Application of MALDI-TOF MS and machine learning for the detection of SARS-CoV-2 and non-SARS-CoV-2 respiratory infections.

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) could aid the diagnosis of acute respiratory infections (ARIs) owing to its affordability and high-throughput capacity. MALDI-TOF MS has been proposed for use on commonly available respiratory samples, without specialized sample preparation, making this technology especially attractive for implementation in low-resource regions. Here, we assessed the utility of MALDI-TOF MS in differentiating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vs non-COVID acute respiratory infections (NCARIs) in a clinical lab setting in Kazakhstan. Nasopharyngeal swabs were collected from inpatients and outpatients with respiratory symptoms and from asymptomatic controls (ACs) in 2020-2022. PCR was used to differentiate SARS-CoV-2+ and NCARI cases. MALDI-TOF MS spectra were obtained for a total of 252 samples (115 SARS-CoV-2+, 98 NCARIs, and 39 ACs) without specialized sample preparation. In our first sub-analysis, we followed a published protocol for peak preprocessing and machine learning (ML), trained on publicly available spectra from South American SARS-CoV-2+ and NCARI samples. In our second sub-analysis, we trained ML models on a peak intensity matrix representative of both South American (SA) and Kazakhstan (Kaz) samples. Applying the established MALDI-TOF MS pipeline "as is" resulted in a high detection rate for SARS-CoV-2+ samples (91.0%), but low accuracy for NCARIs (48.0%) and ACs (67.0%) by the top-performing random forest model. After re-training of the ML algorithms on the SA-Kaz peak intensity matrix, the accuracy of detection by the top-performing support vector machine with radial basis function kernel model was at 88.0%, 95.0%, and 78% for the Kazakhstan SARS-CoV-2+, NCARI, and AC subjects, respectively, with a SARS-CoV-2 vs rest receiver operating characteristic area under the curve of 0.983 [0.958, 0.987]; a high differentiation accuracy was maintained for the South American SARS-CoV-2 and NCARIs. MALDI-TOF MS/ML is a feasible approach for the differentiation of ARI without specialized sample preparation. The implementation of MALDI-TOF MS/ML in a real clinical lab setting will necessitate continuous optimization to keep up with the rapidly evolving landscape of ARI.IMPORTANCEIn this proof-of-concept study, the authors used matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and machine learning (ML) to identify and distinguish acute respiratory infections (ARI) caused by SARS-CoV-2 versus other pathogens in low-resource clinical settings, without the need for specialized sample preparation. The ML models were trained on a varied collection of MALDI-TOF MS spectra from studies conducted in Kazakhstan and South America. Initially, the MALDI-TOF MS/ML pipeline, trained exclusively on South American samples, exhibited diminished effectiveness in recognizing non-SARS-CoV-2 infections from Kazakhstan. Incorporation of spectral signatures from Kazakhstan substantially increased the accuracy of detection. These results underscore the potential of employing MALDI-TOF MS/ML in resource-constrained settings to augment current approaches for detecting and differentiating ARI.

Association of soluble PD-L1 and NLR combination with 1-Year mortality in patients with COVID-19.

PURPOSE: Understanding the relationship between patient immune characteristics, disease severity, and mortality represents a critical step in the fight against COVID-19. Elevated levels of programmed death ligand-1 (PD-L1) and Neutrophil-lymphocyte ratio (NLR) are linked to increased severity of acute COVID-19 in patients. This study aimed to investigate the association of the combination of sPD-L1 and NLR with 1-year Mortality in patients with COVID-19. METHODS: A prospective study was conducted involving patients with COVID-19 in Karaganda, Kazakhstan. The level of sPD-L1 in the blood serum was evaluated by ELISA. The effect of biomarkers on the development of mortality was analyzed with multivariate regression. RESULTS: The risk of mortality within one year HR was 2.46 if the plasma sPD-L1 value of more than 277.13 pg/ml, and for NLR more than 2.46 HR was 2.87. The model of combining sPD-L1 and NLR resulted in an improvement in the predictive accuracy of the Hazard Ratio 7.6 (95 % CI: 3.02-19.11). CONCLUSION: The combination of two immune-mediated markers (sPD-L1 and NLR), which reflect the systemic inflammatory balance of activation and exhaustion, can complement each other and improve the assessment of the risk of death in patients with COVID-19.

