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Record W3108260246 · doi:10.1002/adfm.202007483

Regenerating Hair in Prevascularized Tissue Space Formed by a Controllable Foreign Body Reaction

2020· article· en· W3108260246 on OpenAlexafffund
Lunan Yang, Yong Miao, Yuqing Liu, Shiyi Chen, Yuxin Chen, Wei‐Wen Liu, Jin Wang, Wen Zhong, Quan Wang, Zhiqi Hu, Malcolm Xing

Bibliographic record

VenueAdvanced Functional Materials · 2020
Typearticle
Languageen
FieldMedicine
TopicHair Growth and Disorders
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaCanada Foundation for Innovation
KeywordsMaterials scienceTransplantationIn vivoFabricationDissipationBiomedical engineeringModulusRegeneration (biology)BiophysicsTissue engineeringNanotechnologyComposite materialCell biologyBiologySurgeryPathologyMedicine

Abstract

fetched live from OpenAlex

Abstract Intracutaneous transplantation of trichogenic cells is a currently endorsed strategy to realize hair regeneration in vivo. However, skin is not the most advantageous transplant site due to the robust mechanical property and deficient physiological perfusion. Herein, a subcutaneous space made of prevascularized collagen fibers (PVCF) generated by controlling the duration of in situ foreign body reaction is reported. In contrast to skin, an optimally preprogrammed PVCF presents a larger tissue volume (171 mm 3 ), low Young's modulus (1/2‐fold), high flexibility high mechanical energy dissipation rate (1.3‐fold), highly permeable surface structure, and plentiful vascular network. Remarkably, cells transplanted into PVCF suffer less apoptosis and necrosis, and generate more mature hairs (≈289 per site) than that in intracutaneous transplantation (≈177 per site). The RNA‐sequencing profiling indicates similar trends under molecular level. This work provides a promising strategy to improve graft site microenvironment physiologically and mechanically, with a low‐cost and simple fabrication process.

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)0.001
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 score0.031
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.011
GPT teacher head0.237
Teacher spread0.226 · 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

Citations12
Published2020
Admission routes2
Has abstractyes

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