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Record W2963348990 · doi:10.1039/c9tb01070j

<i>In situ</i>biosynthesized gold nanoclusters inhibiting cancer development<i>via</i>the PI3K–AKT signaling pathway

2019· article· en· W2963348990 on OpenAlexaff
Maonan Wang, Zeqian Yu, Huan Feng, Jianling Wang, Lishan Wang, Yu Zhang, Lihong Yin, Ying Du, Hui Jiang, Xuemei Wang, Jiahua Zhou

Bibliographic record

VenueJournal of Materials Chemistry B · 2019
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsPancreas Centre (Canada)
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsNanoclustersIn situPI3K/AKT/mTOR pathwayProtein kinase BMaterials scienceCancerCancer researchNanotechnologyCell biologySignal transductionChemistryBiologyMedicineInternal medicineOrganic chemistry

Abstract

fetched live from OpenAlex

Nanomaterials have made great breakthroughs in drug delivery. However, in previous studies, nanomaterials have been mostly used as vehicles to transport drugs into tumors. Herein, we first found that the in situ biosynthesized gold nanoparticles (Au NCs) can inhibit cancer development via the inhibition of some signaling pathways. Classical cell phenotypic assay tests and orthotropic liver tumor model both showed that the in situ biosynthesized Au NCs could inhibit tumor development. With the help of the RNA-seq analysis, we found that the in situ biosynthesized Au NCs could significantly inhibit the PI3K-AKT signaling pathway, thus effectively impeding tumor development. This facile and effective tumor targeting theranostics in vivo can effectively cure cancers in future clinical applications.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.009
GPT teacher head0.218
Teacher spread0.209 · 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 source (direct Gemma or distilled Codex), 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

Citations20
Published2019
Admission routes1
Has abstractyes

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Same venueJournal of Materials Chemistry BSame topicNanoparticle-Based Drug DeliveryFrench-language works237,207