MétaCan
Menu
Back to cohort
Record W2983278329 · doi:10.1016/j.jacbts.2019.07.005

Preservation of Post-Infarction Cardiac Structure and Function via Long-Term Oral Formyl Peptide Receptor Agonist Treatment

2019· article· en· W2983278329 on OpenAlexaff
Ricardo Alexandrino Garcia, Bruce R. Ito, John A. Lupisella, Nancy Carson, Mei-Yin Hsu, Gayani Fernando, Madeleine Héroux, Michel Bouvier, Elizabeth A. Dierks, Ellen K. Kick, David A. Gordon, Jian Chen, Gabe Mintier, Marilyn Carrier, Stéphane St-Onge, Himanshu Shah, Jordan Towne, Marcela S. Bucardo, Xiuying Ma, Carol S. Ryan, Nicholas R. Wurtz, Jacek Ostrowski, Francisco Villarreal

Bibliographic record

VenueJACC Basic to Translational Science · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicS100 Proteins and Annexins
Canadian institutionsInstitute for Research in Immunology and CancerUniversité de Montréal
FundersBristol-Myers Squibb
KeywordsVentricular remodelingAgonistInflammationReceptorMyocardial infarctionHeart failureMedicineCardiac function curveInternal medicineCardiologyPharmacology

Abstract

fetched live from OpenAlex

Dysregulated inflammation following myocardial infarction (MI) promotes left ventricular (LV) remodeling and loss of function. Targeting inflammation resolution by activating formyl peptide receptors (FPRs) may limit adverse remodeling and progression towards heart failure. This study characterized the cellular and signaling properties of Compound 43 (Cmpd43), a dual FPR1/FPR2 agonist, and examined whether Cmpd43 treatment improves LV and infarct remodeling in rodent MI models. Cmpd43 stimulated FPR1/2-mediated signaling, enhanced proresolution cellular function, and modulated cytokines. Cmpd43 increased LV function and reduced chamber remodeling while increasing proresolution macrophage markers. The findings demonstrate that FPR agonism improves cardiac structure and function post-MI.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.243
Teacher spread0.234 · 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

Citations54
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJACC Basic to Translational ScienceSame topicS100 Proteins and AnnexinsFrench-language works237,207