A review on the nexus of autophagy genes from the perspective of polycystic ovary syndrome.

Polycystic ovary syndrome or PCOS is an endocrine disorder in women of reproductive age. It is a diversified multi factorial disorder and diagnosis is very complicated because of its overlapping symptoms some of which are irregular menstrual cycle, acne in face, excess level of androgen (AE), insulin resistance, obesity, cardiovascular disease, mood disorder and type 2 diabetes (T2DM). PCOS may be caused by hormonal imbalance, genetic and epigenetic vulnerability, hypothalamic and ovarian troubles. PCOS is essentially hyperandrogenimia with oligo-anovulation. This review explains the abnormal regulation of autophagy related genes and proteins in different cells at various stages which leads to the genesis of PCOS. During nutrient starvation cells face stress condition, which it tries to overcome by activating its macroautophagy mechanism and by degrading the cytoplasmic material. This provides energy to the cell facilitating its survival. Downregulation of autophagy related genes in endometria has been observed in PCOS women. PCOS can be managed by maintaining proper lifestyle and medical treatment. Healthy meals and regular exercise can prevent the excessive weight and also reduce the PCOS complications. Medicines such as metformin, clomiphene, and the oral contraceptive pill can also balance the hormonal level. The imbalance in regulation of autophagy genes has been discussed with correlation to PCOS. The different management strategies for PCOS have also been summarized.

Elucidating the evolution of monkeypox virus genomes through phylo-geo-network and haplogroup analysis.

BACKGROUND: As the world settles down from the COVID-19 pandemic, many countries are faced with an unexpected outbreak of monkeypox infection. Monkeypox is a zoonotic disease caused by monkeypox virus (MPXV), which is an enveloped, double stranded DNA virus belonging to the Poxviridae family. Presently, we construct and analyze the phylo-geo-network and the corresponding haplogroups. Presently, we performed the haplogroup analysis with their defining mutations and phylogenetic lineage study along with geographical distributions with the aim to understand the evolutionary path of the MPXV across the world. RESULTS: Information about 719 full length genomes of MPXV were collected from GISAID repository and the sequences extracted from NCBI. The alignment of 719 MPXV genomes and their subsequent analysis revealed a total of 1530 segregating sites of which 330 were parsimony informative (PI) sites. The variations had a positive value of Tajima's D statistic indicating some mutations being prevalent and hence balancing selection. A total of 39 haplogroups were observed in the phylo-geo-network and their defining mutations along with the evolutionary path has been discussed. The phylo-geo-network revealed the nodal haplogroup is represented by GISAID ID 13889450, haplogroup A1, an isolate from Germany, having a total of 296 identical sequences in the study incident across 22 countries. The localized evolution is highlighted by country specific sequences and haplogroups. USA had a total of 58 genomes and 13 haplogroups as compared to Peru (89 genomes, 7 haplogroups) and Germany (26 genomes, 6 haplogroups). CONCLUSIONS: The evolution of MPXV can be happening in a localized manner and hence accumulation of variations in the MPXV genomes needs to be monitored in order to be prepared for any possible threats.

Nonsynonymous mutations in VEGF receptor binding domain alter the efficacy of bevacizumab treatment.

Vascular endothelial growth factor (VEGF) mediated angiogenesis is crucial for tumor progression. Isoforms of VEGF bind to different VEGF receptors (VEGFRs) to initiate angiogenesis specific cellular signaling. Inhibitors that target both the receptors and ligands are in clinical use to impede angiogenesis. Bevacizumab, a monoclonal antibody (mAb) approved by the Food and Drug Administration (FDA), binds in the VEGF receptor binding domain (RBD) of all soluble isoforms of VEGF and inhibits the VEGF-VEGFR interaction. Bevacizumab is also used in combination with other chemotherapeutic agents for a better therapeutic outcome. Understanding the intricate polymorphic character of VEGFA gene and the influence of missense or nonsynonymous mutations in the form of nonsynonymous polymorphisms (nsSNPs) on RBD of VEGF may aid in increasing the efficacy of this drug. This study has identified 18 potential nsSNPs in VEGFA gene that affect the VEGF RBD structure and alter its binding pattern to bevacizumab. The mutated RBDs, modeled using trRosetta, in addition to the changed pattern of secondary structure, post translational modification and stability compared to the wild type, have shown contrasting binding affinity and molecular interaction pattern with bevacizumab. Molecular docking analysis by ClusPro and visualization using PyMol and PDBsum tools have detected 17 nsSNPs with decreased binding affinity to bevacizumab and therefore may impact the treatment efficacy. Whereas VEGF RBD expressed due to rs1267535717 (R229H) nsSNP of VEGFA has increased affinity to the mAb. This study suggests that genetic characterization of VEGFA before bevacizumab mediated cancer treatment is essential in predicting the appropriate efficacy of the drug, as the treatment efficiency may vary at individual level.

