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Aims Type 2 diabetes is considered to be one of the essential risks of adverse outcomes in coronavirus disease 2019 (COVID-19). 1 Metformin and insulin were suggested to affect the outcomes. However, divergent views are still expressed. We aim to gain further insight into metformin and insulin in both pre-admission and in-hospital usage in COVID-19 patients with pre-existed type 2 diabetes. Main methods This is a multicentral retrospective study of the hospital confirmed COVID-19 patients between January 19 to April 09, 2020, who admitted to 3 main hospitals in Xiangyang city, China. The effect of type 2 diabetes, metformin, and insulin on COVID-19 were analyzed, respectively. Clinical characteristics, blood laboratory indices, clinical observational indices, and outcomes of these cases were collected. Key findings A total of 407 confirmed COVID-19 patients (including 50 pre-existed type 2 diabetes) were eligible in our study. COVID-19 patients with type 2 diabetes had more adverse outcomes than non-diabetes (OR 2 : mortality: 1.46 [95% CI 3 1.11, 1.93]; P < 0.001). Pre-admission metformin usage showed a declined intensive care unit admission rate in a dose-dependent fashion (OR 0.04 [95% CI 0.00, 0.99]; adjust P = 0.049). While in-hospital insulin usage attempted to increase the invasive ventilation (8 [34.8%] vs. 1 [3.7%], adjust P = 0.043), independent of age and blood glucose. Significance Our study indicated that pre-admitted metformin usage may have beneficial effects on COVID-19 with pre-existed type 2 diabetes, insulin should be used sparingly in the hospital stay.

This study investigated the effects of high-dose long-term antioxidant free radicals on the mortality rate, creatinine (Cr) value, partial pressure of oxygen (PaO2), alanine aminotransferase (ALT), as well as the incidence rates of lung fibrosis and dysfunction in the treatment of patients with severe paraquat (PQ) poisoning [toxic dose, 20 ml stock solution (20% w/v)]. A total of 23 cases of severe PQ poisoning treated in Xiangyang First People's Hospital, Hubei University of Medicine were collected (group 1), and they received conventional treatments such as immunosuppressive agents and/or hemoperfusion. Six patients were given high-dose long-term antioxidant therapy on the basis of conventional treatments (group 2). After treatment, 6 out of the 23 patients in group 1 survived, and all the 6 patients in group 2 survived, with the survival rate of 26.1 vs. 100% (p<0.01). The lowest PaO2 value in group 1 was lower than that in group 2 (70.26±16.38 vs. 91.17±3.43 mmHg, p<0.01). The highest ALT value in group 1 was higher than that in group 2 (216.74±126.23 vs. 52.50±24.83 U/l, p<0.01). There was no significant difference in the incidence rate of lung fibrosis between the two groups of survived patients, but there were 6 patients that died of severe lung fibrosis in group 1. Besides, the incidence rate of lung dysfunction in patients in group 2 was significantly lower than that in survived patients in group 1 (p<0.01). High-dose long-term antioxidants are the most critical treatment option to improve the survival rate of high-dose PQ poisoning, they increase the patient's PaO2, enhance liver function, reduce lung fibrosis and refine lung dysfunction.

Background: Rapid quantification of liver metastasis for diagnosis and follow-up is an unmet medical need in patients with secondary liver malignancies. We present a 3D-quantification model of neuroendocrine liver metastases (NELM) using gadoxetic-acid (Gd-EOB)-enhanced MRI as a useful tool for multidisciplinary cancer conferences (MCC). Methods: Manual 3D-segmentations of NELM and livers (149 patients in 278 Gd-EOB MRI scans) were used to train a neural network (U-Net architecture). Clinical usefulness was evaluated in another 33 patients who were discussed in our MCC and received a Gd-EOB MRI both at baseline and follow-up examination (n = 66) over 12 months. Model measurements (NELM volume; hepatic tumor load (HTL)) with corresponding absolute (ΔabsNELM; ΔabsHTL) and relative changes (ΔrelNELM; ΔrelHTL) between baseline and follow-up were compared to MCC decisions (therapy success/failure). Results: Internal validation of the model’s accuracy showed a high overlap for NELM and livers (Matthew’s correlation coefficient (φ): 0.76/0.95, respectively) with higher φ in larger NELM volume (φ = 0.80 vs. 0.71; p = 0.003). External validation confirmed the high accuracy for NELM (φ = 0.86) and livers (φ = 0.96). MCC decisions were significantly differentiated by all response variables (ΔabsNELM; ΔabsHTL; ΔrelNELM; ΔrelHTL) (p < 0.001). ΔrelNELM and ΔrelHTL showed optimal discrimination between therapy success or failure (AUC: 1.000; p < 0.001). Conclusion: The model shows high accuracy in 3D-quantification of NELM and HTL in Gd-EOB-MRI. The model’s measurements correlated well with MCC’s evaluation of therapeutic response.

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