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
Deep-Learning-Based Recovery of Missing Optical Marker Trajectories in 3D Motion Capture Systems
Bioengineering 2024, 11(6), 560; https://doi.org/10.3390/bioengineering11060560 (registering DOI) - 1 Jun 2024
Abstract
Motion capture (MoCap) technology, essential for biomechanics and motion analysis, faces challenges from data loss due to occlusions and technical issues. Traditional recovery methods, based on inter-marker relationships or independent marker treatment, have limitations. This study introduces a novel U-net-inspired bi-directional long short-term
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Motion capture (MoCap) technology, essential for biomechanics and motion analysis, faces challenges from data loss due to occlusions and technical issues. Traditional recovery methods, based on inter-marker relationships or independent marker treatment, have limitations. This study introduces a novel U-net-inspired bi-directional long short-term memory (U-Bi-LSTM) autoencoder-based technique for recovering missing MoCap data across multi-camera setups. Leveraging multi-camera and triangulated 3D data, this method employs a sophisticated U-shaped deep learning structure with an adaptive Huber regression layer, enhancing outlier robustness and minimizing reconstruction errors, proving particularly beneficial for long-term data loss scenarios. Our approach surpasses traditional piecewise cubic spline and state-of-the-art sparse low rank methods, demonstrating statistically significant improvements in reconstruction error across various gap lengths and numbers. This research not only advances the technical capabilities of MoCap systems but also enriches the analytical tools available for biomechanical research, offering new possibilities for enhancing athletic performance, optimizing rehabilitation protocols, and developing personalized treatment plans based on precise biomechanical data.
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(This article belongs to the Special Issue Biomechanics and Motion Analysis)
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Open AccessArticle
The Effect of Two Different Wavelengths of Diode Laser on the Shear Bond Strength of Composite to Dental Enamel after Bleaching Process: An In Vitro Study
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Reza Pourmahmoudian, Luca Solimei, Stefano Benedicenti, Sogol Saberi and Sima Shahabi
Bioengineering 2024, 11(6), 559; https://doi.org/10.3390/bioengineering11060559 (registering DOI) - 1 Jun 2024
Abstract
Introduction: In recent years, tooth whitening has become one of the most popular ways of achieving the original tooth color. The effect of whitening gel can be improved through heat, light or laser. The bond strength between the enamel and the composite can
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Introduction: In recent years, tooth whitening has become one of the most popular ways of achieving the original tooth color. The effect of whitening gel can be improved through heat, light or laser. The bond strength between the enamel and the composite can be reduced through bleaching and laser radiation. The purpose of this study is to assess the shear bond strength of resin composite to enamel after a bleaching process using hydrogen peroxide, with and without a laser (970 nm and 445 nm lasers). Method: This study used 51 extracted anterior teeth without caries that were divided into three groups. A 40% hydrogen peroxide gel was used on the enamel of all teeth. The control group received bleaching without a laser. Both the second and third treatment groups received bleaching with a laser, one with 970 nm and the other with 445 nm. After the bleaching process, all groups had etching, bonding and curing of the composite performed. Lastly, the shear bond strength between the enamel and the composite was measured and the failure modes were recorded. The data were compared using a one-way ANOVA test. Results: The mean shear bond strength between the enamel and the composite in the 445 nm group three (445 nanometer) was significantly lower than the other groups (p < 0.05). There was no significant difference between the control and the 970 nm groups (p = 0.2). Conclusion: According to the laser wavelengths and parameters that were used in this study and the results of this study, office bleaching with a 445 nm laser weakened the shear bond strength between the enamel and the composite.
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(This article belongs to the Section Biomedical Engineering and Biomaterials)
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Open AccessArticle
Dislodgment Effects of Different Cage Arrangements in Posterior Lumbar Interbody Fusion: A Finite Element Study
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Shih-Chieh Yang, Chih-Lin Wu, Yuan-Kun Tu and Pao-Hsin Liu
Bioengineering 2024, 11(6), 558; https://doi.org/10.3390/bioengineering11060558 - 31 May 2024
Abstract
The vertebral cage has been widely used in posterior lumbar interbody fusion. The risk of cage dislodgment is high for patients undergoing lumbar fusion surgery. Therefore, the main objective of this study was to use a lumbar fusion model to investigate the effects
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The vertebral cage has been widely used in posterior lumbar interbody fusion. The risk of cage dislodgment is high for patients undergoing lumbar fusion surgery. Therefore, the main objective of this study was to use a lumbar fusion model to investigate the effects of cage dislodgment on different cage arrangements after PLIF. Finite element analysis was used to compare three PEEK cage placements, together with the fibula-type cage, with respect to the four kinds of lumbar movements. The results revealed that a horizontal cage arrangement could provide a better ability to resist cage dislodgment. Overall lumbar flexion movements were confirmed to produce a greater amount of cage slip than the other three lumbar movements. The lower part of the lumbar fusion segment could create a greater amount of cage dislodgment for all of the lumbar movements. Using an autograft with a fibula as a vertebral cage cannot effectively reduce cage dislodgment. Considering the maximum movement type in lumbar flexion, we suggest that a horizontal arrangement of the PEEK cage might be considered when a single PEEK cage is placed in the fusion segment, as doing so can effectively reduce the extent of cage dislodgment.
