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Kidney diseases pose a significant global health challenge, requiring precise diagnostic tools to improve patient outcomes. This study addresses this need by investigating three main categories of renal diseases: kidney stones, cysts, and tumors. Utilizing a comprehensive dataset of 12,446 CT whole abdomen and urogram images, this study developed an advanced AI-driven diagnostic system specifically tailored for kidney disease classification. The innovative approach of this study combines the strengths of traditional convolutional neural network architecture (AlexNet) with modern advancements in ConvNeXt architectures. By integrating AlexNet's robust feature extraction capabilities with ConvNeXt's advanced attention mechanisms, the paper achieved an exceptional classification accuracy of 99.85%. A key advancement in this study's methodology lies in the strategic amalgamation of features from both networks. This paper concatenated hierarchical spatial information and incorporated self-attention mechanisms to enhance classification performance. Furthermore, the study introduced a custom optimization technique inspired by the Adam optimizer, which dynamically adjusts the step size based on gradient norms. This tailored optimizer facilitated faster convergence and more effective weight updates, imporving model performance. The model of this study demonstrated outstanding performance across various metrics, with an average precision of 99.89%, recall of 99.95%, and specificity of 99.83%. These results highlight the efficacy of the hybrid architecture and optimization strategy in accurately diagnosing kidney diseases. Additionally, the methodology of this paper emphasizes interpretability and explainability, which are crucial for the clinical deployment of deep learning models. [ABSTRACT FROM AUTHOR]

This study introduces a novel deep learning-based approach for classifying poultry audio signals, incorporating a custom Burn Layer to enhance model robustness. The methodology integrates digital audio signal processing, convolutional neural networks (CNNs), and the innovative Burn Layer, which injects controlled random noise during training to reinforce the model's resilience to input signal variations. The proposed architecture is streamlined, with convolutional blocks, densely connected layers, dropout, and an additional Burn Layer to fortify robustness. The model demonstrates efficiency by reducing trainable parameters to 191,235, compared to traditional architectures with over 1.7 million parameters. The proposed model utilizes a Burn Layer with burn intensity as a parameter and an Adamax optimizer to optimize and address the overfitting problem. Thorough evaluation using six standard classification metrics showcases the model's superior performance, achieving exceptional sensitivity (96.77%), specificity (100.00%), precision (100.00%), negative predictive value (NPV) (95.00%), accuracy (98.55%), F1 score (98.36%), and Matthew's correlation coefficient (MCC) (95.88%). This research contributes valuable insights into the fields of audio signal processing, animal health monitoring, and robust deep-learning classification systems. The proposed model presents a systematic approach for developing and evaluating a deep learning-based poultry audio classification system. It processes raw audio data and labels to generate digital representations, utilizes a Burn Layer for training variability, and constructs a CNN model with convolutional blocks, pooling, and dense layers. The model is optimized using the Adamax algorithm and trained with data augmentation and early-stopping techniques. Rigorous assessment on a test dataset using standard metrics demonstrates the model's robustness and efficiency, with the potential to significantly advance animal health monitoring and disease detection through audio signal analysis. [ABSTRACT FROM AUTHOR]

Pose estimation is a computer vision task used to detect and estimate the pose of a person or an object in images or videos. It has some challenges that can leverage advances in computer vision research and others that require efficient solutions. In this paper, we provide a preliminary review of the state‐of‐the‐art in pose estimation, including both traditional and deep learning approaches. Also, we implement and compare the performance of Hand Pose Estimation (HandPE), which uses PoseNet architecture for hand sign problems, for an ASL dataset by using different optimizers based on 10 common evaluation metrics on different datasets. Also, we discuss some related future research directions in the field of pose estimation and explore new architectures for pose estimation types. After applying the PoseNet model, the experiment results showed that the accuracy achieved was 99.9%, 89%, 97%, 79%, and 99% for the ASL alphabet, HARPET, Yoga, Animal, and Head datasets, comparing those with common optimizers and evaluation metrics on different dataset. [ABSTRACT FROM AUTHOR]

