Carbon monoxide increases utero-placental angiogenesis without impacting pregnancy specific adaptations in mice
Bibliographic record
Abstract
BACKGROUND: Cigarette smokers have a reduced risk of developing preeclampsia, possibly attributed to an increase in carbon monoxide (CO) levels. Carbon monoxide is a gasotransmitter that has been implicated in maintaining vascular tone, increasing angiogenesis, and reducing inflammation and apoptosis at physiological concentrations. Moderately increasing CO concentrations may have therapeutic potential to prevent or treat preeclampsia; however, the effects of CO on pregnancy are under studied. Our objective was to investigate the effect of CO on major angiogenic and inflammatory markers in pregnancy, and to evaluate the effect of CO on indicators of placental health. FINDINGS: Pregnant CD-1 mice were constantly exposed to either ambient air or 250 ppm CO from conception until gestation day (GD)10.5 or GD16.5. Using a qRT-PCR array, we identified that CO increased expression of major angiogenic genes at the implantation site on GD10.5, but not GD16.5. Pro-inflammatory cytokines in the plasma and tissue lysates from implantation sites in treated mice were not significantly different compared to controls. Additionally, CO did not alter the implantation site phenotype, in terms of proliferative capacity, invasiveness of trophoblasts, or abundance of uterine natural killer cells. CONCLUSIONS: This study suggests that CO exposure is pro-angiogenic at the maternal-fetal interface, and is not associated with demonstrable concerns during murine pregnancy. Future studies are required to validate safety and efficacy of CO as a potential therapeutic for vascular insufficiency diseases such as preeclampsia and intrauterine growth restriction.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".