Objective: Metabolic syndrome (MetS) significantly increases the risk of cardiovascular disease and is characterized by a combination of atherogenic dyslipidemia, central obesity, and hypertension. This study investigates additional factors contributing to MetS and their relevance by employing advanced analytical techniques.

Methods: Specifically, we utilized ensemble learning, a supervised learning approach that combines multiple individual models to enhance predictive performance. Input factors were ranked through feature selection techniques, while unsupervised methods were employed to mitigate biases associated with traditional labeling based on textbook criteria. An autoencoder was trained to reduce dimensionality without requiring prior knowledge of the data.

Results: Our findings reveal an impressive accuracy of 98.22% achieved by the gradient boosting classifier when evaluating 23 factors. Contrary to the expectation of identifying only five main factors as per the ATP III guidelines, our analysis highlights HOMA-IR, BMI, age, and cholesterol as critical predictors of MetS. Furthermore, the autoencoder's compressed representation uncovers a distinctive three-state classification, rather than the conventional binary classification of MetS presence or absence. Two clusters showed a strong correlation with MetS and non-MetS classifications, while the third cluster represents instances on the boundary of MetS diagnosis.

Conclusion: It is shown that obesity followed by high blood sugar and hypertriglyceridemia, insulin resistance (HOMA-IR), hypertension, to a lesser extent, age and cholesterol appeared to be the most essential features of the MetS prediction. Additionally, discovery of a distinct third cluster introduces a perspective on MetS, suggesting the potential for developing an early-staged nuanced patient management strategy.

Purpose: To evaluate the antibacterial efficacy of different concentrations of natural cold-pressed flaxseed oil when used as an intra-canal medicament against Enterococcus faecalis.

Materials and methods: The antibacterial efficiency of flaxseed oil against E. faecalis was assessed in two sections using different concentrations. Both sections were compared to calcium hydroxide and tricresol formalin. The first section was on the agar, using two methods: agar diffusion and vaporization. The second section is on the extracted roots contaminated with E. faecalis for 21 days to form biofilms, confirmed by SEM examination, and includes two different methods: direct contact and vaporization. Bacterial swabs were collected before and after medication throughout two-time periods (3 and 7 days). The canal contents were swabbed using paper points kept for 1 minute in the root canal, and the collected samples were diluted and cultivated on plates containing blood agar. Survival fractions were determined by calculating the number of colony-forming units on culture medium after 24 hours.

The oil's minimum inhibitory concentration (MIC) and minimal bactericidal concentration (MBC) against E. faecalis were determined using the micro-broth dilution method.

The active components in flaxseed oil were evaluated using GC-MS and HPLC analysis.

Results: The tested oil demonstrated antibacterial efficacy against E. faecalis in different concentrations and levels. The MBC was 22.5 µl/ml. Tricresol formalin induced powerful antibacterial action, while calcium hydroxide exhibited less effective antibacterial action as compared to flaxseed oil. Flaxseed oil contains numerous biologically active components.

Conclusion: Flaxseed oil exhibits strong antibacterial activity when evaluated against E. faecalis biofilm that has been cultivated in root canals.

Purpose: This article investigates the influence of testicular positioning and surrounding organ compositions on the absorbed dose in the testicles across a wide range of photon energies.

Materials and Methods: Using the Digimouse phantom in Geant4 with the mesh approach, the absorbed dose and deposited energy in mouse testicular tissue were calculated. Organ compositions followed ICRP Publication 145 guidelines. Four identical mono-energetic planar radiation sources (10 × 2.2 cm) emitting photons in the 2–10,000 keV range were positioned equidistantly around the mouse phantom at the head, tail, and both sides, 2 cm away, to ensure uniform irradiation. Simulations were conducted both with surrounding organs in anatomically accurate positions and with these organs replaced by air to assess their impact on dose distribution.

Results: Without surrounding organs, the absorbed dose was minimally influenced (<6%) by radiation source orientation. When surrounding organs were included, significant differences were observed, particularly at low photon energies (<25 keV), where notable radiation shielding occurred. Above 25 keV, adjacent organs increased energy deposition in testicular tissue due to secondary scattering, with absorbed dose differences between opposing orientations (e.g., head vs. tail) ranging from 30–92%. At 25 keV, surrounding organs did not affect energy deposition.

Conclusion: Surrounding organs significantly influence testicular absorbed dose, particularly at low photon energies where shielding dominates, and at higher energies where secondary scattering enhances deposition. These findings highlight the importance of considering organ interactions and source positioning in dosimetry to optimize radiation therapy protocols and reduce risks to sensitive organs.

Purpose: This study compares helical Tomotherapy in the supine position with three-dimensional Conformal Radiotherapy (3D-CRT) delivered in both prone and supine positions in pendulous left-breast cancer patients, aiming to achieve optimal target coverage while minimizing dose to Organs At Risk (OARs).

Materials and Methods: Twenty non-metastatic patients with large pendulous left breasts received three separate treatment plans (3D-prone, 3D-supine, and Tomo-supine). Dose–Volume Histogram (DVH)-based indices, including D_mean, V5Gy, V10Gy, V20Gy, and V30Gy for the heart, ipsilateral lung, contralateral lung, and contralateral breast, as well as CI and HI for the PTV, were evaluated. Plans were generated using TIGRT (3D-CRT) and Accuray Precision® Tomotherapy TPS. Statistical analysis was performed using paired t-tests, and p < 0.05 was considered significant.

