A Second-Generation Oral SARS-CoV-2 Main Protease Inhibitor Clinical Candidate for the Treatment of COVID-19.

Despite the record-breaking discovery, development and approval of vaccines and antiviral therapeutics such as Paxlovid, coronavirus disease 2019 (COVID-19) remained the fourth leading cause of death in the world and third highest in the United States in 2022. Here, we report the discovery and characterization of PF-07817883, a second-generation, orally bioavailable, SARS-CoV-2 main protease inhibitor with improved metabolic stability versus nirmatrelvir, the antiviral component of the ritonavir-boosted therapy Paxlovid. We demonstrate the in vitro pan-human coronavirus antiviral activity and off-target selectivity profile of PF-07817883. PF-07817883 also demonstrated oral efficacy in a mouse-adapted SARS-CoV-2 model at plasma concentrations equivalent to nirmatrelvir. The preclinical in vivo pharmacokinetics and metabolism studies in human matrices are suggestive of improved oral pharmacokinetics for PF-07817883 in humans, relative to nirmatrelvir. In vitro inhibition/induction studies against major human drug metabolizing enzymes/transporters suggest a low potential for perpetrator drug-drug interactions upon single-agent use of PF-07817883.

Prediction of diabetes disease using an ensemble of machine learning multi-classifier models.

BACKGROUND AND OBJECTIVE: Diabetes is a life-threatening chronic disease with a growing global prevalence, necessitating early diagnosis and treatment to prevent severe complications. Machine learning has emerged as a promising approach for diabetes diagnosis, but challenges such as limited labeled data, frequent missing values, and dataset imbalance hinder the development of accurate prediction models. Therefore, a novel framework is required to address these challenges and improve performance. METHODS: In this study, we propose an innovative pipeline-based multi-classification framework to predict diabetes in three classes: diabetic, non-diabetic, and prediabetes, using the imbalanced Iraqi Patient Dataset of Diabetes. Our framework incorporates various pre-processing techniques, including duplicate sample removal, attribute conversion, missing value imputation, data normalization and standardization, feature selection, and k-fold cross-validation. Furthermore, we implement multiple machine learning models, such as k-NN, SVM, DT, RF, AdaBoost, and GNB, and introduce a weighted ensemble approach based on the Area Under the Receiver Operating Characteristic Curve (AUC) to address dataset imbalance. Performance optimization is achieved through grid search and Bayesian optimization for hyper-parameter tuning. RESULTS: Our proposed model outperforms other machine learning models, including k-NN, SVM, DT, RF, AdaBoost, and GNB, in predicting diabetes. The model achieves high average accuracy, precision, recall, F1-score, and AUC values of 0.9887, 0.9861, 0.9792, 0.9851, and 0.999, respectively. CONCLUSION: Our pipeline-based multi-classification framework demonstrates promising results in accurately predicting diabetes using an imbalanced dataset of Iraqi diabetic patients. The proposed framework addresses the challenges associated with limited labeled data, missing values, and dataset imbalance, leading to improved prediction performance. This study highlights the potential of machine learning techniques in diabetes diagnosis and management, and the proposed framework can serve as a valuable tool for accurate prediction and improved patient care. Further research can build upon our work to refine and optimize the framework and explore its applicability in diverse datasets and populations.

The effect of breastfeeding on children's growth indices up to 6 months: An application of multivariate t linear mixed model.

Background: This study aimed to determine the effect of breastfeeding on children's growth indices. Materials and Methods: Longitudinal data of children's growth (height, weight, and head circumference) were as a dependent variable and type of nutrition as an independent variable with using multivariate t linear mixed model. Results: The indicated that the height, weight, and head circumference of infants who were fed with breast milk showed a statistically significant difference (P < 0.05) with those of infants receiving formula. Conclusion: Exclusive feeding with breast milk, especially in the first 6 months of life, has a significant impact on the child's growth indicators compared to formula or, or a combination of both.

Improved inter-residue contact prediction via a hybrid generative model and dynamic loss function.

