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Record W3120523104 · doi:10.3390/nano11010161

Degradable Spirocyclic Polyacetal-Based Core-Amphiphilic Assemblies for Encapsulation and Release of Hydrophobic Cargo

2021· article· en· W3120523104 on OpenAlexafffund
Brandon Andrade‐Gagnon, Marilyne Bélanger‐Bouliga, Phuong Nguyen, Thi Hong Diep Nguyen, Steve Bourgault, Ali Nazemi

Bibliographic record

VenueNanomaterials · 2021
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAmphiphileEncapsulation (networking)Materials sciencePolymer scienceChemical engineeringPolymer chemistryChemistryPolymerComposite materialCopolymerComputer scienceEngineering

Abstract

fetched live from OpenAlex

Polymeric nanomaterials that degrade in acidic environments have gained considerable attention in nanomedicine for intracellular drug delivery and cancer therapy. Among various acid-degradable linkages, spirocyclic acetals have rarely been used to fabricate such vehicles. In addition to acid sensitivity, they benefit from conformational rigidity that is otherwise not attainable by their non-spirocyclic analogs. Herein, amphiphilic spirocyclic polyacetals are synthesized by Cu-catalyzed alkyne-azide "click" polymerization. Unlike conventional block copolymers, which often form core-shell structures, these polymers self-assemble to form core amphiphilic assemblies capable of encapsulating Nile red as a hydrophobic model drug. In vitro experiments show that while release from these materials can occur at neutral pH with preservation of their integrity, acidic pH accelerates efficient cargo release and leads to the complete degradation of assemblies. Moreover, cellular assays reveal that these materials are fully cytocompatible, interact with the plasma membrane, and can be internalized by cells, rendering them as potential candidates for cancer therapy and/or drug delivery.

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 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 score0.005
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

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 head0.039
GPT teacher head0.275
Teacher spread0.236 · 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.

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

Citations9
Published2021
Admission routes2
Has abstractyes

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