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Record W3212390906 · doi:10.3390/app112210856

DNA Dosimetry with Gold Nanoparticle Irradiated by Proton Beams: A Monte Carlo Study on Dose Enhancement

2021· article· en· W3212390906 on OpenAlexafffund
Ngoc Han Huynh, James C. L. Chow

Bibliographic record

VenueApplied Sciences · 2021
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health NetworkToronto Metropolitan University
FundersUniversity of Toronto
KeywordsColloidal goldNanoparticleProtonMonte Carlo methodMaterials scienceIrradiationRadiochemistryDosimetryAbsorbed doseRADIUSProton therapyNanotechnologyChemistryNuclear medicinePhysicsNuclear physicsMedicine

Abstract

fetched live from OpenAlex

Heavy atom nanoparticles, such as gold nanoparticles, are proven effective radiosensitizers in radiotherapy to enhance the dose delivery for cancer treatment. This study investigated the effectiveness of cancer cell killing, involving gold nanoparticle in proton radiation, by changing the nanoparticle size, proton beam energy, and distance between the nanoparticle and DNA. Monte Carlo (MC) simulation (Geant4-DNA code) was used to determine the dose enhancement in terms of dose enhancement ratio (DER), when a gold nanoparticle is present with the DNA. With varying nanoparticle size (radius = 15–50 nm), distance between the gold nanoparticle and DNA (30–130 nm), as well as proton beam energy (0.5–25 MeV) based on the simulation model, our results showed that the DER value increases with a decrease of distance between the gold nanoparticle and DNA and a decrease of proton beam energy. The maximum DER (1.83) is achieved with a 25 nm-radius gold nanoparticle, irradiated by a 0.5 MeV proton beam and 30 nm away from the DNA.

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 score0.023
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.019
GPT teacher head0.282
Teacher spread0.264 · 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

Citations22
Published2021
Admission routes2
Has abstractyes

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