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
Metered-Dose Inhaler Spacer with Integrated Spirometer for Home-Based Asthma Monitoring and Drug Uptake
Bioengineering 2024, 11(6), 552; https://doi.org/10.3390/bioengineering11060552 (registering DOI) - 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
by
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 (registering DOI) - 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.
Full article
(This article belongs to the Special Issue Intelligent Health Management, Nursing and Rehabilitation Technology)
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Open AccessArticle
Coordinating Obstacle Avoidance of a Redundant Dual-Arm Nursing-Care Robot
by
Zhiqiang Yang, Hao Lu, Pengpeng Wang and Shijie Guo
Bioengineering 2024, 11(6), 550; https://doi.org/10.3390/bioengineering11060550 (registering DOI) - 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 (registering DOI) - 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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Open AccessArticle
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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Open AccessArticle
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.
Full article
(This article belongs to the Special Issue Body-Worn Sensors for Biomedical Applications)
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Open AccessReview
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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Open AccessArticle
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 (registering DOI) - 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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Open AccessArticle
Evaluation of Patients’ Levels of Walking Independence Using Inertial Sensors and Neural Networks in an Acute-Care Hospital
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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.
Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Spine Research)
Open AccessArticle
Generation of Tailored Extracellular Matrix Hydrogels for the Study of In Vitro Folliculogenesis in Response to Matrisome-Dependent Biochemical Cues
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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
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Jacob Amontree, Kangfu Chen, Jose Varillas and Z. Hugh Fan
Bioengineering 2024, 11(6), 542; https://doi.org/10.3390/bioengineering11060542 (registering DOI) - 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.
Full article
(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
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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.
Full article
(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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Open AccessArticle
An Improved Two-Shot Tracking Algorithm for Dynamics Analysis of Natural Killer Cells in Tumor Contexts
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Yanqing Zhou, Yiwen Tang and Zhibing Li
Bioengineering 2024, 11(6), 540; https://doi.org/10.3390/bioengineering11060540 - 24 May 2024
Abstract
Natural killer cells (NKCs) are non-specific immune lymphocytes with diverse morphologies. Their broad killing effect on cancer cells has led to increased attention towards activating NKCs for anticancer immunotherapy. Consequently, understanding the motion characteristics of NKCs under different morphologies and modeling their collective
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Natural killer cells (NKCs) are non-specific immune lymphocytes with diverse morphologies. Their broad killing effect on cancer cells has led to increased attention towards activating NKCs for anticancer immunotherapy. Consequently, understanding the motion characteristics of NKCs under different morphologies and modeling their collective dynamics under cancer cells has become crucial. However, tracking small NKCs in complex backgrounds poses significant challenges, and conventional industrial tracking algorithms often perform poorly on NKC tracking datasets. There remains a scarcity of research on NKC dynamics. In this paper, we utilize deep learning techniques to analyze the morphology of NKCs and their key points. After analyzing the shortcomings of common industrial multi-object tracking algorithms like DeepSORT in tracking natural killer cells, we propose Distance Cascade Matching and the Re-Search method to improve upon existing algorithms, yielding promising results. Through processing and tracking over 5000 frames of images, encompassing approximately 300,000 cells, we preliminarily explore the impact of NKCs’ cell morphology, temperature, and cancer cell environment on NKCs’ motion, along with conducting basic modeling. The main conclusions of this study are as follows: polarized cells are more likely to move along their polarization direction and exhibit stronger activity, and the maintenance of polarization makes them more likely to approach cancer cells; under equilibrium, NK cells display a Boltzmann distribution on the cancer cell surface.
Full article
(This article belongs to the Special Issue Recent Advances in Optical Imaging and Machine Learning in Biomedicine)
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Open AccessArticle
Trivial State Fuzzy Processing for Error Reduction in Healthcare Big Data Analysis towards Precision Diagnosis
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Mohd Anjum, Hong Min and Zubair Ahmed
Bioengineering 2024, 11(6), 539; https://doi.org/10.3390/bioengineering11060539 - 24 May 2024
Abstract
There is a significant public health concern regarding medical diagnosis errors, which are a major cause of mortality. Identifying the root cause of these errors is challenging, and even if one is identified, implementing an effective treatment to prevent their recurrence is difficult.
