<?xml version="1.0" encoding="utf-8"?>
			<journal>
			<title>Iranian Journal of Medical Physics</title>
			<title_fa></title_fa>
			<short_title>Iran J Med Phys</short_title>
			<subject>Medical Sciences</subject>
			<web_url>https://ijmp.mums.ac.ir/</web_url>
			<journal_hbi_system_id>0</journal_hbi_system_id>
			<journal_hbi_system_user></journal_hbi_system_user>
			<journal_id_issn></journal_id_issn>
			<journal_id_issn_online>2345-3672</journal_id_issn_online>
			<journal_id_pii></journal_id_pii>
			<journal_id_doi></journal_id_doi>
			<journal_id_iranmedex></journal_id_iranmedex>
			<journal_id_magiran></journal_id_magiran>
			<journal_id_sid></journal_id_sid>
			<journal_id_nlai></journal_id_nlai>
			<journal_id_science></journal_id_science>
			<language>en</language>
			<pubdate>
				<type>jalali</type>
				<year>0</year>
				<month>0</month>
				<day>1</day>
			</pubdate>
			<pubdate>
				<type>gregorian</type>
				<year>2026</year>
				<month>4</month>
				<day>1</day>
			</pubdate>
			<volume>23</volume>
			<number>2</number>
			<publish_type>online</publish_type>
			<publish_edition>1</publish_edition>
			<article_type>fulltext</article_type>
			<articleset><article>
				<language>en</language>
				<article_id_issn></article_id_issn>
				<article_id_issn_online></article_id_issn_online>
				<article_id_pubmed></article_id_pubmed>
				<article_id_pii></article_id_pii>
				<article_id_doi></article_id_doi>
				<article_id_iranmedex></article_id_iranmedex>
				<article_id_magiran></article_id_magiran>
				<article_id_sid></article_id_sid>
				<title_fa></title_fa>
				<title>AI-Driven Identification of Microplastics via STIM-Based Proton Transmission: A Computational Proof-of-Concept</title>
				<subject_fa></subject_fa>
				<subject></subject>
				<content_type_fa></content_type_fa>
				<content_type>Original Paper</content_type>
				<abstract_fa><![CDATA[]]></abstract_fa>
				<abstract><![CDATA[Introduction: This simulation-driven study investigated the potential of a non-destructive detection system concept for microplastic (MP) pollution using a computational framework. Traditional detection methods are often time-consuming or lack the sensitivity required for real-time monitoring. We proposed an integrated approach combining proton Scanning Transmission Ion Microscopy (STIM) with Metal-Organic Framework (MOF) nanofilters and deep learning-based analysis.Material and Methods: Computational modeling was conducted using the Monte Carlo N-Particle extended (MCNPX) code to simulate proton beam interactions (at 30 and 60 MeV) with MPs. Polyimide was utilized as a simulation proxy for the MIL-101(Cr) MOF nanofilters to represent the structural density and elemental composition. The physical changes in the proton Bragg peak and energy loss spectra were analyzed. To automate the detection process, a sequential-attention architecture integrating Bidirectional Long Short-Term Memory (BiLSTM) and Transformer models was developed and trained on the simulated spectral data.Results: The simulation-derived results suggested that the integration of MOF nanofilters could enhance the sensitivity of STIM to minor density variations caused by microplastics within a controlled computational environment. The BiLSTM-Transformer model achieved high predictive accuracy on the simulated dataset, with an R² value of 0.982 and a Mean Absolute Error (MAE) of 0.015. Detailed statistical uncertainties (standard deviations) for the MCNPX outputs are reported in the Results section.Conclusion: This simulation study demonstrates the theoretical feasibility of using AI-enhanced proton STIM for the identification of microplastics. While the current findings are based on deterministic Monte Carlo outputs, the high accuracy of the BiLSTM-Transformer model provides a computational proof-of-concept. Future experimental work is required to bridge the gap between simulation and real-world cyclotron-based monitoring systems, particularly regarding electronic noise and detector efficiency.]]></abstract>
				<keyword_fa></keyword_fa>
				<keyword>Microplastic Scanning Ion Transmission Microscopy (STIM) MIL-101(Cr) Nanofilter MCNPX Simulation Bi LSTM&amp;ndash, Transformer Neural Network</keyword>
				<start_page>76</start_page>
				<end_page>85</end_page>
				<web_url>https://ijmp.mums.ac.ir/article_28412.html</web_url>
			<author_list><author>
				<first_name>Marjan</first_name>
				<middle_name></middle_name>
				<last_name>Atghaei</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>m.atghaei65@gmail.com</email>
				<code>124812</code>
				<coreauthor>No</coreauthor>
