Journal Description
Bioengineering
Bioengineering
is an international, scientific, peer-reviewed, open access journal on the science and technology of bioengineering, published monthly online by MDPI. The Society for Regenerative Medicine (Russian Federation) (RPO) is affiliated with Bioengineering and its members receive discounts on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, PMC, CAPlus / SciFinder, Inspec, and other databases.
- Journal Rank: JCR - Q2 (Engineering, Biomedical)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.7 days after submission; acceptance to publication is undertaken in 3.6 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
4.6 (2022)
Latest Articles
Preliminary Virtual Constraint-Based Control Evaluation on a Pediatric Lower-Limb Exoskeleton
Bioengineering 2024, 11(6), 590; https://doi.org/10.3390/bioengineering11060590 (registering DOI) - 8 Jun 2024
Abstract
Pediatric gait rehabilitation and guidance strategies using robotic exoskeletons require a controller that encourages user volitional control and participation while guiding the wearer towards a stable gait cycle. Virtual constraint-based controllers have created stable gait cycles in bipedal robotic systems and have seen
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Pediatric gait rehabilitation and guidance strategies using robotic exoskeletons require a controller that encourages user volitional control and participation while guiding the wearer towards a stable gait cycle. Virtual constraint-based controllers have created stable gait cycles in bipedal robotic systems and have seen recent use in assistive exoskeletons. This paper evaluates a virtual constraint-based controller for pediatric gait guidance through comparison with a traditional time-dependent position tracking controller on a newly developed exoskeleton system. Walking experiments were performed with a healthy child subject wearing the exoskeleton under proportional-derivative control, virtual constraint-based control, and while unpowered. The participant questionnaires assessed the perceived exertion and controller usability measures, while sensors provided kinematic, control torque, and muscle activation data. The virtual constraint-based controller resulted in a gait similar to the proportional-derivative controlled gait but reduced the variability in the gait kinematics by 36.72% and 16.28% relative to unassisted gait in the hips and knees, respectively. The virtual constraint-based controller also used 35.89% and 4.44% less rms torque per gait cycle in the hips and knees, respectively. The user feedback indicated that the virtual constraint-based controller was intuitive and easy to utilize relative to the proportional-derivative controller. These results indicate that virtual constraint-based control has favorable characteristics for robot-assisted gait guidance.
Full article
(This article belongs to the Special Issue Musculoskeletal Disorders and Diseases: Biomechanical Modeling in Sport, Health, Rehabilitation and Ergonomics)
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Open AccessArticle
Agarose as a Tissue Mimic for the Porcine Heart, Kidney, and Liver: Measurements and a Springpot Model
by
Aadarsh Mishra and Robin O. Cleveland
Bioengineering 2024, 11(6), 589; https://doi.org/10.3390/bioengineering11060589 (registering DOI) - 8 Jun 2024
Abstract
Agarose gels are often used as a tissue mimic. The goal of this work was to determine the appropriate agarose concentrations that result in mechanical properties that match three different porcine organs. Strain tests were carried out with an amplitude varying from 0.01%
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Agarose gels are often used as a tissue mimic. The goal of this work was to determine the appropriate agarose concentrations that result in mechanical properties that match three different porcine organs. Strain tests were carried out with an amplitude varying from 0.01% to 10% at a frequency of 1 Hz on a range of agarose concentrations and porcine organs. Frequency sweep tests were performed from 0.1 Hz to a maximum of 9.5 Hz at a shear strain amplitude of 0.1% for agarose and porcine organs. In agarose samples, the effect of pre-compression of the samples up to 10% axial strain was considered during frequency sweep tests. The experimental measurements from agarose samples were fit to a fractional order viscoelastic (springpot) model. The model was then used to predict stress relaxation in response to a step strain of 0.1%. The prediction was compared to experimental relaxation data, and the results agreed within 12%. The agarose concentrations (by mass) that gave the best fit were 0.25% for the liver, 0.3% for the kidney, and 0.4% for the heart. At a frequency of 0.1 Hz and a shear strain of 0.1%, the agarose concentrations that best matched the shear storage modulus of the porcine organs were 0.4% agarose for the heart, 0.3% agarose for the kidney, and 0.25% agarose for the liver.
