Plasma Virome of HIV-infected Subjects on Suppressive Antiretroviral Therapy Reveals Association of Differentially Abundant Viruses with Distinct T-cell Phenotypes and Inflammation

Background

The plasma virome represents the overall composition of viral sequences present in it. Alteration in plasma virome has been reported in treatment naïve and immunocompromised (CD4 count < 200) people with HIV (PWH). However, the effect of ART on virome composition in PWH on ART with preserved CD4 counts is poorly understood.

Objectives

We aimed to assess the alterations in plasma virome in PWH on ART in comparison to HIV-negative uninfected controls and to further investigate possible associations of plasma viruses with inflammation and immune dysfunction, namely, immunosenescence and immune exhaustion.

Methods

Plasma viral DNA from PWH on ART and controls was used for sequencing on the Illumina Nextseq500 platform, followed by the identification of viral sequences using an automated pipeline, VIROMATCH. Multiplex cytokine assay was performed to measure the concentrations of various cytokines in plasma. Immunophenotyping was performed on PBMCs to identify T cell markers of immunosenescence and immune exhaustion.

Results

In our observational, cross-sectional pilot study, chronically infected PWH on ART had significantly different viral species compositions compared to controls. The plasma virome of PWH showed a significantly high relative abundance of species Human gammaherpesvirus 4, also known as Epstein-Barr virus (EBV). Moreover, EBV emerged as a significant viral taxon differentially enriched in PWH on ART, which further correlated positively with the exhaustion phenotype of T cells and significantly increased TNF-α in PWH on ART. Additionally, a significantly increased proportion of senescent T cells and IL-8 cytokine was detected in PWH on ART.

Conclusion

Altered plasma virome influenced the inflammatory response and T-cell phenotype in PWH on ART.

Exploring chemical space, scaffold diversity, and activity landscape of spleen tyrosine kinase active inhibitors

This study aims to comprehensively characterize 576 inhibitors targeting Spleen Tyrosine Kinase (SYK), a non-receptor tyrosine kinase primarily found in haematopoietic cells, with significant relevance to B-cell receptor function. The objective is to gain insights into the structural requirements essential for potent activity, with implications for various therapeutic applications. Through chemoinformatic analyses, we focus on exploring the chemical space, scaffold diversity, and structure-activity relationships (SAR). By leveraging ECFP4 and MACCS fingerprints, we elucidate the relationship between chemical compounds and visualize the network using RDKit and NetworkX platforms. Additionally, compound clustering and visualization of the associated chemical space aid in understanding overall diversity. The outcomes include identifying consensus diversity patterns to assess global chemical space diversity. Furthermore, incorporating pairwise activity differences enhances the activity landscape visualization, revealing heterogeneous SAR patterns. The dataset analysed in this work has three activity cliff generators, CHEMBL3415598, CHEMBL4780257, and CHEMBL3265037, compounds with high affinity to SYK are very similar to compounds analogues with reasonable potency differences. Overall, this study provides a critical analysis of SYK inhibitors, uncovering potential scaffolds and chemical moieties crucial for their activity, thereby advancing the understanding of their therapeutic potential.

A bioinformatics approach to elucidate conserved genes and pathways in C. elegans as an animal model for cardiovascular research

Cardiovascular disease (CVD) is a collective term for disorders of the heart and blood vessels. The molecular events and biochemical pathways associated with CVD are difficult to study in clinical settings on patients and in vitro conditions. Animal models play a pivotal and indispensable role in CVD research. Caenorhabditis elegans, a nematode species, has emerged as a prominent experimental organism widely utilized in various biomedical research fields. However, the specific number of CVD-related genes and pathways within the C. elegans genome remains undisclosed to date, limiting its in-depth utilization for investigations. In the present study, we conducted a comprehensive analysis of genes and pathways related to CVD within the genomes of humans and C. elegans through a systematic bioinformatic approach. A total of 1113 genes in C. elegans orthologous to the most significant CVD-related genes in humans were identified, and the GO terms and pathways were compared to study the pathways that are conserved between the two species. In order to infer the functions of CVD-related orthologous genes in C. elegans, a PPI network was constructed. Orthologous gene PPI network analysis results reveal the hubs and important KRs: pmk-1, daf-21, gpb-1, crh-1, enpl-1, eef-1G, acdh-8, hif-1, pmk-2, and aha-1 in C. elegans. Modules were identified for determining the role of the orthologous genes at various levels in the created network. We also identified 9 commonly enriched pathways between humans and C. elegans linked with CVDs that include autophagy (animal), the ErbB signaling pathway, the FoxO signaling pathway, the MAPK signaling pathway, ABC transporters, the biosynthesis of unsaturated fatty acids, fatty acid metabolism, glutathione metabolism, and metabolic pathways. This study provides the first systematic genomic approach to explore the CVD-associated genes and pathways that are present in C. elegans, supporting the use of C. elegans as a prominent animal model organism for cardiovascular diseases.

Identification of potential regulatory mechanisms and therapeutic targets for lung cancer

Lung cancer poses a significant health threat globally, especially in regions like India, with 5-year survival rates remain alarmingly low. Our study aimed to uncover key markers for effective treatment and early detection. We identified specific genes related to lung cancer using the BioXpress database and delved into their roles through DAVID enrichment analysis. By employing network theory, we explored the intricate interactions within lung cancer networks, identifying ASPM and MKI67 as crucial regulator genes. Predictions of microRNA and transcription factor interactions provided additional insights. Examining gene expression patterns using GEPIA and KM Plotter revealed the clinical relevance of these key genes. In our pursuit of targeted therapies, Drug Bank pointed to methotrexate as a potential drug for the identified key regulator genes. Confirming this, molecular docking studies through Swiss Dock showed promising binding interactions. To ensure stability, we conducted molecular dynamics simulations using the AMBER 16 suite. In summary, our study pinpoints ASPM and MKI67 as vital regulators in lung cancer networks. The identification of hub genes and functional pathways enhances our understanding of molecular processes, offering potential therapeutic targets. Importantly, methotrexate emerged as a promising drug candidate, supported by robust docking and simulation studies. These findings lay a solid foundation for further experimental validations and hold promise for advancing personalized therapeutic strategies in lung cancer.Communicated by Ramaswamy H. Sarma.

Global analysis of urinary extracellular vesicle small RNAs in autosomal dominant polycystic kidney disease

BACKGROUND

Autosomal dominant polycystic kidney disease (ADPKD) is the most prevalent monogenic renal disease progressing to end-stage renal disease. There is a pressing need for the identification of early ADPKD biomarkers to enable timely intervention and the development of effective therapeutic approaches. Here, we profiled human urinary extracellular vesicles small RNAs by small RNA sequencing in patients with ADPKD and compared their differential expression considering healthy control individuals to identify dysregulated small RNAs and analyze downstream interaction to gain insight about molecular pathophysiology.