				<affiliation>Faculty of Sciences and Modern Technologies, Graduate University of Advanced Technology</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>Mohamad Reza</first_name>
				<middle_name></middle_name>
				<last_name>Rezaie Rayeni Nejad</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>mohamadreza45@gmail.com</email>
				<code>124811</code>
				<coreauthor>Yes</coreauthor>
				<affiliation>Department of Nuclear Engineering, Faculty of Physics and Energy Engineering, Amirkabir University of Technology, Tehran, Iran</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>Hossien</first_name>
				<middle_name></middle_name>
				<last_name>Afarideh</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>hafarideh@aut.ac.ir</email>
				<code>124813</code>
				<coreauthor>No</coreauthor>
				<affiliation>Department of Nuclear Engineering, Faculty of Physics and Energy Engineering, Amirkabir University of Technology, Tehran, Iran</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>Ali</first_name>
				<middle_name></middle_name>
				<last_name>Negarestani</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>a.negarestani@kgut.ac.ir</email>
				<code>124814</code>
				<coreauthor>No</coreauthor>
				<affiliation>Faculty of Sciences and Modern Technologies, Graduate University of Advanced Technology</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author></author_list>
				</article><article>
				<language>en</language>
				<article_id_issn></article_id_issn>
				<article_id_issn_online></article_id_issn_online>
				<article_id_pubmed></article_id_pubmed>
				<article_id_pii></article_id_pii>
				<article_id_doi></article_id_doi>
				<article_id_iranmedex></article_id_iranmedex>
				<article_id_magiran></article_id_magiran>
				<article_id_sid></article_id_sid>
				<title_fa></title_fa>
				<title>Investigating Direct and Bystander Radio-Sensitizing Effects of Genistein in PC-3 Prostate Cancer Cells</title>
				<subject_fa></subject_fa>
				<subject></subject>
				<content_type_fa></content_type_fa>
				<content_type>Original Paper</content_type>
				<abstract_fa><![CDATA[]]></abstract_fa>
				<abstract><![CDATA[Introduction: Radiation therapy is the major treatment modality of prostate cancer. Genistein (GEN) as the most abundant isoflavone in soybeans prevents the repair of DNA breaks, improving the radiosensitivity of cancer cells. It is demonstrated that cancer cells can be eradicated outside the radiation field as a result of the bystander effect. The aim of this study was to investigate the direct and indirect effect of ionizing radiation (IR), GEN and their combination on prostate cancer cell line (PC-3).Material and Methods: Cells were irradiated to 4 Gy IR 24h after treatment with GEN. Then the medium of cells was transferred to non-irradiated cells to induce bystander effect. The direct and indirect effects of GEN, IR, and their combination (GEN+IR) on cell cycle and apoptosis were assessed by flow-cytometry.Results: Cell cycle arrest has occurred in the G2/M phase in the GEN and GEN+IR groups, while no significant differences were observed in the bystander groups. Apoptosis levels were also significantly increased by GEN, IR and combined treatment. Although apoptosis was significantly higher in Bys+IR group compared to control, indirect exposure to both GEN and IR did not enhance apoptosis levels. Conclusion: Overall, the results demonstrate that GEN enhances PC-3 cells radiosensitivity by promoting apoptosis and cell cycle arrest. However, bystander effect was not observed by indirect exposure of PC-3 cells to both GEN and IR. Future research are crucial to explore the radiation-induced bystander effect in the presence of GEN.]]></abstract>
				<keyword_fa></keyword_fa>
				<keyword>Genistein, Radiation, Bystander Effect, Prostatic Neoplasms</keyword>
				<start_page>86</start_page>
				<end_page>92</end_page>
				<web_url>https://ijmp.mums.ac.ir/article_28414.html</web_url>
			<author_list><author>
				<first_name>Reza</first_name>
				<middle_name></middle_name>
				<last_name>Fardid</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>rfardid@sums.ac.ir</email>
				<code>124817</code>
				<coreauthor>Yes</coreauthor>
				<affiliation>Ionizing and Non-Ionizing Radiation Protection Research Center (INIRPRC), Faculty of Paramedical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>Aysuda</first_name>
				<middle_name></middle_name>
				<last_name>Mansuri</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>aysudamansuri13751375@gmail.com</email>
				<code>124818</code>
				<coreauthor>No</coreauthor>
				<affiliation>Department of Radiology, Paramedical School, Shiraz University of Medical Sciences, Shiraz, Iran</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>Mohammad Ali</first_name>