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(This article belongs to the Special Issue Biomechanics Analysis in Tissue Engineering)
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Open AccessArticle
A Novel Dehydrated Human Umbilical Cord Particulate Medical Device: Matrix Characterization, Performance, and Biocompatibility for the Management of Acute and Chronic Wounds
by
Dominique Croteau, Molly Buckley, Morgan Mantay, Courtney Brannan, Annelise Roy, Barbara Barbaro and Sarah Griffiths
Bioengineering 2024, 11(6), 588; https://doi.org/10.3390/bioengineering11060588 (registering DOI) - 8 Jun 2024
Abstract
Chronic wounds present a significant socioeconomic burden forecasted to increase in prevalence and cost. Minimally manipulated human placental tissues have been increasingly employed and proven to be advantageous in the treatment of chronic wounds, showing improved clinical outcomes and cost-effectiveness. However, technological advances
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Chronic wounds present a significant socioeconomic burden forecasted to increase in prevalence and cost. Minimally manipulated human placental tissues have been increasingly employed and proven to be advantageous in the treatment of chronic wounds, showing improved clinical outcomes and cost-effectiveness. However, technological advances have been constrained by minimal manipulation and homologous use criteria. This study focuses on the characterization of a novel dehydrated human umbilical cord particulate (dHUCP) medical device, which offers a unique allogeneic technological advancement and the first human birth tissue device for wound management. Characterization analyses illustrated a complex extracellular matrix composition conserved in the dHUCP device compared to native umbilical cord, with abundant collagens and glycosaminoglycans imbibing an intricate porous scaffold. Dermal fibroblasts readily attached to the intact scaffold of the dHUCP device. Furthermore, the dHUCP device elicited a significant paracrine proliferative response in dermal fibroblasts, in contrast to fibrillar collagen, a prevalent wound device. Biocompatibility testing in a porcine full-thickness wound model showed resorption of the dHUCP device and normal granulation tissue maturation during healing. The dHUCP device is a promising advancement in wound management biomaterials, offering a unique combination of structural complexity adept for challenging wound topographies and a microenvironment supportive of tissue regeneration.
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(This article belongs to the Special Issue Biomaterials for Chronic Wound Healing)
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Open AccessArticle
Meta-Analytic Gene-Clustering Algorithm for Integrating Multi-Omics and Multi-Study Data
by
Ulrich Kemmo Tsafack, Kwang Woo Ahn, Anne E. Kwitek and Chien-Wei Lin
Bioengineering 2024, 11(6), 587; https://doi.org/10.3390/bioengineering11060587 (registering DOI) - 8 Jun 2024
Abstract
Gene pathways and gene-regulatory networks are used to describe the causal relationship between genes, based on biological experiments. However, many genes are still to be studied to define novel pathways. To address this, a gene-clustering algorithm has been used to group correlated genes
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Gene pathways and gene-regulatory networks are used to describe the causal relationship between genes, based on biological experiments. However, many genes are still to be studied to define novel pathways. To address this, a gene-clustering algorithm has been used to group correlated genes together, based on the similarity of their gene expression level. The existing methods cluster genes based on only one type of omics data, which ignores the information from other types. A large sample size is required to achieve an accurate clustering structure for thousands of genes, which can be challenging due to the cost of multi-omics data. Meta-analysis has been used to aggregate the data from multiple studies and improve the analysis results. We propose a computationally efficient meta-analytic gene-clustering algorithm that combines multi-omics datasets from multiple studies, using the fixed effects linear models and a modified weighted correlation network analysis framework. The simulation study shows that the proposed method outperforms existing single omic-based clustering approaches when multi-omics data and/or multiple studies are available. A real data example demonstrates that our meta-analytic method outperforms single-study based methods.
Full article
(This article belongs to the Special Issue Gene Clustering in Microbiological and Biotechnological Research)
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Open AccessArticle
Auscultation-Based Pulmonary Disease Detection through Parallel Transformation and Deep Learning
by
Rehan Khan, Shafi Ullah Khan, Umer Saeed and In-Soo Koo
Bioengineering 2024, 11(6), 586; https://doi.org/10.3390/bioengineering11060586 (registering DOI) - 8 Jun 2024
Abstract
Respiratory diseases are among the leading causes of death, with many individuals in a population frequently affected by various types of pulmonary disorders. Early diagnosis and patient monitoring (traditionally involving lung auscultation) are essential for the effective management of respiratory diseases. However, the
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Respiratory diseases are among the leading causes of death, with many individuals in a population frequently affected by various types of pulmonary disorders. Early diagnosis and patient monitoring (traditionally involving lung auscultation) are essential for the effective management of respiratory diseases. However, the interpretation of lung sounds is a subjective and labor-intensive process that demands considerable medical expertise, and there is a good chance of misclassification. To address this problem, we propose a hybrid deep learning technique that incorporates signal processing techniques. Parallel transformation is applied to adventitious respiratory sounds, transforming lung sound signals into two distinct time-frequency scalograms: the continuous wavelet transform and the mel spectrogram. Furthermore, parallel convolutional autoencoders are employed to extract features from scalograms, and the resulting latent space features are fused into a hybrid feature pool. Finally, leveraging a long short-term memory model, a feature from the latent space is used as input for classifying various types of respiratory diseases. Our work is evaluated using the ICBHI-2017 lung sound dataset. The experimental findings indicate that our proposed method achieves promising predictive performance, with average values for accuracy, sensitivity, specificity, and F1-score of 94.16%, 89.56%, 99.10%, and 89.56%, respectively, for eight-class respiratory diseases; 79.61%, 78.55%, 92.49%, and 78.67%, respectively, for four-class diseases; and 85.61%, 83.44%, 83.44%, and 84.21%, respectively, for binary-class (normal vs. abnormal) lung sounds.
