Identification of Differentially Expressed Genes and Protein-Protein Interaction in Patients With COVID-19 and Diabetes Peripheral Neuropathy: A Bioinformatics and System Biology Approach.

The coronavirus disease 2019 (COVID-19) pandemic has had a significant impact globally, resulting in a higher death toll and persistent health issues for survivors, particularly those with pre-existing medical conditions. Numerous studies have demonstrated a strong correlation between catastrophic COVID-19 results and diabetes. To gain deeper insights, we analysed the transcriptome dataset from COVID-19 and diabetic peripheral neuropathic patients. Using the R programming language, differentially expressed genes (DEGs) were identified and classified based on up and down regulations. The overlaps of DEGs were then explored between these groups. Functional annotation of those common DEGs was performed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Bio-Planet, Reactome, and Wiki pathways. A protein-protein interaction (PPI) network was created with bioinformatics tools to understand molecular interactions. Through topological analysis of the PPI network, we determined hub gene modules and explored gene regulatory networks (GRN). Furthermore, the study extended to suggesting potential drug molecules for the identified mutual DEG based on the comprehensive analysis. These approaches may contribute to understanding the molecular intricacies of COVID-19 in diabetic peripheral neuropathy patients through insights into potential therapeutic interventions.

Isolation of multidrug resistance bacteria from the patients with wound infection and their antibiotics susceptibility patterns: A cross-sectional study.

Introduction: Antimicrobial resistance has become one of the most severe public problems in both developed and developing countries like Bangladesh. In this study, several multi-drug resistant bacteria were isolated from the wound infections and demonstrated their antibiotic susceptibility pattern in Bangladeshi patients. Methods: A total of 699 bacterial isolates were collected from wound swabs and each isolate was identified using gram staining, biochemical assays, antibiotic susceptibility tests with the disk diffusion method, and colony morphology. Samples were taken from January 2018 to December 2019. The analysis was conducted using SPSS (Inc., Chicago, IL, USA), and descriptive statistics were employed to illustrate the findings. Results: We have found 14.4% gram-positive bacteria (n = 100) and 85.6% gram-negative bacteria (n = 595) among the 695 samples by gram staining methods. The most prevalent gram-positive and gram-negative bacteria present in wound infections were Staphylococcus spp. (81.5%) and Pseudomonas spp. (89%), respectively. Antimicrobials that were mostly resistant to gram-negative isolates were Amoxicillin (75.8%), Cefixime (75.5%), Cefuroxime (70.3%), and Ceftazidime (69.6%). On the other hand, cefixime and ceftazidime accounted for 73% of the resistance against gram-positive isolates, followed by amoxicillin (71%), and penicillin-G (69%). Meropenem was found to be the most sensitive antibiotic for gram-negative bacteria. Meropenem and Gentamycin were found to have a percentage of sensitivity for gram-positive bacteria. Based on the assessment of 13 different antimicrobial classes, the percentage of multi-drug resistant bacteria identified in gram-negative bacteria was 84% and in gram-positive bacteria was 79%. Among gram-negative bacterial isolates, 82% pseudomonas spp, 88.5% Klebsiella spp, and 91.6% Proteus spp were reported as multi-drug resistant. On the other hand, Pseudomonas spp, Klebsiella spp, and Proteus spp. were found to be multi-drug resistant in 82%, 88.5%, and 91.6% of gram-negative bacterial isolates, respectively. It was shown that staphylococcus aureus (81%) and staphylococcus spp (78.6%) became gram-positive among gram-positive isolates. Conclusion: According to this study, frequently isolated bacteria have a high frequency of MDR, which is the most pressing issue in public health. This study helps to manage the evidence-based treatment strategy and the urgency of early identification of drug-resistant bacteria that can reduce disease burden.

Identification of differentially expressed genes and their major pathways among the patient with COVID-19, cystic fibrosis, and chronic kidney disease.

The SARS-CoV-2 virus causes Coronavirus disease, an infectious disease. The majority of people who are infected with this virus will have mild to moderate respiratory symptoms. Multiple studies have proved that there is a substantial pathophysiological link between COVID-19 disease and patients having comorbidities such as cystic fibrosis and chronic kidney disease. In this study, we attempted to identify differentially expressed genes as well as genes that intersected among them in order to comprehend their compatibility. Gene expression profiling indicated that 849 genes were mutually exclusive and functional analysis was done within the context of gene ontology and key pathways involvement. Three genes (PRPF31, FOXN2, and RIOK3) were commonly upregulated in the analysed datasets of three disease categories. These genes could be potential biomarkers for patients with COVID-19 and cystic fibrosis, and COVID-19 and chronic kidney disease. Further extensive analyses have been performed to describe how these genes are regulated by various transcription factors and microRNAs. Then, our analyses revealed six hub genes (PRPF31, FOXN2, RIOK3, UBC, HNF4A, and ELAVL). As they were involved in the interaction between COVID-19 and the patient with CF and CKD, they could help researchers identify potential therapeutic molecules. Some drugs have been predicted based on the upregulated genes, which may have a significant impact on reducing the burden of these diseases in the future.

Development of an in silico multi-epitope vaccine against SARS-COV-2 by précised immune-informatics approaches.

The coronavirus family has been infecting the human population for the past two decades, but the ongoing coronavirus called SARS-CoV-2 has posed an enigmatic challenge to global public health security. Since last year, the mutagenic quality of this virus is causing changes to its genetic material. To prevent those situations, the FDA approved some emergency vaccines but there is no assurance that these will function properly in the complex human body system. In point of view, a short but efficient effort has made in this study to develop an immune epitope-based therapy for the rapid exploitation of SARS-CoV-2 by applying in silico structural biology and advancing immune information strategies. The antigenic epitopes were screened from the Surface, Membrane, Envelope proteins of SARS-CoV-2 and passed through several immunological filters to determine the best possible one. According to this, 7CD4+, 10CD8+ and 5 B-cell epitopes were found to be prominent, antigenic, immunogenic, and most importantly, highly conserved among 128 Bangladeshi and 110 other infected countries SARS-CoV-2 variants. After that, the selected epitopes and adjuvant were linked to finalize the multi-epitope vaccine by appropriate linkers. The immune simulation disclosed that the engineered vaccine could activate both humoral and innate immune responses. For the prediction of an effective binding, molecular docking was carried out between the vaccine and immunological receptors (TLRs). Strong binding affinity and good docking scores clarified the stringency of the vaccines. Furthermore, MD simulation was performed within the highest binding affinity complex to observe the stability. Codon optimization and other physicochemical properties revealed that the vaccine would be suitable for a higher expression at cloning level. So, monitoring the overall in silico assessment, we anticipated that our engineered vaccine would be a plausible prevention against COVID-19.