Results: Tomotherapy achieved superior target conformity and homogeneity (higher CI and lower HI) and slightly increased PTV D_mean compared to 3D-CRT. However, it increased low- and intermediate-dose exposure to the ipsilateral lung and contralateral organs while significantly reducing high-dose volumes. The prone 3D-CRT approach demonstrated the lowest ipsilateral lung mean dose among all techniques.

Conclusion: While tomotherapy provides excellent PTV coverage and dose uniformity, it increases low-dose exposure to OARs compared with 3D-CRT. Therefore, the choice of technique should be individualized based on clinical priorities, particularly in younger patients, where secondary cancer risk may be of concern.

Purpose: Beta-emitting radionuclides used in liver cancer therapy generate cytotoxic effects and Cherenkov radiation. This study aims to evaluate the potential of Cherenkov radiation to induce localized hyperthermia in hepatic tumors during radionuclide therapy.

Materials and Methods: Monte Carlo simulations were performed using the GATE platform to model Cherenkov radiation transport and heat deposition in hepatic tumor tissue. The absorbed thermal dose was quantified using the integrated bioheat transfer model, allowing accurate voxel-level mapping of temperature distribution. Six beta-emitting radionuclides, including ³²P, ⁹⁰Y, ¹⁶⁶Ho, ¹⁸⁸Re, ¹⁷⁷Lu, and ¹³¹I, were evaluated to assess their potential for inducing thermal effects through Cherenkov radiation absorption during liver radionuclide therapy.

Results: Among the radionuclides studied, ³²P and ⁹⁰Y generated the highest number of Cherenkov photons in the liver tumor, resulting in significant heat deposition and uniform tumor temperatures ranging from 41 to 49°C, consistent with mild hyperthermia and, in the case of ³²P, partial thermal ablation with approximately 20% of the tumor volume exceeding 60°C. ¹⁶⁶Ho induced moderate heating, raising tumor temperatures to around 41°C in most of the tumor volume. In contrast, radionuclides with lower beta energies, such as ¹⁷⁷Lu, ¹³¹I, and ¹⁸⁸Re, produced minimal Cherenkov photon emission, resulting in negligible thermal effects within the tumor.

Conclusion: The integration of radionuclide therapy with Cherenkov radiation-induced hyperthermia presents a promising strategy for radiosensitization and thermal ablation in hepatocellular carcinoma.

Purpose: The main goal of radiotherapy is to deliver a lethal radiation dose to tumor tissue while minimizing the dose to healthy tissues. Treatment planning in radiotherapy requires precise determination of the treatment volume and specification of the radiation dose to both the tumor and healthy tissues. To define the treatment volume in radiotherapy, margins are considered around the tumor tissue, which may include a portion of normal tissue. Fusion of PET-CT images with CT images in the three-dimensional conformal radiotherapy for lung cancer may improve treatment by more accurately and precisely determining the Gross Tumor Volume (GTV). This study aimed to compare the treatment volumes and dosimetric parameters between conventional treatment planning (using CT images only) and treatment planning using PET-CT image fusion in 3D-conformal radiotherapy for lung cancer.

Materials and Methods: All lung cancer patients who were referred to our Radiotherapy center over two years were examined. PET-CT images and simulation CT scans of 15 patients with non-metastatic lung cancer were analyzed. All patients were treated using the 3D-conformal radiotherapy method. The treatment planning and image fusion were performed using the ISOGRAY treatment planning software. The volumetric as well as dosimetric parameters, such as mean dose, maximum dose in the target volume, and other dose-volume parameters in organs at risk such as lung tissue including V5, V13, and V20 were compared between the two groups (including the conventional treatment planning group (only using CT data) and the treatment planning using the PET-CT image fusion group).

Results: A total of 23 lung cancer patients were included during the study period; of these, eight patients were excluded due to having metastatic lung cancer. The variation in Gross Tumor Volume (GTV) among the different patients was significantly high. The fusion of PET images with CT scans increased the GTV in 11 patients (on average by 48±89.7%) and decreased it in 5 cases (46.4±98.0%). According to the results (considering all patients), no statistically significant difference was noted in the Contoured Tumor Volume (CTV) between the conventional method (based on CT images only) and the method based on the fused images with PET-CT images (P-value > 0.05). There was no statistically significant difference in maximum dose at healthy tissues/Organs At Risk (OARs), including the ipsilateral lung, contralateral lung, skin, and spinal cord, between treatment planning based on CT images and based on fusion with PET-CT data (P-value > 0.05). The mean dose in the lung (in involved side %43±.16.66) decreased (on average by %41±21.00) after fusion with PET-CT images (P-value > 0.05).