Full article
(This article belongs to the Special Issue IoT Technology in Bioengineering Applications)
Open AccessArticle
Development of Subcutaneous SSEA3- or SSEA4-Positive Cell Capture Device
by
Yasuhide Nakayama and Ryosuke Iwai
Bioengineering 2024, 11(6), 585; https://doi.org/10.3390/bioengineering11060585 (registering DOI) - 8 Jun 2024
Abstract
Securing high-quality cell sources is important in regenerative medicine. In this study, we developed a device that can accumulate autologous stem cells in the body. When small wire-assembled molds were embedded in the dorsal subcutaneous pouches of beagles for several weeks, collagen-based tissues
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Securing high-quality cell sources is important in regenerative medicine. In this study, we developed a device that can accumulate autologous stem cells in the body. When small wire-assembled molds were embedded in the dorsal subcutaneous pouches of beagles for several weeks, collagen-based tissues with minimal inflammation formed inside the molds. At 3 weeks of embedding, the outer areas of the tissues were composed of immature type III collagen with large amounts of cells expressing SSEA3 or SSEA4 markers, in addition to growth factors such as HGF or VEGF. When separated from the tissues by collagenase treatment, approximately four million cells with a proportion of 70% CD90-positive and 20% SSEA3- or SSEA4-positive cells were recovered from the single mold. The cells could differentiate into bone or cartilage cells. The obtained cell-containing tissues are expected to have potential as therapeutic materials or cell sources in regenerative medicine.
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(This article belongs to the Section Regenerative Engineering)
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Open AccessArticle
FABP4 Is an Indispensable Factor for Regulating Cellular Metabolic Functions of the Human Retinal Choroid
by
Hiroshi Ohguro, Megumi Watanabe, Tatsuya Sato, Nami Nishikiori, Araya Umetsu, Megumi Higashide, Toshifumi Ogawa and Masato Furuhashi
Bioengineering 2024, 11(6), 584; https://doi.org/10.3390/bioengineering11060584 (registering DOI) - 7 Jun 2024
Abstract
The purpose of the current study was to elucidate the physiological roles of intraocularly present fatty acid-binding protein 4 (FABP4). Using four representative intraocular tissue-derived cell types, including human non-pigmented ciliary epithelium (HNPCE) cells, retinoblastoma (RB) cells, adult retinal pigment epithelial19 (ARPE19) cells
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The purpose of the current study was to elucidate the physiological roles of intraocularly present fatty acid-binding protein 4 (FABP4). Using four representative intraocular tissue-derived cell types, including human non-pigmented ciliary epithelium (HNPCE) cells, retinoblastoma (RB) cells, adult retinal pigment epithelial19 (ARPE19) cells and human ocular choroidal fibroblast (HOCF) cells, the intraocular origins of FABP4 were determined by qPCR analysis, and the intracellular functions of FABP4 were investigated by seahorse cellular metabolic measurements and RNA sequencing analysis using a specific inhibitor for FABP4, BMS309403. Among these four different cell types, FABP4 was exclusively expressed in HOCF cells. In HOCF cells, both mitochondrial and glycolytic functions were significantly decreased to trace levels by BMS309403 in a dose-dependent manner. In the RNA sequencing analysis, 67 substantially up-regulated and 94 significantly down-regulated differentially expressed genes (DEGs) were identified in HOCF cells treated with BMS309403 and those not treated with BMS309403. The results of Gene Ontology enrichment analysis and ingenuity pathway analysis (IPA) revealed that the DEGs were most likely involved in G-alpha (i) signaling, cAMP-response element-binding protein (CREB) signaling in neurons, the S100 family signaling pathway, visual phototransduction and adrenergic receptor signaling. Furthermore, upstream analysis using IPA suggested that NKX2-1 (thyroid transcription factor1), HOXA10 (homeobox A10), GATA2 (gata2 protein), and CCAAT enhancer-binding protein A (CEBPA) were upstream regulators and that NKX homeobox-1 (NKX2-1), SFRP1 (Secreted frizzled-related protein 1) and TREM2 (triggering receptor expressed on myeloid cells 2) were causal network master regulators. The findings in this study suggest that intraocularly present FABP4 originates from the ocular choroid and may be a critical regulator for the cellular homeostasis of non-adipocyte HOCF cells.
Full article
(This article belongs to the Special Issue Pathophysiology and Translational Research of Retinal Diseases)
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Open AccessCommunication
Simplified Models to Assess the Mechanical Performance Parameters of Stents
by
Juan P. Toledo, Jaime Martínez-Castillo, Diego Cardenas, Enrique Delgado-Alvarado, Marco Osvaldo Vigueras-Zuñiga and Agustín L. Herrera-May
Bioengineering 2024, 11(6), 583; https://doi.org/10.3390/bioengineering11060583 - 7 Jun 2024
Abstract
Ischemic heart disease remains a leading cause of mortality worldwide, which has promoted extensive therapeutic efforts. Stenting has emerged as the primary intervention, particularly among individuals aged 70 years and older. The geometric specifications of stents must align with various mechanical performance criteria
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Ischemic heart disease remains a leading cause of mortality worldwide, which has promoted extensive therapeutic efforts. Stenting has emerged as the primary intervention, particularly among individuals aged 70 years and older. The geometric specifications of stents must align with various mechanical performance criteria outlined by regulatory agencies such as the Food and Drug Administration (FDA). Finite element method (FEM) analysis and computational fluid dynamics (CFD) serve as essential tools to assess the mechanical performance parameters of stents. However, the growing complexity of the numerical models presents significant challenges. Herein, we propose a method to determine the mechanical performance parameters of stents using a simplified FEM model comprising solid and shell elements. In addition, a baseline model of a stent is developed and validated with experimental data, considering parameters such as foreshortening, radial recoil, radial recoil index, and radial stiffness of stents. The results of the simplified FEM model agree well with the baseline model, decreasing up to 80% in computational time. This method can be employed to design stents with specific mechanical performance parameters that satisfy the requirements of each patient.
