Immune Cells and Infectious Diseases in Preeclampsia Susceptibility
Bibliographic record
Abstract
Preeclampsia is a severe pregnancy disorder, affecting approximately 10% of pregnancies worldwide, characterised by hypertension and proteinuria after the 20th week of gestation. The condition poses significant risks to both maternal and fetal health, including cardiovascular complications and impaired fetal development. Recent trends indicate a rising incidence of preeclampsia, correlating with factors such as advanced maternal age and cardiovascular comorbidities. Emerging evidence also highlights a notable increase in the association between autoimmune and infectious diseases with preeclampsia. Autoimmune conditions, such as type 1 diabetes and systemic lupus erythematosus, and infections triggered by global health challenges, including leptospirosis, Zika, toxoplasmosis, and Chagas disease, are now recognised as significant contributors to preeclampsia susceptibility by affecting placental formation and function. This review focuses on the immunologic mechanisms underpinning preeclampsia, exploring how immune system dysregulation and infectious triggers exacerbate the condition. It also discusses the pathologic mechanisms, including galectins, that preeclampsia shares with autoimmune and infectious diseases, and their significant risk for adverse pregnancy outcomes. We emphasise the necessity for accurate diagnosis and vigilant monitoring of immune and infectious diseases during pregnancy to optimise management and reduce risks. By raising awareness about these evolving risks and their impact on pregnancy, we aim to enhance diagnostic practices and preventive strategies, ultimately improving outcomes for pregnant women, especially in regions affected by environmental changes and endemic diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".