Occurrence and Health Risk Assessment of Aflatoxins through Intake of Eastern Herbal Medicines Collected from Four Districts of Southern Punjab—Pakistan
Bibliographic record
Abstract
Eastern herbal medicines (HMs) are plant-derived naturally occurring substances with minimum or no industrial processing that have long been used in traditional medicine. Aflatoxins are frequent contaminants of plants. Therefore, these mycotoxins are likely to contaminate HMs and pose a health risk to individuals using them on a regular basis as preventive or curative treatments of various diseases. The present study aimed to determine aflatoxin levels in the most popular Pakistani HM formulations and to assess the health risk associated with the intake of aflatoxins. A total of 400 samples of HM formulations collected from four districts of Punjab were analyzed for the quantification of aflatoxins, out of which 52.5% were found to be contaminated. The average daily dose (ADD) of AFB1 and AFs through the intake of HM formulations ranged between 0.00483 and 0.118 ng/kg bw/day and between 0.00579 and 1.714 ng/kg bw/day, respectively. The margin of exposure (MOE) and population cancer risk ranged from 99.49 to 29378.8 and from 0.00011 to 0.0325 liver cancer cases/105 individuals/year (0.0075–2.455 liver cancer cases/105 individuals/75 years), respectively. Despite the low exposure to aflatoxins from HM formulations in the four studied Punjab (Pakistan) districts, the frequent contamination of the analyzed samples suggests that official measures should be considered to manage the associated risk.
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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 | 0.001 |
| 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".