Bibliographic record
Abstract
PURPOSE OF REVIEW: This review will highlight recent advances in our understanding of the pathogenesis of Kawasaki disease, highlighting the molecular players involved in regulation of T-cell activation and their affect on disease incidence and outcome in both humans and mouse. RECENT FINDINGS: Kawasaki disease is the most common cause of multisystem vasculitis in childhood. The vessels most commonly damaged are the coronary arteries, making Kawasaki disease the number one cause of acquired heart disease in children from the developed world. The contribution of genetics to disease predisposition is clearly implicated, but the mechanisms involved in regulating predisposition to disease susceptibility and outcome are not clearly understood. Two independent approaches have recently identified regulation of T-cell activation as the critical factor in determining susceptibility and severity of Kawasaki disease. Firstly, genetic analysis of affected Japanese children identified ITPKC, 1,4,5-triphosphate 3-kinase C, a kinase involved in regulation of T-cell activation, to be significantly associated with susceptibility to and increased severity of Kawasaki disease. A second independent approach using an animal model of Kawasaki disease has also identified regulation of T-cell activation, specifically costimulation, the second signal regulating optimal T-cell activation as the critical regulator of susceptibility to and severity of disease. SUMMARY: Understanding the molecular players responsible for dysregulation of the immune response in Kawasaki disease will foster development of improved diagnostic/predictive tools and more rational use of therapeutic agents to improve outcome in affected children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".