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Record W4380291275 · doi:10.1002/jbm.b.35293

Conductive polymers for cardiac tissue engineering and regeneration

2023· review· en· W4380291275 on OpenAlexaff
Parvin Shokrollahi, Yadollah Omidi, Luigi X. Cubeddu, Hossein Omidian

Bibliographic record

VenueJournal of Biomedical Materials Research Part B Applied Biomaterials · 2023
Typereview
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTissue engineeringRegeneration (biology)Biomedical engineeringMyocardial infarctionCardiac function curveCardiologyMaterials scienceMedicineHeart failureCell biologyBiology

Abstract

fetched live from OpenAlex

Cardiovascular diseases, such as myocardial infarction, are considered a significant global burden and the leading cause of death. Given the inability of damaged cardiac tissue to self-repair, cell-based tissue engineering and regeneration may be the only viable option for restoring normal heart function. To maintain the normal excitation-contraction coupling function of cardiac tissue, uniform electronic and ionic conductance properties are required. To transport cells to damaged cardiac tissues, several techniques, including the incorporation of cells into conductive polymers (CPs) and biomaterials, have been utilized. Due to the complexity of cardiac tissues, the success of tissue engineering for the damaged heart is highly dependent on several variables, such as the cell source, growth factors, and scaffolds. In this review, we sought to provide a comprehensive overview of the electro CPs and biomaterials used in the engineering and regeneration of heart tissue.

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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

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 head0.160
GPT teacher head0.440
Teacher spread0.280 · 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

Citations38
Published2023
Admission routes1
Has abstractyes

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