Pyrogenic, Lethal, and Emetic Properties of Superantigens in Rabbits and Primates
Bibliographic record
Abstract
Superantigens (SAgs) stimulate large fractions of T cells by circumventing normal antigen presentation through binding both class II major histocompatibility complex (MHC) molecules on antigen-presenting cells, and specific variable regions on the β-chain (Vβ) of the T-cell antigen receptor (TCR) ( 1 , 2 ). The bacterial SAgs produced from coagulase positive staphylococci (Staphylococcus aureus) and group A streptococci (GAS; Streptococcus pyogenes) belong to the large and expanding family of pyrogenic toxins, and various members of this group of SAgs have a clear involvement in the toxic shock syndrome (TSS). Through SAg-mediated stimulation, Tlymphocytes are activated at several orders of magnitude above antigen-specific activation, resulting in the extensive release of cytokines that are believed to be responsible for the most severe features of TSS. SAgs from S. aureus include toxic shock syndrome toxin-1 (TSST-1), multiple staphylococcal enterotoxin (SE) serotypes (A through P, excluding F), while SAgs from S. pyogenes include streptococcal pyrogenic exotoxin (SPE) serotypes (A, C, G, H, I, J) as well as streptococcal superantigen (SSA) and multiple streptococcal mitogenic exotoxin Z (SMEZ) variants ( 3 - 5 ). The toxins within this class of SAgs that have been tested are all pyrogenic, and all are likely to be capable to induce TSS in susceptible hosts if supplied in sufficient quantity. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".