Does short-term exposure to elevated levels of natural gamma radiation in Ramsar cause oxidative stress?
Bibliographic record
Abstract
BACKGROUND: Ramsar, a city in northern Iran, has areas with some of the highest recorded levels of natural radiation among inhabited areas measured on the earth. AIMS: To determine whether short-term exposure to extremely high levels of natural radiation induce oxidative stress. MATERIALS AND METHODS: In this study, 53 Wistar rats were randomly divided into five groups of 10-12 animals. Animals in the 1(st) group were kept for 7 days in an outdoor area with normal background radiation while the 2(nd) , 3(rd) , 4(th) and 5(th) groups were kept in four different outdoor areas with naturally elevated levels of gamma radiation in Ramsar. A calibrated RDS-110 survey meter, mounted on a tripod approximately 1 m above the ground, was used to measure exposure rate at each location. On days 7 and 9 blood sampling was performed to assess the serum levels of catalase (CAT) and malondialdehyde (MDA). On day 8, all animals were exposed to a lethal dose of 8 Gy gamma radiations emitted by a Theratron Phoenix (Theratronics, Canada) Cobalt-60 (55 cGy/min) at Radiotherapy Department of Razi Hospital in Rasht, Iran. RESULTS: Findings obtained in this study indicate that high levels of natural radiation cannot induce oxidative stress. CAT and MDA levels in almost all groups were not significantly different (P = 0.69 and P = 0.05, respectively). After exposure to the lethal dose, CAT and MDA levels in all groups were not significantly different (P = 0.054 and P = 0.163, respectively). CONCLUSIONS: These findings indicate that short-term exposure to extremely high levels of natural radiation (up to 196 times higher than the normal background) does not induce oxidative stress.
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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".