Conclusion: The use of PET-CT data in radiotherapy treatment planning for lung cancer patients undergoing adaptive 3D radiotherapy can have an improving and impactful role. The fusion of PET-CT images with CT data had a significant effect on the tumor volume definition, resulting in changes in the gross tumor volume in most patients. It is recommended to utilize data from both anatomical (CT) and functional (PET) imaging modalities for a better assessment and definition of tumor volume, as each modality has its advantages and limitations. However, by combining them, the tumor volume can be determined with greater precision and accuracy. Additionally, improving the precision in defining tumor volume can reduce the radiation dose received by healthy tissues/organs at risk

Purpose: Osteoporosis is a condition where bone density decreases, impacting bone quality and increasing susceptibility to fractures. Diagnosis typically involves imaging techniques like DEXA scan. To enhance early detection, new predictive algorithms are essential due to limitations in clinical diagnostic software. This study aims to predict osteoporosis with various machine learning (ML) algorithms and pinpoint factors contributing to the disease based on DEXA scans of the femoral neck and lumbar spine.

Methods: In this study, we analyzed the data from 1000 people who were encountered for densitometry at the Rheumatology Clinic in a major metropolitan general hospital for predicting osteoporosis, we used five classification algorithms, including Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Logistic Regression (LR). We evaluated the performance of models using four metrics, including accuracy, sensitivity, specificity, and Area Under the Receiver Operating characteristic Curve (AUROC). All of programing was done using the Python programming language in the Google Colab environment.

Results: Out of 1000 patient records, there were 761 women and 239 men, with a 58.42 mean age. Osteoporosis occurred in 23.5% of cases. ANN and RF, with 89% and 78.6%, had the highest sensitivity, respectively. ANN and SVM, with 96.3% and 94.2%, had the highest specificity. In accuracy, ANN and RF, with 94.6% and 86.5%, were the highest. Based on the AUROC, the ANN method achieved the best performance (0.937), followed by RF (0.837), LR (0.832), SVM (0.769), and DT (0.715).

Conclusion: The ANN model emerged as the strongest performer, achieving high sensitivity, specificity, and overall accuracy. The study’s findings hold promise for enhancing the earlier diagnosis of osteoporosis. Machine learning algorithms can provide an alternative approach to identifying and screening individuals at high risk for osteoporosis and can be used in the development of clinical decision support systems for the diagnosis of osteoporosis.

Background: A very effective treatment for lung cancer is radiation therapy. The goal of this study was to find a better way to treat lung cancer with radiation, and it did so by combining three different types of radiation: conventional radiation treatment, intensity-modulated radiation therapy (IMRT), and volumetric modulated arc therapy (VMAT).

Patients and methods: Thirty people with a diagnosis of unilateral lung cancer participated in this study. Patients were treated using ELEKTA's linear accelerator with 6 MV or 10 MV energy x-ray photon beams. To plan their therapies, the patients underwent a CT simulation using MONACO version 5.1.

Results: In the comparison of VMAT with IMRT, the former demonstrates superiority in several aspects, including enhanced protection of the heart and spinal cord, reduced segments and monitoring units, and more comprehensive coverage of the projected target volume (PTV).

Conclusion: VMAT demonstrates superior results compared to IMRT in administering radiation to lung tumors while preserving the heart and spinal cord.

Purpose: Diseases are unavoidable because of environmental factors, changes in diet, hereditary issues and many other factors; hence, it is important to detect diseases via various techniques in the healthcare sector to identify and diagnose the disease. Therefore, the proposed model focuses on employing advanced techniques for detecting heart disease, thyroid disease, and hepatitis, as these diseases have become common in recent years, along with the prediction of heart rate.

Materials and Methods: The proposed work employs modified PCA (principal component analysis) for dimensionality reduction to extract appropriate features for the model by utilizing two learning rates (LR1 and L2). Furthermore, the modified back propagation (BP) method is used for effective classification of heart, thyroid, hepatitis, and heart rate prediction by incorporating adaptive Gaussian white noise (AWGN). In the proposed model, three different datasets are utilized: a heart disease dataset, a thyroid dataset, a hepatitis dataset for classification, and a heart rate prediction dataset for regression.

Results: The accuracy, precision, recall, and F1 scores obtained by the proposed model for the heart disease dataset are 97.8%, 98%, 98%, and 98%, respectively. Similarly, 97.2%, 98%, 89%, and 93% for the thyroid dataset, respectively. Finally, the accuracy, precision, recall, and F1 score obtained by the proposed model for hepatitis are 95%, 98%, 88%, and 92%, respectively. Like the classification of diseases, heart rate prediction was also evaluated via different metrics, such as the RMSE, MSE, MAE, and R2. The MAE obtained by the proposed model for the heart rate prediction dataset is 0.112; likewise, the R2 obtained is 0.99, the MSE attained is 0.022, and the RMSE value obtained is 0.1488.

Conclusion: The results of the proposed mechanism reflect its ability to detect different diseases effectively. This is due to the successful implementation of advanced AI approaches in the proposed framework.

Background: Small electron fields are used in radiotherapy for superficial tumors and areas close to the skin. However, the impact of tissue heterogeneity on dose distribution in these fields poses considerable challenges. To explore how the variability of cold foam affects dose distribution in small electron fields using Semiflex 3D and Advanced Markus dosimeters.

