Computational approaches for innovative anti-viral drug discovery using Orthosiphon aristatus blume miq against dengue virus.

Dengue virus has emerged as infectious mosquito borne disease involved in lowering platelets and white blood cells (WBC) count particularly. The genome structure is based on several structural and non-structural proteins essential for viral replication and progeny. One of the major proteins of replication is non-structural protein 3 (NS3) that transforms polyproteins into functional proteins with a cofactor non-structural protein (NS2B). Heat Shock Protein 70 (HSP70), is a human protein that assists in replication, viral entry and virion synthesis. Therefore, to inhibit the spread of dengue infection, there is a need of antivirals targeting replication proteins and other human proteins that help in dengue virus multiplication. By systemic approach based on molecular docking, ADMET (absorption, distribution, metabolism, excretion and toxicity) properties and molecular dynamic simulation (MD), potent inhibitors can be predicted. Inhibition of NS2B/NS3 dengue and HSP70 proteins involved in multiple steps in dengue virus progression can be prevented by using different phytochemicals. Molecular docking was performed using AutoDock Vina, PatchDock, and SwissDock. Interactions of obtained complex were observed in PyMOL and PLIP. Validation was checked by PROCHEK, simulation was performed using iMODS followed by preclinical testing by admetSAR. Ladanein, a flavonoid of Orthosiphon aristatus, was obtained as the lead compound to inhibit major replication protein of dengue virus with inhibitory potential against HSP70 protein. In summary, various in silico approaches were used to obtain the best phytochemical having anti-dengue potential.Communicated by Ramaswamy H. Sarma.

Bioinformatics approach for the construction of multiple epitope vaccine against omicron variant of SARS-CoV-2.

The World Health Organization categorized SARS-CoV-2 as a variant of concern, having numerous mutations in spike protein, which have been found to evade the effect of antibodies stimulated by the COVID-19 vaccine. The susceptibility to omicron variant by immunization-induced antibodies are direly required for risk evaluation. To avoid the risk of arising viral illness, the construction of a specific vaccine that triggers the production of targeted antibodies to combat infection remains highly imperative. The aim of the present study is to develop a particular vaccine exploiting bioinformatics approaches which can target B- and T-cells epitopes. Through this approach, novel epitopes of the S protein-SARS-CoV-2 were predicted for the development of a multiple epitope vaccine. Multiple epitopes were selected on the basis of toxicity, immunogenicity and antigenicity, and vaccine subunit was constructed having potential immunogenic properties. The epitopes were linked with 3 types of linker EAAAK, AAY and GPGPG for vaccine construction. Subsequently, vaccine structure was docked with the receptor and cloned in a pET-28a (+) vector. The constructed vaccine was ligated in pET-28a (+) vector in E. coli using the SnapGene tool for the expression study and a good immune response was observed. Several computational tools were used to predict and analyze the vaccine constructed by using spike protein sequence of omicrons. The current study identified a Multi-Epitope Vaccine (MEV) as a significant vaccine candidate that could potentially help the global world to combat SARS-CoV-2 infections.

Comparison Of Hepatitis C Antibody Assays And Evaluation Of Agreement Between Hepatitis C Antigen And PCR Results.

BACKGROUND: Hepatitis C is associated with a wide range of health repercussions. Pakistan is one of the highly prevalent countries of Hepatitis C Virus (HCV) infection. The availability of cost-effective, robust, and reliable screening and diagnostic tests for hepatitis C is important to address the disease burden. Standardization of screening and diagnostic assays in clinical laboratories is crucial for achieving big goals. Objectives of this study are to correlate the results of two different HCV antibody (HCV Ab) assays and to examine the correlation of HCV core antigen (HCV c Ag) results with HCV PCR for HCV infection diagnosis. METHODS: This descriptive cross-sectional study was carried out from November to December 2020 at Dow University of Health Sciences. Total number of 40 HCV Ab samples were analysed by both chemiluminescence (CMIA) and electrochemiluminescence (ECLIA) immunoassays. Tests for HCV RNA PCR and HCV c Ag were performed on all samples. Results of screening and diagnostic assays were correlated and agreements were examined. Statistical analysis for agreement was carried out by using R software version 3.6.3 through AC1 Gwetz Statistic. The study was approved by the institutional ethical review committee. RESULTS: An agreement of 0.73 and 0.95 was found between two different HCV Ab immunoassays and HCV c Ag and HCV PCR, respectively. CONCLUSIONS: We found a good correlation between CMIA and ECLIA for HCV Ab. An excellent correlation was found between HCV c Ag and HCV PCR. Based on our study findings, HCV c Ag is a candidate test for the diagnosis of active HCV infection.