Protein contact maps represent spatial pairwise inter-residue interactions, providing a protein's translationally and rotationally invariant topological representation. Accurate contact map prediction has been a critical driving force for improving protein structure determination. Contact maps can also be used as a stand-alone tool for varied applications such as prediction of protein-protein interactions, structure-aware thermal stability or physicochemical properties. We develop a novel hybrid contact map prediction model, CGAN-Cmap, that uses a generative adversarial neural network embedded with a series of modified squeeze and excitation residual networks. To exploit features of different dimensions, we introduce two parallel modules. This architecture improves the prediction by increasing receptive fields, surpassing redundant features and encouraging more meaningful ones from 1D and 2D inputs. We also introduce a new custom dynamic binary cross-entropy loss function to address the input imbalance problem for highly sparse long-range contacts in proteins with insufficient homologs. We evaluate the model's performance on CASP 11, 12, 13, 14, and CAMEO test sets. CGAN-Cmap outperforms state-of-the-art models, improving precision of medium and long-range contacts by at least 3.5%. As a direct assessment between our model and AlphaFold2, the leading available protein structure prediction model, we compare extracted contact maps from AlphaFold2 and predicted contact maps from CGAN-Cmap. The results show that CGAN-Cmap has a mean precision higher by 1% compared to AlphaFold2 for most ranges of contacts. These results demonstrate an efficient approach for highly accurate contact map prediction toward accurate characterization of protein structure, properties and functions from sequence.

The Role of Deep Learning in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review.

Breast cancer is among the most common and fatal diseases for women, and no permanent treatment has been discovered. Thus, early detection is a crucial step to control and cure breast cancer that can save the lives of millions of women. For example, in 2020, more than 65% of breast cancer patients were diagnosed in an early stage of cancer, from which all survived. Although early detection is the most effective approach for cancer treatment, breast cancer screening conducted by radiologists is very expensive and time-consuming. More importantly, conventional methods of analyzing breast cancer images suffer from high false-detection rates. Different breast cancer imaging modalities are used to extract and analyze the key features affecting the diagnosis and treatment of breast cancer. These imaging modalities can be divided into subgroups such as mammograms, ultrasound, magnetic resonance imaging, histopathological images, or any combination of them. Radiologists or pathologists analyze images produced by these methods manually, which leads to an increase in the risk of wrong decisions for cancer detection. Thus, the utilization of new automatic methods to analyze all kinds of breast screening images to assist radiologists to interpret images is required. Recently, artificial intelligence (AI) has been widely utilized to automatically improve the early detection and treatment of different types of cancer, specifically breast cancer, thereby enhancing the survival chance of patients. Advances in AI algorithms, such as deep learning, and the availability of datasets obtained from various imaging modalities have opened an opportunity to surpass the limitations of current breast cancer analysis methods. In this article, we first review breast cancer imaging modalities, and their strengths and limitations. Then, we explore and summarize the most recent studies that employed AI in breast cancer detection using various breast imaging modalities. In addition, we report available datasets on the breast-cancer imaging modalities which are important in developing AI-based algorithms and training deep learning models. In conclusion, this review paper tries to provide a comprehensive resource to help researchers working in breast cancer imaging analysis.

Stability analysis with general fuzzy measure: An application to social security organizations.

An effective method for evaluating the efficiency of peer decision-making units (DMUs) is data envelope analysis (DEA). In engineering sciences and real-world management problems, uncertainty in input and output data always exists. To achieve reliable results, uncertainties must be taken into account. In this research, a General Fuzzy (GF) approach is designed to cope with uncertainty in the presence of fuzzy observations for categorizing and specifying stability radius and alterations ranges of efficient and inefficient DMUs, which is applicable to real-world decision-making problems. For this purpose, a DEA sensitivity analysis model is presented, which will be modeled by fuzzy sets. Then, by applying the General Fuzzy (GF) approach, the fuzzy DEA sensitivity analysis model is transformed into the equivalent crisp form of fuzzy chance constraints according to specific confidence levels. Finally, a numerical example and a case study of branches of the social security organization are presented to illustrate sensitivity and stability analysis in the presence of fuzzy data. The obtained results provide the input and output changes of the evaluated units according to the attitude and preference of the decision maker with different confidence levels so that the data changes in the fuzzy environment do not change the units' classification from efficient to inefficient and vice versa.

