Clinical characteristics of inpatients with coronavirus disease 2019 (COVID-19) in Sichuan province.

BACKGROUND: The outbreak of COVID-19 has resulted in serious concerns in China and abroad. To investigate clinical features of confirmed and suspected patients with COVID-19 in west China, and to examine differences between severe versus non-severe patients. METHODS: Patients admitted for COVID-19 between January 21 and February 11 from fifteen hospitals in Sichuan Province, China were included. Experienced clinicians trained with methods abstracted data from medical records using pre-defined, pilot-tested forms. Clinical characteristics between severe and non-severe patients were compared. RESULTS: Of the 169 patients included, 147 were laboratory-confirmed, 22 were suspected. For confirmed cases, the most common symptoms from onset to admission were cough (70·7%), fever (70·5%) and sputum (33·3%), and the most common chest CT patterns were patchy or stripes shadowing (78·0%); throughout the course of disease, 19·0% had no fever, and 12·4% had no radiologic abnormality; twelve (8·2%) received mechanical ventilation, four (2·7%) were transferred to ICU, and no death occurred. Compared to non-severe cases, severe ones were more likely to have underlying comorbidities (62·5% vs 26·2%, P = 0·001), to present with cough (92·0% vs 66·4%, P = 0·02), sputum (60·0% vs 27·9%, P = 0·004) and shortness of breath (40·0% vs 8·2%, P <  0·0001), and to have more frequent lymphopenia (79·2% vs 43·7%, P = 0·003) and eosinopenia (84·2% vs 57·0%, P = 0·046). CONCLUSIONS: The symptoms of patients in west China were relatively mild, and an appreciable proportion of infected cases had no fever, warranting special attention.

[Study on fatigue resistance of mechanical nickel-titanium files by phase-locked infrared flaw detection method].

PURPOSE: The movement trend of the posterior teeth and the distribution of the periodontal membrane stress were studied by using three-dimensional digital technology. METHODS: CBCT data of 88 patients admitted to our hospital from June 2017 to June 2022 were selected, and input into Mimics20.0 software for preliminary extraction of all parts and stored with STL files; then the data were repaired and optimized through Geomagic Studio 2014 software. With the help of normal phase extension, the invisible appliance and periodontal membrane were constructed. Finally, the six FEM models were simulated and observed by the current teeth in different groups. Statistical analysis was performed with SPSS 21.0 software package. RESULTS: The effect force of the largest periodontal membrane was distributed in the neck of the tooth, followed by the apical area, with the maximum effect force value in the NA group. In all accessory groups, the periodontal membrane maximum paradigm isoeffect force values of all patients in the accessory vertical rectangular group were significantly smaller than the values obtained in the horizontal rectangular group. CONCLUSIONS: The design of orthodontic tooth accessories has a strong inhibition effect on the position movement of anterior teeth during recovery, which improves the accuracy of tooth three-dimensional movement to a certain extent. Meanwhile, the normal equivalent stress of the periodontal membrane of patients in the initial application of the invisible appliance without brackets is large.

Unsupervised Uncertainty Estimation Using Spatiotemporal Cues in Video Saliency Detection.

In this paper, we address the problem of quantifying the reliability of computational saliency for videos, which can be used to improve saliency-based video processing algorithms and enable more reliable performance and objective risk assessment of saliency-based video processing applications. Our approach to quantify such reliability is twofold. First, we explore spatial correlations in both the saliency map and the eye-fixation map. Then, we learn the spatiotemporal correlations that define a reliable saliency map. We first study spatiotemporal eye-fixation data from the public CRCNS data set and investigate a common feature in human visual attention, which dictates a correlation in saliency between a pixel and its direct neighbors. Based on the study, we then develop an algorithm that estimates a pixel-wise uncertainty map that reflects our supposed confidence in the associated computational saliency map by relating a pixel's saliency to the saliency of its direct neighbors. To estimate such uncertainties, we measure the divergence of a pixel, in a saliency map, from its local neighborhood. In addition, we propose a systematic procedure to evaluate uncertainty estimation performance by explicitly computing uncertainty ground truth as a function of a given saliency map and eye fixations of human subjects. In our experiments, we explore multiple definitions of locality and neighborhoods in spatiotemporal video signals. In addition, we examine the relationship between the parameters of our proposed algorithm and the content of the videos. The proposed algorithm is unsupervised, making it more suitable for generalization to most natural videos. Also, it is computationally efficient and flexible for customization to specific video content. Experiments using three publicly available video data sets show that the proposed algorithm outperforms state-of-the-art uncertainty estimation methods with improvement in accuracy up to 63% and offers efficiency and flexibility that make it more useful in practical situations.