Plant phenolics regulate neoplastic cell growth and survival: a quantitative structure–activity and biochemical analysisThis article is one of a selection of papers published in this special issue (part 2 of 2) on the Safety and Efficacy of Natural Health Products.
Bibliographic record
Abstract
The anti-tumour activities of many plant phenolics at high concentrations (>100 micromol/L) suggest their potential use as dietary supplements in cancer chemoprevention and cancer chemotherapy. However, it is not clear what impact phenolic compounds have at the physiological concentrations obtained through consumption of high phenolic diets on neoplastic cells. In the present study, 54 naturally occurring phenolics were evaluated at physiologically relevant concentrations for their capacity to alter PC12 cell viability in response to serum deprivation, the chemotherepeutic agent etoposide, and the apoptogen C2-ceramide. Surprisingly, novel mitogenic, cytoprotective, and antiapoptotic activities were detected. Quantitative structure-activity relationship modelling indicated that many of these activities could be predicted by compound lipophilicity, steric bulk, and (or) antioxidant capacity, with the exception of inhibition of ceramide-induced apoptosis. Where quantitative structure-activity relationship analysis was insufficient, biochemical assessment demonstrated that the benzoate orsellinic acid blocked downstream caspase-12 activation following ceramide challenge. These findings demonstrate substantive mitogenic, cytoprotective, and antiapoptotic biological activities of plant phenolics on neoplastic cells at physiologically relevant dietary concentrations that should be considered in chemopreventive and chemotherapeutic strategies.
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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) | 0.001 | 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".