Isolating Nanoparticles from Complex Biological Media by Immunoprecipitation
Bibliographic record
Abstract
Polyethylene glycol (PEG) is considered the gold standard to prepare long circulating nanoparticles. The hydrophilic layer that sterically protects PEGylated nanomedicines also impedes their separation from biological media. In this study, we describe an immunoprecipitation method using AntiPEG antibodies cross-linked to magnetic beads to extract three types of radiolabeled PEGylated systems: polymeric nanoparticles, liposomes, and therapeutic proteins. The potential of the method is emphasized by isolating these systems after in vivo administration and ex vivo incubation in human biological fluids. Immunoprecipitation also allows a unique perspective on the size distribution of nanoparticles in the bloodstream after intravenous and intraperitoneal administrations. Further, we highlight the potential of the approach to inform on nanomaterial-associated drug in plasma as well as help characterize the protein corona. Altogether, we believe this method answers an unmet need in nanomedicine research and will contribute a fresh perspective on the interactions of nanomedicines with biological systems.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".