Developing a bi-objective resilience relief logistic considering operational and disruption risks: a post-earthquake case study in Iran.

Today, according to the occurrence of numerous disasters in allover over the world, designing the proper and comprehensive plan for relief logistics has received a lot of attention from crisis managers and people. Besides, considering resilience capability along with operational and disruption risks leads to the robustness of the humanitarian relief chain (HRC), and this comprehensive framework ensures the essential supplies delivery to the beneficiaries and is close to real-world problems. The resilience parameters used for the second objective are obtained by a strong Best Worst Method (BWM). Another supposition of the model is the consideration of uncertainty in all stages of the proposed problem. Moreover, the multiple disasters (sub-sequent minor post disasters) which can increase the initial demand are considered. Furthermore, the proposed model is solved using three well-known metaheuristic algorithms includes non-dominated sorting genetic algorithm (NSGA-II), network reconfiguration genetic algorithm (NRGA), and multi-objective particle swarm optimization (MOPSO), and their performance is compared by several standard multi-objective measure metrics. Finally, the obtained results show the robustness of the proposed approaches, and some directions for future researches are provided.

Integrated Network Analysis to Identify Key Modules and Potential Hub Genes Involved in Bovine Respiratory Disease: A Systems Biology Approach.

Background: Bovine respiratory disease (BRD) is the most common disease in the beef and dairy cattle industry. BRD is a multifactorial disease resulting from the interaction between environmental stressors and infectious agents. However, the molecular mechanisms underlying BRD are not fully understood yet. Therefore, this study aimed to use a systems biology approach to systematically evaluate this disorder to better understand the molecular mechanisms responsible for BRD. Methods: Previously published RNA-seq data from whole blood of 18 healthy and 25 BRD samples were downloaded from the Gene Expression Omnibus (GEO) and then analyzed. Next, two distinct methods of weighted gene coexpression network analysis (WGCNA), i.e., module-trait relationships (MTRs) and module preservation (MP) analysis were used to identify significant highly correlated modules with clinical traits of BRD and non-preserved modules between healthy and BRD samples, respectively. After identifying respective modules by the two mentioned methods of WGCNA, functional enrichment analysis was performed to extract the modules that are biologically related to BRD. Gene coexpression networks based on the hub genes from the candidate modules were then integrated with protein-protein interaction (PPI) networks to identify hub-hub genes and potential transcription factors (TFs). Results: Four significant highly correlated modules with clinical traits of BRD as well as 29 non-preserved modules were identified by MTRs and MP methods, respectively. Among them, two significant highly correlated modules (identified by MTRs) and six nonpreserved modules (identified by MP) were biologically associated with immune response, pulmonary inflammation, and pathogenesis of BRD. After aggregation of gene coexpression networks based on the hub genes with PPI networks, a total of 307 hub-hub genes were identified in the eight candidate modules. Interestingly, most of these hub-hub genes were reported to play an important role in the immune response and BRD pathogenesis. Among the eight candidate modules, the turquoise (identified by MTRs) and purple (identified by MP) modules were highly biologically enriched in BRD. Moreover, STAT1, STAT2, STAT3, IRF7, and IRF9 TFs were suggested to play an important role in the immune system during BRD by regulating the coexpressed genes of these modules. Additionally, a gene set containing several hub-hub genes was identified in the eight candidate modules, such as TLR2, TLR4, IL10, SOCS3, GZMB, ANXA1, ANXA5, PTEN, SGK1, IFI6, ISG15, MX1, MX2, OAS2, IFIH1, DDX58, DHX58, RSAD2, IFI44, IFI44L, EIF2AK2, ISG20, IFIT5, IFITM3, OAS1Y, HERC5, and PRF1, which are potentially critical during infection with agents of bovine respiratory disease complex (BRDC). Conclusion: This study not only helps us to better understand the molecular mechanisms responsible for BRD but also suggested eight candidate modules along with several promising hub-hub genes as diagnosis biomarkers and therapeutic targets for BRD.