Overall Survival Prediction of Glioma Patients With Multiregional Radiomics.

Radiomics-guided prediction of overall survival (OS) in brain gliomas is seen as a significant problem in Neuro-oncology. The ultimate goal is to develop a robust MRI-based approach (i.e., a radiomics model) that can accurately classify a novel subject as a short-term survivor, a medium-term survivor, or a long-term survivor. The BraTS 2020 challenge provides radiological imaging and clinical data (178 subjects) to develop and validate radiomics-based methods for OS classification in brain gliomas. In this study, we empirically evaluated the efficacy of four multiregional radiomic models, for OS classification, and quantified the robustness of predictions to variations in automatic segmentation of brain tumor volume. More specifically, we evaluated four radiomic models, namely, the Whole Tumor (WT) radiomics model, the 3-subregions radiomics model, the 6-subregions radiomics model, and the 21-subregions radiomics model. The 3-subregions radiomics model is based on a physiological segmentation of whole tumor volume (WT) into three non-overlapping subregions. The 6-subregions and 21-subregions radiomic models are based on an anatomical segmentation of the brain tumor into 6 and 21 anatomical regions, respectively. Moreover, we employed six segmentation schemes - five CNNs and one STAPLE-fusion method - to quantify the robustness of radiomic models. Our experiments revealed that the 3-subregions radiomics model had the best predictive performance (mean AUC = 0.73) but poor robustness (RSD = 1.99) and the 6-subregions and 21-subregions radiomics models were more robust (RSD  1.39) with lower predictive performance (mean AUC  0.71). The poor robustness of the 3-subregions radiomics model was associated with highly variable and inferior segmentation of tumor core and active tumor subregions as quantified by the Hausdorff distance metric (4.4-6.5mm) across six segmentation schemes. Failure analysis revealed that the WT radiomics model, the 6-subregions radiomics model, and the 21-subregions radiomics model failed for the same subjects which is attributed to the common requirement of accurate segmentation of the WT volume. Moreover, short-term survivors were largely misclassified by the radiomic models and had large segmentation errors (average Hausdorff distance of 7.09mm). Lastly, we concluded that while STAPLE-fusion can reduce segmentation errors, it is not a solution to learning accurate and robust radiomic models.

A systematic evaluation of learning rate policies in training CNNs for brain tumor segmentation.

Convolutional neural networks (CNNs) have recently emerged as a powerful approach for automatic segmentation of brain tumor subregions on 3D multi-parametric MRI scans. Learning rate is a crucial hyperparameter in the training of CNNs, impacting the performance of the learned model. Different learning rate policies trace unique trajectories in the optimization landscape that converge to local minima with varying generalization properties. In this work, we empirically evaluated nine learning rate policy-optimizer pairs with two state-of-the-art architectures, namely 2D slice-based U-Net and 3D DeepMedicRes, on an augmented brain tumor dataset of 534 subjects. Segmentation performance was quantified in terms of Dice similarity coefficient and Hausdorff distance metrics. The policies were ranked based on the final ranking score (FRS) employed by the BraTS challenge, with the statistical significance of the rankings evaluated by random permutation test. For 2D slice-based U-Net architecture, an overall ranking of learning rate policies showed that the polynomial decay policy with Adam optimizer significantly outperformed other policies for the task of individual and hierarchical segmentation of tumor subregions (p< 10-4). For 3D segment-based DeepMedicRes architecture, polynomial decay policy with Adam optimizer performed significantly better than all other policies, with the exception of polynomial decay with SGD optimizer for the same task (p< 10-4). Based on the FRS, polynomial decay policy with Adam and SGD optimizer occupied the top two positions respectively, but the difference was not statistically significant (p> 0.3). These findings were also validated on the BraTS 2019 Validation dataset which comprised of an additional 125 subjects.

Early Allograft Dysfunction Following Liver Transplant: Impact of Obesity, Diabetes, and Red Blood Cell Transfusion.

