Modulation of arsenic toxicity-induced oxidative damage by coronatine pretreatment in sweet basil (<i>Ocimum basilicum</i>) seedlings
Bibliographic record
Abstract
Coronatine (COR), a functional analogue of jasmonates, affects the accumulation of defense-related metabolites in plants. However, information on how its effects may be mediated by heavy metal stress is scanty. This study was carried out to determine the changes in some physiological parameters in sweet basil (Ocimum basilicum L.) seedlings pretreated with COR under arsenic (As) toxicity. COR was applied to seedlings at the three-leaf stage at 50 and 100 nanomolars (nmol·L−1). Arsenic at 150 and 300 μmol·L−1was imposed after completion of COR treatment. Toxic effects of As were reflected by the reduction in growth parameters and photosynthetic pigments and induction of lipid peroxidation coupled with enhancing hydrogen peroxide and electrolyte leakage in leaves. Under As toxicity, plants treated with COR significantly increased the fresh mass and relative water content in leaves. The increase was accompanied by decreased lipid peroxidation and hydrogen peroxide content and maintenance of chlorophyll pigments. COR supplement also increased the accumulation of phenolic compounds and proline content in the leaf of As-treated seedlings. Pretreatment with COR significantly enhanced activities of catalase (CAT), superoxide dismutase (SOD), and guaiacol peroxidase (POD) in basil leaves under As treatment. The results suggested that COR might alleviate As toxicity in sweet basil by activating antioxidant enzymes and inducing conservative components, thereby preventing membrane peroxidation.
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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 | 0.001 |
| 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".