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: The present study aimed to evaluate the effectiveness of incorporating of nanohydroxyapatite in to hydrogen peroxide bleaching material on color, microhardness and morphological features of dental enamel.

Materials and Methods: 33 sound maxillary first premolar were used for the study. Enamel blocks (7mm× 5mm×3mm) were prepared from the middle third of buccal halves of each tooth. Each dental block was embedded in self-curing acrylic resin with exterior enamel surface exposed for various applications. The dental blocks were  randomly divided into three groups (n=11) according to the bleaching technique. The groups were designed as follows: control; hydrogen peroxide (HP) and hydrogen peroxide with nanohydroxyapatite (HP-nHAp) groups. Color measurements and microhardness tests were conducted before and after treatment. one sample represented each group was selected for morphological analysis.

Results: The  results showed  that both HP and HP-nHAp groups induced color changing. Enamel microhardness loss of HP group was significantly higher than that of HP-nHAp and control groups. The enamel morphological changes was only observed in HP group.

Conclusion: nHAp could significantly reduce the enamel microharness loss caused by HP while preserving enamel surface morphological features without affecting bleaching efficacy.

Purpose: Integrating magnetic Nanoparticles (NPs) into contrast-enhanced Magnetic Resonance (MR) imaging can significantly improve the resolution and sensitivity of the resulting images, leading to enhanced accuracy and reliability in diagnostic information. The present study aimed to investigate the use of targeted trastuzumab-labeled iron oxide (TZ-PEG-Fe₃O₄) NPs to enhance imaging capabilities for the detection and characterization of Breast Cancer (BC) cells.

Materials and Methods: The NPs were synthesized by loading Fe₃O₄NPs with the monoclonal antibody TZ. Initially, Fe₃O₄ NPs were produced and subsequently coated with Polyethylene Glycol (PEG) to form PEG- Fe₃O₄ NPs. The TZ antibody was then conjugated to the PEG- Fe₃O₄ NPs, resulting in TZ-PEG-Fe₃O₄ NPs. The resulting NPs were characterized using standard analytical techniques, including UV-Vis spectroscopy, FTIR, SEM, TEM, VSM, and assessments of colloidal stability.

Results: Analyses indicated that the targeted TZ-PEG-Fe₃O₄ NPs exhibited a spherical morphology and a relatively uniform size distribution, with an average diameter of approximately 60 nm. These results confirmed the successful synthesis and controlled fabrication of the Fe₃O₄ NPs, which is crucial for developing effective Contrast Agents (CAs) for medical imaging applications. Additionally, the study confirmed the biocompatibility and magnetic properties of the synthesized TZ-PEG-Fe₃O₄ NPs.

Conclusion: The findings suggest that the developed targeted TZ-PEG-Fe₃O₄ NPs have significant potential as effective CAs for MR imaging of BC cells.

Background: The prevalence of coronavirus has increased the use of CT scans, a high-exposure imaging technique. This study was designed to estimate organ dose and effective dose to investigate the lifetime attributable risks (LARs) of cancer incidence and mortality in COVID-19 patients. 600 patients who had COVID-19 or were suspected of having it, were included in the current study.

Methods: Dosimetric parameters such as dose length product (DLP), volumetric CT dose index (CTDI_V), and scan length, were used to estimate the patient’s dose and cancer risk. The ImPACT CT dosimetry software was also used to calculate organ doses and effective doses. The cancer risk was calculated using the National Academy of Sciences' Biologic Effects of Ionizing Radiation (BEIR VII) report.

Results: For females, the mean effective dose based on International Commission Radiation Protection 103 (ICRP103) and ICRP 60 was 2.36 ± 0.48 mSv and 1.2 ± 0.28 mSv, respectively. For males, this parameter was 2.31 ± 0.53 mSv and 1.21 ± 0.45 mSv based on ICRP103 and ICRP60, respectively. For males, the mean LAR of all cancer incidence and cancer mortality was 14.79 ± 4.85 and 8.59 ± 2.42 per 100000 people, respectively. For females, these parameters were 23.37 ± 9.59 and 12.61± 3.89 per 100000 people, respectively.

Conclusion: Chest CT scan examination connected with a non-considerable radiation dose and risk of cancer. So according to the ALARA principle, CT protocol must be optimized to limit radiation-induced risk.

Purpose: Evaluating the impact of various surface treatments on the adhesive strength between resin cement and zirconia surface.