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There is a significant public health concern regarding medical diagnosis errors, which are a major cause of mortality. Identifying the root cause of these errors is challenging, and even if one is identified, implementing an effective treatment to prevent their recurrence is difficult. Optimization-based analysis in healthcare data management is a reliable method for improving diagnostic precision. Analyzing healthcare data requires pre-classification and the identification of precise information for precision-oriented outcomes. This article introduces a Cooperative-Trivial State Fuzzy Processing method for significant data analysis with possible derivatives. Trivial State Fuzzy Processing operates on the principle of fuzzy logic-based processing applied to structured healthcare data, focusing on mitigating errors and uncertainties inherent in the data. The derivatives are aided by identifying and grouping diagnosis-related and irrelevant data. The proposed method mitigates invertible derivative analysis issues in similar data grouping and irrelevance estimation. In the grouping and detection process, recent knowledge of the diagnosis progression is exploited to identify the functional data for analysis. Such analysis improves the impact of trivial diagnosis data compared to a voluminous diagnosis history. The cooperative derivative states under different data irrelevance factors reduce trivial state errors in healthcare big data analysis.
Full article
(This article belongs to the Special Issue Biomedical Application of Big Data and Artificial Intelligence)
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Open AccessReview
The Use of Head-Mounted Display Systems for Upper Limb Kinematic Analysis in Post-Stroke Patients: A Perspective Review on Benefits, Challenges and Other Solutions
by
Paolo De Pasquale, Mirjam Bonanno, Sepehr Mojdehdehbaher, Angelo Quartarone and Rocco Salvatore Calabrò
Bioengineering 2024, 11(6), 538; https://doi.org/10.3390/bioengineering11060538 - 24 May 2024
Abstract
In recent years, there has been a notable increase in the clinical adoption of instrumental upper limb kinematic assessment. This trend aligns with the rising prevalence of cerebrovascular impairments, one of the most prevalent neurological disorders. Indeed, there is a growing need for
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In recent years, there has been a notable increase in the clinical adoption of instrumental upper limb kinematic assessment. This trend aligns with the rising prevalence of cerebrovascular impairments, one of the most prevalent neurological disorders. Indeed, there is a growing need for more objective outcomes to facilitate tailored rehabilitation interventions following stroke. Emerging technologies, like head-mounted virtual reality (HMD-VR) platforms, have responded to this demand by integrating diverse tracking methodologies. Specifically, HMD-VR technology enables the comprehensive tracking of body posture, encompassing hand position and gesture, facilitated either through specific tracker placements or via integrated cameras coupled with sophisticated computer graphics algorithms embedded within the helmet. This review aims to present the state-of-the-art applications of HMD-VR platforms for kinematic analysis of the upper limb in post-stroke patients, comparing them with conventional tracking systems. Additionally, we address the potential benefits and challenges associated with these platforms. These systems might represent a promising avenue for safe, cost-effective, and portable objective motor assessment within the field of neurorehabilitation, although other systems, including robots, should be taken into consideration.
Full article
(This article belongs to the Special Issue Bioengineering of the Motor System)
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Open AccessArticle
Biomechanical Modelling of Porcine Kidney
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Aadarsh Mishra and Robin O. Cleveland
Bioengineering 2024, 11(6), 537; https://doi.org/10.3390/bioengineering11060537 - 24 May 2024
Abstract
In this study, the viscoelastic properties of porcine kidney in the upper, middle and lower poles were investigated using oscillatory shear tests. The viscoelastic properties were extracted in the form of the storage modulus and loss modulus in the frequency and time domain.