Medical image classification (IC) is a method for categorizing images according to the appropriate pathological stage. It is a crucial stage in computer-aided diagnosis (CAD) systems, which were created to help radiologists with reading and analyzing medical images as well as with the early detection of tumors and other disorders. The use of convolutional neural network (CNN) models in the medical industry has recently increased, and they achieve great results at IC, particularly in terms of high performance and robustness. The proposed method uses pre-trained models such as Dense Convolutional Network (DenseNet)-121 and Visual Geometry Group (VGG)-16 as feature extractor networks, bidirectional long short-term memory (BiLSTM) layers for temporal feature extraction, and the Support Vector Machine (SVM) and Random Forest (RF) algorithms to perform classification. For improved performance, the selected pre-trained CNN hyperparameters have been optimized using a modified grey wolf optimization method. The experimental analysis for the presented model on the Mammographic Image Analysis Society (MIAS) dataset shows that the VGG16 model is powerful for BC classification with overall accuracy, sensitivity, specificity, precision, and area under the ROC curve (AUC) of 99.86%, 99.9%, 99.7%, 97.1%, and 1.0, respectively, on the MIAS dataset and 99.4%, 99.03%, 99.2%, 97.4%, and 1.0, respectively, on the INbreast dataset. [ABSTRACT FROM AUTHOR]

This paper explores the use of chest CT scans for early detection of COVID-19 and improved patient outcomes. The proposed method employs advanced techniques, including binary cross-entropy, transfer learning, and deep convolutional neural networks, to achieve accurate results. The COVIDx dataset, which contains 104,009 chest CT images from 1,489 patients, is used for a comprehensive analysis of the virus. A sample of 13,413 images from this dataset is categorised into two groups: 7,395 CT scans of individuals with confirmed COVID-19 and 6,018 images of normal cases. The study presents pre-trained transfer learning models such as ResNet (50), VGG (19), VGG (16), and Inception V3 to enhance the DCNN for classifying the input CT images. The binary cross-entropy metric is used to compare COVID-19 cases with normal cases based on predicted probabilities for each class. Stochastic Gradient Descent and Adam optimizers are employed to address overfitting issues. The study shows that the proposed pre-trained transfer learning models achieve accuracies of 99.07%, 98.70%, 98.55%, and 96.23%, respectively, in the validation set using the Adam optimizer. Therefore, the proposed work demonstrates the effectiveness of pre-trained transfer learning models in enhancing the accuracy of DCNNs for image classification. Furthermore, this paper provides valuable insights for the development of more accurate and efficient diagnostic tools for COVID-19. [ABSTRACT FROM AUTHOR]

Background: Anemia is common complication in children with chronic kidney disease (CKD) mainly anemia of chronic disease (ACD), but iron deficiency anemia (IDA) is also common. Soluble transferrin receptor (sTfR) is important marker for IDA. This study aims to evaluate the utility of soluble transferrin Receptor-Ferritin Indices as markers of IDA and for differentiation ACD from IDA in childern with chronic kidney disease. Methods: cross-sectional study was conducted at Department of Pediatric Nephrology at Zagazig University Hospital including 45 anemic children (19 ACD, 13 IDA and 13 mixed). The duration of the study ranges from 6 to 12 months, Soluble serum transferrin receptor and Soluble serum transferrin receptor index were measured. Results: There was significant difference regard sTfR and sTfR index IDA group were significantly higher than other two groups then mixed and finally ACD were significantly lower. Conclusion: sTfR value was useful tool for assessment of iron status in patients with CKD, however, they are at best complementary to the existing indices of serum ferritin and TSAT. [ABSTRACT FROM AUTHOR]

Background: Constipation is among the most frequently reported health problems in old-age people, with significant negative impacts on their health, and quality of life. Aim of study: The aim was to evaluate the effectiveness of an abdominal massage learning package in relieving constipation among older adults. Setting: conducted in outpatient clinic beni-suef university hospitals & two Geriatric Social Clubs in Beni-Suef city. Subjects: on 100 older adults suffering from constipation in the setting. Method: A quasi-experimental study Tools: A structured interview questionnaire sheet was used to collect pre-post data concerning knowledge about constipation and reported practices of abdominal massage. The training program was implemented and its effectiveness after three months. Results: The sample had equal gender distribution. Satisfactory knowledge significantly improved from 53% to 98%, and adequate practice from 30% to 65% post-intervention (p<0.001). The problem of constipation showed significant improvement with 74% reporting relief at the postintervention phase (p<0.001). The predictors of the relief of constipation were the practice score and the knowledge of the benefits of abdominal massage, Odds Ratios 1.66 and 1.58, respectively. Conclusion: Training older adults in abdominal massage technique is effective in relieving their constipation. Recommendations: The technique should be used in geriatric care in various community and healthcare settings. Further research is proposed to assess its effect on older adults' quality of life. [ABSTRACT FROM AUTHOR]

