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Record W4366215739 · doi:10.1039/d2tb01985j

Functionalization of polymeric nanoparticles with targeting VNAR ligands using vinyl sulfone conjugation

2023· article· en· W4366215739 on OpenAlexfundno aff
Adam Leach, Marie Finnegan, Mariana S. Machado, Laura Ferguson, John Steven, P. P. A. Smyth, A.J. Porter, Caroline J. Barelle, Efrosyni Themistou, Christopher J. Scott

Bibliographic record

VenueJournal of Materials Chemistry B · 2023
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilQueen's UniversityQueen's University Belfast
KeywordsSurface modificationSulfoneNanoparticleMaterials sciencePolymer chemistryCombinatorial chemistryPolymerNanotechnologyChemical engineeringChemistryComposite materialEngineering

Abstract

fetched live from OpenAlex

-glycolide) (PLGA) to produce VS-functionalized polymeric nanoparticles. The ability of these novel nanoparticles to attach ligands was then exemplified using a single domain variable new antigen receptor (VNAR) with a free carboxyl terminal cysteine residue. The resulting VNAR-functionalized nanoparticles were found to maintain specific affinity to their cognate antigen (DLL4) for at least 72 h at 4 °C. The simplicity of the degradable amphiphilic block copolymer synthesis and the efficiency of VNAR conjugation to the VS-functionalized nanoparticles show the potential of this platform for therapeutic development.

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 score0.002

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 communication0.0000.000
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 head0.014
GPT teacher head0.232
Teacher spread0.217 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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Same venueJournal of Materials Chemistry BSame topicNanoparticle-Based Drug DeliveryFrench-language works237,207