Fulminant invasive group A streptococcal infection in children

Group A streptococcal infections dominate among invasive streptococcal infections, with the major causative agent, Streptococcus pyogenes, being quite stable in the environment and bearing a large number of chromosome encoded pathogenicity factors or transmitted by horizontal transfer through bacteriophages. Different genetic variants of S. pyogenes can have a different set of pathogenicity factors able to change during pathogen evolution and determine virulence level for specific isolate. With a short incubation period, the disease can proceed with developing invasive infection and toxic shock syndrome with unfavorable outcome within 7 days from disease onset. The purpose of this article is to increase the doctors’ alertness to early recognition and diagnosis, which directly affects adequate treatment in a timely manner and disease outcome. The data on streptococcal morbidity in Russia and worldwide, review of laboratory diagnostic methods and pathogen genetic typing are presented. The maximum number of cases of streptococcal septicemia in Russia was registered in 2022, which accounted for 69% of all cases during the 2014–2022 observation period. The article also describes two clinical cases of fulminant invasive group A streptococcal infection in children with symptoms of acute respiratory viral infections at the onset of the disease. The results of various laboratory diagnostics methods verifying the diagnosis are presented. The genetic characterization of microbial isolates was performed by deep DNA sequencing. In the biological material from patients (including autopsy in one case), S. pyogenes sequence type ST-28, serotypes emm-1.25 and emm-1.0 were identified. The increasing importance of invasive streptococcal infection for health care in Russia and other countries may be associated with a possible change in dominating S. pyogenes genetic variants. In this regard, the study on circulating S. pyogenes genotypes on an ongoing basis as part of surveillance of streptococcal infection and development of vaccine for specific prevention are required

ARDD 2020: from aging mechanisms to interventions

Aging is emerging as a druggable target with growing interest from academia, industry and investors. New technologies such as artificial intelligence and advanced screening techniques, as well as a strong influence from the industry sector may lead to novel discoveries to treat age-related diseases. The present review summarizes presentations from the 7th Annual Aging Research and Drug Discovery (ARDD) meeting, held online on the 1st to 4th of September 2020. The meeting covered topics related to new methodologies to study aging, knowledge about basic mechanisms of longevity, latest interventional strategies to target the aging process as well as discussions about the impact of aging research on society and economy. More than 2000 participants and 65 speakers joined the meeting and we already look forward to an even larger meeting next year. Please mark your calendars for the 8th ARDD meeting that is scheduled for the 31st of August to 3rd of September, 2021, at Columbia University, USA

APOE GENE POLYMORPHISM: THE IMPACT OF APOE4 ALLELE ON SYSTEMIC INFLAMMATION AND ITS ROLE IN THE PATHOGENESIS OF ALZHEIMER’S DISEASE

ApoE is a member of lipoprotein family. It is the most common lipoprotein in the central nervous system (CNS), secreted by astrocytes, microglia, neurons and immunocompetent cells, including lymphocytes, monocytes and macrophages. According to recent data, it has endotheliotropic and immunomodulatory functions, regulating inflammatory activation of mononuclear phagocytes and antigen-induced lymphocyte proliferation. APOE4  allele is a major genetic risk factor of Alzheimer’s disease, with prevalence 3-12 times higher in those who have this allele. Mechanisms that predispose carriers of the allele to earlier clinical presentation of neurodegeneration include changes in lipid metabolism in the CNS, in the buildup of neurotoxic amyloid-beta oligomers, in the clearance of amyloid-beta peptides from the CNS and in regulation of immune response. In this review the functions of ApoE protein in central nervous and immune system and changes in functional activity of the protein in APOE4 carriers are discussed. The impact of APOE4 allele on monocyte phenotype and inflammatory activation of monocytes, on specific cell-mediated immune response to amyloid-beta antigens and on effectiveness of immunomodulatory therapy in patients with Alzheimer’s disease summarized, as well as the possible role of changes in the immune response characteristic for APOE4 carriers in the increased risk of Alzheimer’s disease

Epidemiological significance of detection of SARS-CoV-2 RNA among different groups of population of Moscow and Moscow Region during the COVID-19 outbreak

The Central Research Institute of Epidemiology of Rospotrebnadzor presents priority data obtained from the largescale population PCR-based study of the changes in the rates of circulation of SARS-CoV-2 among relatively healthy residents of Moscow and Moscow Region

Gender-Age Distribution of Patients with COVID-19 at Different Stages of Epidemic in Moscow

