Publications by authors named "Pavel S Demenkov"

Multiple myeloma (MM) is characterized by the uncontrolled proliferation of monoclonal plasma cells and accounts for approximately 10% of all hematologic malignancies. The clinical outcomes of MM can exhibit considerable variability. Variability in both the genetic and epigenetic characteristics of MM undeniably contributes to tumor dynamics.

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Primary open-angle glaucoma (POAG) is the most common form of glaucoma. This condition leads to optic nerve degeneration and eventually to blindness. Tobacco smoking, alcohol consumption, fast-food diets, obesity, heavy weight lifting, high-intensity physical exercises, and many other bad habits are lifestyle-related risk factors for POAG.

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Background/objectives: A modern classification distinguishes between two nosological entities posing an intermediate risk between differentiated and anaplastic carcinoma: poorly differentiated thyroid carcinoma and differentiated high-grade thyroid carcinoma. There are currently few studies searching for the preoperative molecular genetic markers of high-grade papillary thyroid carcinoma (PTC HG), primarily because of a recent WHO reclassification and singling out of a separate entity: high-grade follicular cell-derived nonanaplastic thyroid carcinoma. Therefore, this work was aimed at identifying PTC HG-specific microRNAs and mRNAs that reliably distinguish them from differentiated papillary thyroid carcinoma in preoperative cytology specimens (fine-needle aspiration biopsies).

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Despite considerable investigative efforts, the molecular mechanisms of postoperative delirium (POD) remain unresolved. The present investigation employs innovative methodologies for identifying potential primary and secondary metabolic markers of POD by analyzing serum metabolomic profiles utilizing the genetic algorithm and artificial neural networks. The primary metabolomic markers constitute a combination of metabolites that optimally distinguish between POD and non-POD groups of patients.

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Article Synopsis
  • * Traditional text-mining techniques often lack accuracy due to their inability to grasp semantic and contextual details, while deep-learning models are expensive and can produce misleading information (known as hallucination).
  • * This study introduces a hybrid method using text-mining, graph neural networks (GNNs), and fine-tuned large language models (LLMs) to enhance biomedical knowledge graphs, achieving high accuracy in predicting protein interactions and identifying new connections relevant to conditions like insomnia.
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Molecular genetic events are among the numerous factors affecting the clinical course of papillary thyroid carcinoma (PTC). Recent studies have demonstrated that aberrant expression of miRNA, as well as different thyroid-related genes, correlate with the aggressive clinical course of PTC and unfavorable treatment outcomes, which opens up new avenues for using them in the personalization of the treatment strategy for patients with PTC. In the present work, our goal was to assess the applicability of molecular markers in the preoperative diagnosis of aggressive variants of papillary thyroid cancer.

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  • * The study introduces a denoising autoencoder (DAE) to reduce noise in mass spectrometry (MS) data, improving the accuracy of bacterial strain classification.
  • * The DAE combined with Random Forest classification outperforms traditional methods, making it a more effective approach for precisely identifying Bacillus species in noisy conditions.
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Hepatocellular carcinoma (HCC) has been associated with hepatitis C viral (HCV) infection as a potential risk factor. Nonetheless, the precise genetic regulatory mechanisms triggered by the virus, leading to virus-induced hepatocarcinogenesis, remain unclear. We hypothesized that HCV proteins might modulate the activity of aberrantly methylated HCC genes through regulatory pathways.

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  • The volume of scientific literature is increasing rapidly, with 1.5 million biomedical abstracts added to PubMed in 2021, highlighting the need for advanced information retrieval systems.
  • The authors developed an improved version of ANDDigest, a web-based system that utilizes specialized ontologies and artificial intelligence to effectively search PubMed.
  • This new version employs PubMedBERT classifiers, boosting the accuracy of recognizing short names for molecular genetics entities by 13% across eight biological categories.
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Coal worker's pneumoconiosis (CWP) is an occupationally induced progressive fibrotic lung disease. This irreversible but preventable disease currently affects millions across the world, mainly in countries with developed coal mining industries. Here, we report a pilot study that explores the sputum microbiome as a potential non-invasive bacterial biomarker of CWP status.

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  • Previous studies detailed a technique utilizing a molecular marker panel to detect and classify malignant thyroid tumors from fine-needle aspiration biopsy samples analyzed through quantitative PCR.
  • The current study assessed the specificity of this detection method using samples from 278 patients with known histological diagnoses, revealing varying positive and negative predictive values for different types of thyroid carcinoma.
  • While the method showed high accuracy for papillary, medullary, and Hürthle cell carcinomas, it struggled with follicular carcinoma, as its positive predictive value was below 50%.
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Background: The rapid growth of scientific literature has rendered the task of finding relevant information one of the critical problems in almost any research. Search engines, like Google Scholar, Web of Knowledge, PubMed, Scopus, and others, are highly effective in document search; however, they do not allow knowledge extraction. In contrast to the search engines, text-mining systems provide extraction of knowledge with representations in the form of semantic networks.

