Sun Yat-sen University

About: Sun Yat-sen University is a education organization based out in Guangzhou, Guangdong, China. It is known for research contribution in the topics: Population & Cancer. The organization has 115149 authors who have published 113763 publications receiving 2286465 citations. The organization is also known as: Zhongshan University & SYSU.

Current cancer situation in China: Good or bad news from the 2018 Global Cancer Statistics?

Abstract: Cancer is the leading cause of death in China and depicting the cancer pattern of China would provide basic knowhows on how to tackle it more effectively. In this study we have reviewed several reports of cancer burden, including the Global cancer statistics 2018 and Cancer statistics in China, 2015, along with the GLOBCAN 2018 online database, to investigate the differences of cancer patterns between China, the United States (USA) and the United Kingdom (UK). An estimated 4.3 million new cancer cases and 2.9 million new cancer deaths occurred in China in 2018. Compared to the USA and UK, China has lower cancer incidence but a 30% and 40% higher cancer mortality than the UK and USA, among which 36.4% of the cancer-related deaths were from the digestive tract cancers (stomach, liver, and esophagus cancer) and have relatively poorer prognoses. In comparison, the digestive cancer deaths only took up ≤ 5% of the total cancer deaths in either USA or UK. Other reasons for the higher mortality in China may be the low rate of early-stage cancers at diagnosis and non-uniformed clinical cancer treatment strategies performed by different regions. China is undergoing the cancer transition stage where the cancer spectrum is changing from developing country to developed country, with a rapidly increase cancer burden of colorectal, prostate, female breast cancers in addition to a high occurrence of infection-related and digestive cancers. The incidence of westernized lifestyle-related cancers in China (i.e. colorectal cancer, prostate, bladder cancer) has risen but the incidence of the digestive cancers has decreased from 2000 to 2011. An estimated 40% of the risk factors can be attributed to environmental and lifestyle factors either in China or other developed countries. Tobacco smoking is the single most important carcinogenic risk factor in China, contributing to ~ 24.5% of cancers in males. Chronic infection is another important preventable cancer contributor which is responsible for ~ 17% of cancers. Comprehensive prevention and control strategies in China should include effective tobacco-control policy, recommendations for healthier lifestyles, along with enlarging the coverage of effective screening, educating, and vaccination programs to better sensitize greater awareness control to the general public.

Amorphous nickel hydroxide nanospheres with ultrahigh capacitance and energy density as electrochemical pseudocapacitor materials

Abstract: Nickel hydroxide is a promising material for capacitor electrodes and most research has focussed on the crystalline form. Here, the authors report that amorphous nickel hydroxide nanospheres, which may be synthesized relatively easily, also exhibit excellent integrated electrochemical performance.

Development and Validation of a Clinical Risk Score to Predict the Occurrence of Critical Illness in Hospitalized Patients With COVID-19.

Abstract: Importance Early identification of patients with novel coronavirus disease 2019 (COVID-19) who may develop critical illness is of great importance and may aid in delivering proper treatment and optimizing use of resources. Objective To develop and validate a clinical score at hospital admission for predicting which patients with COVID-19 will develop critical illness based on a nationwide cohort in China. Design, Setting, and Participants Collaborating with the National Health Commission of China, we established a retrospective cohort of patients with COVID-19 from 575 hospitals in 31 provincial administrative regions as of January 31, 2020. Epidemiological, clinical, laboratory, and imaging variables ascertained at hospital admission were screened using Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression to construct a predictive risk score (COVID-GRAM). The score provides an estimate of the risk that a hospitalized patient with COVID-19 will develop critical illness. Accuracy of the score was measured by the area under the receiver operating characteristic curve (AUC). Data from 4 additional cohorts in China hospitalized with COVID-19 were used to validate the score. Data were analyzed between February 20, 2020 and March 17, 2020. Main Outcomes and Measures Among patients with COVID-19 admitted to the hospital, critical illness was defined as the composite measure of admission to the intensive care unit, invasive ventilation, or death. Results The development cohort included 1590 patients. the mean (SD) age of patients in the cohort was 48.9 (15.7) years; 904 (57.3%) were men. The validation cohort included 710 patients with a mean (SD) age of 48.2 (15.2) years, and 382 (53.8%) were men and 172 (24.2%). From 72 potential predictors, 10 variables were independent predictive factors and were included in the risk score: chest radiographic abnormality (OR, 3.39; 95% CI, 2.14-5.38), age (OR, 1.03; 95% CI, 1.01-1.05), hemoptysis (OR, 4.53; 95% CI, 1.36-15.15), dyspnea (OR, 1.88; 95% CI, 1.18-3.01), unconsciousness (OR, 4.71; 95% CI, 1.39-15.98), number of comorbidities (OR, 1.60; 95% CI, 1.27-2.00), cancer history (OR, 4.07; 95% CI, 1.23-13.43), neutrophil-to-lymphocyte ratio (OR, 1.06; 95% CI, 1.02-1.10), lactate dehydrogenase (OR, 1.002; 95% CI, 1.001-1.004) and direct bilirubin (OR, 1.15; 95% CI, 1.06-1.24). The mean AUC in the development cohort was 0.88 (95% CI, 0.85-0.91) and the AUC in the validation cohort was 0.88 (95% CI, 0.84-0.93). The score has been translated into an online risk calculator that is freely available to the public (http://118.126.104.170/) Conclusions and Relevance In this study, a risk score based on characteristics of COVID-19 patients at the time of admission to the hospital was developed that may help predict a patient’s risk of developing critical illness.