Single-cell transcriptomics reveals T-cell heterogeneity and immunomodulatory role of CD4^(+) T native cells in Candida albicans infection

Objective:Candida albicans is a common fungal pathogen that triggers complex host defense mechanisms,including coordinated innate and adaptive immune responses,to neutralize invading fungi effectively.Exploring the immune microenvironment has the potential to inform the development of therapeutic strategies for fungal infections.Methods:The study analyzed individual immune cell profiles in peripheral blood mononuclear cells from Candida albicans-infected mice and healthy control mice using single-cell transcriptomics,fluorescence quantitative PCR,and Western blotting.We investigated intergroup differences in the dynamics of immune cell subpopulation infiltration,pathway enrichment,and differentiation during Candida albicans infection.Results:Our findings indicate that infiltration of CD4^(+)naive cells,regulatory T(Treg)cells,and Microtubules(MT)-associated cells increased after infection,along with impaired T cell activity.Notably,CD4^(+) T cells and plasma cells were enhanced after infection,suggesting that antibody production is dependent on T cells.In addition,we screened 6 hub genes,transcription factor forkhead box protein 3(Foxp3),cytotoxic T-lymphocyte associated protein 4(CTLA4),Interleukin 2 Receptor Subunit Beta(Il2rb),Cd28,C-C Motif Chemokine Ligand 5(Ccl5),and Cd27 for alterations associated with CD4^(+) T cell differentiation.Conclusions:These results provide a comprehensive immunological landscape of the mechanisms of Candida albicans infection and greatly advance our understanding of adaptive immunity in fungal infections.

Identification and validation of a pyroptosis-related prognostic model for colorectal cancer based on bulk and single-cell RNA sequencing data

BACKGROUND Pyroptosis impacts the development of malignant tumors,yet its role in colorectal cancer(CRC)prognosis remains uncertain.AIM To assess the prognostic significance of pyroptosis-related genes and their association with CRC immune infiltration.METHODS Gene expression data were obtained from The Cancer Genome Atlas(TCGA)and single-cell RNA sequencing dataset GSE178341 from the Gene Expression Omnibus(GEO).Pyroptosis-related gene expression in cell clusters was analyzed,and enrichment analysis was conducted.A pyroptosis-related risk model was developed using the LASSO regression algorithm,with prediction accuracy assessed through K-M and receiver operating characteristic analyses.A nomo-gram predicting survival was created,and the correlation between the risk model and immune infiltration was analyzed using CIBERSORTx calculations.Finally,the differential expression of the 8 prognostic genes between CRC and normal samples was verified by analyzing TCGA-COADREAD data from the UCSC database.RESULTS An effective pyroptosis-related risk model was constructed using 8 genes-CHMP2B,SDHB,BST2,UBE2D2,GJA1,AIM2,PDCD6IP,and SEZ6L2(P<0.05).Seven of these genes exhibited differential expression between CRC and normal samples based on TCGA database analysis(P<0.05).Patients with higher risk scores demonstrated increased death risk and reduced overall survival(P<0.05).Significant differences in immune infiltration were observed between low-and high-risk groups,correlating with pyroptosis-related gene expression.CONCLUSION We developed a pyroptosis-related prognostic model for CRC,affirming its correlation with immune infiltration.This model may prove useful for CRC prognostic evaluation.

Identification of risk factors and construction of a nomogram predictive model for post-stroke infection in patients with acute ischemic stroke

BACKGROUND Post-stroke infection is the most common complication of stroke and poses a huge threat to patients.In addition to prolonging the hospitalization time and increasing the medical burden,post-stroke infection also significantly increases the risk of disease and death.Clarifying the risk factors for post-stroke infection in patients with acute ischemic stroke(AIS)is of great significance.It can guide clinical practice to perform corresponding prevention and control work early,minimizing the risk of stroke-related infections and ensuring favorable disease outcomes.AIM To explore the risk factors for post-stroke infection in patients with AIS and to construct a nomogram predictive model.METHODS The clinical data of 206 patients with AIS admitted to our hospital between April 2020 and April 2023 were retrospectively collected.Baseline data and post-stroke infection status of all study subjects were assessed,and the risk factors for poststroke infection in patients with AIS were analyzed.RESULTS Totally,48 patients with AIS developed stroke,with an infection rate of 23.3%.Age,diabetes,disturbance of consciousness,high National Institutes of Health Stroke Scale(NIHSS)score at admission,invasive operation,and chronic obstructive pulmonary disease(COPD)were risk factors for post-stroke infection in patients with AIS(P<0.05).A nomogram prediction model was constructed with a C-index of 0.891,reflecting the good potential clinical efficacy of the nomogram prediction model.The calibration curve also showed good consistency between the actual observations and nomogram predictions.The area under the receiver operating characteristic curve was 0.891(95%confidence interval:0.839–0.942),showing predictive value for post-stroke infection.When the optimal cutoff value was selected,the sensitivity and specificity were 87.5%and 79.7%,respectively.CONCLUSION Age,diabetes,disturbance of consciousness,NIHSS score at admission,invasive surgery,and COPD are risk factors for post-stroke infection following AIS.The nomogram prediction model established based on these factors exhibits high discrimination and accuracy.

