Klebsiella pneumoniae is a common pathogen of nosocomial pneumonia worldwide and community-acquired pneumonia (CAP) in Asia. Previous studies have shown that K. pneumoniae bacteremic CAP is associated with high mortality. We aimed to revisit K. pneumoniae bacteremic pneumonia in the current era and determine the risk factors associated with 28-day mortality. Between January 2014 and August 2020, adult patients with K. pneumoniae bacteremic pneumonia in a medical center in Taiwan were identified. Clinical and microbiological characteristics were compared between CAP and nosocomial pneumonia. Risk factors for 28-day mortality were analyzed using multivariate logistic regression. Among 150 patients with K. pneumoniae bacteremic pneumonia, 52 had CAP and 98 had nosocomial pneumonia. The 28-day mortality was 52% for all patients, 36.5% for CAP, and 60.2% for nosocomial pneumonia. Hypervirulent K. pneumoniae was more prevalent in CAP (61.5%) than in nosocomial pneumonia (16.3%). Carbapenem-resistant K. pneumoniae was more prevalent in nosocomial pneumonia (58.2%) than in CAP (5.8%). Nosocomial pneumonia, a higher Severe Organ Failure Assessment score, and not receiving appropriate definitive therapy were independent risk factors for 28-day mortality. In conclusion, revisiting K. pneumoniae bacteremic pneumonia in the current era showed a high mortality rate. Host factors, disease severity, and timely effective therapy affect the treatment outcomes of these patients.
To overcome the challenges brought about by abnormal weather and the growing industrial water consumption in Taiwan, the Taiwanese government is transporting water from the northern to the southern part of the country to help with droughts occurring in Taoyuan and Hsinchu. In addition, the government invested NTD 2.78 billion to build the backup water pipelines necessary in Taiyuan and Hsinchu, which help ensure a stable and safe water supply required for regional economic development. The construction adheres to the four major strategic goals of “open source, throttling, dispatch, and backup”. However, the leakage rate of water pipelines remains high. To help with large-scale right-of-way applications and the timeliness of emergency repairs, establishing a system that can detect the locations of leakages is vital. This study intended to apply artificial intelligence (AI) deep learning to develop a water pipe leakage and location identification system. This research established an intelligent sound-assisted water leak identification system, developed and used a localized AI water leak diagnostic instrument to capture on-site dynamic audio, and integrated Internet of Things (IoT) technology to simultaneously identify and locate the leakage. Actual excavation verification results show that the accuracy of the convolutional neural network (CNN) after training is greater than 95%, and the average absolute error calculated between the output data and the input data of the encoder is 0.1021, confirming that the system has high reliability and can reduce the cost of excavation by 26%.
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