Novel synthetic organosulfur compounds induce apoptosis of human leukemic cells
Bibliographic record
Abstract
It has been well documented that natural organosulfur compounds (OSCs) derived from plants such as garlic, onions and mahogany trees possess antiproliferative properties; however, the essential chemical features of the active OSC compounds remain unclear. To investigate the association between OSC structure and growth inhibitory activity, we synthesized novel relatives of dysoxysulfone, a natural OSC derived from the Fijian medicinal plant, Dysoxylum richii. In this study, we have examined the antiproliferative effects of these novel OSCs on a model human leukemic cell system and show that the compounds segregate into three groups. Group I, consisting of compounds A, B, G and J, did not affect either cell proliferation or the cell cycle profile of the leukemic cell lines. Group II, consisting of compounds F and H, induced the cells to undergo apoptosis from the G2/M phase of the cell cycle. Group III, consisting of compounds C, D, E and I, decreased cell proliferation and induced apoptosis throughout the cell cycle. The apoptotic agonists of Group II and III shared a common disulfide moiety, essential for leukemic cell cytotoxicity. Interestingly, Group II compounds did not affect cell viability of normal human diploid cells, suggesting the regions flanking the disulfide group contributes to the specificity of cell killing. Thus, we provide evidence that structure-activity analysis of natural products can identify novel compounds for the development of new therapeutics that can trigger apoptosis in a tumor-specific manner.
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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".