Material and methods: Using an STL file, 60 monolithic zirconia discs (Vita YZ HT) with dimensions of 10 millimeters in diameter and 2 millimeters in height were produced. They were machined, sintered, and the surface was smoothed using 600, 800, and 1200 grit aluminum oxide paper. Four groups were created based on the surface treatment applied to the discs: no treatment (control), sandblasting, potassium hydrogen difluoride and Zircos-E solution. Resin cement cylinders (Panavia V5; Kuraray Noritake) were applied on zirconia discs using a custom mold. The shear bond strength was assessed subsequent to thermocycling. The scanning electron microscope (SEM) has been utilised to analyse the morphological alterations of a specimen from every group. A post-hoc Tukey's test (P < 0.05) and a two-way ANOVA were used to statistically analyse the data.

Results: The data analysis showed that the maximum shear bond strength values, measured at 128.933 ± 2.764Mpa, were obtained via airborne particle abrasion with 50-µm Al2O3. The values obtained by the control group were the lowest, at 50.933 ± 9.573 Mpa. The use of 50-µm Al2O3 in airborne particle abrasion caused a significant increase in shear bond strength values (p<0.05).

Conclusion: The adhesive strength between zirconia and resin cement was improved by surface treatments, and airborne particle abrasion with 50-µm Al2O3 was shown to be an effective way to increase bond strength.

Introduction

Laryngeal cancer is a critical health issue, often treated using advanced radiation therapy techniques such as Intensity-Modulated Radiation Therapy (IMRT). The gamma index is a widely used metric for quality assurance in radiotherapy, assessing the agreement between planned and delivered dose distributions.

Objective

This study aims to evaluate the feasibility and accuracy of laryngeal IMRT treatment plans using three gamma analysis algorithms and varying evaluation parameters, including dose difference (DD%), distance-to-agreement (DTA).

Result

Gamma passing rates (GPR) for the laryngeal IMRT plans demonstrated high accuracy, with over 90% of pixels passing the criteria in most cases. Composite gamma analysis showed 53.89% of pixels meeting both DD and DTA criteria simultaneously, while individual evaluation revealed the impact of stricter thresholds on GPR. Subtraction analysis identified dose discrepancies, emphasizing the need for accurate calibration.

Conclusion
This study highlights the effectiveness of gamma analysis in ensuring the accuracy of IMRT treatment plans for laryngeal cancer. The findings underscore the importance of rigorous PSQA, parameter optimization, and advanced algorithms to enhance treatment precision.

Purpose: This study aims to explore the effect of mean dose constraint in optimization shells on the reduction of normal lung dose in lung SBRT plans.

Materials and Methods: This study investigated 28 VMAT-based lung SBRT plans optimized with three artificial shells, which were re-generated with same setup and an additional mean dose constraint besides the maximum dose limit.  Dosimetric measurements of target volume and organs at risk (OARs) were compared between the original plans and re-generated ones using Wilcoxon signed-rank test at 5% level significance (two-tailed).

Results: Replanning resulted in slight improvements in some parameters, such as R50% and Gradient measure (GM) respectively reduced by 1.3% and 1.0% with p<0.05, but slight increases in others, such as D2cm and Maximum target dose. However, those increases were not statistically significant. The Conformity Index (CI) and V105% values remained largely unchanged after replanning. The parameters for dose deposited in normal lung tissue showed statistically significant reductions ranging from 1.0% to 1.7%. In addition, the mean dose to the spinal cord, esophagus, and skin were slightly reduced, but the mean dose to the heart showed a slight increase.

Conclusion: The study found that adding mean dose constraints to optimization shells in lung SBRT plans can reduce normal lung dose while maintaining dose conformity to the target. However, there may be slight changes in some OARs such as the spinal cord, esophagus, and skin. These changes were not statistically significant.

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: Magnetoencephalography (MEG) is a brain imaging method with a high temporal-spatial resolution by recording neural magnetic fields. The data quality of this imaging method is reduced for reasons such as the failure of one or more sensors. This study aims to explore the efficiency of the various data reconstruction techniques in magnetoencephalography for the retrieval of poor-quality channels.
Materials and Methods: We compared three surface reconstruction methods (Mean, Median, and Trimmed mean), two partial differential equations (modified Poisson and Diffusion equation), and a Finite Element-based interpolation method using data from 11 young adults (aged 30±12). Each technique was assessed in terms of time taken for reconstruction, R-squared, root mean squared error (RMSE), and signal-to-noise ratio (SNR) compared to a reference signal. Statistical tests (P-value < 0.05) were used to analyze the relationships between the mentioned evaluation criteria. Generalized Linear Models revealed that surface reconstruction methods and finite-element interpolation outperformed partial differential equations.
Results: The Trimmed mean method achieved the highest R-squared (0.882 ± 0.0610) and lowest RMSE (0.0155 ± 0.00904) with a reconstruction time of 9.5154 microseconds for a 500 milliseconds epoch of a magnetoencephalography channel data.
Conclusion: The surface reconstruction methods can recover the noisy or lost signal in magnetoencephalography with a suitable error and required time.