Coffee bean production can encounter challenges due to fluctuations in global coffee prices, impacting the economic stability of some countries that heavily depend on coffee production. The primary objective is to evaluate how effectively various pre-trained models can predict coffee types using advanced deep learning techniques. The selection of an optimal pre-trained model is crucial, given the growing popularity of specialty coffee and the necessity for precise classification. We conducted a comprehensive comparison of several pre-trained models, including AlexNet, LeNet, HRNet, Google Net, Mobile V2 Net, ResNet (50), VGG, Efficient, Darknet, and DenseNet, utilizing a coffee-type dataset. By leveraging transfer learning and fine-tuning, we assess the generalization capabilities of the models for the coffee classification task. Our findings emphasize the substantial impact of the pre-trained model choice on the model's performance, with certain models demonstrating higher accuracy and faster convergence than conventional alternatives. This study offers a thorough evaluation of pre-trained architectural models regarding their effectiveness in coffee classification. Through the evaluation of result metrics, including sensitivity (1.0000), specificity (0.9917), precision (0.9924), negative predictive value (1.0000), accuracy (1.0000), and F1 score (0.9962), our analysis provides nuanced insights into the intricate landscape of pre-trained models. [ABSTRACT FROM AUTHOR]

Prostate cancer, the most common cancer in men, is influenced by age, family history, genetics, and lifestyle factors. Early detection of prostate cancer using screening methods improves outcomes, but the balance between overdiagnosis and early detection remains debated. Using Deep Learning (DL) algorithms for prostate cancer detection offers a promising solution for accurate and efficient diagnosis, particularly in cases where prostate imaging is challenging. In this paper, we propose a Prostate Cancer Detection Model (PCDM) model for the automatic diagnosis of prostate cancer. It proves its clinical applicability to aid in the early detection and management of prostate cancer in real-world healthcare environments. The PCDM model is a modified ResNet50-based architecture that integrates faster R-CNN and dual optimizers to improve the performance of the detection process. The model is trained on a large dataset of annotated medical images, and the experimental results show that the proposed model outperforms both ResNet50 and VGG19 architectures. Specifically, the proposed model achieves high sensitivity, specificity, precision, and accuracy rates of 97.40%, 97.09%, 97.56%, and 95.24%, respectively. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had a significant impact on human migration worldwide, affecting transportation patterns in cities. Many cities have issued "stay-at-home" orders during the outbreak, causing commuters to change their usual modes of transportation. For example, some transit/bus passengers have switched to driving or car-sharing. As a result, urban traffic congestion patterns have changed dramatically, and understanding these changes is crucial for effective emergency traffic management and control efforts. While previous studies have focused on natural disasters or major accidents, only a few have examined pandemic-related traffic congestion patterns. This paper uses correlations and machine learning techniques to analyze the relationship between COVID-19 and transportation. The authors simulated traffic models for five different networks and proposed a Traffic Prediction Technique (TPT), which includes an Impact Calculation Methodology that uses Pearson's Correlation Coefficient and Linear Regression, as well as a Traffic Prediction Module (TPM). The paper's main contribution is the introduction of the TPM, which uses Convolutional Neural Network to predict the impact of COVID-19 on transportation. The results indicate a strong correlation between the spread of COVID-19 and transportation patterns, and the CNN has a high accuracy rate in predicting these impacts. [ABSTRACT FROM AUTHOR]

Black fungus is a rare and dangerous mycology that usually affects the brain and lungs and could be life-threatening in diabetic cases. Recently, some COVID-19 survivors, especially those with co-morbid diseases, have been susceptible to black fungus. Therefore, recovered COVID-19 patients should seek medical support when they notice mucormycosis symptoms. This paper proposes a novel ensemble deep-learning model that includes three pre-trained models: reset (50), VGG (19), and Inception. Our approach is medically intuitive and efficient compared to the traditional deep learning models. An image dataset was aggregated from various resources and divided into two classes: a black fungus class and a skin infection class. To the best of our knowledge, our study is the first that is concerned with building black fungus detection models based on deep learning algorithms. The proposed approach can significantly improve the performance of the classification task and increase the generalization ability of such a binary classification task. According to the reported results, it has empirically achieved a sensitivity value of 0.9907, a specificity value of 0.9938, a precision value of 0.9938, and a negative predictive value of 0.9907. [ABSTRACT FROM AUTHOR]

