MétaCan
Menu
Back to cohort
Record W2782429100 · doi:10.1074/jbc.r117.000366

The role of microRNAs in glucocorticoid action

2018· review· en· W2782429100 on OpenAlexaff
Sally A. Clayton, Simon W. Jones, Mariola Kurowska‐Stolarska, Andrew R. Clark

Bibliographic record

VenueJournal of Biological Chemistry · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsInstitute of Infection and Immunity
FundersMedical Research CouncilVersus Arthritis
KeywordsmicroRNAComputational biologyComplementarity (molecular biology)Context (archaeology)BiologySet (abstract data type)Action (physics)InformaticsMechanism (biology)Computer scienceBioinformaticsGeneticsGene

Abstract

fetched live from OpenAlex

Glucocorticoids (GCs) are steroids with profound anti-inflammatory and immunomodulatory activities. Synthetic GCs are widely used for managing chronic inflammatory and autoimmune conditions, as immunosuppressants in transplantation, and as anti-tumor agents in certain hematological cancers. However, prolonged GC exposure can cause adverse effects. A detailed understanding of GCs' mechanisms of action may enable harnessing of their desirable actions while minimizing harmful effects. Here, we review the impact on the GC biology of microRNAs, small non-coding RNAs that post-transcriptionally regulate gene expression. Emerging evidence indicates that microRNAs modulate GC production by the adrenal glands and the cells' responses to GCs. Furthermore, GCs influence cell proliferation, survival, and function at least in part by regulating microRNA expression. We propose that the beneficial effects of GCs may be enhanced through combination with reagents targeting specific microRNAs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.856
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.315
Teacher spread0.283 · 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 teacher head, not a consensus.

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

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

Citations74
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Biological ChemistrySame topicMicroRNA in disease regulationFrench-language works237,207