Enhancement of antiphotoaging properties of Cannabis sativa stem water extracts by fermentation with Lacticaseibacillus casei
Bibliographic record
Abstract
Skin photoaging, driven primarily by UVB radiation, leads to collagen degradation and oxidative stress, contributing to the visible signs of aging, such as wrinkles and loss of skin elasticity. This process is mediated by the upregulation of matrix metalloproteinase-1 (MMP-1), which is triggered by reactive oxygen species, and the activation of photoaging-related signaling pathways, including ERK, JNK, and p65. In the present study, we evaluated the antiphotoaging potential of fermented and non-fermented Cannabis sativa stem water extracts, focusing on their ability to suppress MMP-1 expression and reduce oxidative stress in UVB-irradiated human dermal fibroblasts. Unlike previous studies that have primarily focused on leaves or flowers, our study highlights the stems of C. sativa as a novel and underutilized source of bioactive compounds for skin protection. Using Lacticaseibacillus casei for fermentation, we observed enhanced bioactivity in the fermented extracts, particularly in terms of a 6.6% greater inhibition of MMP-1 expression and 68.3% increased flavonoid content, compared to the non-fermented extracts. Fermented water extract demonstrated the most potent suppression of UVB-induced signaling pathways and collagen breakdown. Our findings suggest that fermentation enhances the antiphotoaging properties of C. sativa stems, offering a promising potential for natural, plant-based skin care solutions aimed at preventing UVB-induced skin aging.
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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".