Mathematical Modeling of the Co-Infection Dynamics of HIV and Tuberculosis Incorporating Inconsistency in HIV Treatment

A non-linear HIV-TB co-infection has been formulated and analyzed. The positivity and invariant region has been established. The disease free equilibrium and its stability has been determined. The local stability was determined and found to be stable under given conditions. The basic reproduction number was obtained and according to findings, co-infection diminishes when this number is less than unity, and persists when the number is greater than unity. The global stability of the endemic equilibrium was calculated. The impact of HIV on TB was established as well as the impact of TB on HIV. Numerical solution was also done and the findings indicate that when the rate of HIV treatment increases the latent TB increases while the co-infected population decreases. When the rate of HIV treatment decreases the latent TB population decreases and the co-infected population increases. Encouraging communities to prioritize the consistent treatment of HIV infected individuals must be emphasized in order to reduce the scourge of HIV-TB co-infection.

Preoperative blood markers and intra-abdominal infection after colorectal cancer resection

BACKGROUND Colorectal cancer(CRC)has one of the highest morbidity and mortality rates among digestive tract tumors.Intra-abdominal infection(IAI)is a common postoperative complication that affects the clinical outcomes of patients with CRC and hinders their rehabilitation process.However,the factors influencing abdominal infection after CRC surgery remain unclear;further,prediction models are rarely used to analyze preoperative laboratory indicators and postoperative complications.AIM To explore the predictive value of preoperative blood markers for IAI after radical resection of CRC.METHODS The data of 80 patients who underwent radical resection of CRC in the Anorectal Surgery Department of Suzhou Hospital affiliated with Anhui Medical University were analyzed.These patients were categorized into IAI(n=15)and non-IAI groups(n=65)based on whether IAI occurred.Influencing factors were compared;general data and laboratory indices of both groups were identified.The relationship between the indicators was assessed.Further,a nomogram prediction model was developed and evaluated;its utility and clinical applic-ability were assessed.RESULTS The risk factors for IAI after radical resection of CRC were neutrophil-lymphocyte ratio(NLR),platelet-lymphocyte ratio(PLR),systemic immune-inflammation index(SII),and carcinoembryonic antigen(CEA)levels.NLR was correlated with PLR and SII(r=0.604,0.925,and 0.305,respectively),while PLR was correlated with SII(r=0.787).The nomogram prediction model demonstrated an area under the curve of 0.968[95%confidence interval(CI):0.948-0.988]in the training set(n=60)and 0.926(95%CI:0.906-0.980)in the validation set(n=20).The average absolute errors of the calibration curves for the training and validation sets were 0.032 and 0.048,respectively,indicating a good model fit.The decision curve analysis curves demonstrated high net income above the 5%threshold,indicating the clinical practicality of the model.CONCLUSION The nomogram model constructed using NLR,PLR,SII,and CEA levels had good accuracy and reliability in predicting IAI after radical resection of CRC,potentially aiding clinical treatment decision-making.

