Effects of exposure to pesticides during pregnancy on placental maturity and weight of newborns: A cross-sectional pilot study in women from the Chihuahua State, Mexico
Bibliographic record
Abstract
It is known that pesticides cross the placental barrier and can cause alterations in the development of placental structures resulting in adverse effects in reproduction. The objectives of this study were to investigate the effects of pesticide exposure during pregnancy on placental maturity and to evaluate the relationship between placental maturity, gestational age and birth weight. We collected the placentas from singleton pregnancies from women exposed (n = 9) and non-exposed (n = 45 full-term and n = 31 preterm) to pesticides as evaluated geographically, by questionnaire and by acetylcholinesterase levels. Placental morphometry from the central and peripheral regions was examined by microscopy and staining with hematoxylin and eosin. The placental maturity index (PMI) was estimated by dividing the number of epithelial plates in terminal villi to their thickness in 1 mm(2) of the placental parenchyma. Gestational age, birth weight and the following characteristics of the mother were also recorded: pre-pregnancy body mass index, weight gain during pregnancy and hemoglobin concentrations. Birth weight and the gestational age were correlated with PMI (r = .54 and r = .44, respectively; p < .01). Pesticide exposure was associated with a higher PMI (beta = 7.38, p = .01) after adjusting by variables related to placental maturity. In conclusion, the results suggest a relationship between prenatal exposure to pesticides and placental maturity and may potentially affect the nutrient transport from the mother to the fetus.
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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 | 0.000 | 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".