Comparative gene expression analysis after exposure to 123I-iododeoxyuridine, γ- and α-radiation—potential biomarkers for the discrimination of radiation qualities
Bibliographic record
Abstract
Gene expression analysis was carried out in Jurkat cells in order to identify candidate genes showing significant gene expression alterations allowing robust discrimination of the Auger emitter 123I, incorporated into the DNA as 123I-iododeoxyuridine (123IUdR), from α- and γ-radiation. The γ-H2AX foci assay was used to determine equi-effect doses or activity, and gene expression analysis was carried out at similar levels of foci induction. Comparative gene expression analysis was performed employing whole human genome DNA microarrays. Candidate genes had to show significant expression changes and no altered gene regulation or opposite regulation after exposure to the radiation quality to be compared. The gene expression of all candidate genes was validated by quantitative real-time PCR. The functional categorization of significantly deregulated genes revealed that chromatin organization and apoptosis were generally affected. After exposure to 123IUdR, α-particles and γ-rays, at equi-effect doses/activity, 155, 316 and 982 genes were exclusively regulated, respectively. Applying the stringent requirements for candidate genes, four (PPP1R14C, TNFAIP8L1, DNAJC1 and PRTFDC1), one (KLF10) and one (TNFAIP8L1) gene(s) were identified, respectively allowing reliable discrimination between γ- and 123IUdR exposure, γ- and α-radiation, and α- and 123IUdR exposure, respectively. The Auger emitter 123I induced specific gene expression patterns in Jurkat cells when compared with γ- and α-irradiation, suggesting a unique cellular response after 123IUdR exposure. Gene expression analysis might be an effective tool for identifying biomarkers for discriminating different radiation qualities and, furthermore, might help to explain the varying biological effectiveness at the mechanistic level.
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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) | 0.001 | 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".