Pesticides in human milk collected from Jinhua, China: Levels, influencing factors and health risk assessment
Bibliographic record
Abstract
In China, many studies have been carried out on pesticide residues in human milk, yet all of them are on organochlorine pesticides (OCPs) and mostly focused on large, economically developed cities. In this study, 27 pesticides including OCPs, pyrethroid pesticides (PYRs) and organophosphate pesticides (OPPs) in human milk were investigated in Jinhua, an inland and medium sized city in China. Method based on QuEChERS extraction and gas chromatography-mass spectrometer (GC-MS) determination was adopted to analyze the above pesticide residues. The influencing factors as well as the health risks were also evaluated. Results show that PYRs and OPPs in human milk samples were both undetectable. Regarding OCPs, the detection rate of hexachlorobenzene (HCB), β-hexachlorocyclohexane (β-HCH) and p,p'-dichlorodiphenyl-dichloroethylene (p,p'-DDE) were 83.6%, 36.4% and 58.2%, respectively, and their mean value were 29.4, 32.0 and 85.2 ng/g lipid, respectively. p,p'-DDE levels in human milk was significantly (p < 0.05) related to maternal age, but no association was detected between OCPs residues and other factors (living environment, dietary habit, living style, etc.), suggesting that OCPs in human milk in Jinhua were originated from nonspecific source. All estimated daily intake of pesticides (EDIpesticides) by infants were under the guideline suggested by Food and Agriculture Organization (FAO) and China Ministry of Health (CMH). Yet 9% of EDIsHCB and 16% of EDIsHCHs exceeded the guideline recommended by Health Canada. The associations between DDE residues and the delivery way as well as HCBs residues and the birth weight were seemly significant, yet the significance disappeared when consider age or gestational age as a cofounder, indicating that OCPs residue in mother's body in Jinhua has no obvious influence on fetus development and the delivery way.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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 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".