Bioaffinity-based surface-immobilization of antibodies to capture endothelial colony-forming cells
Bibliographic record
Abstract
Abstract Maximizing the re-endothelialization of vascular implants such as prostheses or stents has the potential to significantly improve their long-term performance. Endothelial progenitor cell capture stents with surface-immobilized antibodies show significantly improved endothelialization in the clinic. However, most current antibody-based stent surface modification strategies rely on antibody adsorption or direct conjugation via amino or carboxyl groups which leads to poor control over antibody surface concentration and/or molecular orientation, and ultimately bioavailability for cell capture. Here, we assess the utility of a bioaffinity-based surface modification strategy consisting of a surface-conjugated cysteine-tagged protein G molecules that immobilize Immunoglobulin G (IgG) antibodies via the Fc domain to capture circulating endothelial colony-forming cells (ECFCs). The cysteine-tagged protein G was grafted onto aminated substrates at different concentrations as detected by an enzyme-linked immunosorbent assay and fluorescence imaging. Different IgG antibodies were successfully immobilized on the protein G-modified surfaces and higher antibody surface concentrations were achieved compared to passive adsorption methods. Surfaces with immobilized antibodies targeting endothelial surface proteins, such as CD144, significantly enhanced the capture of circulating ECFCs in vitro compared to surfaces with non-endothelial specific antibodies such as anti-CD14. This work presents a potential avenue for enhancing the clinical performance of vascular implants by using covalent grafting of protein G to immobilize IgG antibodies more effectively. Table of Contents Antibody immobilization via surface-conjugated recombinant cysteine-protein G provides an effective approach to capture circulating therapeutic cells.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".