Embryonic Resorption and Polycyclic Aromatic Hydrocarbons: Putative Immune-mediated Mechanisms
Bibliographic record
Abstract
Polycyclic aromatic hydrocarbons (PAHs) are released into the environment as a result of incomplete fossil fuel combustion from industrial furnaces, wood-burning stoves, and automobile exhaust fumes; however, the primary source of human exposure to these compounds is cigarette smoke. Embryonic and fetal loss after treatment with high doses of PAHs have been well documented in animal studies; however, few studies have addressed the reproductive consequences of long-term, low-level exposure to these chemicals. We previously reported that low doses of PAHs administered to ICR mice over a period of 9 weeks prior to conception resulted in early embryonic resorptions, whereby treated dams lost approximately 50% of their litter. During the course of these studies, we observed greater numbers of infiltrating uterine natural killer (uNK) cells into the placenta of PAH-exposed conceptuses. While exposure to high levels of PAHs has been shown to be immunosuppressive, increasing evidence suggests that chronic, low-dose exposure to PAHs may stimulate immune cells. Thus, we hypothesized that low-dose, chronic PAH exposure in our mouse model is mediating embryonic resorption by hyperstimulating maternal immune cells. In this review of the literature, we outline the rationale of our argument and present preliminary data, focussing upon PAH-mediated alterations in uNK cell dynamics and how these changes may be linked to early embryonic resorptions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".