2.Cytomegalovirus Pneumonia in Patients with Rheumatic Diseases After Immunosuppressive Therapy: A Single Center Study in China.
Yu XUE ; Li JIANG ; Wei-Guo WAN ; Yu-Ming CHEN ; Jiong ZHANG ; Zhen-Chun ZHANG ;
Chinese Medical Journal 2016;129(3):267-273
BACKGROUNDRheumatic diseases involve multiple organs that are affected by immunological mechanisms. Treatment with corticosteroids and immunosuppressive agents may also increase the frequency of infection. Cytomegalovirus (CMV) is a widespread herpes virus and a well-recognized pathogen, which causes an opportunistic and potentially fatal infection in immunocompromised patients. This retrospective study aimed to investigate the clinical and laboratory characteristics of CMV pneumonia in patients with rheumatic diseases after immunosuppressive therapy in a single center in Shanghai, China.
METHODSEight hundred and thirty-four patients with rheumatic diseases who had undergone CMV-DNA viral load tests were included, and the medical records of 142 patients who were positive for CMV-DNA in plasma samples were evaluated. GraphPad Prism version 5.013 (San Diego, CA, USA) was used to conduct statistical analysis. The correlation between CMV-DNA viral loads and lymphocyte counts was assessed using the Spearman rank correlation coefficient test. Significance between qualitative data was analyzed using Pearson's Chi-squared test. The cut-off thresholds for CMV-DNA viral load and lymphocyte count were determined by receiver operating characteristic (ROC) curve analysis.
RESULTSOne hundred and forty-two patients had positive CMV viral load tests. Of these 142 patients, 73 patients with CMV pneumonia were regarded as symptomatic, and the other 69 were asymptomatic. The symptomatic group received higher doses of prednisolone (PSL) and more frequently immunosuppressants than the asymptomatic group (P < 0.01). The symptomatic group had lower lymphocyte counts, especially CD4+ T-cells, than the asymptomatic group (P < 0.01). By ROC curve analysis, when CD4+ T-cell count was <0.39 × 109/L, patients with rheumatic diseases were at high risk for symptomatic CMV infection. The CMV-DNA load was significantly higher in the symptomatic patients than that in asymptomatic patients (P < 0.01; threshold viral loads: 1.75 × 104 copies/ml). Seven patients had a fatal outcome, and they had lower peripheral lymphocyte counts (P < 0.01), including CD4+ and CD8+ T-cells (P < 0.01).
CONCLUSIONSWhen CD4+ T-cell count is <0.39 × 109/L, patients are at high risk for pulmonary CMV infection. Patients are prone to be symptomatic with CMV-DNA load >1.75 × 104 copies/ml. Lymphopenia (especially CD4+ T-cells), presence of symptoms, and other infections, especially fungal infection, are significant risk factors for poor outcome, and a higher PSL dosage combined with immunosuppressants may predict CMV pneumonia.
CD4-Positive T-Lymphocytes ; metabolism ; China ; Cytomegalovirus ; pathogenicity ; Cytomegalovirus Infections ; genetics ; immunology ; therapy ; virology ; Humans ; Immunosuppression ; methods ; Pneumonia ; genetics ; immunology ; therapy ; virology ; Polymerase Chain Reaction ; Retrospective Studies ; Rheumatic Diseases ; genetics ; immunology ; therapy ; virology ; Viral Load
3.Applications of systems approaches in the study of rheumatic diseases.
Ki Jo KIM ; Saseong LEE ; Wan Uk KIM
The Korean Journal of Internal Medicine 2015;30(2):148-160
		                        		
		                        			
		                        			The complex interaction of molecules within a biological system constitutes a functional module. These modules are then acted upon by both internal and external factors, such as genetic and environmental stresses, which under certain conditions can manifest as complex disease phenotypes. Recent advances in high-throughput biological analyses, in combination with improved computational methods for data enrichment, functional annotation, and network visualization, have enabled a much deeper understanding of the mechanisms underlying important biological processes by identifying functional modules that are temporally and spatially perturbed in the context of disease development. Systems biology approaches such as these have produced compelling observations that would be impossible to replicate using classical methodologies, with greater insights expected as both the technology and methods improve in the coming years. Here, we examine the use of systems biology and network analysis in the study of a wide range of rheumatic diseases to better understand the underlying molecular and clinical features.
		                        		