Simple animal model of Helicobacter pylori infection

Helicobacter pylori(H.pylori)has become accepted as a human pathogen for the development of gastritis and gastroduodenal ulcer.To develop a simple rat model of chronic H.pylori infection,male Sprague-Dawley rats were pretreated with streptomycin suspended in tap water(5 mg/mL)for 3 d.The rats were inoculated by gavage at 1 mL/rat with H.pylori suspension(5×108-5×1010 CFU/mL)twice daily at an interval of 4 h for three consecutive days.Two weeks after inoculation,rats were sacrificed and the stomachs were removed.Antral biopsies were performed for urease test and the stomachs were taken for histopathology.Successful H.pylori inoculation was defined as a positive urease test and histopathology.We reported a 69.8%-83.0%success rate for H.pylori infection using the urease test,and hematoxylin and eosin staining confirmed the results.Histopathological analysis detected bacteria along the mucous lining of the surface epithelium and crypt lumen and demonstrated mild to moderate gastric inflammation in successfully inoculated rats.We developed a simple rat model of chronic H.pylori infection for research into gastric microcirculatory changes and therapy with plant products.

DYNAMICAL BEHAVIOR OF A STOCHASTIC HBV INFECTION MODEL WITH LOGISTIC HEPATOCYTE GROWTH

This paper is concerned with a stochastic HBV infection model with logistic growth. First, by constructing suitable stochastic Lyapunov functions, we establish sufficient conditions for the existence of ergodic stationary distribution of the solution to the HBV infection model. Then we obtain sufficient conditions for extinction of the disease. The stationary distribution shows that the disease can become persistent in vivo.

Imported cases of 2019-novel coronavirus (2019-nCoV) infections in Thailand: Mathematical modelling of the outbreak

Outbreak of a new emerging disease is usually an important consideration in medicine and public health. In December 2019, a new emerging disease started in China and becomes the global concern in early January 2020[1,2]. The disease, 2019-novel coronavirus(2019-nCoV) infection, already existed outside China and the importation of disease is the cause of emerging 2019-nCoV infections in new settings.

Neonatal rhesus monkeys as an animal model for rotavirus infection

AIM To establish a rotavirus(RV)-induced diarrhea model using RV SA11 in neonatal rhesus monkeys for the study of the pathogenic and immune mechanisms of RV infection and evaluation of candidate vaccines.METHODS Neonatal rhesus monkeys with an average age of 15-20 d and an average weight of 500 g ± 150 g received intragastric administration of varying doses of SA11 RV( 107 PFUs/mL, 106 PFUs/mL, or 105 PFUs/mL, 10 mL/animal) to determine whether the SA11 strain can effectively infect these animals by observing their clinical symptoms, fecal shedding of virus antigen by ELISA, distribution of RV antigen in the organs by immunofluorescence, variations of viral RNA load in the organs by qRT-PCR, histopathological changes in the small intestine by HE staining, and apoptosis of small intestinal epithelial cells by TUNEL assay.RESULTS The RV monkey model showed typical clinical diarrhea symptoms in the 108 PFUs SA11 group, where we observed diarrhea 1-4 d post infection(dpi) and viral antigen shed in the feces from 1-7 dpi. RV was found in jejunal epithelial cells. We observed a viral load of approximately 5.85 × 103 copies per 100 mg in the jejunum at 2 dpi, which was increased to 1.09 × 105 copies per 100 mg at 3 dpi. A relatively high viral load was also seen in mesenteric lymph nodes at 2 dpi and 3 dpi. The following histopathological changes were observed in the small intestine following intragastric administration of SA11 RV: vacuolization, edema, and atrophy. Apoptosis in the jejunal villus epithelium was also detectable at 3 dpi.CONCLUSION Our results indicate that we have successfully established a RV SA11 strain diarrhea model in neonatal rhesus monkeys. Future studies will elucidate the mechanisms underlying the pathogenesis of RV infection, and we will use the model to evaluate the protective effect of candidate vaccines.

Establishment of a Predictive Diagnostic Model for Acute Mycoplasma Pneumoniae Infection in Elderly Patients with Community-acquired Pneumonia

We established a diagnostic model to predict acute Mycoplasma pneumoniae （M. pneumonia） infection in elderly Community-acquired pneumonia （CAP） patients. We divided 456 patients into acute and non-acute M. pneumoniae infection groups. Binary logistic regression and receiver operating characteristic （ROC） curves were used to establish a predictive model. The following independent factors were identified： age 〉 70 years; serum cTNT level 〉 0.0S ng/mL; lobar consolidation; mediastinal lymphadenopathy; and antibody titer in the acute phase 〉 1：40. The area under the ROC curve of the model was 0.923 and a score of 2 7 score predicted acute M. pneumoniae infection in elderly patients with CAP. The predictive model developed in this study has high diagnostic accuracy for the identification of elderly acute M. pneumoniae infection.

