Staphylococcus saprophyticus is a Gram-positive coccus responsible for the occurrence of cystitis in sexually active, young females. While effective antibiotics against this organism exist, resistant strains are on the rise. Therefore, prevention via vaccines appears to be a viable solution to address this problem. In comparison to traditional techniques of vaccine design, computationally aided vaccine development demonstrates marked specificity, efficiency, stability, and safety. In the present study, a novel, multi-epitope vaccine construct was developed against S. saprophyticus by targeting fully sequenced proteomes of its five different strains, which were examined using a pangenome and subtractive proteomic strategy to characterize prospective vaccination targets. The three immunogenic vaccine targets which were utilized to map the probable immune epitopes were verified by annotating the entire proteome. The predicted epitopes were further screened on the basis of antigenicity, allergenicity, water solubility, toxicity, virulence, and binding affinity towards the DRB*0101 allele, resulting in 11 potential epitopes, i.e., DLKKQKEKL, NKDLKKQKE, QDKLKDKSD, NVMDNKDLE, TSGTPDSQA, NANSDGSSS, GSDSSSSNN, DSSSSNNDS, DSSSSDRNN, SSSDRNNGD, and SSDDKSKDS. All these epitopes have the efficacy to cover 99.74% of populations globally. Finally, shortlisted epitopes were joined together with linkers and three different adjuvants to find the most stable and immunogenic vaccine construct. The top-ranked vaccine construct was further scrutinized on the basis of its physicochemical characterization and immunological profile. The non-allergenic and antigenic features of modeled vaccine constructs were initially validated and then subjected to docking with immune receptor major histocompatibility complex I and II (MHC-I and II), resulting in strong contact. In silico cloning validations yielded a codon adaptation index (CAI) value of 1 and an ideal percentage of GC contents (46.717%), indicating a putative expression of the vaccine in E. coli. Furthermore, immune simulation demonstrated that, after injecting the proposed MEVC, powerful antibodies were produced, resulting in the sharpest peaks of IgM + IgG formation (>11,500) within 5 to 15 days. Experimental testing against S. saprophyticus can evaluate the safety and efficacy of these prophylactic vaccination designs.

As the Urtica dioica L. whole plant’s essential oil has presented significant multiple activities, it was therefore evaluated using the GC–MS technique. This essential oil was investigated for its antioxidant, phytotoxic, and antibacterial activities in vitro. The GC–MS analysis data assisted in the identification of various constituents. The study of the essential oil of U. dioica showed potential antioxidant effects and antibacterial activity against the selected pathogens Escherichia coli -ATCC 9837 (E. coli), Bacillus subtilis-ATCC 6633 (B. subtilis), Staphylococcus aureus-ATCC6538 (S. aureus), Pseudomonas aeruginosa-ATCC 9027 (P. aeruginosa), and Salmonella typhi-ATCC 6539 (S. typhi). The library of 23 phytochemicals was docked by using MOE software, and three top virtual hits with peroxiredoxin protein [PDB ID: 1HD2] and potential target protein [PDB ID: 4TZK] were used; hence, the protein–ligand docking results estimated the best binding conformations and a significant correlation with the experimental analysis, in terms of the docking score and binding interactions with the key residues of the native active binding site. The essential oil in the silico pharmacokinetic profile explained the structure and activity relationships of the selected best hits, and their additional parameters provided insight for further clinical investigations. Therefore, it is concluded that the U. dioica essential oil could be a potent antioxidant and antibacterial agent for aromatherapy through its topical application, if further tested in a laboratory and validated.

Urokinase receptors regulate the interplay between inflammation, immunity, and blood clotting. The soluble urokinase plasminogen activator system is an immunologic regulator affecting endothelial function and its related receptor; the soluble urokinase plasminogen activator receptor (suPAR) has been reported to impact kidney injury. This work aims to measure serum levels of suPAR in COVID-19 patients and correlate the measurements with variable clinicolaboratory parameters and patient outcomes. In this prospective cohort study, 150 COVID-19 patients and 50 controls were included. The circulating suPAR levels were quantified by Enzyme-linked immunosorbent assay (ELISA). Routine COVID-19 laboratory assessments, including CBC, CRP, LDH, serum creatinine, and estimated glomerular filtration rates, were performed. The need for oxygen therapy, CO-RAD score, and survival rates was assessed. Bioinformatic analysis and molecular docking were run to explore the urokinase receptor structure/function and to characterize molecules as potential anti-suPAR therapeutic targets, respectively. We found higher circulating suPAR levels in COVID-19 patients vs. controls (p < 0.001). Circulating suPAR levels positively correlated with COVID-19 severity, the need for O₂ therapy, the total leukocytes count, and the neutrophils to lymphocyte ratio, while they were negatively correlated with the O₂ saturation level, albumin, blood calcium, lymphocytic count, and GFR. In addition, the suPAR levels were associated with poor prognostic outcomes such as a high incidence of acute kidney injury (AKI) and mortality rate. Kaplan–Meier curves showed a lower survival rate with higher suPAR levels. The logistic regression analysis confirmed the significant association of suPAR levels with the occurrence of AKI related to COVID-19 and with increased mortality probability within three months of COVID-19 follow-up. Some compounds that can act similarly to uPAR were discovered and tested by molecular docking to identify the possible ligand–protein interactions. In conclusion, higher circulating suPAR levels were associated with COVID-19 severity and could be considered a putative predictor of AKI development and mortality.

