Involvement of PAR-4 in Cannabinoid-Dependent Sensitization of Osteosarcoma Cells to TRAIL-Induced Apoptosis
Bibliographic record
Abstract
The synthetic cannabinoid WIN 55,212-2 is a potent cannabinoid receptor agonist with anticancer potential. Experiments were performed to determine the effects of WIN on proliferation, cell cycle distribution, and programmed cell death in human osteosarcoma MG63 and Saos-2 cells. Results show that WIN induced G2/M cell cycle arrest, which was associated with the induction of the main markers of ER stress (GRP78, CHOP and TRB3). In treated cells we also observed the conversion of the cytosolic form of the autophagosome marker LC3-I into LC3-II (the lipidated form located on the autophagosome membrane) and the enhanced incorporation of monodansylcadaverine and acridine orange, two markers of the autophagic compartments such as autolysosomes. WIN also induced morphological effects in MG63 cells consisting in an increase in cell size and a marked cytoplasmic vacuolization. However, WIN effects were not associated with a canonical apoptotic pathway, as demonstrated by the absence of specific features, and only the addition of TRAIL to WIN-treated cells led to apoptotic death probably mediated by up-regulation of the tumor suppressor factor PAR-4, whose levels increased after WIN treatment, and by the translocation of GRP78 on cell surface.
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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".