Repeated short climatic change affects the epidermal differentiation program and leads to matrix remodeling in a human organotypic skin model
Bibliographic record
Abstract
Human skin is subject to frequent changes in ambient temperature and humidity and needs to cope with these environmental modifications. To decipher the molecular response of human skin to repeated climatic change, a versatile model of skin equivalent subject to "hot-wet" (40°C, 80% relative humidity [RH]) or "cold-dry" (10°C, 40% RH) climatic stress repeated daily was used. To obtain an exhaustive view of the molecular mechanisms elicited by climatic change, large-scale gene expression DNA microarray analysis was performed and modulated function was determined by bioinformatic annotation. This analysis revealed several functions, including epidermal differentiation and extracellular matrix, impacted by repeated variations in climatic conditions. Some of these molecular changes were confirmed by histological examination and protein expression. Both treatments (hot-wet and cold-dry) reduced the expression of genes encoding collagens, laminin, and proteoglycans, suggesting a profound remodeling of the extracellular matrix. Strong induction of the entire family of late cornified envelope genes after cold-dry exposure, confirmed at protein level, was also observed. These changes correlated with an increase in epidermal differentiation markers such as corneodesmosin and a thickening of the stratum corneum, indicating possible implementation of defense mechanisms against dehydration. This study for the first time reveals the complex pattern of molecular response allowing adaption of human skin to repeated change in its climatic environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".