Airway inflammation and hyperresponsiveness in asthma is regulated by the circadian clock through REV-ERBa
Bibliographic record
Abstract
Background: We have previously shown that the molecular clock protein, REV-ERBα, is important in determining time of day variation in airway hyperresponsiveness (AHR) in a Rev-erbα global knock out murine model of allergic airway disease (AAD). However, it is unclear which peripheral clock governs this response. Aim: To determine if the peripheral molecular clock in the lung, regulates AHR. Methods: Using the Cre-lox system we generated mice with Rev-erbα deleted in CCSP expressing airway epithelial cells (CCSP-Rev-erba-/-, KO) & employed a 5-wk AAD model using i.n HDM. AHR was measured by increasing methacholine concentration both non-invasively (DCP, SciReq) every 6 hours & invasively (FlexiVent) followed by an analysis of immune cells & lung pathology. Results & Discussion: Preliminary analysis shows, Tidal mid-expiratory flow (EF50) was reduced & varied by time of day in HDM WT mice compared to PBS control (p=0.007, ANOVA). In HDM WT mice, EF50 decreased, reaching a trough between 1900 & 0100. In comparison to WT mice, KO mice showed baseline reduced EF50 (PBS treated) across the day and HDM treated KO mice lost time of day variation in EF50 (Figure 1). Conclusion: AHR is regulated by the molecular clock present in the bronchial epithelial cell of airways. Ongoing investigation of immune cells & pathology will further elucidate the role of Rev-erbα in AHR. Figure 1: Mid EF50 variation by time of day.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".