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Background: Coronavirus disease-2019 (COVID-19) epidemic is spreading globally. Sex differences in the severity and mortality of COVID-19 emerged. This study aims to describe the impact of sex on outcomes in COVOD-19 with a special focus on the effect of estrogen.Methods: We performed a retrospective cohort study which included 413 patients (230 males and 183 females) with COVID-19 from three designated hospitals in China with a follow up time from January 31, 2020, to April 17, 2020. Women over 55 were considered as postmenopausal patients according to the previous epidemiological data from China. The interaction between age and sex on in-hospital mortality was determined through Cox regression analysis. In addition, multivariate Cox regression models were performed to explore risk factors associated with in-hospital mortality of COVID-19.Results: Age and sex had significant interaction for the in-hospital mortality (P < 0.001). Multivariate Cox regression showed that age (HR 1.041, 95% CI 1.009–1.073, P = 0.012), male sex (HR 2.033, 95% CI 1.007–2.098, P = 0.010), the interaction between age and sex (HR 1.118, 95% CI 1.003–1.232, P = 0.018), and comorbidities (HR 9.845, 95% CI 2.280–42.520, P = 0.002) were independently associated with in-hospital mortality of COVID-19 patients. In this multicentre study, female experienced a lower fatality for COVID-19 than male (4.4 vs. 10.0%, P = 0.031). Interestingly, stratification by age group revealed no difference in-hospital mortality was noted in women under 55 compared with women over 55 (3.8 vs. 5.2%, P = 0.144), as well as in women under 55 compared with the same age men (3.8 vs. 4.0%, P = 0.918). However, there was significantly difference in women over 55 with men of the same age group (5.2 vs. 21.0%, P = 0.007). Compared with male patients, female patients had higher lymphocyte (P < 0.001) and high-density lipoprotein (P < 0.001), lower high sensitive c reaction protein level (P < 0.001), and lower incidence rate of acute cardiac injury (6.6 vs. 13.5%, P = 0.022).Conclusion: Male sex is an independent risk factor for COVID-19 in-hospital mortality. Although female mortality in COVID-19 is lower than male, it might not be directly related to the effect of estrogen. Further study is warranted to identify the sex difference in COVID-19 and mechanisms involved.

Background Information regarding characteristics and risk factors of COVID-19 amongst middle-aged (40–59 years) patients without comorbidities is scarce. Methods We therefore conducted this multicentre retrospective study and collected data of middle-aged COVID-19 patients without comorbidities at admission from three designated hospitals in China. Results Among 119 middle-aged patients without comorbidities, 18 (15.1%) developed into severe illness and 5 (3.9%) died in hospital. ARDS (26, 21.8%) and elevated D-dimer (36, 31.3%) were the most common complications, while other organ complications were relatively rare. Multivariable regression showed increasing odds of severe illness associated with neutrophil to lymphocyte ratio (NLR, OR, 11.238; 95% CI 1.110–1.382; p < 0.001) and D-dimer greater than 1 µg/ml (OR, 16.079; 95% CI 3.162–81.775; p = 0.001) on admission. The AUCs for the NLR, D-dimer greater than 1 µg/ml and combined NLR and D-dimer index were 0.862 (95% CI, 0.751–0.973), 0.800 (95% CI 0.684–0.915) and 0.916 (95% CI, 0.855–0.977), respectively. SOFA yielded an AUC of 0.750 (95% CI 0.602–0.987). There was significant difference in the AUC between SOFA and combined index (z = 2.574, p = 0.010). Conclusions More attention should be paid to the monitoring and early treatment of respiratory and coagulation abnormalities in middle-aged COVID-19 patients without comorbidities. In addition, the combined NLR and D-dimer higher than 1 μg/ml index might be a potential and reliable predictor for the incidence of severe illness in this specific patient with COVID-19, which could guide clinicians on early classification and management of patients, thereby relieving the shortage of medical resource. However, it is warranted to validate the reliability of the predictor in larger sample COVID-19 patients.

AbstractLung cancer is one of the most harmful malignant tumors to human health. The accurate judgment of the pathological type of lung cancer is vital for treatment. Traditionally, the pathological type of lung cancer requires a histopathological examination to determine, which is invasive and time consuming. In this work, a novel residual neural network is proposed to identify the pathological type of lung cancer via CT images. Due to the low amount of CT images in practice, we explored a medical-to-medical transfer learning strategy. Specifically, a residual neural network is pre-trained on public medical images dataset luna16, and then fine-tuned on our intellectual property lung cancer dataset collected in Shandong Provincial Hospital. Data experiments show that our method achieves 85.71% accuracy in identifying pathological types of lung cancer from CT images and outperforming other models trained with 2054 labels. Our method performs better than AlexNet, VGG16 and DenseNet, which provides an efficient, non-invasive detection tool for pathological diagnosis.