IS Mamedov, IV Zolkina, V. Sukhorukov, AI Krapivkin
{"title":"Determining the diagnostic value of the markers of congenital metabolic disorders by chromatography–mass spectrometry","authors":"IS Mamedov, IV Zolkina, V. Sukhorukov, AI Krapivkin","doi":"10.24075/brsmu.2024.003","DOIUrl":"https://doi.org/10.24075/brsmu.2024.003","url":null,"abstract":"Thorough investigation of metabolome by mass spectrometry is of great importance for personalized and preventive medicine. It is only timely laboratory diagnosis involving the use of high-tech chromatographic analysis methods that can help identify the patients with disorders of amino acid and acylcarnitine metabolism. The study was aimed to determine the efficacy of conventional and additional markers of metabolic disorders of amino acids and acylcarnitines detected by chromatography–mass spectrometry for the diagnosis of congenital metabolic disorders in children, as well as to create specific panels of the most effective indicators and determine the potential diagnostic efficacy of indentification of the relationships between the levels of amino acids and acylcarnitines in pediatric patients with congenital metabolic disorders. We assessed amino acid and acylcarnitine profiles in blood spots by high-performance liquid chromatography–tandem mass spectrometry in patients aged 6 months to 16 years (48 boys and 32 girls) with suspected aminoacidopathy and organic aciduria/acidemia. The comparison group consisted of 35 children with suspected peroxisomal metabolic disorders, the control group included 40 generally healthy children of various age groups. The data obtained were used to conduct the analysis of correlations between the groups of markers. Strong correlation was revealed for the levels of metabolically most closely related compounds (r < 0.8, р < 0.001). However, a similar relationship between metabolically not closely related compounds (correlation coefficient 0.45–0.73 (р < 0.001)) was revealed for some groups of compounds. Thus, the acylcarnitine profile can be proposed as an additional potential marker to be used in cases of borderline phenylalanine levels, and the sum of normalized acylcarnitine levels (С12+С16) can be a potential secondary marker of phenylketonuria.","PeriodicalId":504934,"journal":{"name":"Bulletin of Russian State Medical University","volume":"20 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139966934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Deep learning in modelling the protein–ligand interaction: new pathways in drug development","authors":"AD Barykin, TV Chepurnykh, ZM Osipova","doi":"10.24075/brsmu.2024.002","DOIUrl":"https://doi.org/10.24075/brsmu.2024.002","url":null,"abstract":"The deep learning technologies have become the driver of the revolutionary changes in scientific research in various fields. The AlphaFold-2 neural network software development that has solved the semicentennial problem of 3D protein structure prediction based on primary amino acid sequence is the most obvious example of using such technologies in structural biology and biomedicine. The use of deep learning methods for the prediction of protein–ligand interactions can considerably simplify predicting, speed up the development of new effective pharmaceuticals and change the concept of drug design.","PeriodicalId":504934,"journal":{"name":"Bulletin of Russian State Medical University","volume":"39 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139892789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Deep learning in modelling the protein–ligand interaction: new pathways in drug development","authors":"AD Barykin, TV Chepurnykh, ZM Osipova","doi":"10.24075/brsmu.2024.002","DOIUrl":"https://doi.org/10.24075/brsmu.2024.002","url":null,"abstract":"The deep learning technologies have become the driver of the revolutionary changes in scientific research in various fields. The AlphaFold-2 neural network software development that has solved the semicentennial problem of 3D protein structure prediction based on primary amino acid sequence is the most obvious example of using such technologies in structural biology and biomedicine. The use of deep learning methods for the prediction of protein–ligand interactions can considerably simplify predicting, speed up the development of new effective pharmaceuticals and change the concept of drug design.","PeriodicalId":504934,"journal":{"name":"Bulletin of Russian State Medical University","volume":"125 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139832620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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