Prenatal exposure to pesticide mixture in Argentina: A pilot study in puerperal women from Santa Fe province
Bibliographic record
Abstract
Abstract Introduction The epidemiological investigation of congenital anomalies (CA) represents a challenge due to the multiplicity of associated risk factors, notably environmental ones. The monitoring of genotoxic effects in different populations is a useful tool in human biomonitoring and has great biological importance in estimating the exposure risks to complex mixtures of chemical substances. Objective This study aimed to determine the presence of environmental xenobiotics and evaluate their genotoxic effect, in mothers of newborns with and without CA, and the possible association/correlation between those biomarkers and CA. Materials and methods A descriptive case and control cross‐sectional study was developed on 290 postpartum women from Santa Fe, Argentina. Results Significant differences were observed between both groups, for places of residence and gynecological variables. Metabolites of organochlorine (OC), organophosphate (OP), and pyrethroid (PYR) pesticides were detected. The most frequently detected compounds were atrazine (ATZ) (57.14%), carbendazim (CBZ) (46.42%), and methylparaben (46.42%), among others. A positive correlation was found between the number of pesticides in blood and genotoxic variables. On the other hand, mothers of children with genitourinary anomalies were the ones with the highest concentrations of ATZ and OP. Discussion and conclusion These results showed a deep background in the health reality of Santa Fe, which could greatly impact the health of future adults, who have been born preterm. On the other hand, the mixture of pesticides detected confirms the environmental living conditions of women and the transplacental exposure to these compounds in each pregnancy. The potential effects on long‐term descendent health are unknown and unpredictable.
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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".