Toxicity of black nightshade (<i>Solanum nigrum</i>) extracts on <i>Alternaria brassicicola</i>, causal agent of black leaf spot of Chinese cabbage (<i>Brassica pekinensis</i>)
Bibliographic record
Abstract
Abstract Root extracts of black nightshade (Solanum nigrum) were analyzed for activity against isolates ABA‐31 and ABA‐104 of Alternaria brassicicola, the causal agent of black leaf spot of Chinese cabbage (Brassica pekinensis). Preliminary results showed that dried root tissues of black nightshade extracted with 70% ethanol contained antifungal properties against A. brassicicola. Ethanol root extracts were used for further fractionations using ethyl acetate, n‐butanol and water. Among the three extracts, the n‐butanol fraction showed the strongest antifungal activity by its suppression of conidial germination of A. brassicicola. The n‐butanol extract of S. nigrum roots was fractionated further into six fractions (I–VI). Among the six fractions tested, fraction V showed a strong inhibitory effect on conidial germination of A. brassicicola and thereby suppressed lesion development of black leaf spot of Chinese cabbage at a concentration of 25 ppm or higher. Nuclear magnetic resonance analysis indicated that fraction V contained a mixture of saponins, and results of further bio‐guided fractionation and bioassay suggested that saponins in fraction V were key chemical components in the control of A. brassicicola. The potential of using black nightshade for developing natural products for the control of fungal plant diseases is discussed.
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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) | 0.001 | 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".