Sleep-Disordered Breathing and Its Association with Nocturnal Enuresis at the Primary Schools in Saudi Arabia: A Cross-Sectional Study.

The correlation between nocturnal enuresis (NE) and sleep-disordered breathing (SDB) was reported. We aim to determine whether there is an association between NE and SDB in children and to assess the prevalence of SDB and NE in primary school children aged 6-12 years in Saudi Arabia. A cross-sectional observational study was conducted among the caregivers of children aged 6-12 years in all Saudi Arabia regions. The data were gathered through a self-administered online questionnaire. It included demographic information, weight and height, and associated comorbidities, in addition to the weekly frequencies of snoring symptoms and of enuresis, as well as of unrefreshing sleep using Likert-type response scales. Counts and percentages, the mean ± standard deviation, chi-square test, independent samples t-test, and regression analysis were used in the statistical analysis using R v 3.6.3. The questionnaire was completed by 686 respondents. Most respondents did not report any comorbidities in their children (77.1%). Asthma and adenotonsillar hypertrophy were reported in 16.2% and 15.6% of children, respectively. Unrefreshing sleep, mouth breathing at night, snoring, chronic nasal obstruction, and difficulty breathing while asleep were reported once or twice per week in 38%, 34%, 28%, 18%, and 18% of children, respectively. The prevalence of NE was 22.3%, with about 36.6% of children having NE two or more times per week. Significantly, NE was reported in 26.6% of children who slept before 10 PM compared to 19% of children who slept after 10 PM; in 28.6% of children who snored or loudly snored (57.1%) three times or more per week; and in 51.2% and 27.5% of children with difficulty breathing while asleep and who breathed through their mouth at night for one or two nights per week, respectively. A multivariable regression analysis showed that male gender (OR = 1.52, p = 0.010), obesity (OR = 1.24, p = 0.028), early sleeping time (OR = 1.40, p = 0.048), loud snoring for three or more nights per week (OR = 1.54, p = 0.001), difficulty breathing for one or two nights per week (OR = 1.85, p = 0.010), and mouth breathing at night for one or two nights per week (OR = 1.55, p = 0.049) were associated with higher odds of NE. Our study revealed that 22.3% of primary school children reported suffering from NE. SDB is a common problem among children with NE. The exact mechanism that links SDB to the increase in the risk of NE is unknown. Male gender, obesity, early sleeping time, loud snoring, difficulty breathing, and mouth breathing at night are potential independent risk factors of NE in school-age children.

Colon Cancer Diagnosis Based on Machine Learning and Deep Learning: Modalities and Analysis Techniques.

The treatment and diagnosis of colon cancer are considered to be social and economic challenges due to the high mortality rates. Every year, around the world, almost half a million people contract cancer, including colon cancer. Determining the grade of colon cancer mainly depends on analyzing the gland's structure by tissue region, which has led to the existence of various tests for screening that can be utilized to investigate polyp images and colorectal cancer. This article presents a comprehensive survey on the diagnosis of colon cancer. This covers many aspects related to colon cancer, such as its symptoms and grades as well as the available imaging modalities (particularly, histopathology images used for analysis) in addition to common diagnosis systems. Furthermore, the most widely used datasets and performance evaluation metrics are discussed. We provide a comprehensive review of the current studies on colon cancer, classified into deep-learning (DL) and machine-learning (ML) techniques, and we identify their main strengths and limitations. These techniques provide extensive support for identifying the early stages of cancer that lead to early treatment of the disease and produce a lower mortality rate compared with the rate produced after symptoms develop. In addition, these methods can help to prevent colorectal cancer from progressing through the removal of pre-malignant polyps, which can be achieved using screening tests to make the disease easier to diagnose. Finally, the existing challenges and future research directions that open the way for future work in this field are presented.

Functional and radiological outcomes after treatment of congenital pseudarthrosis of the tibia using the Ilizarov technique: a retrospective single-center study.

BACKGROUND: Congenital pseudarthrosis of the tibia (CPT) is a challenging problem in orthopedic practice, with high rates of non-union, refracture, and residual deformities. After union, long-term follow-up is required to manage late post-union complications. This study aimed to assess the outcomes of the Ilizarov technique in the management of CPT. MATERIALS AND METHODS: This retrospective study included patients with CPT treated with the Ilizarov method between 2005 and 2018. Intramedullary rods were used in 9 cases and iliac bone graft was used in 12 cases. An orthosis was applied till the end of follow-up in all cases. The American Orthopaedic Foot and Ankle Society (AOFAS) scale was used for the evaluation of the functional outcomes. RESULTS: This study included 16 patients, 11 males and 5 females, with an average age of 5.4 ± 2.8 years. Seven cases had multiple previous surgeries. Six patients had neurofibromatosis. The mean follow-up period was 5.8 ± 3.4 years. The average AOFAS score improved significantly from 47.5 ± 7.6 preoperatively to 78.9 ± 8.9 at the latest follow-up. Union was achieved in 15 cases, and persistent non-union occurred in one case. The clinical results were excellent in one patient, good in seven cases, fair in 6, and poor in 2 cases. The radiological results were excellent in one patient, good in seven cases, fair in seven, and poor in one case. CONCLUSIONS: The Ilizarov technique combined with intramedullary rod and primary or secondary bone graft provides a high union rate of CPT and can achieve simultaneous effective management of problems related to pseudarthrosis, including non-union, deformity, limb shortening, and adjacent joint contracture and subluxation. Level of evidence Level IV.

