Predictive biomarkers and initial analysis of maternal immune alterations in postpartum preeclampsia reveal an immune-driven pathology
Bibliographic record
Abstract
Introduction: Postpartum preeclampsia (PPPE) is an under-diagnosed condition, developing within 48 hours to 6 weeks following an uncomplicated pregnancy. The etiology of PPPE is still unknown, leaving patients vulnerable and making the identification and treatment of patients requiring postpartum care an unmet need. We aimed to understand the immune contribution to PPPE at the time of diagnosis, as well as uncover the predictive potential of perinatal biomarkers for the early postnatal identification of high-risk patients. Methods: Placentas were collected at delivery from uncomplicated pregnancies (CTL) and PPPE patients for immunohistochemistry analysis. In this initial study, blood samples in PPPE patients were collected at the time of PPPE diagnosis (48h-25 days postpartum; mean 7.4 days) and compared to CTL blood samples taken 24h after delivery. Single-cell transcriptomics, flow cytometry, intracellular cytokine staining, and the circulating levels of inflammatory mediators were evaluated in the blood. Results: trimester blood pressures can be valuable non-invasive and predictive biomarkers of PPPE with strong clinical application prospects. Furthermore, changes in immune cell populations, as well as cytokine production by CD14+, CD4+, and CD8+ cells, suggested a dampened response with an exhausted phenotype including decreased IL1β, IL12, and IFNγ as well as elevated IL10. Discussion: Understanding maternal immune changes at the time of diagnosis and prenatally within the placenta in our sizable cohort will serve as groundwork for pre-clinical and clinical research, as well as guiding clinical practice for example in the development of immune-targeted therapies, and early postnatal identification of patients who would benefit from more thorough follow-ups and risk education in the weeks following an uncomplicated pregnancy.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".