Failing maternal-fetal tolerance in Systemic Lupus Erythematosus (FaMaLE): a prospective cohort study for finding the molecular mechanisms behind pregnancy complications
Bibliographic record
Abstract
ABSTRACT Introduction Pregnant women with systemic lupus erythematosus (SLE) have an increased risk of maternal complications and adverse fetal outcomes. These include preeclampsia, preterm birth and fetal growth restriction. Interestingly, this increased risk persists in subsequent pregnancies, whereas it decreases in healthy women due to the development of maternal-fetal tolerance. As maternal-fetal tolerance is crucial for a healthy pregnancy, we hypothesize that its failure contributes to the increased risk of pregnancy complications in women with SLE. Therefore, we initiated the FaMaLE study to investigate the failure of maternal-fetal tolerance in pregnant women with SLE. Methods and analysis In the FaMaLE study, women with SLE and healthy women are included in their first trimester of pregnancy (< 14 weeks gestational age (GA)) at Amsterdam UMC. Throughout the pregnancy, data on SLE disease activity, pregnancy course, and medication use are collected. Peripheral blood is collected once per trimester, within 48 hours before delivery and 5-12 weeks post-partum. In addition, the placenta is collected after delivery. Whole blood, peripheral blood mononuclear cells (PBMC) and placenta samples are freshly analyzed by flow cytometry to assess immune cell composition. The resulting data are analyzed in relation to SLE disease course, pregnancy course and pregnancy outcomes. Ethics and dissemination The study has been approved by the Amsterdam UMC Medical Ethics Committee and all participating women will be asked to provide informed consent. The findings will be disseminated through peer-reviewed publications, presentations at scientific meetings and via patient organizations. STRENGTHS AND WEAKNESSES - A unique prospective longitudinal study design, featuring the collection of serum, plasma and PBMC throughout and after pregnancy, alongside placental cells and biopsies from the same participants. This is complemented by detailed clinical data on SLE disease course and pregnancy course, and outcomes. - Fresh flow cytometry analyses allow immediate assessment of cell composition in blood and placenta, without freeze/thawing effects - The study does not include pre-pregnancy collection of serum, plasma and PBMC; however detailed clinical data are collected during this period.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".