Methods: Dosimetric measurements were performed using an Elekta Vera-HD linear accelerator with 10 MeV and 12 MeV electron beams. Square field sizes of 2×2, 3×3, 4×4, 5×5, and 6×6 cm² were investigated. Dose distributions were assessed using Semiflex 3D and Advanced Markus ionization chambers. Percentage Depth Dose (PDD) curves were analyzed, revealing that at 10 MeV, the depth of maximum dose (d_max) was 2.2 cm, while at 12 MeV, it increased to 2.7 cm.

Results: The results confirm that the OF increases with both field size and beam energy. Larger field sizes enhance lateral electron scattering, and higher beam energy enables deeper penetration and broader dose distribution, further increasing the OF. A minimum field size of 3 cm × 3 cm is recommended, as differences between dosimeters were observed in 2 cm × 2 cm and 3 cm × 3 cm fields, but remained below 2% for larger fields. The study also found that increasing heterogeneity reduces the OF, with air-equivalent heterogeneities consistently decreasing the OF across all field sizes and energy levels.

Conclusion: Monte Carlo algorithms in treatment planning systems (TPS) model heterogeneities using CT images and Hounsfield Unit (HU) values. However, their accuracy depends on CT image quality and device calibration, and HU-to-parameter conversion may not fully account for tissue variability. Combining computational simulations with experimental validation is recommended to improve TPS accuracy, especially in electron mode and when dealing with heterogeneities.

Purpose : Computed tomography (CT) is an essential diagnostic imaging modality in pediatric medicine; however, exposure to ionizing radiation poses a potential risk of radiation-induced malignancy. This multicenter study evaluated organ-specific radiation doses and estimated lifetime cancer risks in pediatric patients undergoing chest and abdominal CT imaging across multiple Iraqi healthcare centers.

Material & Methods: Data from 200 pediatric patients (100 chest CT, 100 abdominal CT) were collected from three hospitals in Dohuk, Mosul, Anbar, and Baghdad, Iraq. CT acquisition parameters (kilo-voltage (kVp), pitch, slice thickness, scan length) and dose metrics (CTDI_vol, DLP, effective dose) were recorded. Organ doses to lungs, thyroid, breast, and heart were estimated using imPACT software based on scanner-reported parameters. Lifetime Attributable Risk of cancer incidence was calculated using BEIR VII risk models with linear interpolation for pediatric age groups (1–5, 6–10, 11–15 years).

Results: Radiation dose metrics increased significantly with age. Mean CTDI_vol ranged from 8.1 ± 1.1 mGy (1–5 years) to 20.8 ± 1.2 mGy (11–15 years), with effective doses of 3.2 ± 0.3 to 6.7 ± 0.5 mSv. Organ doses demonstrated parallel trends: lung doses 4.8–8.9 mSv, thyroid doses 4.3–8.0 mSv. Cancer risk was highest in younger children (LAR: 0.16 ± 0.02 cancers/10,000 person-years/mSv), decreasing with age but remaining clinically significant.

Conclusions: This study demonstrates that as children age and body size increase, CT dose and cancer risk increase; therefore, dose optimization and the use of appropriate protocols are essential.

Purpose: Language and memory impairments are common in patients with temporal lobe epilepsy (TLE). Precise identification of language and memory areas is crucial for both identifying the seizure zone and predicting the preservation or recovery of cognitive functions after brain surgery. Functional MRI (fMRI), with its unique ability to elucidate the brain's compensatory mechanisms in response to epilepsy, aids in visualizing brain activity and reveals how different parts of the brain have adapted.

Methods: We conducted a study on 22 left TLE and 13 right TLE patients, as well as 17 healthy control subjects, using task-based fMRI for memory and language mapping. This was done to examine the effective areas responsible for language and memory functions and detect abnormal activations. All participants underwent the CANTAB® neuropsychological test.

Results: We discovered a significant increase in the participation of the left temporal lobe and parahippocampal gyrus in the encoding of non-verbal memory, and increased activation in the left precuneus and right parahippocampal gyrus during the retrieval of non-verbal memory (P < 0.05). In verbal memory tasks, increased participation of the frontal lobes in the encoding and retrieval of verbal memory, along with a significant contribution from the left cingulate gyrus during memory retrieval, indicates compensatory mechanisms (P < 0.05).

Conclusions: Our findings suggest that, compared to healthy controls, TLE patients exhibited activation in more diverse brain regions during language tasks, yet achieved similar functional test results.

Purpose: Defective tube or miscalibration has been implicated as the causes of Computed Tomography (CT) ring artifacts, implying that images acquired with such scanner should demonstrate ‘’ring(s)’’ artefact. However, ‘’ring(s)’’ artefact is frequently seen in moderate to severe hydrocephalic images but lacking on non-hydrocephalic images acquired using same machine. Hence we investigate causes of persisting ‘’ring(s)’’ artifact demonstrable on hydrocephalic CT images.

