ABSTRACT
Background: Since December of 2019, novel coronavirus (SARS-CoV-2)-induced pneumonia (COVID-19) exploded in Wuhan, and rapidly spread throughout China. Patients with COVID-19 demonstrated quite different appearances and outcomes in clinical manifestations. We aimed to figure out whether risk factors of the cystatin C (CysC) and the CysC rangeability are influencing the prognosis of COVID-19 patients with or without type 2 diabetes mellitus (T2DM).Methods: 675 T2DM patients and 602 non-T2DM patients were divided into low CysC group, high CysC group and low CysC rangeability group, high CysC rangeability group according to the serum CysC level and the change range of CysC. Demographic characteristics, clinical data and laboratory results of the four groups were collected and analyzed.Results: Our data showed that COVID-19 patients with high CysC level and CysC rangeability had more organic damage and higher mortality rate compared to those with low level or low rangeability of CysC. Furthermore, patients with higher CysC level and CysC rangeability also demonstrated higher blood lymphocytes (lymph), C-reactive protein (CRP), alanine aminotransferase (ALT), aspartate aminotransferase (AST) which may greatly influence disease progression and poor prognosis of COVID-19. After adjusting for possible confounders, multivariate analysis revealed that CysC≤0.93mg/dl as a reference, CysC＞0.93mg/dl were significantly associated with the risk of heart failure (OR=2.401, 95% CI: 1.118–5.156) and all-cause death (OR=2.734, 95% CI: 1.098-6.811); referring to CysC rangeability≤0, CysC rangeability＞0 significantly associated with all-cause death (OR=4.029, 95% CI: 1.864-8.706). Further grouped by T2DM, these associations were stronger in T2DM than in non-T2DM.Conclusions: It suggests that CysC level and CysC rangeability contribute to clinical manifestations and may influence the prognosis of COVID-19. The CysC is considered as a potential risk factor of the prognosis of COVID-19. Special medical care and appropriate intervention should be performed in COVID-19 patients with elevated CysC during hospitalization and later clinical follow-up, especially for those with T2DM.
Subject(s)
Heart Failure , Diabetes Mellitus, Type 2 , Pneumonia , Neurocognitive Disorders , Death , COVID-19ABSTRACT
Background：Dental departments generally employ cone-beam computed tomography (CBCT) instead of conventional computed tomography (CT), due to its lower price, smaller dosage, and high spatial resolution. During the corona virus disease 2019 (COVID-19) outbreak, CBCT is highly recommended to replace intraoral radiography because it greatly reduces the risk of exposure to salivary droplets. However, CBCT's inability to quantitatively measure tissue attenuation limits its application in differential diagnosis. Methods:We employed a U-Net based network to generate synthetic CT from dental CBCT. The deep neural network can be trained end-to-end to learn the complex mapping between CBCT and CT values. By the U-Net architecture, low-level and high-level features are both utilized to get fine detailed synthetic CT. We applied our method on the collected dataset contains 62 patients. Results:Experimental results on four metrics -- mean absolute error (MAE), root-mean-square error (RMSE), structural similarity index (SSIM), and peak-signal-to-noise ratio (PSNR) -- showed significant improvement of the synthetic CT compared to the original CBCT data. The MAE and RMSE improvement percentages are 64.44% and 66.44%.The MAE level of synthetic CT for most of the tissues are small enough to separate most important tissues,including dentin and cancellous bone, dentin and root canal，implants and cortical bone.Conclusions:CBCT and synthetic CT values can be used to distinguish different high-attenuation structures that are of interest to dentists. The application of CBCT assisted by this U-net based network in medical imaging of other parts of the body is promising.