Predicting Parkinson disease related genes based on PyFeat and gradient boosted decision tree.

Identifying genes related to Parkinson's disease (PD) is an active research topic in biomedical analysis, which plays a critical role in diagnosis and treatment. Recently, many studies have proposed different techniques for predicting disease-related genes. However, a few of these techniques are designed or developed for PD gene prediction. Most of these PD techniques are developed to identify only protein genes and discard long noncoding (lncRNA) genes, which play an essential role in biological processes and the transformation and development of diseases. This paper proposes a novel prediction system to identify protein and lncRNA genes related to PD that can aid in an early diagnosis. First, we preprocessed the genes into DNA FASTA sequences from the University of California Santa Cruz (UCSC) genome browser and removed the redundancies. Second, we extracted some significant features of DNA FASTA sequences using the PyFeat method with the AdaBoost as feature selection. These selected features achieved promising results compared with extracted features from some state-of-the-art feature extraction techniques. Finally, the features were fed to the gradient-boosted decision tree (GBDT) to diagnose different tested cases. Seven performance metrics were used to evaluate the performance of the proposed system. The proposed system achieved an average accuracy of 78.6%, the area under the curve equals 84.5%, the area under precision-recall (AUPR) equals 85.3%, F1-score equals 78.3%, Matthews correlation coefficient (MCC) equals 0.575, sensitivity (SEN) equals 77.1%, and specificity (SPC) equals 80.2%. The experiments demonstrate promising results compared with other systems. The predicted top-rank protein and lncRNA genes are verified based on a literature review.

Early detection of Alzheimer's disease using single nucleotide polymorphisms analysis based on gradient boosting tree.

Alzheimer's disease (AD) is a degenerative disorder that attacks nerve cells in the brain. AD leads to memory loss and cognitive & intellectual impairments that can influence social activities and decision-making. The most common type of human genetic variation is single nucleotide polymorphisms (SNPs). SNPs are beneficial markers of complex gene-disease. Many common and serious diseases, such as AD, have associated SNPs. Detection of SNP biomarkers linked with AD could help in the early prediction and diagnosis of this disease. The main objective of this paper is to predict and diagnose AD based on SNPs biomarkers with high classification accuracy in the early stages. One of the most concerning problems is the high number of features. Thus, the paper proposes a comprehensive framework for early AD detection and detecting the most significant genes based on SNPs analysis. Usage of machine learning (ML) techniques to identify new biomarkers of AD is also suggested. In the proposed system, two feature selection techniques are separately checked: the information gain filter and Boruta wrapper. The two feature selection techniques were used to select the most significant genes related to AD in this system. Filter methods measure the relevance of features by their correlation with dependent variables, while wrapper methods measure the usefulness of a subset of features by training a model on it. Gradient boosting tree (GBT) has been applied on all AD genetic data of neuroimaging initiative phase 1 (ADNI-1) and Whole-Genome Sequencing (WGS) datasets by using two feature selection techniques. In the whole-genome approach ADNI-1, results revealed that the GBT learning algorithm scored an overall accuracy of 99.06% in the case of using Boruta feature selection. Using information gain feature selection, the proposed system achieved an average accuracy of 94.87%. The results show that the proposed system is preferable for the early detection of AD. Also, the results revealed that the Boruta wrapper feature selection is superior to the information gain filter technique.

Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles.

