The landscape of the COVID-19 pandemic in Poland emerging from epidemiological and genomic data.

The COVID-19 pandemic has profoundly affected all aspects of our lives. Through real-time monitoring and rapid vaccine implementation, we succeeded in suppressing the spread of the disease and mitigating its consequences. Finally, conclusions can be summarized and drawn. Here, we use the example of Poland, which was seriously affected by the pandemic. Compared to other countries, Poland has not achieved impressive results in either testing or vaccination, which may explain its high mortality (case fatality rate, CFR 1.94%). Through retrospective analysis of data collected by the COVID-19 Data Portal Poland, we found significant regional differences in the number of tests performed, number of cases detected, number of COVID-19-related deaths, and vaccination rates. The Masovian, Greater Poland, and Pomeranian voivodeships, the country's leaders in vaccination, reported high case numbers but low death rates. In contrast, the voivodeships in the eastern and southern parts of Poland (Subcarpathian, Podlaskie, Lublin, Opole), which documented low vaccination levels and low case numbers, had higher COVID-19-related mortality rates. The strong negative correlation between the CFR and the percentage of the population that was vaccinated in Poland supports the validity of vaccination. To gain insight into virus evolution, we sequenced more than 500 genomes and analyzed nearly 80 thousand SARS-CoV-2 genome sequences deposited in GISAID by Polish diagnostic centers. We showed that the SARS-CoV-2 variant distribution over time in Poland reflected that in Europe. Haplotype network analysis allowed us to follow the virus transmission routes and identify potential superspreaders in each pandemic wave.

The RdRp genotyping of SARS-CoV-2 isolated from patients with different clinical spectrum of COVID-19.

BACKGROUND: The evolution of SARS-CoV-2 has been observed from the very beginning of the fight against COVID-19, some mutations are indicators of potentially dangerous variants of the virus. However, there is no clear association between the genetic variants of SARS-CoV-2 and the severity of COVID-19. We aimed to analyze the genetic variability of RdRp in correlation with different courses of COVID-19. RESULTS: The prospective study included 77 samples of SARS-CoV-2 isolated from outpatients (1st degree of severity) and hospitalized patients (2nd, 3rd and 4th degree of severity). The retrospective analyses included 15,898,266 cases of SARS-CoV-2 genome sequences deposited in the GISAID repository. Single-nucleotide variants were identified based on the four sequenced amplified fragments of SARS-CoV-2. The analysis of the results was performed using appropriate statistical methods, with p < 0.05, considered statistically significant. Additionally, logistic regression analysis was performed to predict the strongest determinants of the observed relationships. The number of mutations was positively correlated with the severity of the COVID-19, and older male patients. We detected four mutations that significantly increased the risk of hospitalization of COVID-19 patients (14676C > T, 14697C > T, 15096 T > C, and 15279C > T), while the 15240C > T mutation was common among strains isolated from outpatients. The selected mutations were searched worldwide in the GISAID database, their presence was correlated with the severity of COVID-19. CONCLUSION: Identified mutations have the potential to be used to assess the increased risk of hospitalization in COVID-19 positive patients. Experimental studies and extensive epidemiological data are needed to investigate the association between individual mutations and the severity of COVID-19.

Development and Validation of Computational Fluid Dynamics Model of Ladle Furnace with Electromagnetic Stirring System.

