Neuroscience informatics最新文献

{"title":"Evaluating the effect of point-sampling on univariate point and interval forecasting of cerebral physiologic signals using ARIMA modeling in acute traumatic neural injury","authors":"Nuray Vakitbilir ,&nbsp;Kevin Y. Stein ,&nbsp;Tobias Bergmann ,&nbsp;Noah Silvaggio ,&nbsp;Amanjyot Singh Sainbhi ,&nbsp;Abrar Islam ,&nbsp;Logan Froese ,&nbsp;Rakibul Hasan ,&nbsp;Mansoor Hayat ,&nbsp;Marcel Aries ,&nbsp;Frederick A. Zeiler","doi":"10.1016/j.neuri.2025.100248","DOIUrl":"10.1016/j.neuri.2025.100248","url":null,"abstract":"<div><div>High-resolution physiological signals, such as intracranial pressure (ICP) and regional cerebral oxygen saturation (rSO₂), are critical for managing traumatic brain injury (TBI) by enabling continuous monitoring of cerebral autoregulation and vascular reactivity. These signals provide essential insights into brain perfusion dynamics, supporting timely clinical interventions. However, the high temporal resolution of these data introduces challenges in real-time use, integration into predictive models, and computational efficiency. Consequently, resolution reduction techniques are essential for simplifying the data while retaining critical features necessary for accurate prediction and modeling. Using the Multi-omic Analytics and Integrative Neuroinformatics in the HUman Brain (MAIN-HUB) Lab database, high-frequency cerebral physiologic dataset, we aimed to evaluate the effects of point-sampling resolution reduction on point and interval predictions using the autoregressive integrated moving average (ARIMA) model for both raw signals and derived indices. Temporal resolution was reduced by selecting the first value within non-overlapping intervals, ranging from 1-min (min) to 12-h windows. A total of 125 patient data was analyzed across various temporal resolutions. The results indicated that ARIMA models performed well at higher resolutions (e.g., 1-min), capturing short-term physiological dynamics with lower errors. However, as resolution decreased, errors and variability increased, particularly for signals like mean arterial pressure (MAP) and cerebral perfusion pressure (CPP), which exhibit rapid or complex physiological changes. The findings underscore the need to carefully balance temporal resolution, model performance, and computational efficiency, especially when dealing with high-frequency physiological data in clinical settings.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"6 1","pages":"Article 100248"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790330","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":"Letter to editor regarding “A systematic review and meta-analysis on the diagnostic accuracy of artificial intelligence and computer-aided diagnosis of lumbar prolapsed intervertebral disc”","authors":"Estanislao Arana","doi":"10.1016/j.neuri.2025.100235","DOIUrl":"10.1016/j.neuri.2025.100235","url":null,"abstract":"","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100235"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220107","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":"Functional MRI in hypertension – A systematic review of brain connectivity, regional activity, and cognitive impairment","authors":"Sathya Sabina Muthu ,&nbsp;Suresh Sukumar ,&nbsp;Rajagopal Kadavigere ,&nbsp;Shivashankar K.N. ,&nbsp;K. Vaishali ,&nbsp;Ramesh Babu M.G. ,&nbsp;Hari Prakash Palaniswamy ,&nbsp;Abhimanyu Pradhan ,&nbsp;Winniecia Dkhar ,&nbsp;Nitika C. Panakkal ,&nbsp;Sneha Ravichandran ,&nbsp;Dilip Shettigar ,&nbsp;Poovitha Shruthi Paramashiva","doi":"10.1016/j.neuri.2025.100233","DOIUrl":"10.1016/j.neuri.2025.100233","url":null,"abstract":"<div><div>Hypertension is increasingly recognized as a key contributor to cognitive decline and brain structure and function alterations. Functional Magnetic Resonance Imaging (fMRI) provides a non-invasive means to detect early disruptions in neural networks before clinical symptoms of cognitive impairment emerge. This systematic review explored the application of fMRI in assessing brain functional changes and cognitive performance in individuals with hypertension. A comprehensive search of electronic databases identified eight relevant studies, most of which employed resting-state fMRI techniques. Findings majorly demonstrated that hypertension is associated with altered connectivity within key neural networks, including the default mode network, frontoparietal network, and salience network. Additional observations included reduced regional homogeneity and changes in low-frequency fluctuations. These neural alterations were linked to decreased memory, executive function, and attention. While the findings support the potential of fMRI as an early biomarker for hypertension-related cognitive impairment, the evidence remains limited by the small number of studies and geographic concentration. Nonetheless, fMRI holds promise for clinical application in identifying individuals at risk and guiding timely interventions. Additional longitudinal studies with broader geographic representation are necessary to confirm these insights and facilitate the integration of fMRI into the routine evaluation and management of hypertension-related brain alterations.