METHODS

This is a cross-sectional study where urine samples were collected from a total of 23 PKD1-ADPKD patients and 28 healthy individuals. Urinary extracellular vesicles were purified, and small RNA was isolated and sequenced. Differentially expressed Small RNA were identified and functional enrichment analysis of the critical miRNAs was performed to identify driver genes and affected pathways.

RESULTS

miR-320b, miR-320c, miR-146a-5p, miR-199b-3p, miR-671-5p, miR-1246, miR-8485, miR-3656, has_piR_020497, has_piR_020496 and has_piR_016271 were significantly upregulated in ADPKD patient urine extracellular vesicles and miRNA-29c was significantly downregulated. Five 'driver' target genes (FBRS, EDC3, FMNL3, CTNNBIP1 and KMT2A) were identified.

CONCLUSIONS

The findings of the present study make significant contributions to the understanding of ADPKD pathogenesis and to the identification of novel biomarkers and potential drug targets aimed at slowing disease progression in ADPKD.

Conserved Cardiovascular Network: Bioinformatics Insights into Genes and Pathways for Establishing Caenorhabditis elegans as an Animal Model for Cardiovascular Diseases

Cardiovascular disease (CVD) is a collective term for disorders of the heart and blood vessels. The molecular events and biochemical pathways associated with CVD are difficult to study in clinical settings on patients and in vitro conditions. Animal models play a pivotal and indispensable role in cardiovascular disease (CVD) research. Caenorhabditis elegans , a nematode species, has emerged as a prominent experimental organism widely utilised in various biomedical research fields. However, the specific number of CVD-related genes and pathways within the C. elegans genome remains undisclosed to date, limiting its in-depth utilisation for investigations. In the present study, we conducted a comprehensive analysis of genes and pathways related to CVD within the genomes of humans and C. elegans through a systematic bioinformatic approach. A total of 1113 genes in C. elegans orthologous to the most significant CVD-related genes in humans were identified, and the GO terms and pathways were compared to study the pathways that are conserved between the two species. In order to infer the functions of CVD-related orthologous genes in C. elegans, a PPI network was constructed. Orthologous gene PPI network analysis results reveal the hubs and important KRs: pmk-1, daf-21, gpb-1, crh-1, enpl-1, eef-1G, acdh-8, hif-1, pmk-2, and aha-1 in C. elegans. Modules were identified for determining the role of the orthologous genes at various levels in the created network. We also identified 9 commonly enriched pathways between humans and C. elegans linked with CVDs that include autophagy (animal), the ErbB signalling pathway, the FoxO signalling pathway, the MAPK signalling pathway, ABC transporters, the biosynthesis of unsaturated fatty acids, fatty acid metabolism, glutathione metabolism, and metabolic pathways. This study provides the first systematic genomic approach to explore the CVD-associated genes and pathways that are present in C. elegans, supporting the use of C. elegans as a prominent animal model organism for cardiovascular diseases.

A computational biology approach to identify potential protein biomarkers and drug targets for sporadic amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease caused by the loss of upper and lower motor neurons. The sporadic ALS (sALS) is a multigenic disorder and the complex mechanisms underlying its onset are still not fully delineated. Despite the recent scientific advancements, certain aspects of ALS pathogenic targets need to be yet clarified. The aim of the presented study is to identify potential genetic biomarkers and drug targets for sALS, by analysing gene expression profiles, presented in the publicly available GSE68605 dataset, of motor neurons cells obtained from sALS patients. We used different computational approaches including differential expression analysis, protein network mapping, candidate protein biomarker (CPB) identification, elucidation of the role of functional modules, and molecular docking analysis. The resultant top ten up- and downregulated genes were further used to construct protein-protein interaction network (PPIN). The PPIN analysis resulted in identifying four CPBs (namely RIOK2, AKT1, CTNNB1, and TNF) that commonly overlapped with one another in network parameters (degree, bottleneck and maximum neighbourhood component). The RIOK2 protein emerged as a potential mediator of top five functional modules that are associated with RNA binding, lipoprotein particle receptor binding in pre-ribosome, and interferon, cytokine-mediated signaling pathway. Furthermore, molecular docking analysis revealed that cyclosporine exhibited the highest binding affinity (-8.6 kJ/mol) with RIOK2, and surpassed the FDA-approved ALS drugs, such as riluzole and edaravone. This suggested that cyclosporine may serve as a promising candidate for targeting RIOK2 downregulation observed in sALS patients. In order to validate our computational results, it is suggested that in vitro and in vivo studies may be conducted in future to provide a more detailed understanding of ALS diagnosis, prognosis, and therapeutic intervention.

Comprehensive multiomics and in silico approach uncovers prognostic, immunological, and therapeutic roles of ANLN in lung adenocarcinoma

The anillin actin-binding protein (ANLN) is immensely overexpressed in cancers, including lung cancer (LC). Phytocompounds have gained interest due to their broader potential and reduced unwanted effects. Screening numerous compounds presents a challenge, but in silico molecular docking is pragmatic. The present study aims to identify the role of ANLN in lung adenocarcinoma (LUAD), along with identification and interaction analysis of anticancer and ANLN inhibitory phytocompounds followed by molecular dynamics (MD) simulation. Using a systematic approach, we found that ANLN is significantly overexpressed in LUAD and mutated with a frequency of 3.73%. It is linked with advanced stages, clinicopathological parameters, worsening of relapse-free survival (RFS), and overall survival (OS), pinpointing its oncogenic and prognostic potential. High-throughput screening and molecular docking of phytocompounds revealed that kaempferol (flavonoid aglycone) interacts strongly with the active site of ANLN protein via hydrogen bonds, Vander Waals interactions, and acts as a potent inhibitor. Furthermore, we discovered that ANLN expression was found to be significantly higher (p) in LC cells compared to normal cells. This is a propitious and first study to demonstrate ANLN and kaempferol interactions, which might eventually lead to removal of rout from cell cycle regulation posed by ANLN overexpression and allow it to resume normal processes of proliferation. Overall, this approach suggested a plausible biomarker role of ANLN and the combination of molecular docking subsequently led to the identification of contemporary phytocompounds, bearing symbolic anticancer effects. The findings would be advantageous for pharmaceutics but require validation using in vitro and in vivo methods. HIGHLIGHTS: • ANLN is significantly overexpressed in LUAD. • ANLN is implicated in the infiltration of TAMs and altering plasticity of TME. • Kaempferol (potential ANLN inhibitor) shows important interactions with ANLN which could remove the alterations in cell cycle regulation, imposed by ANLN overexpression eventually leading to normal process of cell proliferation.

