T cell miRNA in multiple sclerosis pathogenesis (P5209)
Bibliographic record
Abstract
Abstract Multiple sclerosis (MS) is an immune-mediated disease that results in destruction of the myelin sheath and axonal degeneration. Interestingly, while myelin-specific T cells are present in peripheral blood mononuclear cells (PBMC) of both healthy controls and MS patients, they only become pathogenic in MS patients. This suggests that some regulatory checkpoint is bypassed in MS T cells, unleashing their autoimmune potential. By regulating protein-coding mRNAs, microRNAs (miRNAs) control important cellular processes. Specifically, we have previously shown that dysregulated miRNAs in MS patient’s naïve T cells promote pro-inflammatory Th1 immune responses. In this study, we explored whether differences in miRNA expression in memory T cells of MS patients further contribute to the pathogenesis of MS. Memory CD4 T cells were purified by magnetic bead sorting to 95% purity and subjected to a comprehensive Nanostring technology miRNA profiling. In a study on healthy (n=17) and seven MS patients (n=21), a number of miRs were found to be significantly different in MS compared to healthy controls. Among the identified miRs, many target genes known to be involved in T cell proliferation and apoptosis, activation, adhesion and regulation. These results identify miRNAs in memory T cells as MS biomarkers of biological significance and potential clinical value, and reveal potential new therapeutic targets in MS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".