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100233"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060025","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":"Enhancing neuromolecular imaging classification in low-data regimes with generative machine learning: A case study in HDAC PET/MR imaging of alcohol use disorder","authors":"Tyler N. Meyer ,&nbsp;Olga Andreeva ,&nbsp;Roger D. Weiss ,&nbsp;Wei Ding ,&nbsp;Iris Shen ,&nbsp;Changning Wang ,&nbsp;Ping Chen ,&nbsp;Tewodros Mulugeta Dagnew","doi":"10.1016/j.neuri.2025.100225","DOIUrl":"10.1016/j.neuri.2025.100225","url":null,"abstract":"<div><h3>Introduction</h3><div>Positron Emission Tomography (PET) is a vital modality for investigating brain related disorders. However, data scarcity especially for novel molecular targets like neuroepigenetic enzymes combined with difficult-to-recruit patient populations limits the development of machine learning (ML) models. Our primary objective is to enhance single-subject classification of neuromolecular imaging data and facilitate biomarker discovery. We demonstrate our approach using histone deacetylase (HDAC) PET/MR imaging in Alcohol Use Disorder (AUD).</div></div><div><h3>Methods</h3><div>We propose <em>Catalysis Training pipeline</em>, a framework that augments real imaging data with high-quality synthetic data generated by a Wasserstein Conditional Generative Adversarial Network (WCGAN). Using [¹¹C]Martinostat PET/MR imaging, we extracted 1-D standardized uptake value ratio (SUVR) tabular features representing HDAC enzyme expression density across eight cingulate subregions. These were used to train and test ML classifiers, including Support Vector Machine (SVM), XGBoost, and Random Forest, under leave-one-out cross-validation.</div></div><div><h3>Results</h3><div>Integrating synthetic data in the training process improved classification accuracy significantly: +26% for XGBoost and Random Forest (from 59% to 85%), and +18% for SVM (from 70% to 88%). Synthetic samples improved model generalizability. Key hemispheric and subregional cingulate HDAC patterns were also identified as potential biomarkers.</div></div><div><h3>Conclusion</h3><div>Our results demonstrate that generative AI can help overcome data scarcity in low-data regime neuroimaging applications. Catalysis Training provides a scalable strategy to enhance ML-driven biomarker discovery and disease classification, especially for rare or difficult-to-study disorders like AUD. Clinically, cingulate HDAC expression measured by [¹¹C]Martinostat PET/MR shows promise as an objective biomarker for AUD, complementing DSM-based diagnosis and informing novel treatment strategies.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100225"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886494","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":"Deep learning for fetal brain imaging: A systematic review and framework towards privacy-preserving neurodevelopmental informatics","authors":"Sayma Alam Suha,&nbsp;Rifat Shahriyar","doi":"10.1016/j.neuri.2025.100241","DOIUrl":"10.1016/j.neuri.2025.100241","url":null,"abstract":"<div><div>Fetal neurodevelopment is a complex process of neural growth during pregnancy, where early detection of abnormalities is vital, and deep learning offers promising techniques for this purpose. The objective of this systematic review is to investigate deep learning applications in fetal neurodevelopment, aiming to synthesize cutting-edge research, examine methodologies, identify research gaps, and propose a federated learning framework. Following PRISMA 2020 guidelines, 55 peer-reviewed articles were selected from an initial 900 records across major databases and additional sources where each article was examined through six specific data extraction criteria. Peer-reviewed articles from 2005 to 2025, specifically those exploring automated deep learning for fetal neurodevelopment using clinical images were included, while non-deep learning analyses were excluded. Risk of bias was qualitatively assessed based on design, data diversity, validation, and reporting. Key scopes of the studies included brain segmentation and regionalization (50.91%), structural measurement (12.73%), image reconstruction, enhancement and synthesis (21.82%) and predictive modeling and clinical classification (14.55%) which also distinguishes between tasks involving pixel-level analysis and image-level predictions. The 55 included studies used diverse datasets (753 to 433,000 images) as well as synthetic image data in some recent works covering wide-ranging gestational ages, mainly using MRI and ultrasound images. The systematic analysis explicitly categorizes each study by task type, applied methodology (U-Net variants, transformer-based models, CNNs, implicit neural representations), and corresponding evaluation metrics—segmentation (DSC, IoU, HD95), classification (Accuracy, Precision, AUC), regression (MAE, RMSE, R²), and reconstruction (PSNR, SSIM), facilitating standardized performance comparisons and establishing clear benchmarks for future research in automated fetal brain imaging. Significant gaps that were identified include inadequate data diversity, privacy measures, limited clinical interpretability and validity of AI models, and insufficient integration of multimodal data. To address these challenges, a unified framework is proposed that integrates multimodal data fusion, explainable artificial intelligence (XAI) paradigms, and federated learning architectures complemented by synthetic data generation techniques to ensure robust privacy preservation in real-world application. This work was not specifically funded, and the review was not registered.