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Record W3207902372 · doi:10.1038/s41578-021-00381-1

A framework for developing sex-specific engineered heart models

2021· review· en· W3207902372 on OpenAlexaff
Roberta I. Lock, Hadel Al Asafen, Sharon Fleischer, Manuel A. Tamargo, Yimu Zhao, Milica Radisic, Gordana Vunjak‐Novakovic

Bibliographic record

VenueNature Reviews Materials · 2021
Typereview
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsUniversity of Toronto
FundersNational Heart, Lung, and Blood InstituteNational Institute of Biomedical Imaging and BioengineeringNational Aeronautics and Space AdministrationNational Institutes of HealthNational Science Foundation
KeywordsTissue engineeringContext (archaeology)DiseaseModalitiesStem cellMedicineBiologyBioinformaticsPathologyBiomedical engineering

Abstract

fetched live from OpenAlex

The convergence of tissue engineering and patient-specific stem cell biology has enabled the engineering of in vitro tissue models that allow the study of patient-tailored treatment modalities. However, sex-related disparities in health and disease, from systemic hormonal influences to cellular-level differences, are often overlooked in stem cell biology, tissue engineering and preclinical screening. The cardiovascular system, in particular, shows considerable sex-related differences, which need to be considered in cardiac tissue engineering. In this Review, we analyse sex-related properties of the heart muscle in the context of health and disease, and discuss a framework for including sex-based differences in human cardiac tissue engineering. We highlight how sex-based features can be implemented at the cellular and tissue levels, and how sex-specific cardiac models could advance the study of cardiovascular diseases. Finally, we define design criteria for sex-specific cardiac tissue engineering and provide an outlook to future research possibilities beyond the cardiovascular system.

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)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.002

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 head0.129
GPT teacher head0.407
Teacher spread0.278 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations47
Published2021
Admission routes1
Has abstractyes

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