Antimicrobial and Antihaemolytic Activities of Crude Extracts of Some Commonly Used Tea and Coffee in Nigeria
Bibliographic record
Abstract
Current indiscriminate abuse of existing antibiotics in clinical and veterinary treatments lead to an upsurge in antimicrobial resistant strains of microorganisms and aggressive search for alternatives which are readily available, less expensive with little or no side effect. Tea and Coffee are beverages consumed daily in every household in Nigeria. This study examines the antimicrobial and anti-haemolytic properties of commonly available Tea and Coffee in Nigerian market. The antimicrobial potencies of the extracts were assessed through disc diffusion method on pathogens of both man and animal origin while the anti-haemolytic assay was carried out through colorimetric method. The extracts were slightly acidic at full strength and no antifungal property was observed. Broad spectrum and bactericidal effects were observed against Staphylococcus aureus, Salmonella pullorum, Shigella dysenteriae and Streptococcus pneumonia. These activities were concentration dependent. Very poor activity was observed against Escherichia coli . Bactericidal rate of coffee was at 6hrs but ranged between 18 and 24 hrs for tea extracts. Tea extracts greatly inhibited the haemolytic potential of alpha toxins while coffee performed poorly. Tea and Coffee could thus serve as supportive treatment for some bacterial infections without fear of side effects, since they are naturally taken as daily beverages.
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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 | 0.001 | 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".