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Record W1964298890 · doi:10.1038/srep03733

Engineering the heart: Evaluation of conductive nanomaterials for improving implant integration and cardiac function

2014· article· en· W1964298890 on OpenAlexaff
Jin Zhou, Jun Chen, Hongyu Sun, Xiaozhong Qiu, Yongchao Mou, Zhiqiang Liu, Yuwei Zhao, Xia Li, Yao Han, Cuimi Duan, Rongyu Tang, Chunlan Wang, Wen Zhong, Jie Liu, Ying Luo, Malcolm Xing, Changyong Wang

Bibliographic record

VenueScientific Reports · 2014
Typearticle
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsUniversity of ManitobaChildren's Hospital Research Institute of Manitoba
FundersNational Natural Science Foundation of China
KeywordsNanomaterialsNanotechnologyCardiac function curveMyocardial infarctionMaterials scienceIn vitroTissue engineeringCarbon nanotubeBiomedical engineeringChemistryHeart failureCardiologyMedicineBiochemistry

Abstract

fetched live from OpenAlex

Recently, carbon nanotubes together with other types of conductive materials have been used to enhance the viability and function of cardiomyocytes in vitro. Here we demonstrated a paradigm to construct ECTs for cardiac repair using conductive nanomaterials. Single walled carbon nanotubes (SWNTs) were incorporated into gelatin hydrogel scaffolds to construct three-dimensional ECTs. We found that SWNTs could provide cellular microenvironment in vitro favorable for cardiac contraction and the expression of electrochemical associated proteins. Upon implantation into the infarct hearts in rats, ECTs structurally integrated with the host myocardium, with different types of cells observed to mutually invade into implants and host tissues. The functional measurements showed that SWNTs were essential to improve the performance of ECTs in inhibiting pathological deterioration of myocardium. This work suggested that conductive nanomaterials hold therapeutic potential in engineering cardiac tissues to repair myocardial infarction.

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)0.004
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.178

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
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 head0.023
GPT teacher head0.273
Teacher spread0.250 · 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

Citations172
Published2014
Admission routes1
Has abstractyes

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