Respiratory syncytial virus (RSV) infection is the main cause of lower respiratory tract infection in children. However, there is no effective treatment for RSV infection. Here, we aimed to identify potential biomarkers to aid in the treatment of RSV infection. Children in the acute and convalescence phases of RSV infection were recruited and proteomic analysis was performed to identify differentially expressed proteins (DEPs). Subsequently, promising candidate proteins were determined by functional enrichment and protein-protein interaction network analysis, and underwent further validation by western blot both in clinical and mouse model samples. Among the 79 DEPs identified in RSV patient samples, 4 proteins (BPGM, TPI1, PRDX2, and CFL1) were confirmed to be significantly upregulated during RSV infection. Functional analysis showed that BPGM and TPI1 were mainly involved in glycolysis, indicating an association between RSV infection and the glycolysis metabolic pathway. Our findings provide insights into the proteomic profile during RSV infection and indicated that BPGM, TPI1, PRDX2, and CFL1 may be potential therapeutic biomarkers or targets for the treatment of RSV infection.

This work described a simple and feasible colorimetric immunoassay. Using hemin (a horseradish peroxidase (HRP) mimic enzyme) and the mimic enzyme-chromogenic substrate system, it can qualitatively and quantitatively detect a-fetoprotein (AFP) at an ultralow concentration. The glucose oxidase (GOx) catalyzed oxidation of glucose leads to the formation of gluconic acid and hydrogen peroxide (H2O2). The latter can oxidize 4-aminoatipyrine (4-AAP) to chromogenic products, and the reaction was catalyzed by hemin. With the increase of H2O2, the absorbance increased, and the color of the solution changed from colorless to pink. On the basis of the system, monitored by recording the color or absorbance (λ = 505 nm), a new immunoassay protocol with GOx-labeled anti-AFP detection antibody was designed. A wide linear dependence was obtained in the range from 0.075 to 280 ng mL-1 with a low detection limit (LOD) of 0.0247 ng mL-1 (S/N = 3).

The objective of the present study was to evaluate the predictive values of percent body fat (PBF) and body mass index (BMI) for cardiovascular risk factors, especially when PBF and BMI are conflicting. BMI was calculated by the standard formula and PBF was determined by bioelectrical impedance analysis. A total of 3859 ambulatory adult Han Chinese subjects (2173 males and 1686 females, age range: 18-85 years) without a history of cardiovascular diseases were recruited from February to September 2009. Based on BMI and PBF, they were classified into group 1 (normal BMI and PBF, N = 1961), group 2 (normal BMI, but abnormal PBF, N = 381), group 3 (abnormal BMI, but normal PBF, N = 681), and group 4 (abnormal BMI and PBF, N = 836). When age, gender, lifestyle, and family history of obesity were adjusted, PBF, but not BMI, was correlated with blood glucose and lipid levels. The odds ratio (OR) and 95% confidence interval (CI) for cardiovascular risk factors in groups 2 and 4 were 1.88 (1.45-2.45) and 2.06 (1.26-3.35) times those in group 1, respectively, but remained unchanged in group 3 (OR = 1.32, 95%CI = 0.92-1.89). Logistic regression models also demonstrated that PBF, rather than BMI, was independently associated with cardiovascular risk factors. In conclusion, PBF, and not BMI, is independently associated with cardiovascular risk factors, indicating that PBF is a better predictor.