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Record W2143470156 · doi:10.1139/bcb-2014-0095

The role of miR-29 in pulmonary fibrosis

2014· review· en· W2143470156 on OpenAlexvenueno aff
Leah Cushing, Ping‐Ping Kuang, Jining Lü

Bibliographic record

VenueBiochemistry and Cell Biology · 2014
Typereview
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstituteMinisterio de Economía y Competitividad
KeywordsPulmonary fibrosisIdiopathic pulmonary fibrosisFibrosisMedicineExtracellular matrixLungPathogenesisPathologyParenchymaCancer researchImmunologyBiologyInternal medicine

Abstract

fetched live from OpenAlex

Pulmonary fibrosis is a pathological condition in which lungs become scarred due to the excess extracellular matrix (ECM) deposition and structural alterations in the interstitium of lung parenchyma. Many patients with interstitial lung diseases (ILDs) caused by long-term exposure to toxic substances, chronic infections, or autoimmune responses develop fibrosis. Etiologies for many ILDs are unknown, such as idiopathic pulmonary fibrosis (IPF), a devastating, relentless form of pulmonary fibrosis with a median survival of 2-3 years. Despite several decades of research, factors that initiate and sustain the fibrotic response in lungs remain unclear and there is no effective treatment to block progression of fibrosis. Here we summarize recent findings on the antifibrotic activity of miR-29, a small noncoding regulatory RNA, in the pathogenesis of fibrosis by regulating ECM production and deposition, and epithelial-mesenchymal transition (EMT). We also describe interactions of miR-29 with multiple profibrotic and inflammatory pathways. Finally, we review the antifibrotic activity of miR-29 in animal models of fibrosis and highlight miR-29 as a promising therapeutic reagent or target for the treatment of pulmonary fibrosis.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.009
GPT teacher head0.263
Teacher spread0.255 · 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 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

Citations105
Published2014
Admission routes1
Has abstractyes

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