Brain Informatics最新文献

{"title":"Enhancing cerebral infarct classification by automatically extracting relevant fMRI features.","authors":"Vitaly I Dobromyslin, Wenjin Zhou","doi":"10.1186/s40708-025-00259-w","DOIUrl":"10.1186/s40708-025-00259-w","url":null,"abstract":"<p><p>Accurate detection of cortical infarct is critical for timely treatment and improved patient outcomes. Current brain imaging methods often require invasive procedures that primarily assess blood vessel and structural white matter damage. There is a need for non-invasive approaches, such as functional MRI (fMRI), that better reflect neuronal viability. This study utilized automated machine learning (auto-ML) techniques to identify novel infarct-specific fMRI biomarkers specifically related to chronic cortical infarcts. We analyzed resting-state fMRI data from the multi-center ADNI dataset, which included 20 chronic infarct patients and 30 cognitively normal (CN) controls. This study utilized automated machine learning (auto-ML) techniques to identify novel fMRI biomarkers specifically related to chronic cortical infarcts. Surface-based registration methods were applied to minimize partial-volume effects typically associated with lower resolution fMRI data. We evaluated the performance of 7 previously known fMRI biomarkers alongside 107 new auto-generated fMRI biomarkers across 33 different classification models. Our analysis identified 6 new fMRI biomarkers that substantially improved infarct detection performance compared to previously established metrics. The best-performing combination of biomarkers and classifiers achieved a cross-validation ROC score of 0.791, closely matching the accuracy of diffusion-weighted imaging methods used in acute stroke detection. Our proposed auto-ML fMRI infarct-detection technique demonstrated robustness across diverse imaging sites and scanner types, highlighting the potential of automated feature extraction to significantly enhance non-invasive infarct detection.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"16"},"PeriodicalIF":0.0,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12173967/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144318216","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":"Detecting label noise in longitudinal Alzheimer's data with explainable artificial intelligence.","authors":"Paolo Sorino, Angela Lombardi, Domenico Lofù, Tommaso Colafiglio, Antonio Ferrara, Fedelucio Narducci, Eugenio Di Sciascio, Tommaso Di Noia","doi":"10.1186/s40708-025-00261-2","DOIUrl":"10.1186/s40708-025-00261-2","url":null,"abstract":"<p><p>Reliable classification of cognitive states in longitudinal Alzheimer's Disease (AD) studies is critical for early diagnosis and intervention. However, inconsistencies in diagnostic labeling, arising from subjective assessments, evolving clinical criteria, and measurement variability, introduce noise that can impact machine learning (ML) model performance. This study explores the potential of explainable artificial intelligence to detect and characterize noisy labels in longitudinal datasets. A predictive model is trained using a Leave-One-Subject-Out validation strategy, ensuring robustness across subjects while enabling individual-level interpretability. By leveraging SHapley Additive exPlanations values, we analyze the temporal variations in feature importance across multiple patient visits, aiming to identify transitions that may reflect either genuine cognitive changes or inconsistencies in labeling. Using statistical thresholds derived from cognitively stable individuals, we propose an approach to flag potential misclassifications while preserving clinical labels. Rather than modifying diagnoses, this framework provides a structured way to highlight cases where diagnostic reassessment may be warranted. By integrating explainability into the assessment of cognitive state transitions, this approach enhances the reliability of longitudinal analyses and supports a more robust use of ML in AD research.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"15"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12151964/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144259081","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":"AI-driven multi-agent reinforcement learning framework for real-time monitoring of physiological signals in stress and depression contexts.","authors":"Thanveer Shaik, Xiaohui Tao, Lin Li, Haoran Xie, Hong-Ning Dai, Feng Zhao, Jianming Yong","doi":"10.1186/s40708-025-00262-1","DOIUrl":"10.1186/s40708-025-00262-1","url":null,"abstract":"<p><strong>Purpose: </strong>Effective patient monitoring is crucial for timely healthcare interventions and improved outcomes, especially in managing conditions influenced by stress and depression, which can manifest through physiological changes. Traditional monitoring systems often struggle with the complexity and dynamic nature of such conditions, leading to delays in identifying critical scenarios. This study proposes a novel multi-agent deep reinforcement learning (DRL) framework to address these challenges by monitoring vital signs and providing real-time decision-making capabilities.</p><p><strong>Methods: </strong>Our framework deploys multiple learning agents, each dedicated to monitoring specific physiological features such as heart rate, respiration, and temperature. These agents interact with a generic healthcare monitoring environment, learn patients' behavior patterns, and estimate the level of emergency to alert Medical Emergency Teams (METs) accordingly. The study evaluates the proposed system using two real-world datasets-PPG-DaLiA and WESAD-designed to capture physiological and stress-related data. The performance is compared with baseline models, including Q-Learning, PPO, Actor-Critic, Double DQN, and DDPG, as well as existing monitoring frameworks like WISEML and CA-MAQL. Hyperparameter optimization is also performed to fine-tune learning rates and discount factors.