Abstract

Purpose: Magnetoencephalography is the recording of magnetic fields resulting from the activities of brain neurons and provides the possibility of direct measurement of their activity in a non-invasive manner. Despite its high spatial and temporal resolution, magnetoencephalography has a weak amplitude signal, drastically reducing the signal-to-noise ratio in case of environmental noise. Therefore, signal reconstruction methods can be effective in recovering noisy and lost information.

Materials and Methods: The magnetoencephalography signal of 11 healthy young subjects was recorded in a resting state. Each signal contains the data of 148 channels which were fixed on a helmet. The performance of three different reconstruction methods has been investigated by using the data of adjacent channels from the selected track to interpolate its information. These three methods are the surface reconstruction methods, partial differential equations algorithms, and finite element-based methods. Afterward to evaluate the performance of each method, R-square, root mean square error, and signal-to-noise ratio between the reconstructed signal and the original signal were calculated. The relation between these criteria was checked through proper statistical tests with a significance level of 0.05.

Results: The mean method with the root mean square error of 0.016  0.009 (mean  SD) at the minimum time (3.5 microseconds) could reconstruct an epoch. Also, the median method with a similar error but in 5.9 microseconds with a probability of 99.33% could reconstruct an epoch with an R-square greater than 0.7.

Conclusion: The mean and median methods can reconstruct the noisy or lost signal in magnetoencephalography with a suitable percentage of similarity to the reference by using the signal of adjacent channels from the damaged sensor.

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: Diabetes, resulting from insufficient insulin production or utilization, causes extensive harm to the body. The conventional diagnostic methods are often invasive. The classification of diabetes is essential for effective management. The progression in research and technology has led to additional classification approaches. Machine Learning (ML) algorithms have been deployed for analyzing the huge dataset and classifying diabetes.

Materials and Methods: The classification and the regression of diabetic and non-diabetic are performed using the XGBoost mechanism. On the other hand, the proposed class-centric Focal XG-Boost is applied to elevate the model performance by measuring the similarity among the features. The prediction of the model is based on the classification and regression rates of diabetic and non-diabetic individuals, which are anticipated using applicable and effectual metrics to estimate their working performance.

The dataset used in the Class-Centric Focal XG Boost model is attained using the Arduino Uno Kit. The data collection is done under a sampling rate of 100 Hz. The data are gathered from Bharati Hospital Pathology Laboratories, located in Pune.

Results: The inclusive outcomes of the proposed model with their appropriate Exploratory Data Analysis (EDA) among classification and regression, with the suitable dataset used in the study are exemplified.

Conclusion: The proposed Class-Centric Focal XG Boost model has numerous advantages and is less delicate to the hyperparameters than the conventional XGBoost algorithm. As a part of the real-time application of the Class-Centric Focal XG Boost model, the model can be utilized in other communicable and communicable disease classification and detection.

Purpose: In this study, we propose a novel generalizable hybrid underlying mechanism m for mapping Human Pose Estimation (HPE) data to muscle synergy patterns, which can be highly efficient in improving visual biofeedback.

Materials and Methods: In the first step, Electromyography (EMG) data from the upper limb muscles of twelve healthy participants are collected and pre-processed, and muscle synergy patterns are extracted from it. Concurrently, kinematic data are detected using the OpenPose model. Through synchronization and normalization, the Successive Variational Mode Decomposition (SVMD) algorithm decomposes synergy control patterns into smaller components. To establish mappings, a custom Bidirectional Gated Recurrent Unit (BiGRU) model is employed. Comparative analysis against popular models validates the efficacy of our approach, revealing the generated trajectory as potentially ideal for visual biofeedback. Remarkably, the combined SVMD-BiGRU model outperforms alternatives.

Results: the results show that the trajectory generated by the model is potentially suitable for visual biofeedback systems. Remarkably, the combined SVMD-BiGRU model outperforms alternatives. Furthermore, empirical assessments have demonstrated the adept ability of healthy participants to closely adhere to the trajectory generated by the model output during the test phase.

Conclusion: Ultimately, the incorporation of this innovative mechanism at the heart of visual biofeedback systems has been revealed to significantly elevate both the quantity and quality of movement.

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: The phantom-less patient-specific quality assurance (PSQA) for intensity‐modulated radiotherapy (IMRT) plan verification has been exploited recently. The aim of this study was the feasibility of the PSQA of the plan based on a log file and onboard detector for prostate patients in helical tomotherapy.

