Background: Arabic Sign Language (ArSL) recognition remains limited in terms of technological development, compared to American Sign Language (ASL). This disparity restricts communication accessibility for individuals with hearing impairments in Arabic-speaking regions, in offline environments with limited computational resources.Objective: This study aimed to develop a robust offline recognition system for ArSL by integrating Principal Component Analysis (PCA) for dimensionality reduction, Scale-Invariant Feature Transform (SIFT) for feature extraction, and Convolutional Neural Networks (CNNs) for gesture classification.Material and Methods: This experimental, quantitative research used a curated dataset of ArSL gestures, obtained from Kaggle. Preprocessing involved normalization, contrast enhancement, and noise reduction. SIFT was used to extract invariant features, while PCA reduced computational complexity. CNN architectures were trained to recognize gestures, assessed using accuracy, precision, recall, F1-score, loss, confusion matrix, and Receiver Operating Characteristic (ROC) curve.Results: The system achieved an accuracy of 86.64%, surpassing conventional models, such as SIFT combined with Support Vector Machines (SIFT+SVM) (84.45%). The integration of PCA and SIFT enhanced recognition efficiency and reduced model complexity. Deep learning methods showed superior adaptability and precision across gesture types. Conclusion: This study presents a robust offline ArSL recognition system that enhances communication, education, and social participation for individuals with hearing impairments in Arabic-speaking regions.
Background: Drug abuse causes substantial psychological and physical harm to individuals, highlighting the critical need for advanced diagnostic and treatment methodologies.Objective: This study aimed to develop a highly accurate automatic detection system for substance abuse, specifically targeting Methamphetamine (Meth), Cannabis (Can), and Opioid (Op) users.Material and Methods: This descriptive study developed a drug abuse detection system based on nonlinear Electroencephalogram (EEG) signal analysis combined with a Support Vector Machine (SVM) classifier. It also examined changes in EEG signal complexity associated with Meth, Can, and Op abuse by extracting determinism and complexity parameters using Recurrence Quantification Analysis (RQA).Results: The observed decrease in EEG complexity in the Op and Meth groups suggests that these substances may reduce cognitive or behavioral complexity. Conversely, increased complexity in the Can group compared to the Healthy Control (HC) group may indicate enhanced complexity associated with cannabis use. The classification system achieved 88.77% accuracy, 87.69% sensitivity, and 96.30% specificity. Conclusion: The designed automatic diagnostic assistance system, leveraging nonlinear brain data analysis, effectively differentiates Meth, Op, and Can users from HC individuals.
Background: The assessment of treatment-induced changes in glioma and the evaluation of glioma prognosis are crucial components of effective treatment management. Radiomics models based on Positron Emission Tomography (PET) imaging can provide critical insights into therapeutic response monitoring.Objective: This systematic review aimed to evaluate the performance of PET-based radiomics models in distinguishing treatment-related changes and predicting the prognosis of glioma.Material and Methods: In this systematic review, the articles were searched from the Web of Science databases, MEDLINE, PubMed, and EMBASE. The search terms were “amino acid PET”, “PET”, “glioblastoma”, “glioma”, “positron emission tomography”, “machine learning”, “deep learning”, “radiomics”, “artificial intelligence”, “AI”, “prognosis”, “outcome”, “post treatment changes”, “treatment-related changes”, “progression”, “true progression” “pseudo-progression”, and “necrosis”. The titles, abstracts, and full text of the recognized citations were reviewed by two independent reviewers and then the selected articles were abstracted by two independent reviewers based on a standard grid. PRISMA checklist was applied to assess the overall quality of evidence for each outcome.Results: The PET-based radiomics models outperform conventional PET parameter models, such as maximum tumor-to-brain ratios and mean tumor-to-brain ratios in distinguishing post-treatment changes and predicting glioma prognosis. The model integrating radiomics features and the conventional PET parameters achieved superior diagnostic performance compared to radiomics and conventional parameter models solely in differentiation treatment related changes. Conclusion: PET based radiomics models demonstrate enhanced capability in differentiating tumor recurrence from treatment-related changes. The implementation of these models can facilitate personalized treatment plans and increase the patient’s overall survival or quality of life.
