Biocybernetics and Biomedical Engineering最新文献

{"title":"Multiwavelength laser doppler holography (MLDH) in spatiotemporal optical coherence tomography (STOC-T)","authors":"Dawid Borycki ,&nbsp;Egidijus Auksorius ,&nbsp;Piotr Węgrzyn ,&nbsp;Kamil Liżewski ,&nbsp;Sławomir Tomczewski ,&nbsp;Ieva Žičkienė ,&nbsp;Karolis Adomavičius ,&nbsp;Karol Karnowski ,&nbsp;Maciej Wojtkowski","doi":"10.1016/j.bbe.2024.03.002","DOIUrl":"https://doi.org/10.1016/j.bbe.2024.03.002","url":null,"abstract":"<div><p>Spatiotemporal optical coherence tomography (STOC-T) is the novel modality for high-speed, crosstalk- and aberration-free volumetric imaging of biological tissue <em>in vivo</em>. STOC-T extends the Fourier-Domain holographic Optical Coherence Tomography by the spatial phase modulation that enables the reduction of spatial coherence of the tunable laser. By reducing the spatial coherence of the laser, we suppress coherent noise, and, consequently, improve the imaging depth. Furthermore, we remove geometrical aberrations computationally in postprocessing. We recently demonstrated high-speed, high-resolution STOC-T of human retinal imaging <em>in vivo</em>. Here, we show that the dataset produced by STOC-T can be processed differently to reveal blood flow in the human retina <em>in vivo</em>. To render the blood flow, we first pre-process STOC-T holographic data to access the approximated information about the Doppler-shifted optical field backscattered from the sample. Then, we analyze it using methods from the laser Doppler flowmetry, namely, by analyzing the Doppler broadening caused by moving light scatterers (red blood cells). However, contrary to conventional approaches, we use multiple illumination wavelengths. This enables us to render the structural volumetric and blood flow images from the same dataset concurrently. Our method, denoted as multiwavelength laser Doppler holography (MLDH), links laser Doppler flowmetry with multiwavelength holographic detection to enable noninvasive visualization and possible blood flow quantification at different human retina layers at high speeds and high transverse resolution <em>in vivo</em>.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 1","pages":"Pages 264-275"},"PeriodicalIF":6.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0208521624000111/pdfft?md5=fbd875c488dc4651bec90fa168ae6951&pid=1-s2.0-S0208521624000111-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140113616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-scale local-global transformer with contrastive learning for biomarkers segmentation in retinal OCT images","authors":"Xiaoming Liu ,&nbsp;Yuanzhe Ding ,&nbsp;Ying Zhang ,&nbsp;Jinshan Tang","doi":"10.1016/j.bbe.2024.02.001","DOIUrl":"https://doi.org/10.1016/j.bbe.2024.02.001","url":null,"abstract":"<div><p>Quantitative analysis of biomarkers in Optical Coherence Tomography (OCT) images plays an import role in the diagnosis and treatment of retinal diseases. However, biomarker segmentation in retinal OCT images is very hard due to the large variations in size and shape of retinal biomarkers, blurred boundaries, low contrast, and speckle interference. We proposed a novel <strong>M</strong>ulti-<strong>s</strong>cale <strong>L</strong>ocal-<strong>G</strong>lobal <strong>T</strong>ransformer network (MsLGT-Net) for biomarker segmentation in retinal OCT images. The network combines the proposed <strong>M</strong>ulti-scale <strong>F</strong>usion <strong>A</strong>ttention (MFA) module, <strong>L</strong>ocal-<strong>G</strong>lobal <strong>T</strong>ransformer (LGT) module, and <strong>C</strong>ontrastive <strong>L</strong>earning <strong>E</strong>nhancement (CLE) module to tackle the challenges of biomarker segmentation. Specifically, the proposed MFA module aims to enhance the network’s ability to learn multi-scale features of retinal biomarkers by effectively combining the local detail information and contextual semantic information of biomarkers at different scales, and improve the representation ability for different classes of biomarkers. The LGT module is designed to learn local and global information adaptively from multi-scale fused features to address the challenge of small biomarker segmentation. In addition, to distinguish features between different types of retinal biomarkers, we propose the CLE module to enhance the feature representation of different biomarkers. Our proposed method is validated on one public dataset and one local dataset. The experimental results show that the proposed method is more effective than other state-of-the-art methods.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 1","pages":"Pages 231-246"},"PeriodicalIF":6.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139743583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time-resolved near-infrared spectroscopy in monitoring acute ischemic stroke patients – Case study","authors":"Aleksandra Kuls-Oszmaniec ,&nbsp;Michał Kacprzak ,&nbsp;Magdalena Morawiec ,&nbsp;Piotr Sawosz ,&nbsp;Urszula Fiszer ,&nbsp;Marta Leńska-Mieciek","doi":"10.1016/j.bbe.2023.12.006","DOIUrl":"https://doi.org/10.1016/j.bbe.2023.12.006","url":null,"abstract":"<div><p>Stroke is a leading cause of disability and death worldwide, with acute ischemic stroke<span> (AIS) accounting for the majority of cases. Early and accurate diagnosis of AIS is crucial for improving patient outcomes. Non-invasive monitoring techniques, such as time domain near-infrared spectroscopy (tdNIRS), have shown potential for real-time monitoring of AIS patients at the bedside. However, there is a need for further research to evaluate the effectiveness of tdNIRS in the acute phase of stroke. In this study, we present the results of a case report using tdNIRS to monitor AIS patients without any additional stimulation. The tdNIRS technique allows for non-invasively assessing cerebral oxygenation in absolute units, enabling accurate measurement of changes in oxygenated and deoxygenated hemoglobin concentrations in the brain. Our aim was to determine the feasibility of tdNIRS in monitoring AIS patients.