Specific inflammatory profile in each pregnancy complication: A comparative study
Bibliographic record
Abstract
PROBLEM: Pre-eclampsia (PE), preterm birth (PTB) and intra-uterine growth restriction (IUGR) affect 5%-12% of pregnancies. They have been associated with placental inflammation, although the detection of inflammatory mediators in the maternal circulation is still controversial. Our goal was to determine the inflammatory changes occurring in the second part of pregnancy to identify profiles distinguishing pathological pregnancies from each other. METHOD OF STUDY: We performed a nested case-control study of 200 women randomly selected from a cohort recruited at the CHU de Quebec-Universite Laval, Quebec, Canada. Women with uncomplicated term pregnancy (CTRL); PE (severe or not); PTB or IUGR (N = 50/each) were included. Plasma samples, obtained from the late second trimester and at delivery, were analysed for over 30 selected mediators (including cytokines/alarmins), by multiplex, ELISA or specific assays. Demographic and obstetrical information were obtained for classification. RESULTS: In CTRL, we observed significant differences between 2nd trimester and delivery, with increased levels of inflammatory mediators (ex. MCP-1, IL-6), supporting an inflammatory profile towards term. Increased levels of IL-6, CXCL10 and CRP were observed in PE as compared to CTRL. In PTB, we observed increased CXCL9 in 2nd trimester and decreased progesterone at delivery. In IUGR, increased HMGB1 and IL-1α were observed only in the 2nd trimester. CONCLUSIONS: Our work showed significant inflammatory changes in uncomplicated pregnancies towards delivery, supporting that normal delivery is pro-inflammatory, although not to the same extent as in pathological pregnancies. Inflammatory profiles are specific to each pregnancy complication which may help to understand the contribution of inflammation to the clinical presentation of these conditions.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| 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".