Purpose: In this work, nanocomposite with different weight ratios reduce graphene oxide/copper doping-anatase (rGO/Cu-TiO₂) has been successfully prepared using the photolysis method to evaluate the role of rGO/Cu in photovoltaic properties performance application as a photoanodes.

Materials and Methods: The X-Ray Diffraction (XRD), Raman spectrum, and X-Ray Photoelectron (XPS) results analysis confirmed successfully incorporating rGO/Cu in the TiO₂ crystal structure. Transmission Electron Microscopy (TEM) reveals the formation of spherical agglomeration nanoparticles with a size approximately equal to 18nm.

Results: The current density–voltage curves (J-V) and Intensity-Modulated Photocurrent Spectroscopy (IMPS) showed that the incorporation of rGO sheets enhances the ability of N3 loading of (rGO/Cu-TiO₂) photoanodes with faster charge transfer.

Conclusion: Our results illustrate that optimal Cu and rGO can increase the efficiency of dye-sensitized solar cells (4.56%) by 8.2% higher than TiO₂ DSSCs (3.52%).

Purpose: Identification and categorization of brain tumors is a cyclical process in which tumor components are assessed and suggestions for therapy are made based on their classifications. Many imaging techniques are used for this work. Because MRI provides better soft tissue than CT, and MRI does not involve radiation. The currently available manual method is inefficient and hence we provide an advanced method by using the deep learning concepts.

Materials and Methods: This MRI creates detailed images of our body's organs and tissues by using a computer's radio wave and an attracting field. Deep Learning (DL), a subset of Machine Learning (ML), is helpful for the categorization and identification of issues. This project uses one dataset consisting of three categories (Meningioma, Glioma, Pituitary.

Results: In this work, the first stage is pre-processing concerning two datasets. Later involves detection by using a Convolution Neural Network algorithm (CNN). The suggested CNN performs admirably, with the greatest overall accuracy for the datasets coming in at 94.3% and 96.1%. The final results demonstrate the model's capability for brain tumor classification and detection problems.

Conclusion: The proposed system helps to automatically differentiate between the types of Tumors from the normal, future it can be improved to analyze the brain tumor and classification, which will be more useful in the treatment. A few more sectors of artificial intelligence can also be incorporated along with the proposed system to increase the standard of the proposed system.

Purpose: This in-vitro study was conducted to illustrate the influence of Traditional Endodontic Access Cavity (TEAC) and Conservative Endodontic Access Cavity (CEAC) procedures on the capacity of AF F One (Fanta Dental Materials Co., Shanghai, China) and AF Blue R3 (Fanta Dental Materials Co., Shanghai, China), a continuous rotation and reciprocation endodontic rotary file systems respectively, to shape the root canals of maxillary premolar teeth without transportation or centering deviation.

Materials and Methods: Randomly, forty maxillary premolar teeth were categorized into four groups. Group 1 was accessed and instrumented conventionally with AF F One, Group 2 was accessed and instrumented conventionally with AF Blue R3, Group 3 was accessed and instrumented conservatively with AF F One, and Group 4 was accessed and instrumented conservatively with AF Blue R3. With the use of Cone-Beam Computed Tomography (CBCT) imaging (Planmeca ProMax, Helsinki, ‎Finland), before and after instrumentation pictures of the root canals were captured for the purpose of calculating the transportation and centering ability.

Results: After checking the normality of data distribution, which was accomplished using the Shapiro-Wilk test, an independent sample t-test was performed to compare the data of various groups with a 95% level of confidence. Equivalent in terms of transportation and centering capacity, the two distinct endodontic instruments have comparable shaping capacities. No statistically significant difference (P > 0.05) was seen among the endodontic access cavity approaches for the two file systems investigated.

Conclusion: Compared to TEAC preparation, CEAC preparation has no effect on the capacity of the endodontic files to shape the root canal system.

Purpose: Timely detection of breast cancer is essential for improving treatment outcomes, particularly in the field of oncology. Several diagnostic techniques are available, and one promising approach is the use of Quantum Dots (QDs) for accurate and early detection. This study focuses on the utilization of cadmium selenium QDs with and without silver coating, which can modulate the transfer intensity of light sources.

Materials and Methods: Cadmium selenium QDs with silver coating (CdSe@Ag₂S) were synthesized and characterized. These QDs were then mixed with blood samples containing different concentrations of hemoglobin to simulate breast cancer conditions. The mixture was injected into phantom vessels representing breast tissue, and the transmitted light intensity was measured using a power meter. The light source used operated in the near-infrared range at a wavelength of 635 nm.