Full article
(This article belongs to the Section Biosignal Processing)
Open AccessArticle
Design and Evaluation of ScanCap: A Low-Cost, Reusable Tethered Capsule Endoscope with Blue-Green Illumination Imaging for Unsedated Screening and Early Detection of Barrett’s Esophagus
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Cheima Hicheri, Ahad M. Azimuddin, Alex Kortum, Joseph Bailey, Yubo Tang, Richard A. Schwarz, Daniel Rosen, Shilpa Jain, Nabil M. Mansour, Shawn Groth, Shaleen Vasavada, Ashwin Rao, Adrianna Maliga, Leslie Gallego, Jennifer Carns, Sharmila Anandasabapathy and Rebecca Richards-Kortum
Bioengineering 2024, 11(6), 557; https://doi.org/10.3390/bioengineering11060557 - 31 May 2024
Abstract
Esophageal carcinoma is the sixth-leading cause of cancer death worldwide. A precursor to esophageal adenocarcinoma (EAC) is Barrett’s Esophagus (BE). Early-stage diagnosis and treatment of esophageal neoplasia (Barrett’s with high-grade dysplasia/intramucosal cancer) increase the five-year survival rate from 10% to 98%. BE is
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Esophageal carcinoma is the sixth-leading cause of cancer death worldwide. A precursor to esophageal adenocarcinoma (EAC) is Barrett’s Esophagus (BE). Early-stage diagnosis and treatment of esophageal neoplasia (Barrett’s with high-grade dysplasia/intramucosal cancer) increase the five-year survival rate from 10% to 98%. BE is a global challenge; however, current endoscopes for early BE detection are costly and require extensive infrastructure for patient examination and sedation. We describe the design and evaluation of the first prototype of ScanCap, a high-resolution optical endoscopy system with a reusable, low-cost tethered capsule, designed to provide high-definition, blue-green illumination imaging for the early detection of BE in unsedated patients. The tethered capsule (12.8 mm diameter, 35.5 mm length) contains a color camera and rotating mirror and is designed to be swallowed; images are collected as the capsule is retracted manually via the tether. The tether provides electrical power and illumination at wavelengths of 415 nm and 565 nm and transmits data from the camera to a tablet. The ScanCap prototype capsule was used to image the oral mucosa in normal volunteers and ex vivo esophageal resections; images were compared to those obtained using an Olympus CV-180 endoscope. Images of superficial capillaries in intact oral mucosa were clearly visible in ScanCap images. Diagnostically relevant features of BE, including irregular Z-lines, distorted mucosa, and dilated vasculature, were clearly visible in ScanCap images of ex vivo esophageal specimens.
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(This article belongs to the Special Issue Novel, Low Cost Technologies for Cancer Diagnostics and Therapeutics)
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Explainable Precision Medicine in Breast MRI: A Combined Radiomics and Deep Learning Approach for the Classification of Contrast Agent Uptake
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Sylwia Nowakowska, Karol Borkowski, Carlotta Ruppert, Patryk Hejduk, Alexander Ciritsis, Anna Landsmann, Magda Marcon, Nicole Berger, Andreas Boss and Cristina Rossi
Bioengineering 2024, 11(6), 556; https://doi.org/10.3390/bioengineering11060556 - 31 May 2024
Abstract
In DCE-MRI, the degree of contrast uptake in normal fibroglandular tissue, i.e., background parenchymal enhancement (BPE), is a crucial biomarker linked to breast cancer risk and treatment outcome. In accordance with the Breast Imaging Reporting & Data System (BI-RADS), it should be visually
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In DCE-MRI, the degree of contrast uptake in normal fibroglandular tissue, i.e., background parenchymal enhancement (BPE), is a crucial biomarker linked to breast cancer risk and treatment outcome. In accordance with the Breast Imaging Reporting & Data System (BI-RADS), it should be visually classified into four classes. The susceptibility of such an assessment to inter-reader variability highlights the urgent need for a standardized classification algorithm. In this retrospective study, the first post-contrast subtraction images for 27 healthy female subjects were included. The BPE was classified slice-wise by two expert radiologists. The extraction of radiomic features from segmented BPE was followed by dataset splitting and dimensionality reduction. The latent representations were then utilized as inputs to a deep neural network classifying BPE into BI-RADS classes. The network’s predictions were elucidated at the radiomic feature level with Shapley values. The deep neural network achieved a BPE classification accuracy of 84 ± 2% (p-value < 0.00001). Most of the misclassifications involved adjacent classes. Different radiomic features were decisive for the prediction of each BPE class underlying the complexity of the decision boundaries. A highly precise and explainable pipeline for BPE classification was achieved without user- or algorithm-dependent radiomic feature selection.
