Identification of novel first-trimester serum biomarkers for early prediction of preeclampsia
Bibliographic record
Abstract
BACKGROUND: Preeclampsia (PE) is a leading cause of maternal and perinatal mortality and morbidity worldwide, but effective early prediction remains a challenge due to the lack of reliable biomarkers. METHODS: Based on the extensive human biobank of our large-scale assisted reproductive cohort platform, the first-trimester serum levels of 48 cytokines, total immunoglobulins (Igs), anti-phosphatidylserine (aPS) antibodies, and several previously reported PE biomarkers [including placental growth factor (PlGF), soluble fms-like tyrosine kinase-1 (sFlt-1), and activin A] were measured in 34 women diagnosed with PE and 34 matched normotensive controls. RESULTS: The PE group has significantly higher first-trimester serum levels of interleukin (IL)-2Rα, IL-9, tumor necrosis factor-β (TNF-β), RANTES, hepatocyte growth factor (HGF), total IgM, and total IgG, and aPS IgG optical density (OD) value, as well as lower first-trimester serum levels of PlGF and total IgA and aPS-IgG immune complexes (IC) OD value than the control group. Combining top five first-trimester serum biomarkers (total IgM, total IgG, PlGF, aPS IgG, and total IgA) achieved superior predictive value [area under the curve (AUC) and 95% confidence interval (CI) 0.983 (0.952-1.000), with a sensitivity of 100% and a specificity of 94.1%] for PE development compared to PlGF and PlGF/sFlt-1 independently [AUC and 95% CI 0.825 (0.726-0.924) and 0.670 (0.539-0.800), respectively]. CONCLUSION: We identified novel first-trimester serum biomarkers and developed an effective first-trimester prediction model using immune-related factors and PlGF for PE, which could facilitate the development of early diagnostic strategies and provide immunological insight into the further mechanistic exploration of PE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".