Affinity-Enhanced Multimeric VEGF (Vascular Endothelial Growth Factor) and PlGF (Placental Growth Factor) Variants for Specific Adsorption of sFlt-1 to Restore Angiogenic Balance in Preeclampsia
Bibliographic record
Abstract
Preeclampsia is a potentially life-threatening multisystem disease affecting 4% to 8% of pregnant women after the 20th week of gestation. An excess of placental expressed antiangiogenic soluble VEGF (vascular endothelial growth factor)-receptor 1 (soluble FMS-like tyrosine kinase 1) scavenges VEGF and PlGF (placental growth factor), causing generalized endothelial dysfunction. Interventions to restore the angiogenic balance in preeclamptic pregnancies are intensively studied and improve maternal and neonatal outcomes. Especially extracorporeal strategies to remove sFlt-1 are promising in human pregnancy. However, available apheresis systems adsorb sFlt-1 unspecifically and with low efficiency. Affinity-enhanced ligands are needed to improve performance and compatibility of apheresis treatments. Using computerized molecular modeling, we developed multimeric VEGF molecules comprised of single-chain VEGF 165 dimers (scVEGF 165 ). A short peptide linker hampers intrachain dimerization to induce assembly preferably as tetrameric molecules as visualized in negative staining electron microscopy. scVEGF 165 multimers possess 1.2-fold higher affinity for sFlt-1 as compared to the available antibodies or monomeric VEGF. Consequently, scVEGF multimers have the ability to competitively release sFlt-1 bound PlGF and, in particular, VEGF. In ex vivo adsorption experiments using serum samples from patients with preeclampsia, scVEGF multimers reduce sFlt-1 levels by 85% and increase PlGF and VEGF levels by 20- and 9-fold, respectively. Finally, performance and stability of sFlt-1 capturing scVEGF 165 multimers were scrutinized on different matrices of which biocompatible agarose matrix yielded optimal results. We introduce the first VEGF-based highly efficient sFlt-1 apheresis system that is directly applicable in vivo due to utilization of inert agarose matrix, using a homomultimeric form of VEGF 165 to restore the angiogenic balance in preeclampsia.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".