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Изучение общих генных сигнатур и механизмов между тремя аутоиммунными заболеваниями по массовому секвенированию РНК в сочетании с последовательности одноклеточного РНК.

ВАЖНЕЙШАЯ ИНФОРМАЦИЯ: Появляющиеся данные подчеркивают наличие механизмов коморбидности среди аутоиммунных заболеваний (АИД), при этом инновационные технологии, такие, как секвенирование РНК в одной клетке (scRNA-seq), значительно улучшают разведку в этой области. Цель этого исследования заключалась в изучении общих генов трех AID-Multiple Sclerosis (MS), Systemic Lupus Erythematosus (SLE) и Rheumatoid Artritis (RA) с использованием баз данных биоинформатики, а также в выявлении потенциальных биомаркеров для ранней диагностики. Мы извлекли стеноскопические данные о пациентах MS, SLE и RA из об

Artificial Intelligence in the Management of Hereditary and Acquired Hemophilia: From Genomics to Treatment Optimization.

Hemophilia, an X-linked bleeding disorder, is characterized by a deficiency in coagulation factors. It manifests as spontaneous bleeding, leading to severe complications if not properly managed. In contrast, acquired hemophilia is an autoimmune condition marked by the development of inhibitory antibodies against coagulation factors. Both forms present significant diagnostic and therapeutic challenges, highlighting the need for advanced genetic, molecular, laboratory, and clinical assessments. Recent advances in artificial intelligence have opened new avenues for the management of hemophilia. M

Cancer evolution: mathematical models and computational inference.

Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilisti

Возможности и препятствия для глубокого изучения биологии и медицины.

Глубокое обучение описывает класс алгоритмов машинного обучения, которые способны объединять исходные ресурсы в слои промежуточных характеристик. Эти алгоритмы недавно продемонстрировали впечатляющие результаты в различных областях. Биология и медицина являются богатыми данными дисциплинами, однако данные являются сложными и зачастую недопонятными. Поэтому методы глубокого обучения могут быть особенно подходящими для решения проблем в этих областях. Мы изучаем возможности углубленного изучения различных биомедицинских проблем - стационарная классификация, фундаментальные биологические процессы

Challenges Faced by Clinicians in the Personalized Treatment Planning: A Literature Review and the First Results of the Russian National Cancer Program.

Advances in cancer molecular profiling have enabled the development of more effective approaches to the diagnosis and personalized treatment of tumors. However, treatment planning has become more labor intensive, requiring hours or even days of clinician effort to optimize an individual patient case in a trial-and-error manner. Lessons learned from the world cancer programs provide insights into ways to develop approaches for the treatment strategy definition which can be introduced into clinical practice. This article highlights the variety of breakthroughs in patients' cancer treatment and s

Artificial intelligence in cancer immunotherapy: current trends in predicting response and personalizing treatment.

Artificial intelligence (AI) can transform cancer immunotherapy by enabling more accurate prediction of treatment responses, the discovery of specific biomarkers, and the development of personalised treatment plans. Traditional single-marker biomarkers (PD-L1, TMB, MSI) lack consistency across tumour types and cannot be used to assess tumour heterogeneity or the dynamic tumour microenvironment (TME). This review synthesises developments in multimodal AI models that combine genomics, transcriptomics, radiomics, digital pathology (pathomics), circulating biomarkers, and clinical evidence to crea

Увязка патогенеза с падением риска при множественном склерозе.

Множественный склероз — это хроническое неврологическое расстройство, характеризующееся постепенной инвалидностью, при этом падение является значительным следствием его физических и когнитивных расстройств. В настоящем обзоре рассматриваются основные вкладчики, с тем чтобы снизить риск для лиц с множественным склерозом, и анализируются более широкие последствия этих факторов, такие, как боязнь падения. К основным факторам, связанным с падением риска, относятся аномалии, когнитивная дисфункция и усталость. Эти факторы часто взаимодействуют друг с другом, что приводит к ограничению мобильности и

Artificial intelligence for risk assessment and outcome prediction in malignant haematology.

