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Record W2418058365 · doi:10.1039/c6tb01016d

Development of a reactive oxygen species (ROS)-responsive nanoplatform for targeted oral cancer therapy

2016· article· en· W2418058365 on OpenAlexaff
Qing Li, Yong Wen, Xinru You, Fenghe Zhang, Vishva Shah, Xing Chen, Dongdong Tong, Xiujuan Wei, Linlin Yin, Jun Wu, Xin Xu

Bibliographic record

VenueJournal of Materials Chemistry B · 2016
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsRoyal Military College of Canada
FundersNational Natural Science Foundation of China
KeywordsReactive oxygen speciesCancer therapyCancerMaterials scienceCancer researchNanotechnologyMedicineChemistryBiochemistryInternal medicine

Abstract

fetched live from OpenAlex

In this study, for effective oral cancer therapy, a new targeted and ROS-triggered drug delivery nanoplatform was developed from the RGD-PEG-TK-PLGA polymer, in which the ROS-responsive TK containing linker was connected with PEG and PLGA. RGD in the drug delivery system (DDS) presented here was used to target cancer cells. This new nanoplatform shows high stability, good targeting ability, excellent ROS sensitivity and excellent biocompatibility. Loaded with DOX and alpha-TOS, the formulated nanoparticles (NPs) demonstrate much better cellular uptake efficiency and higher inhibition performance towards the oral tongue Cal27 cancer cell line. In vivo anticancer evaluation indicates that DOX and alpha-TOS loaded RGD-PEG-TK-PLGA NPs have no toxicity to mice and showed significantly improved therapeutic efficacy against tumors. Therefore, this polymeric NP platform presents great potential as a new DDS for oral cancer chemotherapy.

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 categoriesInsufficient payload (model declined to judge)
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 score0.008
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.269
Teacher spread0.240 · 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

Citations52
Published2016
Admission routes1
Has abstractyes

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