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100241"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465619","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":"Smart web interface for student mental health prediction using machine learning with blockchain technology","authors":"Mishu Deb Nath ,&nbsp;Md. Khabir Uddin Ahamed ,&nbsp;Omayer Ahmed ,&nbsp;Tanvir Ahmed ,&nbsp;Sujit Roy ,&nbsp;Mohammed Nasir Uddin","doi":"10.1016/j.neuri.2025.100236","DOIUrl":"10.1016/j.neuri.2025.100236","url":null,"abstract":"<div><div>Student mental health is becoming a growing global concern, with more students facing psychological stress, anxiety, and related disorders. These mental health challenges often develop gradually and, if ignored, can negatively affect a student's academic performance and personal life. Early detection is essential, but high costs, limited resources, and time constraints often hinder it. The study proposes a machine learning-based approach to predict and assess student mental health, addressing this problem. Using rich psychological and behavioral data, the system can identify early signs of mental distress. An extensive evaluation of 12 machine learning models identified the top six performers. Logistic regression, Decision Tree, Extra Tree, Adaboost, Gradient Boosting, and XGBoost. Among these, the fine-tuned Random Forest algorithm achieved the highest performance, with an impressive accuracy of 95.6%. To ensure practical implementation, a Streamlit-based application was developed. This application enables educators and mental health professionals to perform real-time analysis and receive predictions in a clear and user-friendly format. The study incorporates blockchain technology to ensure the secure handling of sensitive data. Data collected through the Web interface, such as responses to mental health questionnaires, is securely stored using blockchain technology. This integrated system offers a reliable and scalable solution for monitoring and supporting student mental health.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100236"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220108","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":"Morphometric characterization of early- and late-onset Parkinson's disease: An ROI-based study of classification and correlation","authors":"Sadhana Kumari ,&nbsp;Bharti Rana ,&nbsp;Shefali Chaudhary ,&nbsp;Roopa Rajan ,&nbsp;S. Senthil Kumaran ,&nbsp;Achal Kumar Srivastava ,&nbsp;Leve Joseph Devarajan","doi":"10.1016/j.neuri.2025.100228","DOIUrl":"10.1016/j.neuri.2025.100228","url":null,"abstract":"<div><h3>Introduction</h3><div>Parkinson's disease (PD) is associated with progressive neurodegeneration, particularly involving cortico-basal ganglia-thalamo-cortical circuits that underlie motor and cognitive functions. We investigated the morphological brain features derived from structural MRI to differentiate early (EOPD) and late-onset PD (LOPD) from age-related healthy controls.</div></div><div><h3>Methods</h3><div>3D T1-weighted MRI was acquired in 114 subjects (27 EOPD, 32 YHC, 28 LOPD, and 27 OHC). Gray matter volume (GMV), white matter volumes (WMV), fractal dimension (FD), gyrification index (GI), and cortical thickness (CT) were extracted using CAT12 software. Three tasks, (i) identification of statistically significant regions, (ii) automatic diagnosis using machine learning using individual and combined features, and (iii) correlation study were performed to quantify the relationship between morphological features and clinical variables.</div></div><div><h3>Results</h3><div>EOPD exhibited a reduction in GMV and cortical complexity in frontal, parietal and temporal lobes compared to YHC. We achieved the highest classification accuracy of 89.06% using FD and CT for EOPD vs YHC, 90.91% using GMV, WMV and FD for LOPD vs OHC and 89.29% using WMV and FD for EOPD vs LOPD after data augmentation for class balancing. EOPD revealed a negative correlation of GMV with UPDRS II (in medial frontal cortex, precuneus and supplementary motor cortex), FD with UPDRS III in pericalcarine; GI and UPDRS II in transverse temporal and pars opercularis; CT with UPDRS III in superior frontal regions.</div></div><div><h3>Conclusion</h3><div>Distinct morphometric changes were observed in patients with EOPD and LOPD in comparison with HC, suggesting the utility of morphological measures in early diagnosis of PD.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100228"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908997","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":"Power-to-power cross-frequency coupling as a novel approach for temporal lobe seizure detection and analysis","authors":"Bar Lehmann,&nbsp;Andrei V. Medvedev","doi":"10.1016/j.neuri.2025.100240","DOIUrl":"10.1016/j.neuri.2025.100240","url":null,"abstract":"<div><h3>Objective</h3><div>Power-to-power cross-frequency coupling (CFC) is a novel method to index the dynamic spatio-temporal interactions between brain rhythms, including high frequency oscillations (HFOs). This research evaluates this promising method's capacity for seizure detection with intracranial EEG. Seizures can be conceptualized as composites of different electrographic patterns including (1) spike, (2) ripple-on-spike, and (3) ripple-on-oscillation. This study also performs a basic CFC analysis of each of these components which has potential to further the understanding of epileptogenic processes.