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Record W2753884864 · doi:10.1021/acsomega.7b00829

Synthesis, Self-Assembly, and Drug Delivery Characteristics of Poly(methyl caprolactone-<i>co</i>-caprolactone)-<i>b</i>-poly(ethylene oxide) Copolymers with Variable Compositions of Hydrophobic Blocks: Combining Chemistry and Microfluidic Processing for Polymeric Nanomedicines

2017· article· en· W2753884864 on OpenAlexafffund
Zheqi Xu, Changhai Lü, Carly Lindenberger, Yimeng Cao, Jeremy E. Wulff, Matthew G. Moffitt

Bibliographic record

VenueACS Omega · 2017
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCaprolactoneEthylene oxideAmphiphileCopolymerCrystallinityPolymerDrug deliveryMaterials sciencePolymer chemistryChemistryChemical engineeringNanotechnologyOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide The synthesis, characterization, and self-assembly of a series of biocompatible poly(methyl caprolactone- co -caprolactone)- b -poly(ethylene oxide) amphiphilic block copolymers with variable MCL contents in the hydrophobic block are described. Self-assembly gives rise to polymeric nanoparticles (PNPs) with hydrophobic cores that decrease in crystallinity as the MCL content increases, and their morphologies and sizes show nonmonotonic trends with MCL content. PNPs loaded with the anticancer drug paclitaxel (PAX) give rise to in vitro PAX release rates and MCF-7 GI 50 (50% growth inhibition concentration) values that decrease as the MCL content increases. We also show for selected copolymers that microfluidic manufacturing at a variable flow rate enables further control of PAX release rates and enhances MCF-7 antiproliferation potency. These results indicate that more effective and specific drug delivery PNPs are possible through tangential efforts combining polymer synthesis and microfluidic manufacturing.

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)
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 score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.007
GPT teacher head0.228
Teacher spread0.220 · 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

Citations24
Published2017
Admission routes2
Has abstractyes

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