Non-synonymous Single Nucleotide Polymorphisms in Human ACE2 Gene May Affect the Infectivity of SARS-CoV-2 Omicron Subvariants.

BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes the coronavirus disease 2019 (COVID-19), which first appeared in December 2019. Angiotensin I converting enzyme 2 (ACE2) receptor, present on the host cells, interacts with the receptor binding domain (RBD) of spike (S) protein of SARS-CoV-2 and facilitates the viral entry into host cells. METHODS: Non-synonymous single nucleotide polymorphisms (nsSNPs) in the ACE2 gene may have an impact on the protein's stability and its function. The deleterious or harmful nsSNPs of the ACE2 gene that can change the strength as well as the pattern of interaction with the RBD of S protein were selected for this study. RESULTS: The ACE2:RBD interactions were analyzed by protein-protein docking study. The missense mutations A242V, R708W, G405E, D292N, Y633C, F308L, and G405E in ACE2 receptor were found to interact with RBD of Omicron subvariants with stronger binding affinity. Among the other selected nsSNPs of human ACE2 (hACE2), R768W, Y654S, F588S, R710C, R710C, A191P, and R710C were found to have lower binding affinity for RBD of Omicron subvariants. CONCLUSION: The findings of this study suggest that the nsSNPs present in the human ACE2 gene alter the structure and function of the protein and, consequently, the susceptibility to Omicron subvariants.

Microsatellite diversity and complexity in the viral genomes of the family Caliciviridae.

BACKGROUND: Microsatellites or simple sequence repeats (SSR) consist of 1-6 nucleotide motifs of DNA or RNA which are ubiquitously present in tandem repeated sequences across genome in viruses: prokaryotes and eukaryotes. They may be localized to both the coding and non-coding regions. SSRs play an important role in replication, gene regulation, transcription, and protein function. The Caliciviridae (CLV) family of viruses have ss-RNA, non-enveloped, icosahedral symmetry 27-35 nm in diameter in size. The size of the genome lies between 6.4 and 8.6 kb. RESULTS: The incidence, composition, diversity, complexity, and host range of different microsatellites in 62 representatives of the family of Caliciviridae were systematically analyzed. The full-length genome sequences were assessed from NCBI ( https://www.ncbi.nlm.nih.gov ), and microsatellites were extracted through MISA software. The average genome size is about 7538 bp ranging from 6273 (CLV61) to 8798 (CLV47) bp. The average GC content of the genomes was ~ 51%. There are a total of 1317 SSRs and 53 cSSRs in the studied genomes. CLV 41 and CLV 49 contain the highest and lowest value of SSRs with 32 and 10 respectively, while CLV16 had maximum cSSR incidence of 4. There were 29 species which do not contain any cSSR. The incidence of mono-, di-, and tri-nucleotide SSRs was 219, 884, and 206, respectively. The most prevalent mono-, di-, and tri-nucleotide repeat motifs were "C" (126 SSRs), AC/CA (240 SSRs), and TGA/ACT (23 SSRs), respectively. Most of the SSRs and cSSRs are biased toward the coding region with a minimum of ~ 90% incident SSRs in the genomes' coding region. Viruses with similar host are found close to each other on the phylogenetic tree suggesting virus host being one of the driving forces for their evolution. CONCLUSIONS: The Caliciviridae genomes does not conform to any pattern of SSR signature in terms of incidence, composition, and localization. This unique property of SSR plays an important role in viral evolution. Clustering of similar host in the phylogenetic tree is the evidence of the uniqueness of SSR signature.