Full article
(This article belongs to the Special Issue Devices for Vascular Intervention)
Open AccessArticle
Longitudinal Observation of Micromotion upon Loading of Implant–Abutment Connection
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Kohei Yamashita, Yu Kataoka, Motohiro Munakata, Kikue Yamaguchi, Myu Hayashi and Daisuke Baba
Bioengineering 2024, 11(6), 582; https://doi.org/10.3390/bioengineering11060582 - 7 Jun 2024
Abstract
While technological advances have made implants a good treatment option with a good long-term prognosis, peri-implantitis, which results in alveolar bone resorption around implants, has been observed in some cases. Micromotion at the implant abutment connection can cause peri-implantitis. However, the temporal
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While technological advances have made implants a good treatment option with a good long-term prognosis, peri-implantitis, which results in alveolar bone resorption around implants, has been observed in some cases. Micromotion at the implant abutment connection can cause peri-implantitis. However, the temporal progression of micromotion upon loading remains unclear. Therefore, we aimed to longitudinally measure micromotion upon loading application on an implant. Implants with Morse-tapered connections were prepared. Custom titanium abutments were fabricated and tightened onto implant bodies at 35 N. A 100 N vertical load was applied for 200,000 cycles. Micromotion was measured when the load was applied, as was the total implant length and removal torque before and after loading. The micromotion was measured from the position data of the jig of the testing machine during loading. The average removal torque was 30.67 N after 10 min of tightening and 27.95 N after loading, indicating a decrease due to loading. The implant length reduced by 3.6 μm under the load. The average micromotion was 0.018 mm at 2 cycles, 0.016 mm at 100,000 cycles, and 0.0157 mm at 200,000 cycles, indicating implant length reduction under the load but not reaching 0. The micromotion between the implant and abutment under a cyclic load decreased over time but did not completely cease. These results highlight the relationship between micromotion and loading, underscoring the importance of careful monitoring and management to mitigate potential complications, such as peri-implantitis, and ensure optimal performance and durability of the implant.
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(This article belongs to the Section Biomedical Engineering and Biomaterials)
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Open AccessArticle
TGF-β Isoforms and Local Environments Greatly Modulate Biological Nature of Human Retinal Pigment Epithelium Cells
by
Nami Nishikiori, Tatsuya Sato, Toshifumi Ogawa, Megumi Higashide, Araya Umetsu, Soma Suzuki, Masato Furuhashi, Hiroshi Ohguro and Megumi Watanabe
Bioengineering 2024, 11(6), 581; https://doi.org/10.3390/bioengineering11060581 - 7 Jun 2024
Abstract
To characterize transforming growth factor-β (TGF-β) isoform (TGF-β1~3)-b’s biological effects on the human retinal pigment epithelium (RPE) under normoxia and hypoxia conditions, ARPE19 cells cultured by 2D (two-dimensional) and 3D (three-dimensional) conditions were subjected to various analyses, including (1) an analysis of barrier
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To characterize transforming growth factor-β (TGF-β) isoform (TGF-β1~3)-b’s biological effects on the human retinal pigment epithelium (RPE) under normoxia and hypoxia conditions, ARPE19 cells cultured by 2D (two-dimensional) and 3D (three-dimensional) conditions were subjected to various analyses, including (1) an analysis of barrier function by trans-epithelial electrical resistance (TEER) measurements; (2) qPCR analysis of major ECM molecules including collagen 1 (COL1), COL4, and COL6; α-smooth muscle actin (αSMA); hypoxia-inducible factor 1α (HIF1α); and peroxisome proliferator-activated receptor-gamma coactivator (PGC1α), a master regulator for mitochondrial respiration;, tight junction-related molecules, Zonula occludens-1 (ZO1) and E-cadherin; and vascular endothelial growth factor (VEGF); (3) physical property measurements of 3D spheroids; and (4) cellular metabolic analysis. Diverse effects among TGF-β isoforms were observed, and those effects were also different between normoxia and hypoxia conditions: (1) TGF-β1 and TGF-β3 caused a marked increase in TEER values, and TGF-β2 caused a substantial increase in TEER values under normoxia conditions and hypoxia conditions, respectively; (2) the results of qPCR analysis supported data obtained by TEER; (3) 3D spheroid sizes were decreased by TGF-β isoforms, among which TGF-β1 had the most potent effect under both oxygen conditions; (4) 3D spheroid stiffness was increased by TGF-β2 and TGF-β3 or by TGF-β1 and TGF-β3 under normoxia conditions and hypoxia conditions, respectively; and (5) the TGF-β isoform altered mitochondrial and glycolytic functions differently under oxygen conditions and/or culture conditions. These collective findings indicate that the TGF-β-induced biological effects of 2D and 3D cultures of ARPE19 cells were substantially diverse depending on the three TGF-β isoforms and oxygen levels, suggesting that pathological conditions including epithelial–mesenchymal transition (EMT) of the RPE may be exclusively modulated by both factors.