</span></p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 1","pages":"Pages 149-160"},"PeriodicalIF":6.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139503513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review of artificial neural network techniques for analysis of foot plantar pressure","authors":"Chongguang Wang ,&nbsp;Kerrie Evans ,&nbsp;Dean Hartley ,&nbsp;Scott Morrison ,&nbsp;Martin Veidt ,&nbsp;Gui Wang","doi":"10.1016/j.bbe.2024.01.005","DOIUrl":"https://doi.org/10.1016/j.bbe.2024.01.005","url":null,"abstract":"<div><p>Plantar pressure distribution offers insights into foot function, gait mechanics, and foot-related issues. This systematic review presents an analysis of the use of artificial neural network techniques in the context of plantar pressure analysis. 60 studies were included in the review. Sample size, pathology, pressure sensor number, data collection device, utilization of other sensor devices, ground-truth methods, pre-processing dataset, neural network type, and evaluation metrics were evaluated. Utilization of customized wearable footwear devices for the acquisition of data was common amongst both healthy participants and patients. Inertial measurement units emerged as an effective compensatory measure to address the limitations associated with the distribution of plantar pressure. Ground truth methods predominantly relied on the usage of both annotations and reference devices. Multilayer perceptron, convolutional neural networks, and recurrent neural networks were identified as the most frequently employed artificial neural network algorithms across the reviewed studies. Finally, the evaluation of performance largely drew upon statistical descriptions and other machine learning methods. This review provides a comprehensive understanding of the use of artificial neural network techniques in plantar pressure analysis, highlighting opportunities for future research.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 1","pages":"Pages 197-208"},"PeriodicalIF":6.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0208521624000056/pdfft?md5=9b7f95a54d7af9620bb2a34de80b906f&pid=1-s2.0-S0208521624000056-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139674820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disruptions in brain functional connectivity: The hidden risk for oxygen-intolerant professional divers in simulated deep water","authors":"Emanuela Formaggio ,&nbsp;Lucio Pastena ,&nbsp;Massimo Melucci ,&nbsp;Lucio Ricciardi ,&nbsp;Silvia Francesca Storti","doi":"10.1016/j.bbe.2024.01.004","DOIUrl":"https://doi.org/10.1016/j.bbe.2024.01.004","url":null,"abstract":"<div><p>In this study, we investigated the effects of oxygen toxicity on brain activity and functional connectivity (FC) in divers using a closed-circuit oxygen breathing apparatus. We acquired and analyzed electroencephalographic (EEG) signals from a group of normal professional divers (PD) and a group that developed oxygen intolerance, i.e., oxygen-intolerant professional divers (OPD), to evaluate the potential risk of a dive and understand the physiological mechanisms involved. The results highlighted a significant difference in the baseline levels of <span><math><mi>α</mi></math></span> rhythm between PD and OPD, with PD exhibiting a lower level to counteract the effects of increased <span><math><msub><mrow><mi>O</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> inhalation, while OPD showed a higher level that resulted in a pathological state. Connectivity analysis revealed a strong correlation between cognitive and motor regions, and high levels of <span><math><mi>α</mi></math></span> synchronization at rest in OPDs. Our findings suggest that a pathological condition may underlie the higher <span><math><mi>α</mi></math></span> levels observed in these individuals when facing the stress of high <span><math><msub><mrow><mi>O</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> inhalation. These findings support the hypothesis that oxygen modulates brain networks, and have important implications for understanding the neural mechanisms involved in oxygen toxicity. The study also provides a unique opportunity to investigate the impact of neurophysiological activity in simulated critical scenarios, and opens up new perspectives in the screening and monitoring of divers.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 1","pages":"Pages 209-217"},"PeriodicalIF":6.