Background: Though fracture is known complication of stenting, pseudoaneurysm asscoiated with stent fracture is an extremely rare complication. This has previoulsy been described to occur at least one or more years following initial stent placement. Here we present a case of multi-site stent fracture leading to two separate SFA pseudoaneurysms within one year of placement, successfully treated with covered stents. Case presentation: A 72-year-old male presented with severe claudication of his left lower extremity (Rutherford 3), found to have long segment SFA chronic total occlusion (CTO). Patient successfully underwent endovascular revascularization. Follow-up duplex ultrasound (US) at one year demonstrated a focus of severe in-stent restenosis (ISR). During repeat angiogram for treatment of the stenosis, stent fracture and pseudoaneurysm was seen in the distal SFA, which was treated successfully with a self-expanding covered stent. Additional stent fractures and pseudoanerusyms were subseuqently identified on follow-up, necessitating a third angiogram, and these were successfully repaired using overlapping covered stents, without further recurrence. Conclusions: Superficial femoral artery stent fractures leading to pseudoaneurysms are extremely rare, particularly within first year of stent placement. Endovascular repair with covered stents has proven to be an effective treatment option with decreased procedural morbidity compared to surgical repair. [ABSTRACT FROM AUTHOR]

The aim of this paper is the green synthesis of copper nanoparticles (Cu NPs) via Quinoa seed extract. X-ray diffraction (XRD) results confirmed the production of the pure crystalline face center cubic system of the Cu NPs with an average crystallite size of 8.41 nm. Infrared spectroscopy (FT-IR) analysis confirmed the capping and stabilization of the Cu NPs bioreduction process. UV visible spectroscopy (UV–Vis). surface plasmon resonance revealed the absorption peak at 324 nm with an energy bandgap of 3.47 eV. Electrical conductivity was conducted assuring the semiconductor nature of the biosynthesized Cu NPs. Morphological analysis was investigated confirming the nano-characteristic properties of the Cu NPs as polycrystalline cubic agglomerated shapes in scanning electron microscopy (SEM) analysis. Transmission electron microscopy (TEM) analysis also was used to assess the cubic shapes at a particle size of 15.1 ± 8.3 nm and a crystallinity index about equal to 2.0. Energy dispersive spectroscopy (EDX) was conducted to investigate the elemental composition of the Cu NPs. As a potential utility of the biosynthesized Cu NPs as nano adsorbents to the removal of the Cefixime (Xim) from the pharmaceutical wastewater; adsorption studies and process parameters were being investigated. The following strategic methodology for maximum Xim removal was conducted to be solution pH 4, Cu NPs dosage 30 mg, Xim concentration 100 mg/L, and absolute temperature 313 K. The maximum monolayer adsorption capacity was 122.9 mg/g according to the Langmuir isothermal model, and the kinetic mechanism was pseudo-second-order. Thermodynamic parameters also were derived as spontaneous chemisorption endothermic processes. Antibacterial activity of the Xim and Xim@Cu NPs was investigated confirming they are highly potent against each Gram-negative and Gram-positive bacterium. [ABSTRACT FROM AUTHOR]

Retinal optical coherence tomography (OCT) imaging is a valuable tool for assessing the condition of the back part of the eye. The condition has a great effect on the specificity of diagnosis, the monitoring of many physiological and pathological procedures, and the response and evaluation of therapeutic effectiveness in various fields of clinical practices, including primary eye diseases and systemic diseases such as diabetes. Therefore, precise diagnosis, classification, and automated image analysis models are crucial. In this paper, we propose an enhanced optical coherence tomography (EOCT) model to classify retinal OCT based on modified ResNet (50) and random forest algorithms, which are used in the proposed study's training strategy to enhance performance. The Adam optimizer is applied during the training process to increase the efficiency of the ResNet (50) model compared with the common pre-trained models, such as spatial separable convolutions and visual geometry group (VGG) (16). The experimentation results show that the sensitivity, specificity, precision, negative predictive value, false discovery rate, false negative rate accuracy, and Matthew's correlation coefficient are 0.9836, 0.9615, 0.9740, 0.9756, 0.0385, 0.0260, 0.0164, 0.9747, 0.9788, and 0.9474, respectively. [ABSTRACT FROM AUTHOR]

