Journal of Medical Signals & Sensors最新文献

{"title":"Artificial Intelligence-based Automated International Classification of Diseases Coding: A Systematic Review.","authors":"Seyyedeh Fatemeh Mousavi Baigi, Masoumeh Sarbaz, Ali Darroudi, Fatemeh Dahmardeh Kemmak, Reyhane Norouzi Aval, Khalil Kimiafar","doi":"10.4103/jmss.jmss_76_24","DOIUrl":"10.4103/jmss.jmss_76_24","url":null,"abstract":"<p><p>Automated clinical coding, facilitated by artificial intelligence (AI) techniques like natural language processing and machine learning, has emerged as a promising approach to enhance coding efficiency and accuracy in healthcare. This review synthesizes current knowledge about AI-based automated coding of the International Classification of Diseases (ICD), with a focus on its challenges, benefits, and future research directions. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a systematic search was conducted across PubMed, Embase, Scopus, and Web of Science databases on January 1, 2024. Studies discussing challenges, advantages, and research gaps in AI-driven ICD coding were included. Out of 12,641 identified records, eight studies met the inclusion criteria. These studies highlighted six key challenges: extensive label space, imbalanced label distribution, lengthy documents, coding interpretability issues, ethical concerns, and lack of transparency. Ten major benefits of AI-based ICD coding were identified, including improved decision-making, data standardization, and increased coding accuracy. In addition, eight future directions were proposed, emphasizing interdisciplinary collaboration, transfer learning, transparency enhancement, and active learning techniques. Despite significant challenges, AI-based ICD coding holds substantial potential to revolutionize clinical coding by improving efficiency and accuracy. This review provides a comprehensive synthesis of current evidence and actionable insights for advancing research and practical implementation of automated ICD coding systems.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"15 ","pages":"22"},"PeriodicalIF":1.1,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12373374/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144972787","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":"Human Stress Classification Using Cardiovascular and Respiratory Data Based on Machine Learning Techniques.","authors":"Mahdis Yaghoubi, Navid Adib, Abolfazl Rezaei Monfared, Shirin Ashtari Tondashti, Saeed Akhavan","doi":"10.4103/jmss.jmss_71_24","DOIUrl":"10.4103/jmss.jmss_71_24","url":null,"abstract":"<p><strong>Background: </strong>Stress, a widespread mental health concern, significantly impacts people well-being and performance. This study proposes a novel approach to stress detection by fusing cardiovascular and respiratory data.</p><p><strong>Methods: </strong>Fifteen participants underwent a mental stress induction task while their electrocardiogram (ECG) and respiration signals were recorded. A real-time peak detection algorithm was developed for ECG signal processing, and both time and frequency domain features were extracted from ECG and respiration signals. Various machine learning models, including Support Vector Machine, K-Nearest Neighbors, bagged decision trees, and random forests, were employed for classification, with accurate labeling achieved through the NASA-TLX questionnaire.</p><p><strong>Results: </strong>The results demonstrate that combining respiration and cardiovascular features significantly enhances stress classification performance compared to using each modality alone, achieving an accuracy of 95.6% ±1.7%. Forward feature selection identifies key discriminative features from both modalities.</p><p><strong>Conclusions: </strong>This study demonstrates the efficacy of multimodal physiological data integration for accurate stress detection, outperforming single-modality approaches and comparable studies in the literature. The findings highlight the potential of real-time monitoring systems in enhancing stress and health management.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"15 ","pages":"24"},"PeriodicalIF":1.1,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12373377/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144972801","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":"A New Method for Dynamic Brain Connectivity Analysis Based on Tensor Decomposition in Tinnitus Using High-density Electroencephalogram in Source Domain.","authors":"Moein Bahman, Seyed Saman Sajadi, Iman Ghodrati Toostani, Bahador MakkiAbadi","doi":"10.4103/jmss.jmss_75_24","DOIUrl":"10.4103/jmss.jmss_75_24","url":null,"abstract":"<p><strong>Background: </strong>Functional connectivity (FC), defined as the statistical reliance among different brain regions, has been an effective tool for studying cognitive brain functions. The majority of existing FC-based research has relied on the premise that networks are temporally stationary. However, there exist few research that support nonstationarity of FC which can be due to cognitive functioning. However, still there is a gap in tracking the dynamics of FC to gain a deeper understanding of how brain networks form and adapt in response to therapeutic interventions by identifying the change points that signify substantial shifts in network connectivity across the participants.