Gene biomarkers and classifiers for various subtypes of HTLV-1-caused ATLL cancer identified by a combination of differential gene co­expression and support vector machine algorithms.

Adult T-cell leukemia/lymphoma (ATLL) is pathogen-caused cancer that is progressed after the infection by human T-cell leukemia virus type 1. Four significant subtypes comprising acute, lymphoma, chronic, and smoldering have been identified for this cancer. However, there are no trustworthy prognostic biomarkers for these subtypes. We utilized a combination of two powerful network-based and machine-learning algorithms including differential co-expressed genes (DiffCoEx) and support vector machine-recursive feature elimination with cross-validation (SVM-RFECV) methods to categorize disparate ATLL subtypes from asymptomatic carriers (ACs). The results disclosed the significant involvement of CBX6, CNKSR1, and MAX in chronic, MYH10 and P2RY1 in acute, C22orf46 and HNRNPA0 in smoldering subtypes. These genes also can classify each ATLL subtype from AC carriers. The integration of the results of two powerful algorithms led to the identification of reliable gene classifiers and biomarkers for diverse ATLL subtypes.

Transcriptomic analysis of human cytomegalovirus to survey the indirect effects on renal transplant recipients.

Post-transplant human cytomegalovirus (HCMV) viremia has been linked to adverse "indirect effects" among transplant patients. HCMV-created immunomodulatory mechanisms could be associated with the indirect effects. OBJECTIVE: In the present study, the RNA-Seq whole transcriptome of renal transplant (RT) patients was analyzed to seek the underlying pathobiologic pathways associated with the long-term indirect effects of HCMV. METHODS: To investigate the activated biological pathways in HCMV infection, total RNA was extracted from PBMCs of 2 RT patients with active HCMV and 2 RT patients without infection and then were sequenced using RNA-Seq. The resulted raw data were analyzed by conventional RNA-Seq software to determine the Differentially Expressed Genes (DEGs). Afterward, Gene Ontology (GO) and pathway enrichment analyses were conducted to determine the enriched pathways and biological processes by DEGs. Eventually, the relative expressions of some significant genes were validated in the twenty external RT patients. RESULT: The analysis of RNA-Seq data related to RT patients with HCMV active viremia led to the identification of 140 up-regulated and 100 down-regulated DEGs. KEGG pathway analysis revealed the enrichment of DEGs in IL18 signaling, AGE-RAGE signaling pathway in diabetic complications, signaling by GPCR, Platelet activation, signaling and aggregation, Estrogen signaling pathway and signaling by Wnt due to HCMV infection. The expression levels of six genes involved in enriched pathways including F3, PTX3, ADRA2B, GNG11, GP9, HBEGF were then verified using RT-qPCR. The results were in consistent with RNA-Seq resultsoutcomes. CONCLUSION: This study specifies some pathobiological pathways which are activated in HCMV active infection and could be linked to the adverse indirect effects caused by HCMV infection in transplant patients.

Identification of novel miRNAs potentially involved in the pathogenesis of adult T-cell leukemia/lymphoma using WGCNA followed by RT-qPCR test of hub genes.

BACKGROUND: Adult T-cell Lymphoma/Leukemia (ATLL) is characterized by the malignant proliferation of T-cells in Human T-Lymphotropic Virus Type 1 and a high mortality rate. Considering the emerging roles of microRNAs (miRNAs) in various malignancies, the analysis of high-throughput miRNA data employing computational algorithms helps to identify potential biomarkers. METHODS: Weighted gene co-expression network analysis was utilized to analyze miRNA microarray data from ATLL and healthy uninfected samples. To identify miRNAs involved in the progression of ATLL, module preservation analysis was used. Subsequently, based on the target genes of the identified miRNAs, the STRING database was employed to construct protein-protein interaction networks (PPIN). Real-time quantitative PCR was also performed to validate the expression of identified hub genes in the PPIN network. RESULTS: After constructing co-expression modules and then performing module preservation analysis, four out of 15 modules were determined as ATLL-specific modules. Next, the hub miRNA including hsa-miR-18a-3p, has-miR-187-5p, hsa-miR-196a-3p, and hsa-miR-346 were found as hub miRNAs. The protein-protein interaction networks were constructed for the target genes of each hub miRNA and hub genes were identified. Among them, UBB, RPS15A, and KMT2D were validated by Reverse-transcriptase PCR in ATLL patients. CONCLUSION: The results of the network analysis of miRNAs and their target genes revealed the major players in the pathogenesis of ATLL. Further studies are required to confirm the role of these molecular factors and to discover their potential benefits as treatment targets and diagnostic biomarkers.

