Purpose: Orthodontic archwires play an important part in the enamel demineralization during orthodontic treatment. Dental caries is thought to be caused by the adhesion and colonization of mutans streptococci on these surfaces, followed by the formation of pathogenic plaque. This research was conducted with the purpose of testing and comparing the adhesion of mutans streptococci to a variety of aesthetic archwires as well as a conventional archwire made of NiTi.
Materials and Methods: Four types of nickel-titanium archwires with round cross-section 0.016 inch were used in the current study, one type uncoated NiTi and three types of coated NiTi (rhodium, gold, and flexy blue). After two hours of agitation in 2 ml of sterile Unstimulated Whole Saliva (UWS), 5 pieces of each archwire were incubated in a streptococcus mutans suspension for 5-, 90-, and 180-minutes time interval. Bacterial adhesion was assessed by a microbial culture technique and the amount of bacterial adhesion was counted by colony forming unit.
Results: There were no statistically significant differences in mutans streptococci adhesion among archwires at (90 and 180 minutes), while at 5 minutes, the mutans streptococci adhesion on gold-coated and rhodium-coated were significantly less than uncoated NiTi archwires.
Conclusion: Clinical use of esthetic-coated archwires may provide the same risks for bacterial adhesion compared with uncoated conventional archwires and increased mutans streptococci adhesion was significantly related to longer incubation time.

Purpose: This study aimed to identify quantitative EEG changes in migraine patients as compared to a control group, and to explore the relationship between these changes and the clinical characteristics of migraine patients.
Materials and Methods: In this study, a total of 38 participants were recruited from the neurophysiology unit at Ghazi Al-Hariri Hospital. Among them, 18 were healthy individuals with no history of chronic headaches or neurological diseases, and the remaining 20 were diagnosed migraine patients. To provide a clearer understanding of the sample size, the migraine patients were further divided into two groups: ictal and inter-ictal, based on the presence or absence of headaches during the testing. EEG recordings were conducted for 10 minutes each with eyes closed and eyes open conditions. Subsequently, absolute power values for Delta, Theta, Alpha, and Beta brain waves were calculated in each group.
Results: The quantitative EEG analysis revealed a significant decrease in Theta and Beta waves, following a descending pattern from the control group to inter-ictal migraine patients and then to ictal migraine patients. Conversely, Alpha waves exhibited an ascending pattern, increasing from the control group to inter-ictal and ictal migraine patients. When comparing the absolute power of Alpha waves between patients with migraine without aura (M_woA) and migraine with aura (M_wA), the results demonstrated that during closed-eye conditions, Alpha waves were higher in M_wA than in M_woA. However, under open-eye conditions, the Alpha wave amplitude significantly decreased in M_wA compared to M_woA, during both ictal and inter-ictal phases.
Conclusion: Neurophysiological abnormalities have been identified in the brains of migraine patients. Comparing quantitative EEG results between migraine patients with aura and those without aura, it was found that Alpha waves were more responsive to EEG tasks in migraine patients with aura, both during inter-ictal and ictal phases.

Purpose: The incorporation of Nanoparticles (NPs) in Computed Tomography (CT) imaging significantly enhances the contrast, clarity, and sensitivity of CT scans, leading to a substantial improvement in the accuracy and reliability of diagnostic information obtained from the images. The objective of the current research was to investigate the application of gold (Au) NPs in enhancing the imaging capabilities of Breast Cancer (BC) cells.
Materials and Methods: Au NPs were synthesized by loading Trastuzumab (TZ) on PEGylated Au NPs. Firstly, Au NPs were produced and coated with PEG-SH to form PEG-Au NPs. Next, TZ was coupled with OPSS-PEG-SVA to enable its attachment to the PEG-Au NPs. The resulting NPs were characterized for their structure, size, and morphology using standard analytical techniques. To assess the potential of the developed NPs for CT scan imaging of BC cells, SKBr-3 cells were treated with Au NPs and TZ-PEG-Au NPs. Additionally, the cytotoxicity of the NPs was evaluated with the MTT technique.
Results: The SEM and TEM analyses revealed that the synthesized NPs exhibited a spherical shape and displayed a relatively uniform size distribution (approximately 45 nm). The results showed that the developed Au NPs have acceptable biocompatibility and superior X-ray attenuation properties compared to a commonly used contrast agent.
Conclusion: Based on our results, it can be concluded that the proposed TZ- Polyethylene Glycol-Au NPs are suitable for CT imaging of BC cells.

