Macromolecular Pairing On Nanoparticle Surface Modulates Immune Response
Bibliographic record
Abstract
Many nanoparticles in the blood activate the complement system, an integral part of the innate immune system that renders nanoparticles susceptible to phagocytosis by immune cells like polymorphonuclear leukocytes and tissue macrophages[1]. Complement activation by nanoparticles also compromises nanocarrier stability (e.g., liposome and lipid nanoparticles), causing drug leakage, promoting premature clearance of nanoparticles by the blood and tissue phagocytic cells, and compromising their therapeutic efficacy for intended non-phagocytic cell targets, and when uncontrolled, might induce adverse reactions and promote disease progression. Nanoparticle-mediated complement activation is multiparametric and is modulated by physicochemical properties including size, shape, and surface characteristics as well as non-specific protein binding [2], [3]. Recently, we showed poly (amido amine) dendrimers evade complement activation due to the Angstrom-scale spacing arrangement (the ASSA phenomenon) of their surface functional motifs [3]. Considering this, we hypothesise that immune cells might also respond differently to nanoparticles that display surface ligands/functional groups in ASSA arrangement. Here, we extend our studies by functionalizing polymeric nanoparticle surfaces with a library of fully characterised dendrimers and assess surface properties with a wide range of state-of-the-art biophysical modalities. The results show how precision surface patterning with dendrimers can control and modulate immune responses through assessment of serum protein deposition by shot-gun proteomics and macrophage challenge.
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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".