Materials and Methods: The research included hydrocephalic and hydrocdphalous mimicking phantom CT images with ‘’ring’’ band(s) designated as inner, middle and outer band, and non-hydphalous CT image without ‘’ring’’ band divided into four quadrants - anterior right (AR), anterior left (AL), posterior right (PR), and posterior left (PL). The CT number was measured at four perpendicular points per ‘’ring’’ and randomly at four points in each quadrant. CT number measurement was carried out using DICOM image viewer version 22504.418.1.0., Region of Interest (ROI) = 0.10cm²), and with the outermost ‘’ring’’ band taken control, the relationships between CT number mean values between ‘’ring’’ band, and each quadrant was analyzed.

Results: Dunnette multiple comparison test shows a significant difference in CT numbers between the Inner, Middle and Outer ‘’ring’’ bands with mean pixel values -1.194, -3.167, and 0.007 for hydrocephalous mimicking phantom image. Substantial difference in CT numbers between the Outer and Middle ‘’ring’’ band and similarity between the Outer and Inner ‘’ring’’ band on hydrocephalic image was also found with mean pixel values of 2.444, -1.593 and 2.222. For the non-hydrocephalic images, similarity in CT numbers across the four quadrants was noted with mean pixel values of AR = 17.89, AL=18.56, PR=17.17, and PL=14.00.

Conclusion:  CT ring artefacts may be patient based, indicating onset of pathologic process and diagnostic indicator.

Purpose: Emotion detection using electroencephalogram (EEG) signals has attracted increasing research attention due to its potential applications in affective computing and brain–computer interfaces. However, the inherently non-linear and non-stationary nature of EEG signals poses major challenges for designing a deep network framework capable of achieving high recognition accuracy. To address this, we propose a hybrid deep learning architecture that integrates one-dimensional convolutional neural networks (1D-CNNs) with gated recurrent units (GRUs) and long short-term memory networks (LSTMs) to enhance feature extraction and emotion classification.

Materials and Methods: Experiments were conducted on the DEAP dataset, with a special emphasis on decreasing the number of EEG channels from the full set of 32 to 8 and 4, while maintaining robust performance. CNN layers were utilized to capture spatial dependencies, whereas GRU and LSTM layers modeled temporal dynamics. To optimize performance, the Adamax optimizer and exponential linear unit (ELU) activation function were applied, ensuring stable convergence and efficient training.

Results: For an eight-channel EEG configuration, the proposed 1D-CNN-LSTM/GRU models achieved accuracies of 99.92% and 99.82%, respectively. When the input was further reduced to four channels, the models maintained a strong classification capability, achieving accuracies of 96.38% and 96.53%, respectively. These findings demonstrate that a considerable reduction in EEG channels does not necessarily compromise recognition performance.

Conclusion: The proposed hybrid framework successfully balances accuracy and efficiency, showing that EEG-based emotion recognition can achieve near-perfect performance with fewer channels. This reduction lowers computational cost, simplifies system design, and enhances practicality for real-world applications. Future work will involve applying the framework to alternative datasets and exploring adaptive channel selection strategies to further generalize its applicability.

Objective: The aim of this preclinical study was to evaluate the dosimetry and radiation safety of [⁶⁴Cu]Cu-DOTATATE, a promising PET radiopharmaceutical for imaging SSTR2-positive neuroendocrine tumors (NETs), and to establish its potential for clinical translation.

Materials and Methods: [⁶⁴Cu]Cu-DOTATATE was synthesized by radiolabeling DOTATATE with [⁶⁴Cu]CuCl₂, and its radiochemical purity was assessed using HPLC and RTLC. Biodistribution studies were conducted in rats at 2, 4, 12, and 24 h post-injection, and the organs' radioactivity was measured using an HPGe detector. Dosimetry calculations were performed based on the MIRD schema using animal biodistribution data to estimate absorbed doses in human organs.

Results: [⁶⁴Cu]Cu-DOTATATE demonstrated excellent radiochemical purity (>99%) and stability over 12 h in PBS and human serum. Biodistribution analysis showed rapid blood clearance and high uptake in SSTR2-expressing organs, such as the pancreas and adrenals, with renal excretion as the primary elimination route. The estimated absorbed doses in key organs were highest in the pancreas (0.205 mGy/MBq), followed by kidneys and liver. Comparative dosimetry with other SSTR-targeted agents, like ⁶⁸Ga-DOTATATE, revealed similar or lower radiation doses in non-target organs.

Conclusion: [⁶⁴Cu]Cu-DOTATATE exhibited favorable pharmacokinetics and an acceptable radiation dose profile, supporting its potential as a safe and effective PET imaging agent for SSTR2-positive NETs. The extended half-life of [⁶⁴Cu] enhances its clinical utility with improved tumor-to-background contrast and centralized production logistics, paving the way for its use in clinical settings.

Background: Radiosynovectomy (RSV) is a therapeutic modality for inflammatory joint diseases, traditionally employing beta-emitting radionuclides. However, the clinical potential of alpha-emitting radionuclides has not been extensively explored. This study seeks to compare the dosimetric profiles of alpha (225Ac and 227Th) and beta-emitting radionuclides (32P, 188Re, and 90Y) within a voxelized knee joint phantom.

