Protective Mechanisms of Polyphenol-Enriched Blueberry Preparation in Preventing Inflammation in the Skin against UVB-Induced Damage in an Animal Model
Bibliographic record
Abstract
UVB significantly impacts the occurrence of cutaneous disorders, ranging from inflammatory to neoplastic diseases. Polyphenols derived from plants have been found to exhibit photoprotective effects against various factors that contribute to skin cancer. During the fermentation of the polyphenol-enriched blueberry preparation (PEBP), small oligomers of polyphenols were released, thus enhancing their photoprotective effects. This study aimed to investigate the protective effects of PEBP on UVB-induced skin inflammation. Topical preparations of polyphenols were applied to the skin of dorsally shaved mice. Mice were subsequently exposed to UVB and were sacrificed 90 min after UVB exposure. This study revealed that pretreatment with PEBP significantly inhibited UVB-induced recruitment of mast and neutrophil cells and prevented the loss of skin thickness. Furthermore, the findings show that PEBP treatment resulted in the downregulation of miR-210, 146a, and 155 and the upregulation of miR-200c and miR-205 compared to the UVB-irradiated mice. Additionally, PEBP was found to reduce the expression of IL-6, IL-1β, and TNFα, inhibiting COX-2 and increasing IL-10 after UVB exposure. Moreover, DNA methylation analysis indicated that PEBP might potentially reduce the activation of inflammation-related pathways such as MAPK, Wnt, Notch, and PI3K-AKT signaling. Our finding suggests that topical application of PEBP treatment may effectively prevent UVB-induced skin damage by inhibiting inflammation.
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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 | 0.001 |
| 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".