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0208521624000044/pdfft?md5=5edb8ca083818f99257fa9753df94806&pid=1-s2.0-S0208521624000044-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139709621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Video-based HR measurement using adaptive facial regions with multiple color spaces","authors":"Arpita Panigrahi ,&nbsp;Hemant Sharma ,&nbsp;Atin Mukherjee","doi":"10.1016/j.bbe.2023.12.001","DOIUrl":"https://doi.org/10.1016/j.bbe.2023.12.001","url":null,"abstract":"<div><p><span><span><span>Driven by the desire for feasible and convenient healthcare, non-contact heart rate (HR) monitoring based on consumer-grade cameras has gained significant recognition among researchers. However, this technology suffers from performance reliability and consistency in realistic situations of motion artifacts, illumination variations, and skin tones, limiting it to emerge as an alternative to conventional methods. Considering these challenges, this paper suggests an effective technique for HR measurement from facial </span>RGB<span> videos. The face being the region of interest (ROI) is divided into several small sub-ROIs of even size. A group of quality sub-ROIs is formed and weighted based on the fundamental periodicity coefficient to handle spatial non-uniform illumination and facial motions. Five different color spaces are considered, and the most suitable color component from each space is chosen to alleviate the influence of temporal illumination variation and other factors. The resultant color signals are denoised using the ensemble empirical mode decomposition and integrated using the </span></span>principal component analysis to derive a pulsating component representing the blood </span>volumetric<span> changes for HR computation. Experiments are conducted over three standard datasets, namely PURE, UBFC, and COHFACE. The obtained mean absolute error values are 1.16 beats per minute (bpm), 1.56 bpm, and 2.10 bpm for PURE, UBFC, and COHFACE datasets, respectively, indicating the performance of the technique well above the clinically acceptable threshold. In comparison, the technique showed performance superiority over the state-of-art methods. These outcomes substantiate the potential of alternative color spaces for accurate and reliable HR monitoring from facial videos in challenging scenarios.</span></p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 1","pages":"Pages 68-82"},"PeriodicalIF":6.4,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139050378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Insulin resistance: Risk factors, diagnostic approaches and mathematical models for clinical practice, epidemiological studies, and beyond","authors":"Janusz Krzymien ,&nbsp;Piotr Ladyzynski","doi":"10.1016/j.bbe.2023.12.004","DOIUrl":"https://doi.org/10.1016/j.bbe.2023.12.004","url":null,"abstract":"<div><p><span>Insulin resistance (IR) is a multifactorial metabolic disorder associated with the development of cardiometabolic syndrome, cardiovascular diseases and obesity. Factors such as inflammation, </span>hyperinsulinemia<span>, hyperglucagonemia, mitochondrial dysfunction, glucotoxicity and lipotoxicity<span><span> contribute to the development of IR. Despite being extensively studied for over 60 years, assessing the incidence of IR, developing effective prevention strategies, and implementing appropriate therapeutic approaches remain challenging. This review explores the multifaceted nature of IR, including its association with various conditions such as obesity, primary hypertension, dyslipidemia, obstructive sleep apnea, </span>Alzheimer's disease<span>, non-alcoholic fatty liver disease, polycystic ovary syndrome, chronic kidney disease and cancer. Additionally, we discuss the complexity of diagnosing and quantifying IR, emphasizing the lack of absolute, common criteria for classification. We delve into the use of mathematical models<span> in clinical and epidemiological studies<span>, focusing on the choice between insulin, triglycerides, or waist-to-hip ratio as IR determinants. Furthermore, we highlight the importance of reliable input data and caution in interpreting results when utilizing mathematical models for IR assessment. This narrative review aims to provide insights into the challenges and considerations involved in conducting IR diagnostics, with implications for clinical practice, epidemiological research, and future advancements in this field.</span></span></span></span></span></p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 1","pages":"Pages 55-67"},"PeriodicalIF":6.4,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139050400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid lightweight breast cancer classification framework using the histopathological images","authors":"Daniel Addo ,&nbsp;Shijie Zhou ,&nbsp;Kwabena Sarpong ,&nbsp;Obed T. Nartey ,&nbsp;Muhammed A. Abdullah ,&nbsp;Chiagoziem C. Ukwuoma ,&nbsp;Mugahed A. Al-antari","doi":"10.1016/j.bbe.2023.12.003","DOIUrl":"https://doi.org/10.1016/j.bbe.2023.12.003","url":null,"abstract":"<div><p>A crucial element in the diagnosis of breast cancer is the utilization of a classification method that is efficient, lightweight, and precise. Convolutional neural networks (CNNs) have garnered attention as a viable approach for classifying histopathological images. However, deeper and wider models tend to rely on first-order statistics, demanding substantial computational resources and struggling with fixed kernel dimensions that limit encompassing diverse resolution data, thereby degrading the model’s performance during testing. This study introduces BCHI-CovNet, a novel lightweight artificial intelligence (AI) model for histopathological breast image classification. Firstly, a novel multiscale depth-wise separable convolution is proposed. It is introduced to split input tensors into distinct tensor fragments, each