Analysis of hypoxia‐inducible factor‐1α accumulation and cell cycle in geldanamycin‐treated human cervical carcinoma cells by laser scanning cytometry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| 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) | 0.000 | 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 it