The global prevalence of osteoporosis in the world: a comprehensive systematic review and meta-analysis.

BACKGROUND: Osteoporosis affects all sections of society, including families with people affected by osteoporosis, government agencies and medical institutes in various fields. For example, it involves the patient and his/her family members, and government agencies in terms of the cost of treatment and medical care. Providing a comprehensive picture of the prevalence of osteoporosis globally is important for health policymakers to make appropriate decisions. Therefore, this study was conducted to investigate the prevalence of osteoporosis worldwide. METHODS: A systematic review and meta-analysis were conducted in accordance with the PRISMA criteria. The PubMed, Science Direct, Web of Science, Scopus, Magiran, and Google Scholar databases were searched with no lower time limit up till 26 August 2020. The heterogeneity of the studies was measured using the I2 test, and the publication bias was assessed by the Begg and Mazumdar's test at the significance level of 0.1. RESULTS: After following the systematic review processes, 86 studies were selected for meta-analysis. The sample size of the study was 103,334,579 people in the age range of 15-105 years. Using meta-analysis, the prevalence of osteoporosis in the world was reported to be 18.3 (95% CI 16.2-20.7). Based on 70 studies and sample size of 800,457 women, and heterogenicity I2: 99.8, the prevalence of osteoporosis in women of the world was reported to be 23.1 (95% CI 19.8-26.9), while the prevalence of osteoporosis among men of the world was found to be 11.7 (95% CI 9.6-14.1 which was based on 40 studies and sample size of 453,964 men.). The highest prevalence of osteoporosis was reported in Africa with 39.5% (95% CI 22.3-59.7) and a sample size of 2989 people with the age range 18-95 years. CONCLUSION: According to the medical, economic, and social burden of osteoporosis, providing a robust and comprehensive estimate of the prevalence of osteoporosis in the world can facilitate decisions in health system planning and policymaking, including an overview of the current and outlook for the future; provide the necessary facilities for the treatment of people with osteoporosis; reduce the severe risks that lead to death by preventing fractures; and, finally, monitor the overall state of osteoporosis in the world. This study is the first to report a structured review and meta-analysis of the prevalence of osteoporosis worldwide.

A chimeric hemagglutinin-based universal influenza virus vaccine approach induces broad and long-lasting immunity in a randomized, placebo-controlled phase I trial.

Seasonal influenza viruses constantly change through antigenic drift and the emergence of pandemic influenza viruses through antigenic shift is unpredictable. Conventional influenza virus vaccines induce strain-specific neutralizing antibodies against the variable immunodominant globular head domain of the viral hemagglutinin protein. This necessitates frequent re-formulation of vaccines and handicaps pandemic preparedness. In this completed, observer-blind, randomized, placebo-controlled phase I trial (NCT03300050), safety and immunogenicity of chimeric hemagglutinin-based vaccines were tested in healthy, 18-39-year-old US adults. The study aimed to test the safety and ability of the vaccines to elicit broadly cross-reactive antibodies against the hemagglutinin stalk domain. Participants were enrolled into five groups to receive vaccinations with live-attenuated followed by AS03-adjuvanted inactivated vaccine (n = 20), live-attenuated followed by inactivated vaccine (n = 15), twice AS03-adjuvanted inactivated vaccine (n = 16) or placebo (n = 5, intranasal followed by intramuscular; n = 10, twice intramuscular) 3 months apart. Vaccination was found to be safe and induced a broad, strong, durable and functional immune response targeting the conserved, immunosubdominant stalk of the hemagglutinin. The results suggest that chimeric hemagglutinins have the potential to be developed as universal vaccines that protect broadly against influenza viruses.

Development and Assessment of a Pooled Serum as Candidate Standard to Measure Influenza A Virus Group 1 Hemagglutinin Stalk-Reactive Antibodies.

