Aims: Stagnation is widely believed to predispose water systems to colonization by Legionella. A model plumbing system was constructed to determine the effect of flow regimes on the presence of Legionella within microbial biofilms. Methods and Results: The plumbing model contained three parallel pipes where turbulent, laminar and stagnant flow regimes were established. Four sets of experiments were carried out with Reynolds number from 10 000 to 40 000 and from 355 to 2000 in turbulent and laminar pipes, respectively. Legionella counts recovered from biofilm and planktonic water samples of the three sampling pipes were compared with to determine the effect of flow regime on the presence of Legionella. Significantly higher colony counts of Legionella were recovered from the biofilm of the pipe with turbulent flow compared with the pipe with laminar flow. The lowest counts were in the pipe with stagnant flow. Conclusions: We were unable to demonstrate that stagnant conditions promoted Legionella colonization. Significance and Impact of the Study: Plumbing modifications to remove areas of stagnation including deadlegs are widely recommended, but these modifications are tedious and expensive to perform. Controlled studies in large buildings are needed to validate this unproved hypothesis.

The study was to determine the efficacy of copper-silver ionization against the formation of Pseudomonas aeruginosa , Stenotrophomonas maltophilia , and Acinetobacter baumannii in biofilms and planktonic phases. At concentrations below the EPA limits, ionization has potential to control the three waterborne pathogens, in addition to Legionella , in hospital water systems for nosocomial infection control.

The most robust and economical method for laboratory diagnosis of tuberculosis (TB) is to identify mycobacteria acid-fast bacilli (AFB) under acid-fast staining, despite its disadvantages of low sensitivity and labor intensity. In recent years, artificial intelligence (AI) has been used in TB-smear microscopy to assist medical technologists with routine AFB smear microscopy. In this study, we evaluated the performance of a TB automated system consisting of a microscopic scanner and recognition program powered by artificial intelligence and machine learning. This AI-based system can detect AFB and classify the level from 0 to 4+. A total of 5930 smears were evaluated on the performance of this automatic system in identifying AFB in daily lab practice. At the first stage, 120 images were analyzed per smear, and the accuracy, sensitivity, and specificity were 91.3%, 60.0%, and 95.7%, respectively. In the second stage, 200 images were analyzed per smear, and the accuracy, sensitivity, and specificity were increased to 93.7%, 77.4%, and 96.6%. After removing disqualifying smears caused by poor staining quality and smear preparation, the accuracy, sensitivity, and specificity were improved to 95.2%, 85.7%, and 96.9%, respectively. Furthermore, the automated system recovered 85 positive smears initially identified as negative by manual screening. Our results suggested that the automated TB system could achieve higher sensitivity and laboratory efficiency than manual microscopy under the quality control of smear preparation. Automated TB smear screening systems can serve as a screening tool at the first screen before manual microcopy.

Microscopic examination of acid-fast mycobacterial bacilli (AFB) in sputum smears remains the most economical and readily available method for laboratory diagnosis of pulmonary tuberculosis (TB). However, this conventional approach is low in sensitivity and labor-intensive. An automated microscopy system incorporating artificial intelligence and machine learning for AFB identification was evaluated. The study was conducted at an infectious disease hospital in Jiangsu Province, China, utilizing an intelligent microscope system. A total of 1000 sputum smears were included in the study, with the system capturing digital microscopic images and employing an image recognition model to automatically identify and classify AFBs. Referee technicians served as the gold standard for discrepant results. The automated system demonstrated an overall accuracy of 96.70% (967/1000), sensitivity of 91.94% (194/211), specificity of 97.97% (773/789), and negative predictive value (NPV) of 97.85% (773/790) at a prevalence of 21.1% (211/1000). Incorporating AI and machine learning into an automated microscopy system demonstrated the potential to enhance the sensitivity and efficiency of AFB detection in sputum smears compared to conventional manual microscopy. This approach holds promise for widespread application in TB diagnostics and potentially other fields requiring labor-intensive microscopic examination.