Exploring In Vitro Inhibition of the Causal agent of Banana Xanthomonas Wilt by Medicinal Plant Extracts sourced from North Kivu province, Eastern Democratic Republic of Congo.
Bibliographic record
Abstract
Abstract Banana Wilt caused by Xanthomonas vasicola pv. musacearum ( Xvm ), has emerged as a significant threat to food security in eastern DR Congo (Kivu). Currently, the only means of combatting this biotic constraint is through best agricultural practices. The aim of this study was to evaluate the effectiveness of medicinal plants used in Kivu in inhibiting of Xvm . Three in vitro experiments were conducted at Uganda's National Agriculture Research Organization (NARO) laboratories and the International Institute of Tropical Agriculture (IITA) in Kivu. The bacterial samples were collected in banana cultivated in Kivu and isolated on Yeast Extract Peptone Agar (YPGA). Pure Xvm colonies were used for identification via Polymerase Chain Reaction (PCR) and greenhouse inoculation trials. A completely randomized design was used for the inhibition tests (1) on Mueller Hinton Agar (MHA) using disc diffusion with 10 extracts, (2) in liquid YPG Broth, and (3) on MHA using disc diffusion with 19 extracts. The first two trials used ten plant species extracts diluted in petroleum ether, while the third trial used 19 plant extracts diluted in methanol. After maceration, filtration, and solvent evaporation, 10mg of extract was diluted in 80 µl of distilled water + 10µl of Dimethylsulfoxide (DMSO). Ten µl of this solution was impregnated on perforated discs of Whatman filter paper. Zingiber officinale (ginger) and Ricinus communis (castor) were the most effective plant extracts in suppressing Xvm . Of the thirteen plant species identified as effective against the pathogen, the Myrtaceae and Euphorbiaceae families were the most represented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".