 Methods: A three-dimensional graphic model of the knee joint was transformed into a high-resolution voxelized phantom. Monte Carlo simulations were performed to assess the distribution of absorbed doses within critical anatomical structures: the joint cavity, synovial membrane, articular cartilage, and bone. Dose-Volume Histograms (DVHs) were generated to analyze the spatial distribution of radiation doses for each radionuclide, with maximum and mean absorbed doses and the Homogeneity Index (HI) calculated

Results: The alpha-emitting radionuclides demonstrated markedly superior maximum and mean absorbed doses in comparison to their beta-emitting counterparts. Notably, 227Th provided the highest doses (Dmax: 266.10 Gy/MBq in the joint cavity), while 225Ac exhibited considerable localized energy deposition. The HI values for alpha emitters (0.34 for 227Th) indicated a greater degree of dose uniformity compared to beta emitters (e.g., HI of 4.30 for 90Y). Nevertheless, the use of alpha emitters resulted in increased radiation exposure to surrounding tissues, raising concerns about their appropriateness for the treatment of rheumatoid arthritis.

 Conclusions: This study underscores the dosimetric advantages of alpha-emitting radionuclides in terms of localized dose delivery and uniformity within targeted tissues. However, the risk of heightened exposure to non-target tissues suggests that beta-emitting radionuclides may continue to be the preferred option for clinical applications in RSV.

Abstract

Introduction: Radiation shielding is a critical concern in angiography, where minimizing ionizing radiation exposure is essential for both patients and medical personnel. Composite materials incorporating high-Z nanoparticles into polymer matrices offer promising lead-free alternatives with enhanced flexibility and radiation absorption capabilities. This study aimed to design and evaluate silicon polymer-based composite shields reinforced with binary nanoparticle combinations for angiography applications.

Methods: MCNPX Monte Carlo simulations were conducted to calculate mass attenuation coefficients (MAC) across the angiography energy range (20–140 keV) for composites containing Bi-W, Bi-Ba, and W-Ba nanoparticles at concentrations of 10%, 20%, and 40%. Simulation results were validated against standard XCom reference data.

Results: Excellent agreement was observed between MCNPX simulations and XCom data, confirming the validity of our computational approach. Higher metal concentrations consistently yielded superior shielding performance at low X-ray energies (20–60 keV), with the Bi-W 40% composite achieving the highest MAC of 25.49 cm²/g at 20 keV. Distinct K-edge effects were identified for tungsten (69.5 keV) and bismuth (90.5 keV), contributing to enhanced attenuation at specific energies. However, the attenuation advantage of denser composites diminished at higher energies (100–140 keV), indicating energy-dependent performance.

Conclusion: Silicon polymer composites reinforced with binary high-Z nanoparticles, particularly Bi-W 40%, demonstrate significant potential as lightweight, lead-free shielding materials for angiography. The validated MCNPX approach provides a reliable framework for optimizing nanoparticle-filled composites. 

Background: Radiotherapy is a cornerstone of breast cancer (BC) treatment, but its efficacy is often limited by radioresistance and toxicity to adjacent healthy tissues. This study aimed to investigate, the novel synergistic radiosensitizing effects of combining gold nanoparticles (GNPs) and curcumin (CUR) with ionizing radiation on MCF-7 breast cancer cells.

Materials and Methods: MCF-7 (breast cancer) and L929 (healthy murine fibroblast) cells were treated with various concentrations of CUR and GNPs, alone and in combination, with and without radiation. Cytotoxicity was evaluated using the MTT assay, and dose-response curves were generated. The half-maximal inhibitory concentration (IC₅₀) of CUR was calculated via the median-effect equation using CompuSyn software. Lactate levels were measured using a Lactic Acid Assay Kit, and superoxide dismutase (SOD) enzyme activity was assessed by colorimetric method.

Results: CUR and GNPs both reduced cell viability in a dose-dependent manner, with MCF-7 cells showing significantly higher sensitivity than L929 cells. The combined treatment of CUR + GNPs + radiation exhibited a marked synergistic cytotoxic effect, significantly greater than any single or dual treatment (p < 0.05). This combination also induced apoptosis, substantially decreased lactate levels (indicating metabolic disruption), and reduced SOD activity in cancer cells.

Conclusion: The triple combination of gold nanoparticles, curcumin, and radiation demonstrates a novel and selective synergistic radiosensitizing effect against breast cancer cells while sparing normal cells.

Abstract

Introduction: Photodynamic therapy (PDT) has attracted considerable attention as a non-invasive and selective method for cancer treatment. In this study, the photodynamic effects of nanomicelles loaded with the dye toluidine blue (TB) were evaluated on human lung cancer (QU-DB) and non-cancerous (MRC-5) cell lines. Nanomicelles, as biocompatible carriers, possess the ability to improve solubility, stability, and targeted accumulation of a photosensitizer (PS), while showing enhanced tumor tissue accumulation through the enhanced permeability and retention (EPR) effect.

Methods: The TB-loaded nanomicelles were synthesized using the direct dissolution method and underwent physicochemical characterization. Then, cells were irradiated with laser red light (λ = 655 nm) at doses of 10, 20, and 40 J/cm². Post-irradiation, cell viability was quantified via the MTT assay, enabling the calculation of key therapeutic indices such as IC₅₀, ED₅₀, and the synergy index (SI).