Purpose: This research aimed to evaluate how different concentrations of MgO nanoparticles influence the hardness, surface roughness, and SEM investigation of VerSiltal 50 silicone elastomeric materials that vulcanize at room temperature.
Materials and Methods: Using different weight percentages of MgO nanoparticles, 60 samples were created (0%, 0.5%, and 1% by wt.). The analysis made use of thirty samples from each group. Tests for surface roughness and surface hardness were performed on two experimental groups that contained 0.5% and 1% weight of MgO nanoparticles. Descriptive statistics and analysis of variance with multiple comparison tests were used to evaluate the data, and significance was indicated by a P value < 0.05. Scanning electron microscopy was used to measure the surface topography (SEM). Energy Dispersive X-ray spectroscopy (EDS) can be used to determine the distribution of Magnesium Oxide within the VST-50 silicone matrix.
Results: Surface roughness and hardness increased as the percentage of MgO nanoparticles increased from 0.5 wt. % to 1 wt. %, compared with those in the control group. The SEM test showed a good dispersion of the nanofillers and incorporation within the polymeric matrix of silicone. It showed that there was a slight little agglomeration of Nano filler particles as filler loading increased.
Conclusion: Compared to the control group, the means for surface roughness and hardness increased significantly in the 0.5 and 1 wt.% MgO experimental groups.

Purpose: One of the indirect methods that has been proposed as a way of the detection of anemia is blood attenuation in non-contrast Computed Tomography (CT) scans. Some indices of non-contrast CT scans have been studied as a clue. Most known of such indices include aortic blood density and the difference between blood density and aortic wall density. In the current study, we aimed to evaluate the left ventricle blood attenuation and its relation to patients’ hemoglobin levels.
Materials and Methods: A total of 523 patients who underwent non-contrast chest CT scan with available hemoglobin levels within 48 hours of interval from CT scan acquisition were recruited for this study. Left ventricle blood attenuation was measured and the correlation with hemoglobin levels was evaluated.
Results: There was found to be a linear correlation between blood attenuation in the left ventricle and hemoglobin levels (r=0.33). Our results showed that the highest level of accuracy for diagnosis of anemia is in the Hounsfield Unit of 37.5 for women and 38.5 for men (with 68% sensitivity and 60% specificity) which can be regarded as a reliable threshold.
Conclusion: It can be concluded that the attenuation of the blood in the left ventricle can potentially be a hint for anemia and further evaluation for Hb levels.

Purpose: Brain connectivity studies unveil the intricate interactions within neural networks. Various approaches exist to explore brain connectivity, yet the debate between the efficacy of linear versus non-linear methods remains unresolved due to the advantages and limitations of each.
This study aims to provide a comprehensive evaluation of neuroimaging data analysis to gain insights into the functional aspects of the brain, particularly in the context of Alzheimer's Disease (AD). The objective is to identify potential pathways for early intervention and prevention, despite the controversies arising from diverse neuroimaging modalities and analytical techniques.
Materials and Methods: Using fMRI data, both linear and non-linear approaches are investigated. The linear approach employs the Pearson Correlation Coefficient (PCC) to create whole-brain graphs. For non-linear approaches, Distance Correlation (DC) and the kernel trick are utilized. Functional brain networks are constructed and sparsified for each AD stage, followed by calculating global graph measures.
Results: The findings indicate that non-linear approaches are more effective in distinguishing between different stages of AD. Among these, the kernel trick method performs better than the DC technique. Polynomial kernel (degree 3) showed better group separability, with significantly different graph measures such as clustering, transitivity, modularity, and small-worldness. Kernel analysis revealed that within-region connectivity was more disrupted in AD. Notably, the functional graphs of the brain are more significantly degraded in the early stages of AD.
Conclusion: In the initial phases of AD, both functional integration and segregation of the brain are compromised, with a more pronounced decline in functional segregation as the disease progresses. The clustering coefficient, indicative of brain functional segregation, emerges as the most distinguishing feature across all stages of AD, highlighting its potential as a biomarker for early diagnosis.

