DIAGNOSTIC PERFORMANCE OF CENTRAL NERVOUS SYSTEM INFECTIONS IN PATIENTS WITH NEUROSURGICAL INTENSIVE CARE USING METAGENOMIC NEXT-GENERATION SEQUENCING: A PROSPECTIVE OBSERVATIONAL STUDY.

ABSTRACT: Background. Identifying the causative pathogens of central nervous system infections (CNSIs) is crucial, but the low detection rate of traditional culture methods in cerebrospinal fluid (CSF) has made the pathogenic diagnosis of CNSIs a longstanding challenge. Patients with CNSIs after neurosurgery often overlap with inflammatory and bleeding. Metagenomic next-generation sequencing (mNGS) has shown some benefits in pathogen detection. This study aimed to investigate the diagnostic performance of mNGS in the etiological diagnosis of CNSIs in patients after neurosurgery. Methods. In this prospective observational study, we enrolled patients with suspected CNSIs after neurosurgical operations who were admitted to the intensive care unit of Beijing Tiantan Hospital. All enrolled patients' CSF was tested using mNGS and pathogen culture. According to comprehensive clinical diagnosis, the enrolled patients were divided into CNSIs group and non-CNSIs group to compare the diagnostic efficiency of mNGS and pathogen culture. Results. From December 2021 to March 2023, 139 patients were enrolled while 66 in CNSIs group and 73 in non-CNSIs. The mNGS exceeded culture in the variety and quantity of pathogens detected. The mNGS outperformed traditional pathogen culture in terms of positive percent agreement (63.63%), accuracy (82.01%), and negative predictive value (75.00%), with statistically significant differences ( P < 0.05) for traditional pathogen culture. The mNGS also detected bacterial spectrum and antimicrobial resistance genes. Conclusions. Metagenomics has the potential to assist in the diagnosis of patients with CNSIs who have a negative culture.

Reconstructing Floorplans from Point Clouds Using GAN.

This paper proposed a method for reconstructing floorplans from indoor point clouds. Unlike existing corner and line primitive detection algorithms, this method uses a generative adversarial network to learn the complex distribution of indoor layout graphics, and repairs incomplete room masks into more regular segmentation areas. Automatic learning of the structure information of layout graphics can reduce the dependence on geometric priors, and replacing complex optimization algorithms with Deep Neural Networks (DNN) can improve the efficiency of data processing. The proposed method can retain more shape information from the original data and improve the accuracy of the overall structure details. On this basis, the method further used an edge optimization algorithm to eliminate pixel-level edge artifacts that neural networks cannot perceive. Finally, combined with the constraint information of the overall layout, the method can generate compact floorplans with rich semantic information. Experimental results indicated that the algorithm has robustness and accuracy in complex 3D indoor datasets; its performance is competitive with those of existing methods.

Image Restoration Quality Assessment Based on Regional Differential Information Entropy.

With the development of image recovery models, especially those based on adversarial and perceptual losses, the detailed texture portions of images are being recovered more naturally. However, these restored images are similar but not identical in detail texture to their reference images. With traditional image quality assessment methods, results with better subjective perceived quality often score lower in objective scoring. Assessment methods suffer from subjective and objective inconsistencies. This paper proposes a regional differential information entropy (RDIE) method for image quality assessment to address this problem. This approach allows better assessment of similar but not identical textural details and achieves good agreement with perceived quality. Neural networks are used to reshape the process of calculating information entropy, improving the speed and efficiency of the operation. Experiments conducted with this study's image quality assessment dataset and the PIPAL dataset show that the proposed RDIE method yields a high degree of agreement with people's average opinion scores compared with other image quality assessment metrics, proving that RDIE can better quantify the perceived quality of images.

[Design of ENT endoscopic HD video image system].

Endoscopes have been widely used in ENT (Ear-Nose-Throat) disease diagnosis. This paper mainly designs a high-definition (HD) endoscopic video image system, as a subsystem of digital HD ENT head and neck surgery comprehensive diagnostic workstation, permit to display, record, store and transport of HD video or image, which are needed in clinical examination, diagnosis, treatment and teaching. The system is mainly composed of camera control module, video processing module, video display and storage module, human interactive module and picture & text workstation interactive interface module, etc.