Results: The transmitted light intensity from vessels containing normal hemoglobin concentration was measured at 5.24 mW. However, in cancerous breast tissue, the intensity decreased to 4.56 mW and 3.34 mW for two and four times the hemoglobin concentrations, respectively. When the CdSe QDs were combined with different hemoglobin concentrations, the intensities of transmitted light were found to be 3.14 mW, 2.26 mW, and 1.22 mW for normal, twice, and four times the concentration of hemoglobin in turn. Furthermore, when the test was conducted using CdSe@Ag₂S QDs, the intensities of transmitted light were 1.83 mW, 2.52 mW, and 3.31 mW for the same hemoglobin concentrations, respectively.

Conclusion: This study concludes that the combination of different hemoglobin concentrations with QDs enables the differentiation between healthy and cancerous blood, enabling the early detection of breast cancer during its initial stages of development. Early detection of breast cancer has significant potential for improving treatment outcomes in the field of oncology.

Purpose: The purpose of this study is to investigate the potential of non-linear electroencephalography-based features in depression detection.

Materials and Methods: First, the Electroencephalography (EEG) signal was recorded from 25 normal and 25 depressed subjects. After preprocessing these signals, non-linear features including the Sum of Logarithmic (SL) and second-order spectral Moment (2M) of the amplitudes of diagonal elements in the bispectrum, the normalized Entropy (En) of bispectrum in beta and gamma frequency bands, Katz Fractal Dimension (KFD), and Lempel-Ziv Complexity (LZC) are extracted from them. Then, the ability of these features in depression detection was investigated using Mann-Whitney statistical test. Also, the classification performance of significant features was evaluated using a support vector machine (SVM) classifier.

Results: The results of the statistical analysis demonstrate that bispectral 2M, SL, and KFD features show significant differences between depressed and healthy groups in the Eyes-Closed (EC) condition. Also, bispectral 2M and SL in the gamma frequency band show significant differences between the two groups in parietal and temporal regions in the EC condition and only in the temporal region in the Eyes-Open (EO) condition. Bispectral En does not show a significant difference in the whole 19 channel, but it shows significant differences in the frontal region and beta frequency band. Between these features, gamma bispectral 2M in the temporal region and EO condition shows the highest classification result with 78.6±7.2% accuracy.

Conclusion: Findings confirm that bispectral 2M in the gamma frequency band and EO condition can classify depression and healthy subjects.

Purpose: The concrete construction of the musculoskeletal modeling is efficiently performed using information obtained from patients rather than collected from cadavers. In this study, we have endeavored to propose an automated technique that calculates the skeletal muscle pennation angle of patient ultrasound images and compares it with manual evaluations of the same images.

Materials and Methods: The proposed technique consists of three steps after the process of collecting the data from 30 volunteers of different muscles of the upper and lower limb. The first step is to improve the contrast in the image and identify the important details in the image through the use of two methods that depend on a fuzzy inference system, and this step is considered essential to prepare the image in the next step. The Hough Transform was used to follow the muscle fibers and draw them as lines, this is the second step. The third step is to calculate the angle and compare it with the manual evaluation that was done depending on the ultrasound machine options.

Results: The results reveal that there is a slightly difference between manual and automated evaluations of pennation angle for biceps (upper limb muscle) and gastrocnemius (lower limb muscle) as 8.6% and 0.45% respectively. Furthermore, the manual assignment of pennation angles is significantly slower, taking minutes, while the automated approach takes only a few seconds. Automated measurements take 85% more time compared to manual measurements.

Conclusion: There is no significant difference between measurements based on t-test. In future work, we aspire to a wider application of this technique to other muscles in the body and to activate it as an option available in the ultrasound device.

