Human plasma protein adsorption to elastin-like polypeptide nanoparticles
Bibliographic record
Abstract
Elastin-like polypeptides (ELPs) are being developed for numerous biomedical applications. There is a limited understanding of ELP biocompatibility, with conflicting results in the literature. Protein adsorption is the fate determining event for blood-contacting biomaterials. The aim of this study is to elucidate the biocompatibility of ELP-based nanoparticles by examining the adsorbed proteome from platelet poor human plasma as a function of the physicochemical properties of these nanoparticles: diameter, amino acid hydrophobicity, and chain length. It was found that all ELP constructs had adsorbed an extremely large amount of albumin and high levels of immunoglobulin G and activated complement factor 3. Variations in the compositions of the proteomes across the eight nanoparticle systems studied were observed for plasminogen, fibronectin, activated fibrinogen, and coagulation modulating antithrombin and alpha2 macroglobulin. Plasma clotting experiments showed that ELP-based nanoparticles slightly inhibited normal blood clotting, with shorter and/or more hydrophilic constructs showing a greater difference from the control than longer or more hydrophobic constructs. These results indicate that ELP nanoparticles, regardless of chain length, particle diameter, or amino acid hydrophobicity, may have the potential to stimulate a humoral immune response via immunoglobulin G and activated complement factor 3 despite the large amounts of albumin adsorbed at the blood-material interface.
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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".