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Record W4394497956 · doi:10.6084/m9.figshare.22796070

Epigenetic Activation of Circadian Clock Genes Elicits Inflammation in Experimental Murine Dry Eye

2023· dataset· en· W4394497956 on OpenAlexaff
Ruifen Wei, Qianqian Chen, Qinxiang Zheng, Peter S. Reinach, Xiying Tan, Chengjie Pan, Wei Xu, Louis Tong, Wei Chen

Bibliographic record

VenueFigshare · 2023
Typedataset
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsCircadian rhythmCircadian clockInflammationEpigeneticsCLOCKGeneBiologyCell biologyNeuroscienceGeneticsImmunology

Abstract

fetched live from OpenAlex

To explore whether circadian clock genes contribute to elicit inflammation in experimental dry eye (EDE). RNA sequencing analyzed mRNA expression patterns in EDE model. RT-qPCR and/or Western blot determined the expression of inflammatory factors and circadian genes during EDE. MethylTarget™ assays determined the promoter methylation levels of Per genes in vivo. Per2 or Per3 knockdown assessed their effects on inflammatory factors in vitro. We utilized an intelligently controlled environmental system (ICES) to establish a mouse EDE model. The significant upregulated genes were enriched for circadian rhythms. Therein lied oscillatory and time-dependent upregulation of PER2 and PER3, as well as their promoter hypomethylation during EDE. Silencing PER2 or PER3 significantly decreased inflammatory factor expression and also reversed such increased inflammatory response in azacitidine (AZA) treatment in vitro model. Our findings suggest that DNA methylation mediated the upregulation of PER2 and PER3, leading to inflammatory response in EDE.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.035
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0350.013

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 head0.049
GPT teacher head0.298
Teacher spread0.249 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2023
Admission routes1
Has abstractyes

Explore more

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