Neurochemical Protection of Lutein against Sodium Nitroprusside-Induced Oxidative Damage in the Nauphoeta cinerea Model
Bibliographic record
Abstract
Abstract Objective: Lutein (LTN) is a lipophilic carotenoid widely present in green leafy vegetables such as broccoli and spinach, where it plays a crucial antioxidant role. Although its protective effects against oxidative damage are well established, its interaction with compounds such as sodium nitroprusside (SNP) remains poorly understood. This study evaluated the effects of SNP, lutein, and their combination in Nauphoeta cinerea, aiming to determine potential protective mechanisms. Methods: Toxicity was assessed after 24 h of exposure using biochemical analyses and molecular docking simulations. Results and Discussion: Sodium nitroprusside exhibited mild toxicity, whereas lutein reduced these effects without inducing toxicity on its own. Lower doses of lutein provided significant protection, whereas higher doses caused physiological stress. The combination of lutein and SNP mitigated nitroprusside-induced toxicity and reduced iron levels in the model. In silico analyses indicated comparable molecular interactions for SNP and lutein, with docking simulations revealing predominant alkyl interactions with the target protein. Conclusions: Lutein can modulate SNP-induced toxicity in Nauphoeta cinerea, with protective effects at lower concentrations but potential stress at higher doses.
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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".