Project description:we adopted DIA-MS method to profile serum proteome signatures of acute asthma children and convalescent ones. As result, we identified 747 proteins were identified in 46 serum samples and detected 37 differentially expressed proteins that could clearly separate asthmatic and healthy children.
Project description:Patients with gallbladder carcinoma (GBC), the most aggressive malignancy of the biliary tract, have a poor prognosis. Here, we report our identification of somatic mutations of GBCs in 57 tumor-normal pairs by use of a combination of exome sequencing and ultra-deep sequencing of cancer-related genes. The mutation pattern is defined by a dominative prevalence of C>T mutations at TCN sites. Genes with a significant frequency of non-silent mutations include TP53 (47.1%), KRAS (7.8%), and ERBB3 (11.8%). Moreover, ErbB signaling (including EGFR, ERBB2, ERBB3, ERBB4 and their downstream genes) is the most extensively mutated pathway, affecting 36.8% (21 of 57) of the GBC samples. Multivariate analyses further reveal that patients with ErbB pathway mutations have a worse outcome (P = 0.001). These findings provide insight into the somatic mutational landscape in GBC and highlight the key role of the ErbB signaling pathway in the pathogenesis of GBCs.
Project description:IntroductionHospital readmissions are common, expensive, and a key target of the Medicare Value Based Purchasing (VBP) program. Risk assessment tools have been developed to identify patients at high risk of hospital readmission so they can be targeted for interventions aimed at reducing the rate of readmission. One such tool is the HOSPITAL score that uses seven readily available clinical variables to predict the risk of readmission within 30 days of discharge. The HOSPITAL score has been internationally validated in large academic medical centers. This study aims to determine if the HOSPITAL score is similarly useful in a moderate sized university affiliated hospital in the midwestern United States.Materials and methodsAll adult medical patients discharged from the SIU-SOM Hospitalist service from Memorial Medical Center (MMC) from October 15, 2015 to March 16, 2016, were studied retrospectively to determine if the HOSPITAL score was a significant predictor of hospital readmission within 30 days.ResultsDuring the study period, 998 discharges were recorded for the hospitalist service. The analysis includes data for the 931 discharges. Patients who died during the hospital stay, were transferred to another hospital, or left against medical advice were excluded. Of these patients, 109 (12%) were readmitted to the same hospital within 30 days. The patients who were readmitted were more likely to have a length of stay greater than or equal to 5 days (55% vs. 41%, p = 0.005) and were more likely to have been admitted more than once to the hospital within the last year (100% vs. 49%, p < 0.001). A receiver operating characteristic evaluation of the HOSPITAL score for this patient population shows a C statistic of 0.77 (95% CI [0.73-0.81]), indicating good discrimination for hospital readmission. The Brier score for the HOSPITAL score in this setting was 0.10, indicating good overall performance. The Hosmer-Lemeshow goodness of fit test shows a ? (2) value of 1.63 with a p value of 0.20.DiscussionThis single center retrospective study indicates that the HOSPITAL score has good discriminatory ability to predict hospital readmissions within 30 days for a medical hospitalist service at a university-affiliated hospital. This data for all causes of hospital readmission is comparable to the discriminatory ability of the HOSPITAL score in the international validation study (C statistics of 0.72 vs. 0.77) conducted at considerably larger hospitals (975 average beds vs. 507 at MMC) for potentially avoidable hospital readmissions.ConclusionsThe internationally validated HOSPITAL score may be a useful tool in moderate sized community hospitals to identify patients at high risk of hospital readmission within 30 days. This easy to use scoring system using readily available data can be used as part of interventional strategies to reduce the rate of hospital readmission.