Purpose: The hippocampus is a crucial brain region responsible for memory, spatial navigation, and emotion regulation. Precise hippocampus segmentation from Magnetic Resonance Imaging (MRI) scans is vital in diagnosing various neurological disorders. Traditional segmentation methods face challenges due to the hippocampus's complex structure, leading to the adoption of deep learning algorithms. This study compares four deep learning frameworks to segment hippocampal parts, including concurrent, separated, ordinal, and attention-based strategies.

Materials and Methods: This research utilized 3D T1-weighted MR images with manually delineated hippocampus head and body labels from 260 participants. The images were randomly split into five folds for experimentation, each time one of those designated as the test set and the rest as the training set.

Results: The findings indicate that both the concurrent and separated frameworks perform better than the ordinal and attention-based frameworks regarding the Dice and Jaccard coefficients. In head segmentation, the separated framework had a Dice similarity of 0.8748, a Jaccard similarity of 0.7794, and a Hausdorff distance of 5.4160. In body segmentation, the concurrent framework had a Dice similarity of 0.8616, a Jaccard similarity of 0.7591, and a sensitivity of 0.8437. Statistical results from the one-way ANOVA test showed a significant difference in performance for the body part (P-value=0.008), but not for the head region (P-value=0.652) between concurrent and separated frameworks. Comparing the concurrent with ordinal and attention-based frameworks showed a significant difference in both body and head regions (P-value<0.001 for both comparisons).

Conclusion: Researchers must consider the differences between various frameworks while selecting a segmentation method for their specific task. Understanding the strengths and weaknesses of every framework is essential for deciding on the top-rated segmentation approach for precise applications.

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.

Objective: The initial evaluation of trauma poses a formidable and time-intensive challenge. This study aims to scrutinize the diagnostic efficacy and utility of integrating machine learning models with radiomics features for the identification of blunt traumatic kidney injuries in abdominal CT images.

Methods: This investigation involved the collection of 600 CT scan images encompassing individuals with varying degrees of kidney damage resulting from trauma, as well as images from healthy subjects, sourced from the Kaggle dataset. An experienced radiologist performed the segmentation of axial images, and radiomics features were subsequently extracted from each region of interest. Initially, 30 machine learning models were deployed, with a final selection narrowed down to three models: Light Gradient-Boosting Machine (LGBM), Ridge Classifier, and Adaptive Boosting (AdaBoost). The performance of these chosen models was subjected to a more comprehensive examination.

Results: The AdaBoost model exhibited notable performance in diagnosing mild kidney injury, achieving accuracy and sensitivity rates of 93% and 94%, respectively. Furthermore, for severe kidney injury, the AdaBoost model demonstrated a remarkable sensitivity of 96% and an accuracy of 97%. The Area Under the Curve (AUC) values for this model were also calculated, yielding values of 92.91% and 97.04% for mild and severe renal injuries, respectively.

Conclusion: The artificial intelligence models employed in this study hold significant potential to enhance patient care by providing valuable assistance to radiologists and other medical professionals in the diagnosis and staging of trauma-related kidney injuries. These models offer the capability to prioritize positive studies, expedite evaluations, and accurately identify more severe injuries that may necessitate immediate intervention. Of course, in this study, the compatibility of artificial intelligence tools with the clinical environment has not been discussed, and only the ability of machine learning models to interpret CT scan images has been investigated.

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: Lung cancer is the most common and deadly type of disease that is the cause of one million deaths around the world every year. Due to the present level of medical research, identifying lung tumors on chest computed tomography (CT) images have become a significant process in modern medicine. Enhancing treatment and reducing lung cancer mortality can be achieved by promptly identifying and accurately diagnosing suspected malignant lung tumors. While many deep learning algorithms have been developed recently for the classification of lung cancer, getting high accuracy in lung cancer classification is still a challenge. An advanced deep learning technique is developed to boost the effectiveness of early lung cancer diagnosis.

