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Record W1975317174 · doi:10.1016/j.colcom.2014.07.004

Determining the Size Dependence of Colloidal Gold Nanoparticle Uptake in a Tumor-like Interface (Hypoxic)

2014· article· en· W1975317174 on OpenAlexaff
Mehrnoosh Neshatian, Stephen Chung, Darren Yohan, Celina Yang, Devika B. Chithrani

Bibliographic record

VenueColloids and Interface Science Communications · 2014
Typearticle
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsSt. Michael's HospitalOntario Institute for Cancer ResearchToronto Metropolitan University
Fundersnot available
KeywordsColloidNanoparticleColloidal goldNanotechnologyMaterials scienceInterface (matter)Chemical physicsChemistryMoleculePhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Colloidal gold nanoparticles (GNPs) are being used as drug delivery vehicles and radiation dose enhancers in cancer therapy. Oxygen concentration in human tumours is highly heterogeneous with many regions at very low levels of oxygen (hypoxia). A majority of tumours contain regions with oxygen pressure values of less than 0.7% in the gas phase. The purpose of this study was to investigate how the size of the NPs affects their uptake process in a tumour-like hypoxic environment. We used GNPs of diameter 15, 50, and 74 nm, and carried out our experiment under 0.2% (hypoxic) and 21% (normoxic) oxygen levels using MCF-7 and HeLa cells. Our results showed that NPs of size 50 nm had the highest uptake following prolonged exposure to hypoxia. There was no significant toxicity introduced by NPs under hypoxic conditions. These findings will play a vital role in the optimization of GNP-based therapeutics in cancer treatment.

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 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 score0.336
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.268
Teacher spread0.251 · 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

Citations20
Published2014
Admission routes1
Has abstractyes

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