Potential Biomarkers for Parkinson Disease from Functional Enrichment and Bioinformatic Analysis of Global Gene Expression Patterns of Blood and Substantia Nigra Tissues

The Parkinson disease (PD) is the second most common neurodegenerative disorder affecting the central nervous system and motor functions. The biological complexity of PD is yet to reveal potential targets for intervention or to slow the disease severity. Therefore, this study aimed to compare the fidelity of blood to substantia nigra (SN) tissue gene expression from PD patients to provide a systematic approach to predict role of the key genes of PD pathobiology. Differentially expressed genes (DEGs) from multiple microarray data sets of PD blood and SN tissue from GEO database are identified. Using the theoretical network approach and variety of bioinformatic tools, we prioritized the key genes from DEGs. A total of 540 and 1024 DEGs were identified in blood and SN tissue samples, respectively. Functional pathways closely related to PD such as ERK1 and ERK2 cascades, mitogen-activated protein kinase (MAPK) signaling, Wnt, nuclear factor-κB (NF-κB), and PI3K-Akt signaling were observed by enrichment analysis. Expression patterns of 13 DEGs were similar in both blood and SN tissues. Comprehensive network topological analysis and gene regulatory networks identified additional 10 DEGs functionally connected with molecular mechanisms of PD through the mammalian target of rapamycin (mTOR), autophagy, and AMP-activated protein kinase (AMPK) signaling pathways. Potential drug molecules were identified by chemical-protein network and drug prediction analysis. These potential candidates can be further validated in vitro/in vivo to be used as biomarkers and/or novel drug targets for the PD pathology and/or to arrest or delay the neurodegeneration over the years, respectively.

Identification of new oxospiro chromane quinoline-carboxylate antimalarials that arrest parasite growth at ring stage

Malaria still threatens half the globe population despite successful Artemisinin-based combination therapy. One of the reasons for our inability to eradicate malaria is the emergence of resistance to current antimalarials. Thus, there is a need to develop new antimalarials targeting Plasmodium proteins. The present study reported the design and synthesis of 4, 6 and 7-substituted quinoline-3-carboxylates 9(a-o) and carboxylic acids 10(a-b) for the inhibition of Plasmodium N-Myristoyltransferases (NMTs) using computational biology tools followed by chemical synthesis and functional analysis. The designed compounds exhibited a glide score of -9.241 to -6.960 kcal/mol for PvNMT and -7.538 kcal/mol for PfNMT model proteins. Development of the synthesized compounds was established via NMR, HRMS and single crystal X-ray diffraction study. The synthesized compounds were evaluated for their in vitro antimalarial efficacy against CQ-sensitive Pf3D7 and CQ-resistant PfINDO lines followed by cell toxicity evaluation. In silico results highlighted the compound ethyl 6-methyl-4-(naphthalen-2-yloxy)quinoline-3-carboxylate (9a) as a promising inhibitor with a glide score of -9.084 kcal/mol for PvNMT and -6.975 kcal/mol for PfNMT with IC50 values of 6.58 µM for Pf3D7 line. Furthermore, compounds 9n and 9o exhibited excellent anti-plasmodial activity (Pf3D7 IC50 = 3.96, 6.71 µM, and PfINDO IC50 = 6.38, 2.8 µM, respectively). The conformational stability of 9a with the active site of the target protein was analyzed through MD simulation and was found concordance with in vitro results. Thus, our study provides scaffolds for the development of potent antimalarials targeting both Plasmodium vivax and Plasmodium falciparum.Communicated by Ramaswamy H. Sarma.

Low carbohydrate intake correlates with trends of insulin resistance and metabolic acidosis in healthy lean individuals

Introduction

Both obesity and a poor diet are considered major risk factors for triggering insulin resistance syndrome (IRS) and the development of type 2 diabetes mellitus (T2DM). Owing to the impact of low-carbohydrate diets, such as the keto diet and the Atkins diet, on weight loss in individuals with obesity, these diets have become an effective strategy for a healthy lifestyle. However, the impact of the ketogenic diet on IRS in healthy individuals of a normal weight has been less well researched. This study presents a cross-sectional observational study that aimed to investigate the effect of low carbohydrate intake in healthy individuals of a normal weight with regard to glucose homeostasis, inflammatory, and metabolic parameters.

Methods

The study included 120 participants who were healthy, had a normal weight (BMI 25 kg/m2), and had no history of a major medical condition. Self-reported dietary intake and objective physical activity measured by accelerometry were tracked for 7 days. The participants were divided into three groups according to their dietary intake of carbohydrates: the low-carbohydrate (LC) group (those consuming <45% of their daily energy intake from carbohydrates), the recommended range of carbohydrate (RC) group (those consuming 45-65% of their daily energy intake from carbohydrates), and the high-carbohydrate (HC) group (those consuming more than 65% of their daily energy intake from carbohydrates). Blood samples were collected for the analysis of metabolic markers. HOMA of insulin resistance (HOMA-IR) and HOMA of β-cell function (HOMA-β), as well as C-peptide levels, were used for the evaluation of glucose homeostasis.

Results

Low carbohydrate intake (<45% of total energy) was found to significantly correlate with dysregulated glucose homeostasis as measured by elevations in HOMA-IR, HOMA-β% assessment, and C-peptide levels. Low carbohydrate intake was also found to be coupled with lower serum bicarbonate and serum albumin levels, with an increased anion gap indicating metabolic acidosis. The elevation in C-peptide under low carbohydrate intake was found to be positively correlated with the secretion of IRS-related inflammatory markers, including FGF2, IP-10, IL-6, IL-17A, and MDC, but negatively correlated with IL-3.

Discussion

Overall, the findings of the study showed that, for the first time, low-carbohydrate intake in healthy individuals of a normal weight might lead to dysfunctional glucose homeostasis, increased metabolic acidosis, and the possibility of triggering inflammation by C-peptide elevation in plasma.

Network pharmacology and bioinformatics approach reveals the therapeutic mechanism of action of curcumin in Alzheimer disease

Curcumin is a natural anti-inflammatory and antioxidant substance which plays a major role in reducing the amyloid plaques formation, which is the major cause of Alzheimer's disease (AD). Consequently, a methodical approach was used to select the potential protein targets of curcumin in AD through network pharmacology. In this study, through integrative methods, AD targets of curcumin through SwissTargetPrediction database, STITCH database, BindingDB, PharmMapper, Therapeutic Target Database (TTD), Online Mendelian Inheritance in Man (OMIM) database were predicted followed by gene enrichment analysis, network construction, network topology, and docking studies. Gene ontology analysis facilitated identification of a list of possible AD targets of curcumin (74 targets genes). The correlation of the obtained targets with AD was analysed by using gene ontology (GO) pathway enrichment analyses and Kyoto Encyclopaedia of Genes and Genomes (KEGG). We have incorporated the applied network pharmacological approach to identify key genes. Furthermore, we have performed molecular docking for analysing the mechanism of curcumin. In order to validate the temporospatial expression of key genes in human central nervous system (CNS), we searched the Human Brain Transcriptome (HBT) dataset. We identified top five key genes namely, PPARγ, MAPK1, STAT3, KDR and APP. Further validated the expression profiling of these key genes in publicly available brain data expression profile databases. In context to a valuable addition in the treatment of AD, this study is concluded with novel insights into the therapeutic mechanisms of curcumin, will ease the treatment of AD with the clinical application of curcumin.