BACKGROUND AND OBJECTIVES: This paper presents an in-depth review of the state-of-the-art genetic variations analysis to discover complex genes associated with the brain's genetic disorders. We first introduce the genetic analysis of complex brain diseases, genetic variation, and DNA microarrays. Then, the review focuses on available machine learning methods used for complex brain disease classification. Therein, we discuss the various datasets, preprocessing, feature selection and extraction, and classification strategies. In particular, we concentrate on studying single nucleotide polymorphisms (SNP) that support the highest resolution for genomic fingerprinting for tracking disease genes. Subsequently, the study provides an overview of the applications for some specific diseases, including autism spectrum disorder, brain cancer, and Alzheimer's disease (AD). The study argues that despite the significant recent developments in the analysis and treatment of genetic disorders, there are considerable challenges to elucidate causative mutations, especially from the viewpoint of implementing genetic analysis in clinical practice. The review finally provides a critical discussion on the applicability of genetic variations analysis for complex brain disease identification highlighting the future challenges. METHODS: We used a methodology for literature surveys to obtain data from academic databases. Criteria were defined for inclusion and exclusion. The selection of articles was followed by three stages. In addition, the principal methods for machine learning to classify the disease were presented in each stage in more detail. RESULTS: It was revealed that machine learning based on SNP was widely utilized to solve problems of genetic variation for complex diseases related to genes. CONCLUSIONS: Despite significant developments in genetic diseases in the past two decades of the diagnosis and treatment, there is still a large percentage in which the causative mutation cannot be determined, and a final genetic diagnosis remains elusive. So, we need to detect the variations of the genes related to brain disorders in the early disease stages.

A Personalized Computer-Aided Diagnosis System for Mild Cognitive Impairment (MCI) Using Structural MRI (sMRI).

Alzheimer's disease (AD) is a neurodegenerative disorder that targets the central nervous system (CNS). Statistics show that more than five million people in America face this disease. Several factors hinder diagnosis at an early stage, in particular, the divergence of 10-15 years between the onset of the underlying neuropathological changes and patients becoming symptomatic. This study surveyed patients with mild cognitive impairment (MCI), who were at risk of conversion to AD, with a local/regional-based computer-aided diagnosis system. The described system allowed for visualization of the disorder's effect on cerebral cortical regions individually. The CAD system consists of four steps: (1) preprocess the scans and extract the cortex, (2) reconstruct the cortex and extract shape-based features, (3) fuse the extracted features, and (4) perform two levels of diagnosis: cortical region-based followed by global. The experimental results showed an encouraging performance of the proposed system when compared with related work, with a maximum accuracy of 86.30%, specificity 88.33%, and sensitivity 84.88%. Behavioral and cognitive correlations identified brain regions involved in language, executive function/cognition, and memory in MCI subjects, which regions are also involved in the neuropathology of AD.

Evaluation of pooled screening for anti-HCV in two blood services set-ups.

OBJECTIVE: Screening blood donations for anti-HCV is only partially performed in many developing countries due to the relatively high costs of testing. The screening expenditures can be reduced by testing donations in pools. This study evaluates the accuracy and feasibility of pooled screening procedure for anti-HCV in blood banks in Israel and the Palestinian Authority. METHODS: The sensitivity and specificity of tests performed on pool sizes of 6-24 samples were compared to singleton immunoassay testing. All negative samples and those positive for anti-HCV were obtained from the routine work of Magen David Adom Blood Services in Israel and Shifa Hospital blood bank in Palestinian Authority. The experiments were run in parallel with different technologies. RESULTS: The sensitivity of pooled-testing for anti-HCV by Magen David Adom was 94-97% for verified samples. In the Shifa Hospital, the sensitivity was estimated as 96-97% for non-verified samples. Cost-analysis showed benefits up to $2 per donation screened for anti-HCV in Shifa Hospital. CONCLUSIONS: We recommend using manually created pools of up to 6 samples when testing for anti-HCV, but at the cost of 3% loss in sensitivity. Pooling can be considered, in countries which do not perform routine screening, due to their limited economic resources.

Accuracy of hepatitis C virus core antigen testing in pools among seroconverters.

BACKGROUND: Screening blood units for hepatitis C virus (HCV) with nucleic acid testing (NAT) reduces the risk associated with the long "window period" (8-9 weeks) after HCV infection. The feasibility of adding the HCV core antigen assay in pools to the existing anti-HCV individual screening was examined as an alternative of NAT, for early detection of HCV. STUDY DESIGN AND METHODS: Eighteen HCV seroconversion panels were tested for HCV antibodies, HCV antigen, and HCV RNA. Each sample was tested for HCV antigen individually and in pools of 3, 6, and 12. Statistical analyses included estimation of time until detection of the first positive HCV antigen bleed in each pool size, with a locally weighted regression (LOWESS) model. Sensitivity was calculated compared to NAT. RESULTS: Detection of HCV antigen in individual samples and in pools of 3 and 6 significantly preceded the detection of antibodies by 63, 53, and 46 days, respectively. Although the sensitivity of the HCV antigen test decreased with the increase in pool size, the estimated overall sensitivity of the "two-stage" antigen and antibody screening (where NAT of individual samples was the gold standard) was not significantly different between individual and the different pool sizes. CONCLUSION: Screening for HCV antigen in pools of 6 can be considered an efficient and easier-to-implement alternative to the costly NAT for identifying blood donors in the seroconversion period. It may offer a cost-effective approach in resource utilization in poor countries, that, after the implementation of HCV antibody testing, want to further improve blood safety.