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Record W3156009634 · doi:10.3390/biomedicines9040400

Engineered Skin Substitute Regenerates the Skin with Hair Follicle Formation

2021· article· en· W3156009634 on OpenAlexaff
Jinmei Wang, Xiaoxiao Wang, Jundong Xie, Bin Yao, Miaohua Mo, Danjun Ma, Chen Huang, Ren‐He Xu, Xiaobing Fu, Edward E. Tredget, Yaojiong Wu

Bibliographic record

VenueBiomedicines · 2021
Typearticle
Languageen
FieldMedicine
TopicHair Growth and Disorders
Canadian institutionsUniversity of Alberta
FundersNational Key Research and Development Program of ChinaShenzhen Municipal Science and Technology Innovation CouncilChina Postdoctoral Science FoundationScience, Technology and Innovation Commission of Shenzhen MunicipalityNational Natural Science Foundation of China
KeywordsHair follicleDermal papillaeNeogenesisCell biologyRegeneration (biology)Stem cellBiologyKeratinocyteAnatomyCell cultureEndocrinologyGenetics

Abstract

fetched live from OpenAlex

Currently, engineered skin substitutes (ESS) are unable to regenerate cutaneous appendages. Recent studies have shown that skin-derived precursors (SKPs), which are extensively available, have the potential to induce hair follicle neogenesis. Here, we demonstrate that ESS consisting of culture-expanded SKPs and epidermal stem cells (Epi-SCs) reconstitute the skin with hair follicle regeneration after grafting into nude mice. SKPs seeded in a C-GAG matrix proliferated and expressed higher levels of hair induction signature genes—such as Akp2, Sox2, CD133 and Bmp6—compared to dermal fibroblasts. Moreover, when ESS prepared by seeding a mixture of culture-expanded murine SKPs and human adult Epi-SCs into a C-GAG matrix was grafted into full-thickness skin wounds in nude mice, black hairs were generated within 3 weeks. Immunofluorescence analysis showed that the SKPs were localized to the dermal papillae of the newly-formed hair follicle. Our results indicate that SKPs can serve as the hair-inductive cells in ESS to furnish it with hair genesis potential

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score0.305

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.226
Teacher spread0.216 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

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