Background and objectives: COVID-19 patients exhibit a broad range of manifestations, presenting with a flu-like respiratory tract infection that can advance to a systemic and severe disease characterized by pneumonia, pulmonary edema, severe damage to the airways, and acute respiratory distress syndrome (ARDS, causing fatality in 70% of COVID-19 cases). A ‘cytokine storm’ profile is found in most severely influenced COVID-19 patients. The treatment protocol of the disease also includes tocilizumab, which is a humanized monoclonal antibody used to treat autoimmune and inflammatory conditions. This study was designed (1) to assess the role of tocilizumab in COVID-19 patients regarding therapeutic efficacy through evaluation of cytokine release syndrome (CRS) resolution and anticoagulant effect, analyzing clinical safety via monitoring of associated adverse effects profile; and (2) to compare the clinical safety and therapeutic efficacy of institutional treatment regimen (alone) versus tocilizumab added to an institutional treatment module in COVID-19 patients. Materials and Methods: In this study, the endpoints parametric assessment of severely diseased patients of COVID-19 was performed (total n = 172, control group (institutional protocol treatment provided), n = 101 and test group (tocilizumab provided), n = 71) at the Khyber Teaching Institution, MTI, Peshawar. The assessments were compared using non-parametric analyses at baseline and after a follow-up of 12–18 days until the patient discharged or expired. Results: Results of the study revealed an insignificant difference among the control vs. test group in resolving inflammatory parameters (C-reactive protein (CRP) 21.30 vs. 50.07; p = 0.470, ferritin 482.9 vs. 211.5; p = 0.612, lactate dehydrogenase (LDH) 29.12 vs.18.8; p = 0.0863, and D-dimer 464 vs.164.4; p = 0.131). However, a statistically significant difference was found between the control group and test group regarding coagulation parameters (international normalized ratio (INR) 0.12 vs. −0.07; p ≤ 0.001; activated partial thromboplastin time (aPTT) 0.42 vs. −1.16; p ≤ 0.001; prothrombin time (PT) 0.31 vs. −0.96; p ≤ 0.001; platelet count −12.34 vs. −1.47; p = 0.012) and clinical survival rate (89.10 vs. 90.14; p < 0.001). Furthermore, there was significantly higher infection rates and raised alanine aminotransferase (ALT) and alkaline phosphatase (ALP) associated with the tocilizumab group as compared to those receiving institutional treatment (bacterial infections: 0.99% vs. 15.49%; p ≤ 0.01, ALT: 3.96% vs. 28.16%; p ≤ 0.01, ALP: 1.98% vs. 22.53%; p ≤ 0.01). Conclusions: From this study, it was concluded that tocilizumab can be a better drug of choice in terms of efficacy, particularly in resolving coagulopathy in severe COVID-19 patients.

The Nipah virus (NiV), a lethal pathogen from the Paramyxoviridae family, presents a significant global health threat as a result of its high mortality rate and inter-human transmission. This investigation employed in silico methods that were assisted by machine learning to identify small-molecule inhibitors that target the NiV glycoprotein, a critical component of viral entry. Out of the 754 antiviral compounds that were screened using Lipinski’s Rule of Five and DeepPurpose, 333 are identified. Five best hits were identified through molecular docking, each of which exhibited superior binding scores in comparison to the control. This was further refined to three compounds through density functional theory (DFT) analysis, with compound 138,567,123 exhibiting the highest electronic stability (DFT energy: -1976.74 Hartree; HOMO-LUMO gap: 0.83 eV). Its stability was verified by molecular dynamics (MD) simulations, which demonstrated consistent hydrogen bonding and minimal RMSD. Additionally, it possessed the highest docking score (-9.7 kcal/mol) and binding free energy (-24.04 kcal/mol, MM/GBSA). The results underscore ligand 138,567,123 as a promising antiviral candidate for NiV and illustrate the efficacy of machine learning-based in silico drug discovery.