Upregulation of TRAIL expression on human T lymphocytes by interferon b and glatiramer acetate
Bibliographic record
Abstract
We measured the in vivo and in vitro effects of interferon (IFN)beta and glatiramer acetate (GA) on the expression of the regulatory molecule, tumor necrosis factor related apoptosis inducing ligand (TRAIL), in patients with multiple sclerosis (MS). We confirmed the prior observation that TRAIL is enhanced on anti-CD3 activated T cells by the in vitro addition of IFNbeta. T cells from IFNbeta-treated patients stimulated with anti-CD3 only, had higher levels of TRAIL than untreated patients, suggesting that in vivo IFNbeta exposure has an effect on TRAIL expression in association with T cell activation. In vitro IFNbeta-induced TRAIL upregulation on anti-CD3 or phytohemagglutinin-activated T cells was comparable for IFNbeta-treated and non-treated MS patients and controls, indicating that IFN receptors were neither saturated nor down-regulated by current IFNbeta therapy. Although GA in vivo or in vitro did not induce TRAIL, the IFNbeta +GA combination in vitro enhanced TRAIL expression to higher levels than IFNbeta alone on CD4+ T cells obtained from MS patients, regardless of GA treatment status, and healthy donors, and on GA reactive T cell lines derived from GA-treated patients or controls. Whether any observed therapeutic effects of GA/IFNbeta combination therapy will correlate with TRAIL expression and function remains to be determined.
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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".