Role of neutrophil chemoattractant CXCL5 in SARS-CoV-2 infection-induced lung inflammatory innate immune response in an in vivo hACE2 transfection mouse model

Understanding the pathogenesis of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)and clarifying antiviral immunity in hosts are critical aspects for the development of vaccines and antivirals.Mice are frequently used to generate animal models of infectious diseases due to their convenience and ability to undergo genetic manipulation.However,normal adult mice are not susceptible to SARS-CoV-2.Here,we developed a viral receptor(human angiotensin-converting enzyme 2,hACE2)pulmonary transfection mouse model to establish SARS-CoV-2 infection rapidly in the mouse lung.Based on the model,the virus successfully infected the mouse lung 2 days after transfection.Viral RNA/protein,innate immune cell infiltration,inflammatory cytokine expression,and pathological changes in the infected lungs were observed after infection.Further studies indicated that neutrophils were the first and most abundant leukocytes to infiltrate the infected lungs after viral infection.In addition,using infected CXCL5-knockout mice,chemokine CXCL5 was responsible for neutrophil recruitment.CXCL5 knockout decreased lung inflammation without diminishing viral clearance,suggesting a potential target for controlling pneumonia.

A With-In Host Dengue Infection Model with Immune Response and Beddington-DeAngelis Incidence Rate

A model of viral infection of monocytes population by dengue virus is formulated in a system of four ordinary differenttial equations. The model takes into account the immune response and the incidence rate of susceptible and free virus particle as Beddington-DeAngelis functional response. By constructing a block, the global stability of the unin-fected steady state is investigated. This steady state always exists. If this is the only steady state, then it is globally asymptotically stable. If any infected steady state exists, then uninfected steady state is unstable and one of the infected steady states is locally asymptotically stable. These different cases depend on the values of the basic reproduction ratio and the other parameters.

Evaluation of preoperative blood markers for predicting intraabdominal infection during colorectal cancer resection:A commentary on recent findings

This commentary evaluates the study by Liu et al.This study investigates the predictive utility of the neutrophil-lymphocyte ratio,platelet-lymphocyte ratio,systemic immune-inflammation index,and carcinoembryonic antigen levels for post-operative intra-abdominal infection following colorectal cancer(CRC)surge-ry.The study highlights the critical need for analyzing diverse patient demogra-phics and delves into the potential impact of various confounding factors on the predictive accuracy of these markers.Additionally,the commentary advocates for the initiation of prospective studies aimed at validating and enhancing the clinical utility of these biomarkers in the context of CRC treatment.The commentary aims to underscore the importance of broadening the research framework to include a wider patient demographic and more comprehensive factor analyses,thereby enriching the predictive model's applicability and relevance in clinical settings.

Risk factors and prediction model for inpatient surgical site infection after elective abdominal surgery