Purpose: Streptococcus mutans and Lactobacilli are common microorganisms involved in the caries process, while Candida albicans and Streptococcus mutans are linked to higher rates of tooth decay and severe oropharyngeal conditions. Nd: YAG Lasers are utilized in both medicine and dentistry, enhancing dental enamel's acid resistance and harming pathogenic organisms. This study aims to assess the sensitivity of Candida albicans and Streptococcus mutans to Nd: YAG Laser treatment.
Materials and Methods: Microorganisms were exposed to a Q-switched Nd: YAG Laser with a wavelength of 1064 nm, a beam diameter of 4 mm, and energy levels of 100 and 200 mJ for S. mutans, and 300 and 400 mJ for C. albicans. The laser emitted 100 and 200 pulses at a repetition frequency of 3 Hz. All data underwent statistical analysis.
Results: Exposing the Streptococcus mutans and Candida albicans to neodymium dopped-yettrium aluminum garnet, resulted in a reduction in colony forming unit/milliliter (CFU/mL) with increased Laser energy and numbers of pulse with maximum reduction at 200 mJ – 200 pulse and 400 mJ – 200 pulse for Streptococcus mutans and Candida albicans, respectively. A statistically significant differences were shown in the bacterial number (CFU/mL) between each group when compared with the control group. A statistically significant differences were shown in the fungal number (CFU/mL) between each group in comparison with the control group except between 300mJ-100 pulse and control, there is no significant difference.
Conclusion: ND: YAG exhibits antimicrobial effects against Candida albicans and Streptococcus mutans, with a stronger effect on the latter. Additionally, ND: YAG laser therapy promotes wound healing and reduces inflammation, making it an effective tool for managing oral infections. Its deep tissue penetration enables targeted treatment while minimizing harm to surrounding healthy cells. The principle of selective photothermolysis, where the laser energy is preferentially absorbed by pathogens, further enhances its efficacy against these microorganisms.

Purpose: The purpose of this study was to estimate the dose enhancement of Gold, Silver, and Gadolinium on both microscopic and macroscopic scales in liver ¹⁷⁷Lu nano-radionuclide therapy.
Materials and Methods: The ¹⁷⁷Lu radionuclide at the nano-scale was simulated using the MCNP 6.1 Monte Carlo (MC) simulation method. The emitted radiation characteristics, such as type and energy of emitted radiation, were modeled at the center of the tumor. The tumor cell (phantom liver) was filled with cubic voxels with sides of 1µm. These cubic voxels were then filled with GNP, AgNP, and GdNP spherical nanoparticles with a diameter of 30 nm each, and in a concentration of 10 mg/g of tissue.
Results: The DEF was estimated at 5µm, 20µm, 50µm, 70µm, and 100µm from a single ¹⁷⁷Lu nano-radionuclide radiation source at the center of the phantom liver cell, emitting both γ-ray and β- particles. A significant γ-ray DEF of up to 89% was observed at some µm around the source. Additionally, high DEF was derived for β- rays at some µm around the simulated radionuclides compared to greater distances.  Estimated DEF in tumoral tissue including GNP was 89%, 78%, 72%, 47%, and 25% at 5µm, 20µm, 50µm, 70µm, and 100µm respectively from the center. DEF for the other nanoparticles was also derived.
Conclusion: A dramatic DEF was observed in the close vicinity of NPs around ¹⁷⁷Lu as the radiation source, possibly due to the great gradient in dose and dominance of the photo-electric phenomenon.

Purpose: In this research, using the Geant4 software toolbox and metamaterials as a neutron shield, it was tried to introduce the proper metamaterial for this matter.
Materials and Methods: Boron Neutron Capture Therapy )BNCT( treatment is one of the most significant approaches used to treat brain tumors. The neutron source that is the main part of the BNCT method is produced by protons irradiation of ⁷Li converter. The brain tumor tissue, which contains a high concentration of ¹⁰B, is exposed to thermal neutron energy that is moderated by shield material. The dose of alpha particles that produced by the neutron decay of ¹⁰B in tumor tissue can be calculated by changing the metamaterial thickness. The best thickness of metamaterial for minimizing the radioactive elements production in brain tumor is calculated using the Geant4 toolkit.
Results: Waveguide Core (WC( metamaterial with 10 cm thickness is suitable for neutron moderation. The secondary elements produced in brain tumors is less than other thickness that is calculated by taking into account the alpha spectrum in tumor tissue. The alpha spectrum was calculated by the interaction of neutron spectrum released by the WC metamaterial.
Conclusion: The dose of alpha and secondary particles was obtained by the calculation of numbers and energy of these particles in brain tumors. The number of radioactive elements produced in the tumor tissue, as well as the most effective thickness of proper metamaterial to reduce the dose of secondary particles indicated that the WC metamaterial with a thickness of 17 cm is the best material for reducing radiation of neutron source that is produced by 35 MeV proton irradiation of ⁷Li neutron converter.

