Relationship between biofilm formation and antibiotic resistance of Klebsiella pneumoniae and updates on antibiofilm therapeutic strategies.

Klebsiella pneumoniae is a Gram-negative bacterium within the Enterobacteriaceae family that can cause multiple systemic infections, such as respiratory, blood, liver abscesses and urinary systems. Antibiotic resistance is a global health threat and K. pneumoniae warrants special attention due to its resistance to most modern day antibiotics. Biofilm formation is a critical obstruction that enhances the antibiotic resistance of K. pneumoniae. However, knowledge on the molecular mechanisms of biofilm formation and its relation with antibiotic resistance in K. pneumoniae is limited. Understanding the molecular mechanisms of biofilm formation and its correlation with antibiotic resistance is crucial for providing insight for the design of new drugs to control and treat biofilm-related infections. In this review, we summarize recent advances in genes contributing to the biofilm formation of K. pneumoniae, new progress on the relationship between biofilm formation and antibiotic resistance, and new therapeutic strategies targeting biofilms. Finally, we discuss future research directions that target biofilm formation and antibiotic resistance of this priority pathogen.

Pulse-Wave-Pattern Classification with a Convolutional Neural Network.

Owing to the diversity of pulse-wave morphology, pulse-based diagnosis is difficult, especially pulse-wave-pattern classification (PWPC). A powerful method for PWPC is a convolutional neural network (CNN). It outperforms conventional methods in pattern classification due to extracting informative abstraction and features. For previous PWPC criteria, the relationship between pulse and disease types is not clear. In order to improve the clinical practicability, there is a need for a CNN model to find the one-to-one correspondence between pulse pattern and disease categories. In this study, five cardiovascular diseases (CVD) and complications were extracted from medical records as classification criteria to build pulse data set 1. Four physiological parameters closely related to the selected diseases were also extracted as classification criteria to build data set 2. An optimized CNN model with stronger feature extraction capability for pulse signals was proposed, which achieved PWPC with 95% accuracy in data set 1 and 89% accuracy in data set 2. It demonstrated that pulse waves are the result of multiple physiological parameters. There are limitations when using a single physiological parameter to characterise the overall pulse pattern. The proposed CNN model can achieve high accuracy of PWPC while using CVD and complication categories as classification criteria.