BACKGROUND Surgical site infections(SSIs) are the commonest healthcare-associated infection. In addition to increasing mortality, it also lengthens the hospital stay and raises healthcare expenses. SSIs are challenging to predict, with most models having poor predictability. Therefore, we developed a prediction model for SSI after elective abdominal surgery by identifying risk factors.AIM To analyse the data on inpatients undergoing elective abdominal surgery to identify risk factors and develop predictive models that will help clinicians assess patients preoperatively.METHODS We retrospectively analysed the inpatient records of Shaanxi Provincial People’s Hospital from January 1, 2018 to January 1, 2021. We included the demographic data of the patients and their haematological test results in our analysis. The attending physicians provided the Nutritional Risk Screening 2002(NRS 2002)scores. The surgeons and anaesthesiologists manually calculated the National Nosocomial Infections Surveillance(NNIS) scores. Inpatient SSI risk factors were evaluated using univariate analysis and multivariate logistic regression. Nomograms were used in the predictive models. The receiver operating characteristic and area under the curve values were used to measure the specificity and accuracy of the model.RESULTS A total of 3018 patients met the inclusion criteria. The surgical sites included the uterus(42.2%), the liver(27.6%), the gastrointestinal tract(19.1%), the appendix(5.9%), the kidney(3.7%), and the groin area(1.4%). SSI occurred in 5% of the patients(n = 150). The risk factors associated with SSI were as follows: Age;gender;marital status;place of residence;history of diabetes;surgical season;surgical site;NRS 2002 score;preoperative white blood cell, procalcitonin(PCT), albumin, and low-density lipoprotein cholesterol(LDL) levels;preoperative antibiotic use;anaesthesia method;incision grade;NNIS score;intraoperative blood loss;intraoperative drainage tube placement;surgical operation items. Multivariate logistic regression revealed the following independent risk factors: A history of diabetes [odds ratio(OR) = 5.698, 95% confidence interval(CI): 3.305-9.825, P = 0.001], antibiotic use(OR = 14.977, 95%CI: 2.865-78.299, P = 0.001), an NRS 2002 score of ≥ 3(OR = 2.426, 95%CI: 1.199-4.909, P = 0.014), general anaesthesia(OR = 3.334, 95%CI: 1.134-9.806, P = 0.029), an NNIS score of ≥ 2(OR = 2.362, 95%CI: 1.019-5.476, P = 0.045), PCT ≥ 0.05 μg/L(OR = 1.687, 95%CI: 1.056-2.695, P = 0.029), LDL < 3.37 mmol/L(OR = 1.719, 95%CI: 1.039-2.842, P = 0.035), intraoperative blood loss ≥ 200 mL(OR = 29.026, 95%CI: 13.751-61.266, P < 0.001), surgical season(P < 0.05), surgical site(P < 0.05), and incision grade I or Ⅲ(P < 0.05). The overall area under the receiver operating characteristic curve of the predictive model was 0.926, which is significantly higher than the NNIS score(0.662).CONCLUSION The patient’s condition and haematological test indicators form the bases of our prediction model. It is a novel, efficient, and highly accurate predictive model for preventing postoperative SSI, thereby improving the prognosis in patients undergoing abdominal surgery.

Effective Factors in Urinary Tract Infection Prevention among Children: Application of Health Belief Model

Urinary Tract Infection (UTI) is one of the most common bacterial infections among children using preventive manipulations, especially among children less than 6 years old. This study aimed at exploring factors which could affect urinary tract infection prevention among children. A descriptive analytical study was conducted on 70 mothers who had children less than 6 years old in 2013. Multistage cluster sampling method was utilized in which each kindergarten was considered as a cluster and data were collected by a researcher-made questionnaire designed based on Health Belief Model (HBM). Validity and reliability of questionnaire were confirmed by a pilot study. ANOVA and Pearson Correlation Coefficient were conducted using SPSS, version 18. The mean and standard deviation of knowledge and practice were 6.96 ± 2.14 and 15.47 ± 1.54 respectively. Means and standard deviation of Health Belief Model constructs (perceived susceptibility, perceived severity, perceived benefits, perceived barriers and self-efficacy) were 17.64 ± 2.50, 22.02 ± 2.55, 20.90 ± 2.38, 27.56 ± 5.20 and 21.49 ± 3.18 respectively. More participants were in the moderate levels of knowledge (74.3%) and practice (45.7%). These findings revealed a significant relation between practice and perceived susceptibility (p = 0.02), perceived barriers (p = 0.001), self-efficacy (p = 0.014) and cues to action (p = 0.016). Based on the current study, the intervention based on Health Belief Model can be useful to improve preventive behaviors of Urinary Tract Infection.

Providing a Therapeutic Scheduling for HIV Infected Individuals with Genetic Algorithms Using a Cellular Automata Model of HIV Infection in the Peripheral Blood Stream

The aim of this study is to develop two-dimensional cellular automata model of HIV infection that depicts the dynamics involved in the interactions between acquired immune system and HIV infection in the peripheral blood stream. The appropriate biological rules of cellular automata model have been extracted from expert knowledge and the model has been simulated with determined initial conditions. Obtained results have been validated through comparing with the accepted AIDS reference curve. The new rules and states were added to the proposed model to show the effects of applying combined antiretroviral therapy. Our results showed that by applying RTI and PI drugs with maximum drug effectiveness, comparing with cases in which no treatment was applied, the steady state concentrations of healthy (infected) CD4+T cells were increased (decreased) 53% (41%). Also, the use of cART with maximum drug effectiveness led to a 69% reduction in the steady state level of viral load. At this time, obtained results have been validated through comparing with available clinical data. Our results showed good agreement with both reference curve and the clinical data. In the second phase of this study, by applying genetic algorithms, a therapeutic schedule has been provided that its use, while maintaining the quality of the treatment, leads to a 47% reduction in both drug dosage and the side effects of antiretroviral drugs.

