Informatics in Medicine Unlocked最新文献

{"title":"A stochastic model for evaluating the progression of ductal carcinoma in situ breast cancer using Norwegian breast cancer screening program data","authors":"Xiaoxue Li , Harald Weedon-Fekjær , Bo Zhang , Sandra J. Lee","doi":"10.1016/j.imu.2025.101647","DOIUrl":"10.1016/j.imu.2025.101647","url":null,"abstract":"<div><h3>Background</h3><div>Following widespread mammography screening for breast cancer, the incidence of ductal carcinoma in situ (DCIS) has increased sharply. However, the value of detecting DCIS by screening is uncertain as not all DCIS progresses to invasive breast cancer. Knowledge about the sojourn time in the screen-detectable DCIS state and the progression or regression of DCIS to other stages (i.e., the natural history of DCIS) is essential to treat screen-detected DCIS lesions.</div></div><div><h3>Methods</h3><div>We developed a stochastic model for DCIS natural history, characterized by DCIS states, invasive breast cancer states, and transition probabilities between the states. The model included DCIS lesions in the screen-detectable preclinical state and their progression to clinical DCIS, invasive breast cancer, or regression to a state undetectable by screening. Unlike currently available DCIS Markov models, the proposed model assumed no relationship between the sojourn time and transition probabilities in DCIS states and used age-specific transition probabilities. In the absence of ideal data for DCIS modeling, the Norwegian Breast Cancer Screening Program data, specifically arranged by screening round and mode of detection, was applied to obtain maximum likelihood estimates of DCIS natural history parameters, including transition probabilities and the mean sojourn time in the preclinical screen-detectable DCIS state.</div></div><div><h3>Results</h3><div>By indirectly specifying a range of the proportion of breast lesions in the preclinical undetectable DCIS state (S<sub>du</sub>) that progress through the preclinical screen-detectable DCIS state (S<sub>dp</sub>), <em>P</em><sub><em>d</em></sub><em>(t)</em>, not going directly to preclinical invasive breast cancer (S<sub>p</sub>), plausible sets of DCIS natural history parameters were systematically evaluated. All estimates indicated that the mean sojourn time in S<sub>dp</sub> was relatively short (≤3.5 years). For the age group 50–54 years, the best fitting mean sojourn time in S<sub>dp</sub> was 3.4–3.5 years, with mammography sensitivity 0.60–0.61 when <em>P</em><sub><em>d</em></sub><em>(t)</em> was 0.31–0.34. When <em>P</em><sub><em>d</em></sub><em>(t)</em> was larger, mean sojourn times in S<sub>dp</sub> likely varied by the pathway. In general, assuming higher <em>P</em><sub><em>d</em></sub><em>(t)</em>—that is, a higher proportion of DCIS lesions that progress to from S<sub>dp</sub> to S<sub>p</sub>—the mean sojourn time became shorter. Regression to no cancer or undetectable state might be possible, but the quantified level of regression was associated with great uncertainties.</div></div><div><h3>Conclusion</h3><div>While difficult to point to a unique set of DCIS natural history estimates, identifying broader sets of plausible estimates is possible. Estimates reported here provide a comprehensive view of potential progression paths of DCIS while acknowledging the limit","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"55 ","pages":"Article 101647"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regression and classification of Windkessel parameters from non-invasive cardiovascular quantities using a fully connected neural network","authors":"Ahmed Gdoura ,&nbsp;Stefan Bernhard","doi":"10.1016/j.imu.2025.101614","DOIUrl":"10.1016/j.imu.2025.101614","url":null,"abstract":"<div><div>Despite their simplicity, three-element Windkessel models (WK-3) provide an effective and straightforward representation of the aortic input impedance. The WK-3 model not only captures valuable information about the mechanical and structural characteristics of the aortic arch but also generates reliable estimations of the central blood pressure (cBP) wave, a significant cardiovascular risk indicator. However, fitting the parameters of the WK-3 model typically requires invasively collected data, which carries substantial risk and high cost for patients.</div><div>This study aims to enable non-invasive impedance estimation of the WK-3 model using cardiovascular signals. As a proof of concept, we developed and trained a fully connected neural network (FCNN) on an in-silico dataset to predict the WK-3 parameters: characteristic impedance, peripheral arterial resistance, and arterial compliance. These predictions are based on non-invasive parameters, including zero-flow pressure intercept, heart rate, stroke volume, and left ventricular ejection time.</div><div>The proposed model achieved an overall accuracy of 80% with an average area under the curve (AUC) of <span><math><mrow><mn>0</mn><mo>.