Bacterial and heavy metal contamination in selected commonly sold herbal medicine in Blantyre, Malawi
Bibliographic record
Abstract
Background: There has been an increase in use of herbal medicine worldwide. It is either used as a stand-alone or complementary therapy to conventional medicine due to past good experience, poverty and family traditions. In Malawi, there are no regulations governing the supply, acquisition, marketing and quality enforcement of herbal medicine. This compromises its safety thereby exposing consumers to avoidable bacteria and heavy metals leading to various adverse health effects. Methods: Cross-sectional laboratory experiments were conducted to determine bacterial and heavy metal contamination of herbal medicine commonly sold in Blantyre, Malawi. A total of 47 samples which were in three formulations namely liquid, powder and tablet were used in the experiments. 29 samples were used for bacterial limit tests and 18 samples were used for heavy metal analysis. Bacterial contamination was determined by streak plate method and biochemical tests while heavy metals were determined by atomic absorption spectroscopy. Descriptive statistics and t-tests were calculated using Microsoft excel and SPSS software programs. Results: . (25%). Out of 20 contaminated samples, 75% were contaminated with coliforms. From these 75% which were contaminated with coliforms, 93.3% of them exceeded WHO regulatory limit (103 CFU/g for enterobacteria). Although liquid samples had the highest level of bacterial contaminants, the count was not statistically different from other formulations (P = 0.058). For heavy metals, lead and cadmium were detected and 67% of the samples had lead levels exceeding regulatory limits. Conclusion: Levels of bacterial and lead contamination in herbal medicine from Blantyre markets are far above acceptable limits set by WHO and Canadian guidelines. The use of these herbal medicines is a major risk to the health of consumers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".