				<middle_name></middle_name>
				<last_name>Takhshid</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>takhshidma@sums.ac.ir</email>
				<code>124819</code>
				<coreauthor>No</coreauthor>
				<affiliation>Department of Laboratory Sciences, Paramedical School, Shiraz University of Medical Sciences, Shiraz, Iran</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>Malihe</first_name>
				<middle_name></middle_name>
				<last_name>Rostamyari</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>maliheh.rostamyari@gmail.com</email>
				<code>124820</code>
				<coreauthor>No</coreauthor>
				<affiliation>Department of Radiology, Paramedical School, Shiraz University of Medical Sciences, Shiraz, Iran</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>Seyedeh Kimia</first_name>
				<middle_name></middle_name>
				<last_name>Javadi Haghighi</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>kimiajavadi1376@gmail.com</email>
				<code>124821</code>
				<coreauthor>No</coreauthor>
				<affiliation>Department of Radiology, Paramedical School , Shiraz University of Medical Sciences, Shiraz, Iran</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>Mohammad Amin</first_name>
				<middle_name></middle_name>
				<last_name>Mosleh-Shirazi</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>amosleh@sums.ac.ir</email>
				<code>124822</code>
				<coreauthor>No</coreauthor>
				<affiliation>Department of Radio-oncology, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author></author_list>
				</article><article>
				<language>en</language>
				<article_id_issn></article_id_issn>
				<article_id_issn_online></article_id_issn_online>
				<article_id_pubmed></article_id_pubmed>
				<article_id_pii></article_id_pii>
				<article_id_doi></article_id_doi>
				<article_id_iranmedex></article_id_iranmedex>
				<article_id_magiran></article_id_magiran>
				<article_id_sid></article_id_sid>
				<title_fa></title_fa>
				<title>Investigation of Alternative Metals for Gold Nanoparticles in Enhancing Proton Therapy Relative Biological Effectiveness: A Monte Carlo Study</title>
				<subject_fa></subject_fa>
				<subject></subject>
				<content_type_fa></content_type_fa>
				<content_type>Original Paper</content_type>
				<abstract_fa><![CDATA[]]></abstract_fa>
				<abstract><![CDATA[Introduction: This study investigates the enhancement of relative biological effectiveness (RBE) in proton therapy for ocular tumors using heavy metal nanoparticles using Monte Carlo simulation. Material and Methods: Through multiscale Monte Carlo simulations with Geant4-DNA, we evaluated the impact of gold (Au), bismuth (Bi), iridium (Ir), and platinum (Pt) nanoparticles (with 25 nm radius and 30 mg/g concentration) on the RBE of a 62-MeV spread-out Bragg peak (SOBP). A mammalian fibroblast cell nucleus was simulated and DNA damages induced by protons (with and without the presence of nanoparticles) were calculated as biological endpoint for RBE calculation.Results: The results demonstrated that all nanoparticles increased RBE compared to the nanoparticle-free condition. Pt and Ir nanoparticles yielded the greatest enhancement, outperforming Au, while Bi nanoparticles provided the least improvement. The most substantial RBE increase occurred at the distal end of the SOBP, correlating with the region of highest linear energy transfer (LET). Calculations based on DNA damage and cell survival curves confirmed that the choice of biological endpoint significantly influences the computed RBE values. A key finding was that nanoparticle density, rather than atomic number, was the dominant factor in RBE improvement. This suggests that high-density Pt nanoparticles are a more effective radio-sensitizer than Au. Conclusion: The study underscores the importance of incorporating variable RBE models and radiobiological effects of nanoparticles into proton therapy treatment planning systems to improve clinical outcomes.]]></abstract>
				<keyword_fa></keyword_fa>
				<keyword>Proton Therapy RBE Nanoparticle Monte Carlo Simulation Geant4, DNA</keyword>
				<start_page>93</start_page>
				<end_page>102</end_page>
				<web_url>https://ijmp.mums.ac.ir/article_28415.html</web_url>
			<author_list><author>
				<first_name>Fatemeh</first_name>
				<middle_name></middle_name>
				<last_name>Habibi</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>f.habibi198536@yahoo.com</email>
				<code>124823</code>
				<coreauthor>No</coreauthor>
				<affiliation>Department of Physics, Shi.C., Islamic Azad University, Shiraz, Iran</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>Zohreh</first_name>
