Next-generation sequencing as an advanced supplementary tool for the diagnosis of pathogens in lower respiratory tract infections: An observational trial in Xi'an, China.

The application of next-generation sequencing (NGS) in routine clinical analysis is still limited. The significance of NGS in the identification of pathogens of lower respiratory tract infection should be assessed as part of routine clinical bacterial examinations and chest imaging results. In the present study, the alveolar lavage fluid samples of 30 patients (25 males and 5 females, aged 19-92 years old, with a median age of 62) were examined by routine bacterial culture and NGS, and the results of pathogen detection and identification were compared. Chest imaging showed consolidation in all 30 patients (100%), and pleural effusion in 13 of the 30 patients (43.33%). The routine bacterial culture of the lavage solution was only positive in 14 of the 30 patients (46.6%), and negative in 16 patients (53.33%). However, the positive rate of NGS test results of the lavage fluid was 100%. A total of 12 cases (40%) were completely consistent with the routine bacterial culture test, with 56 other pathogens of mixed infection detected, accounting for the short comings of the routine bacterial examination. Although NGS cannot distinguish between live and dead bacteria, it is still a useful detection technology for accurate diagnosis of clinical infectious diseases. It is worthy of adaptation in the clinic for more effective clinical management and treatment of the lower respiratory airway infection in addition to the routine bacterial culture testing.

The study on the inverse problem of applied current thermoacoustic imaging based on generative adversarial network.

Applied Current Thermoacoustic Imaging (ACTAI) is a new imaging method which combines electromagnetic excitation with ultrasound imaging, and takes ultrasonic signal as medium and biological tissue conductivity as detection target. Taking the high contrast advantage of Electrical Impedance Tomography (EIT) and high resolution advantage of ultrasound imaging, ACTAI has broad application prospects in the field of biomedical imaging. Although ACTAI has high excitation efficiency and strong detectable Signal-to-Noise Ratio, yet while under low frequency electromagnetic excitation, it is still a big challenge to reconstruct a high-resolution image of target conductivity. This paper proposes a new method for reconstructing conductivity based on Generative Adversarial Network, and it consists of three main steps: firstly, use Wiener filtering deconvolution to restore the electrical signal output by the ultrasonic probe to a real acoustic signal. Then obtain the initial acoustic source image with filtered backprojection technology. Finally, match the conductivity image with the initial sound source image, which are used as training samples for generating the adversarial network to establish a deep learning model for conductivity reconstruction. After theoretical analysis and simulation research, it is found that by introducing machine learning, the new method can dig out the inverse problem solving model contained in the data, which further reconstruct a high-resolution conductivity image and has strong anti-interference characteristics. The new method provides a new way to solve the problem of conductivity reconstruction in Applied Current Thermoacoustic Imaging.