Identification of a survival associated gene trio in chemical induced breast cancer

Sporadic cases of breast cancer being more prevalent than the hereditary cases, can be largely attributed to environmental pollutants like polycyclic aromatic hydrocarbons (PAHs). The aim of the present study was to identify gene dysregulations and the associations in DMBA (a PAH) induced breast cancer. A breast cancer model was developed in Wistar rats (n = 40), using DMBA. Serum proteomics (2D electrophoresis and MALDI-TOF MS) followed by relative gene expression analysis in mammary glands were conducted to reach to the differential gene signatures. The candidate genes were subjected to survival analysis (by GEPIA2 and KM plotter) and infiltration analysis (by ImmuCellAI) for correlating gene expression with patient survival and immune cell infiltration respectively. Further, the regulatory network investigation (by Cytoscape) was performed to find out the transcription factors (TFs) and miRNAs of the concerned genes. A gene trio (ANXA5, MTG1, PPP2R5B), expressing differentially in early mammary carcinogenesis at 4 months (precancerous stage) till full-fledged cancerous stage (post 6 months) was identified. The altered gene expression was associated with less survival among breast cancer patients (n = 4019). The dysregulated expression also has a correlation with enhanced mammary infiltration of immune cells. Moreover, a regulatory network (comprising of 77 transcription factors and 50 miRNAs) involved in the regulation of candidate genes was also deciphered. The deregulated target genes can therefore be explored for reregulation via identified TFs and miRNAs, and survival thereby improved.

Network medicine in ovarian cancer: topological properties to drug discovery

Network medicine provides network theoretical tools, methods and properties to study underlying laws governing human interactome to identify disease states and disease complexity leading to drug discovery. Within this framework, we investigated the topological properties of ovarian cancer network (OCN) and the roles of hubs to understand OCN organization to address disease states and complexity. The OCN constructed from the experimentally verified genes exhibits fractal nature in the topological properties with deeply rooted functional communities indicating self-organizing behavior. The network properties at all levels of organization obey one parameter scaling law which lacks centrality lethality rule. We showed that $\langle k\rangle $ can be taken as a scaling parameter, where, power law exponent can be estimated from the ratio of network diameters. The betweenness centrality $C_B$ shows two distinct behaviors one shown by high degree hubs and the other by segregated low degree nodes. The $C_B$ power law exponent is found to connect the exponents of distributions of high and low degree nodes. OCN showed the absence of rich-club formation which leads to the missing of a number of attractors in the network causing formation of weakly tied diverse functional modules to keep optimal network efficiency. In OCN, provincial and connector hubs, which includes identified key regulators, take major responsibility to keep the OCN integrity and organization. Further, most of the key regulators are found to be over expressed and positively correlated with immune infiltrates. Finally, few potential drugs are identified related to the key regulators.

In Silico Integrative Approach Revealed Key MicroRNAs and Associated Target Genes in Cardiorenal Syndrome

Cardiorenal syndromes constellate primary dysfunction of either heart or kidney whereby one organ dysfunction leads to the dysfunction of another. The role of several microRNAs (miRNAs) has been implicated in number of diseases, including hypertension, heart failure, and kidney diseases. Wide range of miRNAs has been identified as ideal candidate biomarkers due to their stable expression. Current study was aimed to identify crucial miRNAs and their target genes associated with cardiorenal syndrome and to explore their interaction analysis. Three differentially expressed microRNAs (DEMs), namely, hsa-miR-4476, hsa-miR-345-3p, and hsa-miR-371a-5p, were obtained from GSE89699 and GSE87885 microRNA data sets, using R/GEO2R tools. Furthermore, literature mining resulted in the retrieval of 15 miRNAs from scientific research and review articles. The miRNAs-gene networks were constructed using miRNet (a Web platform of miRNA-centric network visual analytics). CytoHubba (Cytoscape plugin) was adopted to identify the modules and the top-ranked nodes in the network based on Degree centrality, Closeness centrality, Betweenness centrality, and Stress centrality. The overlapped miRNAs were further used in pathway enrichment analysis. We found that hsa-miR-21-5p was common in 8 pathways out of the top 10. Based on the degree, 5 miRNAs, namely, hsa-mir-122-5p, hsa-mir-222-3p, hsa-mir-21-5p, hsa-mir-146a-5p, and hsa-mir-29b-3p, are considered as key influencing nodes in a network. We suggest that the identified miRNAs and their target genes may have pathological relevance in cardiorenal syndrome (CRS) and may emerge as potential diagnostic biomarkers.

Identifying the natural polyphenol catechin as a multi-targeted agent against SARS-CoV-2 for the plausible therapy of COVID-19: an integrated computational approach

The global pandemic crisis, coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has claimed the lives of millions of people across the world. Development and testing of anti-SARS-CoV-2 drugs or vaccines have not turned to be realistic within the timeframe needed to combat this pandemic. Here, we report a comprehensive computational approach to identify the multi-targeted drug molecules against the SARS-CoV-2 proteins, whichare crucially involved in the viral-host interaction, replication of the virus inside the host, disease progression and transmission of coronavirus infection. Virtual screening of 75 FDA-approved potential antiviral drugs against the target proteins, spike (S) glycoprotein, human angiotensin-converting enzyme 2 (hACE2), 3-chymotrypsin-like cysteine protease (3CLpro), cathepsin L (CTSL), nucleocapsid protein, RNA-dependent RNA polymerase (RdRp) and non-structural protein 6 (NSP6), resulted in the selection of seven drugs which preferentially bind to the target proteins. Further, the molecular interactions determined by molecular dynamics simulation revealed that among the 75 drug molecules, catechin can effectively bind to 3CLpro, CTSL, RBD of S protein, NSP6 and nucleocapsid protein. It is more conveniently involved in key molecular interactions, showing binding free energy (ΔGbind) in the range of -5.09 kcal/mol (CTSL) to -26.09 kcal/mol (NSP6). At the binding pocket, catechin is majorly stabilized by the hydrophobic interactions, displays ΔEvdW values: -7.59 to -37.39 kcal/mol. Thus, the structural insights of better binding affinity and favorable molecular interaction of catechin toward multiple target proteins signify that catechin can be potentially explored as a multi-targeted agent against COVID-19.