Purpose: The purpose of this study was to create an Intelligence System (IS) to analyze the Electroencephalogram (EEG) characteristics of patients with mild Traumatic Brain Injury (mTBI) and healthy volunteers. Generally, mTBI research demonstrates that patients suffer from Working Memory (WM). The frontal cortex is involved in the clinical physiology of mTBI and is crucial for delayed memory.
Materials and Methods: The Frontal-Medial Theta (FMT) is one of the most critical factors in mTBI verification. The oscillatory strength of FMT (4-8Hz) over the Frontal-Medial Cortex (FMC) or Supplementary Motor Area (SMA) and the medial-Sensory Motor Cortex (mSMC) is associated with efficient WM performance. The designed IS accesses the FMT of mTBI and healthy subjects by FCz and Cz electrodes placed in FMC or SMA and mSMC, respectively. The Multi-level Discrete Wavelet Transformation (MDWT) of EEG (FCz and Cz) is suggested here to investigate the mTBI. The FMT rhythms of EEG of FCz and Cz channels are extracted through 3-level-DWT. Then, 1768 features [712 features of healthy subjects + 1056 features of mTBI patients] for both the FCz and Cz electrodes were calculated via their FMT using eight statistical feature computations.
Results: The study found that the FMT strength of FCz and Cz electrodes is similar. The Bagging Classifier achieved 83.3333% accuracy with the 20-fold validation for the FCz electrode.
Conclusion: The strength of the FMT-FCz and FMT-Cz electrodes is approximately the same, and both are equally crucial to investigating mild Traumatic Brain Injury.

Purpose: The goal of biomedical researchers is to overcome harmful bacteria' resistance to antibiotics by developing new active chemicals quickly, cheaply, easily, and environmentally.
Materials and Methods: In this instance, iron nanoparticles (Fe-NPs) were created using a green method by mixing an equal volume of Zingiber officinale aqueous extract with a 100 ml solution of ferrous chloride tetrahydrate (FeCl2.4H2O (0.4 M)). The distinctive dark brown color shift of the mixture indicated the production of Fe-NPs.
Results: This suggests that the nanocomposite was created and the reaction occurred. FT-IR, TEM, and UV-Vis spectroscopy were utilized to analyze phytosynthesized Fe-NPs. Overall, the phytosynthesized Fe-NPs show activities that enable their use in various biomedical and biotechnological applications. Additionally, the antimicrobial effect was investigated against Gram-negative bacteria (Escherichia coli).
Conclusion: The antibacterial activity of E. coli was determined, and the highest zone of inhibition was observed at the concentration of 100 μg/mL.

Purpose: Brain tumors are very important for the overall health of humans, which happen due to the uncontrolled increase and duplication of abnormal cells. Therefore, brain tumor segmentation is a very important step in medical diagnosis and can help in early tumor detection, treatment planning, and tumor progression follow-ups. To solve the problems related to manual segmentation such as time-cost, inaccuracy and subjectivity, automatic segmentation with deep learning methods is presented. This study aimed to develop an automatic brain tumor segmentation based on the combination of convolutional and graph neural networks to overcome the shortcomings of each network when they are used individually.
Materials and Methods: The main goal of this study is to propose a novel architecture for brain tumor segmentation from multi-modal MR images and comparison of the results with related SOTA studies. The novel architecture uses a simple Convolutional Neural Network (CNN) and Graph Neural Network (GNN) sequentially. In the first stage, the volumetric 3D image with a combination of all modalities is fed to the simple convolutional network. After retrieving the feature representation of the CNN, a graph model is created and fed to the GNN. The CNN will help to capture local information of patches and GNN will retrieve the global information available in the data which together can provide promising results.
Results: The proposed model used for the segmentation of the BraTS2021 dataset showed the average Dice score of 0.86 and the average Hausdorff of 17.94. The results showed that the combination of CNN and GNN can the performance of the task at hand. Also, the heatmaps extracted can show the importance of adding the GNN into the CNN.
Conclusion: New and creative advancements in artificial intelligence and its applications for medical image segmentation are very promising. We proposed a hybrid network of CNN and GNN to capture local and global information and combine them in a way such that we can recreate an acceptable segmented result which is justified with Dice score and Hausdorff metrics quantitatively. The proposed methodology performed better in comparison with the other related methods. Also, the activation heatmaps confirm the reliability of the approach qualitatively.