</p><p><strong>Results: </strong>Experimental results demonstrate that the proposed multi-agent DRL framework outperforms baseline models in accurately monitoring patients' vital signs under stress and varying conditions. The optimized agents adapt effectively to dynamic environments, ensuring timely detection of critical health deviations. Comparative evaluations reveal superior performance in metrics related to decision-making accuracy and response efficiency, highlighting the robustness of the framework.</p><p><strong>Conclusions: </strong>The proposed AI-driven monitoring system offers significant advancements over traditional methods by handling complex and uncertain environments, adapting to varying patient conditions influenced by stress and depression, and making autonomous, real-time decisions. While the framework demonstrates high accuracy and adaptability, challenges related to data scale and future vital sign prediction remain. Future research will focus on extending predictive capabilities to further enhance proactive healthcare interventions.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"14"},"PeriodicalIF":0.0,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12149378/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144259080","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":"Advancing Alzheimer's disease detection: a novel convolutional neural network based framework leveraging EEG data and segment length analysis.","authors":"Md Nurul Ahad Tawhid, Siuly Siuly, Enamul Kabir, Yan Li","doi":"10.1186/s40708-025-00260-3","DOIUrl":"10.1186/s40708-025-00260-3","url":null,"abstract":"<p><p>Alzheimer's disease (AD) is a progressive neurodegenerative disorder that primarily affects memory, thinking, and behavior, leading to dementia, a severe cognitive decline. While no cure currently exists, recent advancements in preventive drug trials and therapeutic management have increased interest in developing clinical algorithms for early detection and biomarker identification. Electroencephalography (EEG) is noninvasive, cost-effective, and has high temporal resolution, making it a promising tool for automated AD detection. However, conventional machine learning approaches often fall short in accurately detecting AD due to their limited architectures. We also need to investigate the impact of EEG signal segment length on classification accuracy. To address these issues, a deep learning-based framework is proposed to detect AD using EEG data, focusing on determining the optimal segment length for classification. This framework contains EEG data collection, pre-processing for noise removal, temporal segmentation, convolutional neural network (CNN) model training and classification, and finally, evaluation. We have tested different segment lengths to test the impact on AD detection. We have used both 10-fold and leave-one-out cross-validation techniques and obtained accuracy of 97.08% and 96.90%, respectively, on a publicly available dataset from AHEPA General University Hospital of Thessaloniki. We have also tested the generalizability of the proposed model by testing it to detect frontotemporal dementia and obtained better results than existing studies. Furthermore, we have validated our proposed CNN model using several ablation studies and layer-wise extracted feature visualization. This study will establish a pioneering direction for future researchers and technology experts in the field of neurodiseases.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"13"},"PeriodicalIF":0.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12137833/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144217156","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":"Treatment journey clustering with a novel kernel k-means machine learning algorithm: a retrospective analysis of insurance claims in bipolar I disorder.","authors":"Matthew Littman, Huy-Binh Nguyen, Joanna Campbell, Katelyn R Keyloun","doi":"10.1186/s40708-025-00258-x","DOIUrl":"10.1186/s40708-025-00258-x","url":null,"abstract":"<p><p>In real-world psychiatric practice, patients may experience complex treatment journeys, including various diagnoses and lines of therapy. Insurance claims databases could potentially provide insight into outcomes of psychiatric treatment processes, but the diversity of event sequences restricts analyses with currently available methods. Here, we developed a novel kernel k-means clustering algorithm for event sequences that can accommodate highly diverse event types and sequence lengths. The approach, Divisive Optimized Clustering using Kernel K-means for Event Sequences (DOCKKES), also leverages a novel performance metric, the transition score, which measures sequence coherence in individual clusters. The performance of DOCKKES was evaluated in the context of bipolar I disorder, which is characterized by heterogeneous treatment journeys. We conducted a retrospective, observational analysis of a large sample (n = 31,578) of patients with bipolar I disorder from the MarketScan® Commercial Database. Using insurance claims, bipolar episode diagnoses and mental health-related lines of therapy were identified as events of interest for patient clustering. The dataset included 202,122 events; 75% of the cohort experienced unique treatment journeys. Based on an optimal run, DOCKKES identified 16 treatment journey clusters, which were evenly split for initial manic/mixed or depressive episodes (8 clusters each) and varied in sequence length and early lines of therapy. Variability across clusters was also observed for demographics, comorbidities, and mental health-related healthcare resource utilization and cost. This proof-of-concept study demonstrated the use of DOCKKES for integrating information from large datasets, enabling comparisons between patient clusters and evaluation of real-world treatment journeys in the context of evidence-based guidelines.