Hyperglycemia-induced cathepsin L maturation: Implications for diabetic comorbidities and COVID-19 susceptibility (preprint)

Diabetes is the second most frequent chronic comorbidity for COVID-19 mortality, yet the underlying mechanism remains unclear. Previous studies suggest that Cathepsin L (CTSL) is implicated in diabetic complications such as nephropathy and retinopathy. Our previous research identified CTSL as a critical protease that promotes SARS-CoV-2 infection and a potential drug target. Here, we show that individuals with diabetes have elevated blood CTSL levels, which facilitates SARS-CoV-2 infection. Chronic hyperglycemia, as indicated by HbA1c levels, is positively correlated with CTSL concentration and activity in diabetic patients. Acute hyperglycemia induced by a hyperglycemic clamp in healthy individuals increases CTSL activity. In vitro, high glucose, but not high insulin, promotes SARS-CoV-2 infection in wild-type (WT) cells, while CTSL knockout (KO) cells show reduced susceptibility to high glucose-promoted effects. Using lung tissue samples from diabetic and non-diabetic patients, as well as db/db diabetic and control mice, our findings demonstrate that diabetic conditions increase CTSL activity in both humans and mice. Mechanistically, high glucose levels promote CTSL maturation and CTSL translocation from the endoplasmic reticulum (ER) to the lysosome via the ER-Golgi-lysosome axis. This study emphasizes the significance of hyperglycemia-induced cathepsin L maturation in the development of diabetic comorbidities and complications.

A semi-automatic ultrasound image analysis system for the grading diagnosis of COVID-19 pneumonia (preprint)

This paper proposes a semi-automatic system based on quantitative characterization of the specific image patterns in lung ultrasound (LUS) images, in order to assess the lung conditions of patients with COVID-19 pneumonia, as well as to differentiate between the severe / and no-severe cases. Specifically, four parameters are extracted from each LUS image, namely the thickness (TPL) and roughness (RPL) of the pleural line, and the accumulated with (AWBL) and acoustic coefficient (ACBL) of B lines. 27 patients are enrolled in this study, which are grouped into 13 moderate patients, 7 severe patients and 7 critical patients. Furthermore, the severe and critical patients are regarded as the severe cases, and the moderate patients are regarded as the non-severe cases. Biomarkers among different groups are compared. Each single biomarker and a classifier with all the biomarkers as input are utilized for the binary diagnosis of severe case and non-severe case, respectively. The classifier achieves the best classification performance among all the compared methods (area under the receiver operating characteristics curve = 0.93, sensitivity = 0.93, specificity = 0.85). The proposed image analysis system could be potentially applied to the grading and prognosis evaluation of patients with COVID-19 pneumonia.