Enhancing Maize Grain Yield Quality: Arbuscular Mycorrhizal Fungi as a Sustainable Solution Under Antimony Stress
Bibliographic record
Abstract
Antimony (Sb) contamination threatens food security by lowering crop yields, reducing nutritional quality, and harming agroecosystems, underscoring the need for sustainable and eco-friendly strategies to alleviate heavy metal stress. Here arbuscular mycorrhizal fungi (AMF) role to mitigate Sb-induced stress in maize, was examined. AMF-inoculated and non-inoculated plants were grown under control and Sb stress conditions for 10 weeks, and growth, nutrient uptake, metabolic profiles, antioxidant capacity, and antimicrobial activity were assessed. Sb exposure markedly suppressed maize performance, reducing fresh and dry biomass by 66% and 65%, respectively, while also impairing the growth-promoting effects commonly associated with AMF. However, AMF inoculation significantly alleviated Sb toxicity, enhancing fresh biomass by 43% and dry biomass by 40%. The recovery was linked to improved nutrient uptake and the accumulation of primary metabolites, which promoted physiological adjustments. Moreover, AMF-inoculated plants under Sb stress showed enriched bioactive metabolites, leading to stronger antimicrobial activity and a 65% increase in antioxidant capacity. Collectively, these findings demonstrate that AMF enhance maize resilience to Sb stress by promoting growth, nutritional quality, and bioactive properties. This study demonstrates that AMF offer a sustainable strategy to enhance crop resilience and biofortification in contaminated environments.
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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".