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(This article belongs to the Special Issue Advances in Breast Cancer Imaging)
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Open AccessArticle
Failure Diagnosis for Dental Air Turbine Handpiece with Payload Using Feature Engineering and Temporal Convolution Network
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Yi-Cheng Huang and Po-Chen Chen
Bioengineering 2024, 11(6), 555; https://doi.org/10.3390/bioengineering11060555 - 30 May 2024
Abstract
The internal mechanisms of dental air turbine handpieces (DATHs) have become increasingly intricate over time. To enhance the operational reliability of dental procedures and guarantee patient safety, this study formulated temporal convolution network (TCN) prediction models with the functions of causality in time
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The internal mechanisms of dental air turbine handpieces (DATHs) have become increasingly intricate over time. To enhance the operational reliability of dental procedures and guarantee patient safety, this study formulated temporal convolution network (TCN) prediction models with the functions of causality in time sequence, transmitting memory, learning, storing, and fast convergence for monitoring the health and diagnosing the rotor and collet failure of DATHs. A handpiece mimicking a dentist’s hand load of 100 g was employed to repeatedly mill a glass porcelain block back and forth for cutting. An accelerometer was employed to capture vibration signals during free-running of unrestrained operation of the handpiece, aiming to discern the characteristic features of these vibrations. These data were then utilized to create a diagnostic health classification (DHC) for further developing a TCN, a 1D convolutional neural network (CNN), and long short-term memory (LSTM) prediction models. The three frameworks were used and compared for machine learning to establish DHC prediction models for the DATH. The experimental results indicate that, in terms of DHC predicted for the experimental dataset, the square categorical cross-entropy loss function error of the TCN framework was generally lower than that of the 1D CNN, which did not have a memory framework or the drawback of the vanishing gradient problem. In addition, the TCN framework outperformed the LSTM model, which required a longer history to provide sufficient diagnostic ability. Still, high accuracies were achieved both in the direction of feed-drive milling and in the gravity of the handpiece through vibration signals. In general, the failure classification prediction model could accurately predict the health and failure mode of the dental handpiece before the use of the DATH when an embedded sensor was available. Therefore, this model could prove to be a beneficial tool for predicting the deterioration patterns of real dental handpieces in their remaining useful life.
Full article
(This article belongs to the Section Biosignal Processing)
Open AccessArticle
Evaluating Sustainable Practices for Managing Residue Derived from Wheat Straw
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Harikishore Shanmugam, Vijaya Raghavan, Rajinikanth Rajagopal, Bernard Goyette, Linxiang Lyu, Siyuan Zhou and Chunjiang An
Bioengineering 2024, 11(6), 554; https://doi.org/10.3390/bioengineering11060554 - 30 May 2024
Abstract
Farm leftovers, particularly crop residues, are a key source of renewable energy in Canada. The nation’s robust agricultural industry provides ample biomass, derived from forestry and agriculture resources, for energy generation. Crop residues, such as straws and husks, play a crucial role in
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Farm leftovers, particularly crop residues, are a key source of renewable energy in Canada. The nation’s robust agricultural industry provides ample biomass, derived from forestry and agriculture resources, for energy generation. Crop residues, such as straws and husks, play a crucial role in this biomass reservoir, contributing to biofuel production and greenhouse gas mitigation efforts. Focusing on supply chains, waste management, and emission reduction, this study evaluates the sustainability of wheat straw, an agricultural biomass by-product. The environmental issues of various approaches to managing agricultural biomass were explored. Following an evaluation of biomass features, conversion methods, and economic and environmental advantages, the results show anaerobic digestion to be the most sustainable approach. Four metrics were examined in relation to social elements, and numerous aspects were considered as inputs in the evaluation of transportation costs. The use of electric trucks versus fuel-based trucks resulted in an 18% reduction in total operating costs and a 58% reduction in consumption costs. This study examined CO2 emissions over four different transportation distances. The data indicate that a significant reduction of 36% in kg CO2 equivalent emissions occurred when the distance was lowered from 100 km to 25 km. These findings offer insights for creating practical plans that should increase the sustainability of agricultural biomass leftovers.
