SLAS Technology最新文献

{"title":"Bio-inspired EEG signal computing using machine learning and fuzzy theory for decision making in future-oriented brain-controlled vehicles","authors":"Haewon Byeon ,&nbsp;Aadam Quraishi ,&nbsp;Mohammed I. Khalaf ,&nbsp;Sunil MP ,&nbsp;Ihtiram Raza Khan ,&nbsp;Ashit Kumar Dutta ,&nbsp;Rakeshnag Dasari ,&nbsp;Ramswaroop Reddy Yellu ,&nbsp;Faheem Ahmad Reegu ,&nbsp;Mohammed Wasim Bhatt","doi":"10.1016/j.slast.2024.100187","DOIUrl":"10.1016/j.slast.2024.100187","url":null,"abstract":"<div><p>One kind of autonomous vehicle that can take instructions from the driver by reading their electroencephalogram (EEG) signals using a Brain-Computer Interface (BCI) is called a Brain-Controlled Vehicle (BCV). The operation of such a vehicle is greatly affected by how well the BCI works. At present, there are limitations on the accuracy of BCI recognition, the number of distinguishable command categories, and the execution duration of command recognition. Consequently, vehicles that are exclusively controlled by EEG signals demonstrate suboptimal control performance. To address the difficulty of improving the control capabilities of brain-controlled cars while maintaining BCI performance, a fuzzy logic-based technique called as Fuzzy Brain-Control Fusion Control is introduced. This approach uses Fuzzy Discrete Event System (FDES) supervisory theory to verify the accuracy of the driver's brain-controlled directives. Concurrently, a fuzzy logic-based automatic controller is developed to generate decisions automatically in accordance with the present state of the vehicle via fuzzy reasoning. The final decision is then reached through the application of secondary fuzzy reasoning to the accuracy of the driver's instructions and the automated decisions to make adjustments that are more consistent with human intent. A clever BCI gadget known as the Consistent State Visual Evoked Potential (SSVEP) is utilized to show the viability of the proposed technique. We recommend that additional research should be conducted at this time to confirm that our recommended system may further improve the control execution of BCI-fueled cars, regardless of whether BCIs have special limitations.</p></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"29 5","pages":"Article 100187"},"PeriodicalIF":2.5,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2472630324000694/pdfft?md5=659f65be88dc46d1819fc63c1569b7a8&pid=1-s2.0-S2472630324000694-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142114744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility and safety study of advanced prostate biopsy robot system based on MR-TRUS Image flexible fusion technology in animal experiments","authors":"Zipeng Wang,&nbsp;Ming Fan,&nbsp;Qingdong Tao,&nbsp;Qin Zhang,&nbsp;Shuo Lei,&nbsp;Wener Lv","doi":"10.1016/j.slast.2024.100184","DOIUrl":"10.1016/j.slast.2024.100184","url":null,"abstract":"<div><p>The advanced prostate biopsy robot system has broad application prospects in clinical practice, but due to the deformation and distortion between MR-TRUS (magnetic resonance transrectal ultrasound) images, it poses challenges in biopsy accuracy and safety. The study utilized an advanced prostate biopsy robot system based on MR-TRUS image flexible registration technology and conducted experiments on animal models. Retrospective analysis of the puncture accuracy of 12 animal experiments undergoing prostate puncture using MR-TRUS flexible registration technology from May 2022 to October 2023, and observation of intraoperative and 7-day postoperative complications. The study obtained MR-TRUS images and utilized image processing algorithms for registration to reduce image deformation and distortion. Then, precise positioning and operation are carried out through the robot system to execute the prostate biopsy program. The experimental results indicate that the advanced prostate biopsy robot system based on MR-TRUS image flexible registration technology has demonstrated good feasibility and safety in animal experiments. Image registration technology has successfully reduced image distortion and deformation, improving biopsy accuracy. The precise positioning and operation of robot systems play a crucial role in the biopsy process, reducing the occurrence of complications.</p></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"29 5","pages":"Article 100184"},"PeriodicalIF":2.5,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2472630324000669/pdfft?md5=bf16682709f9de99755e20b25cb3ef75&pid=1-s2.0-S2472630324000669-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142099164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnosis of acute hyperglycemia based on data-driven prediction models","authors":"Xinxin Dong ,&nbsp;Wenping Dong ,&nbsp;Xueshan Guo","doi":"10.1016/j.slast.2024.100182","DOIUrl":"10.1016/j.slast.2024.100182","url":null,"abstract":"<div><p>Acute hyperglycemia is a common endocrine and metabolic disorder that seriously threatens the health and life of patients. Exploring effective diagnostic methods and treatment strategies for acute hyperglycemia to improve treatment quality and patient satisfaction is currently one of the hotspots and difficulties in medical research. This article introduced a method for diagnosing acute hyperglycemia based on data-driven prediction models. In the experiment, clinical data from 1000 patients with acute hyperglycemia were collected. Through data cleaning and feature engineering, 10 features related to acute hyperglycemia were selected, including BMI (Body Mass Index), TG (triacylglycerol), HDL-C (High-density lipoprotein cholesterol), etc. The support vector machine (SVM) model was used for training and testing. The experimental results showed that the SVM model can effectively predict the occurrence of acute hyperglycemia, with an average accuracy of 96 %, a recall rate of 84 %, and an F1 value of 89 %. The diagnostic method for acute hyperglycemia based on data-driven prediction models has a certain reference value, which can be used as a clinical auxiliary diagnostic tool to improve the early diagnosis and treatment success rate of acute hyperglycemia patients.