Effect of amino acid composition of e<scp>lastin‐like</scp> polypeptide nanoparticles on nonspecific protein adsorption, macrophage cell viability and phagocytosis
Bibliographic record
Abstract
Development of elastin-like polypeptide (ELP) biomaterials is widespread, but information critical for clinical deployment is limited, with biocompatibility studies focused on a narrow cross-section of ELP sequences. Macrophages can impair biomaterial systems by degrading or isolating the biomaterial and by activating additional immune functions. Their phagocytic response will reveal early immune biocompatibility of ELP nanoparticles (NPs). This study examines that response, induced by the adsorbed protein corona, as a function of ELP guest amino acid, chain length and NP diameter. The breadth of proteins adsorbed to ELP NPs varied, with valine-containing ELP NPs adsorbing fewer types of proteins than leucine-containing constructs. Particle diameter was also a factor, with smaller leucine-containing ELP NPs adsorbing the broadest range of proteins. Macrophage viability was unaffected by the ELP NPs, and their phagocytic capabilities were unimpeded except when incubated with a 500 nm valine-containing 40-mer. This NP significantly decreased the phagocytic capacity of macrophages relative to the control and to a corresponding 500 nm leucine-containing 40-mer. NP size and the proportion of opsonin to dysopsonin proteins likely influenced this outcome. These results suggest that certain combinations of ELP sequence and particle size can result in an adsorbed protein corona, which may hinder macrophage function.
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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) | 0.001 | 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".