Determination of Endogenous Radioresistance Biomarkers on HeLa Cells
Bibliographic record
Abstract
The Genes such as carbonic anhydrase IX (CAIX), hypoxia inducible factor-1 (HIF-1) and vascular endothelial growth factor (VEGF) have been suggested as hypoxic biomarkers in cancer. Indeed, these endogenous biomarkers have been shown to have stronger prognostic value response after treatment by irradiation. However, inconsistent results suggest that factors other than oxygen influence their expression. This present study deciphers the level of expression of different radioresistance biomarkers in both normoxia and hypoxia conditions followed by irradiation of human ovarian tumor cell lines (uterine cervix squamous cell carcinoma (HeLa). HeLa cells were submitted to hypoxia (1% O2) conditions in a Thermo Scientific Heracell i CO2 incubator. The cells were subjected to two doses 4-10 Gy irradiation and re-incubate in their starting conditions for 4 hours, then fixed in 4% paraformaldehyde for 20 min. Protein expressions were assessed by immunocytochemistry staining and fluorescent images were captured by a Axio Imager Z1 fluorescence microscope with oil immersion lens at 63× magnification. In normoxia conditions there was no modification of the level of expression of the CAIX after irradiation. However, an increasing expression level of VEGF was noted. The level of expression of HIF-1 in normoxia was low compared to the other two proteins (CAIX and VEGF). Hypoxia conditions at 2% resulted in a low expression of CAIX and VEGF before and after irradiation at 10 Gy in HeLa cells. HIF-1 had a maximum expression level compared to CAIX and VEGF at 2% oxygen after irradiation in HeLa cells. As tumor hypoxia occurs in a deprived microenvironment, other environmental factors such as irradiation might interact with the effect of low oxygen concentration on gene expression. This study shows that irradiation of HeLa cells has a profound influence on the oxygen dependent induction of certain endogenous hypoxic markers as HIF-1, CAIX, and VEGF.
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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) | 0.002 | 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".