</p></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"29 5","pages":"Article 100182"},"PeriodicalIF":2.5,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2472630324000645/pdfft?md5=a00884f565141f22d42dbc0079216a94&pid=1-s2.0-S2472630324000645-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142099163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative assessment of human motion for health and rehabilitation: A novel fuzzy comprehensive evaluation approach","authors":"Tao Peng","doi":"10.1016/j.slast.2024.100181","DOIUrl":"10.1016/j.slast.2024.100181","url":null,"abstract":"<div><p>In the pursuit of advancing health and rehabilitation, the quintessence of human motion recognition technology has been underscored through its quantitative contributions to physical performance assessment. This research delineates the inception of a novel fuzzy comprehensive evaluation-based recognition method that stands at the forefront of such innovative endeavours. By synergistically fusing multi-sensor data and advanced classification algorithms, the proposed system offers a granular quantitative analysis with implications for health and fitness monitoring, particularly rehabilitation processes. Our methodological approach, grounded in the modal separation technique and Empirical Mode Decomposition (EMD), effectively distills the motion acceleration component from raw accelerometer data, facilitating the extraction of intricate motion patterns. Quantitative analysis revealed that our integrated framework significantly amplifies the accuracy of motion recognition, achieving an overall recognition rate of 90.03 %, markedly surpassing conventional methods, such as Support Vector Machines (SVM), Decision Trees (DT), and K-Nearest Neighbors (KNN), which hovered around 80 %. Moreover, the system demonstrated an unprecedented accuracy of 97 % in discerning minor left-right swaying motions, showcasing its robustness in evaluating subtle movement nuances—a paramount feature for rehabilitation and patient monitoring. This marked precision in motion recognition heralds a new paradigm in health assessment, enabling objective and scalable analysis pertinent to individualized therapeutic interventions. The experimental evaluation accentuates the system's adeptness at navigating the dichotomy between complex, intense motions and finer, subtler movements with a high fidelity rate. It substantiates the method's utility in delivering sophisticated, data-driven insights for rehabilitation trajectory monitoring.</p></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"29 5","pages":"Article 100181"},"PeriodicalIF":2.5,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2472630324000633/pdfft?md5=df3f2f78fefe8cacf7472db24f85932e&pid=1-s2.0-S2472630324000633-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142114746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Establishment and validation of a risk stratification model for stroke risk within three years in patients with cerebral small vessel disease using a combined MRI and machine learning algorithm","authors":"Xiaolong Yang ,&nbsp;Hui Chang","doi":"10.1016/j.slast.2024.100177","DOIUrl":"10.1016/j.slast.2024.100177","url":null,"abstract":"<div><h3>Background</h3><p>Cerebral small vessel disease (CSVD) is a major cause of stroke, particularly in the elderly population, leading to significant morbidity and mortality. Accurate identification of high-risk patients and timing of stroke occurrence plays a crucial role in patient prevention and treatment. The study aimed to establish and validate a risk stratification model for stroke within three years in patients with CSVD using a combined MRI and machine learning algorithm approach.</p></div><div><h3>Methods</h3><p>The assessment encompassed demographic, clinical, biochemical, and MRI-derived parameters. Correlation analysis, logistic regression, receiver operating characteristic (ROC) curve analysis, and nnet neural network algorithm were employed to evaluate the predictive value of machine learning algorithms and MRI parameters for stroke occurrence within 3 years in patients with CSVD.</p></div><div><h3>Results</h3><p>MRI-derived parameters, including average WMH volume, perfusion deficit volume, ischemic core volume, microbleed count, and perivascular spaces, exhibited strong correlations with stroke occurrence (<em>P</em> &lt; 0.001). MRI-derived parameters demonstrated high sensitivities (0.719 to 0.906), specificities (0.704 to 0.877), and AUC values (0.815 to 0.871). The combined model of machine learning algorithms and MRI parameters yielded an AUC value of 0.925, indicating significantly high predictive accuracy for identifying the risk of stroke within three years in CSVD patients.