MétaCan
Menu
Back to cohort
Record W3089330387 · doi:10.1002/marc.202000394

Direct Polymerization Approach to Synthesize Acid‐Degradable Block Copolymers Bearing Imine Pendants for Tunable pH‐Sensitivity and Enhanced Release

2020· article· en· W3089330387 on OpenAlexaff
Xiaolei Hu, Jung Kwon Oh

Bibliographic record

VenueMacromolecular Rapid Communications · 2020
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsConcordia University
Fundersnot available
KeywordsImineCopolymerAmphiphileChemistryPolymerizationCombinatorial chemistryPolymer chemistryCovalent bondMethacrylateOrganic chemistryPolymerCatalysis

Abstract

fetched live from OpenAlex

The development of effective approaches to synthesize smart amphiphilic block copolymers (ABPs) exhibiting acid-responsive degradation through the cleavage of acid-labile imine bonds is extensively explored for controlled release of encapsulated biomolecules, particularly in drug delivery. Here, a new approach based on direct polymerization utilizing a controlled radical polymerization technique to synthesize acid-degradable ABPs bearing pendant imine linkages in hydrophobic block is reported. The approach centers on the synthesis of a novel methacrylate bearing benzoic imine group that can be polymerized to form the hydrophobic imine pendant block. The formed ABPs respond to mild acidic pHs equivalent to tumoral and endosomal/lysosomal acidic environments. This causes the dissociation of self-assembled nanoassemblies through change in their hydrophilic/hydrophobic balance upon the cleavage of pendant imine linkages to the corresponding aldehyde and primary amine, thus leading to the enhanced release of encapsulated drugs. The proof-of-concept results suggest that this robust approach is versatile to further design advanced nanoassemblies responding to dual/multiple stimuli, thus being more effective to intracellular 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.617
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.022
GPT teacher head0.240
Teacher spread0.218 · 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
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

Citations15
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueMacromolecular Rapid CommunicationsSame topicAdvanced Polymer Synthesis and CharacterizationFrench-language works237,207