Association of sTREM-1 and Neutrophil-to-Lymphocyte Ratio as Prognostic Markers in COVID-19 Short- and Long-Term Mortality.

Aim: Current problem related to COVID-19 is various complications after disease, especially long-term mortality after COVID-19. Routine blood tests presented their effectiveness in the diagnosis, prognosis and mortality of COVID-19. The neutrophil-to-lymphocyte ratio (NLR) is an important marker of systemic inflammation. Soluble Trigger receptor expressed on myeloid cells-1 (sTREM-1) is considered an intrinsic enhancer of inflammatory signals. This study examined the predictive value of these markers in COVID-19 mortality. Methods: A prospective study was conducted involving patients with COVID-19 in Karaganda, Kazakhstan. The neutrophil-to-lymphocyte ratio (NLR) was calculated as the absolute number of neutrophils divided by the absolute number of lymphocytes. The level of sTREM-1 in the blood serum was evaluated by ELISA. Results: Plasma sTREM-1 concentration greater than 59.08 pg/mL and an NLR greater than 2.29 had an increased risk of early mortality (hazard ratio = 8.07; 95% CI: 1.03-62.17 and 9.24; 95% CI: 1.202-71.08, respectively); for long-term mortality of sTREM-1 greater than 47.34 pg/mL (hazard ratio = 7.96; 95% CI: 1.072-59.18) and NLR greater than 2.10 (hazard ratio = 11.52; 95% CI: 1.551-85.52). Conclusion: This study suggests that early levels of sTREM-1 and NLR are associated with the risk of 6-month mortality after experiencing COVID-19.

The Association of Endothelin-1 with Early and Long-Term Mortality in COVID-19.

(1) Background: Endothelial dysfunction is a key mechanism in the pathogenesis of COVID-19. High endothelin-1 during COVID-19 is associated with severe complications and increased mortality rates during hospitalization. This study is aimed to investigate the association of endothelin-1 levels with the risk of 30-day and 12-month all-cause mortality in patients with prior COVID-19. (2) Methods: A prospective study was conducted involving patients with COVID-19 in Karaganda, Kazakhstan. The level of endothelin-1 in the blood serum was evaluated by ELISA. Univariate and multivariate Cox regression was used to determine factors and significance of endothelin-1 associated with the risk of mortality within 30 and 365 days from hospitalization. (3) Results: The median endothelin-1 was higher in the group of patients who passed away within 30 days. The group showed statistically significant differences when compared to healthy volunteers from the control group (p = 0.0001), surviving patients (p = 0.001), and those who passed away within a year (p = 0.002). (4) Conclusions: Endothelin-1 levels are associated with increased mortality risk during the acute period of COVID-19, while plasma endothelin-1 level association with COVID-19 survivor mortality risk does not persist after 12 months.

The Epidemiological and Economic Impact of COVID-19 in Kazakhstan: An Agent-Based Modeling.

BACKGROUND: Our study aimed to assess how effective the preventative measures taken by the state authorities during the pandemic were in terms of public health protection and the rational use of material and human resources. MATERIALS AND METHODS: We utilized a stochastic agent-based model for COVID-19's spread combined with the WHO-recommended COVID-ESFT version 2.0 tool for material and labor cost estimation. RESULTS: Our long-term forecasts (up to 50 days) showed satisfactory results with a steady trend in the total cases. However, the short-term forecasts (up to 10 days) were more accurate during periods of relative stability interrupted by sudden outbreaks. The simulations indicated that the infection's spread was highest within families, with most COVID-19 cases occurring in the 26-59 age group. Government interventions resulted in 3.2 times fewer cases in Karaganda than predicted under a "no intervention" scenario, yielding an estimated economic benefit of 40%. CONCLUSION: The combined tool we propose can accurately forecast the progression of the infection, enabling health organizations to allocate specialists and material resources in a timely manner.