Introduction: The use of CT scan in diagnosis is increasing significantly. The purpose of This study is evaluating the normal brain and chest Computed Tomography (CT) scans at six medical imaging departments in Tehran and evaluate the radiation dose and estimate the cancer risk associated with these normal CT scans.
Materials and methods:  The data of 1080 patients between 20 to 50 years old referred to the six medical imaging centers in Tehran hospitals (1^st January to 30^th March) were collected. Patients were categorized into six groups according to their ages. In this study, a radiologist helped us in interpreting the CT scan images. The DRL was assessed and the BEIR VII model was used to estimate the radiation cancer.
Results: Among the 1080 patients, 642 (59%) were males and the average age of the patients was 45.05 ± 22.60 years. Brain CT scans in 65% cases and chest CT scan in 52% were reported normal. The third quartile of CTDI_vol, DLP, and ED values in the brain and chest scans were calculated and introduced as local DRL values. These values were determined as 22.13, 428.58, and 0.65 for CTDI_vol, DLP, and ED values in the brain scan, and 5, 187.35, and 3.71 in the chest scan. The highest risk of cancer incidence in the brain scan was related to leukemia with a value of 0.73 per 100000 exposures, followed by thyroid with a value of 0.62 in women aged 20-25 years. And in the chest scan, the highest risk of cancer incidence was related to breast cancer with a value of 22.4 per 100000 followed by lung cancer with a value of 19.02 in the same age group.
Conclusion: As the age decreases, the risk of cancer increases, therefore, by optimizing the radiation dose and avoiding CT scans without indications, the risk of cancer can be significantly reduced. 

Purpose: Recent years have witnessed a significant amount of research into laser ablation in liquids due to the many potential applications of laser microprocessing of materials, including the synthesis of nanomaterials and nanostructures. This study aims to explore the antibacterial effects of cadmium oxide on both positive and negative bacteria.
Materials and Methods: Pulsed Laser Ablation (PLA) in liquid is a straightforward and environmentally friendly technical technique that works in water or other organic liquids under ambient conditions, in contrast to other, usually chemical methods. Here, 30 milliliters of deionized water were used to create Cadmium Oxide (CdO) nanoparticles using the PLA method. UV-vis spectrometry, FT-IR, X-ray spectroscopy, and FESEM were utilized to analyze the products' morphology, spectral content, and particle size.
Results: The produced nanocomposites were tested as antibacterial agents against gram-negative bacteria (Proteus mirabilis and Escherichia coli) as well as gram-positive bacteria (Staphylococcus aureus and Enterococcus faecalis). The MIC results on the MTP plate with the synthesized Cd NPS compound were as follows: Proteus mirabilis (6.25%), Escherichia coli (12.5%), Enterococcus faecalis (12.5%), and Staphylococcus aureus (12.5%). These findings indicate that the synthesized nanoparticles exhibit comparable antibacterial activity against both Gram-positive and Gram-negative bacteria.
Conclusion: The synthesized nanoparticles made of CdO demonstrated notable antibacterial activity that was effective against all of the bacteria that were tested in the experiments. This indicates that the CdO nanocomposite has the potential to inhibit the growth of a wide variety of bacterial strains.

