Genistein Can Mitigate the Effect of Radiation on Rat Lung Tissue
Bibliographic record
Abstract
We investigated whether genistein could protect the lung from radiation-induced injury. We hypothesized that genistein would reduce the levels of inflammatory cytokines and ROS after irradiation and therefore lead to reduced DNA damage and functional deficits. Whole lungs of Sprague-Dawley rats were irradiated with 18 Gy at approximately 0.5 Gy/min. At 28 weeks a micronucleus assay was used to examine DNA damage and, using immunohistochemical analysis, expression of IL-1alpha, IL-1beta, IL-6, TNF-alpha and TGF-beta, macrophage activation, oxidative stress (8-OHdG) and collagen levels were measured. A TBARS assay was used to measure the level of malondialdehyde. Functional damage was assessed by measuring the breathing rate of the rats over the course of the experiment. The increase in breathing rate after irradiation was damped in rats receiving genistein during the phase of pneumonitis (6-10 weeks), and there was a 50-80-day delay in lethality in this group. Genistein treatment also decreased the levels of the inflammatory cytokines TNF-alpha, IL-1beta and TGF-beta and led to a reduction in collagen content, a reduction in 8-OHdG levels, and complete protection against DNA damage measured in surviving rats at 28 weeks after irradiation. These results demonstrates that genistein treatment can provide partial protection against the early (pneumonitis) effects of lung irradiation and reduce the extent of fibrosis, although not sufficiently to prevent lethality at the radiation dose used in this study.
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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) | 0.001 | 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".