Secondary Infection Surveillance with Metagenomic Next-Generation Sequencing in COVID-19 Patients: A Cross-Sectional Study.

Background: Metagenomic next-generation sequencing (mNGS) is a promising tool for improving antimicrobial therapy and infection control decision-making in complex infections. Secondary infection surveillance using mNGS in COVID-19 patients has rarely been reported. Methods: Respiratory pathogen and antibiotic resistance prediction were evaluated by BALF mNGS for 192 hospitalized COVID-19 patients between December 2022 and February 2023. Results: Secondary infection was confirmed in 83.3% (160/192) of the COVID-19 patients, with bacterial infections (45%, 72/160) predominating, followed by mixed bacterial and fungal infections (20%, 32/160), and fungal infections (17.5%, 28/160). The incidence of bacterial or viral secondary infection was significantly higher in patients who were admitted to the ICU, received mechanical ventilation, or developed severe pneumonia (all p<0.05). Klebsiella pneumoniae (n=30, 8.4%) was the most prevalent pathogen associated with secondary infection followed by Acinetobacter baumannii (n=29, 8.1%), Candida albicans (n=29, 8.1%), Aspergillus fumigatus (n=27, 7.6%), human herpes simplex virus type 1 (n=23, 6.4%), Staphylococcus aureus (n=20, 5.6%) and Pneumocystis jiroveci (n=14, 3.9%). The overall concordance between the resistance genes detected by mNGS and the reported phenotypic resistance in 69 samples containing five clinically important pathogens (ie, K. pneumoniae, A. baumannii, S. aureus, P. aeruginosa and E. coli) that caused secondary infection was 85.5% (59/69). Conclusion: mNGS can detect pathogens causing secondary infection and predict antimicrobial resistance for COVID19 patients. This is crucial for initiating targeted treatment and rapidly detect unsuspected spread of multidrug-resistant pathogens.

Stochastic response analysis for nonlinear vibration systems with adjustable stiffness property under random excitation.

Nonlinear vibration systems with adjustable stiffness property have attracted considerable attentions for their prominent broadband performances. In the present manuscript, we consider the stochastic dynamical systems with adjustable stiffness and proposed a numerical method for the random responses analysis of the Gaussian white noise excited systems. A multi-dimensional Fokker-Plank-Kolmogorov equation governing the joint probability density of the mechanical states is derived according to the theory of diffusion processes. We solve the multi-dimensional equation using a splitting method and obtained the stationary probability densities and the mean-square responses directly. Two classical nonlinear vibration systems with adjustable stiffness, including the energy harvesting system and the Duffing system with Dahl friction, are presented as examples. Their comparisons with the results from Monte-Carlo simulations illustrate the effectiveness of the proposed procedure for both monostable and bistable cases, even for cases with strong excitation. In addition, the splitting method is efficient for higher-dimensional problem and has advantages of simple implementation, less storage of intermediate values and so on. Hence, in terms of the application scope, the proposed procedure is superior to the current mainstream methods for the random response evaluation of nonlinear vibration systems with adjustable stiffness.