Full article
(This article belongs to the Section Cellular and Molecular Bioengineering)
Open AccessArticle
Classification of Muscular Dystrophies from MR Images Improves Using the Swin Transformer Deep Learning Model
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Alfonso Mastropietro, Nicola Casali, Maria Giovanna Taccogna, Maria Grazia D’Angelo, Giovanna Rizzo and Denis Peruzzo
Bioengineering 2024, 11(6), 580; https://doi.org/10.3390/bioengineering11060580 - 7 Jun 2024
Abstract
Muscular dystrophies present diagnostic challenges, requiring accurate classification for effective diagnosis and treatment. This study investigates the efficacy of deep learning methodologies in classifying these disorders using skeletal muscle MRI scans. Specifically, we assess the performance of the Swin Transformer (SwinT) architecture against
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Muscular dystrophies present diagnostic challenges, requiring accurate classification for effective diagnosis and treatment. This study investigates the efficacy of deep learning methodologies in classifying these disorders using skeletal muscle MRI scans. Specifically, we assess the performance of the Swin Transformer (SwinT) architecture against traditional convolutional neural networks (CNNs) in distinguishing between healthy individuals, Becker muscular dystrophy (BMD), and limb–girdle muscular Dystrophy type 2 (LGMD2) patients. Moreover, 3T MRI scans from a retrospective dataset of 75 scans (from 54 subjects) were utilized, with multiparametric protocols capturing various MRI contrasts, including T1-weighted and Dixon sequences. The dataset included 17 scans from healthy volunteers, 27 from BMD patients, and 31 from LGMD2 patients. SwinT and CNNs were trained and validated using a subset of the dataset, with the performance evaluated based on accuracy and F-score. Results indicate the superior accuracy of SwinT (0.96), particularly when employing fat fraction (FF) images as input; it served as a valuable parameter for enhancing classification accuracy. Despite limitations, including a modest cohort size, this study provides valuable insights into the application of AI-driven approaches for precise neuromuscular disorder classification, with potential implications for improving patient care.
Full article
(This article belongs to the Special Issue Radiomics and Artificial Intelligence in the Musculoskeletal System)
Open AccessArticle
Survival Rates of Amalgam and Composite Resin Restorations from Big Data Real-Life Databases in the Era of Restricted Dental Mercury Use
by
Guy Tobias, Tali Chackartchi, Jonathan Mann, Doron Haim and Mordechai Findler
Bioengineering 2024, 11(6), 579; https://doi.org/10.3390/bioengineering11060579 - 7 Jun 2024
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Tooth decay, also known as caries, is a significant medical problem that harms teeth. Treatment is based on the removal of the carious material and then filling the cavity left in the tooth, most commonly with amalgam or composite resin. The consequences of
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Tooth decay, also known as caries, is a significant medical problem that harms teeth. Treatment is based on the removal of the carious material and then filling the cavity left in the tooth, most commonly with amalgam or composite resin. The consequences of filling failure include repeating the filling or performing another treatment such as a root canal or extraction. Dental amalgam contains mercury, and there is a global effort to reduce its use. However, no consensus has been reached regarding whether amalgam or composite resin materials are more durable, and which is the best restorative material, when using randomized clinical trials. To determine which material is superior, we performed a retrospective cohort study using a large database where the members of 58 dental clinics with 440 dental units were treated. The number of failures of the amalgam compared to composite resin restorations between 2014 and 2021 were compared. Our data included information from over 650,000 patients. Between 2014–2021, 260,905 patients were treated. In total, 19,692 out of the first 113,281 amalgam restorations failed (17.49%), whereas significantly fewer composite restorations failed (11.98%) with 65,943 out of 555,671. This study indicates that composite is superior to amalgam and therefore it is reasonable to cease using mercury-containing amalgam.
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Open AccessArticle
Visualized Lead Selection for Arrhythmia Classification Based on a Lead Activation Heatmap Using Multi-Lead ECGs
by
Heng Wang, Tengqun Shen, Shoufen Jiang, Jilin Wang, Yijun Ma and Yatao Zhang
Bioengineering 2024, 11(6), 578; https://doi.org/10.3390/bioengineering11060578 - 7 Jun 2024
Abstract
Visualizing the decision-making process is a key aspect of research regarding explainable arrhythmia recognition. This study proposed a visualized lead selection method to classify arrhythmia for multi-lead ECG signals. The proposed method has several advantages, as it uses a visualized approach to select
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Visualizing the decision-making process is a key aspect of research regarding explainable arrhythmia recognition. This study proposed a visualized lead selection method to classify arrhythmia for multi-lead ECG signals. The proposed method has several advantages, as it uses a visualized approach to select effective leads, avoiding redundant leads and invalid information. It also captures the temporal dependencies of ECG signals and the complementary information between leads. The method deployed a lead activation heatmap (LA heatmap) based on a lead-wise network to select the proper 5 leads from 12-lead ECG heartbeats extracted from the public 2018 Chinese Physiological Signal Challenge database (CPSC 2018 DB), which were then fed into a ResBiTime network combining bidirectional long short-term memory (Bi-LSTM) networks and residual connections for a classification task of nine heartbeat categories (i.e., N, AF, I-AVB, RBBB, PAC, PVC, STD, LBBB, and STE). The results indicate an average precision of 93.25%, an average recall of 93.03%, an average F1-score of 0.9313, and that the proposed method can effectively extract additional information from ECG heartbeat data.
