In order to further the present knowledge of the emerging severe acute respiratory syndrome-associated coronavirus (SARS-CoV), 486 different specimens from 54 patients with a clinical diagnosis of SARS were investigated for the presence of viral RNA, and 314 plasma specimens of 73 patients were examined for IgM and IgG antibodies specific against SARS-CoV using an indirect ELISA. Viral RNA was detectable in 28 of the 54 patients tested. Cumulative data showed that 67 of the 73 SARS patients demonstrated seroconversion by week 5 of illness. In contrast, only 1 of 278 healthy subjects enrolled in the study was found to be positive for the IgG antibody. Coexistence of viral RNA in plasma and specific antibodies was simultaneously observed over three consecutive weeks in two critical cases. In three convalescent patients in particular, cultivable SARS-CoV was detected in stool or urine specimens for longer than 4 weeks (29-36 days). These findings suggest that SARS-CoV may remain viable in the excretions of convalescent patients.

This study analyzes single factors that affect the prognosis of severe acute respiratory syndrome (SARS) and establishes a prognosis model by multivariate analysis. We retrospectively analyzed the clinical features of SARS in 165 clinically confirmed severe cases. Both age and existence of other diseases before SARS were significantly correlated with prognosis (r=0.506 and r=0.457, respectively; P<.001). During the acute phase of SARS, lactate dehydrogenase level, degree of hypoxemia, respiratory rate, alpha -hydroxybutyric dehydrogenase level, creatine kinase isoenzyme-MB, platelet count, and number of involved lobes noted on chest radiographs, and so on, correlated markedly with the prognosis (r=0.257-0.788; P<.05). The multivariate prognosis regression model was associated with degree of hypoxemia and platelet count. The model was defined by the formula Py=1=es/(1+es), where S is [2.490 x degree of hypoxemia]-[0.050 x number of platelets], and it had a high sensitivity (91.67%), specificity (98.33%), and accuracy (96.42%). The model could be used to effectively judge the state of illness and the prognosis.

BackgroundNon-alcoholic steatoheaptitis (NASH), the critical stage of non-alcoholic fatty liver disease (NAFLD), is of chronic progression and can develop cirrhosis even hepatocellular carcinoma (HCC). However, non-invasive biomarkers for NASH diagnosis remain poorly applied in clinical practice. Our study aims at testing the accuracy of the combination of cytokeratin-18 M30 fragment (CK-18-M30), fibroblast growth factor 21 (FGF-21), interleukin 1 receptor antagonist (IL-1Ra), pigment epithelium-derived factor (PEDF) and osteoprotegerin (OPG) in diagnosing NAFLD and NASH.Methods179 patients with biopsy-proven NAFLD were enrolled as training group, 91 age- and gender-matched healthy subjects were recruited at the same time as controls. 63 other NAFLD patients were separately collected as validation group. 45 alcoholic fatty liver disease (AFLD) patients, 50 hepatitis B virus (HBV) patients, 52 hepatitis C virus (HCV) patients were also included. Serum biomarker levels were measured by enzyme-linked immunosorbent assay.ResultsSerum levels of CK-18-M30, FGF-21, IL-1Ra and PEDF increased, while OPG decreased in a stepwise fashion in controls, non-NASH NAFLD patients and NASH patients (P < 0.01). The area under receiver-operating characteristics curve to diagnose NASH was 0.86 for CK-18-M30, 0.89 for FGF-21, 0.89 for IL-1Ra, 0.89 for PEDF and 0.89 for OPG. CK-18-M30 had 70% negative predictive value (NPV) and 79% positive predictive value (PPV) to diagnose NASH. A 5-step approach measuring CK-18-M30 followed by FGF21, IL-1Ra, PEDF and OPG gradually improved the NPV to 76% and PPV to 85%, which reached 80% and 76% respectively in the validation cohort.ConclusionCompared to single biomarker, stepwise combination of CK-18-M30, FGF-21, IL-1Ra, PEDF and OPG can further improve the accuracy in diagnosing NASH.

Shigella sonnei has become the dominant serotype causing shigellosis in Asian countries in recent years. In this study, we characterize the increasing trend of antibiotic resistance profiles and genotypes of S. sonnei isolates in the Beijing area. From January 2002 to December 2007, a total of 1108 Shigella isolates including 362 S. sonnei were recovered from diarrhea patients at the 302nd Hospital in Beijing. While the frequency of S. flexneri gradually decreased, S. sonnei gradually increased and became the dominant species. A total of 362 S. sonnei isolates were further analyzed for their antimicrobial profiles and 272 revived isolates were selected for genotyping analysis, respectively. High-level antimicrobial resistances were observed in sulfamethoxazole/trimethoprim (94.5%), ampicillin (40.3%), piperacillin (36.5%), and ceftriaxone (12.8%) with significant single- and multiple-drug resistance increase trends from 2002 to 2007 (P = 0.0000). Pulsed-field gel electrophoresis analysis indicated that 263 (96.7%) S. sonnei belonged to 1 clonal genotype A, which were further divided into A1-A6 subtypes. While subtype A2 was dominant in the early stage of study years, subtype A4 started to emerge and increased significantly in later years. Antimicrobial resistance rates are statistically different among the 6 subtypes (P = 0.0000), and A4 possessed the highest resistance rates to ampicillin (83.7%) and piperacillin (81.4%). Subtype A3 was highly clustered in inpatients compared to other subtypes (P = 0.0145). This study indicates that a clonal S. sonnei strain has become dominant in the Beijing area, and subtype A4 is responsible for increased antibiotic resistance.

keeping with most other studies, although the recent report by Shah et al. (7 ) demonstrates that this can occur.In summary, we show that DHPLC can detect previously characterized and novel mutations associated with acquired resistance to imatinib. Each PCR product analysis is performed in less than 10 min in an automated instrumentation platform that has increased sensitivity over direct sequencing methods with considerably reduced labor and consumable costs.