Method: For 15 prostate patients, the quality assurance (QA) of the helical tomotherapy plan was performed using the Delta4 phantom and Cheese phantom to evaluate the spatial dose distribution and point dose, respectively. These parameters were also reconstructed by delivery analysis (DA) software using the measured leaf open times (LOTs). The gamma analysis and relative dose difference were used to compare the measured and reconstructed dose with the calculated values. Then, using the relative discrepancy, the log file and onboard detector data were compared with the expected data to assess machine performance.

Results: The mean relative dose difference was within 1.3% among the measurement, reconstruction, and calculation. The results of statistical analysis and p-value showed there is no statistically significant difference in determining the dose difference between the DA-based and conventional QA methods. The gamma values of 3%/3mm, 3%/2mm, 2%/3mm, 2%/2mm, 2%/1mm, and 1%/1mm for the DA-based QA method were the same as the measurement QA method. However, the gamma values of 3%/1mm, 1%/3mm, and 1%/2mm were comparable. The mean percentage difference LOTs was 0.07%, and most differences occurred in very low and some high LOTs. The relative difference was lower than 2.30% for the couch speed, couch movement, monitor unit, and rotation per minute (RPM) gantry between the log file and expected data.

Conclusion: The DA software is an efficient alternative to the measurement-based PSQA method. However, the accuracy of the DA software requires further investigations for gamma analysis at strict criteria. The very low and high LOTs may lead to the dose discrepancy. The tomotherapy machine can accurately implement the planned parameters.

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.

Abstract

Purpose: The dose of Computed tomography (CT) scan exams consists of a large proportion of all medical imaging modalities’ dose burdens. There are different methods to measure and describe radiation in CT. A standardized way is to measure the Computed Tomography Dose Index (CTDI). However, due to the increase in the detector system size along the z-axis in new CT scanners generations, new measurement methods are described in the American Association of Physicists in Medicine-Task Group No.111(AAPM-TG111). This study aims to estimate the equilibrium dose and compare it with the dose displayed in the volume computed tomography dose index (CTDI_vol) at the end of each exam. Eventually, the effective dose was calculated for both methods.

Material and Methods: Using standard phantom of polymethylmethacrylate (PMMA) and pencil ionization chamber, the values of CTDI₁₀₀, ( CTD₁₀₀), CTDI_vol, cumulative dose, equilibrium dose, and effective dose were calculated.

Results: Six protocols performed in two centers and the results indicated that the measurements with a standard CT dosimetry phantom, was varied between average equilibrium dose and CTDI_vol and the discrepancies ranged between 26% to 35%.

Conclusion: the CTDI_Vol is not suitable to evaluate the radiation dose at the end of each scan and the use of an equilibrium dose for dosimetry of new systems is recommended.

Keywords: Multidetector computed tomography, Equilibrium dose, Computed tomography volume dose index, AAPM-TG 111, Radiation dosimetry

Purpose: This topic focuses on a comprehensive evaluation of various diffusion tensor imaging (DTI) estimation methods, such as linear least squares (LLS), weighted linear least squares (WLLS), iterative re-weighted linear least squares (IRLLS) and non-linear least squares (NLS). The article will explore how each method performs in terms of accuracy, efficiency in estimating the diffusion tensor and robustness against noise.

Materials and Methods:  The study compares the methods using simulated diffusion-weighted MRI data. Time complexity and performance were evaluated across key metrics such as TRMSE, RMSE, MSD and ΔSNR.

Results: The results of the study demonstrate that LLS and IRLLS consistently outperform other methods in terms of TRMSE, MSD and SNR, particularly in high-noise scenarios. NLS performs best in reducing RMSE but high noise causes it to fit to noise, so it is not robust. WLLS showed the weakest performance across all metrics.

Conclusion: LLS and IRLLS provide a balance between accuracy and computational efficiency, making them practical for use in DTI analysis.

Purpose: One of the increasing neurological disorders is Alzheimer's, which progressively weakens brain cells and leads to critical cerebral impairments like memory loss. The present diagnostic techniques comprise PET scans, MRI scans, CSF biomarkers, and others that frequently need manual power and time-consuming process which might not offer appropriate results. This emphasizes the requirement for more precise and potential diagnostic solutions.