				<middle_name></middle_name>
				<last_name>Parang</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>zparang@mail.ir</email>
				<code>124824</code>
				<coreauthor>Yes</coreauthor>
				<affiliation>Department of Physics, Shi.C., Islamic Azad University, Shiraz, Iran</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>Nasrin</first_name>
				<middle_name></middle_name>
				<last_name>Hoseini-Motlagh</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>nasrinhosseini_motlagh@iau.ir</email>
				<code>124825</code>
				<coreauthor>No</coreauthor>
				<affiliation>Department of Physics, Shi.C., Islamic Azad University, Shiraz, Iran</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>Alireza</first_name>
				<middle_name></middle_name>
				<last_name>Keshavarz</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>keshavarz@sutech.ac.ir</email>
				<code>124826</code>
				<coreauthor>No</coreauthor>
				<affiliation>Shiraz University of Technology</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author></author_list>
				</article><article>
				<language>en</language>
				<article_id_issn></article_id_issn>
				<article_id_issn_online></article_id_issn_online>
				<article_id_pubmed></article_id_pubmed>
				<article_id_pii></article_id_pii>
				<article_id_doi></article_id_doi>
				<article_id_iranmedex></article_id_iranmedex>
				<article_id_magiran></article_id_magiran>
				<article_id_sid></article_id_sid>
				<title_fa></title_fa>
				<title>Impact of Patient Arm Positioning on Ddose Distribution in Target and Organs at Risk in Right Bbreast Cancer Radiotherapy</title>
				<subject_fa></subject_fa>
				<subject></subject>
				<content_type_fa></content_type_fa>
				<content_type>Original Paper</content_type>
				<abstract_fa><![CDATA[]]></abstract_fa>
				<abstract><![CDATA[Introduction: Breast cancer (BC) ranks as the second most frequently diagnosed cancer among women globally. The aim of this study was to evaluate the effect of the patient’s arm positioning on dose distribution in the organs at risk (OARs) and target in BC radiotherapy for mastectomy patients.Material and Methods: Computed tomography (CT) scans of images of 30 right BC patients were collected. The patients were divided into two groups, each with 15 patients. The first group was with right arm-up positioning, and the second was with both arms-up positioning. The Monaco treatment planning system (TPS) was used for treatment planning. Radiotherapy included a prescribed dose of 50 Gy/25 fractions. For the planning target volume (PTV), the minimum dose (Dmin), mean dose (Dmean), maximum dose (Dmax), homogeneity index (HI), conformity index (CI), and tumor control probability (TCP) were calculated. For the OARs Dmin, Dmean, Dmax, and V20 Gy (%) for the lung and normal tissue complication probability (NTCP) were assessed. We compared the two groups based on PTV and OAR levels.Results: There was no statistical difference between quantities for PTV and OARs for the lung, heart, and spinal cord, but a statistical difference was only found in Dmean and Dmax for the thyroid gland.Conclusion: Although a statistically significant difference in thyroid dose was found, the absolute difference is small and within tolerance limits. Both positioning techniques provide comparable coverage of the target volume and sparing of the critical organs. With regard to patient comfort and ease of reproducibility during radiotherapy treatments, positioning with both arms up is generally favored, while positions with the right arm up may be an alternative if thyroid dose minimization is an issue.]]></abstract>
				<keyword_fa></keyword_fa>
				<keyword>Breast Cancer, Radiotherapy, arm positioning, Treatment Planning System</keyword>
				<start_page>103</start_page>
				<end_page>110</end_page>
				<web_url>https://ijmp.mums.ac.ir/article_28416.html</web_url>
			<author_list><author>
				<first_name>Bahareh</first_name>
				<middle_name></middle_name>
				<last_name>Khalili Najafabad</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>baharekhalili389@gmail.com</email>
				<code>124832</code>
				<coreauthor>No</coreauthor>
				<affiliation>Biomedical Engineering and Medical Physics Department, School of Medicine, Shahid Beheshti University of Medical Sciences,Tehran, Iran</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>Raed Saleh</first_name>
				<middle_name></middle_name>
				<last_name>Majhool</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>majoolraaid99@gmail.com</email>
				<code>124828</code>
				<coreauthor>No</coreauthor>
