This study reports on the use of whole exome sequencing (WES) to diagnose children with inborn errors of metabolism and other disorders in United Arab Emirates. From January 2012 to December 2014, 85 patients (46 % females) were seen in the metabolic center at Tawam Hospital (Abu Dhabi) and WES testing was requested because definitive diagnoses were not reached by conventional methods. Eighty (93 %) patients were <18 years old and 44 (52 %) were A (p.V339M), but urine organic acids was normal. WES confirmed inborn errors of metabolism (five mitochondrial diseases, three lysosomal storage diseases, and six other disorders) in 14 patients and genetic disorders (14 neurological diseases and three non-neurological diseases) in 17 patients. Variants of unknown significance were identified in 48 patients; 12 had “confirmed pathologic variants”and 12 had “likely pathologic variants”, based on consistent phenotypes, biochemical/ segregation studies, or reported pathogenicity. In 24 patients, the variants were inconsistent with phenotypes or biochemical/ familial studies. Although WES provided molecular diagnoses, the results required careful interpretations and many patients required additional investigations. This tool is useful when conventional diagnostic methods fail. Staff competence in obtaining consent/ permission, interpreting the findings, and providing the proper counseling are essential before incorporating this technology into routine clinical practices.

Pompe disease is a rare autosomal recessive disorder caused by a deficiency of the lysosomal enzyme alpha-glucosidase responsible for degrading glycogen. Late-onset Pompe disease has a complex multisystem phenotype characterized by a range of symptoms. An expert panel from the Middle East and North Africa (MENA) region met to create consensus-based guidelines for the diagnosis and treatment of late-onset Pompe disease for the MENA region, where the relative prevalence of Pompe disease is thought to be high but there is a lack of awareness and diagnostic facilities. These guidelines set out practical recommendations and include algorithms for the diagnosis and treatment of late-onset Pompe disease. They detail the ideal diagnostic workup, indicate the patients in whom enzyme replacement therapy should be initiated, and provide guidance on appropriate patient monitoring. These guidelines will serve to increase awareness of the condition, optimize patient diagnosis and treatment, reduce disease burden, and improve patient outcomes.

A fundamental obstacle to healthcare transformation continues to be the acquisition of knowledge and insightful data from complex, high dimensional, and heterogeneous biological data. As technology has improved, a wide variety of data sources, including omics data, imaging data, and health records, have been available for use in healthcare research contexts. Electronic health records (EHRs), which are digitalized versions of medical records, have given researchers a significant chance to create computational methods for analyzing healthcare data. EHR systems typically keep track of all the data relating to a patient’s medical history, including clinical notes, demographic background, and diagnosis details. EHR data can offer valuable insights and support doctors in making better decisions related to disease and diagnostic forecasts. As a result, several academics use deep learning to forecast diseases and track health trajectories in EHR. Recent advances in deep learning technology have produced innovative and practical paradigms for building end-to-end learning models. However, scholars have limited access to online HER databases, and there is an inherent need to address this issue. This research examines deep learning models, their architectures, and readily accessible EHR online databases. The goal of this paper is to examine how various architectures, models, and databases differ in terms of features and usability. It is anticipated that the outcomes of this review will lead to the development of more robust deep learning models that facilitate medical decision-making processes based on EHR data and inform efforts to support the selection of architectures, models, and databases for specific research purposes.

Studies continue to uncover contributing risk factors for breast cancer (BC) development including genetic variants. Advances in machine learning and big data generated from genetic sequencing can now be used for predicting BC pathogenicity. However, it is unclear which tool developed for pathogenicity prediction is most suited for predicting the impact and pathogenicity of variant effects. A significant challenge is to determine the most suitable data source for each tool since different tools can yield different prediction results with different data inputs. To this end, this work reviews genetic variant databases and tools used specifically for the prediction of BC pathogenicity. We provide a description of existing genetic variants databases and, where appropriate, the diseases for which they have been established. Through example, we illustrate how they can be used for prediction of BC pathogenicity and discuss their associated advantages and disadvantages. We conclude that the tools that are specialized by training on multiple diverse datasets from different databases for the same disease have enhanced accuracy and specificity and are thereby more helpful to the clinicians in predicting and diagnosing BC as early as possible.