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Record W2914468281 · doi:10.1039/c8tb03162b

Copolymers containing carbohydrates and other biomolecules: design, synthesis and applications

2019· review· en· W2914468281 on OpenAlexafffund
Zhiyuan Ma, X. X. Zhu

Bibliographic record

VenueJournal of Materials Chemistry B · 2019
Typereview
Languageen
FieldChemistry
TopicCarbohydrate Chemistry and Synthesis
Canadian institutionsUniversité de Montréal
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaChina Scholarship CouncilFonds Québécois de la Recherche sur la Nature et les TechnologiesCentre québécois sur les matériaux fonctionnels
KeywordsBiomoleculeCopolymerMaterials scienceNanotechnologyCombinatorial chemistryChemical engineeringPolymer scienceChemistryPolymerEngineeringComposite material

Abstract

fetched live from OpenAlex

Glycopolymers are synthetic polymers containing pendant carbohydrate groups. Other biological compounds can be incorporated into glycopolymers to prepare both random and block copolymers aimed at bio-related applications: boronic acid can be introduced as a functional group to obtain crosslinked hydrogels; cholesterol and bile acids can be introduced to render the polymers more hydrophobic for the formation of micelles. Sugar-containing block copolymers with biocompatible blocks such as polypeptides, poly(ethylene glycol), poly(lactic acid) and poly(ε-caprolactone) were also prepared for potential application in drug carriers. These glycopolymers interact with lectins or lectin-containing surfaces as natural carbohydrate mimics. This review highlights the recent progress in the synthesis of random or block glycopolymers. Examples of the applications of glycopolymers in the separation and removal of toxins and bacteria, tumor cell recognition and glucose-responsive insulin delivery are presented and discussed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.375
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.043
GPT teacher head0.288
Teacher spread0.244 · 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 teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreReview

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

Citations48
Published2019
Admission routes2
Has abstractyes

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