The stalk domain of the hemagglutinin has been identified as a target for induction of protective antibody responses due to its high degree of conservation among numerous influenza subtypes and strains. However, current assays to measure stalk-based immunity are not standardized. Hence, harmonization of assay readouts would help to compare experiments conducted in different laboratories and increase confidence in results. Here, serum samples from healthy individuals (n = 110) were screened using a chimeric cH6/1 hemagglutinin enzyme-linked immunosorbent assay (ELISA) that measures stalk-reactive antibodies. We identified samples with moderate to high IgG anti-stalk antibody levels. Likewise, screening of the samples using the mini-hemagglutinin (HA) headless construct #4900 and analysis of the correlation between the two assays confirmed the presence and specificity of anti-stalk antibodies. Additionally, samples were characterized by a cH6/1N5 virus-based neutralization assay, an antibody-dependent cell-mediated cytotoxicity (ADCC) assay, and competition ELISAs, using the stalk-reactive monoclonal antibodies KB2 (mouse) and CR9114 (human). A "pooled serum" (PS) consisting of a mixture of selected serum samples was generated. The PS exhibited high levels of stalk-reactive antibodies, had a cH6/1N5-based neutralization titer of 320, and contained high levels of stalk-specific antibodies with ADCC activity. The PS, along with blinded samples of varying anti-stalk antibody titers, was distributed to multiple collaborators worldwide in a pilot collaborative study. The samples were subjected to different assays available in the different laboratories, to measure either binding or functional properties of the stalk-reactive antibodies contained in the serum. Results from binding and neutralization assays were analyzed to determine whether use of the PS as a standard could lead to better agreement between laboratories. The work presented here points the way towards the development of a serum standard for antibodies to the HA stalk domain of phylogenetic group 1.

A cross-reactive mouse monoclonal antibody against rhinovirus mediates phagocytosis in vitro.

Rhinoviruses (RVs) are the main cause of the common cold worldwide. To date, more than 160 types of the virus have been recognized, categorized into three major species - A, B, and C. There are currently no approved vaccines available to prevent infection with RVs. To elicit antibodies against conserved regions located on capsid proteins of RV A viruses, mice were sequentially vaccinated with DNA plasmids encoding capsid proteins of different RV A types. After a final boost with whole virus, antibody-expressing hybridomas were generated. After isotyping, 11 monoclonal antibodies (mAbs) expressing an IgG subtype Fc-domain were selected for further expansion and purification. Three mAbs showed cross-reactivity against multiple strains of RV A viruses by ELISA, including strains A1A, A1B, A15, A16 and A49. Other mAbs had strain-specific binding patterns, with the majority of mAbs showing reactivity to RV-A15, the strain used for the final vaccination. We found that the RV-A15-specific mAbs, but not the cross-reactive mAbs, had neutralizing activity against RV-A15. An antibody dependent cellular phagocytosis (ADCP) assay revealed substantial ADCP activity for one of the cross-reactive mAbs. Epitope mapping of the neutralizing mAbs via escape mutant virus generation revealed a shared binding epitope on VP1 of RV-A15 for several neutralizing mAbs. The epitope of the ADCP-active, non-neutralizing mAb was determined by microarray analysis of peptides generated from the VP1 capsid protein. VP1-specific, cross-reactive antibodies, especially those with ADCP activity, could contribute to protection against RV infections.

Pandemic influenza virus vaccines boost hemagglutinin stalk-specific antibody responses in primed adult and pediatric cohorts.

