MétaCan
Menu
Back to cohort
Record W2051137616 · doi:10.1002/jbm.a.30711

Evaluation of pegylated lipid nanocapsules versus complement system activation and macrophage uptake

2006· article· en· W2051137616 on OpenAlexaff
Arnaud Vonarbourg, Catherine Passirani, Patrick Saulnier, P. Simard, Jean‐Christophe Leroux, Jean‐Pierre Benoît

Bibliographic record

VenueJournal of Biomedical Materials Research Part A · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMaterials scienceComplement systemNanocapsulesPEG ratioPEGylationPolyethylene glycolBiophysicsAntibody opsonizationMacrophageImmune systemIn vitroBiochemistryNanotechnologyNanoparticleImmunologyChemistryOpsoninBiology

Abstract

fetched live from OpenAlex

This work consisted in defining the in vitro behavior of pegylated lipid nanocapsules (LNC) toward the immune system. LNC were composed of an oily core surrounded by a shell of lecithin and polyethylene glycol (PEG) known to decrease the recognition of nanoparticles by the immune system. The "stealth" properties were evaluated by measuring complement activation (CH50 technique and crossed-immunoelectrophoresis (C3 cleavage)) and macrophage uptake. These experiments were performed on 20-, 50-, and 100-nm LNC before and after dialysis. A high density of PEG at the surface led to very low complement activation by LNC with a slight effect of size. This size effect, associated to a dialysis effect in macrophage uptake, was due to differences in density and flexibility of PEG chains related to LNC curvature radius. Thanks to a high density, 660-Da PEG provided LNC a steric stabilization and a protective effect versus complement protein opsonization, but this protection decreased with the increase of LNC size, especially versus macrophage uptake.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.088
GPT teacher head0.383
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations211
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Biomedical Materials Research Part ASame topicRNA Interference and Gene DeliveryFrench-language works237,207