Materials and Methods: In this research, we have proposed an enhanced ensemble deep-learning model for lung cancer classification and segmentation. Initially, we carried out an extensive preprocessing process including image resizing, noise reduction, and contrast enhancement to enhance the image quality. The problem of small sample size is addressed by applying conventional data augmentation techniques like flipping, rotating, zooming, and shearing. Next, six statistical features are retrieved using Improved Empirical Wavelet Transforms (IEWT). After feature extraction, the Enhanced ResNeXt model is used to classify lung cancer into normal, malignant, and benign classes. The interested region of the lung tumor is segmented using the Modified ShuffleNetV2 model. Depending on whether lung cancer is present or not, individuals with lung cancer are classified as normal, malignant, and benign and the experiments are performed on the benchmark datasets LIDC-IDRI and IQ-OTH/NCCD.

Results: The proposed approach achieves an exceptional model accuracy of 99.43% for the IQ-OTH/NCCD dataset and 99.37% for the LIDC-IDRI dataset. The expected outcomes show that the accuracy and efficiency of our proposed ensemble deep learning model outperform other CNNs.

Conclusion: The proposed models beat existing CNN-based models in terms of speed and number of training parameters, which means that using CT scan images to diagnose lung cancer automatically is a suitable option and a strong selection for extensive use in medical environments.

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: The present study aimed to assess the Entrance Skin Doses (ESD) at the thyroid gland region and evaluate the local Diagnostic Reference Level (DRL) in panoramic radiography in a city (Yasuj, Iran).

Materials and Methods: In the current study, 31 patients (17 women and 14 men) with a mean age of 33.90±16.49 years were included. To assess the ESD values, 3 thermoluminescence dosimeters (TLD-100) were attached to the thyroid gland region for each patient. The DRLs were estimated as the third quartile of the ESD values. The ESD variations among the different genders (men and women), devices, and age groups (children [5-10 years], adolescents [11-19 years], and adults [˃19 years]) were calculated. T-test, one-way ANOVA, and Bonferroni's post hoc test were used for parametric tests; Kruskal-Wallis and Spearman's correlation coefficient were used for the non-parametric tests.

Results: Mean ESD and DRL values were obtained at 72±21 µGy and 0.091±0.02 mGy, respectively. For ESDs, there was no significant difference between different genders (men: 76±20 µGy and women: 69±23 µGy) as well as among the three investigated devices (P-value˃ 0.05). The ESD values of children were significantly lower than adolescent and adult patients (P-value<0.001); however, there was no statistically significant difference between the adolescent and adult patients (P-value =0.057).

Conclusion: Compared to national/international, the DRL value in our study was relatively low; patient doses can be decreased in the panoramic examinations by increasing the knowledge of health workers of the radiation parameters, specifically operators.

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.

Purpose: The posterior oblique beams are increasingly common in radiotherapy techniques. The radiation beams traversing through the treatment couch would be attenuated and cause under-dosage in the tumor region. The attenuation of an IGRT carbon fiber Couch for different angles, energies, field sizes, measurement points, couch regions, and the ability of the Eclipse treatment planning system in dose prediction was investigated.

Materials and Methods: Vital Beam linear accelerator and Exact IGRT couch top from Varian were applied. At first, the couch coefficient was used to find the most attenuation angle. Then, at the most attenuation gantry angle, the attenuation measurements were performed in three measurement points of an inhomogeneous thoracic phantom using a farmer ionization chamber for three energies with six field sizes in three regions of an IGRT couch.

Results: In three regions of the IGRT couch and the angle of 130˚, the photon beam was most attenuated. The most significant difference between calculated and measured point doses was 1.855%.

Conclusion: The IGRT treatment couch in posterior oblique gantry angles decreased the dose in the measurement points due to gantry angle, field size, energy, and couch region. The Eclipse treatment planning system can sufficiently predict the tumor dose distribution.

Purpose: The objective of this paper is to review the non-invasive methods for ICP monitoring and the research conducted in the field.

Materials and Methods: A comprehensive literature search was conducted on NIH and PubMed, and papers highlighting the newer methods used in Intracranial Pressure monitoring were reviewed and the related data was included in the paper.

Results:  The prominent methods of non-invasive ICP monitoring reviewed were: Imaging (CT and MRI), Electroencephalogram (EEG), Near-Infrared Spectroscopy (NIRS), Optic Nerve Sheath Diameter (ONSD), and Transcranial Doppler (TCD) Ultrasound.