Materials and Methods: The proposed model utilizes AI-based Deep Learning (DL) techniques for effective multi-class classification of AD such as Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), Mild Cognitive Impairment (MCI), Cognitive Normal (CN) and Alzheimer’s Disease (AD) using Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. The proposed study utilizes Tri Branch Attention Network (TBAN) with Unified Component Incorporation (UCI) by capturing both spatial and channel attention information, by replacing the Squeeze and Excitation (SE) component in the conventional EfficientNet model and helps in addressing the concerns associated to imbalanced spatial feature distribution in images. Further, the incorporation of the proposed TBAN module in the Conv Layer helps, not only in terms of capturing the long-term dependence between the different channels of the network but also helps in retaining the specific location information to enhance the performance of the model. Similarly, the proposed UCI which is used in the MBConv layer deals with regularization, as the accuracy of the model can be dropped due to unbalanced regularization, hence the incorporation of UCI advocates strong regularization for combatting the concerns associated with overfitting and aids in providing better accuracy.

Results: Eventually, the proposed framework is evaluated with different metrics and the accuracy value obtained by the proposed model is 0.95. Likewise, precision, recall, and F1 scores gained by the proposed work are 0.95, 0.95, and 0.95.

Conclusion: The proposed research resolves significant gaps in the present diagnostic practices by implementing emerged AI techniques to improve the efficacy and accuracy of Alzheimer's diagnosis by medical imaging. Through enhancing the abilities of early detection, this proposed model holds the prospective to majorly affect treatment tactics for people affected with Alzheimer's. Finally, it led to better patient consequences and life quality.

Purpose

In epilepsy pre-surgical evaluations, semi-automated quantitative analysis of 18F-FDG brain PET images is a valuable adjunct to visual assessment for localizing seizure onset zones. This study investigates how adjusting image reconstruction parameters can enhance the accuracy of these quantitative results.

Materials and Methods

A total of 234 reconstruction parameters were applied to 18F-FDG brain PET images of a focal epilepsy patient. The parameters encompassed the 3D-Ordered-Subset Expectation Maximization image reconstruction method with resolution recovery (HD) and without (non-HD), various numbers of iterations and subsets (#it×sub), pixel sizes, and Gaussian filters. The accuracy errors were determined using the relative difference percentage (RD%) in measured SUV_max and the absolute Z-scores compared to reference values derived from the normal database reconstruction set serving as the benchmark.

Results

The study revealed that reconstructed images with 5mm or 8mm Full width at half maximum (FWHM) Gaussian filters yielded RD% values above 5% for SUV_max and Z-scores, indicating potential inaccuracy with higher values of post-smoothing filters. The recommended reconstruction sets with RD% values below 5% for both HD and non-HD images were those with a 3mm FWHM Gaussian filter and higher (#it×sub), specifically (5×21, 8×21), (5×21, 6×21), and (7×21, 8×21) for pixel sizes of 1.01 mm, 1.35 mm, and 2.03 mm, respectively.

Conclusions

The findings underscore the significant impact of altering the image reconstruction sets on the SUV_max and Z-scores. Furthermore, the inconsistent fluctuations of Z-scores emphasize the importance of using standard image reconstruction sets to ensure accurate and reliable quantitative outcomes in epilepsy pre-surgical evaluations.

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.

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: In the era of digital medicine, medical imaging serves as a widespread technique for early disease detection, with a substantial volume of images being generated and stored daily in electronic patient records. X-ray angiography imaging is a standard and one of the most common methods for rapidly diagnosing coronary artery diseases. Deep neural networks, leveraging abundant data, advanced algorithms, and powerful computational capabilities, prove highly effective in the analysis and interpretation of images. In this context, Object detection methods have become a promising approach.

Materials and Methods: Deep learning-based object detection models, namely RetinaNet and EfficientDet D3 were utilized to precisely identify the location of coronary artery stenosis from X-ray angiography images. To this aim, data from about a hundred patients with confirmed one-vessel coronary artery disease who underwent coronary angiography at the Research Institute for Complex Problems of Cardiovascular Diseases in Kemerovo, Russia was utilized.

Results: Based on the results of experiments, almost both models were able to accurately detect the location of stenosis. Accordingly, RetinaNet and EfficientDet D3 detected the location of false stenotic segments with a probability of more than 93% in the coronary artery.

Conclusion: It can be stated that our proposed model enables automatic and real-time detection of stenosis locations, assisting in the crucial and sensitive decision-making process for healthcare professionals.

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: There is a known decline in brain volume with age, impacting cognitive health and increasing the risk of diseases such as dementia and Alzheimer's. Physical activity has been shown to have positive effects on brain structure and cognitive function with aging. Still, the association between motor function and brain volume in young adults remains unclear.

