Preeclampsia: Platelet procoagulant membrane dynamics and critical biomarkers
Bibliographic record
Abstract
A state-of-the-art lecture titled "Preeclampsia and Platelet Procoagulant Membrane Dynamics" was presented at the International Society on Thrombosis and Haemostasis (ISTH) Congress in 2022. Platelet activation is involved in the pathophysiology of preeclampsia and contributes to the prothrombotic state of the disorder. Still, it remains unclear what mechanisms initiate and sustain platelet activation in preeclampsia and how platelets drive the thrombo-hemorrhagic abnormalities in preeclampsia. Here, we highlight our findings that platelets in preeclampsia are preactivated possibly by plasma procoagulant agonist(s) and overexpress facilitative glucose transporter-3 (GLUT3) in addition to GLUT1. Preeclampsia platelets are also partially degranulated, procoagulant, and proaggregatory and can circulate as microaggregates/microthrombi. However, in response to exposed subendothelial collagen, such as in injured vessels during cesarean sections, preeclampsia platelets are unable to mount a full procoagulant response, contributing to blood loss perioperatively. The overexpression of GLUT3 or GLUT1 may be monitored alone or in combination (GLUT1/GLUT3 ratio) as a biomarker for preeclampsia onset, phenotype, and progression. Studies to further understand the mediators of the platelet activation and procoagulant membrane dynamics in preeclampsia can reveal novel drug targets and suitable alternatives to aspirin for the management of prothrombotic tendencies in preeclampsia. Finally, we summarize relevant new data on this topic presented during the 2022 ISTH Congress.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".