</p><p><strong>Methods: </strong>The proposed approach in this study is based on tensor representation of FC networks of the source signals of electroencephalogram (EEG) activities yielding a multi-mode tensor. Then analysis of variance has been used to investigate changing points in connectivity of brain activity in sources domain in different conditions of tasks, frequency bands, and among subjects in time. High-density EEG signals (256 channels) were acquired from 30 tinnitus patients under visual (positive emotion induction) and transcranial direct current stimulation (tDCS) stimuli.</p><p><strong>Results: </strong>The proposed method of this study could effectively identify the significant brain connectivity change points, indicating enhanced effectiveness in capturing connectivity shifts comparing to conventional methods. Findings in tinnitus patients suggest that visual stimulation alone may not significantly alter brain connectivity networks.</p><p><strong>Conclusion: </strong>Based on the results, a combination of visual stimulation with simultaneous High-Definition tDCS is recommended, potentially informing optimal intervention strategies to enhance tinnitus treatment effectiveness.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"15 ","pages":"23"},"PeriodicalIF":1.1,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12373379/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144972837","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":"A Nonlinear Method to Identify Seizure Dynamic Trajectory Based on Variance of Recurrence Rate in Human Epilepsy Patients Using EEG.","authors":"Morteza Farahi, Seyed Saman Sajadi, Fateme Karbasi, Seyed Sohrab Hashemi Fesharaki, Jafar Mehvari Habibabadi, Mohsen Reza Haidari, Amir Homayoun Jafari","doi":"10.4103/jmss.jmss_73_24","DOIUrl":"10.4103/jmss.jmss_73_24","url":null,"abstract":"<p><strong>Background: </strong>Surgery is a well-established treatment for drug-resistant epilepsy, but outcomes are often suboptimal, especially when no lesion is visible on preoperative imaging. A major challenge in determining the seizure's origin and spread is interpreting electroencephalogram (EEG) data. Accurately tracing the seizure's signal trajectory, given the brain's complex behavior, remains a crucial hurdle.</p><p><strong>Materials and methods: </strong>In this study, EEG data from 17 patients were analyzed, using the clinical interpretations of the epileptogenic region as the gold standard. Quantification analysis of recurrence plots primarily based on variance in recurrence rate was used to identify the regions involved during seizures based on investigation of the recurrence phenomena between the regions. This method allowed for a stage-wise analysis across EEG electrodes, highlighting simultaneously involved areas.</p><p><strong>Results: </strong>The method effectively distinguished involved from noninvolved regions across anterior, posterior, right temporal, and left temporal areas with macro averaged F-score of 95.54. For the anterior region, it achieved an overall accuracy (correct predictions out of total predictions) of 86.96%, sensitivity (ability to correctly identify seizure-involved regions) of 82.79%, and specificity (ability to correctly identify non-involved regions) of 86.96%. For the other regions, accuracy, sensitivity, and specificity values ranged from 66.0% to 89.13%.</p><p><strong>Conclusions: </strong>This approach could pinpoint brain regions involved in seizures at any stage and could be useful for clinical monitoring and surgical planning. The method's simplicity and strong performance suggest it is promising for the real-time application during epilepsy treatment.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"15 ","pages":"19"},"PeriodicalIF":1.1,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12331190/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144800561","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":"Balancing Radiation Dose Reduction and Image Quality in Chest Computed Tomography using Silicon Rubber-barium Sulfate Composite Shield.","authors":"Mohammad Keshtkar, Saeedeh Yazdanifar","doi":"10.4103/jmss.jmss_61_24","DOIUrl":"10.4103/jmss.jmss_61_24","url":null,"abstract":"<p><strong>Background: </strong>During chest CT examinations, the breasts are exposed to a significant amount of radiation, increasing the risk of radiation-induced cancers. The objective of this study is to develop and evaluate a novel silicon rubber-barium sulfate (BaSO4) composite breast shield for reducing radiation dose in chest computed tomography (CT) examinations while minimizing impact on image quality.</p><p><strong>Methods: </strong>Four breast shields were fabricated: one with 10% bismuth and three with 10%, 15%, and 20% BaSO4. Dose reduction was assessed using a thorax phantom and ionization chamber. Image quality effects were evaluated in the thorax phantom by measuring noise and CT number changes. The 10% barium shield was further tested on 22 patients undergoing chest CT.</p><p><strong>Results: </strong>The 10%, 15%, and 20% barium shields reduced breast dose by 36.8%, 38.6%, and 45.6%, respectively, while the 10% bismuth shield achieved a 63.1% reduction. However, the 10% barium shield had minimal impact on image quality, increasing lung noise by only 0.3 Hounsfield units (HU) and shifting CT numbers by 4.7 HU. In patient studies, 81.8% of scans showed no artifacts, with 18.2% showing slight artifacts.</p><p><strong>Conclusion: </strong>The 10% BaSO4 shield effectively reduced breast dose while maintaining image quality, presenting a viable alternative to bismuth shielding for radiation protection in chest CT examinations.