Exploration of blood-derived coding and non-coding RNA diagnostic immunological panels for COVID-19 through a co-expressed-based machine learning procedure.

Severe acute respiratory syndrome coronavirus 2 (SARS- CoV-2) is the causative virus of the pandemic coronavirus disease 2019 (COVID-19). Evaluating the immunological factors and other implicated processes underlying the progression of COVID-19 is essential for the recognition and then the design of efficacious therapies. Therefore, we analyzed RNAseq data obtained from PBMCs of the COVID-19 patients to explore coding and non-coding RNA diagnostic immunological panels. For this purpose, we integrated multiple RNAseq data and analyzed them overall as well as by considering the state of disease including severe and non-severe conditions. Afterward, we utilized a co-expressed-based machine learning procedure comprising weighted-gene co-expression analysis and differential expression gene as filter phase and recursive feature elimination-support vector machine as wrapper phase. This procedure led to the identification of two modules containing 5 and 84 genes which are mostly involved in cell dysregulation and innate immune suppression, respectively. Moreover, the role of vitamin D in regulating some classifiers was highlighted. Further analysis disclosed the role of discriminant miRNAs including miR-197-3p, miR-150-5p, miR-340-5p, miR-122-5p, miR-1307-3p, miR-34a-5p, miR-98-5p and their target genes comprising GAN, VWC2, TNFRSF6B, and CHST3 in the metabolic pathways. These classifiers differentiate the final fate of infection toward severe or non-severe COVID-19. The identified classifier genes and miRNAs may help in the proper design of therapeutic procedures considering their involvement in the immune and metabolic pathways.

Signaling factors potentially associated to the pathogenesis of Adult T-cell leukemia /lymphoma: A network-analysis and novel findings assessment.

Adult T-cell leukemia/lymphoma (ATLL) is a human T-cell leukemia virus (HTLV) type 1-associated disease of TCD4+ cell transformation. Despite extensive studies on ATLL development and progression, the fundamental processes of HTLV-1 oncogenicity are yet to be understood. This study aimed to integrate high-throughput microarray datasets to find novel genes involved in the mechanism of ATLL progression. For this purpose, five microarray datasets were downloaded from the Gene Expression Omnibus database and then profoundly analyzed. Differentially expressed genes and miRNAs were determined using the MetaDE package in the R software and the GEO2R web tool. The STRING database was utilized to construct the protein-protein interaction network and explore hub genes. Gene ontology and pathway enrichment analysis were carried out by employing the EnrichR web tool. Furthermore, flow cytometry was employed to assess the CD4/CD8 ratio, and quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was used to confirm the high-throughput data analysis results. Four miRNAs, including hsa-mir-146, hsa-mir-451, hsa-mir-31, and hsa-mir-125, were among the statistically significant differentially expressed miRNAs between healthy individuals and ATLL patients. Moreover, 924 differentially expressed genes were identified between normal and ATLL samples. Further network analysis highlighted 59 hub genes mainly regulating pathways implicated in viral interferences, immunological processes, cancer, and apoptosis pathways. Among the identified hub genes, RhoA and PRKACB were most considerable in the high-throughput analysis and were further validated by qRT-PCR. The RhoA and PRKACB expression were significantly down-regulated in ATLL patients compared to asymptomatic carriers (p<0.0001 and p=0.004) and healthy subjects (p=0.043 and p=0.002). Therefore, these corresponding miRNAs and proteins could be targeted for diagnosis purposes and designing effective treatments.