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Aims: Analysis of molecular markers in addition to cytological analysis of fine-needle aspiration (FNA) samples is a promising way to improve the preoperative diagnosis of thyroid nodules. Previously, we have developed an algorithm for the differential diagnosis of thyroid nodules by means of a small set of molecular markers. Here, we aimed to validate this approach using FNA cytology samples of Bethesda categories III and IV, in which preoperative detection of malignancy by cytological analysis is impossible.

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Article Synopsis
  • * Researchers analyzed 494 FNA samples from various thyroid conditions to build an algorithm that differentiates between benign and malignant nodules using specific molecular markers, including certain mRNA levels and DNA ratios.
  • * The algorithm showed high accuracy in distinguishing between benign goiters and malignant tumors, achieving a sensitivity of 97% and a positive predictive value of 98%, which enhances the potential for better patient management in thyroid cancer diagnosis.
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The increase in the number of Web-based resources on posttranslational modification sites (PTMSs) in proteins is accelerating. This chapter presents a set of computational protocols describing how to work with the Internet resources when dealing with PTMSs. The protocols are intended for querying in PTMS-related databases, search of the PTMSs in the protein sequences and structures, and calculating the pI and molecular mass of the PTM isoforms.

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Background: Currently, more than 150 million people worldwide suffer from lymphedema. It is a chronic progressive disease characterized by high-protein edema of various parts of the body due to defects in lymphatic drainage. Molecular-genetic mechanisms of the disease are still poorly understood.

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Comorbid states of diseases significantly complicate diagnosis and treatment. Molecular mechanisms of comorbid states of asthma and hypertension are still poorly understood. Prioritization is a way for identifying genes involved in complex phenotypic traits.

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  • * The updated version of ANDSystem provides a knowledge base for 272 tissues, allowing users to build and filter gene networks based on tissue-specific expression.
  • * An example analysis of the extrinsic apoptotic signaling pathway demonstrated that accounting for tissue differences can significantly alter the structure of gene networks, affecting metrics like betweenness centrality and network density.
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  • Hypertension and bronchial asthma significantly impact global health, affecting roughly 1 billion adults with hypertension and millions suffering from asthma, leading to hundreds of thousands of deaths each year.
  • The study introduced a bioinformatics methodology to analyze the comorbidity of these two diseases by identifying candidate genes that could reveal molecular mechanisms and guide the discovery of new treatments.
  • Gene network analysis showed overlapping genes and interactions between asthma and hypertension, with IL10, TLR4, and CAT identified as key genes for further exploration in understanding their comorbidity.
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  • Researchers developed FunGeneNet, an online tool for analyzing gene networks derived from experimental gene sets by comparing them to random networks.
  • * The tool improves upon existing methods, such as CrossTalkZ, by taking into account different types of interactions between genes rather than just generalized connections.
  • * FunGeneNet has shown high sensitivity and specificity in its analyses, highlighting significant differences in connectivity for specific biological processes like thyroid cancer and apoptosis compared to random networks.*
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Functional sites define the diversity of protein functions and are the central object of research of the structural and functional organization of proteins. The mechanisms underlying protein functional sites emergence and their variability during evolution are distinguished by duplication, shuffling, insertion and deletion of the exons in genes. The study of the correlation between a site structure and exon structure serves as the basis for the in-depth understanding of sites organization.

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Fine needle aspiration cytology (FNAC) is currently the method of choice for malignancy prediction in thyroid nodules. Nevertheless, in some cases the interpretation of FNAC results may be problematic due to limitations of the method. The expression level of some microRNAs changes with the development of thyroid tumors, and its quantitation can be used to refine the FNAC results.

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Background: Study of the relationship between the structural and functional organization of proteins and their coding genes is necessary for an understanding of the evolution of molecular systems and can provide new knowledge for many applications for designing proteins with improved medical and biological properties. It is well known that the functional properties of proteins are determined by their functional sites. Functional sites are usually represented by a small number of amino acid residues that are distantly located from each other in the amino acid sequence.

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Article Synopsis
  • * A significant challenge in gathering this information is that manually analyzing scientific literature is time-consuming, leading to incomplete databases; hence, automated text mining using ANDSystem was employed for more efficient data extraction.
  • * The resulting HCV interactome identified interactions between 969 human proteins and 11 HCV proteins, including 153 previously unreported proteins, providing a more comprehensive resource for understanding these interactions.
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  • Understanding molecular and genetic interactions is essential for addressing research questions in biology and medicine, which is challenging due to the vast number of scientific papers available.* -
  • The ANDSystem package was created to automate the analysis of molecular genetic networks, effectively detailing various interactions between genes, proteins, and other biological components.* -
  • When used alongside existing tools like Pathway Studio and STRING, ANDSystem enhances the quality of molecular network reconstruction, serving as a valuable resource for researchers in multiple scientific disciplines.*
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