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Record W4410010425 · doi:10.1039/d5ob00270b

Chemical modification of sialylated oligosaccharides to functionalize nanostructured lipid carriers: exploring two different strategies

2025· article· en· W4410010425 on OpenAlexaff
Paul Rivollier, Emeline Richard, Antoine Hoang, Aurélien Traversier, Manuel Rosa‐Calatrava, Dorothée Jary, Isabelle Texier, Sébastien Fort

Bibliographic record

VenueOrganic & Biomolecular Chemistry · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsUniversité LavalCentre hospitalier universitaire de Québec
FundersInstitut Carnot PolyNatAgence Nationale de la RechercheMinistère de l'Education Nationale, de l'Enseignement Superieur et de la RechercheLABoratoires d’EXcellence ARCANE
KeywordsChemistryChemical modificationBiochemistryNanotechnologyMaterials science

Abstract

fetched live from OpenAlex

-acetyl-neuraminic acid (Neu5Ac), a major carbohydrate involved in many cellular functions. 6'- and 3'-Sialyllactose were enzymatically produced and directly functionalized on their reducing ends using either oxime ligation or reductive amination. In the first strategy, thiol-modified oligosaccharides were grafted onto maleimide-decorated NLCs, and the second strategy focused on incorporating sialylated glycolipids into the formulation. Both methods successfully produced stable and monodisperse nanoparticles with sizes ranging from 60 to 100 nm. The functionalization efficiency (46 to 86%) was assessed by quantifying Neu5Ac present at the particle surface. The grafting approach yielded safe nanoparticles that show potential for use in anti-adhesive therapies against pathogens, such as the influenza viruses. However, their effectiveness needs to be optimized by further increasing carbohydrate density on the nanoparticle surface.

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 score0.000
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)0.0000.000

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 head0.017
GPT teacher head0.283
Teacher spread0.266 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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