Materials and Methods: This study utilized high-resolution T1-weighted MRI images and motor function test results from 1082 healthy young adults aged 22-37, sourced from the Human Connectome Project Young Adult (HCP-YA). Motor functions were assessed using four tests: Endurance, Gait Speed, Dexterity, and Strength. Correlation analysis and multiple linear regression models were used to evaluate the association between motor functions and brain volumes, adjusting for demographic variables and body mass index (BMI).

Results: Significant positive correlations were found between Endurance and Strength tests with multiple brain volumes, while Dexterity test showed negative correlations. No significant correlations were observed for the Gait Speed test. Multiple linear regression analyses revealed that total brain (β = 0.045, SE = 0.020), total gray matter (GM) (β = 0.035, SE = 0.016), left white matter (WM) (β = 0.058, SE = 0.025), right WM (β = 0.056, SE = 0.025), total WM (β = 0.057, SE = 0.025), and left accumbens (β = -0.072, SE = 0.031) volumes were significantly associated with motor function scores (p < 0.05).

Conclusion: Physical fitness, as measured by motor function tests, is significantly associated with brain structural integrity in young adults. These findings highlight the potential importance of physical activity in maintaining brain health, which could inform strategies to promote active lifestyles and prevent neurodegenerative diseases.

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.

Background: Patients with diabetes are more likely to develop polyhydramnios. The rate of Polyhydramnios among diabetic patients is ascending when contrasted with non-diabetic patients.

Objective: To compare the amniotic fluid index of diabetics and non-diabetics using sonography.

Methods: 200 people participated in a case-control study, 100 of whom were diabetic and the other 100 were non-diabetic. Toshiba XARIO XG was used in the study at the university ultrasound clinic in Green Town. It has a convex probe of 3.5-7.5 MHz frequency. All patients with diabetes and gestational diabetes of age 18-45 years are included during 2^nd & 3^rd trimesters. Any underlying pathologies like hypertension, multiple gestations were excluded in this study. SPSS version 25.0 was utilized for the analysis of the data.

 Results: The mean amniotic fluid index in diabetics and non-diabetics was 21.19 and 13.20 respectively.  In both diabetics and non-diabetics, the amniotic fluid index was found to be statistically significant (p=0.000). The chi-square analysis shows a significant association between AFI category and diabetes status. With the Diabetic group having a higher proportion of cases with Polyhydramnios AFI category and a lower proportion of cases with Normal AFI category compared to the Non-diabetic group. The mean estimated fetal weight in diabetics and non-diabetics was 1341.64 and 1372.53 respectively. Result shows that there was no significant difference in the estimated weight of the fetus between diabetic and non-diabetic females (p=0.088).

Conclusions: Study concluded that diabetes during pregnancy is associated with a significant increase in amniotic fluid levels, leading to a higher likelihood of polyhydramnios.

In the past, heavy drinking was often linked to fatty liver. The prevalence of non-alcoholic fatty liver disease (NAFLD), which affects people who do not consume alcohol, has garnered a lot of attention in the last 20 years. Nearly all fatty liver diseases are now the leading cause of liver disease in industrialized nations. Fatty liver has traditionally been defined as having a hepatic fat content of more than 5% of liver weight. Several medical issues, including those caused by medications, poor diet, and infections, may lead to fatty infiltration of the liver. Modern scientific understanding, however, attributes fatty liver in most individuals to either being overweight or obese or to drinking too much alcohol. This research proposes a stacked ensemble approach to detect NAFLD efficiently and achieves 95.9% correct classification accuracy. It also compares the proposed method with other basic and boosting machine learning approaches. To improve machine learning for trustworthy and reliable NAFLD screening and diagnosis, we apply explainable AI methods to the ensemble model to identify the most influential features and patterns for NAFLD predictions.

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.