Full article
(This article belongs to the Special Issue 10th Anniversary of Bioengineering: Biosignal Processing)
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Open AccessArticle
Integrating Deep Learning with Electronic Health Records for Early Glaucoma Detection: A Multi-Dimensional Machine Learning Approach
by
Alireza Karimi, Ansel Stanik, Cooper Kozitza and Aiyin Chen
Bioengineering 2024, 11(6), 577; https://doi.org/10.3390/bioengineering11060577 - 7 Jun 2024
Abstract
Background: Recent advancements in deep learning have significantly impacted ophthalmology, especially in glaucoma, a leading cause of irreversible blindness worldwide. In this study, we developed a reliable predictive model for glaucoma detection using deep learning models based on clinical data, social and behavior
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Background: Recent advancements in deep learning have significantly impacted ophthalmology, especially in glaucoma, a leading cause of irreversible blindness worldwide. In this study, we developed a reliable predictive model for glaucoma detection using deep learning models based on clinical data, social and behavior risk factor, and demographic data from 1652 participants, split evenly between 826 control subjects and 826 glaucoma patients. Methods: We extracted structural data from control and glaucoma patients’ electronic health records (EHR). Three distinct machine learning classifiers, the Random Forest and Gradient Boosting algorithms, as well as the Sequential model from the Keras library of TensorFlow, were employed to conduct predictive analyses across our dataset. Key performance metrics such as accuracy, F1 score, precision, recall, and the area under the receiver operating characteristics curve (AUC) were computed to both train and optimize these models. Results: The Random Forest model achieved an accuracy of 67.5%, with a ROC AUC of 0.67, outperforming the Gradient Boosting and Sequential models, which registered accuracies of 66.3% and 64.5%, respectively. Our results highlighted key predictive factors such as intraocular pressure, family history, and body mass index, substantiating their roles in glaucoma risk assessment. Conclusions: This study demonstrates the potential of utilizing readily available clinical, lifestyle, and demographic data from EHRs for glaucoma detection through deep learning models. While our model, using EHR data alone, has a lower accuracy compared to those incorporating imaging data, it still offers a promising avenue for early glaucoma risk assessment in primary care settings. The observed disparities in model performance and feature significance show the importance of tailoring detection strategies to individual patient characteristics, potentially leading to more effective and personalized glaucoma screening and intervention.
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(This article belongs to the Section Biosignal Processing)
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Open AccessArticle
Preclinical and Preliminary Evaluation of Perceived Image Quality of AI-Processed Low-Dose CBCT Analysis of a Single Tooth
by
Na-Hyun Kim, Byoung-Eun Yang, Sam-Hee Kang, Young-Hee Kim, Ji-Yeon Na, Jo-Eun Kim and Soo-Hwan Byun
Bioengineering 2024, 11(6), 576; https://doi.org/10.3390/bioengineering11060576 - 7 Jun 2024
Abstract
This study assessed AI-processed low-dose cone-beam computed tomography (CBCT) images for single-tooth diagnosis. Human-equivalent phantoms were used to evaluate CBCT image quality with a focus on the right mandibular first molar. Two CBCT machines were used for evaluation. The first CBCT machine was
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This study assessed AI-processed low-dose cone-beam computed tomography (CBCT) images for single-tooth diagnosis. Human-equivalent phantoms were used to evaluate CBCT image quality with a focus on the right mandibular first molar. Two CBCT machines were used for evaluation. The first CBCT machine was used for the experimental group, in which images were acquired using four protocols and enhanced with AI processing to improve quality. The other machine was used for the control group, where images were taken in one protocol without AI processing. The dose-area product (DAP) was measured for each protocol. Subjective clinical image quality was assessed twice by five dentists, with a 2-month interval in between, using 11 parameters and a six-point rating scale. Agreement and statistical significance were assessed with Fleiss’ kappa coefficient and intra-class correlation coefficient. The AI-processed protocols exhibited lower DAP/field of view values than non-processed protocols, while demonstrating subjective clinical evaluation results comparable to those of non-processed protocols. The Fleiss’ kappa coefficient value revealed statistical significance and substantial agreement. The intra-class correlation coefficient showed statistical significance and almost perfect agreement. These findings highlight the importance of minimizing radiation exposure while maintaining diagnostic quality as the usage of CBCT increases in single-tooth diagnosis.
