Worldwide, Tuberculosis (TB) is caused by Mycobacterium tuberculosis bacteria. Ocimum sanctum, commonly known as holy basil (Tulsi), is an herbaceous perennial that belongs to the family Lamiaceae and is considered one of the most important sources of medicine and drugs for the treatment of various diseases. The presented study aims to discover the potential phenomenon of Ocimum sanctum in the medicament of tuberculosis using a network pharmacology approach. Active ingredients of Ocimum sanctum were fetched through two different databases and from literature review and then targets of these compounds were harvested by SwissTargetPrediction. Potential targets of TB were downloaded from GeneCards and DisGNet databases. After screening of mutual targets, enrichment analysis through DAVID was performed. Protein–protein interaction was performed using the String database and visualized by Cytoscape. Then the target-compound-pathway network was constructed with Cytoscape. In the end, molecular docking was performed to get the potential active ingredients against tuberculosis. Eight active ingredients with 776 potential therapeutic targets were obtained from O. sanctum, 632 intersected targets from two databases were found in TB, 72 common potential targets were found from TB and O. sanctum. The topological analysis exposes those ten targets that formed the core PPI network. Furthermore, molecular docking analysis reveals that active compounds have the greater binding ability with the potential target to suppress TB.

Zoonotic virus spillover in human hosts including outbreaks of Hantavirus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) imposes a serious impact on the quality of life of patients. Recent studies provide a shred of evidence that patients with Hantavirus-caused hemorrhagic fever with renal syndrome (HFRS) are at risk of contracting SARS-CoV-2. Both RNA viruses shared a higher degree of clinical features similarity including dry cough, high fever, shortness of breath, and certain reported cases with multiple organ failure. However, there is currently no validated treatment option to tackle this global concern. This study is attributed to the identification of common genes and perturbed pathways by combining differential expression analysis with bioinformatics and machine learning approaches. Initially, the transcriptomic data of hantavirus-infected peripheral blood mononuclear cells (PBMCs) and SARS-CoV-2 infected PBMCs were analyzed through differential gene expression analysis for identification of common differentially expressed genes (DEGs). The functional annotation by enrichment analysis of common genes demonstrated immune and inflammatory response biological processes enriched by DEGs. The protein–protein interaction (PPI) network of DEGs was then constructed and six genes named RAD51, ALDH1A1, UBA52, CUL3, GADD45B, and CDKN1A were identified as the commonly dysregulated hub genes among HFRS and COVID-19. Later, the classification performance of these hub genes were evaluated using Random Forest (RF), Poisson Linear Discriminant Analysis (PLDA), Voom-based Nearest Shrunken Centroids (voomNSC), and Support Vector Machine (SVM) classifiers which demonstrated accuracy >70%, suggesting the biomarker potential of the hub genes. To our knowledge, this is the first study that unveiled biological processes and pathways commonly dysregulated in HFRS and COVID-19, which could be in the next future used for the design of personalized treatment to prevent the linked attacks of COVID-19 and HFRS.

The purpose of conducting this research is to compare public and private sector banks of Pakistan by evaluating their customer satisfaction. This research is mainly based on primary data which has been collected through a well-structured questionnaire (adapted from three different studies). The questionnaire has been distributed to 351 different respondents on different chosen locations. This paper makes a useful contribution as there are very low number of studies has been conducted in Pakistan on such areas like price, technology, reliability, customer service, location and infrastructure. This research shows that customer satisfaction varies from person to person and, bank managers need to conduct more researches in order to evaluate customer satisfaction more strongly.