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Record W1993383448 · doi:10.1118/1.4823787

Investigation of the effects of cell model and subcellular location of gold nanoparticles on nuclear dose enhancement factors using Monte Carlo simulation

2013· article· en· W1993383448 on OpenAlexafffund
Zhongli Cai, Jean‐Philippe Pignol, Niladri Chattopadhyay, Yongkyu Luke Kwon, Eli Lechtman, Raymond M. Reilly

Bibliographic record

VenueMedical Physics · 2013
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsUniversity Health NetworkUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsMonte Carlo methodColloidal goldCytoplasmPhotonPhysicsNanoparticleMaterials scienceChemistryNanotechnologyOptics

Abstract

fetched live from OpenAlex

PURPOSE: The authors' aims were to model how various factors influence radiation dose enhancement by gold nanoparticles (AuNPs) and to propose a new modeling approach to the dose enhancement factor (DEF). METHODS: The authors used Monte Carlo N-particle (MCNP 5) computer code to simulate photon and electron transport in cells. The authors modeled human breast cancer cells as a single cell, a monolayer, or a cluster of cells. Different numbers of 5, 30, or 50 nm AuNPs were placed in the extracellular space, on the cell surface, in the cytoplasm, or in the nucleus. Photon sources examined in the simulation included nine monoenergetic x-rays (10-100 keV), an x-ray beam (100 kVp), and (125)I and (103)Pd brachytherapy seeds. Both nuclear and cellular dose enhancement factors (NDEFs, CDEFs) were calculated. The ability of these metrics to predict the experimental DEF based on the clonogenic survival of MDA-MB-361 human breast cancer cells exposed to AuNPs and x-rays were compared. RESULTS: NDEFs show a strong dependence on photon energies with peaks at 15, 30/40, and 90 keV. Cell model and subcellular location of AuNPs influence the peak position and value of NDEF. NDEFs decrease in the order of AuNPs in the nucleus, cytoplasm, cell membrane, and extracellular space. NDEFs also decrease in the order of AuNPs in a cell cluster, monolayer, and single cell if the photon energy is larger than 20 keV. NDEFs depend linearly on the number of AuNPs per cell. Similar trends were observed for CDEFs. NDEFs using the monolayer cell model were more predictive than either single cell or cluster cell models of the DEFs experimentally derived from the clonogenic survival of cells cultured as a monolayer. The amount of AuNPs required to double the prescribed dose in terms of mg Au/g tissue decreases as the size of AuNPs increases, especially when AuNPs are in the nucleus and the cytoplasm. For 40 keV x-rays and a cluster of cells, to double the prescribed x-ray dose (NDEF = 2) using 30 nm AuNPs, would require 5.1 ± 0.2, 9 ± 1, 10 ± 1, 10 ± 1 mg Au/g tissue in the nucleus, in the cytoplasm, on the cell surface, or in the extracellular space, respectively. Using 50 nm AuNPs, the required amount decreases to 3.1 ± 0.3, 8 ± 1, 9 ± 1, 9 ± 1 mg Au/g tissue, respectively. CONCLUSIONS: NDEF is a new metric that can predict the radiation enhancement of AuNPs for various experimental conditions. Cell model, the subcellular location and size of AuNPs, and the number of AuNPs per cell, as well as the x-ray photon energy all have effects on NDEFs. Larger AuNPs in the nucleus of cluster cells exposed to x-rays of 15 or 40 keV maximize NDEFs.

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 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 score0.373
Threshold uncertainty score0.185

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 head0.020
GPT teacher head0.246
Teacher spread0.226 · 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

Citations40
Published2013
Admission routes2
Has abstractyes

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