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Record W4353048388 · doi:10.1016/j.jacbts.2023.01.012

Micronized Acellular Matrix Biomaterial Leverages Eosinophils for Postinfarct Cardiac Repair

2023· article· en· W4353048388 on OpenAlexaff
Vishnu Vasanthan, Ali Fatehi Hassanabad, Darrell D. Belke, Guoqi Teng, Carmina Albertine Isidoro, Devjyoti Dutta, Jeannine Turnbull, Justin Deniset, Paul W.M. Fedak

Bibliographic record

VenueJACC Basic to Translational Science · 2023
Typearticle
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
Fundersnot available
KeywordsBiomaterialBiomedical engineeringCardiologyMedicine

Abstract

fetched live from OpenAlex

After ischemic injury, immune cells mediate maladaptive cardiac remodeling. Extracellular matrix biomaterials may redirect inflammation toward repair. Pericardial fluid contains pro-reparative immune cells, potentially leverageable by biomaterials. Herein, we explore how pericardial delivery of a micronized extracellular matrix biomaterial affects cardiac healing. In noninfarcted mice, pericardial delivery increases pericardial and myocardial eosinophil counts. This response is sustained after myocardial infarction, stimulating an interleukin 4 rich milieu. Ultimately, the biomaterial improves postinfarct vascularization and cardiac function; and eosinophil-knockout negates these benefits. For the first time, to our knowledge, we demonstrate the therapeutic potential of pericardial biomaterial delivery and the eosinophil's critical role in biomaterial-mediated postinfarct repair.

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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.323
Teacher spread0.300 · 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

Citations13
Published2023
Admission routes1
Has abstractyes

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