Optimization algorithms are used to improve model accuracy. The optimization process undergoes multiple cycles until convergence. A variety of optimization strategies have been developed to overcome the obstacles involved in the learning process. Some of these strategies have been considered in this study to learn more about their complexities. It is crucial to analyse and summarise optimization techniques methodically from a machine learning standpoint since this can provide direction for future work in both machine learning and optimization. The approaches under consideration include the Stochastic Gradient Descent (SGD), Stochastic Optimization Descent with Momentum, Rung Kutta, Adaptive Learning Rate, Root Mean Square Propagation, Adaptive Moment Estimation, Deep Ensembles, Feedback Alignment, Direct Feedback Alignment, Adfactor, AMSGrad, and Gravity. prove the ability of each optimizer applied to machine learning models. Firstly, tests on a skin cancer using the ISIC standard dataset for skin cancer detection were applied using three common optimizers (Adaptive Moment, SGD, and Root Mean Square Propagation) to explore the effect of the algorithms on the skin images. The optimal training results from the analysis indicate that the performance values are enhanced using the Adam optimizer, which achieved 97.30% accuracy. The second dataset is COVIDx CT images, and the results achieved are 99.07% accuracy based on the Adam optimizer. The result indicated that the utilisation of optimizers such as SGD and Adam improved the accuracy in training, testing, and validation stages. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea (OSA) is a common disease with a high degree of association with and possible etiological factor for several cardiovascular diseases. Patients who are admitted to the Intensive Care Unit (ICU) are incredibly sick, have multiple co-morbidities, and are at substantial risk for mortality. A study of cardiovascular manifestations and disease processes in patients with OSA admitted to the ICU is very intriguing, and its impact is likely significant. Although much is known about these cardiovascular complications associated with OSA, there is still a paucity of high-quality evidence trying to establish causality between the two. Studies exploring the potential impact of therapeutic interventions, such as positive airway pressure therapy (PAP), on cardiovascular complications in ICU patients are also needed and should be encouraged. This study reviewed the literature currently available on this topic and potential future research directions of this clinically significant relationship between OSA and cardiovascular disease processes in the ICU and beyond. [ABSTRACT FROM AUTHOR]

Infectious diseases are an imminent danger that faces human beings around the world. Malaria is considered a highly contagious disease. The diagnosis of various diseases, including malaria, was performed manually, but it required a lot of time and had some human errors. Therefore, there is a need to investigate an efficient and fast automatic diagnosis system. Deploying deep learning algorithms can provide a solution in which they can learn complex image patterns and have a rapid improvement in medical image analysis. This study proposed a Convolution al Neural Network (CNN) model to detect malaria automatically. A Malaria Convolutional Neural Network (MCNN) model is proposed in this work to classify the infected cases. MCNN focuses on detecting infected cells, which aids in the computation of parasitemia, or infection measures. The proposed model achieved 0.9929, 0.9848, 0.9859, 0.9924, 0.0152, 0.0141, 0.0071, 0.9890, 0.9894, and 0.9780 in terms of specificity, sensitivity, precision, accuracy, F1-score, and Matthews Correlation Coefficient, respectively. A comparison was carried out between the proposed model and some recent works in the literature. This comparison demonstrates that the proposed model outperforms the compared works in terms of evaluation metrics. [ABSTRACT FROM AUTHOR]

Background: Carbimazole is a popular drug for hyperthyroidism. However, it has many side effects on different tissues. Bone marrow-derived mesenchymal stem cells (BM-MSCs) are efficient in tissue regeneration. Aim: To appraise the consequence of carbimazole (antithyroid drug) intake on albino rat parotid gland structure and plausible outcome of bone marrow-derived mesenchymal stem cells (BM-MSCs) application at different durations. Material and Methods: Forty adult male albino rats were used and categorized into: Group I: obtained distilled water. Group II: acquired therapeutic dose of carbimazole. Group III: received carbimazole and single dose of BM-MSCs at the start of experiment. Group IV: got carbimazole then by finishing of 3rd week they received solitary injection of BM-MSCs. Preparation of specimens for examination by light, fluorescent and transmission electron microscope was performed. Histomorphometric data of acini area% was statistically analyzed using ANOVA test. Results: Both histological and ultrastructural examinations illustrated that parotid gland has normal structure in Group I and approximately normal features in Group III. Group II demonstrated distorted acini and duct system. Group IV presented normal features in some acini and some areas of duct system but degenerative features in others. Results of fluorescence labeling explored some labeled-BM-MSCs in Group III but apparently copious labeled cells in Group IV. Statistical results showed highest mean acini area% in Group I, subsequently Group III, then Group IV, followed by Group II. Conclusions: Carbimazole has deteriorative outcomes on the parotid gland of albino rats. BM-MSCs ameliorate the damaging upshot of carbimazole in a direct proportion manner with time factor. [ABSTRACT FROM AUTHOR]

