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Record W2208540901 · doi:10.1016/j.stemcr.2015.12.002

Identification of Drugs that Regulate Dermal Stem Cells and Enhance Skin Repair

2015· article· en· W2208540901 on OpenAlexafffund
Sibel Naska, Scott A. Yuzwa, Adam P. W. Johnston, Smitha Paul, Kristen M. Smith, Maryline Paris, Michael V. Sefton, Alessandro Datti, Freda D. Miller, David R. Kaplan

Bibliographic record

VenueStem Cell Reports · 2015
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsLunenfeld-Tanenbaum Research InstituteSickKids FoundationMount Sinai HospitalUniversity of TorontoHospital for Sick Children
FundersCanadian Institutes of Health ResearchHoward Hughes Medical Institute
KeywordsStem cellBiologyEndogenyRegeneration (biology)PopulationPharmacologyCell biologyHuman skinDrugCancer researchBiochemistryMedicineGenetics

Abstract

fetched live from OpenAlex

Here, we asked whether we could identify pharmacological agents that enhance endogenous stem cell function to promote skin repair, focusing on skin-derived precursors (SKPs), a dermal precursor cell population. Libraries of compounds already used in humans were screened for their ability to enhance the self-renewal of human and rodent SKPs. We identified and validated five such compounds, and showed that two of them, alprostadil and trimebutine maleate, enhanced the repair of full thickness skin wounds in middle-aged mice. Moreover, SKPs isolated from drug-treated skin displayed long-term increases in self-renewal when cultured in basal growth medium without drugs. Both alprostadil and trimebutine maleate likely mediated increases in SKP self-renewal by moderate hyperactivation of the MEK-ERK pathway. These findings identify candidates for potential clinical use in human skin repair, and provide support for the idea that pharmacological activation of endogenous tissue precursors represents a viable therapeutic strategy.

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)0.000
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 score0.723
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.020
GPT teacher head0.272
Teacher spread0.252 · 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

Citations21
Published2015
Admission routes2
Has abstractyes

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