Lung cancer is a deadly disease which has high occurrence and death rates, worldwide. Computed Tomography (CT) imaging is being widely used by clinicians for detection of lung cancer. Radiomics extracted from medical images together with Machine Learning (ML) platform has given encouraging results in lung cancer diagnosis. Therefore, this study is proposed with the aim to efficiently apply and evaluate radiomics and ML techniques to classify pulmonary nodules in CT images. Lung Image Data Consortium is utilized in which nodules are given malignancy score 1 through 5 i.e. benign through malignant. Three scenarios are created randomly using these groups: G54 Vs G12, G543 Vs G12, and G54 Vs G123. Radiomics are extracted using Shape, Gray Level Co-occurrence Method, Gray Level Difference Method, and Gray Level Run Length Matrix along with Wavelet Packet Transform. To select a relevant set of features, four techniques i.e. Chi-square test, Analysis of variance, boosted ensemble classification tree and bagged ensemble classification tree are applied. The classification of nodule into benign or malignant is evaluated by using six models of Support vector machine. The results, in Scenario 1, show that MGSVM+Chi-square yields the best outcome compared to rest of the models with 75.3% accuracy, 77.9% sensitivity and 71.5% specificity. In Scenario 2, QSVM+Chi-square yields the best outcome compared to rest of the models with 74.7% accuracy, 70.3% sensitivity and 77.4% specificity. And in Scenario 3, CSVM+BACET yields comparatively better results with70.3% accuracy, 70.6% sensitivity and 62.1% specificity.

Purpose: Adrenal adenomas are best detected and understood using a Positron Emission Tomography/Computed Tomography (PET/CT) scanner. When doing PET scans, the acquisition time of 18-Fluorodeoxyglucose (¹⁸FDG) absorption is crucial as it determines the diagnostic accuracy and quality of the images. This study aimed to investigate the effects of various acquisition periods to assess the efficacy of PET/CT in identifying adrenal adenomas (1.5 vs. 3 minutes).

Materials and Methods: The research included 30 patients who were thought to have adrenal adenomas. Following the ¹⁸FDG injection, PET/CT imaging was performed on each patient using one of two distinct acquisition times: 1.5 or 3 minutes. The image quality was objectively evaluated using a 5-point Likert scale. Experienced nuclear medicine professionals used consensus reading to assess diagnostic performance for adrenal adenoma identification.

Results: The preliminary findings showed that compared to the 1.5-minute acquisition technique, PET/CT imaging with a 3-minute duration after ¹⁸FDG injection produced considerably superior image quality (p < 0.05). In addition, the longer acquisition time significantly increased the chance of detecting the lesion more precisely, improving the visualisation and characterisation of adrenal adenomas. With greater sensitivity and specificity, the 3-minute acquisition methodology showed better diagnostic accuracy for adrenal adenoma identification than the 1.5-minute approach.

Conclusion: The study suggests that extending the acquisition time to 3 minutes improves image quality and diagnostic performance for adrenal adenoma detection, potentially improving patient care by facilitating accurate diagnosis and treatment planning.

Purpose: In this study, the fracture resistance of prosthetic screws was tested using abutments made of titanium, zirconia, and Polyether Ether Ketone (PEEK) on dental implants.

Materials and Methods: From Easy Implant by easy prod, France, dental implants with specified dimensions and prosthetic screws were purchased. Three different materials (Ti, Zr, and PEEK) were used for abutment preparation. The implant-abutment units were subjected to a constant vertical force using a Universal Testing Machine (UTM) until the prosthetic abutment broke. The force that caused fracture was measured, and one-way ANOVA and Tukey's post-hoc tests were used to statistically analyze the data.

Results: For Titanium, Zirconia, and PEEK abutments, the mean fracture resistance (±standard deviation) was 727±31 N, 516±21 N, and 289±23 N, respectively. A substantial difference in fracture resistance was found between the various abutment materials according to the one-way ANOVA (p<.001). Zirconia showed much stronger fracture resistance than PEEK (p <0.05) and Titanium abutments demonstrated significantly higher resistance than both Zirconia and PEEK (p <0.01), according to post-hoc tests.

Conclusion: The type of the material affects the fracture resistance and fracture pattern of the implant abutment. Titanium, Zirconia, and PEEK abutments show different fracture resistance. Titanium requires more force to be fractured while polyether ether ketone shows less required force. This may affect the prosthetic screw fracture and affect the longevity of the implant.

Purpose: Lung cancer treatment often involves radiotherapy, which can lead to an increased risk of secondary cancers in sensitive organs and Organs At Risk (OARs). Understanding this risk is crucial for optimizing treatment strategies and minimizing long-term adverse effects. The objective of this study is to estimate the Secondary Cancer Risks (SCRs) in sensitive organs and OARs using radiation-induced cancer risk prediction models, specifically the Biological Effects of Ionizing Radiation (BEIR) VII model and the International Commission on Radiological Protection (ICRP) model.