Dose prediction of lopinavir/ritonavir for 2019-novel coronavirus (2019-nCoV) infection based on mathematic modeling

Wuhan novel coronavirus or 2019-novel coronavirus(2019-nCoV)infection is a rapidly emerging respiratory viral disease[1].2019-nCoV infection is characterized as febrile illness with possible severe lung complication[1].The disease was firstly reported in China in December 2019 and then spread to many countries(such as Thailand,Japan and Singapore)[2,3].As a new disease,there is a limited knowledge of treatment for the infection.Lu recently proposed that some drug might be useful in treatment of 2019-nCoV infection[3].

Model of Healthcare-Associated Infection Control in Primary Health Care Institutions:A Structural Equation Modeling

The purpose of this study was to construct the model of organization system,managemcnt,training and surveillance in healthcare-associated infection prevention and control(IC)of primary health care institutions and identify its efleet on patient safety and decreasing economic burden by standardizing IC.A cross-sectional survey was conducted with questionnaires.Data were collected from 268 primary health care institutions in Hubei province,China.Hypotheses on the model of IC were analyzed by means of confirmatory factor analysis and structural equation modeling.The results showed that the fit indices of the hypothesized model of IC satisfied recommended levels:root mean square error of approximation(RMSEA)=0.071;comparative fit index(CFI)=0.965;tucker lewis index(TLI)=0.956:weighted root mean square residual(WRMR)=1.014.The model showed that organization system had a direct effect on management(β=0.311.P<0.01),and training(β=0.365,P<0.01).Management and training played an intermediary role that partially promoted organization system impact on surveillance.Results also showed that institutional factors such as the number of physicians、the ninnber of nurses,the designated capacity of beds,the actual number of open beds and surgery trips had positive impacts on management(β=0.050,P<0.01;β=0.181,P<0.01;β-0.111.P<0.01;β=0.064,P<0.01;β=0.084,P=0.04);nd training(β=0.21,P=0.03;β=0.050,P=0.02;β=0.586.P=0.01;0=0.995,P=0.02;β=0.223.P=0.03).In conclusion.the model of organization system,managemcnt,training and surveillancc in IC of primary health care institutions is valuable tor guiding IC practice.

Robust Model-Free Software Sensors for the HIV/AIDS Infection Process

This paper considers the problem of the HIV/AIDS Infection Process filtering characterized by three compounds, namely, the number of healthy T-cells, the number of infected T-cells and free virus particles. Only the first and third of them can be measurable during the medical treatment process. Moreover, the exact parameter values are admitted to be also unknown. So, here we deal with an uncertain dynamic model that excludes the application of classical filtering theory and requires the application of robust filters successfully working in the absence of a complete mathematical model of the considered process. The problem is to estimate the number of infected T-cells based on the available information. Here we admit the presence of stochastic “white noise” in current observations. To do that we apply the Luenberger-like filter (software sensor) with a matrix gain, which should be adjusted at the beginning of the process in such a way that the filtering error would be as less as possible using the Attractive Ellipsoid Method (AEM). It is shown that the corresponding trajectories of the filtering error converge to an ellipsoidal set of a prespecified form in mean-square sense. To generate the experimental data sequences in the test-simulation example, we have used the well-known simplified HIV/ AIDS model. The obtained results confirm the effectiveness of the suggested approach.

Hepatitis C virus: Morphogenesis, infection and therapy

Hepatitis C virus(HCV) is a major cause of liver diseases including liver cirrhosis and hepatocellular carcinoma. Approximately 3% of the world population is infected with HCV. Thus, HCV infection is considered a public healthy challenge. It is worth mentioning, that the HCV prevalence is dependent on the countries with infection rates around 20% in high endemic countries. The review summarizes recent data on HCV molecular biology, the physiopathology of infection(immune-mediated liver damage, liver fibrosis and lipid metabolism), virus diagnostic and treatment. In addition, currently available in vitro, ex vivo and animal models to study the virus life cycle, virus pathogenesis and therapy are described. Understanding of both host and viral factors may in the future lead to creation of new approaches in generation of an efficient therapeutic vaccine.