Tanshinone IIA targeting cell signaling pathways: a plausible paradigm for cancer therapy.

Natural compounds originating from plants offer a wide range of pharmacological potential and have traditionally been used to treat a wide range of diseases including cancer. Tanshinone IIA (Tan IIA), a bioactive molecule found in the roots of the Traditional Chinese Medicine (TCM) herb Salvia miltiorrhiza, has been shown to have remarkable anticancer properties through several mechanisms, such as inhibition of tumor cell growth and proliferation, metastasis, invasion, and angiogenesis, as well as induction of apoptosis and autophagy. It has demonstrated excellent anticancer efficacy against cell lines from breast, cervical, colorectal, gastric, lung, and prostate cancer by modulating multiple signaling pathways including PI3K/Akt, JAK/STAT, IGF-1R, and Bcl-2-Caspase pathways. This review focuses on the role of Tan IIA in the treatment of various cancers, as well as the underlying molecular mechanisms.

Systems biology of the genomes' microsatellite signature of Orthopoxvirus including the Monkeypox virus.

This study is an attempt to extract and analyse the microsatellites or simple sequence repeats (SSRs) from the genomes of eight species of the genus Orthopoxvirus. The average size of genomes included in the study was 205 kb while the GC% was 33% for all but one. A total of 10,584 SSRs and 854 cSSRs were observed. POX2 with the largest genome of 224.499 kb had maximum of 1493 SSRs and 121 cSSRs (compound SSR) while POX7 with the smallest genome of 185.578 kb had minimum incident SSRs and cSSRs at 1181 and 96, respectively. There was significant correlation between genome size and SSR incidence. Di-nucleotide repeats were the most prevalent (57.47%) followed by mono- at 33% and tri- at 8.6%. Mono-nucleotide SSRs were predominantly T (51%) and A (48.4%). A majority of 80.32% SSRs were in the coding region. The three most similar genomes as per heat map POX1, POX7 and POX5 (93% similarity) are adjacent to one another in the phylogenetic tree. Ankyrin/Ankyrin like protein and Kelch protein which are associated with host determination and divergence have the highest SSR density in almost all studied viruses. Thus, SSRs are involved in genome evolution and host determination of viruses.

Context awareness based Sketch-DeepNet architecture for hand-drawn sketches classification and recognition in AIoT.

A sketch is a black-and-white, 2-D graphical representation of an object and contains fewer visual details as compared to a colored image. Despite fewer details, humans can recognize a sketch and its context very efficiently and consistently across languages, cultures, and age groups, but it is a difficult task for computers to recognize such low-detail sketches and get context out of them. With the tremendous increase in popularity of IoT devices such as smartphones and smart cameras, etc., it has become more critical to recognize free hand-drawn sketches in computer vision and human-computer interaction in order to build a successful artificial intelligence of things (AIoT) system that can first recognize the sketches and then understand the context of multiple drawings. Earlier models which addressed this problem are scale-invariant feature transform (SIFT) and bag-of-words (BoW). Both SIFT and BoW used hand-crafted features and scale-invariant algorithms to address this issue. But these models are complex and time-consuming due to the manual process of features setup. The deep neural networks (DNNs) performed well with object recognition on many large-scale datasets such as ImageNet and CIFAR-10. However, the DDN approach cannot be carried out for hand-drawn sketches problems. The reason is that the data source is images, and all sketches in the images are, for example, 'birds' instead of their specific category (e.g., 'sparrow'). Some deep learning approaches for sketch recognition problems exist in the literature, but the results are not promising because there is still room for improvement. This article proposed a convolutional neural network (CNN) architecture called Sketch-DeepNet for the sketch recognition task. The proposed Sketch-DeepNet architecture used the TU-Berlin dataset for classification. The experimental results show that the proposed method beats the performance of the state-of-the-art sketch classification methods. The proposed model achieved 95.05% accuracy as compared to existing models DeformNet (62.6%), Sketch-DNN (72.2%), Sketch-a-Net (77.95%), SketchNet (80.42%), Thinning-DNN (74.3%), CNN-PCA-SVM (72.5%), Hybrid-CNN (84.42%), and human recognition accuracy of 73% on the TU-Berlin dataset.

