Elucidation of anti-human melanoma and anti-aging mechanisms of compounds from green seaweed Caulerpa racemosa
Bibliographic record
Abstract
Human melanoma is linked with aging-related disorders, prompting interest in the development of functional foods derived from natural ingredients to mitigate its incidence. Molecules in green seaweeds such as Caulerpa racemosa can serve this purpose due to their anti-tumor and anti-inflammatory properties. A previous work study compounds profiling has been carried out, and in this research the molecular docking studies targeting receptors associated with melanoma (GRP78, IRE1, BRAF) and aging (mTOR, AMPK, SIRT1) identified four promising compound in an extract of C. racemosa. The current study aims to the mechanism of those compounds at a cellular level using the human A375 (BRAF-V600E mutation) and A375 and B16-F10 cell lines. The MTT assay was used to evaluate the potential of GSCRE compounds against A375 and B16-F10 cell lines, with comparisons made to normal HDFa cell lines. Results indicated that compound C2, also known as Caulersin, demonstrated a significantly different ∆G affinity binding score compared to the control drug Dabrafenib. GSCRE crude extract, particularly C2, showed potential in modulating mTOR, AMPK, and SIRT1 pathways and downregulating GRP78, IRE1, and BRAF signaling (p < 0.05). Interestingly, C2 was less effective in suppressing A375 and B16-F10 cell lines (LD50 C2 < LD50 Dabrafenib/control), with its LD50 value nearly matching that of the Trametinib control in B16-F10 cell lines. Consequently, GSCRE, especially C2 or Caulersin, shows promise as a new molecule for developing functional foods to combat aging and human melanoma. However, further in vivo studies and clinical trials are necessary to confirm these findings.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".