Phytocannabinoids Stimulate Rejuvenation and Prevent Cellular Senescence in Human Dermal Fibroblasts
Bibliographic record
Abstract
In light of the increased popularity of phytocannabinoids (pCBs) and their appearance in beauty products without rigorous research on their rejuvenation efficacy, we decided to investigate the potential role of pCBs in skin rejuvenation. Utilizing healthy and stress-induced premature senescent (SIPS) CCD-1064Sk skin fibroblasts, the effects of pCBs on cellular viability, functional activity, metabolic function, and nuclear architecture were tested. Both delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD) within the range of 0.5 µM to 2.0 µM increased cell growth in a dose-dependent manner while significantly decreasing senescence as measured by beta-galactosidase activity. Utilizing a scratch assay, both THC and CBD (2.0 µM) significantly improved wound healing in both healthy and SIPS fibroblasts. THC and CBD altered nuclear architecture and mRNA levels of cell cycle regulators and genes involved in ECM production. Subsequently, we found ELN, Cyclin D1, PCNA, and BID protein levels altered by SIPS but ameliorated after pCBs exposure in human dermal fibroblasts. Lastly, we compared the efficacy of THC and CBD with common anti-aging nutrient signaling regulators in replicative senescent adult human dermal fibroblasts, CCD-1135Sk. Both THC and CBD were found to improve wound healing better than metformin, rapamycin, and triacetylresveratrol in replicative senescent CCD-1135Sk fibroblasts. Therefore, pCBs can be a valuable source of biologically active substances used in cosmetics, and more studies using clinical trials should be performed to confirm the efficacy of phytocannabinoids.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".