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.
The support qualitative information regards as an additional step in the process of decision-making where the method following by companies to provide information help in the creation of value because it is very important to deliver information to investors about their stratigies and what happen truly inside the companies i.e. every case relating with the expectations of stockhotslder and the prices of markets depending on those expectation ,and if the matter isn’t that there will be lack of confidence thate couldn’t be backed again. The decisions of the investors effected by security ,economic ,political, psychological, emotional ,and financial factors .
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The aim of this stud to isolate and identified of A. fumigatus from different sources and study the genetic diversity among these isolates by using RAPD and ISSR markers.Collected 20 samples from 7samples were isolated A. fumigatusisolates were characterized depending on its morphological, then extracted DNA from its.RAPD markersrandomly bandingwith sitesof genome more than ISSR markers where the primer OPN-07 achieved discriminative power (19.1) and 43 bands, while ISSR6 achieved discriminative power (17.1) with 32 bands.ISSR were more efficiency in specific binding then RAPD, ISSR primers has great a binding to production unique band, when 9 primers from 01 primers, ISSR9 was produce (5) unique bands, while RAPD markers was low ability
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Nanocrystalline aluminophosphate AlPO4-5 molecular sieves were synthesized by hydrothermal method (HTS). Synthesis parameters like time and temperature of crystallization were investigated. Type of template (R) and ratio of R/P2O5 were studied also. Characterization of the synthesized AlPO4-5 were done by powder X-ray diffraction (XRD), scanning electron microscopy (SEM/EDX), Fourier transform infrared (FTIR), differential scanning calorimetry-thermogravimetry analysis (DSC-TGA), and N2 adsorption-desorption BET analysis. XRD patterns results showed excellent crystallinity for two types of templates, di-n-propylamine (DPA) and tetrapropyl ammonium hydroxide (TPAOH) f
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In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial
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