Analysis of hypoxia‐inducible factor‐1α accumulation and cell cycle in geldanamycin‐treated human cervical carcinoma cells by laser scanning cytometry
Bibliographic record
Abstract
BACKGROUND: Tumor hypoxia has been linked to increased disease aggressiveness and poorer treatment outcomes, and the transcription factor hypoxia-inducible factor-1 (HIF-1) has been identified as the key molecule mediating the cellular response to hypoxic microenvironments. The alpha-subunit of this factor is accumulated under hypoxia and rapidly degraded during re-oxygenation, rendering the reliable measurement of HIF-1alpha a difficult task. Heat shock protein 90 (Hsp90) is an essential protein that controls the activity, turnover, and trafficking of a variety of other proteins including HIF-1alpha and cell cycle regulators. Hsp90 inhibitors like geldanamycin therefore have the potential to target tumor-cell survival by at least two mechanisms, compromising the accumulation of HIF-1alpha and cell proliferation. METHODS: We describe here the simultaneous measurement of HIF-1alpha and cell cycle parameters by laser scanning cytometry (LSC) after exposure of two different human cervical carcinoma cell lines to hypoxia and geldanamycin. RESULTS: Our analysis demonstrates that the cell lines react to hypoxia and drug treatment in a distinct way, with SiHa being more affected by low oxygen concentrations than is ME180, which was more sensitive to geldanamycin treatment. Both cell lines respond to geldanamycin with a G(2)/M-phase arrest and a decrease in HIF-1alpha accumulation. Cell death due to geldanamycin occurs in association with mitosis, presumably through mitotic catastrophe. CONCLUSION: Our results indicate that LSC can significantly contribute to the evaluation of in vitro drug effects particularly with respect to tumor hypoxia and the measurement of HIF-1alpha.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".