				<affiliation>Biomedical Engineering and Medical Physics Department, School of Medicine, Shahid Beheshti University of Medical Sciences,Tehran, Iran</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>Mahdi</first_name>
				<middle_name></middle_name>
				<last_name>Ghorbani</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>mhdghorbani@gmail.com</email>
				<code>124827</code>
				<coreauthor>Yes</coreauthor>
				<affiliation>Biomedical Engineering and Medical Physics Department, School of Medicine, Shahid Beheshti University of Medical Sciences,Tehran, Iran</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>Ahmad</first_name>
				<middle_name></middle_name>
				<last_name>Mostaar</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>mostaar@sbmu.ac.ir</email>
				<code>124829</code>
				<coreauthor>No</coreauthor>
				<affiliation>Biomedical Engineering and Medical Physics Department, School of Medicine, Shahid Beheshti University of Medical Sciences,Tehran, Iran</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>Mehdi</first_name>
				<middle_name></middle_name>
				<last_name>Khosravi</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>mhdikhosravi08@yahoo.com</email>
				<code>124830</code>
				<coreauthor>No</coreauthor>
				<affiliation>Medical Physics Department, Radiation Oncology Center, Vali Asr Hospital, Qom, Iran</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>Abdullah</first_name>
				<middle_name></middle_name>
				<last_name>Ali</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>m.physics1987@gmail.com</email>
				<code>124831</code>
				<coreauthor>No</coreauthor>
				<affiliation>Biomedical Engineering and Medical Physics Department, School of Medicine, Shahid Beheshti University of Medical Sciences,Tehran, Iran</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>Meysam</first_name>
				<middle_name></middle_name>
				<last_name>Tavakoli</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>meysamtavakkoli@gmail.com</email>
				<code>124833</code>
				<coreauthor>No</coreauthor>
				<affiliation>Department of Radiation Oncology, Winship Cancer Institute, Emory University, Atlanta, Georgia, USA</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author></author_list>
				</article><article>
				<language>en</language>
				<article_id_issn></article_id_issn>
				<article_id_issn_online></article_id_issn_online>
				<article_id_pubmed></article_id_pubmed>
				<article_id_pii></article_id_pii>
				<article_id_doi></article_id_doi>
				<article_id_iranmedex></article_id_iranmedex>
				<article_id_magiran></article_id_magiran>
				<article_id_sid></article_id_sid>
				<title_fa></title_fa>
				<title>Development and Validation of a TPS-Derived Feature Based Predictive Model for Adaptive Radiotherapy Support: APE-Net</title>
				<subject_fa></subject_fa>
				<subject></subject>
				<content_type_fa></content_type_fa>
				<content_type>Original Paper</content_type>
				<abstract_fa><![CDATA[]]></abstract_fa>
				<abstract><![CDATA[Introduction: Radiotherapy for advanced head and neck cancer (HNC) is complicated by anatomical changes, tumor shrinkage, and organ displacement, which may cause dosimetric inaccuracies and increased toxicity. Adaptive radiotherapy (ART) remains challenging in resource-limited settings due to time and personnel constraints. This study investigated the feasibility of the Anatomy-Preserving Estimation Network (APE-Net), a treatment planning system (TPS)-informed predictive framework for supporting offline adaptive radiotherapy (oART).
Material and Methods: Data from 21 retrospectively analyzed HNC patients were used, including baseline (Plan 1), unadapted (Plan 2), and adapted (Plan 3) treatment plans. Patients underwent cone-beam computed tomography (CBCT) monitoring, with rescan CT (rCT) acquired when anatomical deviations of &gt;1 cm occurred. Plan 2 was recalculated using rCT images and original treatment parameters, while Plan 3 was re-optimized using the Eclipse TPS (Version 16, Varian Medical Systems, Palo Alto, CA, USA) at Moi Teaching and Referral Hospital, Eldoret, Kenya. TPS-derived dosimetric and volumetric features were extracted. 108 synthetic TPS-derived feature samples for augmentation were generated. APE-Net was implemented as a fully connected multilayer perceptron (MLP) trained on TPS-derived feature vectors to estimate adaptive treatment parameters. 
Results: APE-Net trained on both real and synthetic TPS-derived features demonstrated moderate predictive performance, achieving a coefficient of determination (R²) of 0.681, mean absolute error (MAE) of 9.092% and root mean squared error (RMSE) of 16.776%. Training and validation loss curves showed stable convergence with limited divergence.