The impact of Gam-COVID-Vac, an Adv5/Adv26 COVID-19 vaccine, on the biomarkers of endothelial function, coagulation and platelet activation.

COVID-19 vaccines have played a critical role in controlling the COVID-19 pandemic. Although overall considered safe, COVID-19 vaccination has been associated with rare but severe thrombotic events, occurring mainly in the context of adenoviral vectored vaccines. A better understanding of mechanisms underlying vaccine-induced hypercoagulability and prothrombotic state is needed to improve vaccine safety profile. We assessed changes to the biomarkers of endothelial function (endothelin, ET-1), coagulation (thrombomodulin, THBD and plasminogen activator inhibitor, PAI) and platelet activation (platelet activating factor, PAF, and platelet factor 4 IgG antibody, PF4 IgG) within a three-week period after the first (prime) and second (boost) doses of Gam-Covid-Vac, an AdV5/AdV26-vectored COVID-19 vaccine. Blood plasma collected from vaccinees (n = 58) was assayed using ELISA assays. Participants were stratified by prior COVID-19 exposure based on their baseline SARS-CoV-2-specific serology results. We observed a significant post-prime increase in circulating ET-1, with levels sustained after the boost dose compared to baseline. ET-1 elevation following dose 2 was most pronounced in vaccinees without prior COVID-19 exposure. Prior COVID-19 was also associated with a mild increase in post-dose 1 PAI. Vaccination was associated with elevated ET-1 up to day 21 after the second vaccine dose, while no marked alterations to other biomarkers, including PF4 IgG, were seen. A role of persistent endothelial activation following COVID-19 vaccination warrants further investigation.

Comparative clinical and placental pathologic characteristics in pregnancies with and without SARS-CoV-2 infection.

OBJECTIVES: To compare the clinical and morphological characteristics of the "mother-placenta-fetus" system in high risk pregnant women of three groups: no SARS-CoV-2 infection, mild SARS-CoV-2 infection, and severe SARS-CoV-2 infection. METHODS: A case-control study was performed for all deliveries, at 28 weeks' gestation or greater, who had standard indications for placental pathologic examination. Three groups were formed: (1) control group (no SARS-CoV-2 infection), (2) mild SARS-CoV-2 infection, (3) severe SARS-CoV-2 infection. High-risk pregnancies were registered in all cases in the study groups. The examination of the placenta and the selection of fragments of placental tissue were carried out in accordance with the consensus recommendations of the Amsterdam Placental Workshop Group. The sections were subjected to standard processing and stained with hematoxylin and eosin according to the standard protocol. All cases were reviewed by two pathologists, which did not know any information on pregnancy outcome and clinical data. Statistical analysis was performed using SPSS, p<0.05 was considered statistically significant. RESULTS: Women with severe SARS-CoV-2 infection had an increased rate of multimorbidity including diabetes, chronic hypertension and obesity (p<0.01) compared with the other groups. Placentas at severe COVID-19 course were damaged by both chronic and acute injuries, in comparison to the mild and control groups (p<0.001). Also an important finding in severe COVID-19 was diffuse necrosis of the villous trophoblast - homogenization, diffuse circular eosinophilic masses surrounding the chorionic villi. CONCLUSIONS: Women with multimorbidity are an "at-risk" subgroup for severe SARS-CoV-2 infection and greater likelihood of both placental damage and perinatal hypoxic-ischemic events. These results suggest that patient education, SARS-CoV-2 disease monitoring and preventive measures would be of benefit to this group.

Infant Mortality Trends and Determinants in Kazakhstan.

