Adverse effects of Sudanese<i>toombak</i>vs. Swedish snuff on human oral cells
Bibliographic record
Abstract
BACKGROUND: The high incidence of oral cancer in Sudan has been associated with the use of toombak, the local type of smokeless tobacco. However, its specific effects on human oral cells are not known. We aimed to investigate the effects of toombak on primary normal human oral keratinocytes, fibroblasts, and a dysplastic oral keratinocytic cell line, and to compare them with the effects induced by Swedish snuff. METHOD: Aqueous extracts were prepared from moist toombak and Swedish snuff and added in serial dilutions on in vitro monolayer cultured cells. Cell viability, morphology and growth, DNA double-strand breaks (gammaH2AX staining), expression of phosphatidylserine (Annexin V staining), and cell cycle were assessed after various exposure time periods. RESULTS: Significant decrease in cell number, occurrence of DNA double-strain breaks, morphological and biochemical signs of programmed cell death were detected in all oral cell types exposed to clinically relevant dilutions of toombak extract, although to a lesser extent in normal oral fibroblasts and dysplastic keratinocytes. G2/M-block was also detected in normal oral keratinocytes and fibroblasts exposed to clinically relevant dilutions of toombak extract. Swedish snuff extract had less adverse effects on oral cells, mainly at non-clinically relevant dilutions. CONCLUSION: This study indicates a potential for toombak, higher than for Swedish snuff, to damage human oral epithelium. Dysplastic oral keratinocytes were less sensitive than their normal counterparts, suggesting that they might have acquired a partially resistant phenotype to toombak-induced cytotoxic effects while still being prone to DNA damage that could lead to further malignant progression.
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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) | 0.003 | 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".