</div></div><div><h3>Methods</h3><div>In this study, deep learning networks including Stacked Sparse Autoencoder (SSAE) and Long Short Term Memory (LSTM) are trained to detect seizures and help characterize CFC patterns for these three common seizure components. The analysis uses intracranial EEG (iEEG) records from the ieeg.org (Mayo Clinic files) database. Temporal Lobe Epilepsy (TLE) seizures (<span><math><mi>n</mi><mo>=</mo><mn>120</mn></math></span>) from 26 patients were analyzed along with segments of background activity. Power-to-power coupling was calculated between all frequencies 1–250 Hz pairwise using the EEGLAB toolbox. CFC matrices of seizure and background activity were used as training or testing inputs to the autoencoder.</div></div><div><h3>Results</h3><div>The trained network was able to recognize background and seizure segments (not used in training) with a sensitivity of 90.2%, specificity of 96.8% and overall accuracy of 93.4%. The three seizure components (spike, ripple-on-spike, ripple-on-oscillation) were also observed to have unique CFC signatures.</div></div><div><h3>Conclusions</h3><div>The results provide evidence both for (1) the relevance of power-to-power coupling (PPC) for TLE seizure detection in iEEG, as well as (2) there existing unique PPC signatures of three common seizure components.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100240"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465624","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":"Entity-augmented neuroscience knowledge retrieval using ontology and semantic understanding capability of LLM","authors":"Pralaypati Ta ,&nbsp;Sriram Venkatesaperumal ,&nbsp;Keerthi Ram ,&nbsp;Mohanasankar Sivaprakasam","doi":"10.1016/j.neuri.2025.100237","DOIUrl":"10.1016/j.neuri.2025.100237","url":null,"abstract":"<div><div>Neuroscience research publications encompass a vast wealth of knowledge. Accurately retrieving existing information and discovering new insights from this extensive literature is essential for advancing the field. However, when knowledge is dispersed across multiple sources, current state-of-the-art retrieval methods often struggle to extract the necessary information. A knowledge graph (KG) can integrate and link knowledge from multiple sources. However, existing methods for constructing KGs in neuroscience often rely on labeled data and require domain expertise. Acquiring large-scale, labeled data for a specialized area like neuroscience presents significant challenges. This work proposes novel methods for constructing KG from unlabeled large-scale neuroscience research corpus utilizing large language models (LLM), neuroscience ontology, and text embeddings. We analyze the semantic relevance of neuroscience text segments identified by LLM for building the knowledge graph. We also introduce an entity-augmented information retrieval algorithm to extract knowledge from the KG. Several experiments were conducted to evaluate the proposed approaches. The results demonstrate that our methods significantly enhance knowledge discovery from the unlabeled neuroscience research corpus. The performance of the proposed entity and relation extraction method is comparable to the existing supervised method. It achieves an F1 score of 0.84 for entity extraction from the unlabeled data. The knowledge obtained from the KG improves answers to over 52% of neuroscience questions from the PubMedQA dataset and questions generated using selected neuroscience entities.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100237"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465738","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":"Exploring KAN as a next-generation replacement for MLPs in EEG-based seizure detection","authors":"Eman Allogmani","doi":"10.1016/j.neuri.2025.100226","DOIUrl":"10.1016/j.neuri.2025.100226","url":null,"abstract":"<div><div>Epilepsy is a chronic neurological disorder characterized by recurrent seizures due to abnormal brain activity. Accurate detection of seizures from electroencephalogram (EEG) signals is critical, but it is often challenged by signal noise and class imbalance in real-world data. In this study, we systematically evaluate Kolmogorov–Arnold Networks (KANs)—a recent neural architecture based on the Kolmogorov–Arnold representation theorem—as an alternative to Multi-Layer Perceptrons (MLPs) for EEG-based seizure classification, with a focus on model robustness under noisy conditions. This is the first comprehensive evaluation of KAN's robustness under multiplicative noise in the context of EEG seizure detection. Experiments were conducted using two widely used EEG datasets: the Bonn dataset and the CHB-MIT Scalp EEG dataset. Across multiple network configurations and varying levels of multiplicative noise, we assess performance using F1 Score, AUROC, AUPRC, Sensitivity, and Specificity. Our findings show that KAN achieves more stable performance than MLPs under noisy conditions, particularly in smaller architectures. These results suggest that KAN may offer a robust and generalizable approach for seizure detection in noise-prone clinical settings.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100226"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144865012","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}