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Purpose: This study investigated multidrug-resistant (MDR) pathogens and antibiotic strategies of culture-positive spontaneous ascitic infection (SAI) in patients with acute decompensated cirrhosis. Materials and Methods: We retrospectively analyzed 432 acute decompensated cirrhotic patients with culture-positive SAI from 11 teaching hospitals in China (January 2012 to May 2018). A Cox proportional hazards model analysis was conducted to identify independent predictors of 28-day mortality. Results: A total of 455 strains were isolated from 432 ascitic culture samples. Gram-negative bacteria (GNB), gram-positive bacteria (GPB), and fungi caused 52.3, 45.5, and 2.2% of all SAI episodes, respectively. Episodes were classified as nosocomial (41.2%), healthcare-related (34.7%), and community-acquired (24.1%). Escherichia coli (13.4%) and Klebsiella pneumoniae (2.4%) were extended-spectrum β-lactamase producing isolates. The prevalence of methicillin-resistant Staphylococcus aureus was 1.1%. Ceftazidime, cefepime, aztreonam, and amikacin were recommended as first-line antibiotics agents for non-MDR GNB infections; piperacillin/tazobactam and carbapenems for MDR GNB in community-acquired and healthcare-related or nosocomial infections, respectively; and vancomycin or linezolid for GPB infections, regardless of drug-resistance status. Multivariate analysis revealed days of hospital stay before SAI, upper gastrointestinal bleeding, white blood cell count, alanine aminotransferase, serum creatinine concentration, total bilirubin, and international normalized ratio as key independent predictors of 28-day mortality. Conclusion: MDR pathogens and antibiotic strategies were identified in patients with acute decompensated cirrhosis with culture-positive SAI, which may help optimize therapy and improve clinical outcomes. [ABSTRACT FROM AUTHOR]

A low-energy adaptive clustering hierarchy (LEACH) protocol is a low-power adaptive cluster routing protocol which was proposed by MIT's Chandrakasan for sensor networks. In the LEACH protocol, the selection mode of cluster-head nodes is a random selection of cycles, which may result in uneven distribution of nodal energy and reduce the lifetime of the entire network. Hence, we propose a new selection method to enhance the lifetime of network, in this selection function, the energy consumed between nodes in the clusters and the power consumed by the transfer between the cluster head and the base station are considered at the same time. Meanwhile, the improved FTBA algorithm integrating the curve strategy is proposed to enhance local and global search capabilities. Then we combine the improved BA with LEACH, and use the intelligent algorithm to select the cluster head. Experiment results show that the improved BA has stronger optimization ability than other optimization algorithms, which the method we proposed (FTBA-TC-LEACH) is superior than the LEACH and LEACH with standard BA (SBA-LEACH). The FTBA-TC-LEACH can obviously reduce network energy consumption and enhance the lifetime of wireless sensor networks (WSNs). [ABSTRACT FROM AUTHOR]