Informatics in Medicine Unlocked最新文献

{"title":"Denoising diffusion probabilistic models for addressing data limitations in chest X-ray classification","authors":"Evi M.C. Huijben,&nbsp;Josien P.W. Pluim,&nbsp;Maureen A.J.M. van Eijnatten","doi":"10.1016/j.imu.2024.101575","DOIUrl":"10.1016/j.imu.2024.101575","url":null,"abstract":"<div><p>Deep learning plays a crucial role in medical imaging analysis, particularly in tasks such as image classification and segmentation. However, learning from medical imaging datasets presents challenges, including scarcity of labeled examples, class imbalances, and inadequate representation of diverse patient populations. To address these challenges, there has been a growing interest in the use of deep generative models to create synthetic training data, with denoising diffusion probabilistic models (DDPMs) recently gaining attention for their ability to produce realistic and high-quality images. This study explores the potential of a DDPM to generate synthetic chest X-rays for multi-label classifier training. The results indicate that the use of a conditional DDPM has the potential to produce a realistic training set of synthetic chest X-rays. In addition, the study analyzes the impact on classification performance of addressing class imbalance. Balancing the synthetic training set increased the overall classification sensitivity from 0.02 to 0.59, but decreased the overall specificity from 0.99 to 0.71. Furthermore, we investigated the potential of unconditional pre-training to learn general representations, followed by conditional fine-tuning of the DDPM. The results indicate that this approach allows the amount of labeled training data to be reduced to 25% of the original set. Finally, we demonstrate that fidelity and classification metrics do not consistently exhibit the same trends. Integrating a DDPM into the classification pipeline underscores the benefits of having optimal control over the data and efficient use of available unlabeled data. Our research provides insights for making informed decisions about integrating generative models into medical image analysis.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"50 ","pages":"Article 101575"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S235291482400131X/pdfft?md5=629db3cc19c06c57d9e66726c73db9a2&pid=1-s2.0-S235291482400131X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142058076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep convolutional neural networks for filtering out normal frames in reviewing wireless capsule endoscopy videos","authors":"Ehsan Roodgar Amoli ,&nbsp;Pezhman Pasyar ,&nbsp;Hossein Arabalibeik ,&nbsp;Tahereh Mahmoudi","doi":"10.1016/j.imu.2024.101572","DOIUrl":"10.1016/j.imu.2024.101572","url":null,"abstract":"<div><p>Wireless capsule endoscopy (WCE) has emerged as a valuable non-invasive technique for visualizing the entire gastrointestinal (GI) tract. However, manual evaluation of WCE videos is a time-consuming and costly process. In this study, we present a novel diagnostic assistant system that employs deep convolutional neural networks (DCNNs) to accelerate the evaluation process. Our primary objective is to achieve a high negative predictive value (NPV), which is essential for the efficient identification of normal frames. Six distinct DCNN models were developed and implemented with this objective in mind. The models were trained on a limited dataset encompassing common GI pathologies that reflect real clinical scenarios. Each DCNN architecture comprises a convolutional part derived from renowned pre-trained networks and a custom-designed classifier block optimized for high NPV and classification accuracy. Following a comprehensive assessment utilizing the 5-fold cross-validation approach, the VG_BFCG model was identified as the most effective, exhibiting an average test accuracy of 0.946 and an NPV of 0.983. Moreover, in the event of encountering novel pathologies not present in the training data, our models exhibited robustness in NPV, which is of great importance for practical applications. For example, the DN_BFCG model demonstrated consistent performance, with an NPV exceeding 0.99 across a range of new pathologies. This validates the reliability of our models in clinical settings. Our findings suggest that our developed DCNN architectures have the potential to enhance the efficiency and accuracy of WCE video analysis, which could transform the landscape of gastroenterological diagnostics.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"50 ","pages":"Article 101572"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S235291482400128X/pdfft?md5=55cf3c0dc8b0e8f24953f77449be27da&pid=1-s2.0-S235291482400128X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142058277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Agent-based model of measles epidemic development in small-group settings","authors":"Sonya O. Vysochanskaya ,&nbsp;S. Tatiana Saltykova ,&nbsp;Yury V. Zhernov ,&nbsp;Alexander M. Zatevalov ,&nbsp;Artyom A. Pozdnyakov ,&nbsp;Oleg V. Mitrokhin","doi":"10.1016/j.imu.2024.101574","DOIUrl":"10.1016/j.imu.2024.101574","url":null,"abstract":"<div><p>Measles infection is a significant global public health concern, with one patient able to infect 12–18 people in a susceptible population. Mathematical modeling helps understand the factors influencing