Background: Spatially Fractionated Radiotherapy (SFRT) can be implemented using Volumetric-Modulated Arc Therapy (VMAT) in either two-dimensional (2D) or three-dimensional (3D) configurations.Objective: This study aimed to compare the dosimetric and clinical outcomes of two VMAT-based SFRT techniques for large lung tumors.Material and Methods: In this experimental study, SFRT plans were designed for each patient using cylindrical and spherical grid targets. Single-fraction prescription doses of 15 and 20 Gy were delivered to the grid target isocenters using 6 MV Flattening-filter-free (FFF) photon beams.Results: The 2D SFRT plan demonstrated higher Gross Tumor Volume (GTV) mean dose, GTV Equivalent Uniform Dose (EUD), and Valley-to-peak Dose ratio (VPDR) compared to the 3D lattice plan. However, the 3D Lattice Radiotherapy (3D-LRT) technique provided a better therapeutic ratio and more uniform valley-peak dose distribution. Both plans demonstrated therapeutic ratios greater than one with minimal Normal Tissue Complication Probability (NTCP). Conclusion: Both 2D and 3D lattice VMAT-based SFRT techniques effectively delivered high radiation doses with steep dose gradients within the GTV, minimizing normal tissue exposure and reducing the risk of complications.
Background: Hindlimb unloading (HU) mice is a ground-based model that simulates the effects of microgravity. Since microgravity significantly affects the immune system, understanding immune cell function under these conditions is crucial for developing strategies to protect astronauts from infections and malignancies during long space missions.Objective: To evaluate how microgravity affects neutrophils and T cells as the key components of innate and adaptive immunity, the activity of these cells in HU mice was compared with untreated control mice.Material and Methods: In this experimental study, 10 HU male BALB/c mice and 10 untreated control mice were included. Neutrophil-to-lymphocyte ratio (NLR) was evaluated and neutrophil function was assessed using the DHR assay. T cell proliferation was evaluated using the CFSE-dilution assay. IL-4 and IFN-ɣ production by T cell subsets was determined by intracellular cytokine staining with flow cytometry.Results: The capacity for reactive oxygen species (ROS) production in neutrophils did not differ between HU mice and control mice however, NLR was higher in HU mice. The proliferation of both CD4+ and CD8+ T cells was slightly reduced in HU mice. More notably, IL-4 production by CD4+ T cells and IFN-ɣ production by both CD4+ and CD8+ T cells were significantly decreased in HU mice. Conclusion: Hindlimb unloading, simulating microgravity, impairs immune cell functions by reducing cytokine production and T cell proliferation. The increased NLR in HU mice could indicate a heightened inflammatory response. These insights are essential for advancing space biology and medicine, ensuring astronaut health during prolonged space travel.
Background: Selective attention is the ability to concentrate on specific sensory inputs while ignoring other stimuli and sensory inputs, and it is related to job performance, especially in military personnel.Objective: This study aimed to evaluate selective attention in military personnel rather than normal individuals.Material and Methods: In this cross-sectional study, 40 individuals were divided into two groups: military personnel and normal individuals. Participants were shown a modified flanker task in a military environment, and functional magnetic resonance imaging (fMRI) was used to assess brain activation and functional connectivity through an attention task.Results: Military personnel demonstrated quicker response times than civilians in both high- and low-threat environments, particularly in incongruent trials. In high-threat scenarios, the left Medial Frontal Gyrus (MFG) showed increased voxel counts, while the right MFG was more active in low-threat trials. Additionally, military personnel exhibited stronger functional connectivity in attention regions compared to civilians. Conclusion: Functional connectivity analysis reveals that military personnel show increased connectivity in attention regions during high-threat situations, indicating adaptive neural strategies for managing danger. The study also finds that congruent stimuli demand less neural coordination than incongruent ones, resulting in the understanding improvement of threat perception and attentional processes in military contexts, with significant implications for training and performance.
