MétaCan
Menu
Back to cohort
Record W1992144920 · doi:10.1139/bcb-2014-0101

MicroRNA regulatory networks in idiopathic pulmonary fibrosis

2015· review· en· W1992144920 on OpenAlexvenueno aff
Kusum Pandit, Jadranka Milošević

Bibliographic record

VenueBiochemistry and Cell Biology · 2015
Typereview
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsnot available
Fundersnot available
KeywordsmicroRNAEpigeneticsIdiopathic pulmonary fibrosisPulmonary fibrosisTransforming growth factorBiologyPathogenesisFibrosisDiseaseMediatorEpithelial–mesenchymal transitionCancer researchPhenotypeDNA methylationBioinformaticsImmunologyLungMedicineGene expressionPathologyDownregulation and upregulationCell biologyGeneGeneticsInternal medicine

Abstract

fetched live from OpenAlex

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and fatal scarring lung disease of unknown etiology, characterized by changes in microRNA expression. Activation of transforming growth factor (TGF-β) is a key event in the development of IPF. Recent reports have also identified epigenetic modification as an important player in the pathogenesis of IPF. In this review, we summarize the main results of studies that address the role of microRNAs in IPF and highlight the synergistic actions of these microRNAs in regulating TGF-β, the primary fibrogenic mediator. We outline epigenetic regulation of microRNAs by methylation. Functional studies identify microRNAs that alter proliferative and migratory properties of fibroblasts, and induce phenotypic changes in epithelial cells consistent with epithelial-mesenchymal transition. Though these studies were performed in isolation, we identify multiple co-operative actions after assembling the results into a network. Construction of such networks will help identify disease-propelling hubs that can be targeted for therapeutic purposes.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.018
GPT teacher head0.276
Teacher spread0.259 · 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
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

Citations87
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueBiochemistry and Cell BiologySame topicInterstitial Lung Diseases and Idiopathic Pulmonary FibrosisFrench-language works237,207