Occupational Exposure to Low Concentrations of Lead Dust and Oxidative Stress in Mine Workers
Bibliographic record
Abstract
Background: Several epidemiological studies have reported associations between high levels of lead exposure and oxidative stress (OS). However, research on the effects of low-level lead exposure remains limited. This study aims to assess the relationship between OS parameters and exposure to low concentrations of lead dust in mine workers.Methods: This cross-sectional study evaluated 73 lead-exposed workers and 70 age- and sex-matched non-exposed individuals. Demographic data and occupational and medical history were collected through questionnaires. Workers’ exposure to lead dust was assessed by air monitoring, and blood lead levels (BLLs) were calculated based on inhalation exposure. Blood samples were collected to determine OS parameters. Data were analyzed using SPSS version 21.0.Results: The mean exposure of workers to lead dust was 24 μg/ m³ (range: 1.5 to 185 μg/m³), which complied with the OSHAPEL and ACGIH TLV-TWA standards for lead dust. The BLL in the exposed workers was found to be 45.47 μg/dL. A significant association was observed between the SOD/MDA ratio and exposure to lead dust. Additionally, a borderline negative association between lead exposure and superoxide dismutase (SOD) activity was found. A significant relationship was noted between workers’ BMI and OS biomarkers.Conclusion: This study’s findings suggest that chronic exposure to lead dust may affect OS biomarkers, even at concentrations below the current OSHA-PEL and ACGIH TLV-TWA.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".