Materials and Methods: The radiotherapy dosimetric data of 30 lung cancer patients were collected all of whom underwent Computed Tomography (CT) scans. The PCRT-3D Treatment Planning System (TPS) was used for the treatment planning process. The risks were calculated based on the dose distribution in the target volume. The models for Excess Absolute Risk (EAR) and Excess Relative Risk (ERR) values (per 100,000 person-year) were utilized to estimate SCRs in planning target volume, OARs, and sensitive organs.

Results: The results indicate that, according to the BEIR VII model, the estimated EAR of cancer per 100,000 person-years was 38.39 in the heart, 35.83 in the esophagus, 5.49 in the contralateral lung, 2.17 in the liver, and 3.41 in the pancreas. Conversely, using the ICRP model, the EAR was calculated to be 58.73 in the heart, 38.78 in the esophagus, 20.48 in the contralateral lung, 3.49 in the liver, and 5.44 in the pancreas. These findings suggest that lung cancer patients treated with 3DCRT exhibit relatively high SCRs in the heart, esophagus, and contralateral lung organs in both models.

Conclusion: In this study, SCRs in a range of organs in lung cancer patients treated with 3DCRT were quantified. Our findings revealed that there were comparatively high SCRs in the heart in 3DCRT of lung cancer patients. Based on the findings of the current investigation, the ICRP model SCRs are greater in comparison to the BEIR VII model. These findings underscore the importance of considering SCRs in treatment planning and highlight the need for further research to optimize radiation therapy strategies and minimize long-term risks for lung cancer patients.

Purpose: This study aimed to investigate the biological effects of photon radiation and its potential for cancer treatment through targeted radiation therapy by studying direct and indirect DNA damage induced by 15, 30, and 50 keV photon radiation using Geant4-DNA Monte Carlo simulations.

Materials and Methods: Two spherical cells (C and C₂) and their cell nucleus were modeled in liquid water. An atomic DNA model constructed in the Geant4-DNA Monte Carlo simulation toolkit, containing 125,000 chromatin fibers, was placed inside the nucleus of the C₂ cell. The number of direct and indirect single-strand breaks (SSBs), double-strand breaks (DSBs), and hybrid double-strand breaks (HDSB) in the C₂ cell caused by 15, 30, and 50 keV photons were calculated for N₂←CS, N₂←Cy, N₂←C, and N₂←N Target←Source combinations, at the distances of 0, 2.5, and 5 μm between two cells.

Results: Low energy (15 keV) photons emitted within the cell surface and the cell cytoplasm resulted in the highest DNA damage, producing markedly higher SSBs, DSBs, and HDSBs compared to the whole cell and the nucleus sources across 0-5 μm target distances. Increasing the photon energy to 30 and 50 keV showed 81-96% reduced DNA damage. Additionally, the 2.5 μm target distance decreased DSBs up to 53%.

Conclusion: Based on the results, 15 keV photons are more effective for the inhibition or control of cancer cells.

Purpose: Monitoring disease development or viruses that invade our bodies, such as Coronavirus Disease of 2019 (COVID-19), can be effectively carried out using Computed Tomography (CT) imaging tools. However, manual assessment of CT images by consultants is often insufficient for determining the extent of lung damage in COVID-19 patients. Automated evaluation of lung damage addresses this limitation by optimizing healthcare resource utilization. It reduces the workload on radiologists, allowing them to concentrate on more complex cases. Additionally, it ensures accurate and consistent assessments of lung damage, minimizing variability and the potential for human error inherent in manual evaluations.

Materials and Methods: In this study, a new approach was presented for improving CT images of the lung and specifying further lesions. This will help calculate the extent of damage without human intervention. The structure of the proposed technique draws upon four phases (data collection, improvement, segmentation and extraction lung damage region and evaluation). Firstly, 100 patients were recruited between September 29 2020 and July 10, 2022, of whom tested positive for COVID-19 and CT images were collected, then composite technique is implemented to extract the percentage of lung damage of COVID-19 patients.

Results: The study results demonstrated an efficient method for quickly and practically calculating the percentage of lung damage. There is a clear convergence between manual evaluation, done by radiologists, and automatic evaluation using the proposed method, suggesting its potential as an alternative in the absence of a specialist doctor. The differences in the arithmetic mean between the proposed technique and the radiologists' evaluations were 3.5%, 10%, 18%, and 0.98% for radiologists 1, 2, 3, and 4, respectively. Additionally, the findings indicated that individuals aged 20-60 years are the most affected by COVID-19.

