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Record W1972246230 · doi:10.1039/c3tb20544d

Polymeric mesoporous silica nanoparticles as a pH-responsive switch to control doxorubicin intracellular delivery

2013· article· en· W1972246230 on OpenAlexaff
Ye Tian, Aleksandra Głogowska, Wen Zhong, Thomas Klonisch, Malcolm Xing

Bibliographic record

VenueJournal of Materials Chemistry B · 2013
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversity of ManitobaChildren's Hospital Research Institute of Manitoba
Fundersnot available
KeywordsMesoporous silicaDoxorubicinDrug deliveryNanoparticleNanotechnologyMaterials scienceNanomaterialsCancer cellIntracellularConfocal microscopyDrug carrierBiophysicsMesoporous materialCancerChemistryOrganic chemistryBiochemistryChemotherapyMedicineCell biology

Abstract

fetched live from OpenAlex

Cancer is a leading cause of death. Mesoporous nanomaterials with stimuli sensitivity have received increasing interest as efficient anti-cancer drug carriers. Here, we report hybrid mesoporous nanoparticles of PEGylated silica-poly[2-(dimethylamino)ethyl acrylate] (PEGylated MSN-g-PDMAEA) that can deliver and release the anti-cancer drug doxorubicin (DOX) to tumor cells in a pH dependent switch on/off status. The reversible pH sensitivity resulted in nanoparticles with enhanced functionality through controllable release of the anticancer drug. Confocal laser scanning microscopy (CLSM) and three dimensional (3D) image capture of the intracellular localization of nanoparticles revealed the fast and efficient drug delivery in the human hepatocellular carcinoma cell line. The results highlight the potential of these pH sensitive silica nanoparticles as a novel system for the delivery and controlled release of hydrophobic anti-cancer drugs in the treatment of solid tumors.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.002

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 head0.005
GPT teacher head0.212
Teacher spread0.206 · 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; both teacher heads agree on what is shown here.

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

Citations41
Published2013
Admission routes1
Has abstractyes

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