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Record W4399941144 · doi:10.1016/j.jacbts.2024.03.004

Fibroblast-Specific Depletion of Human Antigen R Alleviates Myocardial Fibrosis Induced by Cardiac Stress

2024· article· en· W4399941144 on OpenAlexaff
Mallikarjun Patil, Sarojini Singh, Praveen Kumar Dubey, Sultan Tousif, Prachi Umbarkar, Qinkun Zhang, Hind Lal, Mary Kathryn Sewell-Loftin, Channakeshava Sokke Umeshappa, Yohannes T. Ghebre, Steven M. Pogwizd, Jianyi Zhang, Prasanna Krishnamurthy

Bibliographic record

VenueJACC Basic to Translational Science · 2024
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Immunology Research
Canadian institutionsDalhousie University
FundersNational Heart, Lung, and Blood InstituteNational Institutes of HealthNational Institute of Arthritis and Musculoskeletal and Skin DiseasesAmerican Heart Association
KeywordsMyofibroblastCardiac fibrosisGene knockdownFibroblastFibrosisCancer researchCell biologyMyocardial fibrosisPressure overloadTransforming growth factorBiologyMedicineApoptosisPathologyInternal medicineCell cultureHeart failureCardiac hypertrophy

Abstract

fetched live from OpenAlex

Cardiac fibrosis can be mitigated by limiting fibroblast-to-myofibroblast differentiation and proliferation. Human antigen R (HuR) modulates messenger RNA stability and expression of multiple genes. However, the direct role of cardiac myofibroblast HuR is unknown. Myofibroblast-specific deletion of HuR limited cardiac fibrosis and preserved cardiac functions in pressure overload injury. Knockdown of HuR in transforming growth factor-β1-treated cardiac fibroblasts suppressed myofibroblast differentiation and proliferation. HuR deletion abrogated the expression and messenger RNA stability of cyclins D1 and A2, suggesting a potential mechanism by which HuR promotes myofibroblast proliferation. Overall, these data suggest that inhibition of HuR could be a potential therapeutic approach to limit cardiac 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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.325
Teacher spread0.297 · 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

Citations8
Published2024
Admission routes1
Has abstractyes

Explore more

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