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Background: About 14% of couples experience infertility, and In Vitro Fertilization (IVF) has become one of the most widely used treatment options. However, the overall success rate of IVF remains relatively low, at around 30%. At present, viable embryos are typically selected on the fifth day based primarily on morphological assessment, a method that is both subjective and limited in accuracy. Considering the substantial financial, physical, and emotional costs associated with failed IVF attempts, there is a pressing need for more reliable and effective embryo selection techniques.Objective: This study aimed to increase the accuracy of embryo selection in IVF based on a deep learning-based transfer with the GoogLeNet architecture.Material and Methods: In this experimental study, a retrospective dataset of embryo images was used to develop and evaluate a deep learning-based classification model in the following main phases: data preprocessing, model implementation, and evaluation. Embryo images were standardized through cropping and normalization to ensure consistency across different imaging systems. The GoogLeNet architecture, pre-trained on the ImageNet dataset, was utilized and further modified to adapt to the specific task of embryo viability classification.Results: Evaluation on the test dataset demonstrated that the proposed model achieved strong predictive performance, with accuracy, precision, recall, and F1-score all reaching 97%. This performance surpasses that of existing baseline techniques, highlighting the model’s effectiveness. Conclusion: The proposed transfer learning-based approach using GoogLeNet shows significant potential for improving embryo selection in IVF, thereby reducing the emotional and financial strain associated with repeated IVF failures.
Background: Magnetic fields can affect physiological systems and some diseases, such as diabetes.Objective: The current study aimed to investigate the effect of a low-power magnetic field on the blood parameters of mice, with streptozotocin-induced diabetes.Material and Methods: In this experimental study, 48 adult male mice were divided into six groups of eight. Diabetic mice were exposed to magnetic fields of 0.0005 and 0.005 tesla for 5 and 10 days using Helmholtz coils. Blood samples were collected every other day to measure blood factors and assess the magnetic field’s effects on diabetes-related parameters.Results: In the diabetes groups, blood protein levels decreased without any effect from the magnetic intervention. However, in three out of the four groups, blood albumin increased under the influence of the magnetic field. The induction of a magnetic field led to a decrease in blood Gamma-Glutamyl Transferase (GGT) activity. Additionally, the magnetic field resulted in increasing blood magnesium levels. Conclusion: The effects of the magnetic field and diabetes on the measured blood parameters, including GGT and magnesium were independent. However, the blood albumin level, which was reduced under the influence of induced diabetes, was improved by the magnetic fields, especially in the magnetic field of the 0.005 tesla.
Exposure to blue light, primarily from light-emitting devices like LEDs, has raised concerns regarding its potential health effects. Neonatal intensive care unit (NICU) nurses, frequently exposed to both digital screens and blue light from phototherapy devices, face particular risks. This study aimed to investigate the influence of blue light exposure from neonatal phototherapy devices on the sleep quality of NICU nurses, presenting the first examination of this issue. NICU nurses and matched controls were studied, comparing their sleep quality using the Pittsburgh Sleep Quality Index (PSQI). General health and reaction time were also assessed using standard questionnaires and benchmark software, respectively. The light intensities of active phototherapy devices were recorded. Statistical analysis included an independent t-test, with a P-value of ≤0.05 considered statistically significant. The study found that exposure to blue light from neonatal phototherapy devices adversely affected sleep disturbances and daytime dysfunction in NICU workers. This aligns with research indicating that reducing ambient blue light can improve cognitive performance, alertness, and sleep quality, especially for night shift workers. It also corresponds with studies linking pre-sleep use of light-emitting devices to higher rates of insomnia in various countries. Amber lenses that block blue light have been proposed as a viable solution for sleep issues. This pioneering research underscores the importance of reducing blue light exposure for NICU nurses. Encouraging the use of blue light-blocking glasses is a practical step that can be taken to mitigate the adverse effects of blue light exposure.