</mo><mn>91</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>11</mn></mrow></math></span>. The implementation and best-fitting model are available for download from <span><span>this link</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101614"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103204","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":"Development of deep learning-based classification models for opacity differentiation in pediatric chest radiography","authors":"Germán Enrique Galvis Ruiz ,&nbsp;Johana Benavides-Cruz ,&nbsp;Daniela Muñoz Corredor ,&nbsp;Esteban Morales-Mendoza ,&nbsp;Héctor Daniel Alejandro Cotrino Palma ,&nbsp;Andrés Cely-Jiménez","doi":"10.1016/j.imu.2024.101605","DOIUrl":"10.1016/j.imu.2024.101605","url":null,"abstract":"<div><div>Opacities of non-interstitial origin in a pediatric patient's chest radiograph may indicate either consolidations and/or atelectasis, based on the appropriate clinical context. However, the overlapping and complex symptomatology of respiratory tract diseases in pediatric patients can make it difficult for physicians to interpret opacities. Artificial intelligence models are frequently employed by physicians for diagnostic support in healthcare, especially to evaluate aspects of radiographs that are not visible with the naked eye. In this study, a prediction model based on deep learning was used to differentiate between atelectasis and consolidations in pediatric chest radiographs from a clinical perspective. The radiologist can assist pediatricians in diagnosing respiratory pathologies based on the type of opacities using the machine learning model. We used 1297 chest X-ray images of pediatric patients with opacities including consolidations (<span><math><mrow><mi>n</mi><mo>=</mo><mn>500</mn></mrow></math></span>), atelectasis (<span><math><mrow><mi>n</mi><mo>=</mo><mn>499</mn></mrow></math></span>); and images without opacities (<span><math><mrow><mi>n</mi><mo>=</mo><mn>298</mn></mrow></math></span>). The images were preprocessed, and various deep learning models were applied to determine the model with the best metrics. The InceptionV3 model demonstrated a significant improvement over its initial results.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101605"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178370","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":"Robust assessment of cervical precancerous lesions from pre- and post-acetic acid cervicography by combining deep learning and medical guidelines","authors":"Siti Nurmaini ,&nbsp;Patiyus Agustiyansyah ,&nbsp;Muhammad Naufal Rachmatullah ,&nbsp;Firdaus Firdaus ,&nbsp;Annisa Darmawahyuni ,&nbsp;Bambang Tutuko ,&nbsp;Ade Iriani Sapitri ,&nbsp;Anggun Islami ,&nbsp;Akhiar Wista Arum ,&nbsp;Rizal Sanif ,&nbsp;Irawan Sastradinata ,&nbsp;Legiran Legiran ,&nbsp;Radiyati Umi Partan","doi":"10.1016/j.imu.2024.101609","DOIUrl":"10.1016/j.imu.2024.101609","url":null,"abstract":"<div><div>Cervical cancer remains a major public health challenge, particularly in low-resource settings where access to regular screening and expert medical evaluation is limited. Traditional visual inspection with acetic acid (VIA) has been widely used for cervical cancer screening but is subjective and highly dependent on the expertise of the healthcare provider. This study presents a comprehensive methodology for decision-making regarding cervical precancerous lesions using cervicograms taken before and after the application of acetic acid. By leveraging the power of the deep learning (DL) model with You Only Look Once (Yolo) version 8, Slicing Aided Hyper Inference (SAHI), and oncology medical guidelines, the system aims to improve the accuracy and consistency of VIA assessments. The method involves training a Yolov8xl model on our cervicogram dataset, annotated by two oncologists using VIA screening results, to distinguish between the cervical area, columnar area, and lesions. The model is designed to process cervicography images taken both before and after the application of acetic acid, capturing the dynamic changes in tissue appearance indicative of precancerous conditions. The automated evaluation system demonstrated high sensitivity and specificity in detecting cervical lesions with 90.78 % accuracy, 91.67 % sensitivity, and 90.96 % specificity, outperforming other existing methods. This work represents a significant step towards deploying AI-driven solutions in cervical cancer screening, potentially reducing the global burden of the disease. It can be integrated into existing screening programs, providing a valuable tool for early detection and intervention, especially in regions with limited access to trained medical personnel.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101609"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178786","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":"Prediction of the onset of the RSV epidemic with meteorological data using deep neural networks","authors":"Kazuo Yonekura ,&nbsp;Miya Nishio ,&nbsp;Momoko Kashiwado ,&nbsp;Takuya Naruto ,&nbsp;Masaaki Mori","doi":"10.1016/j.imu.2025.101659","DOIUrl":"10.1016/j.imu.2025.101659","url":null,"abstract":"<div><h3>Background</h3><div>Respiratory syncytial virus (RSV) is a contagious virus that infects nearly all children by the age of two and is a leading cause of hospitalization and mortality among young children. Despite the recent approval of RSV vaccines for elderly and pregnant individuals, immune prophylaxis remains essential for pediatric cases. In Japan, the typical RSV season has shifted, making timely prediction crucial for effective clinical intervention.