Licensed influenza virus vaccines target the head domain of the hemagglutinin (HA) glycoprotein which undergoes constant antigenic drift. The highly conserved HA stalk domain is an attractive target to increase immunologic breadth required for universal influenza virus vaccines. We tested the hypothesis that immunization with a pandemic influenza virus vaccine boosts pre-existing anti-stalk antibodies. We used chimeric cH6/1, full length H2 and H18 HA antigens in an ELISA to measure anti-stalk antibodies in recipients participating in clinical trials of A/H1N1, A/H5N1 and A/H9N2 vaccines. The vaccines induced high titers of anti-H1 stalk antibodies in adults and children, with higher titers elicited by AS03-adjuvanted vaccines. We also observed cross-reactivity to H2 and H18 HAs. The A/H9N2 vaccine elicited plasmablast and memory B-cell responses. Post-vaccination serum from vaccinees protected mice against lethal challenge with cH6/1N5 and cH5/3N4 viruses. These findings support the concept of a chimeric HA stalk-based universal influenza virus vaccine. clinicaltrials.gov: NCT02415842.

Seroprevalence of viral hepatitis A, B, C, D and E viruses in the Hormozgan province southern Iran.

BACKGROUND: Viral hepatitis is a global public health problem affecting millions of people worldwide, causing thousands of deaths due to acute and persistent infection, cirrhosis, and liver cancer. Providing updated serologic data can improve both surveillance and disease control programs. This study is aimed to determine the seroprevalence of markers for viral hepatitis (A, B, C, D and E) and the epidemiology of such infections in the general population of southern Iran's Hormozgan province. METHODS: Between 2016 and 2017, a total of 562 individuals with ages ranging from 1 to 86 years, who visited governmental public laboratories for routine check-ups, were tested for the presence of serological markers to hepatitis virus types A to E using enzyme-linked immunosorbent assays. RESULTS: The overall anti-hepatitis A virus (HAV) antibody seroprevalence was 93.2% (524/562). The prevalence of anti-hepatitis E virus (HEV) antibodies was 15.8% (89/562) among which 1.6% (9/562) of the seropositive individuals also had evidence of recent exposure to the virus (IgM positivity). Two and a half percent (14/562) were positive for hepatitis B surface (HBs) antigen, whereas 11.6% (65/562) tested positive for anti-hepatitis B core (HBc) antibodies. Among anti-HBc positive patients, 11% (7/65) had HBs Ag and 5% (3/65) were positive for anti-hepatitis D virus (HDV) antibodies. The prevalence of anti-hepatitis C virus (HCV) antibodies was 0.7% (4/562). The seroprevalence of anti-HAV, HEV IgG, anti-HBc antibodies, and HBs Ag increased with age. CONCLUSION: The present study confirms a high seroprevalence of HAV infection among the examined population and reveals high levels of endemicity for HEV in the region. Planned vaccination policies against HAV should be considered in all parts of Iran. In addition, improvements on public sanitation and hygiene management of drinking water sources for the studied area are recommended.

Overview of Current Therapeutics and Novel Candidates Against Influenza, Respiratory Syncytial Virus, and Middle East Respiratory Syndrome Coronavirus Infections.

Emergence and re-emergence of respiratory virus infections represent a significant threat to global public health, as they occur seasonally and less frequently (such as in the case of influenza virus) as pandemic infections. Some of these viruses have been in the human population for centuries and others had recently emerged as a public health problem. Influenza viruses have been affecting the human population for a long time now; however, their ability to rapidly evolve through antigenic drift and antigenic shift causes the emergence of new strains. A recent example of these events is the avian-origin H7N9 influenza virus outbreak currently undergoing in China. Human H7N9 influenza viruses are resistant to amantadines and some strains are also resistant to neuraminidase inhibitors greatly limiting the options for treatment. Respiratory syncytial virus (RSV) may cause a lower respiratory tract infection characterized by bronchiolitis and pneumonia mainly in children and the elderly. Infection with RSV can cause severe disease and even death, imposing a severe burden for pediatric and geriatric health systems worldwide. Treatment for RSV is mainly supportive since the only approved therapy, a monoclonal antibody, is recommended for prophylactic use in high-risk patients. The Middle East respiratory syndrome coronavirus (MERS-CoV) is a newly emerging respiratory virus. The virus was first recognized in 2012 and it is associated with a lower respiratory tract disease that is more severe in patients with comorbidities. No licensed vaccines or antivirals have been yet approved for the treatment of MERS-CoV in humans. It is clear that the discovery and development of novel antivirals that can be used alone or in combination with existing therapies to treat these important respiratory viral infections are critical. In this review, we will describe some of the novel therapeutics currently under development for the treatment of these infections.

