Genomic instability in adult men involved in processing electronic waste in Northern China
Bibliographic record
Abstract
BACKGROUND: Managing and recycling electronic waste (e-waste), while useful and necessary, has resulted in significant contamination of several environments in China. The area around Tianjin, China has become one of the world's largest e-waste disposal centers, where electronics are processed by manually disassembly or burning, which can result in serious exposure of workers to a multitude of toxicants. OBJECTIVE: The present study assessed potential genomic damage in workers involved in recycling e-waste. METHODS: To detect cytogenetic and DNA damage, chromosomal aberrations (CA), cytokinesis blocking micronucleus (CBMN) and the comet assay were performed. Concentrations of some trace elements, markers of oxidative stress and polychlorinated biphenyls (PCBs) in whole blood or serum were measured, and relationships among the markers described above, age, and duration of exposure were analyzed. The profiles of expression of genes in lymphocytes in peripheral blood were assessed to determine the status of the regulation of genes involved in genome stability. RESULTS: Concentrations of 28 PCB congeners in the whole blood of the exposed group were significantly (P<0.001) greater than those in the control individuals. Frequency of CA (8.01%) and CBMN (26.3‰) in lymphocytes and the level of DNA damage in the lymphocytes and spermatozoa of the exposed men were also significantly (P<0.0001) greater than those of the controls. There were significant relationships between CA, CBMN, DNA damage and duration of exposure. Concentrations of malondialdehyde (MDA) and lead (Pb) in the blood serum were significantly greater, but activities of superoxide dismutase (SOD), glutathione (GSH) and concentrations of calcium (Ca) and magnesium (Mg) were lower in the serum of the exposed men. MDA, Pb, Ca and Mg were associated with the duration of exposure to handling e-waste. In males involved in handling of e-waste, there were 13 genes - ATM, ATR, ABL1, CHEK1, CHEK2, GADD45A, CDK7, GTSE1, OGG1, DDB1, PRKDC, XRCC1 and CCNH - for which expression of mRNA was up-regulated and 7 genes - BRCA1, GTF2H1, SEMA4A, MRE11A, MUTYH, PNKP and RAD50 - for which the expression of mRNA was down-regulated. CONCLUSIONS: A strong correlation between indicators of damage of DNA, which could result in instability of the genome, and duration of processing e-waste was observed. If proper procedures are not followed, there are significant risks to the health of the individuals involved in such activities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".