Could the Epigenetics of Eosinophils in Asthma and Allergy Solve Parts of the Puzzle?
Bibliographic record
Abstract
Epigenetics is a field of study investigating changes in gene expression that do not alter the DNA sequence. These changes are often influenced by environmental or social factors and are reversible. Epigenetic mechanisms include DNA methylation, histone modification, and noncoding RNA. Understanding the role of these epigenetic mechanisms in human diseases provides useful information with regard to disease severity and development. Several studies have searched for the epigenetic mechanisms that regulate allergies and asthma; however, only few studies have used samples of eosinophil, a proinflammatory cell type known to be largely recruited during allergic or asthmatic inflammation. Such studies would enable us to better understand the factors that influence the massive recruitment of eosinophils during allergic and asthmatic symptoms. In this review, we sought to summarize different studies that aimed to discover differential patterns of histone modifications, DNA methylation, and noncoding RNAs in eosinophil samples of individuals with certain diseases, with a particular focus on those with asthma or allergic diseases.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".