Conclusion: This method serves as a potent tool for swiftly and practically assessing the percentage of lung damage caused by COVID-19. By eliminating the need for human intervention, it enables the evaluation of lung damage autonomously. This feature makes it particularly valuable in telemedicine applications and emergency situations where specialist medical expertise may not be readily available.

Purpose: Breast cancer is the second most common cancer in women. This study aims to evaluate the effect of the patient’s arm positioning on dose distribution in Planning Target Volume (PTV) and Organs At Risk (OARs) in radiotherapy after Breast-Conserving Surgery (BCS).

Materials and Methods: Thirty patients were divided into two groups; each group included 15 patients, including those in the left arm-up position (group 1) and both arms-up positions (group 2). The patients were selected randomly, and both groups were planned based on 16-slice Computed Tomography (CT) with a 5 mm slice thickness. The patients had been treated with 6 MV photon beam energy at the prescribed dose of 50 Gy in 25 fractions, and planning was performed using the Monaco Treatment Planning System (TPS). The results of dose parameters for the PTV, such as minimum dose (D_min), mean dose (D_mean), maximum dose (D_max), Heterogeneity Index (HI), and Conformity Index (CI), were obtained. For OARs, dose parameters such as Dmin, Dmean, and D_max were calculated. TCP for tumors and NTCP for OARs were also evaluated as radiobiological parameters.

Results: There was no statistically significant difference between the two groups in terms of dose parameters in PTV, but there was a difference for the OARs, such as thyroid.

Conclusion: The patient's arm position significantly affects the dose distribution for OARs such as the thyroid (p<0.05), and the position of both arms up (group 2) is relatively better than the left arm up (group 1) due to some clinical reasons.

Purpose: Manually segmenting mammograms is time-consuming and subjective. Hence, developing an automatic method to address challenges like low signal-to-noise ratio, various mass shapes and sizes, and high false positive rates is crucial. In this study, we present an automated approach for mass segmentation to address these challenges effectively.

Materials and Methods: Our proposed system consists of two phases: the pre-processing phase, which includes denoising, contrast enhancement, image cropping, resizing, and augmentation of mammograms; and the model design phase, where UNet++ is employed as an encoder-decoder-based network for segmenting breast masses. The encoder captures relevant information from various regions in the input image, while the decoder reconstructs the spatial location of the target region. We extensively experimented with a publicly accessible CBIS-DDSM dataset to evaluate our proposed system performance.

Results: Based on our findings, our proposed method demonstrates promising results with a precision rate of 91.84%, a True Positive Rate of rate of 93.66%, a Dice Score Coefficient measuring 92.66%, and a Jaccard Index of 86.46%.

Conclusion: The use of UNet++ combined with a pre-processing pipeline in digital mammography has shown promising results in accurately segmenting breast masses and has the potential to significantly improve early breast cancer detection.

Purpose: The objective of this paper is to study the feasibility of using effective connectivity (Granger Causality) (GC) obtained from resting-state functional Magnetic Resonance Imaging (rs-fMRI) data and stacked autoencoder for diagnosing Autism Spectrum Disorder (ASD) and comparing the results with those obtained using functional connectivity (Pearson Correlation Coefficient) (PCC). ASD affects the normal development of the brain in the field of social interactions and communication skills. Because diagnosing ASD using behavioral symptoms is a time-consuming subjective process that needs the exact collaboration of the ASD subject or his/her relatives, in recent years diagnosing ASD using resting-state functional neuroimaging modalities like rs-fMRI, has been taken into consideration.

Materials and Methods: We used rs-fMRI data and compared the use of functional and effective connectivity features using an autoencoder to classify people with ASD from healthy subjects. We used ABIDE dataset and divided the brain into 100 regions using the Harvard-Oxford (HO) Atlas. We calculated the PCC in classification using functional connectivity, and we calculated the GC in classification using effective connectivity. We used a stacked autoencoder to reduce the dimension of feature-space and a multi-layered perceptron (MLP) neural network as a classifier in both classifications.

Results: We achieved an accuracy of 67.8%, a sensitivity of 68.5%, and a specificity of 66.6% in classification using functional connectivity, and we achieved an accuracy of 67.6%, a sensitivity of 73.1%, and a specificity of 60.8% in classification using effective connectivity. 

