Potential protective effects of the edible alga <i>Arthrospira platensis</i> against lead-induced oxidative stress, anemia, kidney injury, and histopathological changes in adult rats
Bibliographic record
Abstract
Oxidative damage has been proposed as a possible mechanism involved in lead toxicity. This study investigated the possible protective effect of dietary Arthrospira platensis supplementation against lead acetate-induced kidney injury in adult male rats. Rats were divided into 4 groups: normal rats (control rats), rats treated with spirulina, rats treated with lead (Pb) (0.344 g/kg body weight), and rats treated with Pb and spirulina. The exposure of rats to Pb for 30 days provoked renal damage with significant increases in hematological parameters, oxidative stress-related parameters (i.e., thiobarbituric acid reactive substances, protein carbonyl content, advanced oxidation protein products, and hydrogen peroxide), creatinine and urea levels in plasma, and uric acid level in urine. Conversely, antioxidant enzyme activities (i.e., catalase, glutathione peroxidase, and superoxide dismutase) and levels of nonprotein thiols, plasma uric acid, and urinary creatinine and urea decreased. The administration of spirulina to Pb-treated rats significantly improved weight, peripheral blood parameters, oxidative stress-related parameters, renal biomarker levels, and antioxidant enzyme activities. Also, rats treated with Pb and spirulina had normal kidney histology. These healing effects are likely the result of the high phenol content and significant antioxidant capacity of A. platensis. Our data strongly suggest that spirulina supplementation improves kidney function and plays an important role in the prevention of complications of Pb intoxication.
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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".