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Record W2982246887 · doi:10.1038/s41467-019-12748-8

Injectable human recombinant collagen matrices limit adverse remodeling and improve cardiac function after myocardial infarction

2019· article· en· W2982246887 on OpenAlexafffund
Sarah McLaughlin, Brian McNeill, James Podrebarac, Katsuhiro Hosoyama, Veronika Sedláková, Greg O. Cron, David Smyth, Richard J. Seymour, Keshav Goël, Wenbin Liang, Katey J. Rayner, Marc Ruel, Erik J. Suuronen, Emilio I. Alarcón

Bibliographic record

VenueNature Communications · 2019
Typearticle
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversity of Ottawa Heart Institute FoundationUniversity of Ottawa
KeywordsMyocardial infarctionVentricular remodelingMedicineCardiologyHeart failureWound healingCardiac function curveInternal medicineAdverse effectBone marrowSurgery

Abstract

fetched live from OpenAlex

Despite the success of current therapies for acute myocardial infarction (MI), many patients still develop adverse cardiac remodeling and heart failure. With the growing prevalence of heart failure, a new therapy is needed that can prevent remodeling and support tissue repair. Herein, we report on injectable recombinant human collagen type I (rHCI) and type III (rHCIII) matrices for treating MI. Injecting rHCI or rHCIII matrices in mice during the late proliferative phase post-MI restores the myocardium's mechanical properties and reduces scar size, but only the rHCI matrix maintains remote wall thickness and prevents heart enlargement. rHCI treatment increases cardiomyocyte and capillary numbers in the border zone and the presence of pro-wound healing macrophages in the ischemic area, while reducing the overall recruitment of bone marrow monocytes. Our findings show functional recovery post-MI using rHCI by promoting a healing environment, cardiomyocyte survival, and less pathological remodeling of the myocardium.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.721
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

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 integrity0.0000.001
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 head0.265
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 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

Citations151
Published2019
Admission routes2
Has abstractyes

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