AI and Neurological Rare & Neuroimmune Diseases ── 2026-10-30
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（1） · ALS（0） · Parkinson's（0） · Hereditary neurodegeneration（0） · Neuromuscular（0） · Multiple sclerosis（1） · NMOSD/MOGAD（0） · Myasthenia gravis（0） · Autoimmune encephalitis/CIDP/GBS（0）
Neurodegeneration and neuroimmunology research and AI
- Abstract
Circular RNAs regulate gene expression through interactions with ribonucleic acid binding proteins, but their binding sites remain difficult to predict across proteins and cellular contexts. Existing methods often focus on transcript features while making limited use of protein-specific information and structural context. Here, we show that an integrated deep learning framework combining circular transcript sequence composition, physicochemical properties, secondary structure, pretrained representations and protein features improves interaction-site prediction across 37 benchmark datasets and generalizes across cell lines; model interpretation recovers conserved single-stranded sequence patterns, while analyses of proteins linked to Alzheimer disease, Parkinson disease and amyotrophic lateral sclerosis identify recurrent pathways involving nucleocytoplasmic transport, lysosomal signaling and guanosine triphosphatase activity and prioritize compounds for experimental follow-up, supporting accurate and interpretable mapping of circular ribonucleic acid protein interactions without implying therapeutic efficacy.
Journal IF-equivalent: 18.6 (OpenAlex 2-year mean citedness, value as of 2026-10-25, retrieved 2026-10-26; not the official Clarivate IF)Reference: Wang Y, Li J, Liu X, You L, Chen CYC. CPRISP: an interpretable deep learning framework predicting circRNA-RBP interactions and guiding therapeutic discovery in neurodegeneration. Nat Commun. 2026 Oct 28 [Epub ahead of print]. doi:10.1038/s41467-026-79009-3.Checked: Abstract only
See all 1 Basic research papers →
ALS: treatment, trials and AI
No new qualifying paper for this issue.
Parkinson's disease and parkinsonism: treatment, trials and AI
No new qualifying paper for this issue.
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
- Abstract
Background/Objectives: Multiple sclerosis (MS) is a chronic demyelinating disease of the central nervous system, and magnetic resonance imaging (MRI) plays a central role in its diagnosis and monitoring. Because different MRI sequences provide complementary information about MS-related pathology, jointly exploiting multiple sequences may improve automated classification. However, many existing automated MS classification studies rely on limited sequence information, are not consistently evaluated under subject-independent protocols, and are constrained by the scarcity of publicly available multi-sequence datasets organized at the subject level.
Methods: To address these gaps, we collected and publicly released a new multi-sequence brain MRI dataset and proposed DenseGated-MSNet, a novel end-to-end multi-input deep learning model for MS versus non-MS classification. DenseGated-MSNet processes each co-acquired MRI sequence in parallel through an independent sequence-specific DenseNet-121 branch and integrates the resulting representations through a proposed input-dependent gated feature-level fusion mechanism. Under a strict subject-independent evaluation protocol, the model configuration was determined on a subject-independent development set, and the performance of the final model was internally evaluated using five-fold subject-independent cross-validation on the same cohort at the image and subject levels.
Results: In internal five-fold subject-independent cross-validation, DenseGated-MSNet achieved a pooled subject-level accuracy of 90.57% (95% CI: 86.44–93.53%) and an AUC of 0.947 (95% CI: 0.918–0.976). The learned gate values also provided sequence-level modulation information, indicating how strongly each MRI sequence was weighted during multi-sequence integration.
Conclusions: Overall, these findings suggest that DenseGated-MSNet can effectively integrate complementary information from multiple MRI sequences for discriminating established MS from selected non-MS controls, while providing sequence-level modulation information. Together with the released dataset, the proposed framework provides a reproducible basis for future subject-independent MS classification studies.
Journal IF-equivalent: 4.0 (OpenAlex 2-year mean citedness, value as of 2026-10-16, retrieved 2026-10-17; not the official Clarivate IF)Reference: Yaman S, Karasekreter N, Çeliköz R, Özgül E, Guler H. DenseGated-MSNet: A Multi-Sequence Gated Fusion Network for Multiple Sclerosis Classification from Brain MRI. Diagnostics. 2026 Oct 28;16(21):3539. doi:10.3390/diagnostics16213539.Checked: Abstract only
See all 1 Multiple sclerosis papers →
NMOSD and MOGAD: treatment, trials and AI
No new qualifying paper for this issue.
Myasthenia gravis: treatment, trials and AI
No new qualifying paper for this issue.
Autoimmune encephalitis, CIDP and Guillain-Barre syndrome: treatment, trials and AI
No new qualifying paper for this issue.