The lack of advance planning in a public health emergency can lead to wasted resources and inadvertent loss of lives. This study is aimed at forecasting the needs for healthcare resources following the expansion of the coronavirus disease 2019 (COVID-19) outbreak in the Republic of Kazakhstan, focusing on hospital beds, equipment, and the professional workforce in light of the developing epidemiological situation and the data on resources currently available. We constructed a forecast model of the epidemiological scenario via the classic susceptible-exposed-infected-removed (SEIR) approach. The World Health Organization's COVID-19 Essential Supplies Forecasting Tool was used to evaluate the healthcare resources needed for the next 12 weeks. Over the forecast period, there will be 104 713.7 hospital admissions due to severe disease and 34 904.5 hospital admissions due to critical disease. This will require 47 247.7 beds for severe disease and 1929.9 beds for critical disease at the peak of the COVID-19 outbreak. There will also be high needs for all categories of healthcare workers and for both diagnostic and treatment equipment. Thus, Republic of Kazakhstan faces the need for a rapid increase in available healthcare resources and/or for finding ways to redistribute resources effectively. Republic of Kazakhstan will be able to reduce the rates of infections and deaths among its population by developing and following a consistent strategy targeting COVID-19 in a number of inter-related directions.

Semenova Y, et al. J Korean Med Sci. 2020 Jun;35(24):e227. https://doi.org/10.3346/jkms.2020.35.e227

Diabetes mellitus (DM) is associated with higher risk of developing infectious disease and COVID-19 is not the exception. There is a need to generate more data on clinical characteristics and risks of COVID19 patients presenting with DM. In this retrospective study we aimed to report on demographic features, clinical data, and outcomes of COVID-19 patients with DM in comparison with age- and sex-matched patients without DM. This was a retrospective study that relied on the nationwide data on all COVID-19 patients who were diagnosed from 14 March to 18 April, 2020. Overall, there were 31 cases with DM for which we randomly matched 4 patients without DM by age and sex. COVID-19 patients with associated DM had less beneficial outcomes and more severe disease course both at hospital admission and final diagnosis, as compared with the age and sex-matched non-DM patients. Diabetics were more predisposed to impaired breathing (29.0 % versus 4.9 % in controls), nausea/vomiting (6.5 % versus 0 % in controls) and weakness/lethargy (45.2 % versus 26.0 % in controls). Finally, 48.4 % of diabetics showed the signs of pneumonia on CT scans versus 20.3 % of non-diabetics (p = 0.001), and 32.3 % of DM patients were admitted to intensive care units as compared with just 5.7 % of non-DM patients (p<0.001). There is a need to envisage early status monitoring and supportive care in this vulnerable category of patients to enable better prognosis.

The data on seroprevalence of anti-SARS-CoV-2 antibodies in Kazakhstani population are non-existent, but are needed for planning of public health interventions targeted to COVID-19 containment. The aim of the study was to estimate the seropositivity of SARS-CoV-2 infection in the Kazakhstani population from 2020 to 2021. We relied on the data obtained from the results from “IN VITRO” laboratories of enzyme-linked immunosorbent assays for class G immunoglobulins (IgG) and class M (IgM) to SARS-CoV-2. The association of COVID-19 seropositivity was assessed in relation to age, gender, and region of residence. Additionally, we related the monitoring of longitudinal seropositivity with COVID-19 statistics obtained from Our World in Data. The total numbers of tests were 68,732 for SARS-CoV-2 IgM and 85,346 for IgG, of which 22% and 63% were positive, respectively. The highest rates of positive anti-SARS-CoV-2 IgM results were seen during July/August 2020. The rate of IgM seropositivity was the lowest on 25 October 2020 (2%). The lowest daily rate of anti-SARS-CoV-2 IgG was 17% (13 December 2020), while the peak of IgG seropositivity was seen on 6 June 2021 (84%). A longitudinal serological study should be envisaged to facilitate understanding of the dynamics of the epidemiological situation and to forecast future scenarios.

In January 2022 Kazakhstan experienced unprecedented nationwide protests that quickly turned to violent riots. Although the number of individuals affected physically by the disturbances were cited, the emotional toll of the events remains undetermined. The aim of this study was to evaluate the comparative rates of acute stress reactions in Kazakhstan in the aftermath of the unrests. A cross-sectional, population-based online survey was conducted one month after the start of the disturbances. The study questionnaire were completed anonymously and included the Primary Care PTSD Screen for DSM-5 (PC-PTSD-5) and the General Anxiety Disorder-7 (GAD-7), as well as socio-demographic and event exposure information. Of the 7021 people who initially agreed to participate, 6510 were able to complete the full survey. For a cut-off of ≥3 on the PC-PTSD-5, 14.8 % of the study participants exhibited symptoms. With a cut-off of ≥4, this percentage reduced to 4.6 %. Participants from Almaty city and Almaty region who experienced the most extensive disturbances showed a doubled prevalence compared to the national level (30.0 % for a cut-off of ≥3, and 10.1 % for a cut-off of ≥4). At the national level, the prevalence of anxiety symptoms, defined as a score of ≥10 on the GAD-7, stood at 10.9 %. This prevalence decreased to 4.2 % when considering a cut-off of ≥15. Health authorities of riot-affected areas ought to be aware of the population level mental health impact of the civil conflict and consider provision of targeted interventions to mitigate the long-term consequence of these lifespan disorders, while also seeking for the peaceful resolution of the ensuing conflicts.

Breast cancer remains a leading cause of cancer-related mortality among women worldwide, with HER2-positive subtypes requiring precise diagnostic approaches to guide targeted therapy. Digital pathology and AI-based tools offer promising solutions, but their development relies heavily on high-quality digital datasets, labelled or annotated. In this study, we present a dataset of digital images of breast cancer tissue samples with immunohistochemical expression of human epidermal growth factor receptor 2 (HER2) classes 0, 1+, 2+, and 3+. Breast cancer tissue samples were formalin-fixed and paraffin-embedded (FFPE), followed by the preparation of paraffin blocks and 5-µm sections. Immunohistochemical staining was performed using a Ventana Benchmark Ultra automated immunostainer with PATHWAY anti-HER2/neu (4B5) rabbit monoclonal antibodies and ULTRA VIEW detection system.