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Background: Grade prognostic assessment (GPA) is widely used to evaluate the prognosis of non-small cell lung cancer (NSCLC) patients with brain metastases (BMs). This study aimed to investigate whether lymph node status (LNS) could be included as one of the GPA variables for NSCLC with BMs. Methods: Overall, 586 patients with NSCLC and BMs were retrospectively analyzed. Overall survival stratified by LNS was analyzed using the Kaplan-Meier method. Multivariate analysis was also performed to identify independent prognostic factors using the Cox proportional hazards progression model. In the updated GPA index, prognostic factors and criteria of GPA score were weighted by effect magnitude relative risk (RR) and statistical significance. Results: In NSCLC patients with BMs, those with lymph node involvement had worse overall survival (mOS, 13.4 months vs. 25.9 months, P <0.001) than those without lymph node involvement. Multivariate analysis showed that LNS might be an independent prognostic factor (RR: 1.702, CI: 1.340-2.162, P <0.001). Finally, five prognostic factors including LNS, the age of the patient, Karnofsky performance status (KPS), the number of BMs, and extracranial metastases were enrolled in our novel GPA index. With the updated GPA index involving the N stage, survival analysis was also performed. Prognostic results were significantly different among these four subgroups (Class A vs.

Background: Overseas imported cases of COVID-19 continue to increase in China, so we conducted this study to review the epidemiological characteristics of these patients.Methods: From February 26 to April 4, 2020, the imported cases from abroad were enrolled in this study. The effect of prevention countermeasures in curbing the spread of COVID-19 was assessed in this study. Moreover, we defined incubation period and confirmed time as from the date of leaving the epicenter to date of symptom onset and date of final diagnosed, respectively, and the interval of symptom onset to final diagnosed time was defined as diagnostic time. Categorical variables were summarized as numbers and percentages, and the difference among the variables were analyzed.Results: For 670 cases imported from abroad, 555 were Chinese and 115 were foreigners. Apparently, confirmed cases had significantly decreased after China was compelled to temporarily suspend the entry of foreign passport holders with valid visas or residence permits; 6 days after implement of controlled measures, the daily new confirmed cases were reduced to 13 cases. Moreover, about 84.3% of patients (166/197) presented symptoms 1 week after leaving the epicenter, and notably seven patients (3.6%) had symptoms 2 weeks after leaving the epicenter. The median incubation period was 3.0 days (inter quartile range, 1.0 to 6.0), the 95th percentile was 11.6 days. Additionally, most of cases (92.9%) were detected positively of nucleic acid after symptom onset with 4 days, the median diagnostic time was 2.0 days (interquartile range, 1.0 to 3.0), and the 95th percentile of the distribution was 5.0 days. Finally, about 5.8% of patients were healthy carriers, and the median confirmed time of asymptomatic patients was 4.0 days (interquartile range, 2.0 to 9.0). The following variables might be associated with confirmed time: symptom type (P = 0.005), exported regions (P &lt; 0.001), and symptom onset time (P &lt; 0.001).Conclusions: The prevention countermeasures for imported cases implemented by the Chinese government played an indispensable role in curbing the spread of COVID-19; the time of departure from epicenter could provide an estimate of the incubation period; and a confirmed time, 2-week quarantine period might need to be prolonged, while asymptomatic patients should be closely monitored.

Conclusions:The morbidity and mortality rates of COVID-19 patients in the regions with a low population density were lower than those of the national average in China. All populations were susceptible to infection by SARS-CoV-2. Asymptomatic patients with positive results should be taken seriously, and the hospitalization time of patients is associated with their age and severity status.

Esophageal squamous cell carcinoma (ESCC) accounts for the main esophageal cancer type, which is related to advanced stage and poor survivals. Therefore, novel diagnostic biomarkers are critically needed. In the current research, we aimed to screen novel diagnostic biomarkers based on machine learning. The expression profiles were obtained from GEO datasets (GSE20347, GSE38129, and GSE75241) and TCGA datasets. Differentially expressed genes (DEGs) were screened between 47 ESCC and 47 nontumor samples. The LASSO regression model and SVM-RFE analysis were carried out for the identification of potential markers. ROC analysis was carried out to assess discriminatory abilities. The expressions and diagnostic values of the candidates in ESCC were demonstrated in the GSE75241 datasets and TCGA datasets. We also explore the correlations between the critical genes and cancer immune infiltrates using CIBERSORT. In this study, we identified 27 DEGs in ESCC: 5 genes were significantly elevated, and 22 genes were significantly decreased. Based on the results of the SVM-RFE and LASSO regression model, we identified five potential diagnostic biomarkers for ESCC, including GPX3, COL11A1, EREG, MMP1, and MMP12. However, the diagnostic values of only GPX3, MMP1, and MMP12 were confirmed in GSE75241 datasets. Moreover, in TCGA datasets, we further confirmed that GPX3 expression was distinctly decreased in ESCC specimens, while the expression of MMP1 and MMP12 was noticeably increased in ESCC specimens. Immune cell infiltration analysis revealed that the expression of GPX3, MMP1, and MMP12 was associated with several immune, such as T cells CD8, macrophages M2, macrophages M0, and dendritic cells activated. Overall, our findings suggested GPX3, MMP1, and MMP12 as novel diagnostic marker and correlated with immune infiltrates in ESCC patients.