In vitro Anti-Inflammatory Effects of Hyaluronic Acid in Ethanol-Induced Damage in Skin Cells
Bibliographic record
Abstract
UNLABELLED: Ethyl alcohol (ethanol) is commonly applied in cosmetic and pharmaceutical preparations, as well as disinfectant for chronic wounds. OBJECTIVE: The present study aimed to appraise physiological levels of ethanol-induced damage in skin cells in vitro and the possible repair by hyaluronic acid (HA). In addition, we aimed to establish cytokine-chemokine networks in the cellular media and the modulation of cytokines such as tumor necrosis factor-alpha (TNF-B), interferon-alpha (IFN-α), transforming growth factor-beta (TGF-B), interleukins (IL) such as IL1-B and IL-6, as well as matrix metalloproteinases (MMP) and tissue inhibitors of metalloproteinases (TIMP). DESIGN AND METHODS: We treated human A431 epidermoid skin cells and mouse fibroblasts with ethanol at a concentration of 100 mM over 24 hours (h). A separate experiment looked at the effects of 2 consecutive treatments with 100 mM ethanol for 24 h each. HA obtained from umbilical cord excision was used at two concentration levels (2% and 4%) to determine its efficacy in the treatment. We measured cytotoxicity and cytokine networks in the media. RESULTS: Treatment of cells with 100 mM ethanol increased cytotoxicity, as well as the release of pro-inflammatory cytokines into the culture medium. CONCLUSIONS: Ethanol may induce cytotoxicity in skin cells by enhancing the effects of pro-inflammatory cytokines. HA reduced the amount of pro-inflammatory cytokines released into the media both in human A431 epidermoid skin cells and in mouse fibroblasts.
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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".