measles outbreaks, including vaccination levels, population density and movement patterns of the people who comprise it. Agent-based modeling, particularly useful in organized populations like hospitals or academic buildings, can predict the dynamics of infectious disease outbreaks. The aim of this work is to create an agent-based model of measles infection, which would predict the effectiveness of various anti-epidemic measures in small-group settings such as academic buildings. In this article, the effects of vaccination and isolation on the measles epidemic process were studied. The modeling found that combinations of vaccination and isolation measures are most effective, and these anti-epidemic measures allow to reduce the number of susceptible people that were infected from 199/199 (100 %) in the absence of measures to 73–80/199 (36.7–40.2 %).</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"50 ","pages":"Article 101574"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824001308/pdfft?md5=e6a75e8f197d989b883ccb50c9260169&pid=1-s2.0-S2352914824001308-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142088805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The influence of electronic health record use on healthcare providers burnout","authors":"Arwa Alumran ,&nbsp;Shatha Adel Aljuraifani ,&nbsp;Zahraa Abdulmajeed Almousa ,&nbsp;Beyan Hariri ,&nbsp;Hessa Aldossary ,&nbsp;Mona Aljuwair ,&nbsp;Nouf Al-kahtani ,&nbsp;Khalid Alissa","doi":"10.1016/j.imu.2024.101588","DOIUrl":"10.1016/j.imu.2024.101588","url":null,"abstract":"<div><h3>Background</h3><div>Electronic health records (EHRs) are critical health information technology tools that ensure accuracy and improved management of patient records. However, the use of EHRs can lead to significant burden and burnout among healthcare providers, potentially affecting the quality of care they deliver.</div></div><div><h3>Objectives</h3><div>The purpose of this study is to determine the extent of burnout among healthcare providers who use EHRs, with the specific objectives of assessing the level of EHR-related burnout in Saudi Arabian hospitals and identifying the key EHR-related factors contributing to this burnout.</div></div><div><h3>Methods</h3><div>A descriptive quantitative cross-sectional study was conducted. A valid and reliable questionnaire was distributed to healthcare providers in Saudi Arabian hospitals to measure their burnout levels associated with EHR usage.</div></div><div><h3>Results</h3><div>The findings indicate that the use of EHRs contributes to healthcare provider burnout, which may diminish the quality of care provided to patients. Several variables were significantly related to the healthcare providers' personal burnout, i.e., their living area, age, job, and year of experience, although only the healthcare provider's age influences their work-related burnout significantly. On the other hand, working hours per week and number of patients per week significantly influence the healthcare provider's EHR-related burnout.</div></div><div><h3>Conclusion</h3><div>The study suggests that EHR usage is a significant factor in healthcare provider burnout. Addressing this issue requires enhanced training, workload reduction, and prompt resolution of EHR-related problems to improve provider well-being and maintain high-quality patient care.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"50 ","pages":"Article 101588"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An evaluation of the effectiveness of machine learning prediction models in assessing breast cancer risk","authors":"Mahmoud Darwich ,&nbsp;Magdy Bayoumi","doi":"10.1016/j.imu.2024.101550","DOIUrl":"10.1016/j.imu.2024.101550","url":null,"abstract":"<div><p>Breast cancer is a prevalent disease that has a potential influence on the lives of countless women globally. Early diagnosis and intervention are crucial for successful treatment and better patient outcomes. Machine learning algorithms have shown promising results in developing accurate and dependable prediction models for breast cancer. In this research, we conduct an extensive overview of various machine learning (ML) techniques employed to develop breast cancer prediction models using diverse datasets. Our study explores the literature on several ML algorithms utilized for breast cancer prediction. We also examine the types of datasets used for training and testing these models, including clinical data, mammography images, and genetic data. Additionally, we evaluate the benefits and limitations of each machine learning algorithm and dataset and offer recommendations for future research. Our aim is to provide a comprehensive understanding of the current state-of-the-art in breast cancer prediction models using ML and to promote the development of precise and effective models to detect breast cancer at an early stage.