Anti-inflammatory effect of selenium nanoparticles on the inflammation induced in irradiated rats
Bibliographic record
Abstract
Selenium (Se) has been reported to possess anti-inflammatory properties, but its bioavailability and toxicity are considerable limiting factors. The present study aimed to investigate the possible anti-inflammatory and analgesic effects of selenium nanoparticles (Nano-Se) on inflammation induced in irradiated rats. Paw volume and nociceptive threshold were measured in carrageenan-induced paw edema and hyperalgesia model. Leukocytic count, tumor necrosis factor-α (TNF-α), prostaglandin E 2 (PGE 2 ), thiobarbituric acid reactive substances (TBAR), and total nitrate/nitrite (NOx) were estimated in the exudate collected from 6 day old air pouch model. Irradiated rats were exposed to 6 Gy gamma (γ)-irradiation. Nano-Se were administered orally in a dose of 2.55 mg/kg once before carrageenan injection in the first model and twice in the second model. The paw volume but not the nociceptive response produced by carrageenan in irradiated rats was higher than that induced in non-irradiated rats. Nano-Se were effective in reducing the paw volume in non-irradiated and irradiated rats but it did not alter the nociceptive threshold. The inflammation induced in irradiated rats increased all the estimated parameters in the exudate whereas; Nano-Se decreased their elevation in non-irradiated and irradiated rats. Nano-Se possess a potential anti-inflammatory activity on inflammation induced in irradiated rats.
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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".