Risk stratification is the backbone of clinical decision-making for patients with haematological malignancies. Models currently used in routine clinical practice often rely on static rules, conventional statistics and expert consensus. While pragmatic and accessible, these models fail to capture the complex biological interactions and patient heterogeneity, especially as data dimensionality grows given the availability of more comprehensive molecular analysis. Artificial intelligence (AI), particularly machine learning (ML), offers the potential to process such high-dimensional data, uncover l

Лекция Barancik Award: Многодисциплинарные исследования будут ключом к остановке, восстановлению и окончанию MS.

За последние 25 лет мы добились небывалых успехов в понимании патогенеза MS и последующем развитии эффективных методов лечения. Совместные усилия в области генетики позволили выявить связь 236 общих вариантов ДНК с подверженностью болезням и первую связь с серьезностью заболеваний, что проложило путь к более эффективной терапии, особенно для прогрессивных форм заболевания. Параллельно и в дополнение к уже существующим экологическим заболеваниям, вызывающим или изменяющим себя, новая совместная работа выявила новые связи с компонентами кишечного микробиома. Это исследование открыло новую и захв

Identification of a novel cuproptosis-related gene signature for multiple myeloma diagnosis.

BackgroundMultiple myeloma (MM) ranks second among the most prevalent hematological malignancies. Recent studies have unearthed the promise of cuproptosis as a novel therapeutic intervention for cancer. However, no research has unveiled the particular roles of cuproptosis-related genes (CRGs) in the prediction of MM diagnosis.MethodsMicroarray data and clinical characteristics of MM patients were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed gene analysis, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature e

Review and meta-analysis of the genetic Minimal Cut Set approach for gene essentiality prediction in cancer metabolism.

Cancer metabolism is a marvellously complex topic, in part, due to the reprogramming of its pathways to self-sustain the malignant phenotype in the disease, to the detriment of its healthy counterpart. Understanding these adjustments can provide novel targeted therapies that could disrupt and impair proliferation of cancerous cells. For this very purpose, genome-scale metabolic models (GEMs) have been developed, with Human1 being the most recent reconstruction of the human metabolism. Based on GEMs, we introduced the genetic Minimal Cut Set (gMCS) approach, an uncontextualized methodology that

Precision Medicine Through Network Language: Integrating Clinical Insight and Data Expertise.

Precision medicine is facing a critical transition driven by the growing complexity of biological data and the insufficient ability of current models to translate such data into clinically meaningful information. Linear, single-gene approaches are no longer adequate to explain the multifactorial nature of most modern diseases, whose phenotypes emerge from combinations of genetic, molecular, and environmental factors. Network-based precision medicine addresses this by providing a systemic framework capable of integrating heterogeneous omics data, interactomes, and clinical information to identi

Mechanistic study of pre-eclampsia and macrophage-associated molecular networks: bioinformatics insights from multiple datasets.

BACKGROUND: Pre-eclampsia is a pregnancy-related disorder characterized by hypertension and proteinuria, severely affecting the health and quality of life of patients. However, the molecular mechanism of macrophages in pre-eclampsia is not well understood. METHODS: In this study, the key biomarkers during the development of pre-eclampsia were identified using bioinformatics analysis. The GSE75010 and GSE74341 datasets from the GEO database were obtained and merged for differential analysis. A weighted gene co-expression network analysis (WGCNA) was constructed based on macrophage content, and

Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.