Conclusion: While invasive methods for ICP monitoring are preferred over non-invasive methods in a clinical setting, with the intraventricular catheter being the gold standard for ICP monitoring, many non-invasive methods for ICP monitoring are considered, especially in settings where invasive ICP monitoring is not possible. The use of non-invasive methods represents an advancement in the field of ICP monitoring. Although not very well known in a clinical setting, non-invasive methods offer more safety and carry a lesser risk of infection.

Background: This review aims to synthesize current literature on recent advances in the diagnosis and treatment of Brain and spinal cord injuries (SCIs), focusing on molecular imaging, cell therapy, brain-computer interfaces (BCIs), and craniosacral therapy (CST).

Methods: A systematic search was conducted in PubMed/MEDLINE, Scopus, Web of Science, Cochrane Library, and Google Scholar to identify relevant articles published between 2015 and 2025. Keywords included "Brain Injury," "Spinal Cord Injury," "Molecular Imaging," "Cell Therapy," "Brain-Computer Interface," and "Craniosacral Therapy."

Results: Molecular imaging techniques, such as fMRI, DTI, and PET, enhance diagnostic accuracy by visualizing neural activity and structural integrity. Cell therapy, particularly with mesenchymal stem cells (MSCs), shows promise in promoting axon regeneration and reducing inflammation. BCIs offer potential for restoring motor function and enhancing neural plasticity. The evidence for CST is mixed, with some studies suggesting benefits in pain relief and cognitive improvement, while others raise concerns about methodological limitations.

Conclusion: Recent advances in molecular imaging, cell therapy, and BCIs offer promising avenues for improving the diagnosis and treatment of BSCI. However, further rigorous research is needed to validate the efficacy of these approaches and to address ethical considerations. While CST has gained attention as a complementary therapy, more high-quality studies are required to determine its effectiveness. This review highlights the need for interdisciplinary collaboration to translate scientific discoveries into clinical practice and to improve the quality of life for individuals affected by BSCI.

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.

Theranostics is emerging as a powerful modality in precision oncology, integrating diagnostic imaging with targeted therapies to enable more effective and individualized cancer management. In parallel, artificial intelligence (AI) and digital twin (DT) technologies are increasingly being explored as enabling frameworks for advancing research, education, and clinical decision support. AI facilitates a range of quantitative and workflow-driven tasks, including organ and lesion segmentation, longitudinal lesion matching and tracking, and absorbed dose estimation, while also contributing to evidence generation, implementation, and evaluation processes. Complementing this, DTs integrate multimodal images, pharmacokinetic models, molecular characteristics, and clinical data to create dynamic, patient-specific representations of disease and treatment response. Together, these technologies improve treatment response and outcome prediction, enhance treatment planning, and support more adaptive and data-informed disease management strategies. In the near term, clinical practitioners and trainees must learn to effectively supervise AI systems, understand algorithmic limitations, and ensure their safe and effective use within clinical workflows. Over time, DT-enabled environments may support immersive, simulation-based learning with continuous feedback and exposure to complex or rare clinical scenarios, reshaping professional training. More broadly, the convergence of AI and DT technologies is driving an evolution in the structure of oncology practice itself. Alongside the four established clinical specialties medical oncology, radiation oncology, surgical oncology, and nuclear oncology a complementary role is emerging: computational oncology. These clinical and computational roles operate synergistically within an integrated health system, advancing data-driven and patient-centered precision oncology.

Purpose: This case report aimed to describe a treatment for severe inflammatory external root resorption (RR).

Materials and Methods: A 13-year-old boy reported the avulsion of his upper left central incisor. The tooth had been avulsed four months prior and was replanted forty minutes later by an emergency service. The canal was thoroughly irrigated with 2% sodium hypochlorite and then filled with calcium hydroxide of a creamy consistency as an intracanal medication due to its antimicrobial properties, using lentulo spirals. The calcium hydroxide was left inside the canal for a month.

Results: Following the diagnosis, treatment involved conventional endodontic therapy with calcium hydroxide dressings, and the root canal was definitively filled after radiographic control of the resorption. At the 6- and 12-month follow-ups, clinical and radiographic examinations revealed no signs or symptoms of any abnormalities. The resorption process had halted, and the radiograph showed the reappearance of the normal lamina dura, indicating successful therapy.

Conclusion: This case report details the treatment of severe external inflammatory RR in a tooth undergoing orthodontic treatment. Successful tooth replantation depends on the effective implementation of the recommended therapy. However, when inflammatory external RR occurs, appropriate endodontic treatment is necessary to eliminate necrotic tissue and bacteria, along with the use of calcium hydroxide dressings.