Investigating Plant Extract in Inhibition of Ralstonia solanacearum Responsible for Potato Wilt
Bibliographic record
Abstract
Controlling the Potato Bacterial wilt disease caused by Ralstonia solanacearum remains a significant challenge for Kivu’s producers in Democratic Republic of Congo, especially due to limited access to healthy planting material. Therefore, this study aimed to identify effective plant species within the traditional medicine in Kivu whose extracts have the potential to inhibit the bacterium. Eighteen plant extracts were tested using the Mueller Hinton Agar diffusion method. The extract was obtained by grinding 65 grams of dry plant powder macerating in 250 ml of methanol and drying. Then 40 mg of extract was mixed with 10 µl of DiMethyl SulfOxide (DMSO) and 50 µl of sterile distilled water (SDW). The bacterium was isolated from necrotic potato vascular tissue exhibiting wilt symptoms. 500 µl of 1 cm2 sterilized tissue ground in 9 ml SDW was inoculated on MacConkey agar and incubated at 28 °C. After 48 hours, biochemical tests and the disease sensitivity were tested by inoculating healthy potato plants. Results showed that Eucalyptus globulus emerged as the statistically most effective species, exhibiting a notable inhibition area of 19.33 mm in diameter. This was followed closely by Capsicum frutescens, Manihot glasiovii, Datura stramonium, Pteridium aquilinum, Galinsoga parviflora, Tithonia diversifolia, and Cupressus sempervirens, each showing an inhibition zone of 17.33 mm. From this list, the five most effective extracts revealed an abundance of phenols after phytochemical screening. Based on these results, it would be interesting to evaluate the effectiveness of the extracts in disinfecting garden tools and to assess the role of phenol in inhibiting the bacterial growth.
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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".