The effect of selected drugs on the mitigation of myocardial injury caused by gamma radiation
Bibliographic record
Abstract
Radiation damage of healthy tissues represents one of the complications of radiotherapy effectiveness. This study is focused on the screening of potentially effective drugs routinely used in medical practice and involved in the mechanism of radiation injury, namely for radiation-induced production of free radicals in the body. Experiments in rats revealed significant reduction of oxidative stress (malondialdehyde) and inflammatory marker (tumor necrosis factor α) in 10 Gy irradiated groups after administration of atorvastatin and a slight decrease after tadalafil administration, which indicates that one of the possible mechanisms for mitigation of radiation-induced cardiac damage could be the modulation of nitric oxide (NO) in endothelium and phosphodiesterase 5. In addition, miRNAs were analyzed as potential markers and therapeutically effective molecules. Expression of miRNA-21 and miRNA-15b showed the most significant changes after irradiation. Atorvastatin and tadalafil normalized changes of miRNA (miRNA-1, miRNA-15b, miRNA-21) expression levels in irradiated hearts. This screening study concludes that administration of specific drugs could mitigate the negative impact of radiation on the heart, but more detailed experiments oriented to other aspects of drug effectiveness and their exact mechanisms are still needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".