Chitosan-induced production of secondary metabolites in plant extracts of Piper auritum, and the in vitro fungicidal activity against Fusarium oxysporum f. sp. vanillae
Bibliographic record
Abstract
Se probó el efecto del quitosano sobre la producción de metabolitos secundarios de extractos etanólicos de Piper auritum, así como la actividad fungicida in vitro contra Fusarium oxysporum f. sp. vanillae. Las plantas de P. auritum se dividieron en seis parcelas y se añadió quitosano comercial a la mitad de ellas. Se midieron las concentraciones de flavonoides, fenoles, terpenos, alcaloides y ácido salicílico en extractos etanólicos de P. auritum y la actividad antifúngica se midió con la concentración efectiva media (CE50). Las concentraciones totales de flavonoides, fenoles y terpenos fueron más altas con el tratamiento con quitosano (sin quitosano 12.8 versus con quitosano 12.4 ?g equivalente de quercetina por mg, 12.6 versus 2.3 ?g equivalente de ácido tánico por 10 mg y 16.3 versus 11.6 mg equivalente de mentol por 100 mg). Mientras que la concentración de alcaloides disminuyó mediante la adición de quitosano (de 148.2 a 84.5 ?g de equivalente de piperina por mg). La adición de quitosano aumentó la concentración de ácido salicílico (de 1.3 a 2.2 ?g de equivalente de ácido salicílico por mg). La concentración de 4 mg mL-1 de extracto etanólico de P. auritum tratado con quitosano inhibió el 100% del crecimiento micelial. El tratamiento con quitosano generó que la CE50 de P. auritum contra F. oxysporum f. sp. vanillae fuera menor (1.5 mg mL-1) respecto al control (5.1 mg mL-1). Se concluye que la adición de quitosano aumentó la producción de metabolitos secundarios y la actividad antifúngica in vitro en extractos de P. auritum.
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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".