</p></div><div><h3>Conclusion</h3><p>The integrated risk stratification model, incorporating machine learning algorithms and MRI parameters, demonstrated strong predictive potential for stroke within three years in patients with CSVD. This model offered valuable insights for personalized interventions and clinical decision-making in the management of CSVD.</p></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"29 5","pages":"Article 100177"},"PeriodicalIF":2.5,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2472630324000591/pdfft?md5=5b5fb18e030bda6475cb416a4a958798&pid=1-s2.0-S2472630324000591-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142001384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A microfluidic model for infantile in vitro digestions: Characterization of lactoferrin digestion","authors":"Pim de Haan ,&nbsp;Daigo Natsuhara ,&nbsp;Vassilis Triantis ,&nbsp;Takayuki Shibata ,&nbsp;Elisabeth Verpoorte","doi":"10.1016/j.slast.2024.100175","DOIUrl":"10.1016/j.slast.2024.100175","url":null,"abstract":"<div><p>We present a miniaturized, flow-through model for infantile <em>in vitro</em> digestions, following up on our previously published <em>in vitro</em> digestive system for adults. Microfluidic ‘chaotic’ mixers were employed as microreactors to help emulate the biochemical processing going on in the infantile stomach and intestine. Simulated digestive fluids were introduced into these micromixers, and the mixtures were incubated for 60 min after both the gastric phase and the intestinal phase. The pH of the infantile stomach was set at 5.3, which is higher than that of adults. This leads to entirely different patterns of digestion for the milk protein, lactoferrin, used in our study as a model compound. It was found that lactoferrin remained undigested as it passed through the gastric phase and reached the intestinal phase intact, unlike in adult digestions. In the intestinal phase, lactoferrin was rapidly digested. Our miniaturized, infantile, <em>in vitro</em> digestive system requires much less labor and chemicals than standard approaches, and shows great potential for future automation.</p></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"29 5","pages":"Article 100175"},"PeriodicalIF":2.5,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2472630324000578/pdfft?md5=cb34d020f1470feb3b6b423332197279&pid=1-s2.0-S2472630324000578-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141996928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application effect of 18F-FDG PET/CT technique in diagnosis and prognosis evaluation of lymphoma","authors":"Chao Huang ,&nbsp;Haihua Hu ,&nbsp;Xuesheng Zheng","doi":"10.1016/j.slast.2024.100176","DOIUrl":"10.1016/j.slast.2024.100176","url":null,"abstract":"<div><p>The objective of the study was to research diagnostic and prognostic values of 18F fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) in patients with diffuse large B-cell lymphoma (DLBCL). The diagnostic sensitivity (Sen) of PET/CT (94.75 %) was remarkably higher than 83.56 % of B-US. Age ≥ 65 years old, maximum focal diameter ≥5 cm, clinical stages III-IV, systemic symptoms, increased lactate dehydrogenase level, high modified international prognostic index score, Ecog score ≥1, B-cell lymphoma 2 (Bcl-2) gene, MYC protein expression rate, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were all factors that influenced the recurrence or progression of DLBCL. With higher MTV and TLG, patients would have a greater probability of recurrence or progression. 18F-FDG PET/CT showed a high diagnostic Sen in lymphoma lesions, and could accurately guide clinical staging. Combined with clinical parameters, laboratory indicators, and metabolic parameters, prognostic indicators of patients could be evaluated more accurately.</p></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"29 5","pages":"Article 100176"},"PeriodicalIF":2.5,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S247263032400058X/pdfft?md5=5b4e1897fd87e51ac31173bb3f52b2ad&pid=1-s2.0-S247263032400058X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141996929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of Artificial Intelligence in rehabilitation science: A scientometric investigation Utilizing Citespace","authors":"","doi":"10.1016/j.slast.2024.100162","DOIUrl":"10.1016/j.slast.2024.100162","url":null,"abstract":"<div><p>This study presents a scientometric analysis of the intersection between rehabilitation science and artificial intelligence (AI) technologies, using data from the Web of Science (WOS) database from 2002 to 2022. The analysis employed a comprehensive search query with key AI-related terms, focusing on a wide range of publications in rehabilitation science. Utilizing the Citespace tool, the study visualizes and quantifies the relationships between key terms, identifies research trends, and assesses the impact of AI technologies in rehabilitation science. Findings reveal a significant increase in AI-related research in this field, particularly from 2017 onwards, peaking in 2021. The United States has been a leading contributor, followed by countries like England, Australia, Germany, and Canada. Major institutional contributions come from Harvard University and the Pennsylvania Commonwealth System of Higher Education, among others. A keyword co-occurrence network constructed through Citespace identifies nine distinct hot topics and various research frontiers, highlighting evolving