Evaluation of direct and indirect DNA Damage under the Photon Irradiation in the Presence of Gold, Gadolinium, and Silver Nanoparticles Using Geant۴-DNA
Publish place: Radiation Physics and Engineering، Vol: 5، Issue: 2
Publish Year: 1403
نوع سند: مقاله ژورنالی
زبان: English
View: 102
This Paper With 9 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_RPE-5-2_004
تاریخ نمایه سازی: 30 فروردین 1403
Abstract:
Radiation therapy aims to maximize doses to cancer cells while minimizing damage to normal tissues. Today, nanoparticles containing high-atomic-number elements, such as gold, gadolinium, and silver, have proven effective as radiosensitizers in radiotherapy to enhance dose delivery for cancer treatment. In this study, we used the Geant۴-DNA toolkit to investigate the effects of multiple nanoparticles (NPs) with varying sizes (radius= ۳.۱۵ to ۵ nm) on DNA damage when exposed to monoenergetic photons with energies of ۱۵, ۴۰, ۵۰, and ۶۸ keV. Direct and indirect single-strand breaks (SSBs), double-strand breaks (DSBs), and hybrid double-strand breaks (Hybrid DSBs) were calculated in the presence and absence of ۱ to ۴ nanoparticles (NPs) of the same total volume of gold, gadolinium, and silver nanoparticles for the ۱ZBB model (selected from the Protein Data Bank (PDB) library). The results show that increasing the number of gold, gadolinium, and silver NPs and decreasing the photon beam energy increases the total number of strand breaks. Furthermore, gold nanoparticles (GNPs) are more effective options than gadolinium nanoparticles (GdNPs), and silver nanoparticles (SNPs) for inhibiting and controlling cancer cells.
Keywords:
Authors
Ali Azizi Ganjgah
Department of Physics, Faculty of Science, University of Guilan, Postal Code ۴۱۹۳۸۳۳۶۹۷, Rasht, Iran
Payvand Taherparvar
Department of Physics, Faculty of Science, University of Guilan, Postal Code ۴۱۹۳۸۳۳۶۹۷, Rasht, Iran
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :