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Smoking is a well known risk factor for coronary artery disease (CAD). However, the effects of smoking on gene expression in the blood of CAD subjects in Hungary have not been extensively studied. This study aimed to identify differentially expressed genes (DEGs) associated with smoking in CAD subjects. Eleven matched samples based on age and gender were selected for analysis in this study. All subjects were non-obese, non-alcoholic, non-diabetic, and non-hypertensive and had moderate to severe stenosis of one or more coronary arteries, confirmed by coronary angiography. Whole blood samples were collected using PAXgene tubes. Next-generation sequencing was employed using the NextSeq 500 system to generate high-throughput sequencing data for transcriptome profiling. The differentially expressed genes were analyzed using the R programming language. Results: The study revealed that smokers exhibited non-significant higher levels of total cholesterol, low-density lipoprotein-cholesterol, and triglycerides compared to non-smokers (p > 0.05), although high-density lipoprotein-cholesterol was also elevated. Despite this, the overall lipid profile of smokers remained less favorable. Non-smokers had a higher BMI (p = 0.02). Differential gene expression analysis identified 58 DEGs, with 38 upregulated in smokers. The key upregulated genes included LILRB5 (log2FC = 2.88, p = 1.05 × 10−5) and RELN (log2FC = 3.31, p = 0.024), while RNF5_2 (log2FC = −5.29, p = 0.028) and IGHV7-4-1_1 (log2FC = −2.86, p = 0.020) were notably downregulated. Heatmap analysis showed a distinct clustering of gene expression profiles between smokers and non-smokers. However, GO analysis did not identify significant biological pathways associated with the DEGs. Conclusions: This research illuminates smoking’s biological effects, aiding personalized medicine for predicting and treating smoking-related diseases.

In this simulation study, we compare the dynamics and thermal behavior of different ideal flow crystallizers. The first step in creating mathematical models for the crystallizers was the implementation of the population balance equation. The population balance equation was completed with mass balance equations for the solute and the solvent as well as in the case of non-isothermal crystallizers with an energy balance equation. The solution to the population balance equation, which is a partial differential equation, can only be performed numerically. Using the method of moments, which calculates the moments of the population density function, gives a mathematically simpler model for simulating and analyzing the crystallizers. All crystallizers studied are considered mixed suspension and mixed product crystallizers. In this simulation study, the investigated crystallizers are the batch mixed suspension and mixed product isothermal crystallizer, the batch mixed suspension and mixed product non-isothermal crystallizer, and the continuous mixed suspension and mixed product removal (CMSMPR) non-isothermal crystallizer equipped with a cooling jacket. We consider citric acid as the solid material to be crystallized, and a water–glycol system is used as a cooling medium. Considering the nucleation kinetics, we applied both primary and secondary nucleation. In the case of a crystal growth kinetic, we assumed a size-independent growth rate. The highest expected value and the variance of the crystal product occur in the isotherm batch case, which can be explained by the high crystallization rate caused by the high supersaturation. Contrary to this, in the non-isothermal batch case, the final mean particle size and variance are the lowest. In continuous mode, the variance and mean values are between the values obtained in the two other cases. In this case, the supersaturation is maintained at a constant level in the steady state, and the average residence time of the crystal particles also has an important influence on the crystal size distribution. In the case of non-isothermal crystallization, the simulation studies show that the application of the energy balance provides different dynamics for the crystallizers. The implementation of an energy balances into the mathematical model enables the calculation of the thermal behavior of the crystallizers, enabling the model to be used more widely.

Hypertrophic cardiomyopathy (HCM) is a primary disease of the myocardium most commonly caused by mutations in sarcomeric genes. We aimed to perform a nationwide large-scale genetic analysis of a previously unreported, representative HCM cohort in Hungary. A total of 242 consecutive HCM index patients (127 men, 44 ± 11 years) were studied with next generation sequencing using a custom-designed gene-panel comprising 98 cardiomyopathy-related genes. A total of 90 patients (37%) carried pathogenic/likely pathogenic (P/LP) variants. The percentage of patients with P/LP variants in genes with definitive evidence for HCM association was 93%. Most of the patients with P/LP variants had mutations in MYBPC3 (55 pts, 61%) and in MYH7 (21 pts, 23%). Double P/LP variants were present in four patients (1.7%). P/LP variants in other genes could be detected in ≤3% of patients. Of the patients without P/LP variants, 46 patients (19%) carried a variant of unknown significance. Non-HCM P/LP variants were identified in six patients (2.5%), with two in RAF1 (p.Leu633Val, p.Ser257Leu) and one in DES (p.Arg406Trp), FHL1 (p.Glu96Ter), TTN (p.Lys23480fs), and in the mitochondrial genome (m.3243A>G). Frameshift, nonsense, and splice-variants made up 82% of all P/LP MYBPC3 variants. In all the other genes, missense mutations were the dominant form of variants. The MYBPC3 p.Gln1233Ter, the MYBPC3 p.Pro955ArgfsTer95, and the MYBPC3 p.Ser593ProfsTer11 variants were identified in 12, 7, and 13 patients, respectively. These three variants made up 36% of all patients with identified P/LP variants, raising the possibility of a possible founder effect for these mutations. Similar to other HCM populations, the MYBPC3 and the MYH7 genes seemed to be the most frequently affected genes in Hungarian HCM patients. The high prevalence of three MYBPC3 mutations raises the possibility of a founder effect in our HCM cohort.