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Record W1967457947 · doi:10.1021/bm4018484

pH-Responsive Nanoemulsions for Controlled Drug Release

2014· article· en· W1967457947 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBiomacromolecules · 2014
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsQueen's University
FundersNational Natural Science Foundation of China
KeywordsCopolymerAcrylic acidChemistryEthylene glycolPolymerSolubilityPolymer chemistryDrug carrierPolystyreneEtherDoxorubicin HydrochlorideAqueous solutionDrug deliveryNuclear chemistryDoxorubicinOrganic chemistry

Abstract

fetched live from OpenAlex

Three ternary graft copolymers bearing polystyrene (PS), poly(ethylene glycol) methyl ether (MPEG), and poly(acrylic acid) (PAA) side chains were synthesized and characterized. At pH = 7.4, these copolymers stabilized doxorubicin (DOX)-containing benzyl benzoate (BBZ) nanoemulsion droplets in water and formed a compact polymer layer to inhibit DOX release. Upon lowering the solution pH to 5.0, the AA groups dissociated less and became less soluble. Moreover, the neutralized AA groups formed presumably H-bonded complexes with the EG units, reducing the solubility of the EG units. This dual action drastically shifted the hydrophilic and hydrophobic balance of the copolymer and caused the original stabilizing polymer layer to rupture and the nanoemulsion droplets to aggregate, releasing DOX. The rate and extent of DOX release could be increased by matching the numbers of PAA and MPEG chains per graft copolymer. In addition, these nanoemulsions were not toxic and entered human carcinoma cells, releasing DOX there. Thus, these nanoemulsions have potential as drug delivery vehicles.

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.

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)0.001
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 score0.024
Threshold uncertainty score0.787

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.001

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 head0.010
GPT teacher head0.271
Teacher spread0.261 · 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