		                        		
		                        		
		                        			Animals
		                        			;
		                        		
		                        			Antirheumatic Agents/therapeutic use
		                        			;
		                        		
		                        			Biomedical Research/*methods
		                        			;
		                        		
		                        			Cytokines/genetics/metabolism
		                        			;
		                        		
		                        			Genetic Markers
		                        			;
		                        		
		                        			Genetic Predisposition to Disease
		                        			;
		                        		
		                        			Humans
		                        			;
		                        		
		                        			Inflammation Mediators/metabolism
		                        			;
		                        		
		                        			Molecular Targeted Therapy
		                        			;
		                        		
		                        			Phenotype
		                        			;
		                        		
		                        			Prognosis
		                        			;
		                        		
		                        			*Rheumatic Diseases/drug therapy/genetics/metabolism/physiopathology
		                        			;
		                        		
		                        			Rheumatology/*methods
		                        			;
		                        		
		                        			Risk Factors
		                        			;
		                        		
		                        			Signal Transduction
		                        			;
		                        		
		                        			*Systems Biology
		                        			;
		                        		
		                        			Systems Integration
		                        			
		                        		
		                        	
4.Detection of herpes simplex virus type 1 in rheumatic valvular tissue.
Zhi-gang PAN ; Xiu-nan WANG ; Yan-wen LI ; Hong-yi ZHANG ; Leonard C ARCHARD
Chinese Medical Journal 2005;118(5):370-376
BACKGROUNDRheumatic heart disease (RHD) is the most important sequela of rheumatic fever (RF): evidence that streptococcal infection is aetiological is prominent, but sometimes contradictory. Acute HSV-1 infection in mouse leads to carditis and valvulitis whereas recurrent infection results in inflammatory granulomatous lesions that resemble Aschoff bodies. Cells containing HSV-1 inclusions or virus infected giant cells appear similar to Anitschkow cells or Aschoff cells respectively. We hypothesized that HSV-1 infection also may be involved in RHD.
METHODSFormalin-fixed, paraffin-embedded valvular tissue samples from 32 patients with RHD were investigated for evidence of HSV-1 infection. HSV-1 antigen was detected by immunohistochemistry, using HSV-1-specific monoclonal and polyclonal antibodies. HSV-1 glycoprotein D gene sequences were amplified by nPCR, using beta-globin gene amplification in the same samples as internal control. Valvular tissue from 5 cases of sudden death and 3 cases died of neisseria meningitis without a history of valvular disease was used for comparison. HSV-1-infected lung tissue was used as positive control.
RESULTSHSV-1 antigens were detected in valvular tissues from 21 of 32 (65.6%) patients. Fifteen of these 21 (46.9% of cases), but no antigen-negative sample, were positive also for HSV DNA. Nucleotide sequence of PCR products was homologous to the targeted region of the HSV-1 glycoprotein D gene. HSV-1 antigen was present also in one case of sudden death but viral DNA was not found in any tissue sample from the comparison group. Results from reagent and positive controls were as anticipated.
CONCLUSIONSThis is the first study to show the presence of HSV-1 antigen and genomic DNA in valvular tissues from patients with RHD and provides evidence for an association of HSV-1 infection with some cases of rheumatic valvular disease.
Adolescent ; Adult ; Antigens, Viral ; isolation & purification ; DNA, Viral ; isolation & purification ; Female ; Heart Valve Diseases ; etiology ; virology ; Heart Valves ; pathology ; virology ; Herpes Simplex ; pathology ; virology ; Herpesvirus 1, Human ; immunology ; isolation & purification ; Humans ; Male ; Middle Aged ; Rheumatic Heart Disease ; pathology ; virology ; Viral Envelope Proteins ; genetics
5.Study on medical diagnosis decision support system for heart diseases based on hybrid genetic algorithm.
Hongmei YAN ; Xiaojun DING ; Chenglin PENG ; Shouzhong XIAO
Journal of Biomedical Engineering 2004;21(2):302-305
		                        		
		                        			
		                        			In this study, a medical diagnosis decision support system based on hybrid genetic algorithm has been established to support the diagnosis of five common heart diseases (coronary heart disease, rheumatic valvular heart disease, hypertensive heart disease, chronic cor pulmonale and congenital heart disease). A heart disease database consisting of 352 samples was used for constructing and testing the performance of system. Cross-validation of the experimental results indicate that the system we established shows high capability of classifying these five kinds of heart diseases, the mean accuracy of classification is as high as 90.6%, and the user accuracy and procedure accuracy of each disease are both above 85.0%, showing great application prospect of supporting heart diseases diagnosis in clinics.
		                        		
		                        		
		                        		
		                        			Algorithms
		                        			;
		                        		
		                        			Coronary Disease
		                        			;
		                        		
		                        			diagnosis
		                        			;
		                        		
		                        			Decision Support Systems, Clinical
		                        			;
		                        		
		                        			Decision Trees
		                        			;
		                        		
		                        			Diagnostic Techniques, Cardiovascular
		                        			;
		                        		
		                        			Heart Diseases
		                        			;
		                        		
		                        			diagnosis
		                        			;
		                        		
		                        			genetics
		                        			;
		                        		
		                        			Humans
		                        			;
		                        		
		                        			Pulmonary Heart Disease
		                        			;
		                        		
		                        			diagnosis
		                        			;
		                        		
		                        			Rheumatic Heart Disease
		                        			;
		                        		
		                        			diagnosis
		                        			
		                        		
		                        	
            
Result Analysis
Print
Save
E-mail