The combination of cigarette smoke and solar rays causes effects similar to skin aging in a bilayer skin model
Bibliographic record
Abstract
Skin aging is a multifactorial process influenced by internal and external factors. The contribution of different environmental factors has been well established individually in the last few years. On the one hand, man is rarely exposed to a single factor, and on the other hand, there is very little knowledge about how these extrinsic factors may interact with each other or even how the skin may react to chronic exposure. This study aimed to evaluate the effect on skin aging of a chronic co-exposure of tissue-engineered skin substitutes to cigarette smoke extract (CSE) and solar simulator light (SSL). Skin substitutes were reconstructed according to the self-assembly method and then exposed to CSE followed by irradiation with SSL simultaneously transmitting UVA1, visible light and infrared. When skin substitutes were chronically exposed to CSE and SSL, a significant decrease in procollagen I synthesis and the inhibition of Smad2 phosphorylation of the TGF-β signaling pathway were observed. A 6.7-fold increase in MMP-1 activity was also observed when CSE was combined with SSL, resulting in a decrease in collagen III and collagen IV protein expression. The secretory profile resulting from the toxic synergy was investigated and several alterations were observed, notably an increase in the quantities of pro-inflammatory cytokines. The results also revealed the activation of the ERK1/2 (3.4-fold) and JNK (3.3-fold) pathways. Taken together, the results showed that a synergy between the two environmental factors could provoke premature skin aging.
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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 | 0.001 |
| 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 | 0.001 |
| 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".