Design of an epitope-based peptide vaccine against the SARS-CoV-2: a vaccine-informatics approach

The recurrent and recent global outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has turned into a global concern which has infected more than 42 million people all over the globe, and this number is increasing in hours. Unfortunately, no vaccine or specific treatment is available, which makes it more deadly. A vaccine-informatics approach has shown significant breakthrough in peptide-based epitope mapping and opens the new horizon in vaccine development. In this study, we have identified a total of 15 antigenic peptides [including thymus cells (T-cells) and bone marrow or bursa-derived cells] in the surface glycoprotein (SG) of SARS-CoV-2 which is nontoxic and nonallergenic in nature, nonallergenic, highly antigenic and non-mutated in other SARS-CoV-2 virus strains. The population coverage analysis has found that cluster of differentiation 4 (CD4+) T-cell peptides showed higher cumulative population coverage over cluster of differentiation 8 (CD8+) peptides in the 16 different geographical regions of the world. We identified 12 peptides ((LTDEMIAQY, WTAGAAAYY, WMESEFRVY, IRASANLAA, FGAISSVLN, VKQLSSNFG, FAMQMAYRF, FGAGAALQI, YGFQPTNGVGYQ, LPDPSKPSKR, QTQTNSPRRARS and VITPGTNTSN) that are $80\hbox{--} 90\%$ identical with experimentally determined epitopes of SARS-CoV, and this will likely be beneficial for a quick progression of the vaccine design. Moreover, docking analysis suggested that the identified peptides are tightly bound in the groove of human leukocyte antigen molecules which can induce the T-cell response. Overall, this study allows us to determine potent peptide antigen targets in the SG on intuitive grounds, which opens up a new horizon in the coronavirus disease (COVID-19) research. However, this study needs experimental validation by in vitro and in vivo.

Deciphering key genes in cardio-renal syndrome using network analysis

Cardio-renal syndrome (CRS) is a rapidly recognized clinical entity which refers to the inextricably connection between heart and renal impairment, whereby abnormality to one organ directly promotes deterioration of the other one. Biological markers help to gain insight into the pathological processes for early diagnosis with higher accuracy of CRS using known clinical findings. Therefore, it is of interest to identify target genes in associated pathways implicated linked to CRS. Hence, 119 CRS genes were extracted from the literature to construct the PPIN network. We used the MCODE tool to generate modules from network so as to select the top 10 modules from 23 available modules. The modules were further analyzed to identify 12 essential genes in the network. These biomarkers are potential emerging tools for understanding the pathophysiologic mechanisms for the early diagnosis of CRS. Ontological analysis shows that they are rich in MF protease binding and endo-peptidase inhibitor activity. Thus, this data help increase our knowledge on CRS to improve clinical management of the disease.

Transition and identification of pathological states in p53 dynamics for therapeutic intervention

We study a minimal model of the stress-driven p53 regulatory network that includes competition between active and mutant forms of the tumor-suppressor gene p53. Depending on the nature and level of the external stress signal, four distinct dynamical states of p53 are observed. These states can be distinguished by different dynamical properties which associate to active, apoptotic, pre-malignant and cancer states. Transitions between any two states, active, apoptotic, and cancer, are found to be unidirectional and irreversible if the stress signal is either oscillatory or constant. When the signal decays exponentially, the apoptotic state vanishes, and for low stress the pre-malignant state is bounded by two critical points, allowing the system to transition reversibly from the active to the pre-malignant state. For significantly large stress, the range of the pre-malignant state expands, and the system moves to irreversible cancerous state, which is a stable attractor. This suggests that identification of the pre-malignant state may be important both for therapeutic intervention as well as for drug delivery.

Brain Disease Network Analysis to Elucidate the Neurological Manifestations of COVID-19

Although COVID-19 largely causes respiratory complications, it can also lead to various extrapulmonary manifestations resulting in higher mortality and these comorbidities are posing a challenge to the health care system. Reports indicate that 30–60% of patients with COVID-19 suffer from neurological symptoms. To understand the molecular basis of the neurologic comorbidity in COVID-19 patients, we have investigated the genetic association between COVID-19 and various brain disorders through a systems biology-based network approach and observed a remarkable resemblance. Our results showed 123 brain-related disorders associated with COVID-19 and form a high-density disease-disease network. The brain-disease-gene network revealed five highly clustered modules demonstrating a greater complexity of COVID-19 infection. Moreover, we have identified 35 hub proteins of the network which were largely involved in the protein catabolic process, cell cycle, RNA metabolic process, and nuclear transport. Perturbing these hub proteins by drug repurposing will improve the clinical conditions in comorbidity. In the near future, we assumed that in COVID-19 patients, many other neurological manifestations will likely surface. Thus, understanding the infection mechanisms of SARS-CoV-2 and associated comorbidity is a high priority to contain its short- and long-term effects on human health. Our network-based analysis strengthens the understanding of the molecular basis of the neurological manifestations observed in COVID-19 and also suggests drug for repurposing.

Molecular crosstalk: Notch can manipulate Hes1 and miR-9 behavior

We propose a Hes1-Notch-miR-9 regulatory network and studied the regulating mechanism of miR-9 and Hes1 dynamics driven by Notch. Change in Notch concentration, which serves as a stress signal, can trigger the dynamics of Hes1 and miR-9 at five different states, namely, sTable (2), sustain (1) and mixed (2) states those may correspond to different cellular states. Further, this Notch stress signal introduce time reversal oscillation, which behaves as backward wave, after a certain threshold value of the stress signal and defends the system from moving to apoptosis. We also observe heterogeneous patterns of Hes1, miR-9 and other molecular species in various two dimensional parameter spaces and found that the variability in the patterns is triggered by Hill coefficient and Hes1 stress signal. The phase or bifurcation diagram in time period of oscillation (T_N) driven by Notch signal provides all five states, predicts minimum threshold value T_N^c beyond which tendency to build up backward wave starts and T_N^c serves as bifurcation point of the system.

Kinless hubs are potential target genes in prostate cancer network

Complex disease networks can be studied successfully using network theoretical approach which helps in finding key disease genes and associated disease modules. We studied prostate cancer (PCa) protein-protein interaction (PPI) network constructed from patients' gene expression datasets and found that the network exhibits hierarchical scale free topology which lacks centrality lethality rule. Knockout experiments of the sets of leading hubs from the network leads to transition from hierarchical (HN) to scale free (SF) topology affecting network integration and organization. This transition, HN → SF, due to removal of significant number of the highest degree hubs, leads to relatively decrease in information processing efficiency, cost effectiveness of signal propagation, compactness, clustering of nodes and energy distributions. A systematic transition from a diassortative PCa PPI network to assortative networks after the removal of top 50 hubs then again reverting to disassortativity nature on further removal of the hubs was also observed indicating the dominance of the largest hubs in PCa network intergration. Further, functional classification of the hubs done by using within module degrees and participation coefficients for PCa network, and leading hubs knockout experiments indicated that kinless hubs serve as the basis of establishing links among constituting modules and heterogeneous nodes to maintain network stabilization. We, then, checked the essentiality of the hubs in the knockout experiment by performing Fisher's exact test on the hubs, and showed that removal of kinless hubs corresponded to maximum lethality in the network. However, excess removal of these hubs essentially may cause network breakdown.

