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Titlebook: Clinical Metabolomics Applications in Genetic Diseases; Anas M. Abdel Rahman Book 2023 The Editor(s) (if applicable) and The Author(s), un

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楼主: JAR
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Book 2023is model has been successfully applied in various conditions, including inborn errors of metabolism, primary immunodeficiency, and endocrine disorders. However, integrating metabolomics with other omics datasets and clinical phenotypes requires careful study design, analytical tools, and data analys
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Clinical Metabolomics Applications in Genetic Diseases978-981-99-5162-8
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The Advanced Technology and Clinical Application in Metabolomics,t underlie various physiological conditions and diseases. This chapter highlights analytical methods for clinical metabolomics research and personalized medicine. Several innovative clinical metabolomics projects have reached up to patient services are discussed in this chapter.
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Metabolomics: A Pipeline for Biomarker Discovery in Genetic Diseases,biomarker discovery in GD. It discusses the workflow followed, the methods used for sample analysis and data interpretation, and the major challenges and limitations in applying the metabolomic approach for biomarker discovery in GD. Moreover, the chapter provides an overview of the biomarkers ident
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Bioinformatic Tools for Clinical Metabolomics,se), data analysis and biomarker discovery tools such as MetaboAnalyst, and resources for interpreting or characterizing disease mechanisms, such as the Small Molecule Pathway Database (SMPDB). Using these and other well-known bioinformatic resources, clinicians are now able to make more precise dia
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Untargeted Metabolomics in Newborn Screening,patient outcomes. Breaking the diagnostic barrier can mean new research, new drugs, and ultimately increased survival. In this chapter, we overview the concept of untargeted metabolomics as applied to newborn screening, how it fares compared to the well-standardized tests of the targeted screening,
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Metabolomics of Rare Endocrine, Genetic Disease: A Focus on the Pituitary Gland,bolomic analysis techniques in pituitary disease will be a helpful clinical tool for significantly improving diagnosis and, potentially, the therapeutic approach by identifying highly specific disease biomarkers and novel molecular pathogenic mechanisms.
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