Sex‐dependent expression levels of VAV1 and P2X7 in PBMC of multiple sclerosis patients
Bibliographic record
Abstract
Multiple sclerosis (MS) is an inflammatory autoimmune disorder of the central nervous system and the leading cause of progressive neurological disability in young adults. It decreases the patient's lifespan by about 10 years and affects women more than men. No medication entirely restricts or reverses neurological degradation. However, early diagnosis and treatment increase the possibility of a better outcome. To identify new MS biomarkers, we tested the expression of six potential markers (P2X4, P2X7, CXCR4, RGS1, RGS16 and VAV1) using qPCR in peripheral blood mononuclear cells (PBMC) of MS patients treated with interferon β (IFNβ), with glatiramer acetate (GA) or untreated. We showed that P2X7 and VAV1 are significantly induced in MS patients. In contrast, the expression of P2X4, CXCR4, RGS1 and RGS16 was not significantly modified by MS in PBMC. P2X7 and VAV1 are essentially induced in female patients, suggesting these markers are connected to sex-specific mechanisms. Strikingly, VAV1 expression is higher in healthy women than healthy men and IFNβ treatment of MS reduced VAV1 expression in female MS patients while it up-regulated VAV1 in male MS patients. Our data point to the differential, sex-dependent value of MS markers and treatment effects. Although rgs16 expression in PBMC was not a valid MS marker in patients, the strong upregulation of P2X4 and P2X7 induced in the spinal cord of WT mice by EAE was abrogated in rgs16KO mice suggesting that rgs16 is required for P2X4 and P2X7 induction by neurological diseases.
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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) | 0.003 | 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".