Purpose: Pain is an unpleasant sensation that is important in all therapeutic conditions. So far, some researches have been done on pain assessment and cognition, and researchers have come to evaluate pain through different tests and methods. Since the occurrence of pain causes along with activation of a long network in brain regions, so recognition of dynamical changes of the brain in pain states is helpful for pain detection using Electroencephalogram (EEG) signal.
Materials and methods: The aim of this research is to investigate dynamical changes of the brain for pain detection using EEG at the time of happening phasic pain. For this purpose, at the first step phasic pain is produced using coldness, then dynamical features via EEG are analyzed via Recurrence Quantification Analysis (RQA) method and finally Rough neural network classifier has been used for achieving accuracy to detect and categorize pain and non-pain states.
Results: The performance of the classification procedure is 95.25 4%. That is compared with other research, it is a novel method of using rough neural network for distinguishing pain from non-pain states.
Conclusion: The simulation results proved that cerebral behaviors are detectable during pain. Also, one of the most merits of the proposed method is the high accuracy of classifier for an investigation into dynamical fatures of the brain during happening pain. Finally, pain detection can improve and upgrade medical methods. 

Purpose: Nowadays, detecting brain tumors is a crucial application. If a tumor is discovered later on, the medical issues are significant. Therefore, early diagnosis is essential. Magnetic Resonance Imaging (MRI) is the most recent detection, diagnosis, and assessment technology.
Materials and Methods: In this study, MRI images are segmented before input to a pulse-coupled neural network model to identify the existence of a tumor in the brain picture. The doctor may turn to this model for assistance if there are more input MRI brain pictures. This work preprocesses the images using normalization smoothing with linear filter and adaptive histogram. Statistical and Local Binary Patterns (LBP) features are extracted from the preprocessed images to perform the classification process. The Deep Convolutional Network (DCNN) is used to segment the image. The Pulse Coupled Neural Networks (PCNN) categorize the input images as normal and tumor.
Results: Accuracy, sensitivity, specificity, and precision are the various metrics evaluated. This work achieves 99.35 accuracies, 99.78 sensitivity, 98.45 specificities, and 97.61 precision. This work is compared with previous implementations to measure performance.
Conclusion: The comparison analysis improves tumor segmentation and classification accuracy. The suggested method yields great outcomes.

Purpose: At resting state, the human brain releases cycles of Electroencephalography (EEG), which has been proven aberrant in persons with schizophrenia. Deep learning methods and patterns found in EEG of brain activity are helpful features for verifying schizophrenia. The proposed study demonstrates the applicability of alpha-EEG rhythm in a Gated-Recurrent-Unit-based deep learning model for studying schizophrenia.
Materials and Methods: This study suggests Rudiment Densely-Coupled Convolutional Gated Recurrent Unit (RDCGRU) for the EEG rhythm (gamma, beta, alpha, theta, and delta) based diagnoses of schizophrenia. The model includes multiple 1-D-Convolution (Con-1-D) folds with steps greater than 1, which enables the model to programmatically and effectively learn how to reduce the incoming signal. The Con-1-D layers and numerous Gated Recurrent Unit (GRU) layers comprise the Exponential-Linear-Unit activation function. This powerful activation function facilitates in-deep-network training and improves classification performance. The Densely-Coupled Convolutional Gated Recurrent Unit (DCGRU) layers enable RDCGRU to address the training accuracy loss brought on by vanishing or exploding gradients. This makes it possible to develop intense, deep versions of RDCGRU for more complex problems. The sigmoid activation function is implemented in the digital classifier's output nodes.
Results: The RDCGRU framework performs efficiently with alpha-EEG rhythm (88.06%) and harshly with delta-EEG rhythm (60.05%). The research achievements: In EEG rhythm-based schizophrenia verification, GRU cells with the RDCGRU deep learning model performed better with alpha-EEG rhythm.
Conclusion: The α-EEG rhythm is a crucial component of the RDCGRU deep learning model for studying schizophrenia using EEG rhythms. In our investigation of RDCGRU deep learning architectures, we noticed that Con-1-D layers connected special learning networks function well with the α-EEG rhythm for the EEG rhythm-based verification of schizophrenia.

