Innate immune dysregulation in multisystem inflammatory syndrome in children (MIS-C)
Bibliographic record
Abstract
Abstract MIS-C is a systemic inflammation disorder with poorly characterised immunopathological mechanisms. We compared changes in the systemic immune response in children with MIS-C (n = 12, 5–13 years) to healthy controls (n = 14, 5–15 years). Analysis was done in whole blood treated with LPS. Expression of CD11b and Toll-like receptor-4 (TLR4) in neutrophils and monocytes were analysed by flow cytometry. Serum cytokines (IL-1β, IL-2, IL-6, IL-8, IL-10, IL-Ira, TNF-α, TNF-β, IFN-Υ, VEGF, EPO and GM-CSF) and mRNA levels of inflammasome molecules (NLRP3, ASC and IL-1β) were evaluated. Subpopulations of lymphocytes (CD3+, CD19+, CD56+, CD4+, CD8+, TCR Vδ1+, TCR Vδ2+) were assessed at basal levels. Absolute counts of neutrophils and NLR were high in children with MIS-C while absolute counts of lymphocytes were low. Children with MIS-C had increased levels of IL-6, IL-10, TNF-β and VEGF serum cytokines at the basal level, and significantly increased TNF-β post-LPS, compared to controls. IL-1RA and EPO decreased at baseline and post-LPS in MIS-C patients compared to controls. The percentage of CD3+ cells, NK cells and Vδ1 was lower while B cells were higher in children with MIS-C than in controls. Dysregulated immune response in children with MIS-C was evident and may be amenable to immunomodulation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".