An Influenza Virus Hemagglutinin-Based Vaccine Platform Enables the Generation of Epitope Specific Human Cytomegalovirus Antibodies.

Human cytomegalovirus (CMV) is a highly prevalent pathogen with ~60%-90% seropositivity in adults. CMV can contribute to organ rejection in transplant recipients and is a major cause of birth defects in newborns. Currently, there are no approved vaccines against CMV. The epitope of a CMV neutralizing monoclonal antibody against a conserved region of the envelope protein gH provided the basis for a new CMV vaccine design. We exploited the influenza A virus as a vaccine platform due to the highly immunogenic head domain of its hemagglutinin envelope protein. Influenza A variants were engineered by reverse genetics to express the epitope of an anti-CMV gH neutralizing antibody that recognizes native gH into the hemagglutinin antigenic Sa site. We determined that the recombinant influenza variants expressing 7, 10, or 13 residues of the anti-gH neutralizing antibody epitope were recognized and neutralized by the anti-gH antibody 10C10. Mice vaccinated with the influenza/CMV chimeric viruses induced CMV-specific antibodies that recognized the native gH protein and inhibited virus infection. In fact, the influenza variants expressing 7-13 gH residues neutralized a CMV infection at ~60% following two immunizations with variants expressing the 13 residue gH peptide produced the highest levels of neutralization. Collectively, our study demonstrates that a variant influenza virus inserted with a gH peptide can generate a humoral response that limits a CMV infection.

Widespread circulation of West Nile virus, but not Zika virus in southern Iran.

West Nile virus (WNV) and Zika virus (ZIKV) are mosquito-borne viral infections. Over the past few decades, WNV has been associated with several outbreaks involving high numbers of neuroinvasive diseases among humans. The recent re-emergence of ZIKV has been associated with congenital malformation and also with Guillain-Barre syndrome in adults. The geographic range of arthropod-borne viruses has been rapidly increasing in recent years. The objectives of this study were to determine the presence of IgG specific antibodies and the genome of WNV and ZIKV in human samples, as well as WNV and ZIKV genomes in wild-caught mosquitoes in urban and rural areas of the Hormozgan province, in southern Iran. A total of 494 serum samples were tested for the presence of WNV and ZIKV IgG antibodies using ELISA assays. One hundred and two (20.6%) samples were reactive for WNV IgG antibodies. All serum samples were negative for ZIKV IgG antibodies. Using the multivariable logistic analysis, age (45+ vs. 1-25; OR = 3.4, 95% C.I.: 1.8-6.3), occupation (mostly outdoor vs. mostly indoor; OR = 2.4, 95% C.I.: 1.1-5.2), and skin type(type I/II vs. type III/IV and type V/VI; OR = 4.3, 95% C.I.: 1.7-10.8 and OR = 2.7, 95% C.I.: 1.3-5.5 respectively, skin types based on Fitzpatrick scale) showed significant association with WNV seroreactivity. We collected 2,015 mosquitoes in 136 pools belonging to 5 genera and 14 species. Three pools of Culex pipiens complex were positive for WNV RNA using real-time reverse transcription polymerase chain reaction (rtRT-PCR). ZIKV RNA was not detected in any of the pools. All WNV ELISA reactive serum samples were negative for WNV RNA. In conclusion, we provided evidence of the establishment of WNV in southern Iran and no proof of ZIKV in serum samples or in mosquito vectors. The establishment of an organized arbovirus surveillance system and active case finding strategies seems to be necessary.

Molecular diagnosis of genital tract infections among HIV-positive women in Iran.

