Biocybernetics and Biomedical Engineering最新文献

{"title":"Wavelet-Hilbert transform based bidirectional least squares grey transform and modified binary grey wolf optimization for the identification of epileptic EEGs","authors":"Chang Liu ,&nbsp;Wanzhong Chen ,&nbsp;Tao Zhang","doi":"10.1016/j.bbe.2023.04.003","DOIUrl":"10.1016/j.bbe.2023.04.003","url":null,"abstract":"<div><p>Wavelet based seizure detection is an importance topic for epilepsy diagnosis via electroencephalogram (EEG), but its performance is closely related to the choice of wavelet bases. To overcome this issue, a fusion method of wavelet packet transformation (WPT), Hilbert transform based bidirectional least squares grey transform (HTBiLSGT), modified binary grey wolf optimization (MBGWO) and fuzzy K-Nearest Neighbor (FKNN) was proposed. The HTBiLSGT was first proposed to model the envelope change of a signal, then WPT based HTBiLSGT was developed for EEG feature extraction by performing HTBiLSGT for each subband of each wavelet level. To select discriminative features, MBGWO was further put forward and employed to conduct feature selection, and the selected features were finally fed into FKNN for classification. The Bonn and CHB-MIT EEG datasets were used to verify the effectiveness of the proposed technique. Experimental results indicate the proposed WPT based HTBiLSGT, MBGWO and FKNN can respectively lead to the highest accuracies of 100% and 98.60 ± 1.35% for the ternary and quinary classification cases of Bonn dataset, it also results in the overall accuracy of 99.48 ± 0.61 for the CHB-MIT dataset, and the proposal is proven to be insensitive to the choice of wavelet bases.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49373643","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":"Combining homomorphic filtering and recurrent neural network in gait signal analysis for neurodegenerative diseases detection","authors":"Masume Saljuqi ,&nbsp;Peyvand Ghaderyan","doi":"10.1016/j.bbe.2023.04.001","DOIUrl":"10.1016/j.bbe.2023.04.001","url":null,"abstract":"<div><p>Automatic, cost-effective, and reliable detection of neurodegenerative diseases (NDs) is one of the important issues in clinical practice. The main idea of the proposed method in this study is to utilize the advantages of gait time series that can provide low-cost and non-invasive measures, homomorphic filtering that can effectively separate muscle activity from body dynamic and recurrent neural network or cascade forward neural network that can learn sequential time-varying data. Experimental results on gait time series of 16 healthy control subjects, 13 patients with amyotrophic lateral sclerosis, 15 patients with Parkinson’s disease and 20 patients with Huntington’s disease have demonstrated high detection performance with an accuracy rate of 100 % using K-fold cross validation for all three types of NDs outperforming other existing methods. The results have also indicated that<!--> <!-->the use of real cepstral coefficients, oscillation components, or basic statistics feature set has improved the detection performance.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47032478","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":"Hybrid EEG-fNIRS brain-computer interface based on the non-linear features extraction and stacking ensemble learning","authors":"Asmaa Maher ,&nbsp;Saeed Mian Qaisar ,&nbsp;N. Salankar ,&nbsp;Feng Jiang ,&nbsp;Ryszard Tadeusiewicz ,&nbsp;Paweł Pławiak ,&nbsp;Ahmed A. Abd El-Latif ,&nbsp;Mohamed Hammad","doi":"10.1016/j.bbe.2023.05.001","DOIUrl":"10.1016/j.bbe.2023.05.001","url":null,"abstract":"<div><p>The Brain-computer interface (BCI) is used to enhance the human capabilities. The hybrid-BCI (hBCI) is a novel concept for subtly hybridizing multiple monitoring schemes to maximize the advantages of each while minimizing the drawbacks of individual methods. Recently, researchers have started focusing on the Electroencephalogram (EEG) and “Functional Near-Infrared Spectroscopy” (fNIRS) based hBCI. The main reason is due to the development of artificial intelligence (AI) algorithms such as machine learning approaches to better process the brain signals. An original EEG-fNIRS based hBCI system is devised by using the non-linear features mining and ensemble learning (EL) approach. We first diminish the noise and artifacts from the input EEG-fNIRS signals using digital filtering. After that, we use the signals for non-linear features mining. These features are “Fractal Dimension” (FD), “Higher Order Spectra” (HOS), “Recurrence Quantification Analysis” (RQA) features, and Entropy features. Onward, the Genetic Algorithm (GA) is employed for Features Selection (FS). Lastly, we employ a novel Machine Learning (ML) technique using several algorithms namely, the “Naïve Bayes” (NB), “Support Vector Machine” (SVM), “Random Forest” (RF), and “K-Nearest Neighbor” (KNN). These classifiers are combined as an ensemble for recognizing the intended brain activities. The applicability is tested by using a publicly available multi-subject and multiclass EEG-fNIRS dataset. Our method has reached the highest accuracy, F1-score, and sensitivity of 95.48%, 97.67% and 97.83% respectively.