Inherited metabolic disorders (IMDs) are a diverse group of hereditary abnormalities that leads to a defect in metabolic pathway. Its diagnosis has been transformed by the innovations of molecular genetics and computational biology. Conventionally, diagnosis of IMDs is dependent on clinical findings and biochemical tests. Yet, these methods are limited due to a heterogeneity of such disorders and a large number of genes involved. The main objective of this review is to highlight the role of next-generation sequencing (NGS), including targeted gene panels, whole-exome sequencing (WES), and whole-genome sequencing (WGS), in the diagnosis of IMDs and providing reliable information in identifying genetic causes, and to explore the integrated analysis of several molecular layers such as genomics, transcriptomics, proteomics, metabolomics, and epigenetics. Targeted mass spectrometry and untargeted metabolomics methods are essential approaches for screening and identifying the metabolic patterns that act as a diagnosis biomarker to confirm the biochemical phenotypes associated with IMDs. Moreover, a new diagnostic model has been developed from the combination data of transcriptomics and proteomics to determine whether a gene mutation leads to a protein's dysfunction or not. The review concludes that the IMDs diagnosis should be lied in a fully integrated between molecular genetics techniques with multi-omics pipeline enhanced by artificial intelligence (AI) and machine learning (ML), which will provide a more rapid, accurate, and accessible path to diagnosis and, ultimately, more effective treatment.
This review examines how artificial intelligence (AI) including machine learning (ML), deep learning (DL), and the Internet of Things (IoT) is transforming operations across exploration, production, and refining in the Middle Eastern oil and gas sector. Using a systematic literature review approach, the study analyzes AI adoption in upstream, midstream, and downstream activities, with a focus on predictive maintenance, emission monitoring, and digital transformation. It identifies both opportunities and challenges in applying AI to achieve environmental and economic goals. Although adoption levels vary across the region, countries such as Saudi Arabia, the UAE, and Qatar are leading initiatives that align with global sustainability targets.
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AI in teaching English is reshaping language learning. While interest in AI-supported education is growing worldwide, research in this area is still emerging in Iraq. This review synthesizes empirical AI-based intervention studies to enhance English language learning in Iraqi higher education, and the perceptions of stakeholders regarding AI tools in language instruction. The reviewed intervention studies, comprising studies employed different AI platforms to support grammar instruction, speaking fluency, writing feedback, and pragmatic competence. These interventions yielded improvements in learners’ performance, motivation, and communicative confidence. In parallel, perception-focused studies revealed positive attitudes toward A
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Air stripping for removal of Trichloroethylene (TCE), Chloroform (CF) and Dichloromethane (DCM) from water were studied in a bubble column (0.073 m inside dia. and 1.08 m height with several sampling ports). The contaminated water was prepared from deionized water and VOCs. The presence of VOCs in feed solution was single, binary or ternary components. They were diluted to the concentrations ranged between 50 mg/l to 250 mg/l. The experiments were carried out in batch experiments which regard the bubble column as stirred tank and only gas was bubbled through stationary liquid. In this case transient measurements of VOC concentration in the liquid phase and the measured concentra
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Utilizing phase change materials in thermal energy storage systems is commonly considered as an alternative solution for the effective use of energy. This study presents numerical simulations of the charging process for a multitube latent heat thermal energy storage system. A thermal energy storage model, consisting of five tubes of heat transfer fluids, was investigated using Rubitherm phase change material (RT35) as the. The locations of the tubes were optimized by applying the Taguchi method. The thermal behavior of the unit was evaluated by considering the liquid fraction graphs, streamlines, and isotherm contours. The numerical model was first verified compared with existed experimental data from the literature. The outcomes re
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Background: Tumor necrosis factor-alpha (TNF-α) and interleukins play important roles in the pathogenesis of rheumatoid arthritis (RA). Genetic research has been employed to find many of the missing connections between genetic risk variations and causal genetic components. Objective: The goal of this study is to look at the genetic variations of TNF-α and interleukins in Iraqi RA patients and see how they relate to disease severity or response to biological therapy. Method: Using specific keywords, the authors conducted a systematic and comprehensive search to identify relevant Iraqi studies examining the genetic variations of TNF-α and interleukins in Iraqi RA patients and how they relate to disease severity or response to biolo
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