Priming seeds with salicylic acid modulates membrane integrity, antioxidant defense, and gene expression in <i>Medicago sativa</i> grown under iron deficiency and salinity
Bibliographic record
Abstract
Exposure of plants to adverse environmental conditions reduces their growth and productivity. Currently, seed priming with phytohormones is considered one of the most reliable and cost-effective approaches that can help alleviate the toxic effects of environmental stress. In this context, the present study aims to investigate the effect of priming alfalfa seeds with salicylic acid (SA) on oxidative stress markers, including malonyldialdehyde, protein content, activities of antioxidant enzymes, and expression of genes encoding these enzymes in leaves and roots of alfalfa (Gabes ecotype) grown under saline stress, iron deficiency, or both. Our results showed that the application of salt stress and iron deficiency separately or simultaneously induces changes in the activities of antioxidant enzymes, but these are organ- and stress-dependent. The Gabes ecotype was able to increase the activities of these enzymes under salt stress to alleviate oxidative damage. Indeed, priming seeds with 100 μM SA significantly increases the enzymatic activities of APX, GPX, CAT, and SOD. Therefore, this concentration can be considered optimal for the induction of iron deficiency tolerance. Our results showed not only that Gabes ecotype was able to tolerate salt stress by maintaining high expression of the Fe-SOD isoform, but also that the pretreatment of seeds with 100 μM SA improved the tolerance of this ecotype to iron deficiency by stimulating Fe-SOD expression and inhibiting CAT and APXc.
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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".