Conclusion: Although the accuracy obtained using functional and effective connectivity are almost similar, the sensitivity is notably higher using effective connectivity. Since sensitivity is more important than specificity in the medical diagnosis, it seems that using effective connectivity features may outperform the ASD diagnosis in practice. The purpose of this paper is to diagnose ASD using effective connectivity measures and deep neural network by rs-fMRI data, but we compare its results with functional connectivity measures. As far as we know, this is the first time that Granger Causality (GC) and stacked autoencoder have been used to diagnose ASD together.

Purpose: Electromyography (EMG) is widely used to measure grip strength to evaluate neuromuscular activity, and thus it is possible to predict the health status of the heart muscle of those infected with the Coronavirus (COVID-19) and the extent of its relationship according to gender and age.

Materials and Methods: Fifty participants, equally divided between males and females, ages 18 to 65, were recorded for muscle force in kilogram and potential for action signal in mV (3-10 KHz) using skin surface EMG models with grip strength data acquisition. The outcomes were then compared to the volunteers' health status, familial relationships, case history, and history of COVID-19 variation infection.

Results: Based on the analysis of recorded data related to force, frequency, intervals, and amplitude, it was found that females exhibited a significant variation (p<0.05) in force and frequency over 5 minutes, in contrast to males, who showed a significant variation (p<0.05), particularly after 3 minutes when both genders showed signs of fatigue. However, certain chronic diseases such as hypertension, diabetes, and sudden deaths may have contributed to these variations. Particularly, SARS.CoV-2 variant infection showed a significant variation (p<0.05) in the EMG result for the delta variant more than the omicron for females and more impact in male smokers.

Conclusion: Findings indicated that EMG testing can predict the likelihood of Cardiovascular Disease (CVD) disease and health status based on a family history of chronic diseases like hypertension, diabetes, and CVD, which are independently connected to COVID-19 variant infections in both genders.

The management of radiation dose in pediatric X-ray examinations represents a critical concern in medical imaging, given the heightened vulnerability of children to ionizing radiation. Globally, the reliance on radiographic examinations in pediatrics necessitates stringent dose optimization strategies to mitigate associated risks. This study underscores the pivotal role of computed radiography (CR) systems in enhancing radiation dose management. Through a comprehensive analysis incorporating recent advancements in CR technology, this research delineates the significant reductions in radiation exposure achievable with the adoption of CR systems. Comparing traditional radiographic methods with CR systems within a Moroccan pediatric population, our findings reveal a marked improvement in dose efficiency, without compromising diagnostic efficacy. These results advocate for a broader transition towards computed radiography, highlighting its potential to set new standards in pediatric radiology practices worldwide. This study not only contributes to the body of knowledge on pediatric radiation dose management but also paves the way for implementing safer radiographic examination protocols, ensuring a protective environment for our most vulnerable patients.

Purpose: This study aimed to estimate the rate of temperature rise during the radiofrequency capacitive heating (13.56 MHz, 300 watts) to defined geometries including 6 simple geometric models, a virtual phantom, and a real section of the human pelvis obtained by CT-scan. The importance of this study is in the process of Hyperthermia Treatment Planning (HTP).

Materials and Methods: In this research, COMSOL software has been used to numerical model and simulate the three-dimensional (3D). First, six models with simple cylindrical geometry were developed to simulate the Radiofrequency (RF) capacitive hyperthermia treatment sessions. The diameter of the capacitor plates used was 25 cm, which was placed on a layer of water. To perform hyperthermia treatment planning with real geometry based on CT images, the pelvic area was downloaded from the slicer software and the generated mesh was transferred to COMSOL. Finally, a virtual phantom was used to validate the simulation, which means that the results of this simulation have been confirmed by experimental studies in the literature.

Results: The findings of this study indicated that capacitive hyperthermia is an effective deep treatment method especially for lean patients, so that for all models, an increase in temperature to a depth of 12 cm was observed. The thermometric data obtained from the simulation method showed a good agreement with the results obtained from the clinical and tissue equivalent phantom thermometry. The results showed that the simulation can predict temperature changes during capacitive hyperthermia for lean patients with greater accuracy than obese patients.

Conclusion: The results of comparing temperature profiles of the models taken from the platform provided with the experimental studies, showed relatively good simulation accuracy, that can be used to develop software for capacitive heating treatment planning.