BACKGROUND AND OBJECTIVES: Human immunodeficiency virus (HIV)-infected women are usually at a higher risk of sexually transmitted infections (STIs) than others. The objective of this study was to characterize the prevalence of human papilloma virus (HPV), herpes simplex virus (HSV), Chlamydia trachomatis (CT), and Neisseria gonorrhoeae (NG), and associated risk factors among HIV-infected women in Fars province, Iran. MATERIALS AND METHODS: In this cross-sectional study, cervical swab samples were collected from 71 HIV-infected women, aged 17-45 years (mean ± standard deviation: 31.11 ± 6.58 years), and tested for HPV, HSV, CT, and NG using PCR assays. RESULTS: Overall, 77.5% of patients were positive for the tested STIs with the following distribution: 36 (50.7%) HPV, 7 (9.9%) HSV, 4 (5.6%) NG, and 27 (38%) CT. From those, 39 (55%) were positive for only one infection, while 16 (22.5%) were positive for multiple infections. We observed that the prevalence of all tested STIs increased by age, except for HSV which showed a slight decrease, although not statistically significant. Socio-economic factors such as low educational level, multiple sex partners, and being a sex worker significantly correlated with higher positive prevalence of STIs in the studied population. CONCLUSION: A high prevalence of STIs was observed among HIV-infected women in this region. These data might prompt policy makers and STI experts to focus on providing a comprehensive sex education, including participation in screening programs for STIs among high-risk groups.

Antigenic sites in influenza H1 hemagglutinin display species-specific immunodominance.

Hemagglutination inhibition (HI) titers are a major correlate of protection for influenza-related illness. The influenza virus hemagglutinin possesses antigenic sites that are the targets of HI active antibodies. Here, a panel of mutant viruses each lacking a classically defined antigenic site was created to compare the species-specific immunodominance of the antigenic sites in a clinically relevant hemagglutinin. HI active antibodies of antisera from influenza virus-infected mice targeted sites Sb and Ca2. HI active antibodies of guinea pigs were not directed against any specific antigenic site, although trends were observed toward Sb, Ca2, and Sa. HI titers of antisera from infected ferrets were significantly affected by site Sa. HI active antibodies of adult humans followed yet another immunodominance pattern, in which sites Sb and Sa were immunodominant. When comparing the HI profiles among different species by antigenic cartography, animals and humans grouped separately. This study provides characterizations of the antibody-mediated immune responses against the head domain of a recent H1 hemagglutinin in animals and humans.

A diagnostic one-step real-time reverse transcription polymerase chain reaction method for accurate detection of influenza virus type A.

INTRODUCTION: Influenza A is known as a public health concern worldwide. In this study, a novel one-step real-time reverse transcription polymerase chain reaction (rtRT-PCR) assay was designed and optimized for the detection of influenza A viruses. MATERIAL AND METHODS: The primers and probe were designed based on the analysis of 90 matrix nucleotide sequence data of influenza type A subtypes from the GenBank database of the National Center for Biotechnology Information (NCBI). The influenza virus A/Tehran/5652/2010 (H1N1 pdm09) was used as a reference. The rtRT-PCR assay was optimized, compared with that of the World Health Organization (WHO), and its analytical sensitivity, specificity and reproducibility were evaluated. In total, 64 nasopharyngeal swabs from patients with influenza-like illness (ILI) and 41 samples without ILI symptoms were tested for the virus, using conventional cell culture, direct immunofluorescence antibody (DFA) methods, and one-step rtRT-PCR with the designed primer set and probe and the WHO's. RESULTS: The optimized assay results were similar to the WHO's. The optimized assay results were similar to WHO's, with non-significant differences for 10-103 copies of viral RNA/reaction (p > 0.05). It detected 10 copies of viral RNA/reaction with high reproducibility and no cross reactivity with other respiratory viruses. A specific cytopathic effect was observed in 6/64 (9.37%) of the ILI group using conventional culture and DFA staining methods; however, it was not seen in non-ILI. Also, the results of our assay and the WHO's were similar to those of viral isolation and DFA staining. CONCLUSIONS: Given the high specificity, sensitivity and reproducibility of this novel assay, it can serve as a reliable diagnostic tool for the detection of influenza A viruses in clinical specimens and lab experiments.