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Record W2058590767 · doi:10.1371/journal.pone.0122774

Recovery of Aging-Related Size Increase of Skin Epithelial Cells: In vivo Mouse and In vitro Human Study

2015· article· en· W2058590767 on OpenAlexaff
Igor Sokolov, Natali V. Guz, Swaminathan P. Iyer, Amy Hewitt, Nina A. Sokolov, Joseph S. Erlichman, Craig D. Woodworth

Bibliographic record

VenuePLoS ONE · 2015
Typearticle
Languageen
FieldMedicine
TopicSkin Protection and Aging
Canadian institutionsQueen's University
FundersDivision of Civil, Mechanical and Manufacturing InnovationNational Cancer Institute
KeywordsIn vitroCytochalasin BHuman skinIn vivoSkin AgingCellCytochalasin DCytochalasinAndrologyCell biologyBiologyCell culturePathologyMedicineCytoskeletonDermatologyBiochemistryGenetics

Abstract

fetched live from OpenAlex

The size increase of skin epithelial cells during aging is well-known. Here we demonstrate that treatment of aging cells with cytochalasin B substantially decreases cell size. This decrease was demonstrated on a mouse model and on human skin cells in vitro. Six nude mice were treated by topical application of cytochalasin B on skin of the dorsal left midsection for 140 days (the right side served as control for placebo treatment). An average decrease in cell size of 56±16% resulted. A reduction of cell size was also observed on primary human skin epithelial cells of different in vitro age (passages from 1 to 8). A cell strain obtained from a pool of 6 human subjects was treated with cytochalasin B in vitro for 12 hours. We observed a decrease in cell size that became statistically significant and reached 20-40% for cells of older passage (6-8 passages) whereas no substantial change was observed for younger cells. These results may be important for understanding the aging processes, and for cosmetic treatment of aging skin.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.033
GPT teacher head0.254
Teacher spread0.221 · 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 designObservational
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

Citations8
Published2015
Admission routes1
Has abstractyes

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Same venuePLoS ONESame topicSkin Protection and AgingFrench-language works237,207