The Effects of Nutrient Signaling Regulators in Combination with Phytocannabinoids on the Senescence-Associated Phenotype in Human Dermal Fibroblasts
Bibliographic record
Abstract
Identifying effective anti-aging compounds is a cornerstone of modern longevity, aging, and skin-health research. There is considerable evidence of the effectiveness of nutrient signaling regulators such as metformin, resveratrol, and rapamycin in longevity and anti-aging studies; however, their potential protective role in skin aging is controversial. In light of the increasing appearance of phytocannabinoids in beauty products without rigorous research on their rejuvenation efficacy, we decided to investigate the potential role of phytocannabinoids in combination with nutrient signaling regulators in skin rejuvenation. Utilizing CCD-1064Sk skin fibroblasts, the effect of metformin, triacetylresveratrol, and rapamycin combined with phytocannabinoids on cellular viability, functional activity, metabolic function, and nuclear architecture was tested. We found triacetylresveratrol combined with cannabidiol increased the viability of skin fibroblasts (p < 0.0001), restored wound-healing functional activity (p < 0.001), reduced metabolic dysfunction, and ameliorated nuclear eccentricity and circularity in senescent fibroblasts (p < 0.01). Conversely, metformin with or without phytocannabinoids did not show any beneficial effects on functional activity, while rapamycin inhibited cell viability (p < 0.01) and the speed of wound healing (p < 0.001). Therefore, triacetylresveratrol and cannabidiol can be a valuable source of biologically active substances used in aging and more studies using animals to confirm the efficacy of cannabidiol combined with triacetylresveratrol should be performed.
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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".