Whole genome sequence analysis of Neisseria meningitidis strains circulating in Kazakhstan, 2017-2018.

Neisseria meningitidis (meningococcus) is a cosmopolitan bacterium that is often found in the upper respiratory tract of asymptomatic humans. However, N. meningitidis also causes meningeal inflammation and/or sepsis in humans with a periodic resurgence in incidence and high mortality rates. The pathogen is highly diverse genetically and antigenically, so that genotyping is considered important for vaccine matching to circulating strains. Annual incidence of meningococcal disease in Kazakhstan ranges between 0.2 and 2.5 cases per 100 thousand population. In total, 78 strains of N. meningitidis were isolated from clinical patients and contact persons during the years 2017-2018 in Kazakhstan. Of these, 41 strains including four from the patients and 37 from contacts, were sequenced using Illumina MiSeq. In silico typing was completed using the Neisseria pipeline 1.2 on the Galaxy Workflow Management System and PubMLST. Whole genome SNP (single nucleotide polymorphisms) trees were built using BioNumerics 8. Seven-gene multilocus sequence typing (MLST) identified ten sequence types (ST), two of which have not been previously described (ST-16025; ST-16027). ST-16025 was detected in two patients with invasive meningococcal disease in 2017 and 2018 in Akmola region and 16 contacts in 2017 in Turkistan region. This prevalent type ST-16025 demonstrates considerable intertypic diversity as it consists of three subcomplexes with a distance of more than 2000 SNPs. Invasive and carrier strains belong to different serogroups (MenB and MenC), PorA and FetA_VR. Two invasive strains were MenB, one MenC and one MenW (Hajj lineage). The strains from the contact persons were: MenC (n = 18), cnl (n = 9), MenY (n = 7), MenW (n = 1), MenB (n = 1) and one unidentifiable. Different numbers of alleles were present: 12, 11, 7, and 7 alleles for PorA, FetA, fHbp, and NHBA, respectively. This study is the first report of the genetic diversity of N. meningitidis strains in Kazakhstan. Despite limitations with the studied sample size, important conclusions can be drawn based on data produced. This study provides evidence for regulatory authorities with regard to changing routine diagnostic protocols to increase the collecting of samples for WGS.

Comparative Analysis of Clinical and Laboratory Data in Children with Multisystem Inflammatory Syndrome Associated with SARS-CoV-2 in the Republic of Kazakhstan.

Background and Objectives: Data with more severe mutations of the SARS-CoV-2 virus, compared with the original wild-type strain of COVID-19 disease, were reported worldwide. The study aims to describe the clinical and laboratory manifestations of a multisystem inflammatory syndrome in children (MIS-C) associated with SARS-CoV-2 in the Republic of Kazakhstan and to compare the severity of the disease depending on the time of the circulating variant of SARS-CoV-2 virus. Material and methods: A retrospective, multicentre, nationwide study of 89 children with MIS-C who received inpatient treatment from August 1, 2020, to December 1, 2021. The patients were allocated into two groups: 1(2020) - 45 children and 2 (2021) - 44 children. Study periods were characterized by the circulation of different strains of the SARS-CoV-2 virus. Results: In children with MIS-C in 2021, acute renal failure, disseminated intravascular coagulation syndrome, and shock were statistically more frequently found, which led to fairly common admittance to the intensive care unit. When comparing laboratory data, the children with MIS-C in 2021 had higher values of inflammation markers: ferritin, procalcitonin, erythrocyte sedimentation rate, leukocytes, and neutrophils. Furthermore, these children had a lower level of lymphocytes than children with MIS-C in 2020. Conclusions: MIS-C is a severe, life-threatening systemic disease characterized by multiple organ damage and important inflammatory changes in laboratory parameters. A more aggressive clinical course of MIS-C in 2021 may be associated with the emergence of new SARS-CoV-2 strains.

Ability of Procalcitonin and C-Reactive Protein for Discriminating between Bacterial and Enteroviral Meningitis in Children Using Decision Tree.

Bacterial meningitis (BM) is a public health burden in developing countries, including Central Asia. This disease is characterized by a high mortality rate and serious neurological complications. Delay with the start of adequate therapy is associated with an increase in mortality for patients with acute bacterial meningitis. Cerebrospinal fluid culture, as a gold standard in bacterial meningitis diagnosis, is time-consuming with modest sensitivity, and this is unsuitable for timely decision-making. It has been shown that bacterial meningitis differentiation from viral meningitis could be done through different parameters such as clinical signs and symptoms, laboratory values, such as PCR, including blood and cerebrospinal fluid (CSF) analysis. In this study, we proposed the method for distinguishing the bacterial form of meningitis from enteroviral one. The method is based on the machine learning process deriving making decision rules. The proposed fast-and-frugal trees (FFTree) decision tree approach showed an ability to determine procalcitonin and C-reactive protein (CRP) with cut-off values for distinguishing between bacterial and enteroviral meningitis (EVM) in children. Such a method demonstrated 100% sensitivity, 96% specificity, and 98% accuracy in the differentiation of all cases of bacterial meningitis in this study. These findings and proposed method may be useful for clinicians to facilitate the decision-making process and optimize the diagnostics of meningitis.

COVID-19 in the paediatric population of Kazakhstan.

Background: To date, there have been no studies of COVID-19 infection in children in Central Asia, particularly the Republic of Kazakhstan. This report analyses the epidemiological data on COVID-19 infection in children in Kazakhstan.Methods: The study included 650 paediatric patients diagnosed with COVID-19. Demographic and epidemiological data and the symptoms and radiological evidence of complications were collected and analysed. Children were sub-divided into four groups: neonates/infants, young children, older children and adolescents.Results: All of the 650 children were under 19 years of age, 56.3% of whom were male, and 122 (18.8%) were newborns and infants. The majority of cases (n = 558, 85.8%) were asymptomatic and only four cases were severe (0.6%). The symptoms were as follows in descending order: cough (14.8%), sore throat (12.8%), fever (9.1%) and rhinorrhoea (5.5%). Diarrhoea (2%), dyspnoea (1.8%) and muscle pain were rare (1.1%). Only three children required intensive care, including invasive ventilation. One patient had acute respiratory distress syndrome. There were no deaths.Conclusion: Most cases of COVID-19 infection in children in Kazakhstan were asymptomatic or the symptoms were mild. Only three patients required intensive care.