Infant mortality rate (IMR) is a crucial indicator of healthcare performance and a reflection of a country's socioeconomic development. We analyzed the trends of IMR in Central Asia (CA) countries and its determinants in Kazakhstan, which is a middle-income country. Linear regression was used for IMR trend analysis in CA countries from 2000 to 2020 and for exploring associations between IMR and socioeconomic factors, health service-related factors, and population health indicators-related factors. A gamma generalized linear model was applied to define associations with various determinants. Our analysis revealed that IMR has decreased in all CA countries, with Kazakhstan having the lowest rate in 2000 and 2020. Our results suggest that socioeconomic indicators, such as total unemployment, Gini index, current health expenditure, gross domestic product (GDP), proportion of people living in poverty, and births by 15-19-year-old mothers, were associated with increased infant mortality rates. Improving socioeconomic conditions, investing in healthcare systems, reducing poverty and income inequality, and improving access to education, are all potential issues for further development. Addressing these factors may be critical for improving maternal and child health outcomes in the region.

Bacterial Co-Infections and Antimicrobial Resistance in Patients Hospitalized with Suspected or Confirmed COVID-19 Pneumonia in Kazakhstan.

Our study was carried out to characterize respiratory tract microbiota in patients with "COVID-like pneumonia" in Kazakhstan and analyze differences between COVID-19 positive and negative groups. Sputum samples were collected from hospitalized patients, ≥18 years old, in the three cities in Kazakhstan with the highest COVID-19 burden in July 2020. Isolates were identified by MALDI-TOF MS. Susceptibility testing was performed by disk diffusion. We used SPSS 26 and MedCalc 19 for statistical analysis. Among 209 patients with pneumonia, the median age was 62 years and 55% were male. RT-PCR-confirmed SARS-CoV-2 cases were found in 40% of patients, and 46% had a bacterial co-infection. Co-infection was not associated with SARS-CoV-2 RT-PCR test results, but antibiotic use was. The most frequent bacteria were Klebsiella pneumoniae (23%), Escherichia coli (12%), and Acinetobacter baumannii (11%). Notably, 68% of Klebsiella pneumoniae had phenotypic evidence of extended-spectrum beta-lactamases in disk diffusion assays, 87% of Acinetobacter baumannii exhibited resistance to beta-lactams, and >50% of E. coli strains had evidence of ESBL production and 64% were resistant to fluoroquinolones. Patients with a bacterial co-infection had a higher proportion of severe disease than those without a co-infection. The results reinforce the importance of using appropriate targeted antibiotics and effective infection control practices to prevent the spread of resistant nosocomial infections.

Is It Possible to Predict COVID-19? Stochastic System Dynamic Model of Infection Spread in Kazakhstan.

BACKGROUND: Since the start of the COVID-19 pandemic, scientists have begun to actively use models to determine the epidemiological characteristics of the pathogen. The transmission rate, recovery rate and loss of immunity to the COVID-19 virus change over time and depend on many factors, such as the seasonality of pneumonia, mobility, testing frequency, the use of masks, the weather, social behavior, stress, public health measures, etc. Therefore, the aim of our study was to predict COVID-19 using a stochastic model based on the system dynamics approach. METHOD: We developed a modified SIR model in AnyLogic software. The key stochastic component of the model is the transmission rate, which we consider as an implementation of Gaussian random walks with unknown variance, which was learned from real data. RESULTS: The real data of total cases turned out to be outside the predicted minimum-maximum interval. The minimum predicted values of total cases were closest to the real data. Thus, the stochastic model we propose gives satisfactory results for predicting COVID-19 from 25 to 100 days. The information we currently have about this infection does not allow us to make predictions with high accuracy in the medium and long term. CONCLUSIONS: In our opinion, the problem of the long-term forecasting of COVID-19 is associated with the absence of any educated guess regarding the dynamics of ß(t) in the future. The proposed model requires improvement with the elimination of limitations and the inclusion of more stochastic parameters.

COVID-19 Pandemic Impact on the Maternal Mortality in Kazakhstan and Comparison with the Countries in Central Asia.