Background: Movement of the Planning Target Volume (PTV) is considered one of the main challenges in radiotherapy for prostate cancer.Objective: The current study aimed to assess the correlation and impact of rectal and bladder volume changes on PTV shift during tomotherapy for prostate cancer, calculate PTV margins using the Van Herk formula to optimize treatment accuracy, and reduce healthy tissue irradiation.Material and Methods: This prospective study investigates PTV displacement and calculates PTV margin considering changes in rectum, bladder, and prostate volumes in 20 prostate cancer patients undergoing tomotherapy. PTV contouring, including prostate and seminal vesicles was performed on patient CT images. Systematic and random PTV motion errors were measured on Mega Voltage Computed Tomography (MVCT) images relative to the reference CT. PTV margin for 95% prescription dose coverage was calculated using the van Herk formula. The correlation between PTV displacement and prostate volume, rectum volume changes, bladder volume changes, age, and patient weight was investigated.Results: Linear regression analysis showed that changes in rectum and bladder volumes were significantly correlated with PTV displacement. The PTV margin was calculated using the van Herk formula, effectively achieving 95% prescription dose coverage. The largest PTV displacement range was in the anterior direction and related to the seminal vesicles. Conclusion: Significant PTV displacements were observed in prostate cancer patients undergoing tomotherapy. Rectum and bladder volume changes are key parameters associated with PTV displacement. Clinical Target Volume (CTV) to PTV margin for delivery of 95% of the prescribed dose is different and non-homogeneous in different parts of the target volume.
Bacteria, part of the three domains of life (Eukarya, Archaea, and Bacteria), are constantly exposed to man-made electromagnetic fields, which often exceed the intensity of natural electromagnetic sources. In response to this exposure, bacteria have developed various defensive and resistant traits. This article presents an overview of both historical and recent research on how bacteria adapt to common sources of Radiofrequency Electromagnetic Fields (RF-EMF). The widespread use of mobile phones and Wi-Fi, both utilizing Radiofrequency (RF) radiation, raises potential public health concerns, which have been addressed by international organizations like the World Health Organization (WHO). Understanding how bacteria adapt to EMF is important for mitigating the risk of increased pathogenicity of radio-resistant bacteria in the human environment.
Background: Functional Magnetic Resonance Imaging (fMRI) is a powerful modality for investigating changes in healthy brains and those with disorders. Anosmia, an olfactory disorder, is commonly associated with traumatic brain injury, particularly in patients suffering from severe trauma.Objective: In this study, we aimed to utilize Resting-State fMRI (rs-fMRI) to examine changes in Functional Connectivity (FC) networks between Healthy Controls (HCs) and patients with Post-Traumatic Anosmia (PTA).Material and Methods: In this retrospective study, we performed rs-fMRI on forty-four PTA patients and forty-three HCs. The Sniffin’ Sticks test was used to assess olfactory function. Seed-based Analysis (SBA) and Independent Component Analysis (ICA) were conducted using MATLAB-based imaging software.Results: PTA patients showed lower Threshold-Discrimination-Identification (TDI) scores compared to HCs. SBA revealed increased FC correlations in the anterior cingulate cortex, piriform, insular cortex, and prefrontal area in PTA patients. Using ICA on the whole brain network, we found increased FC in the right frontal pole, cerebellum, right putamen, anterior cingulate cortex, postcentral gyrus, orbitofrontal cortex, and amygdala in PTA compared to HCs. In PTA patients, global efficiency of the entire brain network showed a significant association with olfactory performance. Conclusion: This study suggests that neural-level olfactory deficits following head trauma are most accurately characterized through SBA and ICA analyses in higher-order regions outside the primary olfactory cortex.
Background: Glioblastoma Multiforme (GBM) is a highly aggressive brain tumor with a poor prognosis. Despite advancements in radiotherapy, its effectiveness is limited due to challenges in delivering high doses without harming healthy tissues.Objective: The current study aimed to determine whether Urtica dioica extract could enhance the cytotoxic effects of radiation on U87MG glioma cells and explore the underlying mechanisms.Material and Methods: This in-vitro study was conducted on the U87MG glioma cell line and investigated the effects of Urtica dioica extract (at various concentrations) and irradiation (2 Gy) on cell viability, cell cycle distribution, and apoptosis.Results: Urtica dioica extract exhibited a concentration and time-dependent cytotoxic effect on U87MG cells. Notably, combining the extract with radiotherapy resulted in a significantly greater reduction in cell viability compared to either treatment alone. Cell cycle analysis revealed that the combination treatment induced G2/M phase arrest more effectively than either treatment alone. Additionally, Urtica dioica extract enhanced the pro-apoptotic effects of radiation, indicated by a significant increase in the late apoptotic cell population. Conclusion: This study demonstrates the radiosensitizing properties of Urtica dioica extract in U87MG glioma cells. The extract promotes cell cycle arrest and apoptosis, potentially leading to improved radiotherapy efficacy. These findings suggest Urtica dioica as a promising complementary therapy for GBM treatment.
