Methyl jasmonate enhances isoflavone biosynthesis and antioxidant activities in Fusarium sulawense-infected soybean sprouts
Bibliographic record
Abstract
Soybean sprouts are a popular and nutrient-dense traditional food in several countries; thus, they are susceptible to numerous infectious diseases during germination. This study examined the impact of methyl jasmonate (MeJA) at concentrations of 100 and 300 μM on isoflavone levels, antioxidant activities, and disease index against Fusarium sulawense in soybean sprouts. The application of 300 μM MeJA resulted in a 26 % reduction in the disease index of F. sulawense -infected soybean sprouts after 7 d of germination. The isoflavone content in MeJA-treated soybean sprouts increased, particularly on day 5 (7.71–9.57 g kg −1 ). Ten isoflavones were quantified, with genistin (∼1.3 g kg −1 ) being the most abundant and acetyl daidzin (∼0.19 g kg −1 ) the least abundant. On day 5, superoxide dismutase (SOD) and catalase (CAT) exhibited peak levels of 117 and 193 kU kg −1 , respectively, in F. sulawense -infected soybean sprouts when treated with 100 μM MeJA. The total phenolic content (TPC) in MeJA-treated soybean sprouts increased by 2-fold from day 1 to day 7, except for F. sulawense treatment. MeJA boosted the expression of several isoflavone biosynthesis-related genes in germinated soybeans, including PAL , C4H , 4CL , CHS , IFS , and CHI . Collectively, this study suggests that the application of MeJA in F. sulawense -infected soybean sprouts may enhance isoflavone biosynthesis and antioxidant activities.
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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".