</div></div><div><h3>Objective</h3><div>This study aims to predict the onset of RSV epidemics in Japan using meteorological data, based on the hypothesis that meteorological data affect the spread of RSV.</div></div><div><h3>Methods</h3><div>We collected weekly RSV case counts from the Japanese National Institute of Infectious Diseases and daily meteorological data from the Japan Meteorological Agency for the period 2012–2023. Using aggregated weather features (mean, max, min), we constructed a binary classification task to identify the onset of RSV spread. Machine learning models including a support vector machine (SVM), XGBoost, and a deep neural network (DNN) were evaluated.</div></div><div><h3>Results</h3><div>The DNN outperformed other models, achieving the highest F1 score (0.71) and recall (0.83), particularly with a 3-week-ahead prediction horizon. The model demonstrated early detection capability across multiple prefectures, although performance varied geographically, with lower F1 scores in some northern regions.</div></div><div><h3>Conclusion</h3><div>Meteorological data can be effectively utilized to predict the onset of RSV epidemics in Japan. The proposed DNN-based model offers a promising tool for supporting timely prophylactic measures, although further refinement and integration of additional factors are needed to improve generalizability.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"57 ","pages":"Article 101659"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144240370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantum computing research in medical sciences","authors":"Saleh Alrashed ,&nbsp;Nasro Min-Allah","doi":"10.1016/j.imu.2024.101606","DOIUrl":"10.1016/j.imu.2024.101606","url":null,"abstract":"<div><div>With the emergence of ever-improving quantum computers, technology is making its way to revolutionize many fields, and the medical sector is no exception. Recent efforts have explored applications of quantum computing in areas such as drug discovery, patient privacy, and information security. It is expected that, with improved and stable quantum computing technologies, the medical sector will benefit significantly in many areas, including efficient patient care, reduced clinical trial durations, enhanced imaging technologies, and post-quantum cryptography, to name a few.</div><div>In this work, we highlight recent advancements in the medical sector driven by quantum computing, encompassing computation, optimization, security, machine learning, data processing, simulation, and healthcare perspectives. We also discuss the limitations of current technologies, and the challenges associated with the quantum computing revolution.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101606"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178835","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 role of walking-tracking apps and chronic medical conditions for adult students’ quality of life: A cross-sectional study from Saudi Arabia","authors":"Manal Almalki","doi":"10.1016/j.imu.2024.101610","DOIUrl":"10.1016/j.imu.2024.101610","url":null,"abstract":"<div><h3>Background</h3><div>The COVID-19 pandemic significantly altered health behaviors, particularly among adult students in Saudi Arabia. The increased use of walking-tracking apps and the challenges faced by individuals with chronic medical conditions have influenced overall quality of life (QOL).</div></div><div><h3>Objective</h3><div>To assess the influence of having a medical condition and the use of walking-tracking apps on QOL among adult students in Saudi Arabia.</div></div><div><h3>Methods</h3><div>An online questionnaire was utilized in June 2024 to measure QOL using the WHOQOL-BREF scale, which covers physical health, psychological well-being, social relationships, and environmental health. Participants were grouped based on their use of walking-tracking apps and the presence of a chronic medical condition. Statistical analysis included independent t-tests, Pearson correlations, and chi-square tests to determine significant associations (p &lt; 0.05).</div></div><div><h3>Results</h3><div>The sample consisted of 412 participants. The chi-square test revealed a significant association between having a medical condition and using a walking-tracking app (p = 0.037), with individuals without medical conditions being more likely to use these apps. However, despite the high prevalence of app usage (65.3 %), no significant improvements in QOL were observed for app users across any of the QOL domains. Participants with medical conditions reported significantly higher QOL scores in all domains, particularly in psychological health (p &lt; 0.001) and social relationships (p = 0.001). Positive correlations were observed for factors like meaningful life, concentration, and access to healthcare among those with medical conditions.</div></div><div><h3>Conclusion</h3><div>Students with chronic medical conditions reported higher QOL whereas the use of walking-tracking apps had limited direct impact on their QOL. Future studies should explore factors that play a critical role in enhancing QOL beyond physical health and technology usage, including social support and the Saudi healthcare system.