Innate immune responses following Kawasaki disease and toxic shock syndrome
Bibliographic record
Abstract
The pathogenesis of Kawasaki disease (KD) remains unknown and there is accumulating evidence for the importance of the innate immune system in initiating and mediating the host inflammatory response. We compared innate immune responses in KD and toxic shock syndrome (TSS) participants more than two years after their acute illness with control participants to investigate differences in their immune phenotype. Toxic shock syndrome shares many clinical features with KD; by including both disease groups we endeavoured to explore changes in innate immune responses following acute inflammatory illnesses more broadly. We measured the in vitro production of interferon (IFN)-γ, tumour necrosis factor (TNF)-α, interleukin (IL)-1β, IL-6, IL-1 receptor antagonist (IL-1ra), and IL-10 following whole blood stimulation with toll-like receptor and inflammasome ligands in 52 KD, 20 TSS, and 53 control participants in a case-control study. Analyses were adjusted for age, sex, and unstimulated cytokine concentrations. Compared to controls, KD participants have reduced IL-1ra production in response to stimulation with double stranded RNA (geometric mean ratio (GMR) 0.37, 95% CI 0.15, 0.89, p = 0.03) and increased IL-6 production in response to incubation with Lyovec™ (GMR 5.48, 95% CI 1.77, 16.98, p = 0.004). Compared to controls, TSS participants have increased IFN-γ production in response to peptidoglycan (GMR 4.07, 95% CI 1.82, 9.11, p = 0.001), increased IL-1β production to lipopolysaccharide (GMR 1.64, 95% CI 1.13, 2.38, p = 0.01) and peptidoglycan (GMR 1.61, 95% CI 1.11, 2.33, p = 0.01), and increased IL-6 production to peptidoglycan (GMR 1.45, 95% CI 1.10, 1.92, p = 0.01). Years following the acute illness, individuals with previous KD or TSS exhibit a pro-inflammatory innate immune phenotype suggesting a possible underlying immunological susceptibility or innate immune memory.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".