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Record W2122277387 · doi:10.2174/157016112799959332

Clinically Relevant Extracellular-Matrix Scaffolds for Cell Transplantation and Vascular Repair

2012· review· en· W2122277387 on OpenAlexaff
Rashmi Tiwari-Pandey, Hadi Toeg, Frank W. Sellke, Marc Ruel

Bibliographic record

VenueCurrent Vascular Pharmacology · 2012
Typereview
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsUniversity of Ottawa
FundersNational Heart, Lung, and Blood Institute
KeywordsExtracellular matrixTransplantationCell biologyTissue engineeringMedicineBiomaterialRegeneration (biology)Cell adhesionCellBiomedical engineeringChemistryBiologySurgeryBiochemistry

Abstract

fetched live from OpenAlex

Due to the very limited ability of cardiac tissue to self-regenerate, the replacement of damaged cardiomyocytes and the repair of damaged extracellular matrix (ECM) are highly sought-after therapeutic strategies. Cell transplantation in ECM scaffolds has been shown to improve retention, phenotype, and function in vascular and muscle repair. In addition to cellular patches that involve the use of biomaterial scaffolds in combination with cells, acellular patches may have a role in intrinsically recruiting cells to damaged areas. This review focuses on the clinically relevant ECM scaffolds, their interactions with cells to stimulate functions such as adhesion, migration, proliferation, and differentiation, and their intrinsic role in ECM remodeling leading to vascular and possibly myocardial 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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.066
GPT teacher head0.406
Teacher spread0.340 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

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