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"49 ","pages":"Article 101550"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824001060/pdfft?md5=826ec3dd50562effce6bd5c21273ad87&pid=1-s2.0-S2352914824001060-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141636633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of Alzheimer's disease using deep learning models: A systematic literature review","authors":"Eqtidar M. Mohammed ,&nbsp;Ahmed M. Fakhrudeen ,&nbsp;Omar Younis Alani","doi":"10.1016/j.imu.2024.101551","DOIUrl":"10.1016/j.imu.2024.101551","url":null,"abstract":"<div><p>Alzheimer's disease (AD) is a progressive neurological disease considered the most common form of late-stage dementia. Usually, AD leads to a reduction in brain volume, impacting various functions. This article comprehensively analyzes the AD context in fivefold main topics. Firstly, it reviews the main imaging techniques used in diagnosing AD disease. Secondly, it explores the most proposed deep learning (DL) algorithms for detecting the disease. Thirdly, the article investigates the commonly used datasets to develop DL techniques. Fourthly, we conducted a systematic review and selected 45 papers published in highly ranked publishers (Science Direct, IEEE, Springer, and MDPI). We analyzed them thoroughly by delving into the stages of AD diagnosis and emphasizing the role of preprocessing techniques. Lastly, the paper addresses the remaining practical implications and challenges in the AD context. Building on the analysis, this survey contributes to covering several aspects related to AD disease that have not been studied thoroughly.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"50 ","pages":"Article 101551"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824001072/pdfft?md5=7db39c49a1c80d680ed4e5967260663c&pid=1-s2.0-S2352914824001072-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141706596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing the causes and impact of essential medicines and supplies shortages in the supply chain of the Ministry of health in Saudi Arabia: A quantitative survey study","authors":"Fatin Alshibli ,&nbsp;Khaled Alqarni ,&nbsp;Hasan Balfaqih","doi":"10.1016/j.imu.2024.101457","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101457","url":null,"abstract":"<div><h3>Background</h3><p>Investigating the causes and impact of essential medicines and supplies shortages in the supply chain of the MOH in Saudi Arabia could be the initial step in setting innovative strategies for mitigating this issue. This study aimed to identify the key factors contributing to essential medicines and supplies shortages in the supply chain of the MOH in Saudi Arabia and assess their impact on healthcare delivery.</p></div><div><h3>Methods</h3><p>A structured questionnaire was designed to collect relevant data on the causes and impact of essential medicines and supplies shortages. A representative sample of healthcare professionals, from various healthcare MOH facilities in Saudi Arabia. The Statistical Package for the Social Sciences (SPSS) software version 26 was used for the data analysis.</p></div><div><h3>Results</h3><p>A total of 379 respondents participated in the study, 73.7% were males, 51.2% were aged 36–45 years, 23.5% were supply chain professionals, and 32.9% reported an experience of &gt;15 years. 90.0% of the participants reported that they personally have experienced shortages of essential medicines and supplies in the MOH supply chain in KSA. Inadequate planning, forecasting, and procurement were identified as the most significant contributing factors for shortages by about half (48.5%). At least two-thirds of the participants agreed with all strategies adopted for mitigating the issue of shortages.</p></div><div><h3>Conclusions</h3><p>The impact of shortages on patients and healthcare professionals was found to be substantial. The study also identified several key strategies to reduce shortages that received strong support from the participants.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"45 ","pages":"Article 101457"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000133/pdfft?md5=27790d17d48f7149525d2da42ab6fbc5&pid=1-s2.0-S2352914824000133-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139737685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of data augmentation and transfer learning on the performance of deep learning models for the segmentation of the hip on 3D magnetic resonance images","authors":"Eros Montin ,&nbsp;Cem M. Deniz ,&nbsp;Richard Kijowski ,&nbsp;Thomas Youm ,&nbsp;Riccardo Lattanzi","doi":"10.1016/j.imu.2023.101444","DOIUrl":"10.1016/j.imu.2023.101444","url":null,"abstract":"<div><p>Different pathologies of the hip are characterized by the abnormal shape of the bony structures of the joint, namely the femur and the acetabulum. Three-dimensional (3D) models of the hip can be used for diagnosis, biomechanical simulation, and planning of surgical treatments. These models can be generated by building 3D surfaces of the joint's structures segmented on magnetic resonance (MR) images. Deep learning can avoid time-consuming manual segmentations, but its performance depends on the amount and quality of the available training data. Data augmentation and transfer learning are two approaches used when there is only a limited number of datasets. In particular, data augmentation can be used to artificially increase the size and diversity of the training datasets, whereas transfer learning can be used to build the desired model on top of a model previously trained with similar data. This study investigates the effect of data augmentation and transfer learning on the performance of deep learning for the automatic segmentation of the femur and acetabulum on 3D MR images of patients diagnosed with femoroacetabular impingement. Transfer