Influence of 2-Nitroimidazoles in the Response of FaDu Cells to Ionizing Radiation and Hypoxia/Reoxygenation Stress
Bibliographic record
Abstract
Cellular adaptations to hypoxia promote resistance to ionizing radiation (IR). This presents a challenge for treatment of head and neck cancer (HNC) that relies heavily on radiotherapy. Standard radiosensitizers often fail to reach diffusion-restricted hypoxic cells, whereas nitroimidazoles (NIs) [such as iodoazomycin arabinofuranoside (IAZA) and fluoroazomycin arabinofuranoside (FAZA)] can preferentially accumulate in hypoxic tumours. Here, we explored if the hypoxia-selective uptake of IAZA and FAZA could be harnessed to make HNC cells (FaDu) susceptible to radiation therapy. Cellular response to treatment was assessed through clonogenic survival assays and by monitoring DNA damage (immunofluorescence staining of DNA damage markers, γ-H2AX and p-53BP1, and by alkaline comet assay). The effects of reoxygenation were studied using the following assays: estimation of nucleoside incorporation to assess DNA synthesis rates, immunofluorescent imaging of chromatin-associated replication protein A as a marker of replication stress, and quantification of reactive oxygen species (ROS). Both IAZA and FAZA sensitized hypoxic HNC cells to IR, albeit the former is a better radiosensitizer. Radiosensitization by these compounds was restricted only to hypoxic cells, with no visible effects under normoxia. IAZA and FAZA impaired cellular adaptation to reoxygenation; high levels of ROS, reduced DNA synthesis capacity, and signs of replication stress were observed in reoxygenated cells. Overall, our data highlight the therapeutic potentials of IAZA and FAZA for targeting hypoxic HNC cells and provide rationale for future preclinical studies.
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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".