Study Effect of the Aqueous and Alcoholic Extracts of Radish and Ginger on the Gram Negative Bacteria that Cause Urinary Tract Infection in Balad City
Bibliographic record
Abstract
This study aimed to investigate the inhibitory effect of aqueous and alcoholic extracts of radish and ginger on bacterial species isolated from urinary tract and auditory system infections. Prior research has shown the potential of natural plant extracts as alternatives to synthetic antibiotics. In this study, extracts were tested at concentrations of 10, 25, 50, and 100 mg/ml for each extract against six different bacterial species. Results demonstrated that the hot alcoholic extract was more efficient than the hot water extracts of the two plants, with 100 mg/ml concentration having the highest inhibition ability. In addition, the inhibitory effect increased with increasing extract concentration. The hot alcoholic extracts of both ginger and radish were found to have higher inhibitory effects against the bacterial species, with the hot alcoholic ginger extract being more effective than the radish water extract. Furthermore, the synergistic effects of the hot alcoholic and water extracts of radish and ginger were tested against all of the identified bacterial species. Finally, sensitivity testing demonstrated that the isolated bacteria showed differences in their sensitivity and resistance to different antibiotics. Overall, this study highlights the potential of natural plant extracts as a source of new antibiotics for the treatment of bacterial infections. Existing research has shown the potential of natural plant extracts as alternatives to synthetic antibiotics for the treatment of bacterial infections, including UTIs. However, there is a lack of studies investigating the inhibitory effect of specific plant extracts, such as radish and ginger, on bacterial species isolated from UTIs and auditory system infections. This study aims to address this research gap by investigating the inhibitory effect of aqueous and alcoholic extracts of radish and ginger on six different bacterial species. The importance of this research gap lies in the increasing prevalence of antibiotic resistance and the need for alternative treatments. The research motivation is to explore the potential of natural plant extracts as a source of new antibiotics for the treatment of bacterial infections.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".