Developing a Novel Methodology by Integrating Deep Learning and HMM for Segmentation of Retinal Blood Vessels in Fundus Images.

Accurate segregation of retinal blood vessels network plays a crucial role in clinical assessments, treatments, and rehabilitation process. Owing to the presence of acquisition and instrumentation anomalies, precise tracking of vessels network is challenging. For this, a new fundus image segmentation framework is proposed by combining deep neural networks, and hidden Markov model. It has three main modules: the Atrous spatial pyramid pooling-based encoder, the decoder, and hidden Markov model vessel tracker. The encoder utilized modified ResNet18 deep neural networks model for low-and-high-levels features extraction. These features are concatenated in module-II by the decoder to perform convolution operations to obtain the initial segmentation. Previous modules detected the main vessel structure and overlooked some small capillaries. For improved segmentation, hidden Markov model vessel tracker is integrated with module-I and-II to detect overlooked small capillaries of the vessels network. In last module, final segmentation is obtained by combining multi-oriented sub-images using logical OR operation. This novel framework is validated experimentally using two standard DRIVE and STARE datasets. The developed model offers high average values of accuracy, area under the curve, and sensitivity of 99.8, 99.0, and 98.2%, respectively. Analysis of the results revealed that the developed approach offered enhanced performance in terms of sensitivity 18%, accuracy 3%, and specificity 1% over the state-of-the-art approaches. Owing to better learning and generalization capability, the developed approach tracked blood vessels network efficiently and automatically compared to other approaches. The proposed approach can be helpful for human eye assessment, disease diagnosis, and rehabilitation process.

Evaluating the transmission feasibility of SARS-CoV-2 Omicron (B.1.1.529) variant to 143 mammalian hosts: insights from S protein RBD and host ACE2 interaction studies.

In comparison to previously known severe respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, the newly emerged Omicron (B.1.1.529) variant shows higher infectivity in humans. Exceptionally high infectivity of this variant raises concern of its possible transmission via other intermediate hosts. The SARS-CoV-2 infectivity is established via the association of spike (S) protein receptor binding domain (RBD) with host angiotensin I converting enzyme 2 (hACE2) receptor. In the course of this study, we investigated the interaction between Omicron S protein RBD with the ACE2 receptor of 143 mammalian hosts including human by protein-protein interaction analysis. The goal of this study was to forecast the likelihood that the virus may infect other mammalian species that coexist with or are close to humans in the household, rural, agricultural, or zoological environments. The Omicron RBD was found to interact with higher binding affinity with the ACE2 receptor of 122 mammalian hosts via different amino acid residues from the human ACE2 (hACE2). The rat (Rattus rattus) ACE2 was found to show the strongest interaction with Omicron RBD with a binding affinity of -1393.6 kcal/mol. These distinct strong binding affinity of RBD of Omicron with host ACE2 indicates a greater potential of new host transmissibility and infection via intermediate hosts. Though expected but the phylogenetic position of the mammalian species may not dictate the Omicron RBD binding to the host ACE2 receptor suggesting an involvement of multiple factors in guiding host divergence of the variant.

Microsatellite signature analysis of twenty-one virophage genomes of the family Lavidaviridae.