Integration of gene co-expression analysis and multi-class SVM specifies the functional players involved in determining the fate of HTLV-1 infection toward the development of cancer (ATLL) or neurological disorder (HAM/TSP).

Human T-cell Leukemia Virus type-1 (HTLV-1) is an oncovirus that may cause two main life-threatening diseases including a cancer type named Adult T-cell Leukemia/Lymphoma (ATLL) and a neurological and immune disturbance known as HTLV-1 Associated Myelopathy/Tropical Spastic Paraparesis (HAM/TSP). However, a large number of the infected subjects remain as asymptomatic carriers (ACs). There is no comprehensive study that determines which dysregulated genes differentiate the pathogenesis routes toward ATLL or HAM/TSP. Therefore, two main algorithms including weighted gene co-expression analysis (WGCNA) and multi-class support vector machines (SVM) were utilized to find major gene players in each condition. WGCNA was used to find the highly co-regulated genes and multi-class SVM was employed to identify the most important classifier genes. The identified modules from WGCNA were validated in the external datasets. Furthermore, to find specific modules for ATLL and HAM/TSP, the non-preserved modules in another condition were found. In the next step, a model was constructed by multi-class SVM. The results revealed 467, 3249, and 716 classifiers for ACs, ATLL, and HAM/TSP, respectively. Eventually, the common genes between the WGCNA results and classifier genes resulted from multi-class SVM that also determined as differentially expressed genes, were identified. Through these step-wise analyses, PAIP1, BCAS2, COPS2, CTNNB1, FASLG, GTPBP1, HNRNPA1, RBBP6, TOP1, SLC9A1, JMY, PABPC3, and PBX1 were found as the possible critical genes involved in the progression of ATLL. Moreover, FBXO9, ZNF526, ERCC8, WDR5, and XRCC3 were identified as the conceivable major involved genes in the development of HAM/TSP. These genes can be proposed as specific biomarker candidates and therapeutic targets for each disease.

Decoding pathogenesis factors involved in the progression of ATLL or HAM/TSP after infection by HTLV-1 through a systems virology study.

BACKGROUND: Human T-cell Leukemia Virus type-1 (HTLV-1) is a retrovirus that causes two diseases including Adult T-cell Leukemia/Lymphoma (ATLL cancer) and HTLV-1 Associated Myelopathy/Tropical Spastic Paraparesis (HAM/TSP, a neurodegenerative disease) after a long latency period as an asymptomatic carrier (AC). There are no obvious explanations about how each of the mentioned diseases develops in the AC carriers. Finding the discriminative molecular factors and pathways may clarify the destiny of the infection. METHODS: To shed light on the involved molecular players and activated pathways in each state, differentially co-expressed modules (DiffCoEx) algorithm was employed to identify the highly correlated genes which were co-expressed differently between normal and ACs, ACs and ATLL, as well as ACs and HAM/TSP samples. Through differential pathway analysis, the dysregulated pathways and the specific disease-genes-pathways were figured out. Moreover, the common genes between the member of DiffCoEx and differentially expressed genes were found and the specific genes in ATLL and HAM/TSP were introduced as possible biomarkers. RESULTS: The dysregulated genes in the ATLL were mostly enriched in immune and cancer-related pathways while the ones in the HAM/TSP were enriched in immune, inflammation, and neurological pathways. The differential pathway analysis clarified the differences between the gene players in the common activated pathways. Eventually, the final analysis revealed the involvement of specific dysregulated genes including KIRREL2, RAB36, and KANK1 in HAM/TSP as well as LTB4R2, HCN4, FZD9, GRIK5, CREB3L4, TACR2, FRMD1, LHB, FGF3, TEAD3, GRIN2D, GNRH2, PRLH, GPR156, and CRHR2 in ATLL. CONCLUSION: The identified potential prognostic biomarkers and therapeutic targets are proposed as the most important platers in developing ATLL or HAM/TSP. Moreover, the proposed signaling network clarifies the differences between the functional players in the activated pathways in ACs, ATLL, and HAM/TSP.

