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Reconstitution of Skin Fibrosis Development Using a Tissue Engineering Approach

2012· article· en· W179470260 on OpenAlexaff
Véronique Moulin

Bibliographic record

VenueMethods in molecular biology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSkin and Cellular Biology Research
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsDermisEpidermis (zoology)Skin equivalentFibrosisIn vivoHuman skinTissue engineeringPathologyExtracellular matrixArtificial skinIn vitroBiologyMedicineBiomedical engineeringCell biologyKeratinocyteAnatomy

Abstract

fetched live from OpenAlex

Skin fibrosis is involved in several pathologies as hypertrophic scar or scleroderma. The determination of the mechanisms at the origin of these problems is however difficult due to the low number of in vivo models. To bypass this absence of animal models, studies typically use human pathological cells cultured in a monolayer way on plastic. However, cell behavior is different according to the fact that cells are on plastic or embedded in matrix. Using a tissue engineering method, we have developed new in vitro models to study these pathologies of the skin. Human pathological cells are used to reconstitute a three dimensional fibrotic tissue comprising the dermal and the epidermal parts of the skin. This method is called the self-assembly approach and is based on the cell capacity to reconstitute in vitro their own environment as in vivo. In this chapter, protocols generating reconstructed pathological skin using this approach are detailed. The methods include extraction and culture of human skin keratinocytes and fibroblasts from very small cutaneous biopsies. In addition, a description of the protocols for the production of fibrotic dermal sheets can be found to obtain a model of fibrotic dermis that can be associated or not with a fully differentiated epidermis.

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)0.002
metaresearch head score (Gemma)0.000
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: Methods · Consensus signal: Methods
Teacher disagreement score0.069
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.039
GPT teacher head0.386
Teacher spread0.348 · 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
GenreMethods

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

Citations15
Published2012
Admission routes1
Has abstractyes

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