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Micro- and nanotechnology in cardiovascular tissue engineering

2011· review· en· W2097438103 on OpenAlexafffund
Boyang Zhang, Yun Xiao, Anne Hsieh, Nimalan Thavandiran, Milica Radisic

Bibliographic record

VenueNanotechnology · 2011
Typereview
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsHeart and Stroke FoundationUniversity of Toronto
FundersNational Heart, Lung, and Blood InstituteCanadian Institutes of Health Research
KeywordsTissue engineeringMicrofabricationNanotechnologyMaterials scienceBiomedical engineeringFabricationEngineeringPathologyMedicine

Abstract

fetched live from OpenAlex

While in nature the formation of complex tissues is gradually shaped by the long journey of development, in tissue engineering constructing complex tissues relies heavily on our ability to directly manipulate and control the micro-cellular environment in vitro. Not surprisingly, advancements in both microfabrication and nanofabrication have powered the field of tissue engineering in many aspects. Focusing on cardiac tissue engineering, this paper highlights the applications of fabrication techniques in various aspects of tissue engineering research: (1) cell responses to micro- and nanopatterned topographical cues, (2) cell responses to patterned biochemical cues, (3) controlled 3D scaffolds, (4) patterned tissue vascularization and (5) electromechanical regulation of tissue assembly and function.

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.002
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 head0.024
GPT teacher head0.273
Teacher spread0.249 · 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.

Study designOther design
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

Citations70
Published2011
Admission routes2
Has abstractyes

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