Protective effects of selenium in tacrolimus-induced lung toxicity: potential role of heme oxygenase 1
Bibliographic record
Abstract
The present study aimed to evaluate the protective effects of selenium (Sel) administration against tacrolimus (Tac) – induced lung toxicity and to assess the relation between heme oxygenase 1 (HO-1) and these effects. The study was conducted on 36 Wistar male albino rats equally divided into four groups: (i) normal control; (ii) Sel (0.1 mg/kg per day p.o. for four weeks); (iii) TAC 3 mg/mL as single oral dose on 27th day; and (iv) Tac + Sel. Lung tissues, lung homogenate, and bronchoalveolar lavage of the sacrificed animals were investigated biochemically and histopathologically, by immunohistochemistry or by PCR. The Tac group showed significantly lower expression of HO-1. Administration of Sel was associated with increased HO-1 expression. Oxidative (malondialdehyde, reduced glutathione, superoxide dismutase, myeloperoxidase, and glutathione peroxidase activity) and nitrosative stress (nitric oxide) markers and markers of inflammation (interleukin 1β (IL-1β), IL-6, and IL-10) showed changes corresponding to HO-1 levels in rat groups. Tac group showed the highest expression of caspase-3. Sel exerted a protective role against Tac-induced lung toxicity.
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) | 0.000 | 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".