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Record W2606172895 · doi:10.1111/exd.13359

Hair follicle dermal stem cells and skin‐derived precursor cells: Exciting tools for endogenous and exogenous therapies

2017· article· en· W2606172895 on OpenAlexaff
Natacha A. Agabalyan, Nicole L. Rosin, Waleed Rahmani, Jeff Biernaskie

Bibliographic record

VenueExperimental Dermatology · 2017
Typearticle
Languageen
FieldMedicine
TopicHair Growth and Disorders
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHair follicleStem cellProgenitor cellRegeneration (biology)MesenchymeMesenchymal stem cellCell biologyBiologySkin repairHair lossDermisImmunologyMedicinePathologyWound healingAnatomyGenetics

Abstract

fetched live from OpenAlex

Understanding the cellular interactions and molecular signals underlying hair follicle (HF) regeneration may have significant implications for restorative therapies for skin disease that diminish hair growth, whilst also serving to provide fundamental insight into the mechanisms underlying adult tissue regeneration. One of the major, yet underappreciated, players in this process is the underlying HF mesenchyme. Here, we provide an overview of a mesenchymal progenitor pool referred to as hair follicle dermal stem cells (hfDSCs), discuss their potential functions within the skin and their relationship to skin-derived precursors (SKPs), and consider unanswered questions about the function of these specialized fibroblasts. We contend that dermal stem cells provide an important reservoir of renewable dermal progenitors that may enable development of novel restorative therapies following hair loss, skin injury or disease.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.044
GPT teacher head0.284
Teacher spread0.240 · 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 source (direct Gemma or distilled Codex), 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

Citations36
Published2017
Admission routes1
Has abstractyes

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