learning was applied starting from a model trained for the segmentation of the bony structures of the shoulder joint, which bears some resemblance to the hip joint. Our results suggest that data augmentation is more effective than transfer learning, yielding a Dice similarity coefficient compared to ground-truth manual segmentations of 0.84 and 0.89 for the acetabulum and femur, respectively, whereas the Dice coefficient was 0.78 and 0.88 for the model based on transfer learning. The Accuracy for the two anatomical regions was 0.95 and 0.97 when using data augmentation, and 0.87 and 0.96 when using transfer learning. Data augmentation can improve the performance of deep learning models by increasing the diversity of the training dataset and making the models more robust to noise and variations in image quality. The proposed segmentation model could be combined with radiomic analysis for the automatic evaluation of hip pathologies.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"45 ","pages":"Article 101444"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914823002903/pdfft?md5=abcbc7caad8856414e021c946ab6d3f4&pid=1-s2.0-S2352914823002903-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139394936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning prediction of in-hospital recurrent infarction and cardiac death in patients with myocardial infarction","authors":"Yu. Kononova ,&nbsp;L. Abramyan ,&nbsp;A. Funkner ,&nbsp;A. Babenko","doi":"10.1016/j.imu.2023.101443","DOIUrl":"10.1016/j.imu.2023.101443","url":null,"abstract":"<div><h3>Background and aim</h3><p>The aim of the study is to identify statistical patterns in patients with myocardial infarction (MI) during hospitalization that allow predicting the development of acute conditions (recurrent myocardial infarction, cardiac death).</p></div><div><h3>Methods</h3><p>We identified 3471 episodes of patients treated with a diagnosis acute MI in Almazov National Medical Research Centre. For modelling we selected episodes with acute MI with cardiac surgery operations. Classical machine learning models were chosen as forecasting models: decision trees and ensembles based on them, logistic regression and support vector machine.</p></div><div><h3>Results</h3><p>The important signs for predicting recurrent MI were the minimum values of hemoglobin, the echocardiography parameters end systolic volume and pulmonary regurgitation, and the minimum value of leukocyte level. Predictors of lethal outcome during hospitalization were advanced age, high values of leukocytes, low values of hemoglobin, high values of alanine aminotransferase.</p></div><div><h3>Conclusion</h3><p>The obtained results make it possible to predict the development of a lethal outcome and re-infarction based on simple parameters that are easily available in clinical practice.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"45 ","pages":"Article 101443"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914823002897/pdfft?md5=bb41d1493e009ea8208601ab5ee4ee58&pid=1-s2.0-S2352914823002897-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139455903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harnessing the therapeutic potential of Coccinia grandis phytochemicals in diabetes: A computational, DFT calculation and MMGBSA perspective on aldose reductase inhibition","authors":"Nasim Ahmed ,&nbsp;Faria Farzana Perveen ,&nbsp;Mahfuza Akter ,&nbsp;Abdullah Al Mamun ,&nbsp;Md. Nurul Islam","doi":"10.1016/j.imu.2024.101477","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101477","url":null,"abstract":"<div><p>The role of aldose reductase (ALR), the key enzyme of the polyol pathway, has been firmly established in hyperglycemia-induced diabetic complications. Therefore, the present study focused on the screening of phytochemicals reported in <em>Coccinia grandis</em> against ALR using <em>in-silico</em> methodologies encompassing molecular docking, pharmacokinetics, molecular dynamic simulation, free energy calculation (MMGBSA), and quantum mechanics. A comprehensive array of 101 compounds from <em>C. grandis</em> documented in IMPPAT database and different literatures have been selected in this study. These compounds were meticulously docked with the ALR (PDB ID: 1EL3), yielding docking scores spanning a range of −5.8 to −11.0 kcal/mol compared to the positive control epalrestat with a score of −7.9kcal/mol. Among them, four compounds have been emerged as the most promising ALR inhibitors: tiliroside, lukianol B, formononetin, and trachelogenin, with docking scores of −11.0, −10.7, −10.4, and −10.2, respectively. Importantly, these compounds exhibited notable stability throughout 100 ns dynamic simulations compared to the control drug, aligning with Lipinski's rule of 5, standard ADME properties, and evincing an absence of anomalous toxic effects. Therefore, these compounds hold great promise as leads to the development of potent ALR inhibitors; however, further studies are needed to warrant their uses in ameliorating diabetic complications.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"46 ","pages":"Article 101477"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000339/pdfft?md5=818a5394c29a43f7f724a5ee0e4721f0&pid=1-s2.0-S2352914824000339-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140188026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}