Genomic Analysis and Molecular Characteristics in Carbapenem-Resistant Klebsiella pneumoniae Strains.

Klebsiella pneumoniae is an important bacterium and responsible for both infections acquired in hospital and community because of its multidrug resistance and the virulence. The aim of this research was to investigate clonal lineages, antibiotic resistance profiles, and virulence factors of the hospital isolated carbapenem-resistant strains. Fifty carbapenem-resistant isolates were phenotypically confirmed extended-spectrum beta-lactamases ESBLs producers. MLST analysis revealed 94% sequence type 11. These isolates mainly belonged to three clones according to the PFGE DNA patterns. PFGE patterns have good discrimination than ST profiles. One isolate, K. pneumoniae KPX, undergoing whole-genome sequencing comprised one circular chromosome and four circular plasmids. This isolate harbored a variety of antimicrobial resistance and virulence determinants. The closest relative of K. pneumoniae KPX was another ST11 clinical isolate recovered Sichuan. In addition, KPC-2 (98.0%), SHV-11 (98.0%), TEM-1 (76.0%), CTX-M (76.0%), oqxB1(66%), qnrS (70%), Int1 (42.0%), sul1 (82.0%), sul2 (96.0%), iutA (88%), iucC(10%), and rmpA2 (100%) genes were presented in multiple drug-resistant isolates. The dataset presented in this study provided the genomic and epidemiological analysis of carbapenem-resistant K. pneumoniae in hospital settings. Antimicrobial-resistant profiles suggested the presence of significant selective antibiotic pressure. Appropriate surveillance is essential to the development of effective control strategies in the prevention of nosocomial infection.

Characterization and Significance of Sub-Visible Particles and Colloids in a Submerged Anaerobic Membrane Bioreactor (SAnMBR).

The distribution, composition and morphological structure of subvisible particles and colloids (0.01-10 µm) in the supernatant of a lab-scale submerged anaerobic membrane bioreactor (SAnMBR), and their role in membrane fouling, was investigated. Photometric analysis showed that the supernatant and membrane foulants were dominated by particles and colloids (0.45-10 µm), which accounted for over 90% of the total organics (proteins and polysaccharides). Excitation-emission matrix (EEM) fluorescence spectra and monosaccharide analysis showed that these particles and colloids were rich in fluorescent proteins, rhamnose, ribose and arabinose, all of which could be related to cellular and extracellular substances. Fluorescence and scanning electron microscopy confirmed the presence of bacterial cells in/on the subvisible particles and colloids. The microparticles (5-10 µm) were primarily composed of Streptobacilli and/or filamentous bacteria in the form of microcolonies, while the submicrometer particles and colloids (1-5 µm and 100 kDa-1 µm) had more free/single cocci and bacilli. The ratio of live/dead cells varied in different size-fractions, and the particles (1-10 µm) contained more live cells compared with the colloids (100 kDa-1 µm). Our findings suggest that bacterial cells in/on the particles and colloids could have an important effect on fouling in SAnMBRs as they represent pioneering species attaching to membranes to form fouling layers/biofilm. Such insights reveal that previous foulant-characterization studies in MBRs tended to overestimate organic fouling, while the biofouling induced by these bacteria in/on the particles and colloids was overlooked.

Assessing usability of intelligent guidance chatbots in Chinese hospitals: Cross-sectional study.

Objective: This study aimed to assessing usability of intelligent guidance chatbots (IGCs) in Chinese hospitals. Methods: A cross-sectional study based on expert survey was conducted between August to December 2023. The survey assessed the usability of chatbots in 590 Chinese hospitals. One-way ANOVA was used to analyze the impact of the number of functions, human-like characteristics, number of outpatients, and staff size on the usability of the IGCs. Results: The results indicate that there are 273 (46.27%) hospitals scoring above 45 points. In terms of function development, 581(98.47%) hospitals have set the number of functions between 1 and 5. Besides, 350 hospitals have excellent function implementation, accounting for 59.32%. In terms of the IGC's human-like characteristic, 220 hospitals have both an avatar and a nickname. Results of One-way ANOVA show that, the number of functions(F = 202.667, P < 0.001), human-like characteristics(F = 372.29, P < 0.001), staff size(F = 9.846, P < 0.001), and the number of outpatients(F = 5.709, P = 0.004) have significant impact on the usability of hospital IGCs. Conclusions: This study found that the differences in the usability of hospital IGCs at various levels of the number of functions, human-like characteristics, number of outpatients, and staff size. These findings provide insights for deploying hospital IGCs and can inform improvements in patient's experience and adoption of chatbots.