High-performance porphyrin-like graphene quantum dots for immuno-sensing of Salmonella typhi.

The extraordinary optical properties of porphyrins have inspired their applications in various fields. Herein, we introduce iron porphyrin bio-mimicked graphene quantum dots (Fe-N-GQDs) as a novel paramagnetic and fluorescent label. The Fe-N-GQD was prepared by the mechanochemical mixing of Fe, N, and C sources followed by pyrolysis at high-temperature and next, the solvothermal treatment was performed. The Fe-N sites in graphene matrix, the structural alterations during the solvothermal treatment, the optical properties, and paramagnetic behaviour were studied using FTIR, Raman and X-ray spectroscopies, and Vibrating sample magnetometer. The structural studies revealed that under solvothermal condition, Fe-N doped graphene sheets cut into ultra-small Fe-N-GQDs containing well-dispersed particles with an average diameter of about 2.5 nm. As a result of Fe-N doping, the photoluminescence quantum yield was enhanced to 86% and strong paramagnetic behaviour was observed. Due to the rich oxygen-containing groups at Fe-N-GQDs surface, it has proper sites for bio-conjugation. The bioconjugated Fe-N-GQDs serve as donors in a prominent fluorescence resonance energy transfer system, while graphene oxide acts as an acceptor. The proposed immunosensor was successfully applied for the detection of Salmonella Typhi Vi antigen in real human serum in the concentration range from 1 pg/mL to 1 µg/mL with the detection limit of 1 pg/mL.

HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) versus adult T-cell leukemia/lymphoma (ATLL).

OBJECTIVES: Human T cell leukemia virus-1 (HTLV-1) infection may lead to one or both diseases including HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) or adult T cell leukemia lymphoma (ATLL). The complete interactions of the virus with host cells in both diseases is yet to be determined. This study aims to construct an interaction network for distinct signaling pathways in these diseases based on finding differentially expressed genes (DEGs) between HAM/TSP and ATLL. RESULTS: We identified 57 hub genes with higher criteria scores in the primary protein-protein interaction network (PPIN). The ontology-based enrichment analysis revealed following important terms: positive regulation of transcription from RNA polymerase II promoter, positive regulation of transcription from RNA polymerase II promoter involved in meiotic cell cycle and positive regulation of transcription from RNA polymerase II promoter by histone modification. The upregulated genes TNF, PIK3R1, HGF, NFKBIA, CTNNB1, ESR1, SMAD2, PPARG and downregulated genes VEGFA, TLR2, STAT3, TLR4, TP53, CHUK, SERPINE1, CREB1 and BRCA1 were commonly observed in all the three enriched terms in HAM/TSP vs. ATLL. The constructed interaction network was then visualized inside a mirrored map of signaling pathways for ATLL and HAM/TSP, so that the functions of hub genes were specified in both diseases.

Identification of dysregulated pathways underlying HTLV-1-associated myelopathy/tropical spastic paraparesis through co-expression network analysis.

Human T cell lymphotropic virus-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) is a pathogen-caused disease which is associated with the progressive neurological disorder. HAM/TSP affects the expression level of several proteins and dysregulates some biological pathways. To identify the interaction patterns among expressed genes in HAM/TSP patients, weighted gene co-expression network analysis (WGCNA) was applied. Three microarray datasets regarding HAM/TSP were merged, and the co-expression network was constructed among genes. A total of 38 modules were identified. Three preserved modules in HAM/TSP in comparison to the healthy subjects which also had the most connected proteins and enriched in the biological pathways were selected. These modules were enriched in pathways related to immune systems, cell cycle, viral infection, and neuronal systems. Moreover, the involvement of novel immunological-related proteins including C1QB, GBP5, PSME1, SERPING1, and UBE2C; neurological-related proteins including TUBA4A, TUBB8, and TP63; and also proteins including TRPC6, PRKG2, OPRD1, PRKACA, and TUBB4A involved in the cGMP-PKG signaling pathway, thyroid hormone synthesis, and recruitment of mitotic centrosome proteins and complexes were found. Therefore, tracing these proteins and the identified modules can shed light on the pathogenesis mechanism of HAM/TSP and help to find potential therapeutic targets. However, further experimental validation should be performed to confirm the proposed functional players.

