Ocrelizumab modulates the IL-2 signaling pathway and associated lncRNAs in multiple sclerosis
Bibliographic record
Abstract
B cell-depleting monoclonal antibody, is widely used in multiple sclerosis (MS), yet its molecular impact on immune regulation remains incompletely defined. Given the importance of the interleukin-2 (IL-2) signaling axis in immune tolerance, we investigated the expression of key genes in this pathway, and their associated long non-coding RNAs in an Iranian cohort of relapsing-remitting MS patients. Peripheral Blood Mononuclear Cells (PBMC) from 20 untreated patients, 20 Ocrelizumab (Xacrel®)-treated stable RRMS patients for whom at least six months had passed since the last dose, and 20 healthy controls were analyzed by RT-PCR. Treatment resulted in reduced IL2RA and FOXP3 but not IL2 expression levels and normalization of FLICR and RP11-536 K7.5 levels in MS patients. Correlation analysis revealed a strong IL2RA-FOXP3 association and inverse IL2RA-RP11-536 K7.5 correlation in treated patients. Lower IL2RA and RP11-536 K7.5 levels correlated with higher EDSS scores. ROC analysis highlighted IL2, IL2RA, and FOXP3 as strong classifiers in treated patients, and RP11-536 K7.5 in untreated cases. FOXP3 expression positively correlates with the number of Ocrelizumab infusions, indicating reinforcement of regulatory T-cell activity with ongoing therapy. These findings highlight IL-2 pathway modulation and lncRNA regulation as therapeutic effects of Ocrelizumab.
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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) | 0.001 | 0.000 |
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".