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Record W2594469236 · doi:10.1088/1361-6528/aa645f

A strategy for photothermal conversion of polymeric nanoparticles by polyaniline for smart control of targeted drug delivery

2017· article· en· W2594469236 on OpenAlexaff
Chaoqun You, Hongshuai Wu, Mingxin Wang, Senlin Wang, Tianyi Shi, Yanghui Luo, Bai‐Wang Sun, Xiangyang Zhang, Jin Zhu

Bibliographic record

VenueNanotechnology · 2017
Typearticle
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsMinistry of Education and Child Care
Fundersnot available
KeywordsPolyanilinePhotothermal therapyMaterials scienceNanocarriersNanotechnologyDrug deliveryNanoparticleCytotoxicityControlled releasePhotothermal effectIn vitroPolymerChemistryPolymerization

Abstract

fetched live from OpenAlex

The near-infrared (NIR)-mediated novel strategy to control the drug release from nanocarriers has developed rapidly in recent decades. Polyaniline as a non-cytotoxic and electroactive material for studying cellular proliferation has attracted great attention in recent years. In the present work, polyaniline-mediated polymeric nanoparticles were developed to target the delivery of cisplatin and release it in a controllable way. The prepared polyaniline nanoparticles displayed a size of 90 ± 1.0 nm, a favorable morphology in water, and could be targeted to tumors through the high affinity between trastuzumab and the overexpressed Her2 in tumor cells. In addition, the developed nanoparticles demonstrated exciting photothermal conversion efficiency induced by NIR light and achieved significant cell inhibition efficiency (93.97%) in vitro when exposed to an 808 nm NIR laser with the power of 1.54 W for 5 min. Therefore, the developed external control release delivery system with excellent specificity and high cytotoxicity exhibited great potential in cell research and our research demonstrated that the polyaniline also has potential in the application of photothermal conversion in biomedicine.

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 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 score0.021
Threshold uncertainty score0.700

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 head0.008
GPT teacher head0.220
Teacher spread0.211 · 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

Citations37
Published2017
Admission routes1
Has abstractyes

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