Pregnant individuals with uncomplicated pregnancies display pro‐inflammatory immune changes when exposed to the COVID‐19 pandemic
Bibliographic record
Abstract
PROBLEM: The COVID-19 pandemic has been shown to have a detrimental impact on the mental health of pregnant individuals, and chronic stress can alter the immune profile. However, the effects of the COVID-19 pandemic on the immune system in pregnancy are still poorly understood. We aimed to evaluate the impact of pandemic exposure on the maternal immune profile in uncomplicated pregnancies. METHOD OF STUDY: We collected blood and placenta samples from pregnant individuals exposed and unexposed to the pandemic to compare their immune and inflammatory profiles. We performed co-culture with circulating maternal immune cells and endothelial cells to assess endothelial activation. Statistical analysis was performed using unpaired t-test, Mann-Whitney, or Fisher's exact test as appropriate. RESULTS: In exposed individuals, we observed a decreased proportion of Th2 cells (p < .0001) and Treg/Th17 ratio (p < .05), as well as an increased Th1/Th2 ratio (p < .0001). Levels of IL-1β (p < .01) and IL-18 (p < .01) were increased in the circulation of exposed participants, whilst other mediators were significantly decreased (IFNγ, IL-8, MCP-1, amongst others). Furthermore, we observed increased production of ICAM, hallmark of endothelial activation, when we co-cultured endothelial cells with immune cells from exposed individuals. Vaccination status impacted the cellular profile with increased proportions of Th1 and B cells in vaccinated participants. CONCLUSION: Overall, we observed a pro-inflammatory bias in the circulation of pregnant individuals exposed to the COVID-19 pandemic, with otherwise uncomplicated pregnancies. Our work also supports an association between the increased risk of endothelial activation/hypertension and SARS-CoV2 infection, which might be driven in part by exposure to the pandemic and associated stressors.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".