Capture of Flowing Endothelial Cells Using Surface-Immobilized Anti-Kinase Insert Domain Receptor Antibody
Bibliographic record
Abstract
In humans, self-endothelialization of synthetic grafts is severely limited, but a recent interesting idea is to attract endothelial progenitor cells (EPCs) from peripheral blood onto grafts via antibodies directed at proposed EPC markers. Results with anti-CD34 antibodies have shown some promise, but it is unclear whether CD34 is the best marker for cells with re-endothelializing potential. Much evidence points to kinase insert domain receptor (KDR) as an important indicator of endothelial potential if not a definitive marker. Because KDR is not an adhesion molecule (like CD34), we first demonstrated the ability to use adsorbed and protein G-oriented antibody to this receptor to capture flowing cells onto a solid surface. Using endothelial cells and smooth muscle cells, we show in a model system under low shear rates the ability to selectively capture cells by this receptor. Furthermore, our results indicate that concomitant flow of cells lacking the receptor does not affect the efficiency of capture of KDR(+) cells but that orienting the antibody significantly increases the efficiency of capture.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".