Conclusion: APE-Net demonstrates feasibility as a TPS-informed machine-learning framework for supporting predictive oART workflows in resource-limited settings.]]></abstract>
				<keyword_fa></keyword_fa>
				<keyword>Head and Neck Cancer (HNC) Limited, Resource Setting Synthetic TPS, Derived Feature Predictive Modelling Anatomical Changes</keyword>
				<start_page>111</start_page>
				<end_page>116</end_page>
				<web_url>https://ijmp.mums.ac.ir/article_28417.html</web_url>
			<author_list><author>
				<first_name>daniel</first_name>
				<middle_name>Warurii</middle_name>
				<last_name>Ndegwa</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>danielwarurii1@gmail.com</email>
				<code>124834</code>
				<coreauthor>Yes</coreauthor>
				<affiliation>P.O. BOX 109-60400 Chuka, KENYA</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>Martin</first_name>
				<middle_name>M</middle_name>
				<last_name>Mutie</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>mmutie@chuka.ac.ke</email>
				<code>124835</code>
				<coreauthor>No</coreauthor>
				<affiliation>P.O. Box 109 – 60400 Chuka
P.O. Box 109 – 60400 Chuka</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>Paul</first_name>
				<middle_name>K</middle_name>
				<last_name>Kamweru</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>pkkamweru@chuka.ac.ke</email>
				<code>124836</code>
				<coreauthor>No</coreauthor>
				<affiliation>Department of Physical Sciences, Chuka University</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>James</first_name>
				<middle_name></middle_name>
				<last_name>Ngure</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>jayngure@gmail.com</email>
				<code>124837</code>
				<coreauthor>No</coreauthor>
				<affiliation>Department of Radiotherapy, Nakuru County Referral and Teaching Hospital</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author></author_list>
				</article><article>
				<language>en</language>
				<article_id_issn></article_id_issn>
				<article_id_issn_online></article_id_issn_online>
				<article_id_pubmed></article_id_pubmed>
				<article_id_pii></article_id_pii>
				<article_id_doi></article_id_doi>
				<article_id_iranmedex></article_id_iranmedex>
				<article_id_magiran></article_id_magiran>
				<article_id_sid></article_id_sid>
				<title_fa></title_fa>
				<title>Monte Carlo Simulation of DNA Break Prediction Following Electron Irradiation Using Geant4-DNA Toolkit</title>
				<subject_fa></subject_fa>
				<subject></subject>
				<content_type_fa></content_type_fa>
				<content_type>Original Paper</content_type>
				<abstract_fa><![CDATA[]]></abstract_fa>
				<abstract><![CDATA[Introduction: The interaction of ionizing radiation with cells involves complex physical and chemical processes that can cause single-strand breaks (SSB) and double-strand breaks (DSB). DSB is one of the most deleterious forms of DNA damage caused by radiation exposure and is also poorly repaired.  Misrepaired or unrepaired damage can finally lead to genomic mutations, which may initiate cancer or cell death. 
Material and Methods: The Monte Carlo simulation toolkit Geant4-DNA is used in this study to simulate the physical and chemical processes that can damage the DNA of human fibroblast cells resulting from an incident electron on the DNA. 
Results: Electrons are classified as low-Linear Energy Transfer (LET) radiation, typically depositing energy at a rate of approximately 0.2 to 15 keV µm-1 in water or soft tissue; therefore, we examined the occurrence of SSB and DSB vs. electron energy of 0.5, 0.8, 1, 10, and 100 keV. Since there are no published data for human fibroblast cells, our simulation results were compared with available experimental data and findings from other simulation models of other cell types. Our results demonstrated consistent agreement with previously published studies.