Full article
(This article belongs to the Special Issue Computed Tomography for Oral and Maxillofacial Applications)
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Open AccessArticle
nnSegNeXt: A 3D Convolutional Network for Brain Tissue Segmentation Based on Quality Evaluation
by
Yuchen Liu, Chongchong Song, Xiaolin Ning, Yang Gao and Defeng Wang
Bioengineering 2024, 11(6), 575; https://doi.org/10.3390/bioengineering11060575 - 6 Jun 2024
Abstract
Accurate and automated segmentation of brain tissue images can significantly streamline clinical diagnosis and analysis. Manual delineation needs improvement due to its laborious and repetitive nature, while automated techniques encounter challenges stemming from disparities in magnetic resonance imaging (MRI) acquisition equipment and accurate
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Accurate and automated segmentation of brain tissue images can significantly streamline clinical diagnosis and analysis. Manual delineation needs improvement due to its laborious and repetitive nature, while automated techniques encounter challenges stemming from disparities in magnetic resonance imaging (MRI) acquisition equipment and accurate labeling. Existing software packages, such as FSL and FreeSurfer, do not fully replace ground truth segmentation, highlighting the need for an efficient segmentation tool. To better capture the essence of cerebral tissue, we introduce nnSegNeXt, an innovative segmentation architecture built upon the foundations of quality assessment. This pioneering framework effectively addresses the challenges posed by missing and inaccurate annotations. To enhance the model’s discriminative capacity, we integrate a 3D convolutional attention mechanism instead of conventional convolutional blocks, enabling simultaneous encoding of contextual information through the incorporation of multiscale convolutional features. Our methodology was evaluated on four multi-site T1-weighted MRI datasets from diverse sources, magnetic field strengths, scanning parameters, temporal instances, and neuropsychiatric conditions. Empirical evaluations on the HCP, SALD, and IXI datasets reveal that nnSegNeXt surpasses the esteemed nnUNet, achieving Dice coefficients of 0.992, 0.987, and 0.989, respectively, and demonstrating superior generalizability across four distinct projects with Dice coefficients ranging from 0.967 to 0.983. Additionally, extensive ablation studies have been implemented to corroborate the effectiveness of the proposed model. These findings represent a notable advancement in brain tissue analysis, suggesting that nnSegNeXt holds the promise to significantly refine clinical workflows.
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(This article belongs to the Special Issue Machine Learning Methods for Biomedical Imaging)
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Open AccessArticle
The Recombinant Lactobacillus Strains with the Surface-Displayed Expression of Amuc_1100 Ameliorate Obesity in High-Fat Diet-Fed Adult Mice
by
Xueni Zhang, Lei Jiang, Cankun Xie, Yidi Mo, Zihao Zhang, Shengxia Xu, Xiaoping Guo, Ke Xing, Yina Wang and Zhijian Su
Bioengineering 2024, 11(6), 574; https://doi.org/10.3390/bioengineering11060574 - 6 Jun 2024
Abstract
Excessive dietary fat intake is closely associated with an increased risk of obesity, type 2 diabetes, cardiovascular disease, gastrointestinal diseases, and certain types of cancer. The administration of multi-strain probiotics has shown a significantly beneficial effect on the mitigation of obesity induced by
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Excessive dietary fat intake is closely associated with an increased risk of obesity, type 2 diabetes, cardiovascular disease, gastrointestinal diseases, and certain types of cancer. The administration of multi-strain probiotics has shown a significantly beneficial effect on the mitigation of obesity induced by high-fat diets (HFDs). In this study, Amuc_1100, an outer membrane protein of Akkermansia muciniphila, was fused with green fluorescent protein and LPXTG motif anchor protein and displayed on the surface of Lactobacillus rhamnosus (pLR-GAA) and Lactobacillus plantarum (pLP-GAA), respectively. The localization of the fusion protein on the bacterial cell surface was confirmed via fluorescence microscopy and Western blotting. Both recombinant strains demonstrated the capacity to ameliorate hyperglycemia and decrease body weight gain in a dose-dependent manner. Moreover, daily oral supplementation of pLR-GAA or pLP-GAA suppressed the HFD-induced intestinal permeability by regulating the mRNA expressions of tight junction proteins and inflammatory cytokines, thereby reducing gut microbiota-derived lipopolysaccharide concentration in serum and mitigating damage to the gut, liver, and adipose tissue. Compared with Lactobacillus rhamnosus treatment, high-dose pLR-GAA restored the expression level of anti-inflammatory factor interleukin-10 in the intestine. In conclusion, our approach enables the maintenance of intestinal health through the use of recombinant probiotics with surface-displayed functional protein, providing a potential therapeutic strategy for HFD-induced obesity and associated metabolic comorbidities.
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(This article belongs to the Section Biochemical Engineering)
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Modeling of Magnetic Scaffolds as Drug Delivery Platforms for Tissue Engineering and Cancer Therapy
by
Matteo B. Lodi, Eleonora M. A. Corda, Francesco Desogus, Alessandro Fanti and Giuseppe Mazzarella
Bioengineering 2024, 11(6), 573; https://doi.org/10.3390/bioengineering11060573 - 6 Jun 2024
Abstract
Magnetic scaffolds (MagSs) are magneto-responsive devices obtained by the combination of traditional biomaterials (e.g., polymers, bioceramics, and bioglasses) and magnetic nanoparticles. This work analyzes the literature about MagSs used as drug delivery systems for tissue repair and cancer treatment. These devices can be
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Magnetic scaffolds (MagSs) are magneto-responsive devices obtained by the combination of traditional biomaterials (e.g., polymers, bioceramics, and bioglasses) and magnetic nanoparticles. This work analyzes the literature about MagSs used as drug delivery systems for tissue repair and cancer treatment. These devices can be used as innovative drugs and/or biomolecules delivery systems. Through the application of a static or dynamic stimulus, MagSs can trigger drug release in a controlled and remote way. However, most of MagSs used as drug delivery systems are not optimized and properly modeled, causing a local inhomogeneous distribution of the drug’s concentration and burst release. Few physical–mathematical models have been presented to study and analyze different MagSs, with the lack of a systematic vision. In this work, we propose a modeling framework. We modeled the experimental data of drug release from different MagSs, under various magnetic field types, taken from the literature. The data were fitted to a modified Gompertz equation and to the Korsmeyer–Peppas model (KPM). The correlation coefficient ( ) and the root mean square error (RMSE) were the figures of merit used to evaluate the fitting quality. It has been found that the Gompertz model can fit most of the drug delivery cases, with an average RMSE below 0.01 and . This quantitative interpretation of existing experimental data can foster the design and use of MagSs for drug delivery applications.