Results: The results demonstrated that encapsulating TB in nanomicelles increased optical absorption, reduced dye aggregation, and consequently enhanced reactive oxygen species (ROS) generation efficiency. The photodynamic effect of TB-loaded nanomicelles was significantly stronger in the QU-DB cancer cell line compared with free TB, while inducing lower toxicity in the non-tumor MRC-5 cells. Thus, the selective distinction between tumor and non- cancerous cell lines was confirmed. Furthermore, increasing light dose resulted in greater cytotoxicity, with dose-dependent reductions in cell survival. However, no favorable selectivity index was obtained when comparing micellar drug cytotoxicity with the free PS (p > 0.05).

Conclusion: TB-loaded nanomicelles represent a promising drug delivery system to enhance the efficacy and selectivity of PDT in lung cancer while reducing its off-target effects.

Purpose: Recent studies signify that epilepsy has become significant burden to the society due to its unpredictable nature and high cost of treatment. It has been observed that, an integrated neural investigation system is required to aid epileptologist for early diagnosis as well as long-term monitoring of patients. Electroencephalogram (EEG) plays a significant role to study brain’s electrical activity such as epilepsy. An epileptic EEG signal have four necessary distinctive characteristics- sudden changes in signal, amplitude deviations, electro-cerebral negativity, and physiological fields formation by affected EEG channels.
Materials and Methods: In this paper, we proposed an automated system for epileptic seizure prediction using above mentioned characteristics. In the proposed system, the very low-lying amplitude deviation and rhythmic irregularities were identified in EEG signal. The proposed work is based on extraction of features of EEG signal and structure of brain. The proposed system provide dual execution mode ( detection and analysis ) and is able to predict the epileptic seizure waveforms and specific brain regions that will be affected by upcoming seizures.
Results: The proposed system accurately identifies various seizure waveforms in EEG signals of primary dataset and achieves an excellent performance (92.66% accuracy, 94.86% F1 score) in prediction of epileptic seizures and in case of secondary dataset it show 100% accuracy.
Conclusion: Advantage of the proposed system lies on generation of a detailed report on upcoming seizure location and nature prior to the seizure which mitigate patient life risks. This advancement could lead to substantial improvements in the field of neuroscience.

Purpose: Non-alcoholic fatty liver disease (NAFLD) is a prevalent chronic liver condition. Non-invasive imaging techniques are being developed to replace biopsies. In liver steatosis, tissue stiffness decreases, making stiffness assessment crucial for early diagnosis. This study evaluated the ability of two elastography methods—point shear wave (P-SWE) and two-dimensional shear wave (2D-SWE)—to differentiate healthy from fatty liver tissue.

Materials and Methods: Twenty-seven individuals (9 men and 18 women, aged 20-50) without liver disease were examined. Liver stiffness was measured using both P-SWE and 2D-SWE in 15 healthy individuals and 12 with fatty liver. Statistical analysis was conducted using an independent t-test (p<0.05).

Results: The mean and standard deviation of liver stiffness (Young modulus) were 4.33±1.01 kPa for fatty liver and 3.55±0.90 kPa for healthy liver using P-SWE (18% increase, p<0.05). For 2D-SWE, stiffness was 5.03±1.44 kPa in fatty liver and 4.57±1.10 kPa in healthy tissue, with no significant difference (p>0.05). The P-SWE method showed a sensitivity of 50% and specificity of 80% for diagnosing grade I hepatic steatosis.

Conclusion: The P-SWE technique is effective in differentiating fatty liver tissue stiffness from healthy liver, more so than the 2D-SWE method.

Background: Head and neck CT examinations constitute more than 50% of all CT scans in Iran, and their utilization has dramatically increased in recent years, raising concerns about population radiation burden.

Objectives: This study aimed to estimate organ doses, effective dose, and lifetime attributable risk (LAR) of cancer in Iranian patients undergoing routine head and neck CT using Monte Carlo-based dosimetry.

Methods: In this retrospective cross-sectional study, 429 patients (215 females, 214 males; mean age 45.5 ± 17.2 years) from nine imaging centers (2020–2022) were evaluated. Organ and effective doses were calculated using NCICT software with size-specific dose estimate (SSDE) adjustment. LAR of cancer incidence and mortality was estimated using BEIR VII models, adjusted for Iranian life-tables.

Results: Mean CTDIvol and DLP were 36.36 ± 17.17 mGy and 542.44 ± 292.56 mGy·cm, respectively. The mean effective dose was 1.23 ± 0.67 mSv. Proposed national diagnostic reference levels (75th percentile) were 48.31 mGy for CTDIvol and 752 mGy·cm for DLP. The highest organ doses were received by the brain (22.36 mGy) and salivary glands (19.84 mGy). The total LAR of cancer incidence was 181.82 per 100,000 (95% CI 174.13–189.51); females showed a 15.5% higher risk than males (p < 0.01). The "other cancers" category (mainly brain and salivary glands) contributed 98% of the total risk.

Conclusion: Individual risk is low, but high CT frequency (>10 million/year) may cause thousands of radiation-induced cancers. Recommend ALARA adherence, protocol optimization, and national DRLs.