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(This article belongs to the Special Issue Innovative Biotechnological & Microbiological Strategies for Organic Waste Management)
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Betulin Stimulates Osteogenic Differentiation of Human Osteoblasts-Loaded Alginate–Gelatin Microbeads
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Mehmet Ali Karaca, Derya Dilek Kancagi, Ugur Ozbek, Ercument Ovali and Ozgul Gok
Bioengineering 2024, 11(6), 553; https://doi.org/10.3390/bioengineering11060553 - 30 May 2024
Abstract
Osteoporosis, a terminal illness, has emerged as a global public health problem in recent years. The long-term use of bone anabolic drugs to treat osteoporosis causes multi-morbidity in elderly patients. Alternative therapies, such as allogenic and autogenic tissue grafts, face important issues, such
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Osteoporosis, a terminal illness, has emerged as a global public health problem in recent years. The long-term use of bone anabolic drugs to treat osteoporosis causes multi-morbidity in elderly patients. Alternative therapies, such as allogenic and autogenic tissue grafts, face important issues, such as a limited source of allogenic grafts and tissue rejection in autogenic grafts. However, stem cell therapy has been shown to increase bone regeneration and decrease osteoporotic bone formation. Stem cell therapy combined with betulin (BET) supplementation might be adequate for bone remodeling and new bone tissue generation. In this study, the effect of BET on the viability and osteogenic differentiation of hFOB 1.19 cells was investigated. The cells were encapsulated in alginate–gelatin (AlGel) microbeads. In vitro tests were conducted during the 12 d of incubation. While BET showed cytotoxic activity (>1 µM) toward non-encapsulated hFOB 1.19 cells, encapsulated cells retained their functionality for up to 12 days, even at 5 µM BET. Moreover, the expression of osteogenic markers indicates an enhanced osteo-inductive effect of betulin on encapsulated hFOB 1.19, compared to the non-encapsulated cell culture. The 3D micro-environment of the AlGel microcapsules successfully protects the hFOB 1.19 cells against BET cytotoxicity, allowing BET to improve the mineralization and differentiation of osteoblast cells.
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(This article belongs to the Section Regenerative Engineering)
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Metered-Dose Inhaler Spacer with Integrated Spirometer for Home-Based Asthma Monitoring and Drug Uptake
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Cheuk-Yan Au, Kelleen J. X. Koh, Hui Fang Lim and Ali Asgar Saleem Bhagat
Bioengineering 2024, 11(6), 552; https://doi.org/10.3390/bioengineering11060552 - 29 May 2024
Abstract
This work introduces Spiromni, a single device incorporating three different pressurised metered-dose inhaler (pMDI) accessories: a pMDI spacer, an electronic monitoring device (EMD), and a spirometer. While there are devices made to individually address the issues of technique, adherence and monitoring, respectively, for
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This work introduces Spiromni, a single device incorporating three different pressurised metered-dose inhaler (pMDI) accessories: a pMDI spacer, an electronic monitoring device (EMD), and a spirometer. While there are devices made to individually address the issues of technique, adherence and monitoring, respectively, for asthma patients as laid out in the Global Initiative for Asthma’s (GINA) global strategy for asthma management and prevention, Spiromni was designed to address all three issues using a single, combination device. Spiromni addresses the key challenge of measuring both inhalation and exhalation profiles, which are different by an order of magnitude. Moreover, the innovative design prevents exhalation from entering the spacer chamber and prevents medication loss during inhalation using umbrella valves without a loss in flow velocity. Apart from recording the peak exhalation flow rate, data from the sensors allow us to extract other key lung volume and capacities measures similar to a medical pulmonary function test. We believe this low-cost portable multi-functional device will benefit both asthma patients and clinicians in the management of the disease.
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(This article belongs to the Special Issue Fusion on a Chip: Microfluidics Meet Miniaturised Sensors)
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Optimizing Acute Coronary Syndrome Patient Treatment: Leveraging Gated Transformer Models for Precise Risk Prediction and Management
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Yingxue Mei, Zicai Jin, Weiguo Ma, Yingjun Ma, Ning Deng, Zhiyuan Fan and Shujun Wei
Bioengineering 2024, 11(6), 551; https://doi.org/10.3390/bioengineering11060551 - 29 May 2024
Abstract
Background: Acute coronary syndrome (ACS) is a severe cardiovascular disease with globally rising incidence and mortality rates. Traditional risk assessment tools are widely used but are limited due to the complexity of the data. Methods: This study introduces a gated Transformer model utilizing
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Background: Acute coronary syndrome (ACS) is a severe cardiovascular disease with globally rising incidence and mortality rates. Traditional risk assessment tools are widely used but are limited due to the complexity of the data. Methods: This study introduces a gated Transformer model utilizing machine learning to analyze electronic health records (EHRs) for an enhanced prediction of major adverse cardiovascular events (MACEs) in ACS patients. The model’s efficacy was evaluated using metrics such as area under the curve (AUC), precision–recall (PR), and F1-scores. Additionally, a patient management platform was developed to facilitate personalized treatment strategies. Results: Incorporating a gating mechanism substantially improved the Transformer model’s performance, especially in identifying true-positive cases. The TabTransformer+Gate model demonstrated an AUC of 0.836, a 14% increase in average precision (AP), and a 6.2% enhancement in accuracy, significantly outperforming other deep learning approaches. The patient management platform enabled healthcare professionals to effectively assess patient risks and tailor treatments, improving patient outcomes and quality of life. Conclusion: The integration of a gating mechanism within the Transformer model markedly increases the accuracy of MACE risk predictions in ACS patients, optimizes personalized treatment, and presents a novel approach for advancing clinical practice and research.