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43466636","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":"PCcS-RAU-Net: Automated parcellated Corpus callosum segmentation from brain MRI images using modified residual attention U-Net","authors":"Anjali Chandra ,&nbsp;Shrish Verma ,&nbsp;A.S. Raghuvanshi ,&nbsp;Narendra Kuber Bodhey","doi":"10.1016/j.bbe.2023.02.003","DOIUrl":"10.1016/j.bbe.2023.02.003","url":null,"abstract":"<div><h3>Background</h3><p>The Corpus callosum (Cc) in the cerebral cortex is a bundle of neural fibers that facilitates inter-hemispheric communication. The Cc area and area of its sub-regions (also known as parcels) have been examined as a biomarker for cortical pathology and differential diagnosis in neurodegenerative diseases such as Autism, Alzheimer’s disease (AD), and more. Manual segmentation and parcellation of Cc are laborious and time-consuming. The present work proposes a novel work of automated parcellated Cc (PCc) segmentation that will serve as a potential biomarker to study and diagnose neurological disorders in brain MRI images.</p></div><div><h3>Method</h3><p>In this perspective, the present work aims to develop an automated PCc segmentation from mid-sagittal T1- weighted (w) 2D brain MRI images using a deep learning-based fully convolutional network, a modified residual attention U-Net, referred to as PCcS-RAU-Net. The model has been modified to use a multi-class segmentation configuration with five target classes (parcels): rostrum, genu, mid-body, isthmus and splenium.</p></div><div><h3>Results</h3><p>The experimental research uses two benchmark MRI datasets, ABIDE and OASIS. The proposed PCcS-RAU-Net outperformed existing methods on the ABIDE dataset with a DSC of 97.10% and MIoU of 94.43%. Furthermore, the model's performance is validated on the OASIS and Real clinical image (RCI) data and hence verifies the model’s generalization capability.</p></div><div><h3>Conclusion</h3><p>The proposed PCcS-RAU-Net model extracts essential characteristics such as the total area of the Cc (TCcA) to categorize MRI slices into healthy controls (HC) and disease groups. Also, sub-regional areas, Cc1A to Cc5A, help study atrophy progression for early diagnosis.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43691652","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":"Computer aided diagnosis of diabetic macular edema in retinal fundus and OCT images: A review","authors":"Pavithra K.C. ,&nbsp;Preetham Kumar ,&nbsp;Geetha M. ,&nbsp;Sulatha V. Bhandary","doi":"10.1016/j.bbe.2022.12.005","DOIUrl":"10.1016/j.bbe.2022.12.005","url":null,"abstract":"<div><p><span><span>Diabetic Macular Edema (DME) is a potentially blinding consequence of Diabetic Retinopathy (DR) as well as the leading cause of vision loss in diabetics. DME is characterized by a buildup of </span>extracellular fluid inside the macula through hyperpermeable vessels. The presence of DME can be spotted at any level of DR with varying degrees of severity using prominent </span>imaging modalities<span> such as Color Fundus Photography (CFP) and Optical Coherence Tomography<span><span> (OCT). Computerized approaches for screening eye disorders appear to be beneficial, as they provide doctors with detailed insights into abnormalities. Such a system for the evaluation of retinal images<span> can function as a stand-alone disease monitoring system. This review reports the state-of-art automated DME detection methods with traditional Machine Learning (ML) and Deep Learning (DL) techniques employing retinal fundus or OCT images. The paper provides a list of public retinal OCT and fundus imaging datasets for DME detection. In addition, the paper describes the dynamics of advancements in presented methods adopted in the past along with their </span></span>strengths and limitations to highlight the insufficiencies that could be addressed in future investigations.</span></span></p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44275569","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":"Internet service for wound area measurement using digital planimetry with adaptive calibration and image segmentation with deep convolutional neural networks","authors":"Piotr Foltynski,&nbsp;Piotr Ladyzynski","doi":"10.1016/j.bbe.2022.11.004","DOIUrl":"10.1016/j.bbe.2022.11.004","url":null,"abstract":"<div><p><span>Uncontrolled diabetes leads to serious complications comparable to cancer. Infected foot ulcer causes a 5-year mortality of 50%. Proper treatment of foot wounds is essential, and wound area monitoring plays an important role in this area. In this article, we describe an automatic wound area measurement service that facilitates area measurement and the measurement result is based on adaptive calibration for larger accuracy at curved surfaces. Users need to take a digital picture of a wound and calibration markers and send them for analysis using an Internet page. The deep learning model based on </span>convolutional neural