Background: Diabetic Retinopathy (DR) is one of several retinal microvascular complications of Diabetes Mellitus (DM), a disease of increasing global prevalence. However, early detection and treatment can reduce or even prevent DR progression. In this work, Deep Learning (DL) techniques are used to grade DR from an early stage using either binary or multiclass classification as a clinical aid to help reduce the risk of patient vision loss.Objective: The primary objective of this research is to develop a low-cost, fast, and accurate automated system using DL for the early detection and classification of DR from retina fundus images.Material and Methods: This cross-sectional study employed three DL models, namely Convolutional Neural Networks (CNNs), decision tree, and logistic regression, to categorize three distinct clinically graded datasets, namely the Iraqi dataset, the Indian Diabetic Retinopathy Image Dataset (IDRiD) and the Eyepacs dataset, according to DR severity.Results: Evaluation of the DL model results showed that logistic regression emerged as the most effective, where accuracies of 99%, 99.3%, and 99.4% were achieved for the Iraqi, IDRiD, and Eyepacs datasets, respectively. Conversely, the decision-tree model achieved the lowest accuracy across the three datasets with 95.2%, 95.9%, and 96.0%, respectively. Conclusion: The logistic regression model demonstrated the highest overall accuracy of the three models for the classification of DR, with the Iraqi dataset with the highest accuracy of the three datasets.
Background: Treatment Planning Systems (TPS) are designed to calculate dose distributions within the CT imaging field of view. However, the Electronic Portal Imaging Device (EPID) is positioned outside this area, making it challenging to use standard TPS for dose calculations at the EPID level.Objective: The objective of this study is to present an innovative approach to address the limitations of TPS in calculating dose distribution at the EPID level.Material and Methods: In this retrospective quantitative study, the CT image was extended to the EPID level and imported into the TPS. 42 treatments were planned, and doses were calculated. The TPS doses were then compared with the measured doses obtained using an Ion Chamber (IC). The study also investigated the impact of field size, phantom thickness, and air gap for energies of 6, 10, and 15 MV.Results: The average, minimum, and maximum dose differences were 1.91%, 0.02%, and 5.79% when changing the field size from 5×5 cm2 to 20×20 cm2, 3.62%, 0.18%, and 6.91% when the phantom thickness changed from 10 to 30 cm, and 3.5%, 0.4%, and 7.46% when the air gap was varied from 30 to 60 cm respectively. 97% of all changes in IC values can be predicted through the linear relationship with TPS. Conclusion: The validated proposed method in this study, as an innovative approach, effectively addresses the limitation of TPS in calculating dose distribution at the EPID level. This can be used as a reference for comparing the measured dose obtained by EPID in dosimetric verification.
Real-time data collection, sharing, and analysis of health-related information are made feasible using the Internet of Things (IoT) in the healthcare field. IoT could transform patient care, enhance clinical results, and optimize healthcare operations by integrating remote monitoring, automation, and data-driven decision-making. Determining the blood type is essential for safe blood transfusions, organ transplant compatibility, and preventing immunological responses. Additionally, the ABO blood group system prediction supports research on associations between blood types and various medical conditions, such as susceptibility to infections, cardiovascular diseases, and clotting disorders. Antigens (A and B) and the Rhesus (Rh) factor (+ or -) are usually used to determine blood grouping. By combining known antibodies with blood samples, the blood group can be examined by the agglutination reactions through image processing techniques. In this work, we proposed an intelligent portable blood analyser for blood type prediction and determination using an IoT-based system. The blood group identification and detection in blood samples is performed with a fabricated simulation device using a 3D Printer and acrylic materials. This system determines a solution using the adaptive Hough transform algorithm and provides the highest level of efficiency and accuracy in blood group identification and counting. Thus, the proposed system lowers the possibility of transfusion-related allergic responses and stores precise outcomes that exclude human-made errors, enabling us to instantly determine a person’s blood type.
