ArticleFrontiers in neurology2025
Immune inflammatory regulation in Anti-NMDAR encephalitis: insights from transcriptome analysis.
Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- [Association Between T Lymphocyte Mitochondrial Function and Disease Activity in Anti-NMDAR Encephalitis].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is a critical neurological disorder mediated by autoimmune mechanisms, Previous literature suggests that immune inflammatory responses may be involved in the progression of anti NMDAR encephalitis, but its molecular regulatory mechanisms still remain uncertain. We aimed to identify transcriptome-wide landscape of mRNAs and explore the potential pathogenesis for anti-NMDAR encephalitis. Methods: Peripheral blood mononuclear cells were obtained from six patients with anti-NMDAR encephalitis and six controls for RNA extraction and library creation. The Illumina HiSeq platform was used to do transcriptome sequencing. We utilized R software to identify differentially expressed genes (DEGs) and performed a functional enrichment analysis. Furthermore, random forest (RF) and support vector machine-recursive feature elimination (SVM-RFE) were employed to screen for and identify anti-NMDAR encephalitis diagnostic signatures. To verify the findings, we employed quantitative real-time polymerase chain reaction. Receiver operating characteristic curves were utilized to assess the diagnostic values. We evaluated the inflammatory state of anti-NMDAR encephalitis using cell-type identification by computing the relative subsets of RNA transcripts (CIBERSORT) and investigated the relationship between diagnostic biomarkers and immune cell subsets. Results: 899 DEGs were identified (568 upregulated and 331 downregulated), of which 78 were immune-related genes. The DEGs were found to be considerably enriched in immunological inflammation-related pathways, according to the functional enrichment analysis. Insulin-like factor 3 [area under the curve (AUC) = 0.917] and tumor protein translationally controlled regulator 1 (AUC = 0.944) were considered potential diagnostic indicator candidates of anti-NMDAR encephalitis, with statistically significant variations in expression. An immune cell analysis of immune cell proportions suggests that monocytes, CD8 Conclusions: Transcriptome analysis reveals significant activation of peripheral immune-inflammatory pathways in anti-NMDAR encephalitis. INSL3 and TPT1 may serve as potential auxiliary diagnostic biomarkers, while monocyte, CD8+ T cell, and Treg infiltration likely synergistically drive disease progression.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.