Purpose: The human brain is comprised of distinct regions, each contributing uniquely to behavioral control. The execution of even basic tasks necessitates synchronized activities among multiple brain regions. Fundamental cognitive functions hinge on the capacity to retain and flexibly manipulate information, a key role ascribed to Working Memory (WM). This study seeks to enhance our understanding of the neural mechanisms underlying WM and elucidate the coordinated neural activities spanning various brain regions.
Materials and Methods: To achieve this objective, the invasively recorded electrophysiological activities from the Medial Temporal (MT) cortex of humans using a high number of electrodes were analyzed. The subjects did a verbal working memory task including three phases: encoding, maintenance, and retrieval.
Phase synchronization between electrode signals in common frequency rhythms determined by Phase Locking Value (PLV) was used to create brain network graphs.
Results: This study validates prior findings on neural synchronization in the hippocampus, entorhinal cortex, and amygdala during WM within the theta, alpha, and beta bands. Analysis of Phase Locking Value (PLV) dynamics during encoding and maintenance, reveals strong modulation in the theta, alpha, and beta rhythm. Notably, the PLV of theta oscillation between channels within the posterior hippocampal region was significantly reduced during maintenance. Conversely, PLV of theta-alpha rhythms between the Anterior Hippocampal region (AHL) and amygdala/entorhinal cortex was significantly increased by WM.
Conclusion: This study, for the first time, demonstrates the networks involved in WM within MT areas in the human brain. These findings underscore the frequency-specific intricacies in WM modulation, providing valuable insights into neural coordination during specific processing stages.

Purpose: Understanding neural mechanisms is critical for discerning the nature of brain disorders and enhancing treatment methodologies. Functional Magnetic Resonance Imaging (fMRI) plays a vital role in gaining this knowledge by recording various brain regions. In this study, our primary aim was to categorize visual objects based on fMRI data during a natural scene viewing task. We intend to elucidate the challenges and limitations of previous models in order to produce a generalizable model across different subjects using advanced deep learning methods.
Materials and Methods: We've designed a new deep learning model based on transformers for processing fMRI data. The model includes two blocks, the first block receives fMRI data as input and transforms the input data to a set of features called fMRI space. Simultaneously a visual space is extracted from visual images using a pre-trained inceptionv3 network. The model tries to construct the fMRI space similar to the extracted visual space. The other block is a fully connected (FC) network for object recognition based on fMRI space. Using transformer capabilities and an overlapping method, the proposed architecture accounts for structural changes across different voxel sizes of the subjects' brains.
Results: A unique model was trained for all subjects with different brain sizes. The results demonstrated that the proposed network achieves an impressive similarity correlation between visual space and fMRI space around 0.84 for train and 0.86 for test dataset. Furthermore, the classification accuracy was about 70.3%. These outcomes underscored the effectiveness of our fMRI transformer network in extracting features from fMRI data.
Conclusion: The results indicated the potential of our model for decoding images from the brain activities of new subjects. This unveils a novel direction in image reconstruction from neural activities, an area that has remained relatively uncharted due to its inherent intricacies.

Background: Three-dimensional echocardiography (3DE) allows simultaneous evaluation of the entire left ventricular (LV) volume, motion, and mechanical dyssynchrony. This study aimed to provide valuable data on the feasibility and reliability of 3DE in assessing LV dyssynchrony in healthy individuals.
Methods: One hundred healthy volunteers, including both genders, with mean age, weight, and BMI of 39.64±10.21 years, 76.57±14.65 kg, and 27.59±4.3 kg.m^-2, respectively, without evidence of structural heart or chronic disease, were included. 3DE examinations were conducted using a 4-chamber view and the full-volume method for all volunteers. Dyssynchrony was automatically quantified as the systolic dyssynchrony index (SDI) for selected LV segments using Q-lab software. The standard deviation (SD) of the time to attain minimum systolic volume was considered as SDI. This time length was expressed as % R-R to compensate for the variability of heart rate and increase reproducibility. Consequently, a single SDI (global SDI) was available for quantifying the degree of LV dyssynchrony by comparing all segments.
Results: According to echocardiographic findings, the mean global LV-SDI, apical SDI, basal SDI, and mid SDI were 28.68±15.48 msec, 26.16±27.47 msec, 24.41±14.35 msec, and 22.07±18.24 msec, respectively. After correction for RR duration, the mean global LV-SDI was 3.49±1.97%, apical SDI: 3.21±3.58%, basal SDI: 2.97±1.82%, and mid SDI: 2.68±2.19%.
Conclusion: 3DE proves to be a useful tool for evaluating LV dyssynchrony. The data provided include age- and sex-related changes in total and regional SDI in healthy volunteers, serving as a suitable reference for further investigations into LV dyssynchrony changes.

