In mid-March 2020, the World Health Organization declared that COVID-19 was to be characterised as a pandemic. The purpose of this article is to recommend emergency management procedures for dental clinics during this public health emergency. We have implemented a series of emergency management measures to prevent cross-infection in our dental clinic during the COVID-19 pandemic, including personnel scheduling, division of the clinic into functional areas, limitation or delay of non-emergency patients, staff protection and infection controls, clinical environmental disinfection, and the use of online consultation services, among others. Due to public health policy and dental emergency management, the number of dental visitors to our clinic dropped sharply, and no COVID-19 suspected cases or high-risk patients received treatment. There have been no reports of infection of dental staff or patients during dental treatment in China to date. These public health policies and dental emergency management measures were effective in controlling cross-infection of COVID-19 in the dental clinic. We share control measures for COVID-19, and hope that they will be helpful for dental professionals worldwide to continue to provide dental care in a safe and orderly manner. We share control measures for COVID-19, and hope that they will be helpful for dental professionals worldwide to continue to provide dental care in a safe and orderly manner. To evaluate the accuracy of unenhanced attenuation and early biphasic contrast-enhanced computed tomography (CT) in differentiating adrenal metastases (AMs) from lipid-poor adrenal adenomas (AAs). This retrospective study included 37 patients with 50 AMs and 86 patients with 89 lipid-poor AAs. Quantitative data including the longest diameter (LD), the shortest diameter (SD), LD/SD ratio, CT attenuation values (CTu, CTa, CTv), degree of enhancement (DEAP, DEPP, DEpeak, APW, RPW), and peak enhanced/unenhanced (PE/U) CT attenuation ratio were obtained. Qualitative data including enhancement pattern, location, shape, the presence of calcification or haemorrhage, and intra-lesion necrosis were analysed. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also calculated. The PE/U ratio (≤1.25), CTu (≥32.2 HU), DEpeak (≤43.15 HU), DEPP (≤37.65 HU), presence of intralesional necrosis, location (bilateral adrenal glands), and irregular shape were significant variables for differentiating AMs from lipid-poor AAs (p<0.05). Among them, PE/U ratio (≤1.25) was of greater value in differentiating the two adrenal diseases, with sensitivity, specificity, area under the receiver operating curve (ROC) curve (AUC) of 92%, 84%, 0.933, respectively. When at least any three of above criteria were combined, the sensitivity, specificity, PPV, and NPV for diagnosing AMs were 88%, 93%, 88%, and 88%, respectively. These seven CT criteria are conducive to differentiate AMs from lipid-poor AAs. Early biphasic contrast-enhanced CT is a high-efficient and practical imaging tool in differentiating them. These seven CT criteria are conducive to differentiate AMs from lipid-poor AAs. Early biphasic contrast-enhanced CT is a high-efficient and practical imaging tool in differentiating them. To evaluate the density and volume changes in the lungs of silicosis patients and their relationship with the disease severity classification of the International Labor Organization (ILO). The multidetector computed tomography (CT) images of 44 patients diagnosed with silicosis and 32 controls that underwent thoracic CT due to trauma were evaluated. Patients with silicosis were divided into three categories according to the ILO classification. Data related to the total lung volume, total lung mean density, lung opacity score, percentage of lung high opacity, and mean density in the lower and upper lobes were obtained using three-dimensional (3D) software. There was no significant difference between the total lung mean densities of the silicosis and control groups (p=0.213); however, a significant difference was observed between the two groups in terms of the total lung volume (p<0.0001). According to the ILO classification, there was a significant difference between the disease severity categories in relation to the percentage of lung high opacity (p=0.000005). A strong correlation was detected between disease severity and high opacity percentage (p<0.0001, r=0.804). According to the ILO classification, there was also a significant difference between disease severity categories in terms of the lung opacity score (p=0.000144), as well as a moderate correlation between disease severity and opacity score (p<0.0001, r=0.580). Total lung volume is a CT finding that shows variation in exposure to crystalline silica. The percentage of high opacity determined using multidetector CT is an effective parameter in evaluating disease severity. Total lung volume is a CT finding that shows variation in exposure to crystalline silica. The percentage of high opacity determined using multidetector CT is an effective parameter in evaluating disease severity. To study the ability of dual-energy computed tomography (DECT) after successful mechanical thrombectomy (MT) to predict symptomatic intracerebral haemorrhage (sICH) in anterior circulation acute ischaemic stroke (AIS). From June 2018 to February 2020, 102 AIS patients with DECT performed immediately after successful MT were enrolled prospectively. According to the presence of iodine contrast media extravasation (ICME) on DECT and subsequent sICH development, patients were classified into four groups. The neurological outcome was compared among groups. Imaging parameters, together with clinical factors, were investigated for sICH prediction based on a linear logistic regression model after class-imbalance resolved by Synthetic Minority Sampling Technique (SMOTE) method. Among 102 patients, patients (14.7%, 15/102) with the presence of sICH experienced worse outcomes than others without sICH (p<0.001). No case without ICME was observed with sICH development (0/102). https://www.selleckchem.com/products/rbn013209.html The parameters derived from DECT have excellent performance for sICH prediction after successful MT, which is better than clinical predictive model boosted data (area under the curve [AUC] DECT 0.