Stochastic method to control Mycobacterium tuberculosis epidemic

We study the origin of TB (tuberculosis) epidemic and complex distributions of various populations of TB infection within the stochastic framework. The stochastic nature of this disease infection could be linked to the stochastic behaviour at genome level which is exhibited in SNP (single nucleotide polymorphism) distributions of experimentally identified hotspot driver genes. Our results show the emergence of random clusters, and well-defined discrete domains of the respective species populations in the model driven by demographic stochasticity and intrinsic complex species interaction. The multifractal analysis of the time series of the species populations indicate that TB epidemic could be mainly caused by contact communication and is directional. We propose that any TB epidemic may have high chance of approximately periodic recurrence and can be controlled by optimizing some of the parameters involved in the system modelling.

Differential levels of Alpha-1-inhibitor III, Immunoglobulin heavy chain variable region, and Hypertrophied skeletal muscle protein GTF3 in rat mammary tumorigenesis

Early detection of breast cancer can be best facilitated by the development of precancerous markers. Serum proteins being the sensitive signatures, can be the ideal choice. We previously demonstrated the reduced levels of two serum proteins at a very early stage of tumorigenesis in a breast cancer model, developed in Wistar rats by 7,12-dimethylbenz[a]anthracene (DMBA) administration. Here we report the dysregulation of three more proteins in the serum collected at another early stage (15 weeks) of tumorigenesis in the same model. The proteins were identified (as Alpha-1-inhibitor III (Mug1), Immunoglobulin heavy chain variable region (IGHV), and Hypertrophied skeletal muscle protein GTF3) by MALDI-TOF MS after the screening and fingerprinting of serum samples by one-dimensional (1D) and two-dimensional (2D) electrophoresis respectively. Relative expression analysis of corresponding genes was also carried out, and the results were found as supporting the proteomic findings. In addition, the candidate proteins of the study and their corresponding ribonucleic acids (RNAs) were subjected to homology modelling and docking (using softwares like MODELLER, 3dRNA, Autodock4.0, and GROMACS etc), which revealed the binding sites for carcinogen (DMBA) and its nature of interaction with proteins and RNAs. Moreover, the network analysis by GeneMANIA unraveled the protein/gene functional network in which Mug1, IGHV, and GTF3 are involved. Based on the significant protein and gene expression alterations in early tumorigenesis, these proteins may prove very effective in search for biomarkers for the early detection of mammary cancer. Further, these proteins can also be tried as targets for chemotherapy.

Methodology of predicting novel key regulators in ovarian cancer network: a network theoretical approach

Background

Identification of key regulator/s in ovarian cancer (OC) network is important for potential drug target and prevention from this cancer. This study proposes a method to identify the key regulators of this network and their importance.

Methods

The protein-protein interaction (PPI) network of ovarian cancer (OC) is constructed from curated 6 hundred genes from standard six important ovarian cancer databases (some of the genes are experimentally verified). We proposed a method to identify key regulators (KRs) from the complex ovarian cancer network based on the tracing of backbone hubs, which participate at all levels of organization, characterized by Newmann-Grivan community finding method. Knockout experiment, constant Potts model and survival analysis are done to characterize the importance of the key regulators in regulating the network.

Results

The PPI network of ovarian cancer is found to obey hierarchical scale free features organized by topology of heterogeneous modules coordinated by diverse leading hubs. The network and modular structures are devised by fractal rules with the absence of centrality-lethality rule, to enhance the efficiency of signal processing in the network and constituting loosely connected modules. Within the framework of network theory, we device a method to identify few key regulators (KRs) from a huge number of leading hubs, that are deeply rooted in the network, serve as backbones of it and key regulators from grassroots level to complete network structure. Using this method we could able to identify five key regulators, namely, AKT1, KRAS, EPCAM, CD44 and MCAM, out of which AKT1 plays central role in two ways, first it serves as main regulator of ovarian cancer network and second serves as key cross-talk agent of other key regulators, but exhibits disassortive property. The regulating capability of AKT1 is found to be highest and that of MCAM is lowest.

Conclusions

The popularities of these key hubs change in an unpredictable way at different levels of organization and absence of these hubs cause massive amount of wiring energy/rewiring energy that propagate over all the network. The network compactness is found to increase as one goes from top level to bottom level of the network organization.

Identification and Classification of Differentially Expressed Genes and Network Meta-Analysis Reveals Potential Molecular Signatures Associated With Tuberculosis

Tuberculosis (TB) is one of deadly transmissible disease that causes death worldwide; however, only 10% of people infected with Mycobacteriumtuberculosis develop disease, indicating that host genetic factors may play key role in determining susceptibility to TB disease. In this way, the analysis of gene expression profiling of TB infected individuals can give us a snapshot of actively expressed genes and transcripts under various conditions. In the present study, we have analyzed microarray data set and compared the gene expression profiles of patients with different datasets of healthy control, latent infection, and active TB. We observed the transition of genes from normal condition to different stages of the TB and identified and annotated those genes/pathways/processes that have important roles in TB disease during its cyclic interventions in the human body. We identified 488 genes that were differentially expressed at various stages of TB and allocated to pathways and gene set enrichment analysis. These pathways as well as GSEA’s importance were evaluated according to the number of DEGs presents in both. In addition, we studied the gene regulatory networks that may help to further understand the molecular mechanism of immune response against the TB infection and provide us a new angle for future biomarker and therapeutic targets. In this study, we identified 26 leading hubs which are deeply rooted from top to bottom in the gene regulatory network and work as the backbone of the network. These leading hubs contains 31 key regulator genes, of which 14 genes were up-regulated and 17 genes were down-regulated. The proposed approach is based on gene-expression profiling, and network analysis approaches predict some unknown TB-associated genes, which can be considered (or can be tested) as reliable candidates for further (in vivo/in vitro) studies.