Using functional near-infrared spectroscopy (fNIRS) as a complementary and cost-effective neuroimaging technique in sensorimotor tasks due to its applications in brain-computer interface (BCI) research can provide useful information about functional connectivity of brain networks. However, few studies on brain functional connectivity during sensorimotor tasks have often focused on evaluating brain activity electrically. In the present study, a signal processing algorithm using fNIRS-HbO2 data has been suggested to find active parts of the brain for motion and motor imagery in motor imagery task. In this algorithm, first, the wavelet transform was used to remove the noise and preprocess the signal. Then, using correlation analysis, functional connectivity matrices in motion and motor imagery were extracted, and finally, global efficiency ​​(GE) values were calculated. In addition to investigating the conditions of the small-world network in the connectivity matrix, the classification of motion and motor imagery was investigated using a t-test. For this purpose, a 20-channel fNIRS signal was recorded to measure changes in HbO2 concentration in the motor cortex of 12 healthy individuals with a sampling frequency of 10 Hz. The results, in addition to confirming the presence of a small-world network in the graphs from the correlation matrix, showed that the classification of motion and motor imagery of right and left hands will be significant when 40% of the strongest connectivity between channels was selected. The results showed that in the left hemisphere there was stronger connectivity between the channels. In general, the results not only showed the activity of brain networks in performing sensorimotor tasks as small-world networks, but they also reported the role of the dominant hemisphere in performing these tasks.

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.

Radiopharmaceuticals are combinations of two main components, a pharmaceutical component that targets specific moieties, and a radionuclide component that acts through spontaneous degradation for diagnostic, therapeutic purposes, or both simultaneously known as theranostics. By combining diagnostic and therapeutic methods, radiotheranostics play an important role in reducing radiation dosages for patients, increasing treatment effectiveness, controlling side effects, improving patient outcomes, and reducing overall treatment costs. Despite the diagnostic and therapeutic roles, radiopharmaceuticals are beneficial for assessing prognosis, disease progression and possibility of recurrences, treatment planning strategies, and assessing response to treatment. The most incredible role of radiopharmacy is establishing new radiopharmaceuticals to better target and tolerated agents for imaging and treatment in a clinic. These approaches are supported by nuclear medicine non-invasive procedures. It is crucial for radiopharmaceuticals that drug delivery occurs in a highly selective and sensitive manner to minimize the potential radiation risk to patients. This report will provide an overview of the recent progress in radiopharmaceuticals for diagnosis and therapy, including the latest radiotheranostic tracers, key concerns within the field, and future trends and prospects. Additionally, the available and useful radiopharmaceuticals are categorized into separate tables based on their specific characteristics. Presenting information in table format enhances organization and makes the data more understandable and accessible for users. This structured approach allows users to quickly locate relevant information, compare different radiopharmaceuticals, and grasp essential details at a glance. By utilizing tables, we ensure that critical information is not only easy to read but also effectively highlights the unique attributes of each radiopharmaceutical, ultimately improving the decision-making process for healthcare professionals.

Background: The aim of this study is to provide a comprehensive review of recent advances in the application of nanocarriers for targeted drug delivery and radiosensitization in cancer radiotherapy (RT), as well as to examine the challenges, solutions, and future prospects of this technology.

Methods: A comprehensive literature search was conducted in PubMed, Scopus, Web of Science, and Embase, identifying 373 records. Following PRISMA guidelines, 36 studies met inclusion criteria focusing on functionalized nanocarriers in cancer RT. Data extraction covered nanoparticle types, functionalization, therapeutic payloads, cancer models, radiation modalities, and outcomes.

Results: Forty studies were analyzed, categorized into iron oxide-based (10), silver (10), bismuth-based (7), graphene-based (4), gadolinium-based (4), and titanium-based (2) nanoparticles (NPs). Bismuth-based NPs (BiNPs) showed superior radiosensitization with sensitizer enhancement ratios (SERs) of 1.25–1.48 and up to 450% reactive oxygen species (ROS) increase in vivo, achieving ~70% tumor volume reduction without systemic toxicity. Silver NPs (AgNPs) demonstrated dose enhancement factors (DEF) rising from 1.4 to 1.9 and synergistic effects with docetaxel plus 2 Gy radiation. Iron oxide NPs functionalized with HER2 and RGD ligands reduced cell viability by 1.95-fold and achieved DEF of 89.1 in targeted systems. Gadolinium NPs reached SERs up to 2.44 at 65 keV, while graphene-based systems enhanced ROS production by 75.2%. Titanium-based NPs increased ROS levels 2.5-fold. Combination therapies integrating chemotherapeutics such as cisplatin and curcumin with nanocarriers yielded SERs up to 4.29. Radiation modalities included megavoltage X-rays (4–10 MV, n=24), synchrotron keV X-rays (n=2), gamma rays (0.38–1.25 MeV, n=3), and electron beams (6–12 MeV, n=3).

Conclusions: Bismuth-based NPs represent the most promising radiosensitizers due to their high efficacy, safety, and clinical relevance, supporting their advancement toward clinical translation.

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.