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(This article belongs to the Special Issue Intelligent Health Management, Nursing and Rehabilitation Technology)
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Coordinating Obstacle Avoidance of a Redundant Dual-Arm Nursing-Care Robot
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Zhiqiang Yang, Hao Lu, Pengpeng Wang and Shijie Guo
Bioengineering 2024, 11(6), 550; https://doi.org/10.3390/bioengineering11060550 - 29 May 2024
Abstract
Collision safety is an essential issue for dual-arm nursing-care robots. However, for coordinating operations, there is no suitable method to synchronously avoid collisions between two arms (self-collision) and collisions between an arm and the environment (environment-collision). Therefore, based on the self-motion characteristics of
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Collision safety is an essential issue for dual-arm nursing-care robots. However, for coordinating operations, there is no suitable method to synchronously avoid collisions between two arms (self-collision) and collisions between an arm and the environment (environment-collision). Therefore, based on the self-motion characteristics of the dual-arm robot’s redundant arms, an improved motion controlling algorithm is proposed. This study introduces several key improvements to existing methods. Firstly, the volume of the robotic arms was modeled using a capsule-enveloping method to more accurately reflect their actual structure. Secondly, the gradient projection method was applied in the kinematic analysis to calculate the shortest distances between the left arm, right arm, and the environment, ensuring effective avoidance of the self-collision and environment-collision. Additionally, distance thresholds were introduced to evaluate collision risks, and a velocity weight was used to control the smooth coordinating arm motion. After that, experiments of coordinating obstacle avoidance showed that when the redundant dual-arm robot is holding an object, the coordinating operation was completed while avoiding self-collision and environment-collision. The collision-avoidance method could provide potential benefits for various scenarios, such as medical robots and rehabilitating robots.
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(This article belongs to the Section Biomedical Engineering and Biomaterials)
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A Cross-Stage Partial Network and a Cross-Attention-Based Transformer for an Electrocardiogram-Based Cardiovascular Disease Decision System
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Chien-Ching Lee, Chia-Chun Chuang, Chia-Hong Yeng, Edmund-Cheung So and Yeou-Jiunn Chen
Bioengineering 2024, 11(6), 549; https://doi.org/10.3390/bioengineering11060549 - 29 May 2024
Abstract
Cardiovascular disease (CVD) is one of the leading causes of death globally. Currently, clinical diagnosis of CVD primarily relies on electrocardiograms (ECG), which are relatively easier to identify compared to other diagnostic methods. However, ensuring the accuracy of ECG readings requires specialized training
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Cardiovascular disease (CVD) is one of the leading causes of death globally. Currently, clinical diagnosis of CVD primarily relies on electrocardiograms (ECG), which are relatively easier to identify compared to other diagnostic methods. However, ensuring the accuracy of ECG readings requires specialized training for healthcare professionals. Therefore, developing a CVD diagnostic system based on ECGs can provide preliminary diagnostic results, effectively reducing the workload of healthcare staff and enhancing the accuracy of CVD diagnosis. In this study, a deep neural network with a cross-stage partial network and a cross-attention-based transformer is used to develop an ECG-based CVD decision system. To accurately represent the characteristics of ECG, the cross-stage partial network is employed to extract embedding features. This network can effectively capture and leverage partial information from different stages, enhancing the feature extraction process. To effectively distill the embedding features, a cross-attention-based transformer model, known for its robust scalability that enables it to process data sequences with different lengths and complexities, is employed to extract meaningful embedding features, resulting in more accurate outcomes. The experimental results showed that the challenge scoring metric of the proposed approach is 0.6112, which outperforms others. Therefore, the proposed ECG-based CVD decision system is useful for clinical diagnosis.