subject to unique kernel sizes integrating various kernel sizes within one depth-wise convolution to capture both low- and high-resolution patterns. Secondly, an additional pooling module is introduced to capture extensive second-order statistical information across the channels and spatial dimensions. This module works in tandem with an innovative multi-head self-attention mechanism to capture the long-range pixels contributing significantly to the learning process, yielding distinctive and discriminative features that further enrich representation and introduce pixel diversity during training. These novel designs substantially reduce computational complexities regarding model parameters and FLOPs, which is crucial for resource-constrained medical devices. The outcomes achieved by employing the suggested model on two openly accessible datasets for breast cancer histopathological images reveal noteworthy performance. Specifically, the proposed approach attains high levels of accuracy: 99.15 % at 40× magnification, 99.08 % at 100× magnification, 99.22 % at 200× magnification, and 98.87 % at 400× magnification on the BreaKHis dataset. Additionally, it achieves an accuracy of 99.38 % on the BACH dataset. These results highlight the exceptional effectiveness and practical promise of BCHI-CovNet for the classification of breast cancer histopathological images.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 1","pages":"Pages 31-54"},"PeriodicalIF":6.4,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139033249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differentiating age and sex in vertebral body CT scans – Texture analysis versus deep learning approach","authors":"Karolina Nurzynska ,&nbsp;Adam Piórkowski ,&nbsp;Michał Strzelecki ,&nbsp;Marcin Kociołek ,&nbsp;Robert Paweł Banyś ,&nbsp;Rafał Obuchowicz","doi":"10.1016/j.bbe.2023.11.002","DOIUrl":"https://doi.org/10.1016/j.bbe.2023.11.002","url":null,"abstract":"<div><p><span><span>The automated analysis of computed tomography<span> (CT) scans of vertebrae, for the purpose of determining an individual's age and sex constitutes a vital area of research. Accurate assessment of bone age in children facilitates the monitoring of their growth and development. Moreover, the determination of both age and sex has significant relevance in various legal contexts involving human remains. We have built a dataset comprising CT scans of vertebral bodies from 166 patients of diverse genders, acquired during routine cardiac examinations. These images were rescaled to 8-bit data, and textural features were computed using the qMaZda software. The results were analysed employing conventional </span></span>machine learning techniques and deep convolutional networks. The regression model, developed for the automatic estimation of bone age, accurately determined patients' ages, with a </span>mean absolute error of 3.14 years and R2 = 0.79. In the context of classifying patient gender through textural analysis supported by machine learning, we achieved an accuracy of 69 %. However, the application of deep convolutional networks for this task yielded a slightly lower accuracy of 59 %.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 1","pages":"Pages 20-30"},"PeriodicalIF":6.4,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138558885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hollow fiber bioreactor with genetically modified hepatic cells as a model of biologically active function block of the bioartificial liver","authors":"Malgorzata Jakubowska ,&nbsp;Monika Joanna Wisniewska ,&nbsp;Agnieszka Wencel ,&nbsp;Cezary Wojciechowski ,&nbsp;Monika Gora ,&nbsp;Krzysztof Dudek ,&nbsp;Andrzej Chwojnowski ,&nbsp;Beata Burzynska ,&nbsp;Dorota Genowefa Pijanowska ,&nbsp;Krzysztof Dariusz Pluta","doi":"10.1016/j.bbe.2023.11.003","DOIUrl":"https://doi.org/10.1016/j.bbe.2023.11.003","url":null,"abstract":"<div><p>Chronic liver disease and cirrhosis, that can lead to liver failure, are major public health issues, with liver transplantation as the only effective treatment. However, the limited availability of transplantable organs has spurred research into alternative therapies, including bioartificial livers. To date, liver hybrid support devices, using porcine hepatocytes or hepatoma-derived cell lines, have failed to demonstrate efficacy in clinical trials.</p><p>Here, for the first time, we report the construction of a model of biologically active function block of bioartificial liver based on a hollow fiber bioreactor populated with genetically modified hepatic cells. For comprehensive comparison the culturing of hepatic cells was carried out in both static and dynamic conditions in a medium that flowed through porous polysulfone capillaries. The most crucial parameters, such as cell viability, glucose consumption, albumin secretion and urea production, were analyzed in static conditions while glucose usage and albumin production were compared in dynamic cell cultures. This model has the potential to improve the development of bioartificial liver devices and contribute to the treatment of patients with impaired liver function.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 1","pages":"Pages 9-19"},"PeriodicalIF":6.4,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0208521623000621/pdfft?md5=03a6bb7b322d5ddb29c988d0991b1104&pid=1-s2.0-S0208521623000621-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138549545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}