Acute gastroenteritis-related acute kidney injury in a tertiary care center.

BACKGROUND: Acute gastroenteritis (AGE) can cause acute kidney injury (AKI) via hypoperfusion mechanisms. Early detection of AKI caused by AGE can significantly decrease mortality rates. In Saudi Arabia, studies investigating the association between AGE and AKI are limited; thus, we aimed to fill this knowledge gap. OBJECTIVES: Analyze all cases of AGE reported in tertiary-care hospitals to assess the prevalence of AKI among AGE patients. DESIGN: Retrospective cohort SETTINGS: Single tertiary-care center PATIENTS AND METHODS: The study included patients treated for AGE between October 2017 and October 2022. Stool culture was used to diagnose AGE. Inclusion criteria were infective diarrhea and/ or vomiting, and availability of data (demographics, comorbidities, malignancies, length of hospital stay, vital signs at the time of diagnosis, dehydration, causative agents of diarrhea, hemodialysis status, and laboratory data. MAIN OUTCOME MEASURES: Prevalence of AKI among AGE patients and factors associated with development of AKI. SAMPLE SIZE: 300 patients diagnosed with AGE. RESULTS: Of the 300 patients with AGE, 41 (13.6%) had AKI, those older than 60 years were more likely to develop AKI. The most frequent cause of AGE was Salmonella spp. (n=163, 53.3%), whereas AKI was most common in Clostridium difficile AGE patients (n=21, 51.2%). Furthermore, the most common comorbidity in the present study was malignancy, especially leukemia and lymphoma the risk of AKI was independently associated with mild dehydration, higher serum urea concentrations and low GFR values. CONCLUSIONS: Patients hospitalized for diarrheal disease are at an increased risk of developing AKI due to dehydration and comorbid conditions. It is crucial to keep kidney function in mind for AGE patients as this is associated with a high mortality rate and poor prognosis. LIMITATIONS: The main limitation of this study was its retrospective design. Another limitation is that it is limited to a single center. CONFLICTS OF INTEREST: None.

Computational Intelligence Approaches in Developing Cyberattack Detection System.

The Internet plays a fundamental part in relentless correspondence, so its applicability can decrease the impact of intrusions. Intrusions are defined as movements that unfavorably influence the focus of a computer. Intrusions may sacrifice the reputability, integrity, privacy, and accessibility of the assets attacked. A computer security system will be traded off when an intrusion happens. The novelty of the proposed intelligent cybersecurity system is its ability to protect Internet of Things (IoT) devices and any networks from incoming attacks. In this research, various machine learning and deep learning algorithms, namely, the quantum support vector machine (QSVM), k-nearest neighbor (KNN), linear discriminant and quadratic discriminant long short-term memory (LSTM), and autoencoder algorithms, were applied to detect attacks from signature databases. The correlation method was used to select important network features by finding the features with a high-percentage relationship between the dataset features and classes. As a result, nine features were selected. A one-hot encoding method was applied to convert the categorical features into numerical features. The validation of the system was verified by employing the benchmark KDD Cup database. Statistical analysis methods were applied to evaluate the results of the proposed study. Binary and multiple classifications were conducted to classify the normal and attack packets. Experimental results demonstrated that KNN and LSTM algorithms achieved better classification performance for developing intrusion detection systems; the accuracy of KNN and LSTM algorithms for binary classification was 98.55% and 97.28%, whereas the KNN and LSTM attained a high accuracy for multiple classification (98.28% and 970.7%). Finally, the KNN and LSTM algorithms are fitting-based intrusion detection systems.

Classification and Detection of Autism Spectrum Disorder Based on Deep Learning Algorithms.

Autism spectrum disorder (ASD) is a type of mental illness that can be detected by using social media data and biomedical images. Autism spectrum disorder (ASD) is a neurological disease correlated with brain growth that later impacts the physical impression of the face. Children with ASD have dissimilar facial landmarks, which set them noticeably apart from typically developed (TD) children. Novelty of the proposed research is to design a system that is based on autism spectrum disorder detection on social media and face recognition. To identify such landmarks, deep learning techniques may be used, but they require a precise technology for extracting and producing the proper patterns of the face features. This study assists communities and psychiatrists in experimentally detecting autism based on facial features, by using an uncomplicated web application based on a deep learning system, that is, a convolutional neural network with transfer learning and the flask framework. Xception, Visual Geometry Group Network (VGG19), and NASNETMobile are the pretrained models that were used for the classification task. The dataset that was used to test these models was collected from the Kaggle platform and consisted of 2,940 face images. Standard evaluation metrics such as accuracy, specificity, and sensitivity were used to evaluate the results of the three deep learning models. The Xception model achieved the highest accuracy result of 91%, followed by VGG19 (80%) and NASNETMobile (78%).

COVID-19-Related Knowledge and Practices Among Health Care Workers in Saudi Arabia: Cross-sectional Questionnaire Study.

BACKGROUND: Health care workers are at the front line against COVID-19. The risk of transmission decreases with adequate knowledge of infection prevention methods. However, health care workers reportedly lack a proper attitude and knowledge of different viral outbreaks. OBJECTIVE: This study aimed to assess the knowledge and attitude of health care workers in Saudi Arabia toward COVID-19. Assessment of these parameters may help researchers focus on areas that require improvement. METHODS: A cross-sectional questionnaire study was conducted among 563 participants recruited from multiple cities in Saudi Arabia. An online questionnaire was shared via social media applications, which contained questions to health care workers about general information regarding COVID-19 and standard practices. RESULTS: The mean age of the study population was 30.7 (SD 8) years. Approximately 8.3% (47/563) of the health care workers were isolated as suspected cases of COVID-19, and 0.9% (n=5) were found positive. The majority agreed that social distancing, face masks, and hand washing are effective methods for preventing disease transmission. However, only 63.7% (n=359) knew the correct duration of hand washing. Almost 70% (n=394) strictly adhered to hand hygiene practices, but less than half complied with the practice of wearing a face mask. Significant differences in health care workers' attitudes were observed on the basis of their city of residence, their adherence to COVID-19 practices, and their compliance with the use of a face mask. Among the health care workers, 27.2% (n=153) declared that they will isolate themselves at home and take influenza medication if they experience COVID-19 symptoms. CONCLUSIONS: The majority of health care workers in Saudi Arabia presented acceptable levels of general knowledge on COVID-19, but they lack awareness in some crucial details that may prevent disease spread. Intense courses and competency assessments are highly recommended. Prevention of disease progression is the only option for the time being.