The ongoing COVID-19 pandemic around the world and in Russia remains a major event of 2020. All over the world, research is being conducted to comprehensively study the patterns and manifestations of the epidemic  process. The main quantitative characteristics of SARS-CoV-2 transmission dynamics among the population, based on the data of official monitoring over the current situation, play an important role in the development of  the epidemiological surveillance system.The aim of this study is to explore the peculiarities of age-gender distribution of COVID-19 patients in Moscow.Material and methods. The data related to the epidemiological characteristics of age-gender structure of COVID-19 patients in Moscow between March 19, 2020 and April 15, 2020, at different stages of the  epidemic were retrospectively analyzed.Results and discussion. The mean age of COVID-19 patients in Moscow was 46,41±20,58 years. The gender ratio (male/female) among the patients was 52.7/47.3 %, wherein the indicators varied depending upon the  age. Male/female ratio in the age group “under 39” stood at 53.7/46.3 %, and “over 40 years of age” – at  39.3/60.7 %. The predominant age range among male cases was 19 to 39 years old – 35.4 %, while among female patients – 40–59 years (36.5 %). The age distribution of patients in Moscow is indicative of the fact that COVID-19 is a disease that primarily affects older age groups. The age structure of all COVID-19 cases during the observation period is characterized by predominance of adult patients over 19 years of age – 92,7 % (92,6–92,8 %), the share of patients aged 40–59 years is 35,7% (35,5–35,9 %). The differences in the age distribution in males and females are as follows: in the male cohort, the age groups 19–39 years old and 40–59 years old prevail – 35.4 % (35.1–35.7 %) and 34.9 % (34.6–35.2 %), respectively. The age group 40–59 years old – 36.5 % (36.3–36.8%) dominates in the female cohort

Aging and drug discovery

Multiple interventions in the aging process have been discovered to extend the healthspan of model organisms. Both industry and academia are therefore exploring possible transformative molecules that target aging and age‐associated diseases. In this overview, we summarize the presented talks and discussion points of the 5th Annual Aging and Drug Discovery Forum 2018 in Basel, Switzerland. Here academia and industry came together, to discuss the latest progress and issues in aging research. The meeting covered talks about the mechanistic cause of aging, how longevity signatures may be highly conserved, emerging biomarkers of aging, possible interventions in the aging process and the use of artificial intelligence for aging research and drug discovery. Importantly, a consensus is emerging both in industry and academia, that molecules able to intervene in the aging process may contain the potential to transform both societies and healthcareDB is supported by the German Research Foundation (Forschungsstipendium; BA 6276/1-1). CYE is supported by Swiss National Science Foundation [163898]. VNG is supported by grants from National Institutes of Health, and by the Russian Federation grant 14.W03.31.0012. DWL presented the results of research supported in part by research grants and funds from the National Institutes of Health, the Wisconsin Partnership Program, the Progeria Research Foundation, the American Federation for Aging Research, and the University of Wisconsin-Madison School of Medicine and Public Health and Department of Medicine, as well as the facilities and resources of the William S. Middleton Memorial Veterans Hospital. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. This work does not represent the views of the Department of Veterans Affairs or the United States Government. MSL is supported by an LUMC research fellowship and a VIDI grant from the Netherlands scientific organization (NWO- ALW-016.161.320). A.M.-M. is supported by grants from the Instituto de Salud Carlos III co-funded by Instituto de Salud Carlos III and FEdeR (CP14/00105 and PI15/00134). SM was supported by the FWO-OP/Odysseus program (42/FA010100/32/6484). SJO's current work is funded by The Glenn Award from the Glenn Foundation for Medical Research. MR is supported by the Swiss National Science Foundation and the European Union Horizon 2020 program. MSK is supported by grants from the Danish Cancer Society (#R167-A11015_001), the Independent Research Fund Denmark (#7016- 00230B) and the Novo Nordisk Foundation (NNF17OC0027812).Peer reviewe

Aging and drug discovery.

Multiple interventions in the aging process have been discovered to extend the healthspan of model organisms. Both industry and academia are therefore exploring possible transformative molecules that target aging and age-associated diseases. In this overview, we summarize the presented talks and discussion points of the 5th Annual Aging and Drug Discovery Forum 2018 in Basel, Switzerland. Here academia and industry came together, to discuss the latest progress and issues in aging research. The meeting covered talks about the mechanistic cause of aging, how longevity signatures may be highly conserved, emerging biomarkers of aging, possible interventions in the aging process and the use of artificial intelligence for aging research and drug discovery. Importantly, a consensus is emerging both in industry and academia, that molecules able to intervene in the aging process may contain the potential to transform both societies and healthcare

Deep Learning of EEG Data in the NeuCube Brain-Inspired Spiking Neural Network Architecture for a Better Understanding of Depression

In the recent years, machine learning and deep learning techniques are being applied on brain data to study mental health. The activation of neurons in these models is static and continuous-valued. However, a biological neuron processes the information in the form of discrete spikes based on the spike time and the firing rate. Understanding brain activities is vital to understand the mechanisms underlying mental health. Spiking Neural Networks are offering a computational modelling solution to understand complex dynamic brain processes related to mental disorders, including depression. The objective of this research is modeling and visualizing brain activity of people experiencing symptoms of depression using the SNN NeuCube architecture. Resting EEG data was collected from 22 participants and further divided into groups as healthy and mild-depressed. NeuCube models have been developed along with the connections across different brain regions using Synaptic Time Dependent plasticity (STDP) learning rule for healthy and depressed individuals. This unsupervised learning revealed some distinguishable patterns in the models related to the frontal, central and parietal areas of the depressed versus the control subjects that suggests potential markers for early depression prediction. Traditional machine learning techniques, including MLP methods have been also employed for classification and prediction tasks on the same data, but with lower accuracy and fewer new information gained