Gene Expression Profiles in Mouse Liver Cells after Exposure to Different Types of Radiation
Bibliographic record
Abstract
The liver is one of the target organs of radiation-induced cancers by internal exposures. In order to elucidate radiation-induced liver cancers including Thorotrast, we present a new approach to investigate in vivo effects of internal exposure to alpha-particles. Adopting boron neutron capture, we separately irradiated Kupffer cells and endothelial cells in mouse liver in vivo and analyzed the changes in gene transcriptions by an oligonucleotide microarray. Differential expression was defined as more than 3-fold for up-regulation and less than 1/3 for under-regulation, compared with non-irradiated controls. Of 6,050 genes examined, 68 showed differential expression compared with non-irradiated mice. Real-time polymerase chain reaction validated the results of the microarray analysis. Exposure to alpha-particles and gamma-rays produced different patterns of altered gene expression. Gene expression profiles revealed that the liver was in an inflammatory state characterized by up-regulation of positive acute phase protein genes, irrespective of the target cells exposed to radiation. In comparison with chemical and biological hepatotoxicants, inductions of Metallothionein 1 and Hemopexin, and suppressions of cytochrome P450s are characteristic of radiation exposure. Anti-inflammatory treatment could be helpful for the prevention and protection of radiation-induced hepatic injury.
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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".