Evaluation of the anti‐aging effects of Zinc‐α2‐glycoprotein peptide in clinical and in vitro study
Bibliographic record
Abstract
BACKGROUND: Skin aging, characterized by the deterioration of skin density and elasticity, is a common concern among individuals seeking to maintain a youthful appearance. Zinc-α2-glycoprotein (ZAG) is secreted by various body fluids, and is associated with lipolysis and identified as an atopic dermatitis biomarker. This study evaluated the potential of ZAG peptides, which exert multiple benefits such as anti-aging. MATERIALS AND METHODS: We conducted a 4-week clinical trial on patients with noticeable periorbital wrinkles (n = 22) using a ZAG peptide-containing product. The effects of the products on skin density, elasticity, and the depth of periorbital wrinkles were evaluated using Cutometer Dual MPA580, Ultrascan, and Antera 3D CS, respectively. The effect of ZAG peptides on UVB-treated keratinocyte cells was evaluated in vitro to understand the mechanisms underlying its effects against impaired skin barrier function, collagen degradation, and senescence. In addition, the effects of ZAG peptides on cell viability and expression of aging and skin barrier-related genes were assessed using cell counting kit assay and quantitative reverse transcription-polymerase chain reaction, respectively. RESULTS: The patients demonstrated improved skin density, elasticity, and reduced periorbital wrinkles. Further, more than 85% patients scored the product as satisfactory regarding anti-aging effects. Furthermore, ZAG peptides reduced SA-β-gal staining, downregulated the senescence-related genes, and upregulated the skin barrier function-related genes in UVB-irradiated keratinocyte cells. CONCLUSIONS: Our clinical and in vitro findings showed that ZAG peptides exert anti-aging effects and improve skin barrier functions, suggesting their promising potential as therapeutic agents to combat skin aging and improve skin health.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".