Frequent detection of enterovirus D68 and rhinovirus type C in children with acute respiratory infections.

This study aimed to evaluate the prevalence of human rhinoviruses (HRVs) and the emergence of enterovirus D68 (EV-D68) in children. A total of 322 nasopharyngeal swab samples were provided from children with an initial diagnosis of upper and lower respiratory tract infections. A total of 34 and 70 cases were positive for EV-D68 and HRV, respectively. The phylogenetic analysis revealed that the clades A and B are the prevalent genotypes for EV-D68 and the HRV-positive samples belong to three types including HRV-A, HRV-B, and HRV-C. The results showed that EV-D68 and HRV-C are circulating in Iran especially in the winter.

A multimodal deep learning-based drug repurposing approach for treatment of COVID-19.

Recently, various computational methods have been proposed to find new therapeutic applications of the existing drugs. The Multimodal Restricted Boltzmann Machine approach (MM-RBM), which has the capability to connect the information about the multiple modalities, can be applied to the problem of drug repurposing. The present study utilized MM-RBM to combine two types of data, including the chemical structures data of small molecules and differentially expressed genes as well as small molecules perturbations. In the proposed method, two separate RBMs were applied to find out the features and the specific probability distribution of each datum (modality). Besides, RBM was used to integrate the discovered features, resulting in the identification of the probability distribution of the combined data. The results demonstrated the significance of the clusters acquired by our model. These clusters were used to discover the medicines which were remarkably similar to the proposed medications to treat COVID-19. Moreover, the chemical structures of some small molecules as well as dysregulated genes' effect led us to suggest using these molecules to treat COVID-19. The results also showed that the proposed method might prove useful in detecting the highly promising remedies for COVID-19 with minimum side effects. All the source codes are accessible using https://github.com/LBBSoft/Multimodal-Drug-Repurposing.git.

Identifying novel biomarkers of the pediatric influenza infection by weighted co-expression network analysis.

BACKGROUND: Despite the high yearly prevalence of Influenza, the pathogenesis mechanism and involved genes have not been fully known. Finding the patterns and mapping the complex interactions between different genes help us to find the possible biomarkers and treatment targets. METHODS: Herein, weighted gene co-expression network analysis (WGCNA) was employed to construct a co-expression network among genes identified by microarray analysis of the pediatric influenza-infected samples. RESULTS: Three of the 38 modules were found as the most related modules to influenza infection. At a functional level, we found that the genes in these modules regulate the immune responses, protein targeting, and defense to virus. Moreover, the analysis of differentially expressed genes disclosed 719 DEGs between the normal and infected subjects. The comprehensive investigation of genes in the module involved in immune system and viral defense (yellow module) revealed that SP110, HERC5, SAMD9L, RTP4, C19orf66, HELZ2, EPSTI1, and PHF11 which were also identified as DEGs (except C19orf66) have the potential to be as the biomarkers and also drug targeting for the treatment of pediatric influenza. CONCLUSIONS: The WGCN analysis revealed co-expressed genes which were involved in the innate immune system and defense to virus. The differentially expressed genes in the identified modules can be considered for designing drug targets. Moreover, modules can help to find pathogenesis routes in the future.

Reconnaissance of the candidate genes involved in the pathogenesis of human immunodeficiency virus and targeted by antiretroviral therapy.