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(This article belongs to the Section Biosignal Processing)
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Integrating OpenPose and SVM for Quantitative Postural Analysis in Young Adults: A Temporal-Spatial Approach
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Posen Lee, Tai-Been Chen, Hung-Yu Lin, Li-Ren Yeh, Chin-Hsuan Liu and Yen-Lin Chen
Bioengineering 2024, 11(6), 548; https://doi.org/10.3390/bioengineering11060548 - 28 May 2024
Abstract
Noninvasive tracking devices are widely used to monitor real-time posture. Yet significant potential exists to enhance postural control quantification through walking videos. This study advances computational science by integrating OpenPose with a Support Vector Machine (SVM) to perform highly accurate and robust postural
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Noninvasive tracking devices are widely used to monitor real-time posture. Yet significant potential exists to enhance postural control quantification through walking videos. This study advances computational science by integrating OpenPose with a Support Vector Machine (SVM) to perform highly accurate and robust postural analysis, marking a substantial improvement over traditional methods which often rely on invasive sensors. Utilizing OpenPose-based deep learning, we generated Dynamic Joint Nodes Plots (DJNP) and iso-block postural identity images for 35 young adults in controlled walking experiments. Through Temporal and Spatial Regression (TSR) models, key features were extracted for SVM classification, enabling the distinction between various walking behaviors. This approach resulted in an overall accuracy of 0.990 and a Kappa index of 0.985. Cutting points for the ratio of top angles (TAR) and the ratio of bottom angles (BAR) effectively differentiated between left and right skews with AUC values of 0.772 and 0.775, respectively. These results demonstrate the efficacy of integrating OpenPose with SVM, providing more precise, real-time analysis without invasive sensors. Future work will focus on expanding this method to a broader demographic, including individuals with gait abnormalities, to validate its effectiveness across diverse clinical conditions. Furthermore, we plan to explore the integration of alternative machine learning models, such as deep neural networks, enhancing the system’s robustness and adaptability for complex dynamic environments. This research opens new avenues for clinical applications, particularly in rehabilitation and sports science, promising to revolutionize noninvasive postural analysis.
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(This article belongs to the Section Biomechanics and Sports Medicine)
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Mechanical Method for Rapid Determination of Step Count Sensor Settings
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Sydney Lundell and Kenton R. Kaufman
Bioengineering 2024, 11(6), 547; https://doi.org/10.3390/bioengineering11060547 - 27 May 2024
Abstract
With the increased push for personalized medicine, researchers and clinicians have begun exploring the use of wearable sensors to track patient activity. These sensors typically prioritize device life over robust onboard analysis, which results in lower accuracies in step count, particularly at lower
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With the increased push for personalized medicine, researchers and clinicians have begun exploring the use of wearable sensors to track patient activity. These sensors typically prioritize device life over robust onboard analysis, which results in lower accuracies in step count, particularly at lower cadences. To optimize the accuracy of activity-monitoring devices, particularly at slower walking speeds, proven methods must be established to identify suitable settings in a controlled and repeatable manner prior to human validation trials. Currently, there are no methods for optimizing these low-power wearable sensor settings prior to human validation, which requires manual counting for in-laboratory participants and is limited by time and the cadences that can be tested. This article proposes a novel method for determining sensor step counting accuracy prior to human validation trials by using a mechanical camshaft actuator that produces continuous steps. Sensor error was identified across a representative subspace of possible sensor setting combinations at cadences ranging from 30 steps/min to 110 steps/min. These true errors were then used to train a multivariate polynomial regression to model errors across all possible setting combinations and cadences. The resulting model predicted errors with an R2 of 0.8 and root-mean-square error (RMSE) of 0.044 across all setting combinations. An optimization algorithm was then used to determine the combinations of settings that produced the lowest RMSE and median error for three ranges of cadence that represent disabled low-mobility ambulators, disabled high-mobility ambulators, and healthy ambulators (30–60, 20–90, and 30–110 steps/min, respectively). The model identified six setting combinations for each range of interest that achieved a ±10% error in cadence prior to human validation. The anticipated range of errors from the optimized settings at lower walking speeds are lower than the reported errors of wearable sensors (±30%), suggesting that pre-human-validation optimization of sensors may decrease errors at lower cadences. This method provides a novel and efficient approach to optimizing the accuracy of wearable activity monitors prior to human validation trials.
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(This article belongs to the Special Issue Body-Worn Sensors for Biomedical Applications)
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Integration Approaches to Model Bioreactor Hydrodynamics and Cellular Kinetics for Advancing Bioprocess Optimisation
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Vishal Kumar Singh, Ioscani Jiménez del Val, Jarka Glassey and Fatemeh Kavousi
Bioengineering 2024, 11(6), 546; https://doi.org/10.3390/bioengineering11060546 - 27 May 2024
Abstract
Large-scale bioprocesses are increasing globally to cater to the larger market demands for biological products. As fermenter volumes increase, the efficiency of mixing decreases, and environmental gradients become more pronounced compared to smaller scales. Consequently, the cells experience gradients in process parameters, which
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Large-scale bioprocesses are increasing globally to cater to the larger market demands for biological products. As fermenter volumes increase, the efficiency of mixing decreases, and environmental gradients become more pronounced compared to smaller scales. Consequently, the cells experience gradients in process parameters, which in turn affects the efficiency and profitability of the process. Computational fluid dynamics (CFD) simulations are being widely embraced for their ability to simulate bioprocess performance, facilitate bioprocess upscaling, downsizing, and process optimisation. Recently, CFD approaches have been integrated with dynamic Cell reaction kinetic (CRK) modelling to generate valuable information about the cellular response to fluctuating hydrodynamic parameters inside large production processes. Such coupled approaches have the potential to facilitate informed decision-making in intelligent biomanufacturing, aligning with the principles of “Industry 4.0” concerning digitalisation and automation. In this review, we discuss the benefits of utilising integrated CFD-CRK models and the different approaches to integrating CFD-based bioreactor hydrodynamic models with cellular kinetic models. We also highlight the suitability of different coupling approaches for bioprocess modelling in the purview of associated computational loads.