Microsatellites or Simple Sequence Repeats (SSRs) are short motif repeat sequences constituting the most hypervariable regions of genomes. Present study extracts and analyzes the SSRs from genomes of 21 virophages. Genomic sequences were retrieved from NCBI and the microsatellite data was extracted through MISA web server. Phylogenetic analysis was performed by using MAFFT and MEGAX as per standardized protocols. The virophages have a circular/linear ds DNA genome of ~17-30 kb size. The GC% of genomes ranged from 26.8 (PSAV13) to 51.1 (PSAV12). A total of 3664 SSRs and 488 cSSR were observed with an average incidence of 174 and 23 respectively. The total SSR incidence in a genome ranged from 120 (PSAV19) to 264 (PSAV14). The cSSR (compound SSR) incidence ranged from 8 (PSAV12) to 47 (PSAV14). Mono-nucleotide repeats are the most incident microsatellites (1129 SSRs) followed by di-nucleotide (1036 SSRs) and tri-nucleotide repeats (368 SSRs). However, the same is not true for individual genomes. There are 14, 16 and 17 genomes which have no incidence of tetra-, penta- and hexa-nucleotide repeats respectively. Mono 'A' repeats having the maximum representation (average ~33 per genome) in mono-nucleotide repeats. For the di-nucleotide repeats, AT/TA motif had the highest frequency (average ~30) distantly followed by AG/GA; and CT/TC (average 5.6 & 5.5 respectively). A total of 1946 SSRs (76%) were found in the coding region. All genomes had a higher SSR density in non-coding as compared to the coding region. There are fifteen genomes which have at least one gene with no SSR. A total of 41 cSSRs with incidence across minimum of two virophages was observed. There were 12 cSSRs which had multiple presence within the same genome. The heat map of the genomes on one hand corroborates the phylogenetic tree with similar sequences (PSAV2, PSAV5, PSAV6, PSAV17 and PSAV18) being positioned together in the phylogenetic analysis while on the other hand it also highlights the diversity of the studied sequences. The conservation of cSSRs across multiple virophages highlights their potential as biomarkers.

Electromagnetically induced absorption in Rb atoms with circular polarization of laser beams: Effects of neighboring transitions.

We have studied the effects of neighboring transitions on electromagnetically induced absorption (EIA) and electromagnetically induced transparency (EIT) in the D2 transition line in Rb atoms with respect to the same circular polarization configurations ( σ + - σ + ) of coupling and probe lasers. Spectra for the open F g = 2 → F e = 2 transition of 87Rb and the F g = 3 → F e = 2 and 3 transitions of 85Rb exhibit EIA, resulting from the neighboring effect of F g = 2 → F e = 3 and F g = 3 → F e = 4 transitions, respectively. In contrast, EIT is observed for the open F g = 2 → F e = 1 transition of 87Rb, which indicates greater hyperfine energy splittings of 87Rb and weaker neighboring effects than those of 85Rb. Based on the theoretical results, we can confirm that EIT in the case of F g > F e for 87Rb for the same circular polarization configuration transforms into EIA due to the magnitude of the neighboring effects with a decrease in the hyperfine energy splittings.

Development of multivariate classification models for the diagnosis of dengue virus infection.

The dengue virus (DENV) infection is a worldwide cause of serious illness and death. Early and efficient prediction of disease may help in proper medical management to control disease. Keeping this in view, multivariate classification models by combining with Raman spectroscopy have been developed for the diagnosis of DENV infection in human blood sera. For study design, a statistical analysis is performed to select the sample size for training of models. Total 1240 Raman spectra have been acquired from 39 DENV infected and 23 healthy sera samples. Prior to model development, Raman spectra were examined using ANOVA test for significant differences present in the intensities of newly appeared Raman bands at 622, 645, 700, 746, 800, 814, 873, 890, 948, 1002, 1018, 1080, 1235, 1250, 1272, 1386, 1404, 1446, 1609 and 1645 cm-1. The significant differences and characteristic patterns of Raman bands induced by disease played decisive role and are exploited for development of multivariate model. Classification models are developed by utilizing principal component analysis (PCA) to extract discriminant features from multidimensional Raman spectral dataset and followed by support vector machines (SVM) with Polynomial of 5, RBF, and liner kernels. The proposed model for this study is built using 10-fold cross validation technique and evaluated on independent dataset to demonstrate its robustness. PCA-SVM (poly-5) model successfully yielded high diagnostic accuracy of 99.52%, sensitivity of 99.75%, specificity of 99.09% for classification of unknown suspected samples. For comparison, PCA discriminant analysis (PCA-DA), partial least squares regression (PLSR) are PLS-DA have been compared. It is found that PCA-SVM (poly-5) approach is more effective and robust compared to other state-of-the-art approaches and it can be used for clinical prediction of DENV infection in human blood sera.

