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Record W3091285965 · doi:10.1088/1748-605x/abbdbe

Biofabrication and preclinical evaluation of a large-sized human self-assembled skin substitute

2020· article· en· W3091285965 on OpenAlexaff
Fabien Kawecki, Dominique Mayrand, Akram Ayoub, Emilie Attiogbe, Lucie Germain, François A. Auger, Véronique Moulin

Bibliographic record

VenueBiomedical Materials · 2020
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsHôpital de l'Enfant-JésusUniversité LavalHôtel-Dieu de QuébecCentre hospitalier de l'Université Laval
Fundersnot available
KeywordsHuman skinBiofabricationBiomedical engineeringSkin graftingArtificial skinSkin equivalentMaterials scienceSkin transplantationDermatologySurgeryMedicineTissue engineeringIn vitroBiologyKeratinocyte

Abstract

fetched live from OpenAlex

Abstract Severe skin burns are widely treated using split-thickness skin autografts. However, the accessibility of the donor site may be limited depending on the size of the injured surface. As an alternative to skin autografts, our laboratory is clinically investigating a model of human self-assembled skin substitute (SASS) with a standard size of 35 cm 2 . For the management of extensive skin wounds, multiple grafts are required to cover the entire wound bed. Even if SASSs could provide an adequate and efficient treatment, in some cases, the long-term follow-up of the skin graft site reveals the appearance of marks at the junction between SASSs. This study aims to produce a large-sized self-assembled skin substitute (L-SASS; 289 cm 2 ) and evaluate its preclinical potential for skin wound coverage. The L-SASSs and SASSs shared similar contraction behavior on an agar surface, thickness, and epidermal differentiation in vitro . After grafting, similar histological results were obtained for skin substitutes produced with both methods. Hence, the self-assembly approach of tissue engineering is a scaffold-free method that allows the production of living skin substitutes in a large format.

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)0.001
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 score0.206
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.086
GPT teacher head0.409
Teacher spread0.322 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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