Conclusion: The outcomes of our simulation, using the Monte Carlo simulation Geant4-DNA code with the “molecular DNA” based on G4EmDNAPhysics_option6 and G4EmDNAChemistry_option3, are in line with experimental data and simulation results of other cells that have already been published. The maximum DSB value was about 1 keV, with an LET value of about 10 keV μm-1.]]></abstract>
				<keyword_fa></keyword_fa>
				<keyword>DNA DamageMonte Carlo Simulation Geant4, DNA</keyword>
				<start_page>117</start_page>
				<end_page>123</end_page>
				<web_url>https://ijmp.mums.ac.ir/article_28418.html</web_url>
			<author_list><author>
				<first_name>Omar</first_name>
				<middle_name></middle_name>
				<last_name>Qasim</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>scpm23003@uokirkuk.edu.iq</email>
				<code>124838</code>
				<coreauthor>No</coreauthor>
				<affiliation>Department of Physics, College of Science, University of Kirkuk, Kirkuk, Iraq</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>Ali</first_name>
				<middle_name>Hussain</middle_name>
				<last_name>Taqi</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>alitaqi@uokirkuk.edu.iq</email>
				<code>124839</code>
				<coreauthor>Yes</coreauthor>
				<affiliation>Physics Dept., College of Science,kirkuk University ,Kirkuk, IRAQ</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>Shilan</first_name>
				<middle_name></middle_name>
				<last_name>Jabbar</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>shilan.jabbar@uokirkuk.edu.iq</email>
				<code>124840</code>
				<coreauthor>No</coreauthor>
				<affiliation>Department of Biology, College of Education for Women, University of Kirkuk, Kirkuk, Iraq</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author></author_list>
				</article><article>
				<language>en</language>
				<article_id_issn></article_id_issn>
				<article_id_issn_online></article_id_issn_online>
				<article_id_pubmed></article_id_pubmed>
				<article_id_pii></article_id_pii>
				<article_id_doi></article_id_doi>
				<article_id_iranmedex></article_id_iranmedex>
				<article_id_magiran></article_id_magiran>
				<article_id_sid></article_id_sid>
				<title_fa></title_fa>
				<title>Quantitative Dynamic Susceptibility Contrast MRI for Non-Invasive Glioma Grading: The Role of rCBV and rCBF</title>
				<subject_fa></subject_fa>
				<subject></subject>
				<content_type_fa></content_type_fa>
				<content_type>Original Paper</content_type>
				<abstract_fa><![CDATA[]]></abstract_fa>
				<abstract><![CDATA[Introduction: Accurate preoperative grading of gliomas is essential for personalized treatment planning; however, conventional MRI often fails to reliably differentiate low-grade gliomas from high-grade gliomas due to overlapping imaging characteristics. Dynamic susceptibility contrast (DSC) MRI provides quantitative perfusion biomarkers that more directly reflect tumor vascularity and biological aggressiveness. This study aimed to evaluate the diagnostic utility of relative cerebral blood volume (rCBV) and relative cerebral blood flow (rCBF) for non-invasive glioma grading and to characterize regional perfusion patterns across distinct tumor compartments.Material and Methods: Thirty patients with histopathologically confirmed gliomas underwent preoperative structural and DSC-MRI. Quantitative rCBV and rCBF maps were generated using standardized acquisition and post-processing protocols. Regions of interest were placed within the contrast-enhancing tumor, peritumoral edema, tumor core, and contralateral normal-appearing white matter. Perfusion metrics were compared across tumor subregions and between glioma grades. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis.Results: High-grade gliomas exhibited significantly higher rCBV and rCBF than low-grade gliomas (p &lt;0.05). In high-grade gliomas, the enhancing region exhibited markedly increased perfusion relative to the contralateral hemisphere, whereas the tumor core showed significantly reduced values. No significant perfusion differences were observed within the edema region. ROC analysis demonstrated strong diagnostic performance for both biomarkers, with rCBV achieving the best diagnostic performance (AUC=0.941, 95% CI: 0.821, 0.992, p&lt;0.05).Conclusion: Quantitative rCBV and rCBF show promising potential as biologically meaningful indicators of glioma grade, reflecting vascularity-related tumor biology. These metrics enhance non-invasive tumor characterization and may assist in optimizing surgical planning and individualized therapeutic strategies.]]></abstract>
				<keyword_fa></keyword_fa>
				<keyword>magnetic resonance imaging, Dynamic Susceptibility Contrast, Glioma</keyword>
				<start_page>124</start_page>
				<end_page>131</end_page>
				<web_url>https://ijmp.mums.ac.ir/article_28419.html</web_url>
			<author_list><author>
				<first_name>Fateme</first_name>
				<middle_name></middle_name>
				<last_name>Shahedi</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>shahedif96@yahoo.com</email>
				<code>124841</code>
				<coreauthor>No</coreauthor>
				<affiliation>Department of Radiology Technology, School of Paramedical Sciences, Mashhad University of Medical Sciences, Mashhad, Iran</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>Hoda</first_name>
				<middle_name></middle_name>
				<last_name>Zare</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>zareh@mums.ac.ir</email>
				<code>124842</code>
				<coreauthor>Yes</coreauthor>
				<affiliation>Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author></author_list>
				</article><article>
				<language>en</language>
				<article_id_issn></article_id_issn>
				<article_id_issn_online></article_id_issn_online>
				<article_id_pubmed></article_id_pubmed>
				<article_id_pii></article_id_pii>
				<article_id_doi></article_id_doi>
				<article_id_iranmedex></article_id_iranmedex>
				<article_id_magiran></article_id_magiran>
				<article_id_sid></article_id_sid>
				<title_fa></title_fa>
				<title>Shuffle Attention–Enhanced ResNet50 for Robust Skin Lesion Classification on Dermoscopic Images</title>
				<subject_fa></subject_fa>
				<subject></subject>
				<content_type_fa></content_type_fa>
				<content_type>Original Paper</content_type>
				<abstract_fa><![CDATA[]]></abstract_fa>
				<abstract><![CDATA[Introduction: Skin cancer is a prevalent global malignancy, and despite CNN advances in automated diagnosis, challenges in feature discrimination, robustness, and performance stability remain.