focus areas within the field. Burst analysis of keywords indicates a shift from performance and injury-related research to an increasing emphasis on AI and deep learning in recent years. The study also predicts the potential impact of papers, spotlighting works by Kunze KN and others as significantly influencing future research directions. Additionally, it examines the evolution of knowledge bases in AI-related rehabilitation science research, revealing a multidisciplinary core that includes neurology, rehabilitation, and ophthalmology, extending to complementary fields such as medicine and social sciences. This scientometric analysis provides a comprehensive overview of AI's application in rehabilitation science, offering insights into its evolution, impact, and emerging trends over the past two decades. The findings suggest strategic directions for future research, policy-making, and interdisciplinary collaboration in rehabilitation science and AI.</p></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"29 4","pages":"Article 100162"},"PeriodicalIF":2.5,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S247263032400044X/pdfft?md5=47410fc61cb15e6d30e46179130bd9d1&pid=1-s2.0-S247263032400044X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141545548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Th1/Th2 cytokines in early peripheral blood of patients with multiple injuries and its predictive value for SIRS: A bioinformatic analysis","authors":"","doi":"10.1016/j.slast.2024.100150","DOIUrl":"10.1016/j.slast.2024.100150","url":null,"abstract":"<div><p>This study aims to evaluate the changes in helper T lymphocyte (Th)1/Th2 factor levels in peripheral blood of patients with severe multiple injuries and their prognostic value for nosocomial infection using bioinformatic analysis. The experimental group consisted of 180 patients with numerous injuries admitted to our hospital between January 2021 and June 2023, with 80 healthy volunteers serving as controls. Th1 cytokines (interleukin-2 and interferon-γ) and Th2 cytokines (IL-4 and IL-10) were evaluated 48 hours after admission using enzyme-linked immunosorbent assays. The experimental group was separated into two groups: those with systemic inflammatory response syndrome (SIRS) and those without SIRS, for cytokine analysis and SIRS incidence. Furthermore, the study examined Th1 and Th2 cytokine levels in trauma patients in various body locations within the experimental group. A receiver operating characteristic (ROC) curve analysis was performed to determine the predictive value of Th1/Th2 cytokines for SIRS incidence. The experimental group had lower IL-2 and IFN-γ levels compared to the control group, but greater levels of IL-4 and IL-10. There were no significant variations in Th1 and Th2 cytokine levels across the experimental groups. Patients with SIRS had lower levels of IL-2 and IFN-γ but greater levels of IL-4 and IL-10 compared to those without SIRS. Combined cytokine levels have a better predictive value for SIRS than individual cytokines alone. In conclusion, individuals with severe multiple injuries had a change from Th1 to Th2 cytokine profiles, which was most evident in those with SIRS. The combined cytokine levels had a substantial predictive value for SIRS incidence in this patient cohort.</p></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"29 4","pages":"Article 100150"},"PeriodicalIF":2.5,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2472630324000323/pdfft?md5=88059d556d278858a05569705928cb98&pid=1-s2.0-S2472630324000323-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141134250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systematic training of table tennis players' physical performance based on artificial intelligence technology and data fusion of sensing devices","authors":"","doi":"10.1016/j.slast.2024.100151","DOIUrl":"10.1016/j.slast.2024.100151","url":null,"abstract":"<div><p>This research emphasises the value of physical training for table tennis players, particularly as ball speed and spin rate decline and emphasises how important intensity quality is to the game. Chinese table tennis players' dual identities place greater demands on the general growth of their learning and training as a crucial component of talent development preparation. Athletes' general quality, competitive level, and ability to avoid sports injuries are all improved by scientific and focused physical training. In order to achieve the functions of intelligent camera, multi-angle broadcasting, and 3D scene reproduction, this study combines the physical training model of artificial intelligence. This gives the audience a more engaging and in-depth viewing experience. More feature extraction of the match footage is made possible by deep learning and convolutional neural networks when combined with large-scale video data, greatly enhancing the match information for viewers. The experimental findings demonstrate that the accuracy of table tennis human technical movement recognition reaches 98.88 % based on the enhanced AM-Softmax classification algorithm.</p></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"29 4","pages":"Article 100151"},"PeriodicalIF":2.5,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2472630324000335/pdfft?md5=48cd7cdeadb4dd7b106b0e41ef936d66&pid=1-s2.0-S2472630324000335-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141139125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}