Development and rigorous validation of antimalarial predictive models using machine learning approaches

The large collection of known and experimentally verified compounds from the ChEMBL database was used to build different classification models for predicting the antimalarial activity against Plasmodium falciparum. Four different machine learning methods, namely the support vector machine (SVM), random forest (RF), k-nearest neighbour (kNN) and XGBoost have been used for the development of models using the diverse antimalarial dataset from ChEMBL. A well-established feature selection framework was used to select the best subset from a larger pool of descriptors. Performance of the models was rigorously evaluated by evaluation of the applicability domain, Y-scrambling and AUC-ROC curve. Additionally, the predictive power of the models was also assessed using probability calibration and predictiveness curves. SVM and XGBoost showed the best performances, yielding an accuracy of ~85% on the independent test set. In term of probability prediction, SVM and XGBoost were well calibrated. Total gain (TG) from the predictiveness curve was more related to SVM (TG = 0.67) and XGBoost (TG = 0.75). These models also predict the high-affinity compounds from PubChem antimalarial bioassay (as external validation) with a high probability score. Our findings suggest that the selected models are robust and can be potentially useful for facilitating the discovery of antimalarial agents.

In Vitro and In Silico Evaluation of Betulin on Calcium Oxalate Crystal Formation

Objective: The medicinal plant Betula alba has been used for prevention and treatment of kidney stones. Betulin is one of the main phytochemicals of Betula alba. The aim of this study is to investigate the antioxidant and antiurolithiatic activity of betulin in vitro and in silico. For antioxidant activity, 2, 2-diphenyl-1-picrylhydrazyl (DPPH), total reducing capacity, nitric oxide (NO) radical scavenging assay, and superoxide radical scavenging assay were studied. Method: In order to study antiurolithiatic activity, three assays such as crystallization, nucleation, and aggregation of oxalate crystal in urine were performed. In silico experiments were performed by using AutoDock 4.2 tools in order to establish affinity of phytochemicals toward antioxidant enzyme and matrix metalloproteinase (MMP-2 and 9). Results: The results obtained clearly demonstrate the significant scavenging activity of betulin and cystone against DPPH, NO, and superoxide radicals in comparison to standard antioxidant L-ascorbate (L-AA). It has also been observed that betulin has the capacity to inhibit the crystallization, nucleation, and aggregation in comparison to cystone. On the other hand, betulin and L-AA showed strong affinity toward antioxidant enzymes and matrix metalloproteinase as determined by in silico experiments. Conclusions: From this, it may be concluded that the antiurolithiatic activity of betulin is, at least in part, mediated by its antioxidant property.

Draft genome sequence of Dichelobacter nodosus JKS-07 serogroup E from India

OBJECTIVES

Dichelobacter nodosus is an anaerobic bacterium with fastidious growth requirements that is the principal cause of footrot associated with lameness in sheep and goats. In India, D. nodosus serogroups B and E have been recorded as major causes of footrot. Here we report the draft genome sequence of a D. nodosus serogroup E strain (JKS-07) from a case of virulent footrot in India.

METHODS

The whole genome of the D. nodosus JKS-07 serogroup E was sequenced using an Illumina HiSeq 2500 platform and was annotated according to functional gene categories. De novo genome assembly and annotation were performed using Perl scripts developed in-house using the Nr/Nt and UniProt databases.

RESULTS

The assembled genome is 1389350bp and contains 1301 genes. The genome has 45 tRNAs and 9 rRNAs. The draft genome sequence will provide insight into the various genes and regulators involved in D. nodosus growth and survival.

CONCLUSION

Information on the genome of the D. nodosus serogroup E strain is important bearing in mind the fact that both serogroups B and E are associated with virulent footrot, either alone or frequently together. In order to develop an efficacious vaccine against virulent footrot, it is essential to know the serological diversity as well as the virulence status of the D. nodosus strains. Serogroups B and E are potential vaccine candidates to mitigate ovine footrot in India.

Assessment of the key regulatory genes and their Interologs for Turner Syndrome employing network approach

Turner Syndrome (TS) is a condition where several genes are affected but the molecular mechanism remains unknown. Identifying the genes that regulate the TS network is one of the main challenges in understanding its aetiology. Here, we studied the regulatory network from manually curated genes reported in the literature and identified essential proteins involved in TS. The power-law distribution analysis showed that TS network carries scale-free hierarchical fractal attributes. This organization of the network maintained the self-ruled constitution of nodes at various levels without having centrality-lethality control systems. Out of twenty-seven genes culminating into leading hubs in the network, we identified two key regulators (KRs) i.e. KDM6A and BDNF. These KRs serve as the backbone for all the network activities. Removal of KRs does not cause its breakdown, rather a change in the topological properties was observed. Since essential proteins are evolutionarily conserved, the orthologs of selected interacting proteins in C. elegans, cat and macaque monkey (lower to higher level organisms) were identified. We deciphered three important interologs i.e. KDM6A-WDR5, KDM6A-ASH2L and WDR5-ASH2L that form a triangular motif. In conclusion, these KRs and identified interologs are expected to regulate the TS network signifying their biological importance.

Control of apoptosis by SMAR1

The nuclear matrix associated protein SMAR1 is sensitive to p53 and acts as a stress inducer as well as a regulator in the p53 regulatory network. Depending on the amount of stress SMAR1 stimulates, it can drive the p53 dynamics in the system to various dynamical states which correspond to various cellular states. The behavior of p53 in these dynamical states is found to be multifractal, due to the mostly long range correlations and large scale fluctuations imparted by stress. This fractal behavior is exhibited in the topological properties of the networks constructed from these dynamical states, and is a signature of self-organization to optimize information flow in the dynamics. The assortativity found in these networks is due to perturbation induced by stress, and indicates that the hubs in the time series play a significant role in stress management. SMAR1 can also regulate apoptosis in the presence of HDAC1, depending on the stress induced by it.

From ZikV genome to vaccine: in silico approach for the epitope-based peptide vaccine against Zika virus envelope glycoprotein

Zika virus (ZikV) has emerged as a potential threat to human health worldwide. A member of the Flaviviridae, ZikV is transmitted to humans by mosquitoes. It is related to other pathogenic vector-borne flaviviruses including dengue, West Nile and Japanese encephalitis viruses, but produces a comparatively mild disease in humans. As a result of its epidemic outbreak and the lack of potential medication, there is a need for improved vaccine/drugs. Computational techniques will provide further information about this virus. Comparative analysis of ZikV genomes should lead to the identification of the core characteristics that define a virus family, as well as its unique properties, while phylogenetic analysis will show the evolutionary relationships and provide clues about the protein's ancestry. Envelope glycoprotein of ZikV was obtained from a protein database and the most immunogenic epitope for T cells and B cells involved in cell-mediated immunity, whereas B cells are primarily responsible for humoral immunity. We mainly focused on MHC class I potential peptides. YRIMLSVHG, VLIFLSTAV and MMLELDPPF, GLDFSDLYY are the most potent peptides predicted as epitopes for CD4⁺ and CD8⁺ T cells, respectively, whereas MMLELDPPF and GLDFSDLYY had the highest pMHC-I immunogenicity score and these are further tested for interaction against the HLA molecules, using in silico docking techniques to verify the binding cleft epitope. However, this is an introductory approach to design an epitope-based peptide vaccine against ZikV; we hope that this model will be helpful in designing and predicting novel vaccine candidates.

