Effects of IFN-B on TRAIL and Decoy Receptor Expression in Different Immune Cell Populations from MS Patients with Distinct Disease Subtypes
Bibliographic record
Abstract
Using quantitative RT-PCR, we compared mRNA levels for TRAIL [tumor necrosis factor (TNF)-related apoptosis-inducing ligand] and its receptors in various immune cell subsets derived from the peripheral blood of untreated normal subjects (NS) and patients with distinct subtypes of multiple sclerosis (MS): active relapsing-remitting MS (RRA), quiescent relapsing-remitting MS (RRQ), secondary-progressive MS (SPMS) or primary-progressive MS (PPMS). Consistent with a role for TRAIL in the mechanism of action of interferon-β (IFN-β), TRAIL mRNA levels were increased in monocytes from patients clinically responsive to IFN-β (RRQ) but not those unresponsive to this therapeutic (RRA). TRAIL-R3 (decoy receptor) expression was elevated in T cells from untreated RRMS patients while IFN-β therapy reversed this increase suggesting that IFN-β may promote the apoptotic elimination of autoreactive T cells by increasing the amount of TRAIL available to activate TRAIL death receptors. Serum concentrations of soluble TRAIL were increased to a similar extent by IFN-β therapy in RRQ, RRA and SPMS patients that had not generated neutralizing antibodies against this cytokine. Although our findings suggest altered TRAIL signaling may play a role in MS pathogenesis and IFN-β therapy, they do not support use of TRAIL as a surrogate marker for clinical responsiveness to this therapeutic.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".