Th17/Treg ratio derived using DNA methylation analysis is associated with the late phase asthmatic response
Bibliographic record
Abstract
BACKGROUND: The imbalance between Th17 and Treg cells has been studied in various diseases including allergic asthma but their roles have not been fully understood in the development of the late phase asthmatic response. OBJECTIVES: To determine changes in Th17 and Treg cell numbers between isolated early responders (ERs) and dual responders (DRs) undergoing allergen inhalation challenge. To identify gene expression profiles associated with Th17 and Treg cells. METHODS: 14 participants (8 ERs and 6 DRs) with mild allergic asthma underwent allergen inhalation challenge. Peripheral blood was collected prior to and 2 hours post allergen challenge. DNA methylation analysis was used to quantifiy the relative frequencies of Th17, Tregs, total B cells, and total T cells. Gene expression from whole blood was measured using microarrays. Technical replication of selected genes was performed using nanoString nCounter Elements. RESULTS: The Th17/Treg ratio significantly increased in DRs compared to ERs post allergen challenge compared to pre-challenge. Genes significantly correlated to Th17 and Treg cell counts were inversely correlated with each other. Genes significantly correlated with Th17/Treg ratio included the cluster of genes of the leukocyte receptor complex located on chromosome 19q 13.4. CONCLUSIONS: Th17/Treg imbalance post-challenge may contribute to the development of the late phase inflammatory phenotype.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".