Modulation of Complement Activation and Amplification on Nanoparticle Surfaces by Glycopolymer Conformation and Chemistry
Bibliographic record
Abstract
The complement system plays an integral part of a host's innate immunity, and its activation is highly dependent on the chemistry and structure of a "foreign" target surface. We determined that the conformational state of glycopolymer chains, defined by the grafting density (chains/nm(2)), on the nanoparticle (NP) surface acts as a "molecular switch" for complement activation and amplification, and the protein corona on the NP surface dictates this process. A grafting density threshold was determined, below which minimal complement activation was observed and above which substantial complement activation was detected. The glycopolymer-grafted NPs activated complement via the alternative pathway. The chemical structure of pendent sugar units on the grafted polymer was also an important determinant for complement activation. NPs grafted with glucose-containing polymer activated complement at a lower grafting density compared to NPs grafted with galactose-containing polymer. Analysis of complement activation products C3a and SC5b-9 followed a similar pattern. Complement activation on the NP surface was independent of particle size or concentration for a given conformational state of grafted polymer. To gain insight into a putative surface-dependent mechanism of complement activation, we determined the nature of adsorbed protein corona on various NPs through quantitative mass spectrometry. Elevated levels of two pro-complement proteins, factors B and C3, present on the NP surface grafted with glycopolymer chains at high grafting density compared to low grafting density surface, may be responsible for its complement activity. Galactose polymer modified NPs adsorbed more of the negative regulator of complement, factor H, than the glucose surface, providing an explanation for its lower level of complement activation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".