Eosinophil microRNAs Play a Regulatory Role in Allergic Diseases Included in the Atopic March
Bibliographic record
Abstract
(1) Background: The atopic march is defined by the increased prevalence of allergic diseases after atopic dermatitis onset. In fact, atopic dermatitis is believed to play an important role in allergen sensitization via the damaged skin barrier, leading to allergic diseases such as allergic asthma and allergic rhinitis. The eosinophil, a pro-inflammatory cell that contributes to epithelial damage, is one of the various cells recruited in the inflammatory reactions characterizing these diseases. Few studies were conducted on the transcriptome of this cell type and even less on their specific microRNA (miRNA) profile, which could modulate pathogenesis of allergic diseases and clinical manifestations post-transcriptionally. Actually, their implication in allergic diseases is not fully understood, but they are believed to play a role in inflammation-related patterns and epithelial cell proliferation. (2) Methods: Next-generation sequencing was performed on RNA samples from eosinophils of individuals with atopic dermatitis, atopy, allergic rhinitis and asthma to obtain differential counts of primary miRNA (pri-miRNA); these were also analyzed for asthma-related phenotypes such as forced expiratory volume in one second (FEV1), immunoglobulin E (IgE) and provocative concentration of methacholine inducing a 20% fall in forced expiratory volume in 1 s (PC20) levels, as well as FEV1 to forced vital capacity (FEV1/FVC) ratio. (3) Results: Eighteen miRNAs from eosinophils were identified to be significantly different between affected individuals and unaffected ones. Based on counts from these miRNAs, individuals were then clustered into groups using Ward’s method on Euclidian distances. Groups were found to be explained by asthma diagnosis, familial history of respiratory diseases and allergic rhinitis as well as neutrophil counts. (4) Conclusions: The 18 differential miRNA counts for the studying phenotypes allow a better understanding of the epigenetic mechanisms underlying the development of the allergic diseases included in the atopic march.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".