Genetoxicity to human beings caused by the pollution of electronic wastes recycling
Bibliographic record
Abstract
OBJECTIVE: To study cytogenetics change on the residents of three villages in electronic wastes recycling area of a town in Tianjin,and analyze the damaging effect to human beings caused by pollutants of electronic wastes. METHODS:171 residents were randomly selected as population sample chromosome aberration(CA), cytokinesis-blockmicronucleus(CBMN) test and single cell gel electrophoresis(SCGE) were performed. 30 residents who lived near the electronic waste recycling town but never had contact with electronic wastes were recruited as control group. To assess CA,CBMN and DNA damage of samples in different gender and age,exposure group was divided into subgroups according to gender and age. RESULTS:Total rate of CA of the 171 residents was 5.50%,and CBMN rate was 16.99‰. Significant differences were found in both. The same difference was found in DNA percent in the tail(TDNA,%),tail moment(TM) and Olive tail moment(OTM) detected by SCGE compared with control group. The level of chromosome aberration,micronucleus rate and DNA damage of female group were significantly higher than that of male group,but no significant difference was found among three age groups. CONCLUSION:The pollutants of electronic wastes were latent genetic mutagens indeed,causing cytogenetics damage to the population who have been exposed. The harmful effect to humans and their offsprings should not be ignored.
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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 | 0.000 |
| 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".