Maternal mortality ratio is one of the sensitive indicators that can characterize the performance of healthcare systems. In our study we aimed to compare the maternal mortality ratio in the Central Asia region from 2000 to 2020, determine its trends and evaluate the association between the maternal mortality ratio and Central Asia countries' total health expenditures. We also compared the maternal mortality causes before and during the pandemic in Kazakhstan. The data were derived from the public statistical collections of each Central Asian country. During the pre-pandemic period, Central Asian nations had a downward trend of maternal mortality. Maternal mortality ratio in Central Asian countries decreased by 38% from 47.3 per 100,000 live births in 2000 to 29.5 per 100,000 live births in 2020. Except for Uzbekistan, where this indicator decreased, all Central Asian countries experienced a sharp increase in maternal mortality ratio in 2020. The proportion of indirect causes of maternal deaths in Kazakhstan reached 76.3% in 2020. There is an association between the maternal mortality ratio in Central Asian countries and their total health expenditures expressed in national currency units (r max = -0.89 and min = -0.66, p < 0.01). The study revealed an issue in the health data availability and accessibility for research in the region. The findings suggest that there must be additional efforts from the local authorities to enhance the preparedness of Central Asian healthcare systems for the new public health challenges and to improve health data accessibility.

Assessing psychometric challenges and fatigue during the COVID-19 pandemic.

Environmental hazards and/or pandemics may push humans to use different protective methods to maintain their well-being. This study aimed to identify populations vulnerable to psychometric challenges and fatigue during the Coronavirus Disease (COVID-19) pandemic in Kazakhstan. A total of 1,592 participants were recruited and asked to complete the Multidimensional Fatigue Inventory-20 (MFI-20). Participants were classified according to gender and age. Data were analyzed using the Chi-square test (x2) and MedCalc to detect the odds of MFI-scales with a score ≥12 in women compared to men. Young women had significantly higher odds (OR) of reduced activity (OR 2.4, p<0.0001), physical (OR 2.5, p<0.0001), and mental fatigue (OR 3.4, p<0.0001) than young men. Middle-aged women had significantly higher odds of general fatigue (OR 2.1; p<0.0001), reduced motivation (OR 2.1, p<0.0001), physical (OR 2.1, p<0.0001), and mental fatigue (OR 1.9, p<0.0001) than did middle-aged men. Elderly women had significantly higher odds of general fatigue (OR 3.6, p<0.0001), reduced motivation (OR 3.5, p<0.0001), and physical fatigue (OR 3.5, p<0.0001) than men in the same age category. The study highlights that women were more susceptible, with significantly increased odds of experiencing various aspects of fatigue syndrome compared to men during the COVID-19 pandemic in Kazakhstan. Therefore, it is necessary to investigate individual behavioral changes to help identify vulnerable populations and provide relevant evidence for developing protocols and guidelines during pandemics and/or outbreaks.

High SARS-CoV-2 seroprevalence in Karaganda, Kazakhstan before the launch of COVID-19 vaccination.

COVID-19 exposure in Central Asia appears underestimated and SARS-CoV-2 seroprevalence data are urgently needed to inform ongoing vaccination efforts and other strategies to mitigate the regional pandemic. Here, in a pilot serologic study we assessed the prevalence of SARS-CoV-2 antibody-mediated immunity in a multi-ethnic cohort of public university employees in Karaganda, Kazakhstan. Asymptomatic subjects (n = 100) were recruited prior to their first COVID-19 vaccination. Questionnaires were administered to capture a range of demographic and clinical characteristics. Nasopharyngeal swabs were collected for SARS-CoV-2 RT-qPCR testing. Serological assays were performed to detect spike (S)-reactive IgG and IgA and to assess virus neutralization. Pre-pandemic samples were used to validate the assay positivity thresholds. S-IgG and -IgA seropositivity rates among SARS-CoV-2 PCR-negative participants (n = 100) were 42% (95% CI [32.2-52.3]) and 59% (95% CI [48.8-69.0]), respectively, and 64% (95% CI [53.4-73.1]) of the cohort tested positive for at least one of the antibodies. S-IgG titres correlated with virus neutralization activity, detectable in 49% of the tested subset with prior COVID-19 history. Serologically confirmed history of COVID-19 was associated with Kazakh ethnicity, but not with other ethnic minorities present in the cohort, and self-reported history of respiratory illness since March 2020. Overall, SARS-CoV-2 exposure in this cohort was ~15-fold higher compared to the reported all-time national and regional COVID-19 prevalence, consistent with recent studies of excess infection and death in Kazakhstan. Continuous serological surveillance provides important insights into COVID-19 transmission dynamics and may be used to better inform the regional public health response.