Numerical methods are crucial to supporting the development of new technology in different industries, especially steelmaking, where many phenomena cannot be directly measured or observed under industrial conditions. As a result, further designing and optimizing steelmaking equipment and technology are not easy tasks. At the same time, numerical approaches enable modeling of various phenomena controlling material behavior and, thus, understanding the physics behind the processes occurring in different metallurgical devices. With this, it is possible to design and develop new technological solutions that improve the quality of steel products and minimize the negative impact on the environment. However, the usage of numerical approaches without proper validation can lead to misleading results and conclusions. Therefore, in this paper, the authors focus on the development of the CFD-based (computational fluid dynamics) approach to investigate the liquid steel flow inside one metallurgical device, namely a ladle furnace combined with an EMS (electromagnetic stirring) system. First, a numerical simulation of electromagnetic stirring in a scaled mercury model of a ladle furnace was carried out. The numerical results, such as stirring speed and turbulent kinetic energy, were compared with measurements in the mercury model. It was found that the results of the transient multiphase CFD model achieve good agreement with the measurements, but a free surface should be included in the CFD model to simulate the instability of the flow pattern in the mercury model. Based on the developed model, a full-scale industrial ladle furnace with electromagnetic stirring was also simulated and presented. This research confirms that such a coupled model can be used to design new types of EMS devices that improve molten steel flow in metallurgical equipment.

What to Do if the qPCR Test for SARS-CoV-2 or Other Pathogen Lacks Endogenous Internal Control? A Simple Test on Housekeeping Genes.

Some of the products for the molecular diagnosis of infections do not have an endogenous internal control, and this is necessary to ensure that the result is not a false negative. The aim of the project was to design a simple low-cost RT-qPCR test that can confirm the expression of basic metabolism proteins, thus confirming the quality of genetic material for molecular diagnostic tests. Two successful equivalent qPCR assays for the detection of the GADPH and ACTB genes were obtained. The course of standard curves is logarithmic, with a very high correlation coefficient R2 within the range of 0.9955-0.9956. The reaction yield was between 85.5 and 109.7%, and the detection limit (LOD) with 95% positive probability was estimated at 0.0057 ng/µL for GAPDH and 0.0036 ng/µL for ACTB. These tests are universal because they function on various types of samples (swabs, cytology, etc.) and can complement the diagnosis of SARS-CoV-2 and other pathogens, as well as potentially oncological diagnostics.

Glutathione S-Transferase P1 Genetic Variant's Influence on the HbA1c Level in Type Two Diabetic Patients.

GST (glutathione S-transferases) are capable of influencing glucose homeostasis, probably through regulation of the response to oxidant stress. The aim of our study was to investigate the relationship between GSTP1 gene polymorphism and glycated hemoglobin (HbA1c) levels in type two diabetic (T2D) patients. A total of 307 T2D patients were included. Analysis of the GSTP1 gene polymorphism (rs1695) was conducted using the TaqMan qPCR method endpoint genotyping. HbA1c was determined using a COBAS 6000 autoanalyzer. A univariable linear regression and multivariable linear regression model were used to investigate the association between mean HbA1c level and GSTP1 gene polymorphism, age at T2D diagnosis, T2D duration, therapy with insulin, gender, BMI, smoking status. GSTP1 Val/Val genotype, age at T2D diagnosis, T2D duration and therapy with insulin were statistically significant contributors to HbA1c levels (p < 0.05). Multivariable regression analysis revealed that GSTP1 (Val/Val vs. Ile/Ile) was associated with higher HbA1c even after adjustment for variables that showed a statistically significant relationship with HbA1c in univariable analyses (p = 0.024). The results suggest that GSTP polymorphism may be one of the risk factors for higher HbA1c in T2D patients. Our study is limited by the relatively small sample size, cross-sectional design, and lack of inclusion of other oxidative stress-related genetic variants.

Scaling Scientific Cellular Automata Microstructure Evolution Model of Static Recrystallization toward Practical Industrial Calculations.

