Staphylococcal superantigens in colonization and disease
Bibliographic record
Abstract
Superantigens (SAgs) are a family of potent immunostimulatory exotoxins known to be produced by only a few bacterial pathogens, including Staphylococcus aureus. More than 20 distinct SAgs have been characterized from different S. aureus strains and at least 80% of clinical strains harbor at least one SAg gene, although most strains encode many. SAgs have been classically associated with food poisoning and toxic shock syndrome (TSS), for which these toxins are the causative agent. TSS is a potentially fatal disease whereby SAg-mediated activation of T cells results in overproduction of cytokines and results in systemic inflammation and shock. Numerous studies have also shown a possible role for SAgs in other diseases such as Kawasaki disease (KD), atopic dermatitis (AD), and chronic rhinosinusitis (CRS). There is also now a rich understanding of the mechanisms of action of SAgs, as well as their structures and function. However, we have yet to discover what purpose SAgs play in the life cycle of S. aureus, and why such a wide array of these toxins exists. This review will focus on recent developments within the SAg field in terms of the molecular biology of these toxins and their role in both colonization and disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".