Purpose: The application of fertilizers raises concerns regarding their potential to increase natural soil radioactivity, attributed to radioactive elements found in specific types of fertilizers. The purpose of this work is to assess the natural radioactivity levels of different fertilizers by measuring the concentrations of radionuclides ²²⁶Ra, ²³²Th, and ⁴⁰K and other risk factors using a High-Purity Germanium (HPGe) detector with 50% relative efficiency, and to complete this assessment, eight fertilizer samples were collected and were dried, crushed, and sieved for homogenization.

Materials and Methods: They were then sealed in Marinelli beakers (type 533N) and were stored for gamma spectroscopy analysis. Furthermore, SPSS software was employed for data analysis, applying cluster analysis, Pearson correlation, basic statistical evaluations, and multivariate statistical techniques to explore relationships among the radionuclides and hazard indices.

Results: To assess potential radiological health risks, several radiation hazard indices were calculated and ranging between (119.39-17.59) Bq/Kg for the Radium Equivalent Activity (Raeq), (0.32- 0.05) for External Hazard Index (Hex), (0.42- 0.06) for Internal Hazard Index (Hin), (0.07-0.01) mSv/year for Annual Effective Dose Equivalent (AEDE), (57.07- 8.71) nGy/h for D, (0.89- 0.13) for Gamma Index (Iγ), (0.40- 0.01) mSv/year for Annual Gonadal Dose Equivalent (AGDE), ((0.24- 0.06) 10⁻³ for Excess Lifetime Cancer Risk (ELCR),(56.98- 7.85) W/kg for Radioactive Heat Production Rate (RHP). These levels stay within the specified and appropriate boundaries, except the AGDE risk for the Perlite Saudi. The results also indicated the levels in Bq/kg of measured radionuclides for all samples ranging between (54.3±28.4- 6.2±2.3) for ²²⁶Ra, (29.2±6.4- 2.1±0.3) for ²³²Th, and (551.2±38.1- 99.3±11.6) for ⁴⁰K.

Conclusion: The results indicate that the analyzed fertilizers do not present significant radiological health risks to humans or the environment, with radiation hazard factor levels remaining below the global average limits set by the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR).

Purpose: Denture stomatitis, poor oral health, and angular cheilitis can all result from bacterial and fungal colonization. As a result, denture cleaners have been suggested to preserve the longevity of partially removable dentures and the health of the oral mucosa. The purpose of the present study was to investigate the impact of ozone water on Polymethyl Methacrylate (PMMA) by studying wettability, Ultraviolet (UV) absorption, and surface topography following soaking for 10 and 20 minutes at a 2 mg/l concentration.

Materials and Methods: A sixty-disc-shaped sample of polymethacrylate material (Ivoclar Vivadent) was fabricated for the wettability and UV absorption tests, and three bar-shaped samples of polymethacrylate material (Ivoclar Vivadent) were fabricated for the surface topography. Three groups were created: the first was the control group (immersion of samples in distal water). Second group (immersion of samples in 2 mg/l of ozone water solution for 10 min), and third group (immersion of samples in 2 mg/l of ozone water solution for 20 min). The contact angle (a wettability parameter) on the surfaces of the samples was measured after each storage period. The UV absorption test was assessed using a spectrophotometer; ANOVA was used to perform statistical analysis on the data at level 0.0.5; and surface topography was evaluated using Scanning Electron Microscopy (SEM).

Results: Based on the findings of this research, there was no statistically significant difference between the experimental and control groups when testing wettability and UV absorption. There is no change in surface topography when assessed by SEM.

Conclusion: This research concluded that the samples prepared from PMMA material can be safely soaked in an ozone water solution without compromising their properties.

Purpose: Laser photobiostimulation has recently gained recognition as a non-invasive and effective technique for accelerating orthodontic tooth movement and enhancing bone healing. This article evaluates the effects of laser biostimulation at an energy density of 15.9 Joules/cm² on the amount of orthodontic movement and its impact at the histological level.

Materials and Methods: Thirty adult male albino rabbits were randomly chosen to form two groups (n=15 per group): a Control (C) and a Laser Treatment (LT) group. The LT group received laser treatment for three weeks at 976 ± 10 nm and an energy density of 15.9 Joules/cm². Laser irradiation was applied to four specific spots on the lower incisors for 80 seconds, administered on days 0, 3, 6, 9, 11, 13, 16, 18, and 20. Five rabbits from each group were euthanized at 7, 14, and 21 days for subsequent analysis.

Results: The amount of orthodontic movement, the extent of osteogenesis, osteoblasts, and osteoclast counts were significantly larger in the laser-exposed group than in the unexposed group. Notably, bone alkaline phosphatase and tartrate-resistant acid phosphatase 5b activity significantly increased, particularly at two weeks relative to the control group.