PURPOSE: We examined the role of obesity and intraoperative red blood cell (RBC) and platelet transfusion in early allograft dysfunction (EAD) following liver transplantation (LT). METHODS: This is a retrospective analysis of 239 adult deceased-donor LT recipients over a 10-year period. EAD was defined by Olthoff's criteria. Data collection included donor (D) and recipient (R) age, body mass index (BMI) ≥ 35 kg/m2, diabetes mellitus, allograft macrosteatosis, and intraoperative (RBC) and platelet administration. We employed logistic regression to evaluate associations of these factors with EAD. Results are presented as odds ratios (OR) and 95% confidence intervals (CI) with corresponding P values. A P ≤ .05 was considered statistically significant. RESULTS: EAD occurred in 85 recipients (36%). Macrosteatosis data were available for 199 donors. In the multivariate analyses, BMI-D ≥ 35 kg/m2 increased the odds of developing EAD by 156% in the entire cohort (OR 2.56, 95% CI 1.09-6.01) and by 187% in recipients with macrosteatosis data (n = 199, OR 2.87, 95% CI 1.15-7.15). Each unit of RBCs increased the odds for EAD by 8% (OR 1.08, 95% CI 1.02-1.14) and, for the subgroup of 238 recipients with macrosteatosis data, by 9% (OR 1.09, 95% CI 1.02-1.16). CONCLUSION: We found a significant independent association of donor obesity and intraoperative RBC transfusion with EAD but no such association for platelet administration, MELD score, age, recipient obesity, and diabetes.

Patent foramen ovale closure, antiplatelet therapy or anticoagulation in patients with patent foramen ovale and cryptogenic stroke: a systematic review and network meta-analysis incorporating complementary external evidence.

OBJECTIVE: To examine the relative impact of three management options in patients aged <60 years with cryptogenic stroke and a patent foramen ovale (PFO): PFO closure plus antiplatelet therapy, antiplatelet therapy alone and anticoagulation alone. DESIGN: Systematic review and network meta-analysis (NMA) supported by complementary external evidence. DATA SOURCES: Medline, EMBASE and Cochrane CENTRAL. STUDY SELECTION: Randomised controlled trials (RCTs) addressing PFO closure and/or medical therapies in patients with PFO and cryptogenic stroke. REVIEW METHODS: We conducted an NMA complemented with external evidence and rated certainty of evidence using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. RESULTS: Ten RCTs in eight studies proved eligible (n=4416). Seven RCTs (n=3913) addressed PFO closure versus medical therapy. Of these, three (n=1257) addressed PFO closure versus antiplatelet therapy, three (n=2303) addressed PFO closure versus mixed antiplatelet and anticoagulation therapies and one (n=353) addressed PFO closure versus anticoagulation. The remaining three RCTs (n=503) addressed anticoagulant versus antiplatelet therapy. PFO closure versus antiplatelet therapy probably results in substantial reduction in ischaemic stroke recurrence (risk difference per 1000 patients over 5 years (RD): -87, 95% credible interval (CrI) -100 to -33; moderate certainty). Compared with anticoagulation, PFO closure may confer little or no difference in ischaemic stroke recurrence (low certainty) but probably has a lower risk of major bleeding (RD -20, 95% CrI -27 to -2, moderate certainty). Relative to either medical therapy, PFO closure probably increases the risk of persistent atrial fibrillation (RD 18, 95% CI +5 to +56, moderate certainty) and device-related adverse events (RD +36, 95% CI +23 to +50, high certainty). Anticoagulation, compared with antiplatelet therapy, may reduce the risk of ischaemic stroke recurrence (RD -71, 95% CrI -100 to +17, low certainty), but probably increases the risk of major bleeding (RD +12, 95% CrI -5 to +65, moderate certainty). CONCLUSIONS: In patients aged <60 years, PFO closure probably confers an important reduction in ischaemic stroke recurrence compared with antiplatelet therapy alone but may make no difference compared with anticoagulation. PFO closure incurs a risk of persistent atrial fibrillation and device-related adverse events. Compared with alternatives, anticoagulation probably increases major bleeding. PROSPERO REGISTRATION NUMBER: CRD42017081567.

Bone Turnover Markers for Osteoporosis Status Assessment at Baseline in Postmenopausal Pakistani Females.

