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- Wei, Jianhua, et al.
(author)
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Genome and proteomic analysis of risk factors for fatal outcome in children with severe community-acquired pneumonia caused by human adenovirus 7
- 2023
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In: Journal of Medical Virology. - : WILEY. - 0146-6615 .- 1096-9071. ; 95:11
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Journal article (peer-reviewed)abstract
- Introduction: Human adenovirus 7 (HAdV-7) is an important viral pathogen of severe pneumonia in children and a serious threat to health. Methods: A cohort of 45 pediatric patients diagnosed with HAdV-7-associated severe pneumonia and admitted to the Pediatric Intensive Care Unit at the Children's Hospital of Chongqing Medical University from May 2018 to January 2020 were included. Risk factors of death were analyzed by the Cox proportional risk mode with Clinical data, serum, and nasopharyngeal aspirate adenovirus load, Genome analysis, Olink proteomics, and cytokine profile between dead and surviving patients were also analyzed. Results: A total of 45 children with a median age of 12.0 months (interquartile range [IQR]: 6.5, 22.0) were included (female 14), including 14 (31.1%) who died. High serum viral load was an independent risk factor for mortality (hazard ratio [HR] = 2.16, 95% confidence interval [CI], 1.04-4.49, p = 0.039). BTB and CNC homology 1 (BACH1), interleukin-5 (IL-5), and IL-9 levels were significantly correlated with serum viral load (p = 0.0400, 0.0499, and 0.0290; r = 0.4663, 0.3339, and -0.3700, respectively), with significant differences between the dead and survival groups (p = 0.021, 0.001, and 0.021). Conclusions: Severe cytokine storm-associated high serum viral load after HAdV-7 infection may be the main mechanism responsible for poor prognosis in children.
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| 2. |
- Wang, Weixing, et al.
(author)
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Remote Sensing Image Automatic Registration on Multi-scale Harris-Laplacian
- 2015
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In: JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING. - : Springer Science and Business Media LLC. - 0255-660X .- 0974-3006. ; 43:3, s. 501-511
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Journal article (peer-reviewed)abstract
- In order to overcome the difficulty of automatic image registration in image preprocessing, this paper presents an automatic registration algorithm for remote sensing images with different spatial resolutions. The algorithm is studied based on Harris-Laplacian corner detection, which can determine the affine transformation (zoom, rotation, translation) between images of different scales. The corners in the reference and registration images are firstly detected and located by a multi-scale Harris-Laplacian (H-L) corner detector. Secondly, the algorithm chooses SURF (Speeded Up Robust Feature) descriptor to calculate the detected corners descriptors. Then, the multi-resolution corner matching is achieved based on Euclid distance. Finally, according to the LoG (Laplacian Of Gaussian), the scale factor is automatically determined between reference and registration images. A number of remote sensing images are tested, and the experiments show that the studied algorithm can register two remote sensing images of different sizes and resolutions automatically. It also verifies that the algorithm has the lower time cost comparing with the other existing algorithms (e.g. SIFT) within certain detecting accuracy level. This algorithm is also useful for resolving the problem of potential errors due to parallax effects when establishing geometric affine transformation on corners for detecting on buildings with different unknown elevations.
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