Hormonal, liver, and renal function associated with electronic waste (e-waste) exposure in Dhaka, Bangladesh
Bibliographic record
Abstract
Electronic waste (e-waste) contains numerous metals and organic pollutants that have detrimental impacts on human health. We studied 199 e-waste recycling workers and 104 non-exposed workers; analyzed blood, urine, and hair samples to measure heavy metals, hormonal, liver, and renal function. We used quantile regression models to evaluate the impact of Pb, Cd, and Hg on hormonal, liver and renal function, and the role of DNA oxidative damage in mediating the relationship between exposures and outcomes. Exposed workers had higher blood lead (Pb) (median 11.89 vs 3.63 µg/dL), similar blood cadmium (Cd) (1.04 vs 0.99 µg/L) and lower total mercury (Hg) in hair (0.38 vs 0.57 ppm) than non-exposed group. Exposed workers also had elevated median concentrations of total triiodothyronine (TT3), aspartate aminotransferase (AST), alanine aminotransferase (ALT), urinary albumin, albumin creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR) were significantly higher than non-exposed group (p≤0.05). Sex hormones including luteinizing hormone, follicle stimulating hormone, estrogen, progesterone and testosterone concentrations were not significantly different between exposed and non-exposed (all p≥0.05). The median concentration of ALT was 4.00 (95% CI: 0.23, 7.77), urinary albumin was 0.09 (95% CI: 0.06, 0.12) and ACR was 1.31 (95% CI: 0.57, 2.05) units higher in the exposed group compared to non-exposed group. Pb was associated with a 3.67 unit increase in the ALP (95% CI: 1.53, 5.80), 0.01 unit increase in urinary albumin (95% CI: 0.00, 0.01), and 0.07 unit increase in ACR (95% CI: 0.01, 0.13). However, no hormonal, renal, and hepatic parameters were associated with Cd or Hg. Oxidative DNA damage did not mediate exposure-outcome relationships (p≥0.05). Our data indicate e-waste exposure impairs liver and renal functions secondary to elevated Pb levels. Continuous monitoring, longitudinal studies to evaluate the dose-response relationship and effective control measure are required to protect workers from e-waste exposure.
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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 | 0.001 |
| 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".