Analysis of the Compositional Features and Codon Usage Pattern of Genes Involved in Human Autophagy.

Autophagy plays an intricate role in paradigmatic human pathologies such as cancer, and neurodegenerative, cardiovascular, and autoimmune disorders. Autophagy regulation is performed by a set of autophagy-related (ATG) genes, first recognized in yeast genome and subsequently identified in other species, including humans. Several other genes have been identified to be involved in the process of autophagy either directly or indirectly. Studying the codon usage bias (CUB) of genes is crucial for understanding their genome biology and molecular evolution. Here, we examined the usage pattern of nucleotide and synonymous codons and the influence of evolutionary forces in genes involved in human autophagy. The coding sequences (CDS) of the protein coding human autophagy genes were retrieved from the NCBI nucleotide database and analyzed using various web tools and software to understand their nucleotide composition and codon usage pattern. The effective number of codons (ENC) in all genes involved in human autophagy ranges between 33.26 and 54.6 with a mean value of 45.05, indicating an overall low CUB. The nucleotide composition analysis of the autophagy genes revealed that the genes were marginally rich in GC content that significantly influenced the codon usage pattern. The relative synonymous codon usage (RSCU) revealed 3 over-represented and 10 under-represented codons. Both natural selection and mutational pressure were the key forces influencing the codon usage pattern of the genes involved in human autophagy.

Common causes of hearing impairment among children less than 8 years of age in District Bahawalpur, Punjab Pakistan.

OBJECTIVE: To find out the common causes of hearing loss among young children, and to compare the causes along gender lines. Method: The cross-sectional study was conducted from September 2020 to March 2021 at two government hospitals and one private clinic in Bahawalpur, Pakistan, and comprised children of either gender aged <8 years with complaint of decreased hearing. They were examined and tested for hearing impairment. Data was analysed using SPSS 22. RESULTS: Of the 328 subjects, 164(50%) each were girls and boys. The overall mean age was 4.20±2.08 years. Sensorineural hearing loss was found in 178(54.3%), cases, conductive in 118(36%) and mixed type in 32(9.8%). Otitis media with effusion 89(27.1%) was the commonest cause of hearing impairment, followed by consanguinity 73(22.3%). There was no significant difference between the male and female children (p>0.05). CONCLUSIONS: Otitis media with effusion was the commonest cause of hearing impairment, followed by consanguinity. Gender had no significant role to play in this regard.

Diagnosis of dengue virus infection using spectroscopic images and deep learning.

Dengue virus (DENV) infection is one of the major health issues and a substantial epidemic infectious human disease. More than two billion humans are living in dengue susceptible regions with annual infection mortality rate is about 5%-20%. At initial stages, it is difficult to differentiate dengue virus symptoms with other similar diseases. The main objective of this research is to diagnose dengue virus infection in human blood sera for better treatment and rehabilitation process. A novel and robust approach is proposed based on Raman spectroscopy and deep learning. In this regard, the ResNet101 deep learning model is modified by exploiting transfer learning (TL) concept on Raman spectroscopic data of human blood sera. Sample size was selected using standard statistical tests. The proposed model is evaluated on 2,000 Raman spectra images in which 1,200 are DENV-infected of human blood sera samples, and 800 are healthy ones. It offers 96.0% accuracy on testing data for DENV infection diagnosis. Moreover, the developed approach demonstrated minimum improvement of 6.0% and 7.0% in terms of AUC and Kappa index respectively over the other state-of-the-art techniques. The developed model offers superior performance to capture minute Raman spectral variations due to the better residual learning capability and generalization ability compared to others deep learning models. The developed model revealed that it might be applied for diagnosis of DENV infection to save precious human lives.

Analyzing the interaction of human ACE2 and RBD of spike protein of SARS-CoV-2 in perspective of Omicron variant.

