Placental levels of polycyclic aromatic hydrocarbons (PAHs) and their association with birth weight of infants
Bibliographic record
Abstract
As an alarming group of pollutants, polycyclic aromatic hydrocarbons (PAHs) gather much public health concern not only because of their carcinogenic or co-carcinogenic risk but also by interfering with hormone systems or by causing oxidative damage, henceforth liable to toxic actions on reproduction. Accordingly, the present study was aimed to explore the association between in-utero exposure to PAHs by evaluating their placental levels and infant birth weight among 110 healthy and nonsmoking pregnant women. Placental tissue samples were collected instantly after delivery and were analyzed for the presence of sixteen Environmental Protection Agency (EPA) listed PAHs with the help of Gas chromatography equipped with flame ionization detector (GC-FID). Chrysene and benzo(k)fluoranthene were the predominant PAHs detected in tissue samples. To assess the source of origin of PAHs in placenta tissue samples, the ratio of low molecular weight PAHs to high molecular weight (∑LMW/∑HMW PAHs) was calculated, showing the predominance of pyrogenic sources of PAHs possibly responsible for the exposure of the studied population. Results of regression analysis demonstrated the inverse although not significant association of naphthalene (Nap), acenaphthylene (Acy), anthracene (Anth), pyrene (Pyr), benzo(b)fluoranthene (BbF), benzo(k)Fluoranthene (BkF), benzo(a)pyrene (BaP), indeno (123 cd pyrene (IcdP), dibenzo(ah)anthracene (DahA) and benzo(ghi)Perylene (BghiP) with birth weight of neonates. Additionally, the regression model lay light upon the significant association of fluoranthene (Fla) (coefficient= −1.41 gram, p < 0.05) to the depletion trend of birth weight after adjusting for potential covariates. These findings suggest the possible role of an environmental contaminants like PAHs on impairment of fetal growth.
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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 | 0.001 |
| 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) | 0.001 | 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".