Purpose: Accurate target delineation is essential for radiotherapy planning in glioblastoma. While CT provides the electron density information required for dose calculation, MRI offers superior soft-tissue contrast and may improve tumor delineation.

Materials and Methods: Computed Tomography (CT) is routinely used for radiotherapy treatment planning; however, Magnetic Resonance Imaging (MRI) provides superior soft-tissue contrast. This study evaluated the improvement of treatment planning in glioblastoma patients by fusing MRI and CT images, with a focus on changes in Clinical Target Volume (CTV) and radiation dose to the eye region. Thirty patients with histologically confirmed glioblastoma underwent both CT and MRI scans. Two plans were generated per patient: MRI–CT fused (dual-modality) and CT-only (single-modality). Demographics, tumor location, CTV, and eye dosimetry (maximum dose, mean dose, D0.03cc, dose–volume histogram metrics) were collected. CT and MRI protocols, registration, and fusion procedures were performed. Statistical comparisons the Wilcoxon signed-rank test, with significance set at P ≤ 0.05 and 95% Confidence Intervals (CI).

Results: Image fusion significantly reduced CTV in both phases (initial: 443.45±190.64 vs 200.19±133.67 cm³; mean difference −243.27 cm³ [95% CI −316.82, −169.71], P<0.001; boost: 265.72±140.72 vs 84.28±57.48 cm³; mean difference −181.44 cm³ [95% CI −232.92, −129.96], P<0.001). Eye doses were consistently lower with fusion: left eye D_mean (initial: −225.06 cGy, P=0.006; boost: −68.05 cGy, P=0.007), left eye D_max (initial: −620.44 cGy, P=0.010; boost: −142.14 cGy, P=0.015), right eye D_mean (initial: −268.73 cGy, P=0.006; boost: −89.08 cGy, P=0.013), right eye D_max (initial: −662.06 cGy, P=0.004; boost: −135.52 cGy, P=0.024). Across endpoints, 70–93% of cases favored CT–MRI plans.

Conclusion: In 3D-CRT for glioblastoma, CT–MRI fusion improves anatomical specificity, enabling smaller CTVs and meaningfully lower ocular doses. These findings support routine MRI fusion for safer, more conformal planning. MRI–CT fusion enhances target delineation in glioblastoma but may increase eye dose; careful organ-at-risk sparing is warranted.

Abstract

Objective: To evaluate the effectiveness of polymer-based shields containing boron compounds for radiation protection in medical centers, focusing on their performance against neutron and gamma radiation.

Methods: A comprehensive literature review was conducted using databases including PubMed, Scopus, Web of Science, and Embase. Studies published from 2010 to February 2025 were included. The search strategy employed keywords related to polymer-based shields, boron compounds, and radiation protection in medical settings.

Results: Boron-containing polymers demonstrated significant potential for radiation shielding, particularly against neutrons. Nanocomposites incorporating high-Z elements showed improved gamma radiation attenuation. Hexagonal boron nitride (h-BN) nanocomposites exhibited superior neutron absorption properties. Epoxy-based composites with various nanoparticles showed enhanced protection against both neutron and gamma radiation. Recycled high-density polyethylene (R-HDPE) composites containing gadolinium oxide demonstrated promising thermal neutron shielding capabilities.

Conclusion: Polymer-based shields containing boron compounds offer lightweight, flexible, and effective alternatives to traditional shielding materials. These materials show particular promise in medical applications, potentially improving safety for both patients and healthcare providers. However, challenges remain in optimizing material composition, thickness, and long-term stability for practical implementation in clinical settings.

Abstract

Background and Purpose: Radiation therapy of the head and neck (HNC RT) is often associated with xerostomia and dysphagia, making it extremely important to predict these toxicities. This review aims to evaluate the Lyman-Kutcher-Burman (LKB) model against alternatives such as relative seriality (RS) for estimating normal tissue complication probability (NTCP) to improve the management of HNC RT-induced side effects.

Methods: An Extensive literature search was carried out in PubMed, Embase, Web of Science, Google Scholar, and Cochrane Library to trace published studies between 1991 to 2023 on the above topic. Studies were included if they presented quantitative NTCP models for xerostomia and dysphagia induced by radiation therapy. The key LKB parameters (TD₅₀, m, n) were extracted, and comparative analyses were then carried out with the RS and clustering methods based on clinical and dosimetric data.

Results: 60-90% of the cases of HNC RT are reported to have xerostomia, which is raised to 100% when salivary glands are involved. Dysphagia occurs in 50-70% of cases, which increases to 90% with chemotherapy. Xerostomia occurs at doses above 40 Gy, and dysphagia at doses above 60 Gy. These are reduced by up to 50% with intensity-modulated radiation therapy (IMRT) and swallowing exercises. The prediction of radiobiological models is at around 75-85% for xerostomia and 70-80% for dysphagia. LKB prediction accuracy is at 70-75%; RS achieves 75-80%. Machine learning models show 85-90% accuracy.

Conclusion: Advanced radiation therapy techniques, in combination with validated toxicity prediction models, can improve treatment personalization and reduce the incidence of radiation-induced complications in HNC patients. Future research should focus on refining predictive models, investigating long-term treatment outcomes, and developing novel strategies to protect normal tissues.