Blood Components Interactions to Ionic and Nonionic Glyconanogels
Bibliographic record
Abstract
Nanogels are prominent examples of "smart" nanomaterials, which are designed to incorporate biologically relevant (macro)molecules for systemic delivery. Although these systems are carefully engineered, only a handful of studies discuss the blood compatibility of nanogels, and no systematic studies are available on how the presence of net or surface charges impacts the hemocompatibility of these nanomaterials. Therefore, in this study, temperature responsive, galactose based nanogels bearing net positive, negative, or neutral charges, either in the core or shell of nanogels, are prepared and are subsequently evaluated for their blood compatibility profiles. The nanogels containing neutral core and shell, cationic core with neutral shell, anionic core with neutral shell, neutral core with cationic shell, and neutral core with anionic shell are prepared by reversible addition-fragmentation chain transfer (RAFT) polymerization approach. The evaluation of complement activation, blood clot formation, platelet activation, red blood cells aggregation, and hemolysis provides a detailed analysis of structure activity relationship of blood compatibility profile of these nanogels. The data reveal that the physical and biological (blood compatibility) properties can be carefully tuned by embedding the charges in the core of temperature-responsive nanomaterials, protected by neutral carbohydrate based shells.
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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".