No abstract

Tumor markers are beneficial for the diagnosis and therapy monitoring of lung cancer. However, the value of tumor markers in lung cancer histological diagnosis is unknown. In this study, we analyzed the serum levels of six tumor markers (CEA, CYFRA21-1, SCC, NSE, ProGRP, and CA125) in 2097 suspected patients with lung cancer and determined whether the combination of the tumor markers was useful for histological diagnosis of lung cancer. We found that CYFRA21-1 was the most sensitive marker in NSCLC. ProGRP showed a better clinical performance than that of NSE in discriminating between SCLC and NSCLC. The serum level of CYFRA21-1 or SCC was significantly higher in squamous carcinoma (p < 0.05), and the levels of ProGRP and NSE were significantly higher in SCLC (p < 0.05). According to the criteria established, SCLC and NSCLC were discriminated with sensitivity of 87.12 and 62.63% and specificity of 64.61 and 99.5%, respectively. The sensitivity and specificity in the differentiation of adenocarcinoma and squamous carcinoma were 68.1 and 81.63% and 70.73 and 65.93%, with NPV of 46.03 and 68.97% and PPV of 85.82 and 79.47%, respectively. Our results suggested the combination of six tumor markers could discriminate the histological types of lung cancer.

Depth estimation is usually ill-posed and ambiguous for monocular camera-based 3D multi-person pose estimation. Since LiDAR can capture accurate depth information in long-range scenes, it can benefit both the global localization of individuals and the 3D pose estimation by providing rich geometry features. Motivated by this, we propose a monocular camera and single LiDAR-based method for 3D multi-person pose estimation in large-scale scenes, which is easy to deploy and insensitive to light. Specifically, we design an effective fusion strategy to take advantage of multi-modal input data, including images and point cloud, and make full use of temporal information to guide the network to learn natural and coherent human motions. Without relying on any 3D pose annotations, our method exploits the inherent geometry constraints of point cloud for self-supervision and utilizes 2D keypoints on images for weak supervision. Extensive experiments on public datasets and our newly collected dataset demonstrate the superiority and generalization capability of our proposed method. Project homepage is at \url{https://github.com/4DVLab/FusionPose.git}.

No abstract

Objective. We sought to analyze the distribution and antibiotic sensitivity of pathogens in hospitalized patients and to provide a scientific reference for the rational application of antibiotics. Methods. From January 2014 to December 2018, urine cultures from patients in our hospital were collected and analyzed retrospectively for the presence, distribution, and drug sensitivity of pathogens. Results. A total of 42,854 midstream urine cultures were collected from which 11,891 (27.75%) pathogens were isolated, including 8101 (68.13%) strains of gram-negative bacteria, 2580 (21.69%) strains of gram-positive bacteria, and 1210 (10.18%) strains of fungi. Escherichia coli and Enterococci were the most common species of gram-negative and gram-positive bacteria, respectively. Drug sensitivity varied among different pathogens. Clear drug resistance was observed in bacteria, while fungus exhibited relatively lower resistance. Conclusion. Pathogens responsible for urinary tract infections in hospitalized patients are diversiform and display resistance to some antibiotics. Drug resistance monitoring should be enhanced to optimize antimicrobial therapy.