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"15 ","pages":"20"},"PeriodicalIF":1.1,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12331176/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144800562","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":"Designing a Software for Registry of Pregnant Women with Heart Disease in Iran and Preliminary Results.","authors":"Mahdi Kalani, Fateme Mahdikhoshouei, Parvin Bahrami, Amirreza Sajjadieh Khajouei, Minoo Movahedi, Shima Mehdipour, Marzieh Rezvani Habibabadi","doi":"10.4103/jmss.jmss_43_24","DOIUrl":"10.4103/jmss.jmss_43_24","url":null,"abstract":"<p><p>Heart disease in pregnancy is an important health issue worldwide which needs precise care to improve pregnant women health care and reduce maternal mortality rate (MMR). As we know registries play an important role in improvement of health care, so we decided to design a software to take the first step for having a national registry for pregnant women with heart disease in Iran and classify them in a more effective way to reduce mismanagements. A windows-based software with C# language programming was designed and implemented by a group of specialists included two experienced cardiologists, a skilled gynecologist, and a proficient medical doctor programmer. Since the launch of the software, information for 500 pregnant women with heart disease has been entered. The most common types of heart disease in order were congenital heart disease, prosthetic heart valves, valvular disease, and cardiomyopathies. The software developed by our team provides a comprehensive and efficient tool for managing patients with heart disease in pregnancy. The use of this software can help identify high-risk patients early on, leading to better patient outcomes and ultimately contributing to the global goal of reducing MMR. In the field of pregnant women with heart disease, gathering large and accurate data over time can be utilized in artificial intelligence for analysis.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"15 ","pages":"21"},"PeriodicalIF":1.1,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12331175/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144800563","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":"From Image to Sequence: Exploring Vision Transformers for Optical Coherence Tomography Classification.","authors":"Amirali Arbab, Aref Habibi, Hossein Rabbani, Mahnoosh Tajmirriahi","doi":"10.4103/jmss.jmss_58_24","DOIUrl":"10.4103/jmss.jmss_58_24","url":null,"abstract":"<p><strong>Background: </strong>Optical coherence tomography (OCT) is a pivotal imaging technique for the early detection and management of critical retinal diseases, notably diabetic macular edema and age-related macular degeneration. These conditions are significant global health concerns, affecting millions and leading to vision loss if not diagnosed promptly. Current methods for OCT image classification encounter specific challenges, such as the inherent complexity of retinal structures and considerable variability across different OCT datasets.</p><p><strong>Methods: </strong>This paper introduces a novel hybrid model that integrates the strengths of convolutional neural networks (CNNs) and vision transformer (ViT) to overcome these obstacles. The synergy between CNNs, which excel at extracting detailed localized features, and ViT, adept at recognizing long-range patterns, enables a more effective and comprehensive analysis of OCT images.</p><p><strong>Results: </strong>While our model achieves an accuracy of 99.80% on the OCT2017 dataset, its standout feature is its parameter efficiency-requiring only 6.9 million parameters, significantly fewer than larger, more complex models such as Xception and OpticNet-71.</p><p><strong>Conclusion: </strong>This efficiency underscores the model's suitability for clinical settings, where computational resources may be limited but high accuracy and rapid diagnosis are imperative.<b>Code Availability:</b> The code for this study is available at https://github.com/Amir1831/ViT4OCT.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"15 ","pages":"18"},"PeriodicalIF":1.3,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12180780/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144369353","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":"A Comprehensive Survey of Brain-Computer Interface Technology in Health care: Research Perspectives.","authors":"Meenalosini Vimal Cruz, Suhaima Jamal, Sibi Chakkaravarthy Sethuraman","doi":"10.4103/jmss.jmss_49_24","DOIUrl":"10.4103/jmss.jmss_49_24","url":null,"abstract":"<p><p>The brain-computer interface (BCI) technology has emerged as a groundbreaking innovation with profound implications across diverse domains, particularly in health care. By establishing a direct communication pathway between the human brain and external devices, BCI systems offer unprecedented opportunities for diagnosis, treatment, and rehabilitation, thereby reshaping the landscape of medical practice. However, despite its immense potential, the widespread adoption of BCI technology in clinical settings faces several challenges. These include the need for robust signal acquisition and processing techniques and optimizing user training and adaptation. Overcoming these challenges is crucial to unleashing the complete potential of BCI technology in health care and realizing its promise of personalized, patient-centric care. This review work underscores the transformative potential of BCI technology in revolutionizing medical practice. This paper offers a comprehensive analysis of medical-oriented BCI applications by exploring the various uses of BCI technology and its potential to transform patient care.