Artificial intelligence (AI) is increasingly advancing precision immunotherapy by integrating high-dimensional biomedical data to support diagnosis, treatment selection, and longitudinal monitoring in both cancer and autoimmune diseases. This review summarizes AI applications in biomarker discovery, prediction of immune checkpoint inhibitor (ICI) response and toxicity, neoantigen prioritization, CAR-T cell optimization, and therapeutic antibody engineering. In oncology, multimodal models combining multi-omics, medical imaging, and clinical variables improve patient stratification and non-invas

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… These techniques employ advanced algorithms, including fuzzy logic, genetic algorithms, artificial neural networks (ANNs), support vector machines (SVMs), and clustering methods, to analyze large, heterogeneous biological datasets derived from genomics, proteomics, metabolomics, and clinical records( …

Obesity definition, diagnosis, bias, standard operating procedures (SOPs), and telehealth: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2022.

BackgroundThe Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) regarding definition, diagnosis, bias, standard operating procedures (SOPs) and telehealth is intended to provide clinicians an overview of obesity medicine and provide basic organizational tools towards establishing, directing, managing, and maintaining an obesity medical practice.MethodsThis CPS is based upon published scientific citations, clinical perspectives of OMA authors, and peer review by Obesity Medicine Association leadership.ResultsOMA has defined obesity as: "A chronic, progressive, relapsing, and

Intelligence Architectures and Machine Learning Applications in Contemporary Spine Care.

The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies has initiated a paradigm shift in contemporary spine care. This narrative review synthesizes advances across imaging-based diagnostics, surgical planning, genomic risk stratification, and post-operative outcome prediction. We critically assess high-performing AI tools, such as convolutional neural networks for vertebral fracture detection, robotic guidance platforms like Mazor X and ExcelsiusGPS, and deep learning-based morphometric analysis systems. In parallel, we examine the emergence of ambient clini

Искусственный интеллект, видоизменяющий парадигму гематологического диагноза и лечения злокачественных заболеваний: от статической оценки до динамического управления точностью.

Искусственный интеллект (ИИ) все чаще изучается в качестве инструмента содействия более точному и динамичному управлению диагностикой и лечением гематологов. В отличие от предыдущих обзоров, в которых основное внимание уделялось отдельным типам заболеваний или изолированным технологическим путям, в настоящем документе содержится всеобъемлющий обзор текущих прикладных программ АИШ и последних достижений в области диагностики, классификации, оценки прогнозов и принятия решений по лечению лейкемии, лимфомы, множественной миэломы и миэлодиспластических синдромов. Она охватывает ключевые технически

Rational drug repositioning for coronavirus-associated diseases using directional mapping and side-effect inference.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen of coronavirus disease 2019 (COVID-19), has infected hundreds of millions of people and caused millions of deaths. Looking for valid druggable targets with minimal side effects for the treatment of COVID-19 remains critical. After discovering host genes from multiscale omics data, we developed an end-to-end network method to investigate drug-host gene(s)-coronavirus (CoV) paths and the mechanism of action between the drug and the host factor in a directional network. We also inspected the potential side effect of the ca

Towards the integration, annotation and association of historical microarray experiments with RNA-seq.

BackgroundTranscriptome analysis by microarrays has produced important advances in biomedicine. For instance in multiple myeloma (MM), microarray approaches led to the development of an effective disease subtyping via cluster assignment, and a 70 gene risk score. Both enabled an improved molecular understanding of MM, and have provided prognostic information for the purposes of clinical management. Many researchers are now transitioning to Next Generation Sequencing (NGS) approaches and RNA-seq in particular, due to its discovery-based nature, improved sensitivity, and dynamic range. Additiona

Artificial Intelligence for Risk Stratification in Diffuse Large B-Cell Lymphoma: A Systematic Review of Classification Models and Predictive Performances.

Background: Diffuse large B-cell lymphoma (DLBCL) is a biologically heterogeneous malignancy, with various outcomes despite significant advances in therapeutic options. Current conventional prognostic tools, e.g., the International Prognostic Index (IPI), lack sufficient precision at an individual patient level. However, artificial intelligence (AI), including machine learning (ML) and deep learning (DL), can enable specialists to navigate complex datasets, with the final aim of improving prognostic models for DLBCL. Objectives: This scoping review aims to systematically map the current litera