One of the most extensive desalination facilities, the multi-stage flash (MSF) system uses saltwater to create desalinated water, which is then utilized to develop high productivity fresh (sweet) or non-salt water. The operation of MSF plants is depending on using thermal energy to evaporate some vapor (pure water) from the seawater based on the reduction in the operating pressure, which the generated vapor will condense to form the product. This article will outline the design of the brine heater and the one-stage (just the first stage) MSF system. The thermal performance (MSF plant efficiency) of the suggested system (one-stage of MSF), measured by Gain Output Ratio, reaches 0.45 (very low value). Therefore, the MSF plants must be designed with multi-stages to increase its thermal performance. A parametric study has been done to study the effect of tube length and vapor temperature on the number of tubes. [ABSTRACT FROM AUTHOR]

Purpose: Erythroferrone (ERFE) is well acknowledged for its inhibitory function on hepcidin synthesis in the liver during stress erythropoiesis, thereby ensuring sufficient iron supply to bone marrow erythroblasts. Hepcidin plays an indispensable role in the pathogenesis of anemia of chronic disease (ACD). Thus, ERFE was suggested to protect against ACD in various diseases. Rheumatoid arthritis (RA) is commonly involved with ACD and high hepcidin levels, with a further increase of the latter in active states. The present study is a case-control study that aimed to determine the pattern of ERFE expression in RA patients with concomitant ACD and study its relationship with hepcidin, erythropoietin (EPO) and disease activity. Patients and Methods: Fifty-five RA patients with ACD were categorized into active and inactive RA using the disease activity score (DAS28); 15 healthy subjects were included as control subjects. ERFE was measured for patients and control subjects using quantitative real-time polymerase chain reaction, in addition to testing for CBC, ESR, CRP, iron profile parameters and hepcidin. EPO was assessed for patients of both active and inactive RA groups. Results: ERFE and hepcidin showed the highest levels in active RA; ERFE values were similar in control subjects and inactive RA patients, while hepcidin was significantly higher in inactive RA than control subjects. Patients with high ERFE levels had higher RBC, Hct, MCV, hepcidin and EPO levels. Stepwise regression analysis has identified DAS28 and disease duration as the best predictors of ERFE values, whereas ERFE and hepcidin were independent predictors of disease activity. Conclusion: We introduce ERFE as a novel marker of RA activity. Although the inhibitory effect of ERFE on hepcidin is not evident, our results still indicate that ERFE may have a beneficial erythropoietic effect in the context of ACD in RA disease activity. [ABSTRACT FROM AUTHOR]

To assessing nurses ’knowledge about non-pharmacological measures to control hypertension in Basra educational hospitals. Data were collected through a questionnaire of 50 samples that included two axes, The demographic information for nurses and the second their scientific knowledge through a group of questions amounting to 20 questions. The results showed that the nurses answers regarding weight reeducation in over weight individual is needed to reduce high blood pressure, 96% were correct, with highly significant mean of score 2.94, regular physical activity is need to reduce high blood pressure 88% with significant mean of score 2.76. Also, t relaxation techniques such as shopping, watching a movie, meditation and sitting on the beach can reduce high blood pressure were 80%, with significant mean of score 2.68. While the nurses had non correct answer regarding the person’s dependence on a diet Rich in saturated fats, found in animal products, does not affect his high blood pressure, by 70%, with non-significant mean of score 1.86 The study concluded that nurses' knowledge of non-pharmacological measures to control high blood pressure summarizes that the majority of nurses were aware of the importance of non-pharmacological measures to control high blood pressure, and despite this, more knowledge about this must appear through conducting educational courses, educational programs and publication Paying attention to diet in hospitals and encouraging a healthy lifestyle. [ABSTRACT FROM AUTHOR]