networks<span> (CNNs) was trained using 565 wound images and was used for image segmentation to identify the wound and calibration markers. The developed software calculates the wound area based on the number of pixels in the wound region and the calibration coefficient determined from distances between ticks at calibration markers. The result of the measurement is sent back to the user at the provided e-mail address. The median relative error of wound area measurement in the wound models was 1.21%. The efficacy of the CNN model was tested on 41 wounds and 73 wound models. The averaged values for the dice similarity coefficient, intersection over union, accuracy and specificity for wound identification were 90.9%, 83.9%, 99.3% and 99.6%, respectively. The service proved its high efficacy and can be used in wound area monitoring. The service may be used not only by health care specialists but also by patients. Thus, it is important tool for wound healing monitoring.</span></p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42001474","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":"Biphasic monolithic osteochondral scaffolds obtained by diffusion-limited enzymatic mineralization of gellan gum hydrogel","authors":"Krzysztof Pietryga ,&nbsp;Katarzyna Reczyńska-Kolman ,&nbsp;Janne E. Reseland ,&nbsp;Håvard Haugen ,&nbsp;Véronique Larreta-Garde ,&nbsp;Elżbieta Pamuła","doi":"10.1016/j.bbe.2022.12.009","DOIUrl":"10.1016/j.bbe.2022.12.009","url":null,"abstract":"<div><p>Biphasic monolithic materials for the treatment of osteochondral defects were produced from polysaccharide hydrogel, gellan gum (GG). GG was enzymatically mineralized by alkaline phosphatase (ALP) in the presence of calcium glycerophosphate (CaGP). The desired distribution of the calcium phosphate (CaP) mineral phase was achieved by limiting the availability of CaGP to specific parts of the GG sample. Therefore, mineralization of GG was facilitated by the diffusion of CaGP, causing the formation of the CaP gradient. The distribution of CaP was analyzed along the cross section of the GG. The formation of a CaP gradient was mainly affected by the mineralization time and the ALP concentration. The formation of CaP was confirmed by Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy and mapping, as well as energy-dispersive X-ray spectroscopy (EDX) mapping of the interphase. The microstructure of mineralized and non-mineralized parts of the material was characterized by scanning electron microscopy (SEM) and atomic force microscopy (AFM) showing sub-micrometer CaP crystal formation, resulting in increased surface roughness. Compression tests and rheometric analyzes showed a 10-fold increase in stiffness of the GG mineralized part. Concomitantly, micromechanical tests performed by AFM showed an increase of Young's modulus from 17.8 to more than 200 kPa. <em>In vitro</em> evaluation of biphasic scaffolds was performed in contact with osteoblast-like MG-63 cells. The mineralized parts of GG were preferentially colonized by the cells over the non-mineralized parts. The results showed that osteochondral scaffolds of the desired structure and properties can be made from GG using a diffusion-limited enzymatic mineralization method.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48558693","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":"Effects of sampling rate on multiscale entropy of electroencephalogram time series","authors":"Jinlin Zheng ,&nbsp;Yan Li ,&nbsp;Yawen Zhai ,&nbsp;Nan Zhang ,&nbsp;Haoyang Yu ,&nbsp;Chi Tang ,&nbsp;Zheng Yan ,&nbsp;Erping Luo ,&nbsp;Kangning Xie","doi":"10.1016/j.bbe.2022.12.007","DOIUrl":"10.1016/j.bbe.2022.12.007","url":null,"abstract":"<div><p>A physiological system encompasses numerous components that function at various time scales. To characterize the scale-dependent feature, the multiscale entropy (MSE) analysis has been proposed to describe the complex processes on multiple time scales. However, MSE analysis uses the relative scale factors to reveal the time-related dynamics, which may cause in-comparability of results from diverse studies with inconsistent sampling rates. In this study, in addition to the conventional MSE with relative scale factors, we also expressed MSE with absolute time scales (MaSE). We compared the effects of sampling rates on MSE and MaSE of simulated and real EEG time series. The results show that the previously found phenomenon (down-sampling can increase sample entropy) is just the projection of the compressing effect of down-sampling on MSE. And we have also shown the compressing effect of down-sampling on MSE does not change MaSE’s profile, despite some minor right-sliding. In addition, by analyzing a public EEG dataset of emotional states, we have demonstrated improved classification rate after choosing appropriate sampling rate. We have finally proposed a working strategy to choose an appropriate sampling rate, and suggested using MaSE to avoid confusion caused by sampling rate inconsistency. This novel study may apply to a broad range of studies that would traditionally utilize sample entropy and MSE to analyze the complexity of an underlying dynamic process.