OBJECTIVE: To assess bone turnover status in osteopenic and osteoporotic postmenopausal females. STUDY DESIGN: Cross-sectional analytical study. PLACE AND DURATION OF STUDY: The Aga Khan University Hospital, Karachi, from January to December 2013. METHODOLOGY: Across-sectional study was conducted on 203 postmenopausal females undergoing bone mineral density testing (BMD) by DXAscan. Patients with clinical history of any disorder or medications affecting bone turnover were excluded. Bone turnover was assessed with osteocalcin and ß-CTx. Data was analyzed by SPSS version 19. RESULTS: Mean age of the participants was 54 ±4.66 years with a mean BMI of 28.7 ±5.5 kg/m2. Mean ß-CTx (0.28 ±0.24 ng/ml) and osteocalcin (21.5 ±10.6 ng/ml) levels were within the normal reference range. Subjects were grouped into normal (26.6%), osteopenic (44.8%), and osteoporotic (28.6%) based on the t-scores. Serum levels of osteocalcin and ß-CTx between normal, osteopenic, and osteoporotic groups were not significantly different. ß-CTx was negatively and significantly associated with only lumber spine BMD (r = -0.13, p=0.04). Positive association (< 0.0001) was noted between both markers in normal, osteopenic, and osteoporotic females. However, association of these markers with BMD in the 3 groups were not found. Multivariate linear regression showed a positive and significant effect of BMI on BMD (ß= 0.332, p= < 0.0001). ß-CTx had negative but significant effect on BMD (ß= -0.155, p= 0.018) of postmenopausal women. CONCLUSION: Association between baseline levels of BTM and rate of bone loss is variable and site dependent. ß-CTx correlates better with BMD. However, role of osteocalcin in postmenopausal osteoporosis is uncertain and needs further investigation.

The application of a piezo-resistive cardiorespiratory sensor system in an automobile safety belt.

Respiratory and heart failure are conditions that can occur with little warning and may also be difficult to predict. Therefore continuous monitoring of these bio-signals is advantageous for ensuring human health. The car safety belt is mainly designed to secure the occupants of the vehicle in the event of an accident. In the current research a prototype safety belt is developed, which is used to acquire respiratory and heart signals, under laboratory conditions. The current safety belt is constructed using a copper ink based nonwoven material, which works based on the piezo-resistive effect due to the pressure exerted on the sensor as a result of expansion of the thorax/abdomen area of the body for respiration and due to the principle of ballistocardiography (BCG) in heart signal sensing. In this research, the development of a theoretical model to qualitatively describe the piezo-resistive material is also presented in order to predict the relative change in the resistance of the piezo-resistive material due to the pressure applied.

Thermo-Mechanical Behavior of Textile Heating Fabric Based on Silver Coated Polymeric Yarn.

This paper presents a study conducted on the thermo-mechanical properties of knitted structures, the methods of manufacture, effect of contact pressure at the structural binding points, on the degree of heating. The test results also present the level of heating produced as a function of the separation between the supply terminals. The study further investigates the rate of heating and cooling of the knitted structures. The work also presents the decay of heating properties of the yarn due to overheating. Thermal images were taken to study the heat distribution over the surface of the knitted fabric. A tensile tester having constant rate of extension was used to stretch the fabric. The behavior of temperature profile of stretched fabric was observed. A comparison of heat generation by plain, rib and interlock structures was studied. It was observed from the series of experiments that there is a minimum threshold force of contact at binding points of a knitted structure is required to pass the electricity. Once this force is achieved, stretching the fabric does not affect the amount of heat produced.

Raman spectroscopy of xylitol uptake and metabolism in Gram-positive and Gram-negative bacteria.

Visible-wavelength Raman spectroscopy was used to investigate the uptake and metabolism of the five-carbon sugar alcohol xylitol by Gram-positive viridans group streptococcus and the two extensively used strains of Gram-negative Escherichia coli, E. coli C and E. coli K-12. E. coli C, but not E. coli K-12, contains a complete xylitol operon, and the viridans group streptococcus contains an incomplete xylitol operon used to metabolize the xylitol. Raman spectra from xylitol-exposed viridans group streptococcus exhibited significant changes that persisted even in progeny grown from the xylitol-exposed mother cells in a xylitol-free medium for 24 h. This behavior was not observed in the E. coli K-12. In both viridans group streptococcus and the E. coli C derivative HF4714, the metabolic intermediates are stably formed to create an anomaly in bacterial normal survival. The uptake of xylitol by Gram-positive and Gram-negative pathogens occurs even in the presence of other high-calorie sugars, and its stable integration within the bacterial cell wall may discontinue bacterial multiplication. This could be a contributing factor for the known efficacy of xylitol when taken as a prophylactic measure to prevent or reduce occurrences of persistent infection. Specifically, these bacteria are causative agents for several important diseases of children such as pneumonia, otitis media, meningitis, and dental caries. If properly explored, such an inexpensive and harmless sugar-alcohol, alone or used in conjunction with fluoride, would pave the way to an alternative preventive therapy for these childhood diseases when the causative pathogens have become resistant to modern medicines such as antibiotics and vaccine immunotherapy.