Protective effects of 3,4',5,7‐tetrahydroxyflavone against squalene monohydroperoxide‐induced skin wrinkles and its green extraction using deep eutectic solvents
Bibliographic record
Abstract
Abstract Three flavone derivatives, 3,4′,5,7‐tetrahydroxyflavone (3,4′,5,7‐THF) and their glycosides 3,4′,5,7‐tetrahydroxyflavone‐3‐rhamnoglucoside, and 3,4′,5,7‐tetrahydroxyflavone‐3‐glucoside, were identified by partial enzymatic conversion of Camellia sinensis seed extract (CSSE). 2,2‐Diphenyl‐1‐picrylhydrazyl radical scavenging activity of these compounds and lipid peroxidation inhibition effects of 3.4′,5,7‐THF were tested to evaluate their antioxidant properties. To investigate the effects of 3,4′,5,7‐THF on squalene monohydroperoxide (SQLOOH)‐induced wrinkle formation, the dorsal skin of SKH1 hairless mice was used. Four weeks of treatment with 3,4′,5,7‐THF reduced wrinkle formation, in contrast to that observed in the group treated with SQLOOH only. A green and simple method has been suggested for the extraction of 3,4′,5,7‐THF from CSSE hydrolysate with nine deep eutectic solvents. A mixture of choline chloride and xylose yielded the best extraction result. These findings suggest that 3,4′,5,7‐THF is a natural compound with potential application in preventing SQLOOH associated skin aging.
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 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".