Nowadays, Coronavirus (COVID-19) considered one of the most critical pandemics in the earth. This is due its ability to spread rapidly between humans as well as animals. COVID-19 expected to outbreak around the world, around 70 % of the earth population might infected with COVID-19 in the incoming years. Therefore, an accurate and efficient diagnostic tool is highly required, which the main objective of our study. Manual classification was mainly used to detect different diseases, but it took too much time in addition to the probability of human errors. Automatic image classification reduces doctors diagnostic time, which could save human's life. We propose an automatic classification architecture based on deep neural network called Worried Deep Neural Network (WDNN) model with transfer learning. Comparative analysis reveals that the proposed WDNN model outperforms by using three pre-training models: InceptionV3, ResNet50, and VGG19 in terms of various performance metrics. Due to the shortage of COVID-19 data set, data augmentation was used to increase the number of images in the positive class, then normalization used to make all images have the same size. Experimentation is done on COVID-19 dataset collected from different cases with total 2623 where (1573 training, 524 validation, 524 test). Our proposed model achieved 99,046, 98,684, 99,119, 98,90 in terms of accuracy, precision, recall, F-score, respectively. The results are compared with both the traditional machine learning methods and those using Convolutional Neural Networks (CNNs). The results demonstrate the ability of our classification model to use as an alternative of the current diagnostic tool. [ABSTRACT FROM AUTHOR]

Copyright of Kufa Journal for Veterinary Medical Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: The clinical characteristics, risk factors and outcomes of H1N1 influenza cases in Bahrain have not been reported. Objective: To evaluate personal and clinical characteristics, risk factors, and outcomes in adults with confirmed H1N1 infection. Setting: Salmaniya Medical Complex, Bahrain. Design: A Retrospective Observational Study. Method: All H1N1-confirmed-patients admitted between 1 September 2015 and 31 March 2016 were reviewed. Personal and clinical characteristics, risk factors, and outcomes were documented. The analyses were performed using STATA software, version 12 and P-value of <.05 was considered significant. Result: Twenty-six confirmed H1N1 cases were admitted and reviewed. Eight (30.7%) were admitted to the intensive care unit and 5 (19.2%) died. None of the patients received flu vaccine. The age range was 24 to 83 years. The presence of comorbid conditions and smoking were common among the study population; however, it was not statistically significant. A significant association was discovered between mortality and the need for vasopressors (all patients requiring vasopressors died compared to only one among other patients, P-value<.005). Conclusion: In our study of the flu season of 2015-2016, 5 patients died. We recommend vaccination, especially for high risk groups. The study is limited by the small study-population size and one missing file. [ABSTRACT FROM AUTHOR]

Copyright of Kufa Journal for Veterinary Medical Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The aim of the present study was to examine primary cilia in endometrial tissue during the menstrual cycle and to clarify their morphological changes with different grades of endometrial cancer. Images of fluorescence immunostaining taken by confocal microscopy were used to count the number of primary cilia in normal endometrium and endometrioid carcinoma Grade 1 and Grade 3 specimens. To examine the association between autophagy and ciliogenesis in endometrioid carcinoma, the expression of p62/Sequestosome-1, a selective substrate for autophagy, and oral-facial-digital syndrome 1 protein (OFD1), a protein associated with ciliogenesis, were examined using images of fluorescence immunostaining taken by confocal microscopy. The level of p62 expression was confirmed by western blotting. In proliferative and secretory endometrial stromal cells, the percentage of cells that were ciliated was 7.2 and 32.7% (95% confidence interval=21.61-39.79; P<0.01), and the length of the primary cilia was 1.24 µm and 2.34 µm (0.92-1.26; P<0.01), respectively. In stromal cells of endometrioid carcinoma Grade 1 and Grade 3, the percentage of ciliated cells was 13.5 and 2.9% (7.89-15.05; P<0.001), and the length of the primary cilia was 2.02 and 1.14 µm (0.76-0.99; P<0.001), respectively. In both normal menstrual cycle tissue and endometrial carcinomas, the percentage of primary cilia was lower and their length was shorter in tissues with higher proliferative potential. The expression of OFD1 was significantly higher in Grade 3 compared with Grade 1 as indicated by quantifying the intensity of the fluorescence images (133–12248; P=0.046). To the best of our knowledge, this is the first study concerning the distribution of primary cilia in normal endometrium and endometrial cancer tissues. Overall, fewer ciliated cells in the highly malignant endometrial cancer tissues may be associated not only to the proliferation of cancer cells, but also to the excessive accumulation of OFD1 due to dysfunctional autophagy. [ABSTRACT FROM AUTHOR]