Acute Toxicity of Mycotoxigenic Fungi Isolates in Ready-to-Eat Meat in Buea, Cameroon
Bibliographic record
Abstract
Introduction: Meat is an important source of nutrition for many people worldwide. Away from homes, it is widely consumed in restaurants (e.g. small roadside) and non-restaurants (including drinking parlors and open-air)in various forms assnack. Objective: This paper reports on the acute toxicity of mycotoxigenic fungi isolates from various ready-to-eat meat from Buea, Cameroon. Method: Ten varieties of ready-to-eat meat samples (N=130: raw beef (n=18), beef soya (n=29), fried chicken (n=11), chicken soya (n=10), snail soya (n=13), fried pork (n=06), fried cow liver (n=13), roasted goat (n=06), boiled cow skin (n=07) and smoked beef (n=17).were collected from Buea municipality. The samples were cultures, and fungal isolates isolated microscopically. Thereafter, an acute toxicity test using 24 young BALB/c mice (weight range: 22-25g) was performed. Results: A total of 40 mycotoxigenic fungi belonging to five genera (Aspergillus, Penicillium, Fusarium, Rhizomucor, and Rhizopus) were identified. The most dominant species was Aspergillus (unidentified Aspergillus species, 23.1%; and Aspergillus ochraceus,10.3%).The order of Aspergillus fungi proliferation were roasted goat liver(20%), fried chicken (20%), snails soya (20%)ean body weight of test mice significantly decreased (p<0.05) after the first 7 days of feeding with a mycotoxigenic fungi-supplemented diet relative to control mice that ate healthy diets. Aspartate Transaminase (AST, range: 169.9-563.1U/L) and Alanine Transaminase (ALT, range:521.1-707.5U/L) levels were significantly (p<0.05) higher than the reference values for healthy mice (range: 60-100U/L for AST and 25-6U/L for ALT). Conclusion: Given the toxicity potentials of mycotoxigenic fungi, e.g. production of toxic secondary metabolites, not reported in this paper, and demonstrated by the liver toxicity/function test, ready-to-eat meat in Buea, Cameroon may not be adequately safe for consumption.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".