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"12"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12098244/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144120486","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":"HoRNS-CNN model: an energy-efficient fully homomorphic residue number system convolutional neural network model for privacy-preserving classification of dyslexia neural-biomarkers.","authors":"Opeyemi Lateef Usman, Ravie Chandren Muniyandi, Khairuddin Omar, Mazlyfarina Mohamad, Ayoade Akeem Owoade, Morufat Adebola Kareem","doi":"10.1186/s40708-025-00256-z","DOIUrl":"https://doi.org/10.1186/s40708-025-00256-z","url":null,"abstract":"<p><p>Recent advancements in cloud-based machine learning (ML) now allow for the rapid and remote identification of neural-biomarkers associated with common neuro-developmental disorders from neuroimaging datasets. Due to the sensitive nature of these datasets, secure deep learning (DL) algorithms are essential. Although, fully homomorphic encryption (FHE)-based methods have been proposed to maintain data confidentiality and privacy, however, existing FHE deep convolutional neural network (CNN) models still face some issues such as low accuracy, high encryption/decryption latency, energy inefficiency, long feature extraction times, and significant cipher-image expansion. To address these issues, this study introduces the HoRNS-CNN model, which integrates the energy-efficient features of the residue number system FHE scheme (RNS-FHE scheme) with the high accuracy of pre-trained deep CNN models in the cloud for efficient, privacy-preserving predictions and provide some proofs of its energy efficiency and homomorphism. The RNS-FHE scheme's FPGA implementation includes embedded RNS pixel-bitstream homomorphic encoder/decoder circuits for encrypting 8-bit grayscale pixels, with cloud CNN models performing remote classification on the encrypted images. In the HoRNS-CNN architecture, the ReLU activation functions of deep CNNs were initially trained for stability and later adapted for homomorphic computations using a Taylor polynomial approximation of degree 3 and batch normalization to achieve high accuracy. The findings show that the HoRNS-CNN model effectively manages cipher-image expansion with an asymptotic complexity of <math><mrow><mi>O</mi> <mfenced> <msup><mrow><mi>n</mi></mrow> <mn>3</mn></msup> </mfenced> </mrow> </math> , offering better performance and faster feature extraction compared to its peers. The model can predict 400,000 neural-biomarker features in one hour, providing an effective tool for analyzing neuroimages while ensuring privacy and security.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"11"},"PeriodicalIF":0.0,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12044152/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144056981","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":"Explainable CNN for brain tumor detection and classification through XAI based key features identification.","authors":"Shagufta Iftikhar, Nadeem Anjum, Abdul Basit Siddiqui, Masood Ur Rehman, Naeem Ramzan","doi":"10.1186/s40708-025-00257-y","DOIUrl":"https://doi.org/10.1186/s40708-025-00257-y","url":null,"abstract":"<p><p>Despite significant advancements in brain tumor classification, many existing models suffer from complex structures that make them difficult to interpret. This complexity can hinder the transparency of the decision-making process, causing models to rely on irrelevant features or normal soft tissues. Besides, these models often include additional layers and parameters, which further complicate the classification process. Our work addresses these limitations by introducing a novel methodology that combines Explainable AI (XAI) techniques with a Convolutional Neural Network (CNN) architecture. The major contribution of this paper is ensuring that the model focuses on the most relevant features for tumor detection and classification, while simultaneously reducing complexity, by minimizing the number of layers. This approach enhances the model's transparency and robustness, giving clear insights into its decision-making process through XAI techniques such as Gradient-weighted Class Activation Mapping (Grad-Cam), Shapley Additive explanations (Shap), and Local Interpretable Model-agnostic Explanations (LIME). Additionally, the approach demonstrates better performance, achieving 99% accuracy on seen data and 95% on unseen data, highlighting its generalizability and reliability. This balance of simplicity, interpretability, and high accuracy represents a significant advancement in the classification of brain tumor.