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(This article belongs to the Special Issue 10th Anniversary of Bioengineering: Biochemical Engineering)
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LightCF-Net: A Lightweight Long-Range Context Fusion Network for Real-Time Polyp Segmentation
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Zhanlin Ji, Xiaoyu Li, Jianuo Liu, Rui Chen, Qinping Liao, Tao Lyu and Li Zhao
Bioengineering 2024, 11(6), 545; https://doi.org/10.3390/bioengineering11060545 - 27 May 2024
Abstract
Automatically segmenting polyps from colonoscopy videos is crucial for developing computer-assisted diagnostic systems for colorectal cancer. Existing automatic polyp segmentation methods often struggle to fulfill the real-time demands of clinical applications due to their substantial parameter count and computational load, especially those based
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Automatically segmenting polyps from colonoscopy videos is crucial for developing computer-assisted diagnostic systems for colorectal cancer. Existing automatic polyp segmentation methods often struggle to fulfill the real-time demands of clinical applications due to their substantial parameter count and computational load, especially those based on Transformer architectures. To tackle these challenges, a novel lightweight long-range context fusion network, named LightCF-Net, is proposed in this paper. This network attempts to model long-range spatial dependencies while maintaining real-time performance, to better distinguish polyps from background noise and thus improve segmentation accuracy. A novel Fusion Attention Encoder (FAEncoder) is designed in the proposed network, which integrates Large Kernel Attention (LKA) and channel attention mechanisms to extract deep representational features of polyps and unearth long-range dependencies. Furthermore, a newly designed Visual Attention Mamba module (VAM) is added to the skip connections, modeling long-range context dependencies in the encoder-extracted features and reducing background noise interference through the attention mechanism. Finally, a Pyramid Split Attention module (PSA) is used in the bottleneck layer to extract richer multi-scale contextual features. The proposed method was thoroughly evaluated on four renowned polyp segmentation datasets: Kvasir-SEG, CVC-ClinicDB, BKAI-IGH, and ETIS. Experimental findings demonstrate that the proposed method delivers higher segmentation accuracy in less time, consistently outperforming the most advanced lightweight polyp segmentation networks.
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(This article belongs to the Section Biosignal Processing)
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Evaluation of Patients’ Levels of Walking Independence Using Inertial Sensors and Neural Networks in an Acute-Care Hospital
by
Tatsuya Sugimoto, Nobuhito Taniguchi, Ryoto Yoshikura, Hiroshi Kawaguchi and Shintaro Izumi
Bioengineering 2024, 11(6), 544; https://doi.org/10.3390/bioengineering11060544 - 26 May 2024
Abstract
This study aimed to evaluate walking independence in acute-care hospital patients using neural networks based on acceleration and angular velocity from two walking tests. Forty patients underwent the 10-meter walk test and the Timed Up-and-Go test at normal speed, with or without a
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This study aimed to evaluate walking independence in acute-care hospital patients using neural networks based on acceleration and angular velocity from two walking tests. Forty patients underwent the 10-meter walk test and the Timed Up-and-Go test at normal speed, with or without a cane. Physiotherapists divided the patients into two groups: 24 patients who were monitored or independent while walking with a cane or without aids in the ward, and 16 patients who were not. To classify these groups, the Transformer model analyzes the left gait cycle data from eight inertial sensors. The accuracy using all the sensor data was 0.836. When sensor data from the right ankle, right wrist, and left wrist were excluded, the accuracy decreased the most. When analyzing the data from these three sensors alone, the accuracy was 0.795. Further reducing the number of sensors to only the right ankle and wrist resulted in an accuracy of 0.736. This study demonstrates the potential of a neural network-based analysis of inertial sensor data for clinically assessing a patient’s level of walking independence.
