Exposure to Ambient Particulate Matter during Specific Gestational Periods Produces Adverse Obstetric Consequences in Mice
Bibliographic record
Abstract
Background: Epidemiological studies associate inhalation of fine-sized particulate matter (PM2.5) during pregnancy with preterm birth (PTB) and low birth weight (LBW) but disagree over which time frames are most sensitive, or if effects are cumulative. Objectives: Our objective was to provide experimental plausibility for epidemiological observations by testing the hypothesis that exposure to PM2.5 during discrete periods of pregnancy results in PTB and LBW. Methods: For the first study, timed-pregnant B6C3F1 mice were exposed to concentrated ambient PM2.5 (CAPs) or filtered air (FA) throughout pregnancy [6 h/d from gestational day (GD) 0.5 through GD16.5]. A follow-up study examined the effects of CAPs exposure during discrete gestational periods (1: GD0.5–5.5; 2: GD6.5–14.5; 3: GD14.5–16.5; 4: GD0.5–16.5) aligning to milestones during human development. Results: In the first experiment, exposure to 160 μg CAPs/m3 throughout pregnancy decreased gestational term by 0.5 d (∼1.1 wk decrease for humans) and birth weight by 11.4% compared with FA. The follow-up experiment investigated timing of CAPs exposure (mean concentrations at 178, 193, 171, and 173 μg/m3 for periods 1–4, respectively). Pregnancy was significantly shortened (vs. FA) by ∼0.4d when exposure occurred during gestational periods 2 and 4, and by ∼0.5d if exposure occurred during period 3. Exposure during periods 1, 2, and 4 reduced birth weight by ∼10% compared with FA, and placental weight was reduced (∼8%) on GD17.5 if exposure occurred only during period 3. Conclusions: Adverse PM2.5-induced outcomes such as PTB and LBW are dependent upon the periods of maternal exposure. The results of these experimental studies could contribute significantly to air pollution policy decisions in the future. https://doi.org/10.1289/EHP1029
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".