The expression levels of many genes change after treatment of human immunodeficiency virus (HIV)-infected subjects by antiretroviral drugs. High-throughput analysis of tremendous datasets led to the discovery of genes that are implicated in the treatment pathways. In this study, we performed a gene-enrichment analysis after determining the differentially expressed genes (DEGs) between untreated HIV-positive and HIV-negative subjects and also between treated HIV-positive subjects with antiretroviral therapy (ART; who receiving nucleoside reverse transcriptase inhibitor-based ART) and untreated HIV-positive cases in the peripheral blood mononuclear cells (PBMCs), adipose, and muscle tissues. In sum, the genes that activate inflammatory, immune response, proliferation, metabolism, and viral involvement pathways have different expression patterns in the untreated HIV-positive subjects and treated HIV-positive cases. Moreover, the expression levels of the genes including ACLY, ALDH18A1, HADHA, and YARS in the PBMCs tissue and HBEGF, PKN3, DEGS2, and EDN3 in the fat tissue were found to be different in the HIV-infected patients, which can be considered as new biomarkers for HIV infection.

An insight to HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) pathogenesis; evidence from high-throughput data integration and meta-analysis.

BACKGROUND: Human T-lymphotropic virus 1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) is a progressive disease of the central nervous system that significantly affected spinal cord, nevertheless, the pathogenesis pathway and reliable biomarkers have not been well determined. This study aimed to employ high throughput meta-analysis to find major genes that are possibly involved in the pathogenesis of HAM/TSP. RESULTS: High-throughput statistical analyses identified 832, 49, and 22 differentially expressed genes for normal vs. ACs, normal vs. HAM/TSP, and ACs vs. HAM/TSP groups, respectively. The protein-protein interactions between DEGs were identified in STRING and further network analyses highlighted 24 and 6 hub genes for normal vs. HAM/TSP and ACs vs. HAM/TSP groups, respectively. Moreover, four biologically meaningful modules including 251 genes were identified for normal vs. ACs. Biological network analyses indicated the involvement of hub genes in many vital pathways like JAK-STAT signaling pathway, interferon, Interleukins, and immune pathways in the normal vs. HAM/TSP group and Metabolism of RNA, Viral mRNA Translation, Human T cell leukemia virus 1 infection, and Cell cycle in the normal vs. ACs group. Moreover, three major genes including STAT1, TAP1, and PSMB8 were identified by network analysis. Real-time PCR revealed the meaningful down-regulation of STAT1 in HAM/TSP samples than AC and normal samples (P = 0.01 and P = 0.02, respectively), up-regulation of PSMB8 in HAM/TSP samples than AC and normal samples (P = 0.04 and P = 0.01, respectively), and down-regulation of TAP1 in HAM/TSP samples than those in AC and normal samples (P = 0.008 and P = 0.02, respectively). No significant difference was found among three groups in terms of the percentage of T helper and cytotoxic T lymphocytes (P = 0.55 and P = 0.12). CONCLUSIONS: High-throughput data integration disclosed novel hub genes involved in important pathways in virus infection and immune systems. The comprehensive studies are needed to improve our knowledge about the pathogenesis pathways and also biomarkers of complex diseases.

Integrational analysis of miRNAs data sets as a plausible missing linker between Epstein-Barr virus and vitamin D in relapsing remitting MS patients.

Given the multifactorial state of autoimmune complex diseases such as multiple sclerosis (MS), it is not clear if different risk factors act jointly or independently. Despite intensive studies investigating multi aspects of MS risk factors, findings with regards to potential biomarkers that may link these risk factors remained largely inconclusive. System biology or data integration utilizes different validated datasets to extract meaningful information and map the plausible biological pathways and networks. As such, we integrated eight transcriptome datasets to find the differentially expressed miRNAs in peripheral blood (PB) between relapsing remitting MS patients (RRMS) and normal group. After identification the targeted genes of miRNAs, the hub genes were used to construct the underlying protein-protein interaction network and signaling pathways. As results, 9 miRNAs were best exemplified by significant dysregulation including hsa-mir-15a, hsa-mir-484, hsa-mir-30d, hsa-mir-145, hsa-mir-363, has-let-7e, hsa-mir-30a, hsa-let-7b, and hsa-mir-146a. System biology analysis of miRNAs in PB of RRMS patients clearly indicates the involvement of miRNAs in many vital pathways and highlighted the possibility of an association between miRNAs with EBV and vitamin D in MS pathogenesis. Described novel pathways and genes related to miRNAs such as Transient receptor potential channels and Acid sphingomyelinase may provide a potential target for therapeutic approaches although further functional studies are warranted to test these candidates.

