Objectives: Evaluation of clinical course of COVID-19 during pregnancy and maternal and perinatal outcomes of this pregnancy. Methods: 66 women with polymerase chain reaction (PCR)confirmed SARS-CoV-2 and their 42 neonates were included in the prospective observational study. Demographic, epidemiological, clinical, laboratory and instrumental data of pregnancy, delivery, postpartum period, including pharmacotherapy and neonatal outcomes were analyzed. Results: 15 (22.7%) women were asymptomatic, 25 (38%) had mild disease, while moderate and severe forms were detected in 20 (30.2%) and 6 (9.1%) cases, respectively. Additional oxygenation was required in 6 (9%) cases: 4 (6%) received CPAP therapy and 2 (3%)mechanical ventilation. Main clinical symptoms were cough (51.5%), anosmia (34.9%), and hyperthermia (33.3%). Laboratory changes included increased levels of lactate dehydrogenase (LDH), creatinine, D-dimer, and C-reactive protein (CRP), anemia, and leukopenia. All pregnant women received low molecular weight heparin and interferon alfa-2b according to the National clinical recommendations. Antimicrobial drugs included Amoxicillin/Clavulanic acid (46%) and macrolides (28%) or carbapenems in severe cases of disease. Spontaneous abortion was reported in 6.1% of cases. Eight preterm (19%) and 34 term deliveries (81%) occurred. The mean weight of neonates was (3283 ± 477) g, 1-and 5-min Apgar score was (7.8 ± 0.6) and (8.7 ± 0.5), respectively. No cases of neonatal COVID-19 infection were reported. Conclusions: Mostly, the manifestations of COVID-19 were mild. However, 9% of cases were severe, and could contribute to preterm delivery or maternal morbidity. Main predictors of severe COVID-19 course in pregnant women were a decrease in the levels of erythrocytes and lymphocytes and increase in the levels of alanine aminotransferase and CRP. Elimination of the virus in pregnant women required more time due to altered immunity. No evidence of vertical transmission during pregnancy and delivery was found. However, the possibility of this cannot be excluded.

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In order to solve the demographic problem that has arisen in Russia, comprehensive multilateral approaches are required, aimed at increasing the birth rate and reducing child and adult mortality. One approach to solve this problem is directed regulation of the human microbiome.

The need for novel techniques of rapid identification of pathogenic microorganisms arises from the massive spread of drug-resistant nosocomial strains and the emergence of centers for biohazard control. Fourier-transform infrared spectroscopy is a promising alternative to mass spectrometry as it is cost-effective, fast and suitable for field use. The aim of this work was to propose an algorithm for the identification of microorganisms in pure cultures based on the analysis of their Fourier transform infrared spectra. The algorithm is based on the automated principal component analysis of infrared spectra. Unlike its analogues described in the literature, the algorithm is capable of identifying bacteria regardless of the culture medium or growth phase. The training sample included the most prevalent causative agents of infections and sepsis in humans: Staphylococcus aureus (n = 67), Enterococcus faecalis (n = 10), Enterococcus faecium (n = 10), Klebsiella pneumoniae (n = 10), Escherichia coli (n = 10), Serratia marcescens (n = 10), Enterobacter cloacae (n = 10), Acinetobacter baumannii (n = 10), Pseudomonas aeruginosa (n = 10), and Candida albicans (n = 10). The model we built successfully passed a series of blind tests involving clinical isolates of 10 methicillin-resistant (MRSA) and 10 methicillin-sensitive (MSSA) Staphylococcus aureus strains as well as pair mixes of these cultures with clinical isolates of Pseudomonas aeruginosa, Escherichia coli, and Klebsiella pneumoniae.

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