Yusen Eason Lin

Vancomycin-intermediate Staphylococcus aureus (VISA) is a multi-drug-resistant pathogen of significant clinical concern. Various S. aureus strains can cause infections, from skin and soft tissue infections to life-threatening conditions such as bacteremia and pneumonia. VISA infections, particularly bacteremia, are associated with high mortality rates, with 34% of patients succumbing within 30 days. This study aimed to develop predictive models for VISA (including hVISA) bacteremia outcomes using data mining techniques, potentially improving patient management and therapy selection. We focused on three endpoints in patients receiving traditional vancomycin therapy: VISA persistence in bacteremia after 7 days, after 30 days, and patient mortality. Our analysis incorporated 29 risk factors associated with VISA bacteremia. The resulting models demonstrated high predictive accuracy, with 82.0–86.6% accuracy for 7-day VISA persistence in blood cultures and 53.4–69.2% accuracy for 30-day mortality. These findings suggest that data mining techniques can effectively predict VISA bacteremia outcomes in clinical settings. The predictive models developed have the potential to be applied prospectively in hospital settings, aiding in risk stratification and informing treatment decisions. Further validation through prospective studies is warranted to confirm the clinical utility of these predictive tools in managing VISA infections.

Microorganisms, including pathogens that cause skin, respiratory, and urinary tract infections, are widespread in our environment. Despite routine cleaning with bleach and disinfectants, the transmission of pathogens still occurs, leading to potential infectious diseases. This study aimed to determine the antibacterial effect of two coating formulas against common environmental pathogens like Escherichia coli, Staphylococcus aureus, Acinetobacter baumannii, Pseudomonas aeruginosa, Salmonella sp., vancomycin-resistant Enterococcus faecium, and Klebsiella pneumoniae. The antibacterial coatings exhibited efficacy against these pathogens, with a bacterial reduction rate (R) greater than two, even after prolonged bleach cleaning. However, UV exposure may affect the efficacy of such coatings. The antibacterial coatings can eliminate the need for repetitive consumption of toxic detergents and reduce cleaning manpower, making them an environmentally friendly product for safety control and infection prevention.

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.

Background: Microscopic examination of acid-fast stained sputum smears for detecting mycobacterial acid-fast bacilli (AFB) remains the most economical and readily available method for laboratory diagnosis of tuberculosis (TB). However, this conventional approach has limitations, including low sensitivity and labor-intensive procedures. Methods: An automated microscopy system incorporating artificial intelligence (AI) 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 (TB-Scan, Wellgen Medical, Kaohsiung). A total of 1,000 sputum smears were included in the analysis, with the system capturing digital microscopic images and employing an image recognition model to automatically identify and classify AFB. Referee technicians provided the gold standard for discrepant results. Results: The automated system demonstrated an overall accuracy of 95.00% (950/1,000), sensitivity of 91.24% (177/194), and specificity of 95.91% (773/806). Notably, the system identified 21 smears as positive that were previously reported as negative, with referee technicians confirming 17 of these as true positive and retracting the test results accordingly. Recalculating the performance, the accuracy increased to 96.70% (967/1,000), sensitivity to 91.94% (194/211), and specificity to 97.97% (773/789), with a false negative rate of 8.06% (17/211) and a false positive rate of 2.03% (16/789). Conclusions: The incorporation of 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.

The COVID-19 pandemic has underscored the pivotal role of epidemiology in studying pathogenic agents

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.

Introduction: Extracorporeal shock wave lithotripsy (ESWL) is a well-established, popular treatment choice for renal stones. Traditionally, the semi-liquid gel is used as a coupling medium in ESWL. During ESWL, body fluid or blood might transmit between the patients when the probe or gel used in the procedure is contaminated and cause potential nosocomial infections. To solve this problem, we developed a solid coupling medium (isolation coupling pad, referred to as “icPad”) between the patient’s skin and the probe as a shock wave transmission medium to prevent contamination. This study aimed to investigate the isolative efficacy of the icPad in blocking the permeation of microbes. Method : Rhodamine 6G (a fluorescent dye) was used as a tracer to simulate the microorganisms. The penetration of the fluorescent dye on the longitudinal section of the icPad was observed by a microscope after the dye was placed on the body side of the icPad for 40 min. After the shock wave, icPad was extracted with 75% ethanol, and fluorescence intensity was measured with a fluorescence spectrometer. Results: Our results revealed that the body side of icPad is free of fluorescent dye during lithotripsy. Qualitative analysis results confirmed that icPad has an isolative effect on simulating contaminants such as bacteria or viruses. Conclusion: In this in vitro phantom study, a proprietary icPad can be an isolative coupling medium and is speculated to avoid cross-contamination of bacterial or viral infection during ESWL.

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.