Dynamics of p53 and Wnt cross talk

We present the mechanism of interaction of Wnt network module, which is responsible for periodic somitogenesis, with p53 regulatory network, which is one of the main regulators of various cellular functions, and switching of various oscillating states by investigating p53-Wnt model. The variation in Nutlin concentration in p53 regulating network drives the Wnt network module to different states, stabilized, damped and sustain oscillation states, and even to cycle arrest. Similarly, the change in Axin2 concentration in Wnt could able to modulate the p53 dynamics at these states. We then solve the set of coupled ordinary differential equations of the model using quasi steady state approximation. We, further, demonstrate the change of p53 and GSK3 interaction rate, due to hypothetical catalytic reaction or external stimuli, can able to regulate the dynamics of the two network modules, and even can control their dynamics to protect the system from cycle arrest (apoptosis).

Development of a simple chromatographic method for the determination of piracetam in human plasma and its pharmacokinetic evaluation

OBJECTIVE

The objective of study was to develop an accurate and reproducible HPLC method for determination of piracetam in human plasma and to evaluate pharmacokinetic parameters of 800 mg piracetam.

METHODS

A simple, rapid, accurate, precise and sensitive high pressure liquid chromatography method has been developed and subsequently validated for determination of piracetam. This study represents the results of a randomized, single-dose and single-period in 18 healthy male volunteers to assess pharmacokinetic parameters of 800 mg piracetam tablets. Various pharmacokinetic parameters were determined from plasma for piracetam and found to be in good agreement with previous reported values. The data was analyzed by using Kinetica® version 4.4 according to non-compartment model of pharmacokinetic analysis and after comparison with previous studies, no significant differences were found in present study of tested product.

RESULTS

The major pharmacokinetic parameters for piracetam were as follows: t1/2 was (4.40 ± 0.179) h; Tmax value was (2.33 ± 0.105) h; Cmax was (14.53 ± 0.282) µg/mL; the AUC(0-∞) was (59.19 ± 4.402) µg · h/mL. AUMC(0-∞) was (367.23 ± 38.96) µg. (h)(2)/mL; Ke was (0.16 ± 0.006) h; MRT was (5.80 ± 0.227) h; Vd was (96.36 ± 8.917 L).

CONCLUSIONS

A rapid, accurate and precise high pressure liquid chromatography method was developed and validated before the study. It is concluded that this method is very useful for the analysis of pharmacokinetic parameters, in human plasma and assured the safety and efficacy of piracetam, can be effectively used in medical practice.

Hypercalcemic crisis due to primary hyperparathyroidism in systemic lupus erythematosus (SLE)

Severe hypercalcemia is often caused by primary hyperparathyroidism (PHP), which is not commonly seen in patients with systemic lupus erythematosus (SLE). In this case report an adolescent girl with a history of SLE develops mild hypercalcemia secondary to unrecognized PHP that leads to a hypercalcemic crisis with a prolonged recovery. Therefore, early diagnostic evaluation of persistent hypercalcemia in patients with SLE is important for detection and appropriate treatment of PHP to avoid a hypercalcemic crisis and associated prolonged morbidity.

Intercellular synchronization of coupled smooth muscle cells via Ca2+ propagation

The propagation of Ca2+ wave through gap junction in smooth muscle cell is studied as a function of electrical coupling parameter (g) modulated by Ca2+ level in the cell. The range of activation time of Ca2+ propagation with amplitude is found to increase as increase in electrical coupling parameter g, which is identified by increase in critical time of activation, T(F) as a function of g. Then identical Ca2+ oscillators are allowed to interact via electrical and diffusive coupling of Ca2+ ions diffused through gap junctions, and rate of intercellular synchronization among them is studied. The phase diagrams in (T(F) - g) and (T(F) - epsilon) parameter spaces separate oscillation death and damped oscillations regimes which correspond to deactivated and activated regimes of Ca2+ level. The effect of on T(F) is significantly very slow, however it enhances the rate of synchronization among the coupled oscillators. The increase in g comparatively slows down the rate of synchronization of the coupled oscillators as shown in the phase diagram in (epsilon - g) parameter space which separates desynchronized and synchronized regimes.

Comparative analysis of knowledge, attitudes and perceptions about induced abortions among medical and non-medical students of Karachi

OBJECTIVE

To determine the knowledge, attitudes and perceptions regarding induced abortions among university students of Karachi and compare these aspects among medical and non-medical students.

STUDY DESIGN

Cross sectional opinion survey.

METHODS

The study is a cross sectional opinion survey of medical and non-medical students from four educational institutions of Karachi using a pretested questionnaire.

RESULTS

Of the 381 students interviewed, 201 were medical and 180 were non-medical. More medical students were aware of the correct definition of induced abortion. An encouraging finding was the awareness of our study population about the only legally acceptable circumstance for induced abortion in Pakistan, that is, if conceptus is a risk to maternal health (p<0.001). More medical students cited complications that were technical like uterine perforation (p<0.001), cervical trauma (p<0.001), DIC (p<0.001), and sepsis (p<0.001). More medical students thought induced abortion to be a health issue (p=0.001) as compared to non-medical students. Most of the medical students felt the need to modify Pakistan's law regarding abortion (p=0.008) especially for fetal anomalies (13.9%) and rape (7%).

CONCLUSION

Though medical students were more knowledgeable about induced abortions as compared to the non-medical population, there is a need to improve awareness regarding induced and unsafe abortions and their consequences.

Disease pattern and fatality among patients treated at DHQ Hospital, Faisalabad

A pilot study was undertaken to assess the pattern of diseases, fatality and some other ratios among the patients treated at District Headquarters Hospital, Faisalabad during 1984. Out of 4,506 in-patients, maximum admissions 1,196 (26.54%) were of gastrointestinal diseases followed by 795 (17.64%) cases of injuries. Overall fatality was found to be 6.35%. The highest case fatality was found in the infectious diseases group, while maximum of the total deaths (20.98%) occurred due to injuries. Consistency in provisional and final diagnosis was found in 86.20% of total cases. The annual turnover was 41 patients per bed per year.

Acute leukaemia in children--French-American-British (FAB) classification and its relation to clinical features

A retrospective study of 45 patients of acute leukaemia in children (15 years age), in relation to FAB classification, is presented. Acute lymphoblastic leukaemias (ALL) were commoner than acute myeloid leukaemias (AML), with an ALL:AML ratio of 4.63:1. Amongst ALL, L1 was the most frequent type (59%), followed by L2 (35%) and L3 (5.4%), respectively. FAB types M2, M3, M4 and M5 were diagnosed in 2 patients each. An analysis of clinical features in relation to FAB classification is also presented.