Signaling and apoptosis differences between severe hypoxia and desferoxamine treatment of human epithelial cells
Bibliographic record
Abstract
The mechanisms by which cells undergo proliferation arrest or cell death in response to hypoxia are still not completely understood. Originally, we showed that HeLa and Hep3B carcinoma cells undergo different proliferation responses in hypoxia. We now show that these 2 cell lines also have different cell death responses to severe hypoxia, with HeLa showing both cell cycle arrest and apoptosis (as early as 12 h after hypoxia treatment), and Hep3B showing resistance to both. Hypoxia-induced apoptosis in Hela was associated with decreases of both phospho-S473- and -T308-AKT and loss of AKT function, whereas Hep3B cells were resistant to hypoxia-induced apoptosis and did not lose phospho-AKT or AKT function. We then decided to test if our observations were confirmed using a hypoxia mimic, desferoxamine. Desferoxamine treatment yielded cell cycle arrest in HeLa and moderate arrest in Hep3B but, surprisingly, did not induce notable apoptosis of either cell line with up to 24 h of treatment. Hypoxia-treated normal human mammary epithelial cells also showed hypoxia-induced apoptosis. Interestingly, in these cell lines, there was a complete correlation between loss of phospho-AKT and (or) total AKT, and susceptibility to hypoxia-induced apoptosis. Our data suggests a model in which regulated loss of active AKT at a precise time point in hypoxia may be associated with apoptosis in susceptible cells.
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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".