The microgravity environment and high radiation levels in space lead to a significant increase in Reactive Oxygen Species (ROS) production compared to Earth, which can have detrimental effects on astronaut health over time. This study examines the hypothesis that high levels of ROS in living organisms in space may aid pre-selected astronauts’ cells in adapting to the intense radiation encountered during missions to Mars and beyond. By looking at evolutionary biology and past radiation events like the Chernobyl disaster, we suggest that increased ROS could trigger adaptive responses similar to those seen in radiation-resistant organisms such as tardigrades. This paper explores the dual nature of ROS as both harmful agents and vital signaling molecules, evaluating their potential to enhance DNA repair, boost antioxidant defenses, and alter mitochondrial metabolism. We aim to see if managing ROS could be a strategy to prepare astronauts’ cells for space travel, using cytogenetic tests to find individuals with strong adaptive responses.
Background: Lung cancer is a leading cause of cancer-related mortality worldwide, underscoring the need for the development of more effective treatment strategies. Radiotherapy (RT), particularly intensity-modulated radiation therapy (IMRT), has enhanced tumor targeting while minimizing damage to healthy tissues. Nevertheless, radioresistance and challenges posed by the tumor microenvironment limit its efficacy.Objective: Selenium-curcumin-polyethylene glycol 600 nanoparticles (Se-Cur-PEG NPs) analyzed as radiosensitizers in IMRT for lung cancer treatment.Material and Methods: In this experimental study, Se-Cur-PEG NPs were synthesized and characterized for their potential as radiosensitizers.Results: The in vitro toxicity of Se-Cur-PEG NPs against A549 lung cancer cells was evaluated using MTT assays, demonstrating a dose-dependent reduction in cell viability. The combination of Se-Cur-PEG NPs (50 µg mL-1) with IMRT (4 Gy) resulted in a significant enhancement in cell death compared to either treatment alone, indicating a strong synergistic effect (CI=1.21) and a notable sensitizer enhancement ratio (SER=2.5). Intracellular ROS generation analysis confirmed that Se-Cur-PEG NPs amplified IMRT-induced oxidative stress, contributing to increased cancer cell toxicity. Conclusion: These findings suggest that Se-Cur-PEG NPs hold promise as effective radiosensitizers, potentially improving lung cancer RT outcomes.
Background: Helical Tomotherapy (HT) enables daily verification of patient positioning using Megavoltage Computed Tomography (MVCT) during each treatment session.Objective: The present study aimed to investigate the effects of Automatic Registration (AR) compared to a combination of Automatic and Manual Registration (AR+MR) on setup errors. Additionally, the study aimed to determine the corresponding Margins of the Planning Target Volume (MPTV).Material and Methods: In this experimental study, a total of 1513 daily MVCT scans were analyzed from September 2020 to January 2024, which were obtained from 71 patients diagnosed with Head and Neck (HN), cervical, and gastrointestinal cancer. The scans were registered with the planning CT to determine the setup errors of the patients. The analysis compares the setup errors between the AR and the AR+MR techniques in translational (X, Y, and Z axes) and rotational directions (RX, RY, and RZ). Additionally, the study calculated the MPTV.Results: In the AR and AR+MR techniques, the translational setup errors were significantly different in the Z-axis for HN patients. For cervical cancer patients, AR and AR+MR exhibited significantly different translational errors across all axes. Furthermore, they also had notable differences in the Y and Z-axis translational errors for Gastro-Intestinal (GI) patients. Regarding the rotational setup errors, a substantial difference was observed in the Z-axis translational error for cervical cancer patients, and in the Y and Z-axes for GI patients. Conclusion: Human assessment after automatic registration helps ensure that the registration is clinically appropriate, especially in circumstances involving deformable patient anatomy.
