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Record W2802324364

Airway hyper-responsiveness is regulated by the circadian clock through Rev-erbα. Durrington HJ, Begley N, Krakowiak K, Maidstone R, Loudon A, Ray DW

2018· article· en· W2802324364 on OpenAlexaff
Hannah Durrington, Karolina Krakowiak, David Ray, Andrew Loudon, Nicola Begley, Robert Maidstone

Bibliographic record

VenueResearch Explorer (The University of Manchester) · 2018
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsLMC Diabetes & Endocrinology (Canada)
Fundersnot available
KeywordsCircadian rhythmCircadian clockMedicinePsychologyNeuroscience
DOInot available

Abstract

fetched live from OpenAlex

Background Asthma symptoms show marked circadian variation; being worse in the early morning. Airway resistance shows diurnal physiological variation, which is more exaggerated in asthma. Maximal resistance occurs in the early morning. Using a murine model of asthma we investigated whether the timing of allergen challenge influenced airway hyper-responsiveness (AHR) and allergic inflammation. Rev-erbα provides a gate between the core cellular molecular clock and the immune response. We therefore employed the clock gene knock-out mouse, Rev-erbα-/-. Understanding the chronobiology of asthma may open new treatment opportunities, with new, or existing drugs (chronotherapy). Aims • Does time of day influence the effects of allergen challenge on AHR and inflammation? • Is Rev-erbα important in regulating AHR? Method C57/Bl6 female Rev-erbα-/- mice and littermate control mice (aged 8-12 weeks) were challenged with house dust mite, HDM or PBS daily for 3 weeks at either ZT23 (just before lights come on, 6am) or ZT 11 (just before lights are turned off, 6pm). 24 hours after the last challenge AHR was measured by Flexivent. Mice were then sacrificed and bronchoalveolar lavage performed, lungs and blood were collected. An ANCOVA test was performed to analyse AHR data. Results 1. There is baseline diurnal variation in AHR in C57/Bl6 mice. AHR measured at ZT11 is significantly greater than when measured at ZT 23 Figure 1a. 2. HDM challenge at ZT 11 causes significantly greater AHR, compared to HDM challenge at ZT23 Figure 1a. 3. In Rev-erbα-/- mice, baseline diurnal variation in AHR was reversed compared to littermate controls. AHR at ZT23 was greater than at ZT11 Figure 1b. 4. HDM challenge in Rev-erbα-/- mice resulted in greater AHR compared to littermate controls, however, there was no longer a time of day difference by challenge time Figure 1b. 5. HDM caused significantly increased levels of inflammation in both Rev-erbα-/- and littermate mice. However, there was no difference in inflammatory response by time of day in either Rev-erbα-/- or littermate mice. Discussion • The circadian phenotype in asthma may be driven through diurnal changes in AHR independent of inflammatory changes. • The circadian gating of AHR in asthma appears to be dependent upon the action of Rev-erbα • In nocturnal mice, AHR increased at ZT11 (6pm), corresponding to the start of the active phase in humans (morning, 6am) • Further work is underway to determine how the circadian clock controls AHR to methacholine.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.054
GPT teacher head0.306
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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