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Record W2803296862 · doi:10.1039/c8nr03271h

An easy-to-fabricate clearable CuS-superstructure-based multifunctional theranostic platform for efficient imaging guided chemo-photothermal therapy

2018· article· en· W2803296862 on OpenAlexaff
Wenlong Zhang, Jingbo Xiao, Qing Cao, Weiheng Wang, Xuan Peng, Guoqiang Guan, Zhe Cui, Yongfang Zhang, Shige Wang, Rujia Zou, Xinjian Wan, Huiling Qiu, Junqing Hu

Bibliographic record

VenueNanoscale · 2018
Typearticle
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsPancreas Centre (Canada)
FundersDonghua UniversityNational Natural Science Foundation of China
KeywordsPhotothermal therapySuperstructureNanotechnologyMaterials scienceDrug deliveryEngineering

Abstract

fetched live from OpenAlex

Despite drug delivery systems (DDSs) receiving ever-increasing attention, development of a simple, effective, sensitive and clearable drug delivery and multifunctional theranostic nanoplatform for cancer therapy is still highly desirable and remains a challenge. Herein, using a one-step solvothermal method, hollow acanthosphere-like CuS superstructures assembled from ∼10 nm nanoparticles (NPs) were successfully obtained and used as an efficient drug delivery and theranostic platform for photoacoustic (PA) and infrared (IR) thermal imaging-guided cancer combination therapy. The special hollow characteristic of CuS superstructures with mesoporous shells and large cavities grants them high drug loading capacity; they demonstrate near-infrared (NIR)/pH stimuli-sensitive drug release and pronounced synergetic effects of chemo-photothermal therapy both in vitro and in vivo. In particular, our as-fabricated hollow loose CuS superstructures, with easily breakable characteristic, are biodegradable and able to be cleared from the body when their therapy task is completed. This CuS-superstructure-based clearable drug delivery and "all-in-one" cancer theranostic platform might provide possibilities for improving therapeutic efficacy and minimizing adverse effects.

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 score0.284
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 head0.017
GPT teacher head0.250
Teacher spread0.233 · 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

Citations26
Published2018
Admission routes1
Has abstractyes

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