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(This article belongs to the Special Issue Electric, Magnetic, and Electromagnetic Fields in Biology and Medicine: From Mechanisms to Biomedical Applications, Volume II)
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Open AccessArticle
Ex Vivo Biomechanical Bone Testing of Pig Femur as an Experimental Model
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Marijana Kulić, Petra Bagavac, Marijo Bekić and Lovre Krstulović-Opara
Bioengineering 2024, 11(6), 572; https://doi.org/10.3390/bioengineering11060572 - 5 Jun 2024
Abstract
This study investigates the mechanical behavior of femur bones under loading conditions, focusing on the transition from elastic to plastic deformation and eventual fracture. The force–displacement curves reveal distinct phases of deformation, with an initial linear relationship indicating elastic behavior, followed by deviation
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This study investigates the mechanical behavior of femur bones under loading conditions, focusing on the transition from elastic to plastic deformation and eventual fracture. The force–displacement curves reveal distinct phases of deformation, with an initial linear relationship indicating elastic behavior, followed by deviation from linearity marking the onset of plastic deformation. Fracture occurs beyond a critical load, leading to a sharp drop in the force–displacement curve. The maximum fracture force varies among specimens and is influenced by bone geometry, size, cross-sectional area, and cortical thickness. Post-failure analysis highlights additional insights into fracture mechanics and bone material toughness. Reinforcing bones with screws enhances their strength, which is evident in the higher fracture forces observed in force–displacement diagrams. Fixation procedures following fractures further increase bone strength. Comparing specimens with and without strengthening underscores the effectiveness of reinforcement methods in improving bone mechanical properties. After analyzing the results, it is evident that femur bones with reinforcement can withstand greater loads, and they can also absorb higher impact energies while remaining in the elastic deformation range and without suffering permanent plastic damage. This study provides valuable insights into bone biomechanics and the efficacy of reinforcement techniques in enhancing bone strength and fracture resistance.
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(This article belongs to the Section Biomedical Engineering and Biomaterials)
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Classification of Ameloblastoma, Periapical Cyst, and Chronic Suppurative Osteomyelitis with Semi-Supervised Learning: The WaveletFusion-ViT Model Approach
by
Bohui Liang, Hongna Qin, Xiaolin Nong and Xuejun Zhang
Bioengineering 2024, 11(6), 571; https://doi.org/10.3390/bioengineering11060571 - 5 Jun 2024
Abstract
Ameloblastoma (AM), periapical cyst (PC), and chronic suppurative osteomyelitis (CSO) are prevalent maxillofacial diseases with similar imaging characteristics but different treatments, thus making preoperative differential diagnosis crucial. Existing deep learning methods for diagnosis often require manual delineation in tagging the regions of interest
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Ameloblastoma (AM), periapical cyst (PC), and chronic suppurative osteomyelitis (CSO) are prevalent maxillofacial diseases with similar imaging characteristics but different treatments, thus making preoperative differential diagnosis crucial. Existing deep learning methods for diagnosis often require manual delineation in tagging the regions of interest (ROIs), which triggers some challenges in practical application. We propose a new model of Wavelet Extraction and Fusion Module with Vision Transformer (WaveletFusion-ViT) for automatic diagnosis using CBCT panoramic images. In this study, 539 samples containing healthy (n = 154), AM (n = 181), PC (n = 102), and CSO (n = 102) were acquired by CBCT for classification, with an additional 2000 healthy samples for pre-training the domain-adaptive network (DAN). The WaveletFusion-ViT model was initialized with pre-trained weights obtained from the DAN and further trained using semi-supervised learning (SSL) methods. After five-fold cross-validation, the model achieved average sensitivity, specificity, accuracy, and AUC scores of 79.60%, 94.48%, 91.47%, and 0.942, respectively. Remarkably, our method achieved 91.47% accuracy using less than 20% labeled samples, surpassing the fully supervised approach’s accuracy of 89.05%. Despite these promising results, this study’s limitations include a low number of CSO cases and a relatively lower accuracy for this condition, which should be addressed in future research. This research is regarded as an innovative approach as it deviates from the fully supervised learning paradigm typically employed in previous studies. The WaveletFusion-ViT model effectively combines SSL methods to effectively diagnose three types of CBCT panoramic images using only a small portion of labeled data.
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(This article belongs to the Section Biosignal Processing)
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