Purpose: The aim of this study is to investigate the diagnostic reference levels (DRL) for the abdomen-pelvis computed tomography (CT) examinations performed at our medical institution.
Materials and Methods: In total, the data of 600 patients referred to the radiology center of Chamran hospital in Kermanshah from May 1, 2022, to May 1, 2023, were collected. All scans were performed using a GE Healthcare 16-slice. Four imaging protocols have been used for imaging the abdomen and pelvis of patients, which include without contrast media, with oral contrast media, with contrast media injection, and triple-phase. The volume CT dose index (CTDIvol (mGy)) and dose length product (DLP) distribution’s median and 75th percentile values were computed.
Results: Effective dose values in the triple-phase ranged from 33.30 to 38.12 ± 0.1 mSv for patients with different DLP values, for without and oral contrast media ranged from 8.68 to 9.45 ± 0.2 mSv, for with contrast media injection ranged from 10.83 to 11.45 ± 0.1 mSv based on the total value of DLP. We established our DRLs as the median value of CTDIvol (mGy)| and DLP (mGy.cm) as follows: abdomen and pelvis CT without contrast media and oral contrast media: 12 mGy and 605 mGy.cm, respectively; CT abdomen and Pelvis procedure (triple-phase): 11 mGy and 2382 mGy.cm, respectively; abdomen and pelvis CT with contrast media injection protocols: 16 mGy and 1484 mGy.cm, respectively.
Conclusion: The proposed DRL values for all imaging protocols are slightly higher than the European and the American College of Radiologists (ACR) DRL values in DLP and CTDIvol (mGy). The purpose of DRL in terms of CTDIvol (mGy) is comparable with the international guidelines. Thus reducing the scan length is recommended ensuring that patients receive a minimal possible radiation dose while maintaining the image quality.

Novel antimicrobials are needed now more than ever as antibiotic resistance to dangerous bacteria and the number of contagious diseases are on the rise. Nanoparticles (NPs) are innovative and promising therapeutic agents as they possess distinctive physiochemical characteristics and can hinder the growth of microorganisms. As such, their potential use as antimicrobials has garnered significant research interest. Researchers have, similarly, trained their sights on nanotechnology, which is the study and development of materials, tools, and systems that have physical, chemical, and biological characteristics that are more unique than those found in larger systems, due to its potential applications and advantages over existing conventional materials, especially in the dental and medical industries. Therefore, a better understanding of the science behind nanotechnology is needed to comprehend how these materials can be used in everyday life. Zein-coated magnesium oxide nanoparticles (MgO NPs) have recently shown significant promise as a powerful antibacterial compound that can be combined with other materials to fabricate a variety of brand-new dental formulations.

Purpose: Artificial intelligence (AI) techniques have been extensively utilized for diagnosing and prognosis of several diseases in recent years. This study identifies, appraises and synthesizes published studies on the use of AI for the prognosis of COVID-19.
Method: Electronic search was performed using Medline, Google Scholar, Scopus, Embase, Cochrane and ProQuest. Studies that examined machine learning or deep learning methods to determine the prognosis of COVID-19 using CT or chest X-ray images were included. Polled sensitivity, specificity area under the curve and diagnostic odds ratio were calculated. 
Result: A total of 36 articles were included; various prognosis-related issues, including disease severity, mechanical ventilation or admission to the intensive care unit and mortality, were investigated. Several AI models and architectures were employed, such as the Siamense model, support vector machine, Random Forest , eXtreme Gradient Boosting, and convolutional neural networks. The models achieved 71%, 88% and 67% sensitivity for mortality, severity assessment and need for ventilation, respectively. The specificity of 69%, 89% and 89% were reported for the aforementioned variables.
Conclusion: Based on the included articles, machine learning and deep learning methods used for the prognosis of COVID-19 patients using radiomic features from CT or CXR images can help clinicians manage patients and allocate resources more effectively. These studies also demonstrate that combining patient demographic, clinical data, laboratory tests and radiomic features improves model performances.