Association of four genetic variants with colorectal cancer in Kazakhstan population.

The study was conducted to search for polymorphisms located in the 10th chromosome associated with colorectal adenocarcinoma in representatives of the Kazakhstan population. Study was performed with 282 colorectal cancer (CRC) patients and 159 controls. Genotyping of SNPs was performed by QuantStudio 12K Flex PCR. For four significant SNPs inheritance model analysis was performed. Increasing risk of CRC was noted for rs10795668 in log-additive model (OR = 1.45, 95% CI: 1.05-1.99, p = 0.023); for rs1035209 in log-additive model (OR = 1.79, 95% CI: 1.18-2.72, p = 0.003); for rs11190164 in log-additive model (OR = 1.67, 95% CI: 1.17-2.38, p = 0.004). Decreasing risk of CRC was noted for rs10506868 in log-additive model (OR = 0.56, 95% CI: 0.37-0.85, p = 0.006). We detected SNPs that are associated with CRC risk in the Kazakhstan population.

Antibiotic Resistance and Genotypes of Nosocomial Strains of Acinetobacter baumannii in Kazakhstan.

The aim of this study was to determine the prevalence of A. baumannii antibiotic-resistant strains in Kazakhstan and to characterize genotypes related to epidemic "high-risk" clones. Two hundred and twenty four A. baumannii isolates from four cities of Kazakhstan in 2011-2019 were studied. Antibiotic susceptibility testing was performed by using broth microdilutions method according to EUCAST (v 11.0) recommendations. The presence of blaOXA-23-like, blaOXA-24/40-like,blaOXA-58-like,blaVIM,blaIMP, and blaNDM genes was determined by PCR. Genotyping was performed using high-throughput real-time PCR detection of 21 SNPs at 10 chromosomal loci used in existing MLST schemes. Resistance rates to imipenem, meropenem, amikacin, gentamicin, and ciprofloxacin were 81.3%, 78.6%, 79.9%, 65.2%, and 89.3%, respectively. No colistin resistant isolates were detected. The values of the MIC 50% and the MIC 90% of tigecycline were 0.125 mg/L, only four isolates (1.8%) had the ECOFF value >0.5 mg/L. The presence of acquired carbapenemase genes was found in 82.2% strains, including blaOXA-23-like (78.6%) or blaOXA-58-like (3.6%) genes. The spreading of carbapenem resistant A. baumannii strains in Kazakhstan was associated with epidemic "high-risk" clonal groups, predominantly, CG208(92)OXF/CG2PAS (80.8%) and less often CG231(109)OXF/CG1PAS (1.8%).

Level of FABP3, FABP4, Nt-proBNP and Total Cardiovascular Risk in the Population of Central Kazakhstan.

AIM: The study analyzed the level of cytokines playing the significant role in the diagnosis of circulatory system diseases and total cardiovascular risk. MATERIAL AND METHODS: The study involved 1,244 residents of Karaganda region. We had studied baseline participant characteristics, in addition to calculating the total cardiovascular risk and assessment of Fatty Acid Binding Proteins 3 (FABP3), Fatty Acid Binding Proteins 4 (FABP4) and N-Terminal Prohormone of Brain Natriuretic Peptide (NT-proBNP) level. RESULTS: The results showed the combination of high cardiovascular risk (CVR) with increased titers of cardiac markers, reflecting common pathogenic mechanisms in its development, among residents of Karaganda region. CONCLUSION: The combination of high CVR with the increased titers of cardiac markers showed common pathogenic mechanisms in its development, and support the diagnostic and prognostic value of these parameters among residents of Karaganda region, and also reflects the possibility to include these cardiac markers in the program of screening survey of population for early prevention of cardiovascular disease and its complications.