Maternal-Child Exposures to Persistent Organic Pollutants in Dhaka, Bangladesh
Bibliographic record
Abstract
Information about the human burdens of persistent organic pollutants (POPs) in low- and middle-income countries is limited. In particular, studies often include only a small subset of POPs. To address this data gap, we aimed to assess maternal-child exposures to POPs in Dhaka, Bangladesh. We quantified 16 organochlorine pesticides, 12 polychlorinated biphenyls, 21 brominated flame retardants, 18 per- and polyfluorinated alkyl substances, 2 polycyclic aromatic hydrocarbons, and short-chain chlorinated paraffins in 18 pooled samples of human cord blood from 90 mother–infant pairs living in Dhaka, Bangladesh (2014–2015). In all pooled samples, we detected high levels of p , p ′-DDT (median 81.6 ng/g lipid) and its metabolites p , p ′-DDE and p , p ′-DDD (median 551 and 10.7 ng/g lipid, respectively), where the p , p ′-DDE/ p , p ′-DDT ratio ranged from 2.9 to 9.8 indicating recent dichlorodiphenyltrichloroethane (DDT) exposure. We also detected acenaphthene, decabromodiphenyl ethane, o , p ′-DDT, o , p ′-DDE, hexachlorobenzene, β-hexachlorocyclohexane, hexabromobenzene, and perfluorooctanoic acid in a subset of samples. For the other 59 target compounds, concentrations were below the limits of detection, despite using ultra-trace analytical methodology. No trends were observed when stratifying the analyses of detected POP concentrations by maternal age, maternal body mass index, or large fish consumption. These findings highlight recent DDT exposure in Dhaka, but the overall POP burden was otherwise low in this sample of pregnant women/newborns. Future monitoring efforts should focus on newly detected POPs for which burdens may be increasing due to ongoing industrialization in Bangladesh.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".