Temporal Sequence and Functional Implications of Vβ‐Specific T Cell Receptor Down‐regulation and Costimulatory Molecule Expression following In Vitro Stimulation with the Staphylococcal Superantigen Toxic Shock Syndrome Toxin–1
Bibliographic record
Abstract
The superantigen toxic shock syndrome toxin-1 (TSST-1) is implicated as the major cause of staphylococcal toxic shock syndrome. The temporal sequence of early signaling events in human peripheral blood mononuclear cells following TSST-1 stimulation was examined. TSST-1 induced rapid and complete down-regulation of V beta 2-specific T cell receptor (TCR), followed by transient CD154 expression on CD4(+) lymphocytes. This was sequentially followed by the up-regulation of CD86, CD80, CD40, and human leukocyte antigen-DR expression on CD14(+) monocytes. In contrast, S14N, a TSST-1 mutant toxin with a single amino acid substitution that is known to be impaired in interleukin (IL)--2, interferon (IFN)-gamma, and tumor necrosis factor (TNF)-alpha secretion, was deficient in both V beta 2-TCR down-regulation and CD154 and CD80/CD86 expression. Furthermore, pretreatment with monoclonal antibodies against V beta 2-TCR, CD80/CD86, and CD154 significantly inhibited TSST-1-induced IL-2, IFN-gamma, and TNF-alpha secretion. Taken together, these results indicate that early V beta-specific TCR activation, along with CD80/CD86 and CD154 costimulation, are key determinants of the TSST-1-induced proinflammatory cytokine response.
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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".