The newly identified Omicron (B.1.1.529) variant of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has steered concerns across the world due to the possession of a large number of mutations leading to high infectivity and vaccine escape potential. The Omicron variant houses 32 mutations in spike (S) protein alone. The viral infectivity is determined mainly by the ability of S protein Receptor Binding Domain (RBD) to bind to the human Angiotensin I Converting Enzyme 2 (hACE2) receptor. In this paper, the interaction of the RBDs of SARS-CoV-2 variants with hACE2 was analyzed by using protein-protein docking and compared with the novel Omicron variant. Our findings reveal that the Omicron RBD interacts strongly with hACE2 receptor via unique amino acid residues as compared to the Wuhan and many other variants. However, the interacting residues of RBD are found to be the same in Lamda (C.37) variant. This unique binding of Omicron RBD with hACE2 suggests an increased potential of infectivity and vaccine evasion potential of the new variant. The evolutionary drive of the SARS-CoV-2 may not be exclusively driven by RBD variants but surely provides for the platform for emergence of new variants.

Vitamin C supplementation ameliorates liver function profile and antiviral treatment response in Hepatitis C patients.

An imbalance between oxidative stress and antioxidative defence mediates a variety of diseases pathogenesis. The present study aims to assess the possible outcome of supplementation of oral vitamin-C (VC), an antioxidant, in Viral Hepatitis C (HCV) treatment as an adjuvant therapy. 200 HCV-patients were selected, 100 were given Vitamin-C (1000 mg/day) along with anti HCV treatment (sofosbuvir plus daclatasvir) while the other 100 took only anti-HCV treatment for 4weeks. The serum ascorbic acid (Vitamin-C) levels and functions of the liver were tested before and after the VC supplementation. HCV patients with relatively low serum ascorbic acid showed significant improvement after the intake of vitamin C. After 4 weeks of treatment, AST, ALP, albumin, and total, direct and indirect bilirubin were improved significantly in the VC group; whereas only ALT and indirect bilirubin were improved in both groups when associated with the control subjects. Comparing the two treatment groups at 4weeks; more effective and significant improvement was observed in ALT (p<0.01), AST (p<0.001), direct (p<0.01) and indirect bilirubin (p<0.001), total proteins (p<0.001) and albumin (p<0.05) in patients with VC supplementation on anti-viral treatment compared to only anti-viral treatment group. Thus, VC supplementation improves the antiviral therapy outcome by bestowing a beneficial effect in minimizing liver damage in HCV cases.

Assessment of simple sequence repeats signature in hepatitis E virus (HEV) genomes.

BACKGROUND: Hepatitis E virus (HEV) is small (27-34 nm diameter) non-enveloped with positive sense ssRNA genome. Microsatellites or simple sequence repeats (SSR) are short tandem repeat sequences present across coding and non-coding regions of both prokaryotes and eukaryotes. They are involved with genome function and evolution at multiple levels. RESULTS: The complete genome sequences of 22 HEV genomes of the family Hepeviridae and genus Orthohepevirus (21 species) and Piscihepevirus (1 species) were extracted from NCBI database ( http://www.ncbi.nlm.nih.gov/ ). The extraction of microsatellites was done using Imperfect Microsatellite Extractor (IMEx) in 'Advance-Mode'. The average genome size of the studied HEV genomes was 7003nt and it ranged from 6649nt (HEV11) to 7310nt (HEV22). The average GC content of the genomes was ~ 55%. A total of 519 SSRs and 21 cSSRS were extracted from the HEV genomes with an average incidence of 24 per genome ranging from 14 (HEV13) to 34 (HEV19). The cSSR incidence ranged from 0 (eight species) to 4 (HEV19). The genomes with no cSSR incidence had an SSR incidence range from 14 to 28. There were just four hexa-nucleotide repeat motifs and 5 penta-nucleotide repeat motifs observed. The most prevalent mono-, di-, and tri-nucleotide repeat motifs were "C", "GT/TG", and "GAC/CTG" respectively. The studied genomes had a minimum of ~ 90% incident SSRs present in the coding regions. Viruses with same or similar hosts are placed together on the phylogenetic tree implicating viral host being one of the driving forces for evolution. Conclusions Host range in viruses is being decided by multiple factors aided by the unique genome SSR signature and genomes of varied compositions need to be analyzed to forge a widely acceptable rule for predicting the same.