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Record W2416244604 · doi:10.1007/978-1-60327-360-2_36

Serum-Stable, Long-Circulating, pH-Sensitive PEGylated Liposomes

2009· article· en· W2416244604 on OpenAlexafffund
Nicolas Bertrand, Pierre Simard, Jean‐Christophe Leroux

Bibliographic record

VenueMethods in molecular biology · 2009
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsLiposomePharmacologyChemistryMedicineBiochemistry

Abstract

fetched live from OpenAlex

pH-sensitive liposomes have been designed to deliver active compounds, specifically to acidic intracellular organelles and to augment their cytoplasmic concentrations. These systems combine the protective effects of other liposomal formulations with specific environment-controlled drug release. They are stable at physiological pH, but abruptly discharge their contents when endocytosed into acidic compartments, allowing the drug to be released before it is exposed to the harsh environment of lysosomes. Serum-stable formulations with minimal leakage at physiological pH and rapid drug release at pH 5.0-5.5 can be easily prepared by inserting a hydrophobically modified N-isopropylacrylamide/methacrylic acid copolymer (poly(NIPAM-co-MAA)) in the lipid bilayer of sterically stabilized liposomes. The present chapter describes polymer synthesis, as well as the preparation, and characterization of large unilamelar pH-sensitive vesicles.

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)0.000
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
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.352
Teacher spread0.334 · 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

Citations29
Published2009
Admission routes2
Has abstractyes

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