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Abstract Background The application of multiparametric magnetic resonance imaging (mpMRI) for diagnosis of prostate cancer has been recommended by the European Association of Urology (EAU), National Comprehensive Cancer Network (NCCN), and European Society of Urogenital Radiology (ESUR) guidelines. The purpose of this study is to systematically review the literature on assessing the accuracy of mpMRI in patients with suspicion of prostate cancer. Method We searched Embase, Pubmed and Cochrane online databases from January 12,000 to October 272,018 to extract articles exploring the possibilities that the pre-biopsy mpMRI can enhance the diagnosis accuracy of prostate cancer. The numbers of true- and false-negative results and true- and false-positive ones were extracted to calculate the corresponding sensitivity and specificity of mpMRI. Study quality was assessed using QUADAS-2 tool. Random effects meta-analysis and a hierarchical summary receiver operating characteristic (HSROC) plot were performed for further study. Results After searching, we acquired 3741 articles for reference, of which 29 studies with 8503 participants were eligible for inclusion. MpMRI maintained impressive diagnostic value, the area under the HSROC curve was 0.87 (95%CI,0.84–0.90). The sensitivity and specificity for mpMRI were 0.87 [95%CI, 0.81–0.91] and 0.68 [95%CI,0.56–0.79] respectively. The positive likelihood ratio was 2.73 [95%CI 1.90–3.90]; negative likelihood ratio was 0.19 [95% CI 0.14,-0.27]. The risk of publication bias was negligible with P = 0.96. Conclusion Results of the meta-analysis suggest that mpMRI is a sensitive tool to diagnose prostate cancer. However, because of the high heterogeneity existing among the included studies, further studies are needed to apply the results of this meta-analysis in clinic.

This article analyzes Chinese video artist Zhang Peili's latex-glove-themed paintings and conceptual art from the 1980s. It argues that they are an important intervention into post-socialist China's politicized discussions on art's role in society. Building on Mel Chen's discussion of animacy hierarchies as sociopolitical constructs, the article reads Zhang's latex gloves as animate—transgressing boundaries of "aliveness" between humans and objects. It locates Zhang and his art collective, "Pond" Society, amid mid-1980s discourses on art's emotional contagiousness, highlighting that the Chinese art establishment's insistence on Realism and on the Maoist art methodology of "immersion into life" (shenru shenghuo) reflected anxieties about "healthy" art making that could promote national progress. Zhang's series of X? glove paintings from 1986 onward represent strategic promotions of his medical conceptualization of art. Zhang was infected during the 1988 Shanghai hepatitis A epidemic, and his subsequent glove-themed work called for an embrace of viral life. The figure of the glove was also central to Zhang's first two video works, including Document on Hygiene No. 3, which signals a shift away from the insistence that art should reinforce the artist's authority. Instead, Document uses the gloved hand to reflect on where, in a society obsessed with purity and in an art world obsessed with radicalism, we might fundamentally locate agency. This article shows that the history of contemporary Chinese art cannot be understood without considering artists' responses to, and contributions in shaping, the deeply politicized notion of hygiene. [ABSTRACT FROM AUTHOR]

Low-dose computed tomography (LDCT) imaging can significantly reduce the radiation dose to a patient. However, a low radiation dose will cause considerable noise and artifacts in the image, seriously impacting the clinical diagnosis. To better solve the problems, we propose an edge protection and global attention mechanism densely connected convolutional network (EP–GAMNet) for LDCT denoising. First, edge information was extracted using the improved eight-directional Prewitt operator and then, passed to each convolutional block through skip connections. Subsequently, a multiscale feature extractor and global attention mechanism were used for feature extraction. The final predicted images were then obtained by the noise reduction module. Further, a compound loss function based on mean squared error and perceptual loss was used to enhance the texture detail and improve the visual quality of the image. Extensive experiments on Mayo and piglet datasets showed the effectiveness of the proposed method in reducing noise/artifacts while preserving edges. The peak-signal-to-noise ratio value of CT images of the AAPM dataset processed by the new model is 33.5712, and the structural similarity (SSIM) value is 0.9244. Moreover, it performed better than some classical methods in terms of objective indicators and subjective effects. The proposed model is robust and can effectively retain edge information and extract effective features from LDCT images. Moreover, it is effective in improving LDCT image quality. [ABSTRACT FROM AUTHOR]

This study investigates the impact of assimilating Aircraft Meteorological Data Relay (AMDAR) observations on the prediction of two typhoons, Nesat and Haitang (2017), using the Gridpoint Statistical Interpolation (GSI) assimilation system and the Weather Research and Forecasting (WRF) model. Two quality control (QC) methods, Minimum Covariance Determinant (MCD) and one-class Support Vector Machine (OCSVM), were employed to perform QC on the AMDAR observations before data assimilation. The QC results indicated that both methods significantly reduced kurtosis, skewness, and discrepancies between the AMDAR data and the reanalysis data. The data distribution after applying the MCD-QC method exhibited a closer resemblance to a Gaussian distribution. Four numerical experiments were conducted to assess the impact of different AMDAR data qualities on typhoon forecasting, including a control experiment without data assimilation (EXP-CNTL), assimilating all AMDAR observations (EXP-RAW), assimilating observations after applying MCD-QC (EXP-MCD), and assimilating observations after applying OCSVM-QC (EXP-SVM). The results demonstrated that using AMDAR data in assimilation improved the track and intensity prediction of the typhoons. Furthermore, utilizing QC before assimilation enhanced the performance of track forecasting prediction, with EXP-MCD showing the best performance. As for intensity prediction, the three assimilation experiments exhibited varying strengths and weaknesses at different times, with EXP-MCD showing smaller intensity forecast errors on average. [ABSTRACT FROM AUTHOR]