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"15 ","pages":"16"},"PeriodicalIF":1.3,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12180781/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144369352","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":"Introducing a Deep Neural Network Model with Practical Implementation for Polyp Detection in Colonoscopy Videos.","authors":"Hajar Keshavarz, Zohreh Ansari, Hossein Abootalebian, Babak Sabet, Mohammadreza Momenzadeh","doi":"10.4103/jmss.jmss_23_24","DOIUrl":"10.4103/jmss.jmss_23_24","url":null,"abstract":"<p><strong>Background: </strong>Deep learning has gained much attention in computer-assisted minimally invasive surgery in recent years. The application of deep-learning algorithms in colonoscopy can be divided into four main categories: surgical image analysis, surgical operations analysis, evaluation of surgical skills, and surgical automation. Analysis of surgical images by deep learning can be one of the main solutions for early detection of gastrointestinal lesions and for taking appropriate actions to treat cancer.</p><p><strong>Method: </strong>This study investigates a simple and accurate deep-learning model for polyp detection. We address the challenge of limited labeled data through transfer learning and employ multi-task learning to achieve both polyp classification and bounding box detection tasks. Considering the appropriate weight for each task in the total cost function is crucial in achieving the best results. Due to the lack of datasets with nonpolyp images, data collection was carried out. The proposed deep neural network structure was implemented on KVASIR-SEG and CVC-CLINIC datasets as polyp images in addition to the nonpolyp images extracted from the LDPolyp videos dataset.</p><p><strong>Results: </strong>The proposed model demonstrated high accuracy, achieving 100% in polyp/non-polyp classification and 86% in bounding box detection. It also showed fast processing times (0.01 seconds), making it suitable for real-time clinical applications.</p><p><strong>Conclusion: </strong>The developed deep-learning model offers an efficient, accurate, and cost-effective solution for real-time polyp detection in colonoscopy. Its performance on benchmark datasets confirms its potential for clinical deployment, aiding in early cancer diagnosis and treatment.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"15 ","pages":"17"},"PeriodicalIF":1.3,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12180779/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144369319","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":"Radiomics Analysis on Computed Tomography Images for Prediction of Chemoradiation-induced Heart Failure in Breast Cancer by Machine Learning Models.","authors":"Farzaneh Ansari, Ali Neshasteh-Riz, Reza Paydar, Fathollah Mohagheghi, Sahar Felegari, Manijeh Beigi, Susan Cheraghi","doi":"10.4103/jmss.jmss_51_24","DOIUrl":"10.4103/jmss.jmss_51_24","url":null,"abstract":"<p><strong>Background: </strong>This study aimed to evaluate the effectiveness of clinical, dosimetric, and radiomic features from computed tomography (CT) scans in predicting the probability of heart failure in breast cancer patients undergoing chemoradiation treatment.</p><p><strong>Materials and methods: </strong>We selected 54 breast cancer patients who received left-sided chemoradiation therapy and had a low risk of natural heart failure according to the Framingham score. We compared echocardiographic patterns and ejection fraction (EF) measurements before and 3 years after radiotherapy for each patient. Based on these comparisons, we evaluated the incidence of heart failure 3 years postchemoradiation therapy. For machine learning (ML) modeling, we first segmented the heart as the region of interest in CT images using a deep learning technique. We then extracted radiomic features from this region. We employed three widely used classifiers - decision tree, K-nearest neighbor, and random forest (RF) - using a combination of radiomic, dosimetric, and clinical features to predict chemoradiation-induced heart failure. The evaluation criteria included accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (area under the curve [AUC]).</p><p><strong>Results: </strong>In this study, 46% of the patients experienced heart failure, as indicated by EF. A total of 873 radiomic features were extracted from the segmented area. Out of 890 combined radiomic, dosimetric, and clinical features, 15 were selected. The RF model demonstrated the best performance, with an accuracy of 0.85 and an AUC of 0.98. Patient age and V5 irradiated heart volume were identified as key predictors of chemoradiation-induced heart failure.</p><p><strong>Conclusion: </strong>Our quantitative findings indicate that employing ML methods and combining radiomic, dosimetric, and clinical features to identify breast cancer patients at risk of cardiotoxicity is feasible.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"15 ","pages":"14"},"PeriodicalIF":1.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12105806/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152279","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}