Association of ACE2 and TMPRSS2 genes variants with disease severity and most important biomarkers in COVID-19 patients in Bosnia and Herzegovina.

AIM: To assess the association of single nucleotide polymorphisms (SNPs) in the ACE2 and TMPRSS2 genes with COVID-19 severity and key biomarkers. METHODS: The study involved 750 COVID-19 patients from Bosnia and Herzegovina, divided into three groups: mild, moderate, and severe cases. Genetic variations within the ACE2 (rs2285666) and TMPRSS2 (rs2070788) genes were examined with real-time polymerase chain reaction. Biochemical markers were determined with standard procedures. RESULTS: There was a significant difference in the rs2070788 genotype distribution between patients with mild and moderate symptoms, but not between other groups. For the rs2285666 polymorphism, no significant difference in genotype distribution was found. In patients with mild symptoms, carriers of the GG genotype of rs2070788 had significantly higher total bilirubin levels than carriers of the AA genotype. Similarly, carriers of the TT genotype of rs2285666 had significantly higher activated partial thromboplastin time and international normalized ratio, and lower lactate dehydrogenase levels compared with the CC genotype. Among patients with severe symptoms, carriers of the GG genotype showed significantly higher potassium levels than carriers of the AA genotype, while carriers of the TT genotype showed significantly higher erythrocyte count as well as hemoglobin and hematocrit levels compared with the CC genotype. CONCLUSION: This study highlights the role of genetic factors, particularly SNPs in the ACE2 and TMPRSS2 genes, in determining COVID-19 severity, aiding patient risk assessment and prognosis.

Principal component analysis of the oxidative stress, inflammation, and dyslipidemia influence in patients with different levels of glucoregulation.

Background: The aim of the study was to explore the mutual relationship between oxidative stress, inflammation and metabolic biomarkers in subjects with prediabetes (PRE), newly diagnosed type 2 diabetes patients (NT2D) and overt type 2 diabetes (T2D) using principal component analysis (PCA) as a thorough statistical approach. Methods: Glycated hemoglobin, lipid parameters, inflammation (IL-6, CRP and fibrinogen) and oxidative stress markers pro-oxidants (AOPP, PAB, TOS) and antioxidants (PON1, tSHG, TAS) were measured. PCA was applied to explore the factors that the most strongly influenced glucoregulation. Results: A total of 278 subjects were (i.e., 37 PRE, 42 NT2D and 99 T2D) were compared with 100 healthy subjects as a control group (CG). PCA emphasized 4 different factors explaining 49% of the variance of the tested parameters: oxidative stress-dyslipidemia related factor (with positive loading of TG and tSHG, and with negative loading of HDL-c and TAS), dyslipidaemia related factor (i.e., total cholesterol and LDL-c, both with positive loading), Anthropometric related factor (i.e., waist and hip circumference, both with positive loading) and oxidative stressInflammation related factor (i.e., PAB, fibrinogen, and CRP all with positive loading). Out of these 4 factors, only oxidative stress - dyslipidaemia related factor showed a significant predictive capability towards poor glucoregulation. An increase in this factor by one unit showed a 1.6 times higher probability for poor glucoregulation. Conclusions: Redox imbalance (determined with lower TAS and higher tSHG), in addition to higher TG and lower HDLc was associated with poor glucoregulation.

Application of artificial intelligence for the classification of the clinical outcome and therapy in patients with viral infections: The case of COVID-19.

BACKGROUND: With the end of the coronavirus disease 2019 (COVID-19) pandemic, it becomes intriguing to observe the impact of innovative digital technologies on the diagnosis and management of diseases, in order to improve clinical outcomes for patients. OBJECTIVE: The research aims to enhance diagnostics, prediction, and personalized treatment for patients across three classes of clinical severity (mild, moderate, and severe). What sets this study apart is its innovative approach, wherein classification extends beyond mere disease presence, encompassing the classification of disease severity. This novel perspective lays the foundation for a crucial decision support system during patient triage. METHODS: An artificial neural network, as a deep learning technique, enabled the development of a complex model based on the analysis of data collected during the process of diagnosing and treating 1000 patients at the Tesanj General Hospital, Bosnia and Herzegovina. RESULTS: The final model achieved a classification accuracy of 82.4% on the validation data set, which testifies to the successful application of the artificial neural network in the classification of clinical outcomes and therapy in patients infected with viral infections. CONCLUSION: The results obtained show that expert systems are valuable tools for decision support in healthcare in communities with limited resources and increased demands. The research has the potential to improve patient care for future epidemics and pandemics.

Identification of human genetic variants modulating the course of COVID-19 infection with importance in other viral infections.

Introduction: COVID-19 has been a major focus of scientific research since early 2020. Due to its societal, economic, and clinical impact worldwide, research efforts aimed, among other questions, to address the effect of host genetics in susceptibility and severity of COVID-19. Methods: We, therefore, performed next-generation sequencing of coding and regulatory regions of 16 human genes, involved in maintenance of the immune system or encoding receptors for viral entry into the host cells, in a subset of 60 COVID-19 patients from the General Hospital Tesanj, Bosnia and Herzegovina, classified into three groups of clinical conditions of different severity ("mild," "moderate," and "severe"). Results: We confirmed that the male sex and older age are risk factors for severe clinical picture and identified 13 variants on seven genes (CD55, IL1B, IL4, IRF7, DDX58, TMPRSS2, and ACE2) with potential functional significance, either as genetic markers of modulated susceptibility to SARS-CoV-2 infection or modifiers of the infection severity. Our results include variants reported for the first time as potentially associated with COVID-19, but further research and larger patient cohorts are required to confirm their effect. Discussion: Such studies, focused on candidate genes and/or variants, have a potential to answer the questions regarding the effect of human genetic makeup on the expected infection outcome. In addition, loci we identified here were previously reported to have clinical significance in other diseases and viral infections, thus confirming a general, broader significance of COVID-19-related research results following the end of the pandemic period.

Association of trace element status in COVID-19 patients with disease severity.

Caused by the new SARS-CoV-2 coronavirus, COVID-19 (coronavirus disease 2019) evolves with clinical symptoms that vary widely in severity, from mild symptoms to critical conditions, which can even result in the patient's death. A critical aspect related to an individual response to SARS-CoV-2 infection is the competence of the immune system, and it is well known that several trace elements are essential for an adequate immune response and have anti-inflammatory and antioxidant properties that are of particular importance in fighting infection. Thus, it is widely accepted that adequate trace element status can reduce the risk of SARS-CoV-2 infection and disease severity. In this study, we evaluated the serum levels of Cu, Zn, Se, Fe, I and Mg in patients (n = 210) with clinical conditions of different severity ("mild", "moderate", "severe" and "exitus letalis", i.e., patients who eventually died). The results showed significant differences between the four groups for Cu, Zn, Se and Fe, in particular a significant trend of Zn and Se serum levels to be decreased and Cu to be increased with the severity of symptoms. For Mg and I, no differences were observed, but I levels were shown to be increased in all groups.