In Vitro antibacterial and antibiotic-potentiation activities of four edible plants against multidrug-resistant gram-negative species
Bibliographic record
Abstract
BACKGROUND: The present study was designed to investigate the antibacterial activities of the methanol extracts of four Cameroonian edible plants, locally used to treat microbial infections, and their synergistic effects with antibiotics against a panel of twenty nine Gram-negative bacteria including Multi-drug resistant (MDR) phenotypes expressing active efflux pumps. METHODS: The broth microdilution method was used to determine the minimum inhibitory concentrations (MICs) of the extracts [alone and in the presence of the efflux pumps inhibitor (EPI) Phenylalanine-Arginine β-Naphtylamide (PAβN)], and those of antibiotics in association with the two of the most active ones, Piper nigrum and Telfairia occidentalis. The preliminary phytochemical screening of the extracts was conducted according to the standard phytochemical methods. RESULTS: Phytochemical analysis showed the presence of alkaloids and flavonoids in all studied extracts. Other chemical classes of secondary metabolites were selectively present in the extracts. The results of the MIC determination indicated that the crude extracts from P. nigrum and V. amygdalina were able to inhibit the growth of all the twenty nine studied bacteria within a concentration range of 32 to 1024 μg/mL. At a similar concentration range (32 to 1024 μg/mL) the extract from T. occidentalis inhibited the growth of 93.1% of the tested microorganisms. At MIC/2 and MIC/5, synergistic effects were noted between the extracts from P. nigrum and T. occidentalis and seven of the tested antibiotics on more than 70% of the tested bacteria. CONCLUSION: The overall results of the present study provide information for the possible use of the studied edible plants extracts in the control of bacterial infections including MDR phenotypes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".