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In this study, the viscoelastic properties of porcine kidney in the upper, middle and lower poles were investigated using oscillatory shear tests. The viscoelastic properties were extracted in the form of the storage modulus and loss modulus in the frequency and time domain. Measurements were taken as a function of frequency from 0.1 Hz to 6.5 Hz at a shear strain amplitude of 0.01 and as function of strain amplitude from 0.001 to 0.1 at a frequency of 1 Hz. Measurements were also taken in the time domain in response to a step shear strain. Both the frequency and time domain data were fitted to a conventional Standard Linear Solid (SLS) model and a semi-fractional Kelvin–Voigt (SFKV) model with a comparable number of parameters. The SFKV model fitted the frequency and time domain data with a correlation coefficient of 0.99. Although the SLS model well fitted the time domain data and the storage modulus data in the frequency domain, it was not able to capture the variation in loss modulus with frequency with a correlation coefficient of 0.53. A five parameter Maxwell–Wiechert model was able to capture the frequency dependence in storage modulus and loss modulus better than the SLS model with a correlation of 0.85.
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(This article belongs to the Special Issue Biomechanics Analysis in Tissue Engineering)
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Open AccessSystematic Review
Fracture Resistance of Direct versus Indirect Restorations on Posterior Teeth: A Systematic Review and Meta-Analysis
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Carol Moussa, Guillaume Savard, Gael Rochefort, Matthieu Renaud, Frédéric Denis and Maha H. Daou
Bioengineering 2024, 11(6), 536; https://doi.org/10.3390/bioengineering11060536 - 24 May 2024
Abstract
The aim of this systematic review and meta-analysis was to compare static compression forces between direct composite resin restorations and indirect restorations for posterior teeth. All studies comparing mechanical properties of direct versus indirect restorations of posterior teeth were included from 2007 up
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The aim of this systematic review and meta-analysis was to compare static compression forces between direct composite resin restorations and indirect restorations for posterior teeth. All studies comparing mechanical properties of direct versus indirect restorations of posterior teeth were included from 2007 up to February 2024. A meta-analysis was conducted for static compression fracture resistance. Medline, Central, and Embase databases were screened. Twenty-four articles were included in the qualitative synthesis, and sixteen studies were finally included in the quantitative synthesis. There was no difference in terms of fracture resistance between direct and indirect restorations for posterior teeth (p = 0.16 for direct and indirect composite resin restorations and p = 0.87 for direct composite resin restorations and indirect ceramic restorations). Also, sub-group analysis with or without cusp coverage in each group revealed no discernable difference. Based on this study, it can be concluded that the choice between direct and indirect restoration approaches may not significantly impact fracture resistance outcomes. There was no statically significant difference between direct and indirect restorations for posterior teeth in all cases of restorations with or without cusp coverage and no matter the used materials. However, to better evaluate these materials, further studies are warranted.
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(This article belongs to the Special Issue Bioceramic Strategy—the Game of Bioactivity in Endodontic)
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A Self-Polymerizing Mesh of Nano-Tethers for the Mechanical Constraint of Degraded Intervertebral Discs—A Review of 25 Years of Pre-Clinical and Early Clinical Research
by
Thomas Hedman, Adam Rogers and Douglas Beall
Bioengineering 2024, 11(6), 535; https://doi.org/10.3390/bioengineering11060535 - 24 May 2024
Abstract
Genipin polymers are self-forming tensile-load-carrying oligomers, derived from the gardenia fruit, that covalently bond to amines on collagen. The potential therapeutic mechanical benefits of a non-discrete in situ forming mesh of genipin oligomers for degraded spinal discs were first conceived in 1998. Over
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Genipin polymers are self-forming tensile-load-carrying oligomers, derived from the gardenia fruit, that covalently bond to amines on collagen. The potential therapeutic mechanical benefits of a non-discrete in situ forming mesh of genipin oligomers for degraded spinal discs were first conceived in 1998. Over more than two decades, numerous studies have demonstrated the immediate mechanical effects of this injectable, intra-annular polymeric mesh including an early demonstration of an effect on clinical outcomes for chronic or recurrent discogenic low back pain. This literature review focused on articles investigating mechanical effects in cadaveric animal and human spinal discs, biochemical mechanism of action studies, articles describing the role of mechanical degradation in the pathogenesis of degenerative disc disease, initial clinical outcomes and articles describing current discogenic low back pain treatment algorithms. On the basis of these results, clinical indications that align with the capabilities of this novel injectable polymer-based treatment strategy are discussed. It is intended that this review of a novel nano-scale material-based solution for mechanical deficiencies in biologically limited tissues may provide a helpful example for other innovations in spinal diseases and similarly challenging musculoskeletal disorders.