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101610"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178369","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":"Corrigendum to “Early detection of coronary heart disease using ensemble techniques” [Inform Med Unlocked 26 (2021) 100655]","authors":"Vardhan Shorewala ,&nbsp;Shivam Shorewala","doi":"10.1016/j.imu.2024.101598","DOIUrl":"10.1016/j.imu.2024.101598","url":null,"abstract":"","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101598"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178836","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":"Large language models aided patient progression documentation according to the ICD standard","authors":"Nuria Lebeña ,&nbsp;Arantza Casillas ,&nbsp;Alicia Pérez","doi":"10.1016/j.imu.2025.101637","DOIUrl":"10.1016/j.imu.2025.101637","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Healthcare documentation processing is becoming more and more efficient and effective as a result of advances in machine learning and natural language processing (NLP). One challenge in clinical practice is the early detection of future patient potential diagnoses, which is crucial for preventive medicine. Estimating the potential future diagnoses, helps to speed up the management of Electronic Health Records (EHRs) and opens a path towards clinical prevention. It is a challenging task, as there are thousands of possible diseases, and, in general, there is limited data available to train systems due to privacy concerns.</div><div>The objective of his study is to infer future probable diagnoses given patients diagnosis history. In previous works, this task has been carried out using structured data, such as, ICD-coded diagnoses, overlooking unstructured textual information in EHRs. Unlike traditional methods, this study aims to enhance next-diagnosis prediction by integrating patient diagnosis information codified according to the International Classification of Diseases (ICD) with unstructured clinical text.</div></div><div><h3>Methods:</h3><div>We propose a multi-faceted model that integrates structured ICD-encoded patient histories with unstructured EHR text for future diagnosis prediction. Our approach consists of (1) a sequential model trained on structured diagnosis timelines, (2) a Clinical Longformer-based model trained on unstructured EHRs, and (3) an ensemble strategy to combine predictions from both components.</div></div><div><h3>Results:</h3><div>Our proposed ensemble strategy significantly outperforms current state-of-the-art approaches in predicting future diagnoses, achieving a Precision@5 of 72.34% and a Precision@20 of 77.49%. Additionally, it showed high robustness and reliability across different demographic groups and a varying scope of medical history.</div></div><div><h3>Conclusion:</h3><div>This research demonstrates that the integration of structured ICD diagnoses timelines with unstructured EHRs achieves improved results compared to just using structured diagnosis timelines. Notably, the proposed model also maintained high accuracy even with a short-term history of diagnoses.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"55 ","pages":"Article 101637"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739798","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":"Examining the association between genetic polymorphisms and osteoporosis among post-menopausal women: a systematic review","authors":"Zainab Alhalwachi ,&nbsp;Mira Mousa ,&nbsp;Salsabeel Juneidi ,&nbsp;Gabriela Restrepo-Rodas ,&nbsp;Spyridon Karras ,&nbsp;Habiba Alsafar ,&nbsp;Fatme Al Anouti","doi":"10.1016/j.imu.2025.101652","DOIUrl":"10.1016/j.imu.2025.101652","url":null,"abstract":"<div><h3>Purpose</h3><div>Postmenopausal osteoporosis (PMOP) is the most prevalent metabolic bone disease among women, characterized by significant bone density loss and increased fracture risk. With a genetic component, a systematic review was conducted on the association between genetic polymorphisms and PMOP risk.</div></div><div><h3>Methods</h3><div>A comprehensive review of PubMed literature examined genetic polymorphisms linked to PMOP risk. The primary outcome was to identify the most frequently studied genes linked to PMOP. The secondary outcome was to perform a meta-analysis on the top genetic markers to assess their overall association with PMOP risk.</div></div><div><h3>Results</h3><div>Six genes, accounting for 55.08 % of all studies, were strongly associated with PMOP. Of these, the <em>VDR</em> gene was featured in 35 articles (18.72 % of studies), TNFRSF11B in 23 (12.30 %), <em>ESR1</em> in 18 (9.63 %), <em>COL1A1</em> in 12 (6.42 %), <em>MTHFR</em> in 8 (4.27 %), and TGFb1 in 7 (3.74 %). Meta-analysis showed five markers significantly associated with PMOP: SNP rs1544410 (OR_G: 0.74 (0.59, 0.92)), SNP rs11568820 (OR_G: 1.40 (1.03, 1.91)), and SNP rs2228570 (OR_T: 1.39 (1.12, 1.73)) in the <em>VDR</em> gene; and PvuII variant (OR_P: 0.80 (0.67, 0.96)) in the <em>ESR1</em> gene.</div></div><div><h3>Conclusion</h3><div>This review strengthens the importance of conducting a robust, multi-ethnic, large cohort study with functional analysis to corroborate the findings of the six key genes associated with PMOP. Replicating these findings in larger and more diverse datasets is crucial to validate their biological relevance and potential clinical application.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"56 ","pages":"Article 101652"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}