Senecio latifolius induces in vitro hepatocytotoxicity in a human cell lineThis 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 objectives of this study were twofold: (i) to determine the mechanism(s) of Senecio-induced toxicity in human hepatoblastoma cells (HepG2) in vitro and whether such toxicity could be prevented using N-acetyl-cysteine (NAC), and (ii) to evaluate whether caspases are involved in Senecio-induced apoptosis. Cells were treated with aqueous extracts of Senecio (10 mg x mL-1) with and without NAC. Cytotoxicity was determined by using the MTT assay. Total glutathione (GSH) was measured by using the Tietze assay. Cells were also treated with aqueous extracts of Senecio in the presence or absence of 50 micromol/L caspase-3 inhibitor (IDN) for 24 h. Apoptosis was determined by transmission electron microscopy, and DNA fragmentation was determined by ELISA and terminal dUTP nick-end labelling (TUNEL). Senecio produced cytotoxicity and depleted GSH in a concentration- and time-dependent manner. A significant depletion in GSH was observed after 15 min (p < 0.001 vs. control), whereas significant cytotoxicity was only observed after 3 h (p < 0.001 vs. control). Treatment with NAC prevented Senecio-induced GSH depletion and resulted in a significant decrease in Senecio-induced cytotoxicity (p < 0.001 vs. NAC-untreated cells). Treatment with Senecio for 24 h resulted in 22% +/- 2.5% (p < 0.001) apoptosis (vs. control). Pretreatment with 50 mumol caspase inhibitor reduced Senecio-induced apoptosis significantly (vs. non-exposed to IDN) (12% +/- 1.5%; p < 0.05). Our results suggest the mechanism of Senecio-induced cytotoxicity in HepG2 cells in vitro involves depletion of cellular GSH. Cytotoxicity is reduced by supplementation with NAC, which thus prevents GSH depletion. Caspase activation is involved in Senecio-induced apoptosis.
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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) | 0.001 | 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) | 0.003 | 0.001 |
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".