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44909478","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":"In vitro examinations of the anti-inflammatory interleukin functionalized polydopamine based biomaterial as a potential coating for cardiovascular stents","authors":"Przemysław Sareło ,&nbsp;Beata Sobieszczańska ,&nbsp;Edyta Wysokińska ,&nbsp;Marlena Gąsior-Głogowska ,&nbsp;Wojciech Kałas ,&nbsp;Halina Podbielska ,&nbsp;Magdalena Wawrzyńska ,&nbsp;Marta Kopaczyńska","doi":"10.1016/j.bbe.2023.02.001","DOIUrl":"10.1016/j.bbe.2023.02.001","url":null,"abstract":"<div><p><span><span><span>Despite advances in stent technologies, restenosis remains a serious problem of interventional cardiology and is considered as a consequence of the progressing inflammation within the vessel wall. Thus, attempts to extinguish this inflammatory process undoubtedly motivate the development of a coating that exhibits immunomodulatory properties. Hence, we propose a polydopamine-based-coating functionalized with an anti-inflammatory </span>interleukin<span> is reported. By the ATR-FTIR spectroscopy<span><span> and AFM examination the incorporation of cytokines into the </span>coating structure is confirmed, thus effective </span></span></span>functionalization<span><span> is proved. The gradual delivery of cytokines allows to limit the influence of IL-4 and IL-10 deficiency, which is recognized as a restenosis risk factor. A relatively steady cytokine release profile exhibits therapeutic potential in the first days after implantation and in preventing late complications on </span>cellular model. </span></span><em>In vitro</em><span><span> coating studies prove the promotion of endothelialization in the initial stage after implantation, being consistent with present treatment strategies. The limitation of IL-8 and MCP-1 daily release by coating-interacted-endothelium significantly reduce another risk factor of restenosis. Finally, by assessing the changes in THP-1 differentiation, the coating immunological activity is confirmed, so the binding procedure do not impair biological properties of the interleukin. Therefore, it can be concluded that proposed anti-inflammatory coating can reduce the </span>probability of restenosis to a minimum.</span></p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47310259","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":"SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19","authors":"Kiran Kumar Patro ,&nbsp;Allam Jaya Prakash ,&nbsp;Mohamed Hammad ,&nbsp;Ryszard Tadeusiewicz ,&nbsp;Paweł Pławiak","doi":"10.1016/j.bbe.2023.01.005","DOIUrl":"10.1016/j.bbe.2023.01.005","url":null,"abstract":"<div><h3>Background and Objective</h3><p>The global population has been heavily impacted by the COVID-19 pandemic of coronavirus. Infections are spreading quickly around the world, and new spikes (Delta, Delta Plus, and Omicron) are still being made. The real-time reverse transcription-polymerase chain reaction (RT-PCR) is the method most often used to find viral RNA in a nasopharyngeal swab. However, these diagnostic approaches require human involvement and consume more time per prediction. Moreover, the existing conventional test mainly suffers from false negatives, so there is a chance for the virus to spread quickly. Therefore, a rapid and early diagnosis of COVID-19 patients is needed to overcome these problems.</p></div><div><h3>Methods</h3><p>Existing approaches based on deep learning for COVID detection are suffering from unbalanced datasets, poor performance, and gradient vanishing problems. A customized skip connection-based network with a feature union approach has been developed in this work to overcome some of the issues mentioned above. Gradient information from chest X-ray (CXR) images to subsequent layers is bypassed through skip connections. In the script’s title, “SCovNet” refers to a skip-connection-based feature union network for detecting COVID-19 in a short notation. The performance of the proposed model was tested with two publicly available CXR image databases, including balanced and unbalanced datasets.</p></div><div><h3>Results</h3><p>A modified skip connection-based CNN model was suggested for a small unbalanced dataset (Kaggle) and achieved remarkable performance. In addition, the proposed model was also tested with a large GitHub database of CXR images and obtained an overall best accuracy of 98.67% with an impressive low false-negative rate of 0.0074.</p></div><div><h3>Conclusions</h3><p>The results of the experiments show that the proposed method works better than current methods at finding early signs of COVID-19. As an additional point of interest, we must mention the innovative hierarchical classification strategy provided for this work, which considered both balanced and unbalanced datasets to get the best COVID-19 identification rate.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9928742/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9299574","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}