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Record W2589055971 · doi:10.1002/chem.201700059

Near‐Infrared Light and pH Dual‐Responsive Targeted Drug Carrier Based on Core‐Crosslinked Polyaniline Nanoparticles for Intracellular Delivery of Cisplatin

2017· article· en· W2589055971 on OpenAlexaff
Chaoqun You, Hongshuai Wu, Minxing Wang, Yawen Zhang, Jingwen Wang, Yanghui Luo, Lihai Zhai, Bai‐Wang Sun, Xiangyang Zhang, Jin Zhu

Bibliographic record

VenueChemistry - A European Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsMinistry of Education and Child Care
Fundersnot available
KeywordsNanoparticleNanocarriersPolyanilineEthylene glycolDrug deliveryMaterials scienceDrug carrierDynamic light scatteringNanotechnologyPolymerControlled releaseCopolymerChemical engineeringChemistryOrganic chemistryPolymerization

Abstract

fetched live from OpenAlex

Abstract Biodegradable polymeric nanoparticles have received growing interest as one of the most promising agents for drug delivery. In the present work, functional and core‐crosslinked poly(ethylene glycol) with poly(ϵ‐caprolactone) (PEG 5k ‐PCL 10k ) block copolymer and lecithin as biodegradable polymer doped with polyaniline was used to assemble nanoparticles which were prepared for targeted delivery and controlled release of cisplatin. The morphology of the polyaniline nanoparticles was determined by dynamic light scattering and the prepared nanoparticles showed a size of 83(±1) nm and a uniform spherical shape. For targeting to HER2 receptors, Herceptin was applied to guide the nanoparticles to breast cancer cells. Studies on cellular uptake and drug release of the nanocarriers showed that the prepared nanoparticles were efficiently taken up by breast cancer cells and the drug was released efficiently under acidic conditions when exposed to a near‐infrared laser (808 nm, 1.54 W) for 5 min. Our research highlights the great potential of near‐infrared light and pH dual‐responsive release by core‐crosslinked nanoparticles in nanobiomedicine.

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: Empirical
Teacher disagreement score0.079
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 studies0.0000.000
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 head0.012
GPT teacher head0.214
Teacher spread0.202 · 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

Citations50
Published2017
Admission routes1
Has abstractyes

Explore more

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