In silico analysis identifies neuropilin-1 as a potential therapeutic target for SARS-Cov-2 infected lung cancer patients.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), and is highly contagious and pathogenic. TMPRSS2 and Neuropilin-1, the key components that facilitate SARS-CoV-2 infection, are potential targets for treatment of COVID-19. Here we performed a comprehensive analysis on NRP1 and TMPRSS2 in lung to provide information for treating comorbidity of COVID-19 with lung cancer. NRP1 is widely expressed across all the human tissues while TMPRSS2 is expressed in a restricted pattern. High level of NRP1 associates with worse prognosis in multiple cancers, while high level of TMPRSS2 is associated with better survival of Lung Adenocarcinoma (LUAD). Moreover, NRP1 positively correlates with the oncogenic Cancer Associated Fibroblast (CAF), macrophage and endothelial cells infiltration, negatively correlates with infiltration of CD8+ T cell, the tumor killer cell in Lung Squamous cell carcinoma (LUSC). TMPRSS2 shows negative correlation with the oncogenic events in LUAD. RNA-seq data show that NRP1 level is slightly decreased in peripheral blood of ICU admitted COVID-19 patients, unaltered in lung, while TMPRSS2 level is significantly decreased in lung of COVID-19 patients. Our analysis suggests NRP1 as a potential therapeutic target, while sets an alert on targeting TMPRSS2 for treating comorbidity of COVID-19 and lung cancers.

Treatment strategies of hospitalized patients with coronavirus disease-19.

With the outbreak of coronavirus disease-19 (COVID-19), Changsha faced an increasing burden of treating the Wuhan migrants and their infected patients. This study is a retrospective, single-center case series of the 238 consecutive hospitalized patients with confirmed COVID-19 at the First Hospital of Changsha city, China, from 01/21 to 02/14, 2020; the final date of follow-up was 02/27, 2020. Of 238 patients 43.7% visited Wuhan, 58.4% got in touch with Wuhan people, and 47.5% had contacted with diagnosed patients. 37.8% patients had family members infected. 190 cases had mild / general disease, and 48 cases had severe / critical disease. Compared to mild or general patients, more severe or critical patients visited Wuhan (59.6% vs 40.2%; P=0.02) and contacted with Wuhan people (74.5% vs 55.0%; P=0.02). All patients received antiviral treatment, including Lopinavir / Ritonavir (29.3%), Interferon (14.6%) and their combination (40.6%), Arbidol (6.7%), Xuebijing (7.1%) and Chloroquine phosphate (1.3%). Severe and critical patients received glucocorticoid, Gamma-globulin and oxygen inhalation. Some received mechanic ventilation support. As of 02/27, 161 patients discharged. The median length of hospital stay was 13 days. The 10-, 14-, 20- and 28-day discharge rate was 19.1%, 42.8%, 65.0% and 76.4%, respectively. No hospital-related transmission was observed.

From community-acquired pneumonia to COVID-19: a deep learning-based method for quantitative analysis of COVID-19 on thick-section CT scans.

OBJECTIVE: To develop a fully automated AI system to quantitatively assess the disease severity and disease progression of COVID-19 using thick-section chest CT images. METHODS: In this retrospective study, an AI system was developed to automatically segment and quantify the COVID-19-infected lung regions on thick-section chest CT images. Five hundred thirty-one CT scans from 204 COVID-19 patients were collected from one appointed COVID-19 hospital. The automatically segmented lung abnormalities were compared with manual segmentation of two experienced radiologists using the Dice coefficient on a randomly selected subset (30 CT scans). Two imaging biomarkers were automatically computed, i.e., the portion of infection (POI) and the average infection HU (iHU), to assess disease severity and disease progression. The assessments were compared with patient status of diagnosis reports and key phrases extracted from radiology reports using the area under the receiver operating characteristic curve (AUC) and Cohen's kappa, respectively. RESULTS: The dice coefficient between the segmentation of the AI system and two experienced radiologists for the COVID-19-infected lung abnormalities was 0.74 ± 0.28 and 0.76 ± 0.29, respectively, which were close to the inter-observer agreement (0.79 ± 0.25). The computed two imaging biomarkers can distinguish between the severe and non-severe stages with an AUC of 0.97 (p value < 0.001). Very good agreement (κ = 0.8220) between the AI system and the radiologists was achieved on evaluating the changes in infection volumes. CONCLUSIONS: A deep learning-based AI system built on the thick-section CT imaging can accurately quantify the COVID-19-associated lung abnormalities and assess the disease severity and its progressions. KEY POINTS: • A deep learning-based AI system was able to accurately segment the infected lung regions by COVID-19 using the thick-section CT scans (Dice coefficient ≥ 0.74). • The computed imaging biomarkers were able to distinguish between the non-severe and severe COVID-19 stages (area under the receiver operating characteristic curve 0.97). • The infection volume changes computed by the AI system were able to assess the COVID-19 progression (Cohen's kappa 0.8220).