upregulated (SOX17). The AUC values for identifying IgAN in the training and testing set ranged from 0.956 to 0.995. Immune infiltration analysis indicated that hub gene expression correlated with immune cell abundance, suggesting their involvement in IgAN’s immune pathogenesis. (4) Conclusion: This study identifies FOSB, SLC19A2, PER1, and SOX17 as novel hub genes with high diagnostic accuracy for IgAN. These genes, linked to immune-related pathways such as IL-17 signaling and complement activation, offer promising targets for diagnostic development and therapeutic intervention, enhancing our understanding of IgAN’s molecular and immune mechanisms. Full article

(This article belongs to the Special Issue Advanced Biomedical Signal Communication Technology)

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22 pages, 1278 KB

Open AccessReview

Artificial Intelligence-Based Methods and Omics for Mental Illness Diagnosis: A Review

by Glenda Santos de Oliveira, Fábio Henrique dos Santos Rodrigues, João Guilherme de Moraes Pontes and Ljubica Tasic

Bioengineering 2025, 12(10), 1039; https://doi.org/10.3390/bioengineering12101039 - 27 Sep 2025

Abstract

The underlying causes fof major mental illnesses, including anxiety disorders (ADs), depression, and bipolar disorder (BD), remain insufficiently understood, limiting the availability of effective, patient-friendly treatments and accurate diagnostic tests. For instance, anxiety disorders encompass a diverse spectrum of subtypes and may emerge [...] Read more.

The underlying causes fof major mental illnesses, including anxiety disorders (ADs), depression, and bipolar disorder (BD), remain insufficiently understood, limiting the availability of effective, patient-friendly treatments and accurate diagnostic tests. For instance, anxiety disorders encompass a diverse spectrum of subtypes and may emerge at different stages of mental illness, each with distinct symptom profiles. This heterogeneity often complicates differential diagnosis, leading, in many cases, to delayed treatment or inappropriate management. In recent years, technological advances have enabled the development of artificial intelligence (AI)-based approaches that, when integrated with multi-omics data, offer substantial advantages over traditional statistical methods, particularly for analysing large-scale datasets and integrating clinical with bioanalytical information. This review analyses current efforts to identify biomarkers for mental illness and explores the application of machine learning, deep learning, and computational modelling in advancing personalised and precise diagnostics. Full article

(This article belongs to the Special Issue Smart Engineering: Integrating Artificial Intelligence and Bioengineering)

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21 pages, 1558 KB

Open AccessSystematic Review

From Echocardiography to CT/MRI: Lessons for AI Implementation in Cardiovascular Imaging in LMICs—A Systematic Review and Narrative Synthesis

by Ahmed Marey, Saba Mehrtabar, Ahmed Afify, Basudha Pal, Arcadia Trvalik, Sola Adeleke and Muhammad Umair

Bioengineering 2025, 12(10), 1038; https://doi.org/10.3390/bioengineering12101038 - 27 Sep 2025

Abstract

Objectives: The aim of this study was to synthesize current evidence on artificial intelligence (AI) adoption in cardiovascular imaging across low- and middle-income countries (LMICs), highlighting diagnostic performance, implementation barriers, and potential solutions. Methods: We conducted a systematic review of PubMed, [...] Read more.

Objectives: The aim of this study was to synthesize current evidence on artificial intelligence (AI) adoption in cardiovascular imaging across low- and middle-income countries (LMICs), highlighting diagnostic performance, implementation barriers, and potential solutions. Methods: We conducted a systematic review of PubMed, Embase, Cochrane Library, Web of Science, and Scopus for studies evaluating AI-based echocardiography, cardiac CT, or cardiac MRI in LMICs. Articles were screened according to PRISMA guidelines, and data on diagnostic outcomes, challenges, and enabling factors were extracted and narratively synthesized. Results: Twelve studies met the inclusion criteria. AI-driven methods frequently surpassed 90% accuracy in detecting coronary artery disease, rheumatic heart disease, and left ventricular hypertrophy, often enabling task shifting to non-expert operators. Challenges included limited dataset diversity, operator dependence, infrastructure constraints, and ethical considerations. Insights from high-income countries, such as automated segmentation and accelerated imaging, suggest potential for broader AI integration in cardiac MRI and CT. Conclusions: AI holds promise for enhancing cardiovascular care in LMICs by improving diagnostic accuracy and workforce efficiency. However, multi-center data sharing, targeted training, reliable infrastructure, and robust governance are essential for sustainable adoption. This review underscores AI’s capacity to bridge resource gaps in LMICs, offering practical pathways for future research, clinical practice, and policy development in global cardiovascular imaging. Full article

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

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23 pages, 1575 KB

Open AccessSystematic Review

Integrating Spatial Omics and Deep Learning: Toward Predictive Models of Cardiomyocyte Differentiation Efficiency

by Tumo Kgabeng, Lulu Wang, Harry M. Ngwangwa and Thanyani Pandelani

Bioengineering 2025, 12(10), 1037; https://doi.org/10.3390/bioengineering12101037 - 27 Sep 2025

Abstract

Advances in cardiac regenerative medicine increasingly rely on integrating artificial intelligence with spatial multi-omics technologies to decipher intricate cellular dynamics in cardiomyocyte differentiation. This systematic review, synthetising insights from 88 PRISMA selected studies spanning 2015–2025, explores how deep learning architectures, specifically Graph Neural [...] Read more.

Advances in cardiac regenerative medicine increasingly rely on integrating artificial intelligence with spatial multi-omics technologies to decipher intricate cellular dynamics in cardiomyocyte differentiation. This systematic review, synthetising insights from 88 PRISMA selected studies spanning 2015–2025, explores how deep learning architectures, specifically Graph Neural Networks (GNNs) and Recurrent Neural Networks (RNNs), synergise with multi-modal single-cell datasets, spatially resolved transcriptomics, and epigenomics to advance cardiac biology. Innovations in spatial omics technologies have revolutionised our understanding of the organisation of cardiac tissue, revealing novel cellular communities and metabolic landscapes that underlie cardiovascular health and disease. By synthesising cutting-edge methodologies and technical innovations across these 88 studies, this review establishes the foundation for AI-enabled cardiac regeneration, potentially accelerating the clinical adoption of regenerative treatments through improved therapeutic prediction models and mechanistic understanding. We examine deep learning implementations in spatiotemporal genomics, spatial multi-omics applications in cardiac tissues, cardiomyocyte differentiation challenges, and predictive modelling innovations that collectively advance precision cardiology and next-generation regenerative strategies. Full article

(This article belongs to the Special Issue Artificial Intelligence for Computer-Aided Detection in Biomedical Applications)

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