Bibliographic record
Abstract
Studies were conducted to examine the effect of pre and post-treatment of selenium in mercury intoxication (20 micromole/ kg b.w. each given intraperitoneally) in mice in terms of lipid peroxidation (LPO), glutathione (GSH) content, activities of superoxide dismutase (SOD), glutathione peroxidase (GPx), catalase (CAT) and mercury concentration in liver, kidney and brain. No significant alteration was observed in all the organs examined after mercury or selenium treatment in LPO and GSH but administration of selenium (pre and post) resulted in an increase in the level of LPO and GSH. The activity of SOD was depleted in liver and kidney while that of GPx was lowered in liver of mercury exposed animals. Selenium administration resulted in restoration of the depletion of these enzymatic activities. The activity of CAT in liver and brain was enhanced both in mercury and selenium treated animals. Administration of selenium significantly arrested enhanced CAT activity. Kidney showed the highest mercury concentration among the organs examined. Administration of selenium resulted in further enhancement of mercury concentration in the tissues. An increase in selenium level in liver was observed after mercury treatment, which was also restored by mercury selenium co-administration. Our results indicate that the prooxidant effect of selenium was greater by its pretreatment.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".