A 6-year study of complicated urinary tract infections in southern China: prevalence, antibiotic resistance, clinical and economic outcomes.

Urinary tract infections (UTIs) are common nosocomial infections. This study evaluated the prevalence, pathogens, antibiotic resistances, clinical outcomes, and hospitalization costs associated with complicated UTIs in southern China, and risk factors delaying patient discharge. We retrospectively reviewed electronic medical records of 4,284 (61.4% women) complicated UTI-related hospitalizations from 2008 to 2013. Average patient age was 61.1 years and median hospital stay was 11 days. Pathogens were isolated from 1,071 urine and 148 blood specimens. Gram-negative bacteria were the most frequent and included Escherichia coli (48.2%), Klebsiella pneumoniae (9.5%), Pseudomonas aeruginosa (4.9%), and Proteus mirabilis (4.6%), while Enterococcus spp. (14.4%) was the most common Gram-positive bacteria causing UTIs. Both E. coli and K. pneumoniae showed high resistance rates (>45%) to wide-spectrum penicillins, cephalosporins, aztreonam, and ciprofloxacin. Resistances to beta-lactamase inhibitor/beta-lactam antibiotic combination were relatively lower. Imipenem, meropenem, and amikacin had the greatest activity against E.coli and K. pneumoniae. Recurrent infection was a risk factor for mortality. Age, sex, previous surgery, diabetes, and renal insufficiency were significant risk factors for delayed discharge (P<0.01). Response to initial treatment was associated with a lower cost. Initial empiric use of antibiotics least associated with resistance may reduce costs and medical resource usage.

Integrating multiple resources to identify specific transcriptional cooperativity with a Bayesian approach.

MOTIVATION: Limited cohort of transcription factors is capable to structure various gene-expression patterns. Transcriptional cooperativity (TC) is deemed to be the main mechanism of complexity and precision in regulatory programs. Although many data types generated from numerous experimental technologies are utilized in an attempt to understand combinational transcriptional regulation, complementary computational approach that can integrate diverse data resources and assimilate them into biological model is still under development. RESULTS: We developed a novel Bayesian approach for integrative analysis of proteomic, transcriptomic and genomic data to identify specific TC. The model evaluation demonstrated distinguishable power of features derived from distinct data sources and their essentiality to model performance. Our model outperformed other classifiers and alternative methods. The application that contextualized TC within hepatocarcinogenesis revealed carcinoma associated alterations. Derived TC networks were highly significant in capturing validated cooperativity as well as revealing novel ones. Our methodology is the first multiple data integration approach to predict dynamic nature of TC. It is promising in identifying tissue- or disease-specific TC and can further facilitate the interpretation of underlying mechanisms for various physiological conditions. CONTACT: tieliushi01@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

HDAM: a resource of human disease associated mutations from next generation sequencing studies.

BACKGROUND: Next generation sequencing (NGS) technologies have greatly facilitated the rapid and economical detection of pathogenic mutations in human disorders. However, mutation descriptions are hard to be compared and integrated due to various reference sequences and annotation tools adopted in different articles as well as the nomenclature of diseases/traits. DESCRIPTION: The Human Disease Associated Mutation (HDAM) database is dedicated to collect, standardize and re-annotate mutations for human diseases discovered by NGS studies. In the current release, HDAM contains 1,114 mutations, located in 669 genes and associated with 125 human diseases through literature mining. All mutation records have uniform and unequivocal descriptions of sequence changes according to the Human Genome Sequence Variation Society (HGVS) nomenclature recommendations. Each entry displays comprehensive information, including mutation location in genome (hg18/hg19), gene functional annotation, protein domain annotation, susceptible diseases, the first literature report of the mutation and etc. Moreover, new mutation-disease relationships predicted by Bayesian network are also presented under each mutation. CONCLUSION: HDAM contains hundreds rigorously curated human mutations from NGS studies and was created to provide a comprehensive view of these mutations that confer susceptibility to the common disorders. HDAM can be freely accessed at http://www.megabionet.org/HDAM.