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Record W4212935846 · doi:10.1021/acsnano.1c08890

Nanoengineered Sprayable Therapy for Treating Myocardial Infarction

2022· article· en· W4212935846 on OpenAlexafffund
Marcelo Muñoz, Çağla Eren Çimenci, Keshav Goël, Maxime Comtois‐Bona, Mahir Hossain, Christopher D. McTiernan, Matías Zúñiga-Bustos, Alex Ross, Brenda Truong, Darryl R. Davis, Wenbin Liang, Benjamin H. Rotstein, Marc Ruel, Horacio Poblete, Erik J. Suuronen, Emilio I. Alarcón

Bibliographic record

VenueACS Nano · 2022
Typearticle
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsUniversity of Ottawa
FundersFondo Nacional de Desarrollo Científico y TecnológicoSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchGovernment of Ontario
KeywordsContractilityVentricleMedicineMyocardial infarctionIn vivoCardiologyCardiac function curveCardiac muscleInternal medicineBiomedical engineeringHeart failureBiology

Abstract

fetched live from OpenAlex

testing, of a sprayable nanotherapeutic that uses surface engineered custom-designed multiarmed peptide grafted nanogold for on-the-spot coating of an infarcted myocardial surface. When applied to mouse hearts, 1 week after infarction, the spray-on treatment resulted in an increase in cardiac function (2.4-fold), muscle contractility, and myocardial electrical conductivity. The applied nanogold remained at the treatment site 28 days postapplication with no off-target organ infiltration. Further, the infarct size in the mice that received treatment was found to be <10% of the total left ventricle area, while the number of blood vessels, prohealing macrophages, and cardiomyocytes increased to levels comparable to that of a healthy animal. Our cumulative data suggest that the therapeutic action of our spray-on nanotherapeutic is highly effective, and in practice, its application is simpler than other regenerative approaches for treating an infarcted heart.

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 score0.853
Threshold uncertainty score0.382

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 integrity0.0000.000
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 head0.015
GPT teacher head0.260
Teacher spread0.245 · 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

Citations26
Published2022
Admission routes2
Has abstractyes

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