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There is a high mortality and long hospitalization period for severe cases with 2019 novel coronavirus disease (COVID-19) pneumonia. Therefore, it makes sense to search for a potential biomarker that could rapidly and effectively identify severe cases early. Clinical samples from 28 cases of COVID-19 (8 severe cases, 20 mild cases) in Zunyi District from January 29, 2020 to February 21, 2020 were collected and otherwise statistically analysed for biochemical markers. Serum urea, creatinine (CREA) and cystatin C (CysC) concentrations in severe COVID-19 patients were significantly higher than those in mild COVID-19 patients (P<0.001), and there were also significant differences in serum direct bilirubin (DBIL), cholinesterase (CHE) and lactate dehydrogenase (LDH) concentrations between severe and mild COVID-19 patients (P<0.05). Serum urea, CREA, CysC, DBIL, CHE and LDH could be used to distinguish severe COVID-19 cases from mild COVID-19 cases. In particular, serum biomarkers, including urea, CREA, CysC, which reflect glomerular filtration function, may have some significance as potential indicators for the early diagnosis of severe COVID-19 and to distinguish it from mild COVID-19. Glomerular filtration function injury in severe COVID-19 patients should also be considered by clinicians.

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Based on the basic principle of the porosity method in image segmentation, considering the relationship between the porosity of the rocks and the fractal characteristics of the pore structures, a new improved image segmentation method was proposed, which uses the calculated porosity of the core images as a constraint to obtain the best threshold. The results of comparative analysis show that the porosity method can best segment images theoretically, but the actual segmentation effect is deviated from the real situation. Due to the existence of heterogeneity and isolated pores of cores, the porosity method that takes the experimental porosity of the whole core as the criterion cannot achieve the desired segmentation effect. On the contrary, the new improved method overcomes the shortcomings of the porosity method, and makes a more reasonable binary segmentation for the core grayscale images, which segments images based on the actual porosity of each image by calculated. Moreover, the image segmentation method based on the calculated porosity rather than the measured porosity also greatly saves manpower and material resources, especially for tight rocks.