Depression is the most common mental illness worldwide. It has become an important public health problem. This study aimed to determine the global burden of depression and how it has changed between 1990 and 2017. We used information on depression obtained by the Global Burden of Disease (GBD) study from 1990 to 2017. The age-standardized incidence rate (ASR) and estimated annual percentage change (EAPC) were used to assess the global burden of depression. The number of incident cases of depression worldwide increased from 172 million in 1990 to 25,8 million in 2017, representing an increase of 49.86%. The ASR of depression varied widely between the 195 analyzed countries and regions in 2017, being highest in Lesotho (6.59 per 1000) and lowest in Myanmar (1.28 per 1000). The ASR increased the most between 1990 and 2017 in Belgium (EAPC = 0.88, 95% confidence interval [CI] = 0.78 to 0.97), and decreased the most in Cuba (EAPC = -1.26, 95% CI = -1.36 to -1.14). The ASR increased in regions with a high sociodemographic index, such as high-income North America (EAPC = 0.41, 95% CI = 0.31 to 0.51), and decreased significantly in South Asia (EAPC = -0.63, 95% CI = -0.85 to -0.41). The proportions of the population with major depressive disorder and dysthymia were essentially stable both globally and in various countries, with a much larger proportion having major depressive disorder. Depression remains a major public health issue, and governments should support the research necessary to develop better prevention and treatment interventions.

Data mining technology can search for potentially valuable knowledge from a large amount of data, mainly divided into data preparation and data mining, and expression and analysis of results. It is a mature information processing technology and applies database technology. Database technology is a software science that researches manages, and applies databases. The data in the database are processed and analyzed by studying the underlying theory and implementation methods of the structure, storage, design, management, and application of the database. We have introduced several databases and data mining techniques to help a wide range of clinical researchers better understand and apply database technology.

Many high quality studies have emerged from public databases, such as Surveillance, Epidemiology, and End Results (SEER), National Health and Nutrition Examination Survey (NHANES), The Cancer Genome Atlas (TCGA), and Medical Information Mart for Intensive Care (MIMIC); however, these data are often characterized by a high degree of dimensional heterogeneity, timeliness, scarcity, irregularity, and other characteristics, resulting in the value of these data not being fully utilized. Data-mining technology has been a frontier field in medical research, as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models. Therefore, data mining has unique advantages in clinical big-data research, especially in large-scale medical public databases. This article introduced the main medical public database and described the steps, tasks, and models of data mining in simple language. Additionally, we described data-mining methods along with their practical applications. The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.

Thyroid cancer is the most pervasive endocrine cancer worldwide.¹ During the past decades, published studies reported that the incidence of thyroid cancer continues to increase² in countries and regions such as Canada,³ the US,⁴ Australia,⁵ Asia,^6-8 South America,⁹ and Europe.^10-13 Although some regional studies have provided data on the incidence and mortality associated with thyroid cancer,^14,15 studies on thyroid cancer examining the association between the disease and country, sex, age, sociodemographic index (SDI), and other factors are lacking. Comprehensive, in-depth analysis of thyroid cancer in all regions of the world based on a variety of factors may be beneficial for health care planning and resource allocation.

Backgroud: Diabetes mellitus is a common chronic disease and a severe public health issue. The incidence trends for type 1 diabetes (TIDM) and type 2 diabetes (T2DM) have rarely been studied on a global scale. We aimed to determine the temporal and geographical trends of diabetes globally. Methods: Data on diabetes mellitus, including incidence, prevalence from 1990 to 2017 were obtained from the 2017 Global Burden of Disease study. We calculated the estimated annual percentage changes (EAPCs) in age-standardized incidence rate (ASIR) of diabetes mellitus according to sex, region, and disease type. Results: The worldwide incident cases of diabetes mellitus has increased by 102.9% from 11,303,084 cases in 1990 to 22,935,630 cases in 2017 worldwide, while the ASIR increased from 234 /100,000 persons (95% UI, 219–249) to 285/100,000 persons (95% UI, 262–310) in this period [EAPC = 0.87, 95% confidence interval (CI):0.79–0.96]. The global ASIRs of T1DM and T2DM both demonstrated significant increase during 1990–2017, with EAPCs of 0.34 (95% CI,0.30–0.39) and 0.89 (95% CI,0.80–0.97), respectively. The ASIR trends also varied considerably by regions and countries. The increase in ASIR was greatest in high sociodemographic index regions (EAPC = 1.05, 95% CI:0.92–1.17) and lowest in low-SDI regions (EAPC = 0.79, 95% CI:0.71–0.88). Conclusions: Both the number of incident cases and ASIR of diabetes mellitus increased significantly during 1990–2017 worldwide, but the temporal trends varied markedly across regions and countries.

The coronavirus disease 2019 (COVID-19) was first reported in Wuhan, China and rapidly spread in other countries in December 2019. The infected patients presented with fever, respiratory symptoms, sometimes with digestive and other systemic manifestations, and some progressed with a severe acute respiratory syndrome or even death. Associated digestive symptoms were frequently observed in the patients, with an unknown significance and mechanism. ACE2, as the major known functional receptor of the 2019 novel coronavirus (2019-nCoV) attracted our attention. We collected the clinical data of the 2019-nCoV-infected patients from published studies and extracted the data about the incidence of gastrointestinal symptoms. Furthermore, we used online datasets to analyze ACE2 expression in different human organs, especially in the small intestine, to explore the relationship between ACE2 expression patterns and clinical symptoms. We found that diarrhea accounted for a notable proportion of COVID-19 patients, ranging from 8.0% to 12.9%. The results reveal that ACE2 mRNA and protein are highly expressed in the small intestinal enterocytes but not in the goblet cells or intestinal immune cells. High expression of ACE2 on the surface cells in the digestive tract may lead to gastrointestinal symptoms and inflammation susceptibility. Overall, digestive symptoms were common in the COVID-19 patients. ACE2 expression on surface cells of the small intestine may mediate the invasion and amplification of the virus and activation of gastrointestinal inflammation. It is a possible mechanism of digestive symptoms in the COVID-19 patients and explains the presence of the virus in patients' stool samples. The study also highlights the necessity of taking stool samples for suspected patients to help in early diagnosis and assessment of disease status.