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Record W1968221883 · doi:10.1007/s12079-011-0156-9

MEK/ERK inhibitors: proof-of-concept studies in lung fibrosis

2011· article· en· W1968221883 on OpenAlexaff
Andrew Leask

Bibliographic record

VenueJournal of Cell Communication and Signaling · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective Tissue Growth Factor Research
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineMyofibroblastCirrhosisFibrosisLung fibrosisLungMAPK/ERK pathwayCancer researchPulmonary fibrosisKinasePathologyInternal medicineBiology

Abstract

fetched live from OpenAlex

There is no therapy for chronic fibroproliferative diseases, in spite of the fact that current health statistics suggest that these (which include cardiovascular disease, pulmonary fibrosis, diabetic nephropathy, liver cirrhosis and systemic sclerosis) have been estimated to cause approximately 45% of the deaths in the developed world. Recently, many studies have shown that mitogen activated protein kinases (MAPKs) are activated in response to fibrogenic agents and contribute to the formation and function of the myofibroblast, the critical cell type responsible for excessive scarring. A recent report by Madala and colleagues (Am J Respir Cell Mol Biol, 2011) has provided a proof-of-concept study showing that the specific MEK inhibitor ARRY-142886 (ARRY) can both suppress the progression of fibrosis and reverse an animal model of lung fibrosis. Thus MEK inhibition could be a valuable method to treat lung 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 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)0.002
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
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.073
GPT teacher head0.336
Teacher spread0.263 · 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

Citations9
Published2011
Admission routes1
Has abstractyes

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