Evaluation of Trace Elements in the Nails and Hair of Farmers Exposed to Pesticides and Fertilizers
Bibliographic record
Abstract
Exposure to pesticides and fertilizers lowers the level of trace elements in the human body for several reasons. This study aimed to investigate the effect of pesticide exposure to the levels of trace elements of farmers in Bachok and Tumpat, Kelantan, Malaysia. This cross sectional study involved 89 farmers. Demographic data and information on the duration of the exposure to the pesticides and fertilizers, as well as personal protective equipment (PPE) practice habits, were determined through questionnaire. The levels of selenium, manganese, zinc, copper and chromium samples of fingernails, toenails and hair were determined through the use of inductively coupled plasma-mass spectrometry (ICP-MS). The levels of the trace elements were not influenced by gender, age and the period of exposure. Only the manganese levels found in the hair samples (r = 0.250) show a significant positive correlation (p < 0.05) with the working period. PPE practice habit also have significant correlation (p < 0.05) with manganese level in fingernails (r = 0.530) and toenails (r = -0.353), zinc level in hair (r = -0.439) and chromium level in fingernails (r = -0.306). Exposure towards pesticide and fertilizer decreased the trace element level in nails and hair of farmers. Additionally, the level of trace elements can be influenced by health status, working period, dietary habit and PPE practices.
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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 | 0.000 | 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) | 0.002 | 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".