Moreover, most previous studies primarily report single-run performance metrics, providing limited insight into the reproducibility and stability of deep learning models in clinically relevant settings.
Material and Methods: In this study, a Shuffle Attention–enhanced ResNet50 (SA-ResNet50) architecture was developed for automated skin lesion classification. The proposed model was systematically compared with baseline ResNet50 and ResNet50 integrated with the Convolutional Block Attention Module (CBAM). Experiments were conducted on two publicly available dermoscopic datasets, PH2 and HAM10000, using two data-partitioning strategies. To evaluate model robustness, independent-repeated ten times and K-fold cross validation methods were used for experiments, and performance variability was analyzed across repeated runs.
Results: The proposed SA-ResNet50 consistently outperformed the baseline models on both datasets while exhibiting lower performance variability. On the PH2 dataset, the proposed model achieved an accuracy of 98.9 ± 0.4%, sensitivity of 99.2 ± 0.3%, and specificity of 99.6 ± 0.2%. On the HAM10000 dataset with the first data partitioning strategy, SA-ResNet50 achieved an accuracy of 90.2 ± 1.6%, sensitivity of 85.7 ± 2.0%, and specificity of 92.8 ± 1.3%. In second strategy, the proposed model achieved an accuracy of 84.31±3.48%, Macro-F1 score of 71.86±7.23%, Balanced Accuracy of 70.29±9.36%, Matthews Correlation Coefficient (MCC) of 68.15±9.36, and an Area Under the ROC Curve (AUC) of 92.47±1.91, demonstrating robust performance on the highly imbalanced HAM10000 dataset with second data partitioning strategy.
Conclusion: The experimental results demonstrate that integrating the Shuffle Attention module into the ResNet50 backbone improves discriminative feature representation and enhances model robustness and convergence stability for dermoscopic skin lesion classification across benchmark datasets. These findings suggest that the proposed SA-ResNet50 constitutes an effective and reliable framework for automated skin lesion classification.]]></abstract>
				<keyword_fa></keyword_fa>
				<keyword>Skin Lesion Classification, Shuffle Attention, Deep learning, Dermoscopy</keyword>
				<start_page>132</start_page>
				<end_page>141</end_page>
				<web_url>https://ijmp.mums.ac.ir/article_28420.html</web_url>
			<author_list><author>
				<first_name>Seyed Vahab</first_name>
				<middle_name></middle_name>
				<last_name>Shojaedini</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>shojaeddini_va@yahoo.com</email>
				<code>124843</code>
				<coreauthor>Yes</coreauthor>
				<affiliation>Electrical Engineering Department, Iranian Research Organization for Science and Technology, Tehran, Iran</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>Amir</first_name>
				<middle_name></middle_name>
				<last_name>Nooraliei</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>amir.nooraliei@gmail.com</email>
				<code>124844</code>
				<coreauthor>No</coreauthor>
				<affiliation>Department of Computer and IT, Qa .c., Isalamic Azad university , Qazvin , Iran</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>Mahsa</first_name>
				<middle_name></middle_name>
				<last_name>Monajemi</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>mahsamonajjemi@gmail.com</email>
				<code>124845</code>
				<coreauthor>No</coreauthor>
				<affiliation>Department of Biomedical Engineering, Qa .c., Isalamic Azad university , Qazvin , Iran</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author></author_list>
				</article>
			</articleset>
			</journal>