The effects of selenium and vitamin E on lung tissue in rats with sepsis
Bibliographic record
Abstract
PURPOSE: In this study we examined the ability of selenium and vitamin E to prevent sepsis-induced changes in lung tissue. METHODS: Fifty rats were divided into five groups: Group 1: Control group; Group 2: Sepsis group. In this group only cecal ligation and perforation (CLP) was performed. Group 3: Selenium group. An intraperitoneal dose of 100 µg selenium was given for the first two days followed by a daily dose of 40 µg for the next five days. CLP was performed the following day. Group 4: Selenium and vitamin E group. In addition to selenium, vitamin E was given intramuscularly in a dose of 250 mg/kg/day for seven days. CLP was performed the following day. Group 5: Vitamin E group. Vitamin E was given intramuscularly in a dose of 250 mg/kg/day for seven days. CLP was performed the following day. RESULTS: There were significant differences between Group 2 and all other groups in terms of blood gas values (pH, pCO2, SaO2), and leukocyte, C-reactive protein (CRP) and glutathione peroxidase levels (p < 0.005). There was no statistically significant difference between groups 3, 4 and 5 in terms of histopathological changes in lung tissue (p > 0.05), but all groups were significantly different compared with Group 2 (p < 0.05). CONCLUSION: Sepsis-induced lung tissue damage can be reduced or prevented by pre-treatment with of selenium and/or vitamin E in a rat model.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".