AI and Neurological Rare & Neuroimmune Diseases ── 2026-10-14
New papers on neurological rare and neuroimmune diseases and AI, collected from journal feeds, PubMed (searched across all journals) and OpenAlex (a public index used instead of Google Scholar), judged by Jev (an AI that makes language judgements): original research, AI central, which of 9 disease groups, treatment or trials, and then ranked by importance up to a daily limit. Journals are not filtered by impact factor; each paper shows its journal's IF-equivalent (OpenAlex two-year mean citedness) and the date of that value. Each entry shows the full original abstract.
Read by disease: Basic research（0） · ALS（0） · Parkinson's（1） · Hereditary neurodegeneration（0） · Neuromuscular（0） · Multiple sclerosis（0） · NMOSD/MOGAD（0） · Myasthenia gravis（1） · Autoimmune encephalitis/CIDP/GBS（0）
Neurodegeneration and neuroimmunology research and AI
No new qualifying paper for this issue.
ALS: treatment, trials and AI
No new qualifying paper for this issue.
Parkinson's disease and parkinsonism: treatment, trials and AI
- Abstract
The brain diseases such as Fronto-Temporal Dementia (FTD), Parkinson&s;s (PD), and Alzheimer&s;s (AD) are neurodegenerative disease release the neuroinflammation and protein misfolding. However the diagnosis of such diseases ruins challenging due to complexity of the dat and variability of the data. The present study identifies molecular signatures by applying machine learning (ML) techniques by using curated microarray datasets. The datasets are collected from National Center for Biotechnology Information (NCBI ) Gene Expression Omnibus (GEO).Based on R based analysis on GEO (GEO2R) as well as Limma package, the expressed genes (|logFC| ≥ 0.5; adj.P.Val ≤ 0.05) were obtained. ML model with Support Vector Machine (SVM), was developed by training and validating these genes. SVM showed very high precision as well as recall. The heatmaps are ensured the data quality. The work underlines the capability of statistical biomarkers and collective learning to distinguish between disease-specific gene expression profiles, henceforth enabling the development of gene-based diagnostics. In future, to enhance the interpretability of the model and improvement of the model, genomic structural data can be incorporated. This helps to identify the abnormal genes for the further conclusion.
Journal IF: unknown (could not be matched)Reference: Manohar Mathkunti N, Rangaswamy S, Varsha VP, Bhat S. Analysis of Differentially Expressed Genes in Alzheimer's, Parkinson's, and Frontotemporal Dementia Using Machine Learning Algorithm. Interdisciplinary Approaches to Sustainable Research and Development in Science, Technology, and Socio-Economic Studies. 2026 Oct 12 [Epub ahead of print]. doi:10.1201/9781042035762-123.Checked: Abstract only
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Hereditary neurodegenerative diseases: treatment, trials and AI
No new qualifying paper for this issue.
Neuromuscular diseases (muscular dystrophy, SMA): treatment, trials and AI
No new qualifying paper for this issue.
Multiple sclerosis: treatment, trials and AI
No new qualifying paper for this issue.
NMOSD and MOGAD: treatment, trials and AI
No new qualifying paper for this issue.
Myasthenia gravis: treatment, trials and AI
- Abstract
BACKGROUND: Early identification of myasthenia gravis (MG) severe exacerbations that require intensive care unit (ICU) admission remains challenging due disease highly heterogeneous. Existing models seldom incorporate phenotype stratification, limiting interpretability and clinical utility. AIM: This study aimed to develop an explainable machine learning (ML) model to assess phenotype-specific risk factors and predict ICU admission for patients with MG. DESIGN AND METHODS: This retrospective study included 319 hospitalized patients with MG. We utilized clinical variables and systemic inflammation markers for ML modeling. Subgroups were prespecified by thymoma status and age at onset. Primary outcome was need for intensive care during the hospitalization. Model performance was assessed. SHapley Additive exPlanations (SHAP) was employed for interpretation of global and individual features. RESULTS: Of 319 admissions, 33 (10.3%) required ICU care. Four ML models were trained with nested 3-fold validation. eXtreme Gradient Boosting performed best (AUC: 0.94). SHAP ranked MGFA clinical classification, infection during admission, body mass index, and inflammatory indices as principal predictors. Subgroup analyses revealed phenotype-specific patterns, including opposite SHAP directions for inflammatory markers between thymoma and non-thymoma groups and distinct rankings between different age of onset groups. SHAP-based personalized explanations revealed patient-specific variations in risk ranking and directionality of predictive features. A web-based calculator was developed to support real-time clinical implementation of the model, potentially allowing for risk stratification. CONCLUSIONS: An explainable machine learning framework that integrates clinical features with systemic inflammation indices reliably predicts ICU admission in MG and reveals phenotype-specific risk patterns, enabling personalized risk stratification in MG management.
Journal IF-equivalent: 0.5 (OpenAlex 2-year mean citedness, value as of 2026-10-13, retrieved 2026-10-14; not the official Clarivate IF)Reference: Chang CC, Lin KY, Yeh JH, Chiu HC, Liu TC, Lu CJ. Explainable Machine Learning Model for Personalized Assessment of the Need for Intensive Care in Patients with Myasthenia Gravis Based on Phenotype and Inflammatory Markers. QJM: An International Journal of Medicine. 2026 Oct 12 [Epub ahead of print]. doi:10.1093/qjmed/hcag289.Checked: Abstract only
See all 1 Myasthenia gravis papers →
Autoimmune encephalitis, CIDP and Guillain-Barre syndrome: treatment, trials and AI
No new qualifying paper for this issue.