An attempt to bridge the gap between capabilities offered by advanced full-field microstructure evolution models based on the cellular automata method and their practical applications to daily industrial technology design was the goal of the work. High-performance parallelization techniques applied to the cellular automata static recrystallization (CA-SRX) model were selected as a case study. Basic assumptions of the CA-SRX model and developed modifications allowing high-performance computing are presented within the paper. Particular attention is placed on the development of the parallel computation scheme allowing numerical simulations even for a large volume of material. The development of new approaches to handle communication within the distributed environment is also addressed in the paper as a means to obtain higher computational efficiency. Evaluation of model limits was based on the scalability analysis. The investigation was carried out for the 3D and 2D case studies. Therefore, the complex static recrystallization cellular automata simulation taking into account the influence of recovery, nucleation based on accumulated energy, and the progress of recrystallization as a function of stored energy and grain boundary mobility with high-performance computing capabilities is now possible. The research highlighted that parallelization is more effective with an increasing number of cellular automata cells processed during the entire simulation. It was also proven that the developed parallelization scheme and communication mechanism provides a possibility of obtaining scaled speedup over 700 times for 2D and over 800 times for 3D computational domains, which is crucial for future applications in industrial practice. Therefore, the presented approach's main advantage is based on the possibility of running the calculation based on input data obtained directly from high-resolution 3D imaging of the microstructure. With that, the full immersion of the experimental results into the numerical model is possible. The second novelty aspect of this work is related to the identification of the quality of model predictions as a function of model size reductions.

Numerical Study on the Dependency of Microstructure Morphologies of Pulsed Laser Deposited TiN Thin Films and the Strain Heterogeneities during Mechanical Testing.

Numerical study of the influence of pulsed laser deposited TiN thin films' microstructure morphologies on strain heterogeneities during loading was the goal of this research. The investigation was based on the digital material representation (DMR) concept applied to replicate an investigated thin film's microstructure morphology. The physically based pulsed laser deposited model was implemented to recreate characteristic features of a thin film microstructure. The kinetic Monte Carlo (kMC) approach was the basis of the model in the first part of the work. The developed kMC algorithm was used to generate thin film's three-dimensional representation with its columnar morphology. Such a digital model was then validated with the experimental data from metallographic analysis of laboratory deposited TiN(100)/Si. In the second part of the research, the kMC generated DMR model of thin film was incorporated into the finite element (FE) simulation. The 3D film's morphology was discretized with conforming finite element mesh, and then incorporated as a microscale model into the macroscale finite element simulation of nanoindentation test. Such a multiscale model was finally used to evaluate the development of local deformation heterogeneities associated with the underlying microstructure morphology. In this part, the capabilities of the proposed approach were clearly highlighted.

Selective cytotoxicity and antifungal properties of copper(II) and cobalt(II) complexes with imidazole-4-acetate anion or 1-allylimidazole.

The physicochemical properties of metal complexes determine their potential applications as antitumor agents. In this study, the antitumor properties of mononuclear cobalt(II) and copper(II) coordination compounds (stoichiometry: [Co(iaa)2(H2O)2]·H2O (iaa = imidazole-4-acetate anion), [Co(1-allim)6](NO3)2 (1-allim = 1-allylimidazole), [Cu(iaa)2H2O] and [Cu(1-allim)4(NO3)2]) and their ligands have been evaluated on human lung carcinoma A549 cells and normal bronchial BEAS-2B cells. Designing the chemical structure of new antitumor agents the possible interactions with macromolecules such as DNA or proteins should be take into account. PCR gene tlr4 product served as DNA model, whereas lysozyme and phage-derived endolysin (both peptidoglycan degrading enzymes) were applied as protein/enzyme model. The interactions were analysed using PCR-HRM and circular dichroism, FT-IR, spectrophotometry, respectively. Additionally, the antimicrobial properties of the complexes at a non-cytotoxic concentration were analyzed against S. aureus, E. coli, P. aeruginosa and C. albicans strains. The results obtained in this study showed the selective cytotoxicity of metal complexes, mainly [Cu(1-allim)4(NO3)2] towards tumor cells. From all tested compounds, only [Co(iaa)2(H2O)2].H2O non-covalently interacts with DNA. Cu(II) and Co(II) complexes did not affect the secondary conformation of tested proteins but modified the hydrolytic activity of enzymes (lysozyme and endolysin). Moreover, only [Co(iaa)2(H2O)2].H2O exhibited the antifungal properties. In conclusion, Co(II) and Cu(II) metal complexes bearing two imidazole-4-acetate ligands seemed to be promising antitumor and antifungal agents for future drug design and application.