A genosensor for detection of HTLV-I based on photoluminescence quenching of fluorescent carbon dots in presence of iron magnetic nanoparticle-capped Au.

Carbon dots and Fe3O4@Au were synthesized to develop a new biosensor to detect DNA target. We investigated the photoluminescence property of carbon dots (CDs) in the presence of Fe3O4-capped Au (Fe3O4@Au). Firstly, we designed two dedicated probes for unique long sequence region of human T-lymphotropic virus type 1 genome. One of the probes was covalently bound to the CDs. In the absence of target, CDs-probe was adsorbed on the surface of Fe3O4@Au through two possible mechanisms, leading to quenching the fluorescence emission of CDs. The fluorescence emission of CDs was recovered in the presence of target since double-stranded DNA cannot adsorb on the Fe3O4@Au. Also, Fe3O4@Au can adsorb the unhybridized oligonucleotides and improves the accuracy of detection. The specificity of the proposed biosensor was confirmed by BLAST search and assessed by exposing the biosensor to other virus targets. The experimental detection limit of the biosensor was below 10 nM with linear range from 10 to 320 nM.

Molecular evolution and phylodynamics of hepatitis B virus infection circulating in Iran.

Previous local and national Iranian publications indicate that all Iranian hepatitis B virus (HBV) strains belong to HBV genotype D. The aim of this study was to analyze the evolutionary history of HBV infection in Iran for the first time, based on an intensive phylodynamic study. The evolutionary parameters, time to most recent common ancestor (tMRCA), and the population dynamics of infections were investigated using the Bayesian Monte Carlo Markov chain (BMCMC). The effective sample size (ESS) and sampling convergence were then monitored. After sampling from the posterior distribution of the nucleotide substitution rate and other evolutionary parameters, the point estimations (median) of these parameters were obtained. All Iranian HBV isolates were of genotype D, sub-type ayw2. The origin of HBV is regarded as having evolved first on the eastern border, before moving westward, where Isfahan province then hosted the virus. Afterwards, the virus moved to the south and west of the country. The tMRCA of HBV in Iran was estimated to be around 1894, with a 95% credible interval between the years 1701 and 1957. The effective number of infections increased exponentially from around 1925 to 1960. Conversely, from around 1992 onwards, the effective number of HBV infections has decreased at a very high rate. Phylodynamic inference clearly demonstrates a unique homogenous pattern of HBV genotype D compatible with a steady configuration of the decreased effective number of infections in the population in recent years, possibly due to the implementation of blood donation screening and vaccination programs. Adequate molecular epidemiology databases for HBV are crucial for infection prevention and treatment programs.

Prevalence of human influenza virus in Iran: Evidence from a systematic review and meta-analysis.

This systematic review and meta-analysis was conducted to consolidate the information on the prevalence of the human influenza virus, including H1N1 and H3N2 as well as B-type influenza across Iran from 2000 to December 2016. The literature search was based on keywords including "influenza and Iran", "human influenza", "prevalence", "relative frequency", "incidence", and "drug" in MEDLINE (PubMed), Web of Science, Scopus, ScienceDirect, the Iranian Research Institute for Information Science and Technology (IranDoc), the Regional Information Centre for Science & Technology (RICeST), and the Scientific Information Database (SID). The literature search revealed 25 prevalence and seven drug resistance studies. In order to investigate the publication bias among studies, funnel plots and Egger's test were used. Additionally, the statistical tests of I2, Chi2, and Tau2 were computed, and the results were visualized with forest plots. A high level of I2 and Chi2 were obtained among studies, which are representative of the high variation and remarkable heterogeneity between studies. This may be because of various methodologies applied in the studies such as study design, age groups, and different populations. The prevalence of influenza H1N1, H3N2, and B in Iran have not yet been well evaluated. The heterogeneity among studies reveals that more attention should be paid to considering various factors, including gender, population size, and underlying conditions.