Blood metabolomics profiling using mass spectrometry has emerged as a powerful approach for investigating non-cancer diseases and understanding their underlying metabolic alterations. Blood, as a readily accessible physiological fluid, contains a diverse repertoire of metabolites derived from various physiological systems. Mass spectrometry offers a universal and precise analytical platform for the comprehensive analysis of blood metabolites, encompassing proteins, lipids, peptides, glycans, and immunoglobulins. In this comprehensive review, we present an overview of the research landscape in mass spectrometry-based blood metabolomics profiling. While the field of metabolomics research is primarily focused on cancer, this review specifically highlights studies related to non-cancer diseases, aiming to bring attention to valuable research that often remains overshadowed. Employing natural language processing methods, we processed 507 articles to provide insights into the application of metabolomic studies for specific diseases and physiological systems. The review encompasses a wide range of non-cancer diseases, with emphasis on cardiovascular disease, reproductive disease, diabetes, inflammation, and immunodeficiency states. By analyzing blood samples, researchers gain valuable insights into the metabolic perturbations associated with these diseases, potentially leading to the identification of novel biomarkers and the development of personalized therapeutic approaches. Furthermore, we provide a comprehensive overview of various mass spectrometry approaches utilized in blood metabolomics research, including GC-MS, LC-MS, and others discussing their advantages and limitations. To enhance the scope, we propose including recent review articles supporting the applicability of GC×GC-MS for metabolomics-based studies. This addition will contribute to a more exhaustive understanding of the available analytical techniques. The Integration of mass spectrometry-based blood profiling into clinical practice holds promise for improving disease diagnosis, treatment monitoring, and patient outcomes. By unraveling the complex metabolic alterations associated with non-cancer diseases, researchers and healthcare professionals can pave the way for precision medicine and personalized therapeutic interventions. Continuous advancements in mass spectrometry technology and data analysis methods will further enhance the potential of blood metabolomics profiling in non-cancer diseases, facilitating its translation from the laboratory to routine clinical application.

Purpose: Immunodeficiency conditions, which are characterized by reduced immune activity that promotes the development of chronic diseases, are needed for efficient monitoring. A promising area of monitoring and early diagnosis of immunodeficiency diseases is the determination of metabolic biomarkers in the blood. Methods: In this work, we identified a set of lipid biomarkers of immunodeficiency states by performing high-performance liquid chromatography–high-resolution mass spectrometry (HPLC-HRMS) analysis of blood plasma samples from mice and processing them with bioinformatics approaches. Potential biomarkers were selected through statistical analysis and further validated by MS/MS. Conclusions: As a result, 15 lipids were confirmed and selected as potential biomarkers of immunodeficiency states. The selected biomarkers can be further studied and serve as promising targets for the early diagnosis of immunodeficiency diseases.

There are hundreds of methods for analysis of data obtained in mRNA-sequencing. The most of them are focused on small number of genes. In this study, we propose an approach that reduces the analysis of several thousand genes to analysis of several clusters. The list of genes is enriched with information from open databases. Then, the descriptions are encoded as vectors using the pretrained language model (BERT) and some text processing approaches. The encoded gene function pass through the dimensionality reduction and clusterization. Aiming to find the most efficient pipeline, 180 cases of pipeline with different methods in the major pipeline steps were analyzed. The performance was evaluated with clusterization indexes and expert review of the results.

There are hundreds of methods for analysis of data obtained in mRNA-sequencing. The most of them are focused on small number of genes. In this study, we propose an approach that reduces the analysis of several thousand genes to analysis of several clusters. The list of genes is enriched with information from open databases. Then, the descriptions are encoded as vectors using the pretrained language model (BERT) and some text processing approaches. The encoded gene function pass through the dimensionality reduction and clusterization. Aiming to find the most efficient pipeline, 180 cases of pipeline with different methods in the major pipeline steps were analyzed. The performance was evaluated with clusterization indexes and expert review of the results.

The myocardium is cardiac muscle tissue consisting of cardiomyocytes. Coordinated and consistent changes in a cardiomyocyte transmembrane potential cause waves of an electrophysiological excitation. This activity may be registered directly from the myocardium surface with optical mapping, microelectrode array, or other methods. In general, all methods provide some videos or series of images that represent trans-membrane potential. There are some specialized methods for processing such types of data, but general purposed methods of image reconstruction and denoising also may be suitable. In this paper, we test convolutional neural networks for problems of noise reduction, super-resolution, and compressed sensing on transmembrane potential data. The data for our analysis were generated in physically accurate finite element simulation. According to the obtained results, the convolutional neural network can reduce the high levels of noise, increase the spatial resolution of data, and accurately reconstruct the position of the excitation front. A stacked convolutional autoencoder is a sufficient architecture of neural networks for work with transmembrane potential data.

The existing methods for interpreting the differential gene expression analysis results are mainly divided into three categories: cluster analysis, enrichment analysis, and the construction of genetic networks. Despite the rich abilities, all approaches take a lot of time to compute, and the final results are not always sufficient for understanding of the logic that binds genes into groups.In this paper, we propose a complete pipeline in order to make the process of understanding the results of differential gene expression analysis much faster, easier, and more efficient. The pipeline takes in Gene Ontology terms along with descriptions of collected genes, and returns the output of gene clusters, topics they are related to, and a filtered list of most common words that can be found in each of them. The processing involves an artificial neural network model BERT for semantic information extraction, BERTopic for unsupervised topic extraction, dimensional reduction for data simplification, and clustering for the search of dependencies.The pipeline was tested with ablation study and its performance was evaluated by an expert with gene expression datasets from NCBI GEO that include different types of cardiomyopathy: dilated, inflammatory, ischemic, non-ischemic, and healthy individuals.