Klebsiella pneumoniae has become one of the most intractable gram-negative pathogens infecting humans and animals due to its severe antibiotic resistance. Bacteriophages and protein products derived from them are receiving increasing amounts of attention as potential alternatives to antibiotics. In this study, we isolated and investigated the characteristics of a new lytic phage, P1011, which lyses K5 K. pneumoniae specifically among 26 serotypes. The K5-specific capsular polysaccharide-degrading depolymerase dep1011 was identified and expressed. By establishing murine infection models using bovine strain B16 (capable of supporting phage proliferation) and human strain KP181 (incapable of sustaining phage expansion), we explored the safety and efficacy of phage and dep1011 treatments against K5 K. pneumoniae. Phage P1011 resulted in a 60% survival rate of the mice challenged with K. pneumoniae supporting phage multiplication, concurrently lowering the bacterial burden in their blood, liver, and lungs. Unexpectedly, even when confronted with bacteria impervious to phage multiplication, phage therapy markedly decreased the number of viable organisms. The protective efficacy of the depolymerase was significantly better than that of the phage. The depolymerase achieved 100% survival in both treatment groups regardless of phage propagation compatibility. These findings indicated that P1011 and dep1011 might be used as potential antibacterial agents to control K5 K. pneumoniae infection.

The paper examines the application of uncertainty theory to portfolio decision making, specifically focusing on constructing portfolio models based on uncertain preference relations. Firstly, we establish the theoretical foundation by introducing the theory of uncertainty, which includes uncertain measure and normal uncertain distribution. Then, building upon Markowitz portfolio theory, we propose an uncertain preference relation prioritization model with chance constraints and an additive consistency portfolio model to facilitate rational decision making in a complex and uncertain financial environment. Furthermore, empirical analysis validates our model’s feasibility, demonstrating its advantages in maximizing returns and minimizing risks.

Teaching reform constitutes a crucial task faced by universities in the new era. This paper analyzes existing issues in online course teaching modes and proposes recommendations for improving these modes. The present study focuses on multifactor evaluation of course instruction, selecting six factors as research subjects from a dataset provided by Kaggle website. We employ sentiment analysis, semantic network analysis, as well as LSTM-based sentiment analysis to delve into implementing online course education from students’ perspective while uncovering their concerns and learning needs, ultimately offering relevant suggestions. The conclusions drawn herein possess certain reference value for advancing online education.