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(This article belongs to the Special Issue Bioengineering Strategies for the Improvement of Therapies in Spinal Diseases)
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Alexander Friedenstein, Mesenchymal Stem Cells, Shifting Paradigms and Euphemisms
by
Donald G. Phinney
Bioengineering 2024, 11(6), 534; https://doi.org/10.3390/bioengineering11060534 - 23 May 2024
Abstract
Six decades ago, Friedenstein and coworkers published a series of seminal papers identifying a cell population in bone marrow with osteogenic potential, now referred to as mesenchymal stem cells (MSCs). This work was also instrumental in establishing the identity of hematopoietic stem cell
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Six decades ago, Friedenstein and coworkers published a series of seminal papers identifying a cell population in bone marrow with osteogenic potential, now referred to as mesenchymal stem cells (MSCs). This work was also instrumental in establishing the identity of hematopoietic stem cell and the identification of skeletal stem/progenitor cell (SSPC) populations in various skeletal compartments. In recognition of the centenary year of Friedenstein’s birth, I review key aspects of his work and discuss the evolving concept of the MSC and its various euphemisms indorsed by changing paradigms in the field. I also discuss the recent emphasis on MSC stromal quality attributes and how emerging data demonstrating a mechanistic link between stromal and stem/progenitor functions bring renewed relevance to Friedenstein’s contributions and much needed unity to the field.
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(This article belongs to the Special Issue A Tribute to Professor Alexander Friedenstein and His Outstanding Achievements in the Area of Stromal Stem Cells)
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Unraveling the Potential of Attentive Bi-LSTM for Accurate Obesity Prognosis: Advancing Public Health towards Sustainable Cities
by
Hina Ayub, Murad-Ali Khan, Syed Shehryar Ali Naqvi, Muhammad Faseeh, Jungsuk Kim, Asif Mehmood and Young-Jin Kim
Bioengineering 2024, 11(6), 533; https://doi.org/10.3390/bioengineering11060533 - 23 May 2024
Abstract
The global prevalence of obesity presents a pressing challenge to public health and healthcare systems, necessitating accurate prediction and understanding for effective prevention and management strategies. This article addresses the need for improved obesity prediction models by conducting a comprehensive analysis of existing
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The global prevalence of obesity presents a pressing challenge to public health and healthcare systems, necessitating accurate prediction and understanding for effective prevention and management strategies. This article addresses the need for improved obesity prediction models by conducting a comprehensive analysis of existing machine learning (ML) and deep learning (DL) approaches. This study introduces a novel hybrid model, Attention-based Bi-LSTM (ABi-LSTM), which integrates attention mechanisms with bidirectional Long Short-Term Memory (Bi-LSTM) networks to enhance interpretability and performance in obesity prediction. Our study fills a crucial gap by bridging healthcare and urban planning domains, offering insights into data-driven approaches to promote healthier living within urban environments. The proposed ABi-LSTM model demonstrates exceptional performance, achieving a remarkable accuracy of 96.5% in predicting obesity levels. Comparative analysis showcases its superiority over conventional approaches, with superior precision, recall, and overall classification balance. This study highlights significant advancements in predictive accuracy and positions the ABi-LSTM model as a pioneering solution for accurate obesity prognosis. The implications extend beyond healthcare, offering a precise tool to address the global obesity epidemic and foster sustainable development in smart cities.
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(This article belongs to the Special Issue Intelligent IoMT Systems for Brain–Computer Interface)
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