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(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Spine Research)
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Generation of Tailored Extracellular Matrix Hydrogels for the Study of In Vitro Folliculogenesis in Response to Matrisome-Dependent Biochemical Cues
by
Hannah B. McDowell, Kathryn L. McElhinney, Elizabeth L. Tsui and Monica M. Laronda
Bioengineering 2024, 11(6), 543; https://doi.org/10.3390/bioengineering11060543 - 25 May 2024
Abstract
While ovarian tissue cryopreservation (OTC) is an important fertility preservation option, it has its limitations. Improving OTC and ovarian tissue transplantation (OTT) must include extending the function of reimplanted tissue by reducing the extensive activation of primordial follicles (PMFs) and eliminating the risk
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While ovarian tissue cryopreservation (OTC) is an important fertility preservation option, it has its limitations. Improving OTC and ovarian tissue transplantation (OTT) must include extending the function of reimplanted tissue by reducing the extensive activation of primordial follicles (PMFs) and eliminating the risk of reimplanting malignant cells. To develop a more effective OTT, we must understand the effects of the ovarian microenvironment on folliculogenesis. Here, we describe a method for producing decellularized extracellular matrix (dECM) hydrogels that reflect the protein composition of the ovary. These ovarian dECM hydrogels were engineered to assess the effects of ECM on in vitro follicle growth, and we developed a novel method for selectively removing proteins of interest from dECM hydrogels. Finally, we validated the depletion of these proteins and successfully cultured murine follicles encapsulated in the compartment-specific ovarian dECM hydrogels and these same hydrogels depleted of EMILIN1. These are the first, optically clear, tailored tissue-specific hydrogels that support follicle survival and growth comparable to the “gold standard” alginate hydrogels. Furthermore, depleted hydrogels can serve as a novel tool for many tissue types to evaluate the impact of specific ECM proteins on cellular and molecular behavior.
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(This article belongs to the Special Issue Bioengineering Technologies to Advance Reproductive Health)
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A Capillary-Force-Driven, Single-Cell Transfer Method for Studying Rare Cells
by
Jacob Amontree, Kangfu Chen, Jose Varillas and Z. Hugh Fan
Bioengineering 2024, 11(6), 542; https://doi.org/10.3390/bioengineering11060542 - 24 May 2024
Abstract
The characterization of individual cells within heterogeneous populations (e.g., rare tumor cells in healthy blood cells) has a great impact on biomedical research. To investigate the properties of these specific cells, such as genetic biomarkers and/or phenotypic characteristics, methods are often developed for
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The characterization of individual cells within heterogeneous populations (e.g., rare tumor cells in healthy blood cells) has a great impact on biomedical research. To investigate the properties of these specific cells, such as genetic biomarkers and/or phenotypic characteristics, methods are often developed for isolating rare cells among a large number of background cells before studying their genetic makeup and others. Prior to using real-world samples, these methods are often evaluated and validated by spiking cells of interest (e.g., tumor cells) into a sample matrix (e.g., healthy blood) as model samples. However, spiking tumor cells at extremely low concentrations is challenging in a standard laboratory setting. People often circumvent the problem by diluting a solution of high-concentration cells, but the concentration becomes inaccurate after series dilution due to the fact that a cell suspension solution can be inhomogeneous, especially when the cell concentration is very low. We report on an alternative method for low-cost, accurate, and reproducible low-concentration cell spiking without the use of external pumping systems. By inducing a capillary force from sudden pressure drops, a small portion of the cellular membrane was aspirated into the reservoir tip, allowing for non-destructive single-cell transfer. We investigated the surface membrane tensions induced by cellular aspiration and studied a range of tip/tumor cell diameter combinations, ensuring that our method does not affect cell viability. In addition, we performed single-cell capture and transfer control experiments using human acute lymphoblastic leukemia cells (CCRF-CEM) to develop calibrated data for the general production of low-concentration samples. Finally, we performed affinity-based tumor cell isolation using this method to generate accurate concentrations ranging from 1 to 15 cells/mL.
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(This article belongs to the Special Issue Bioanalysis Systems: Materials, Methods, Designs and Applications)
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Open AccessReview
How Do Cartilage Lubrication Mechanisms Fail in Osteoarthritis? A Comprehensive Review
by
Manoj Rajankunte Mahadeshwara, Maisoon Al-Jawad, Richard M. Hall, Hemant Pandit, Reem El-Gendy and Michael Bryant
Bioengineering 2024, 11(6), 541; https://doi.org/10.3390/bioengineering11060541 - 24 May 2024
Abstract
Cartilage degeneration is a characteristic of osteoarthritis (OA), which is often observed in aging populations. This degeneration is due to the breakdown of articular cartilage (AC) mechanical and tribological properties primarily attributed to lubrication failure. Understanding the reasons behind these failures and identifying
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Cartilage degeneration is a characteristic of osteoarthritis (OA), which is often observed in aging populations. This degeneration is due to the breakdown of articular cartilage (AC) mechanical and tribological properties primarily attributed to lubrication failure. Understanding the reasons behind these failures and identifying potential solutions could have significant economic and societal implications, ultimately enhancing quality of life. This review provides an overview of developments in the field of AC, focusing on its mechanical and tribological properties. The emphasis is on the role of lubrication in degraded AC, offering insights into its structure and function relationship. Further, it explores the fundamental connection between AC mechano-tribological properties and the advancement of its degradation and puts forth recommendations for strategies to boost its lubrication efficiency.
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(This article belongs to the Special Issue Inspired by Nature: Advanced Biomaterials and Manufacturing Solutions for Skeletal Tissue Regeneration and Osteoarthritis Treatment)
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