Conclusion: Laser biostimulation offered evidence of improved parameters of teeth movement, providing insight to enhance the orthodontic therapy outcome.

Purpose: Skin Cancer (SC) is one of the most threatening diseases worldwide. Skin cancer diagnosis is still a challenging task. Recently, Deep Learning (DL) algorithms have demonstrated exceptional performance on many tasks compared to the traditional Machine Learning (ML) methods. Particularly, they have been applied to skin disease diagnosis tasks. The aim is to provide a comprehensive overview of the advancements, challenges, and potential applications in this critical domain of dermatology.

Materials and Methods: The review encompasses a wide range of scholarly articles, research papers, and relevant literature focusing on integrating deep learning techniques in skin cancer diagnosis. Materials include studies that employ various imaging modalities such as dermoscopy, histopathology, and other advanced imaging technologies.

The initial phase involves acquiring images of SC from various patients through primary sources and standardized databases. Subsequently, a thorough data cleaning process is implemented, encompassing noise reduction, resizing, and contrast enhancement. Further refinement occurs through the segmentation of the malignant sections, employing edge-based, region-based, and morphological-based techniques. Feature extraction is followed by deep learning approaches, it enhanced with Federated Learning (FL) that is applied to image classification. Finally, leveraging FL-aided deep learning techniques, the images are categorized as either malignant or non-cancerous.

Results: The metrics include Accuracy (AC%), Specificity (Spe%), Sensitivity (Sen%), and Dice Coefficient (DC%), providing a comprehensive evaluation of the classification performance. Generative Adversarial Network (G-AN) demonstrates the highest accuracy 98.5% among the considered techniques, making it the top-performing neural network architecture for skin cancer classification.

Conclusion: This review was undertaken by pulling data from 90 papers published between the years 2019 and 2023, it provides a thorough statistical analysis. A review of various neural network algorithms for skin cancer identification and classification, despite Generative Adversarial Network, has emerged as the most promising approach, underscoring their potential to revolutionize the accurate early diagnosis of skin cancer. Finally, this survey will be beneficial for SCD researchers.

Purpose: This review aimed to comprehensively assess how various physicochemical properties of nanoparticle-based MRI contrast agents—such as size, concentration, surface coating, charge, pH-responsiveness, and surface functionalization—affect their magnetic behavior and relaxivity. Moreover, this study evaluated the synergistic effects of these parameters to provide an integrated understanding of their combined impact on imaging performance.

Materials and Methods: A systematic search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore for studies published between 2015 and 2025. Search terms included combinations of “MRI contrast agents,” “nanoparticles,” “particle size,” “surface coating,” “surface charge,” “polymer type,” “relaxivity,” “drug delivery,” and “circulation time.” The search strategy used Boolean operators (AND, OR), Medical Subject Headings (MeSH), and filters for English-language, peer-reviewed, experimental articles. Inclusion criteria focused on original studies assessing how size, surface characteristics (charge, polymer, pH responsiveness), and concentration affect MRI relaxivity and imaging performance. Data were extracted and synthesized to evaluate trends, thresholds, and correlations among parameters.

Results: The review identified that nanoparticle size below 20 nm significantly enhances T₁ relaxivity, while concentrations above 0.5 mg/mL often lead to signal quenching and increased cytotoxicity. Surface coatings such as PEG and silica were found to improve biocompatibility and alter magnetic response depending on thickness and binding chemistry. Notably, the synergistic effects among these parameters were highlighted, demonstrating that optimized combinations of size, concentration, and surface coating could significantly enhance magnetic behavior and relaxivity, offering a more accurate and efficient MRI performance. This review identified threshold values for key nanoparticle properties—such as size, concentration, and surface coating—that significantly influence MRI relaxivity and imaging performance, providing a clear understanding of their combined effects.

Conclusion: This review highlights that optimizing the design of nanoparticle-based MRI contrast agents requires a synergistic approach, where key parameters—size, concentration, surface coating, and surface functionalization—are co-engineered to enhance magnetic behavior and relaxivity. Specifically, maintaining particle sizes below 20 nm, using biocompatible coatings like PEG or silica, and optimizing concentration between 0.1–0.5 mg/mL were identified as critical factors. This integrated framework provides a guideline for developing next-generation contrast agents with superior imaging performance and minimal toxicity.