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"10"},"PeriodicalIF":0.0,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12044100/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144037051","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":"Breakdown of the compositional data approach in psychometric Likert scale big data analysis: about the loss of statistical power of two-sample t-tests applied to heavy-tailed big data.","authors":"René Lehmann, Bodo Vogt","doi":"10.1186/s40708-025-00253-2","DOIUrl":"10.1186/s40708-025-00253-2","url":null,"abstract":"<p><p>Bipolar psychometric scale data play a crucial role in psychological healthcare and health economics, such as in psychotherapeutic profiling and setting standards. Creating an accurate psychological profile not only benefits the patient but also saves time and costs. The quality of psychotherapeutic measures directly impacts grant funding decisions, influencing managerial choices. Moreover, the accuracy of consumer data analyses affects costs, profits, and the long-term sustainability of decisions. Considering psychometric bipolar scale data as compositional data can enhance the statistical power of well-known paired and unpaired two-sample t-tests, supporting managerial decision-making and the development or implementation of health interventions. This increase in statistical power is observed when the central limit theorem (CLT) holds true in statistics. Through stochastic simulation, this study explores the impact of violating the CLT on statistical power of the unpaired t-test under heavy-tailed data generating processes (DGPs) with finite variance. The findings reveal a reduction in statistical power based on specific parameters like the psychometric limit of quantification, the number of items in a questionnaire, the response scale used, and the dispersion of the DGP.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"9"},"PeriodicalIF":0.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11977074/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143804412","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 models for Alzheimer's disease diagnosis using neuroimaging data: survey, reproducibility, and generalizability evaluation.","authors":"Maryam Akhavan Aghdam, Serdar Bozdag, Fahad Saeed","doi":"10.1186/s40708-025-00252-3","DOIUrl":"10.1186/s40708-025-00252-3","url":null,"abstract":"<p><p>Clinical diagnosis of Alzheimer's disease (AD) is usually made after symptoms such as short-term memory loss are exhibited, which minimizes the intervention and treatment options. The existing screening techniques cannot distinguish between stable MCI (sMCI) cases (i.e., patients who do not convert to AD for at least three years) and progressive MCI (pMCI) cases (i.e., patients who convert to AD in three years or sooner). Delayed diagnosis of AD also disproportionately affects underrepresented and socioeconomically disadvantaged populations. The significant positive impact of an early diagnosis solution for AD across diverse ethno-racial and demographic groups is well-known and recognized. While advancements in high-throughput technologies have enabled the generation of vast amounts of multimodal clinical, and neuroimaging datasets related to AD, most methods utilizing these data sets for diagnostic purposes have not found their way in clinical settings. To better understand the landscape, we surveyed the major preprocessing, data management, traditional machine-learning (ML), and deep learning (DL) techniques used for diagnosing AD using neuroimaging data such as structural magnetic resonance imaging (sMRI), functional magnetic resonance imaging (fMRI), and positron emission tomography (PET). Once we had a good understanding of the methods available, we conducted a study to assess the reproducibility and generalizability of open-source ML models. Our evaluation shows that existing models show reduced generalizability when different cohorts of the data modality are used while controlling other computational factors. The paper concludes with a discussion of major challenges that plague ML models for AD diagnosis and biomarker discovery.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"8"},"PeriodicalIF":0.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11928716/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143674661","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":"Exploring multi-granularity balance strategy for class incremental learning via three-way granular computing.","authors":"Yan Xian, Hong Yu, Ye Wang, Guoyin Wang","doi":"10.1186/s40708-025-00255-0","DOIUrl":"10.1186/s40708-025-00255-0","url":null,"abstract":"<p><p>Class incremental learning (CIL) is a specific scenario in incremental learning. It aims to continuously learn new classes from the data stream, which suffers from the challenge of catastrophic forgetting. Inspired by the human hippocampus, the CIL method for replaying episodic memory offers a promising solution. However, the limited buffer budget restricts the number of old class samples that can be stored, resulting in an imbalance between new and old class samples during each incremental learning stage. This imbalance adversely affects the mitigation of catastrophic forgetting. Therefore, we propose a novel CIL method based on multi-granularity balance strategy (MGBCIL), which is inspired by the three-way granular computing in human problem-solving. In order to mitigate the adverse effects of imbalances on catastrophic forgetting at fine-, medium-, and coarse-grained levels during training, MGBCIL introduces specific strategies across the batch, task, and decision stages. Specifically, a weighted cross-entropy loss function with a smoothing factor is proposed for batch processing. In the process of task updating and classification decision, contrastive learning with different anchor point settings is employed to promote local and global separation between new and old classes. Additionally, the knowledge distillation technology is used to preserve knowledge of the old classes. Experimental evaluations on CIFAR-10 and CIFAR-100 datasets show that MGBCIL outperforms other methods in most incremental settings. Specifically, when storing 3 exemplars on CIFAR-10 with Base2 Inc2 setting, the average accuracy is improved by up to 9.59% and the forgetting rate is reduced by up to 25.45%.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"7"},"PeriodicalIF":0.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914578/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143650755","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}