Background In December 2019, a few coronavirus disease (COVID-19) cases were first reported in Wuhan, Hubei, China. Soon after, increasing numbers of cases were detected in other parts of China, eventually leading to a disease outbreak in China. As this dreadful disease spreads rapidly, the mass media has been active in community education on COVID-19 by delivering health information about this novel coronavirus, such as its pathogenesis, spread, prevention, and containment. Objective The aim of this study was to collect media reports on COVID-19 and investigate the patterns of media-directed health communications as well as the role of the media in this ongoing COVID-19 crisis in China. Methods We adopted the WiseSearch database to extract related news articles about the coronavirus from major press media between January 1, 2020, and February 20, 2020. We then sorted and analyzed the data using Python software and Python package Jieba. We sought a suitable topic number with evidence of the coherence number. We operated latent Dirichlet allocation topic modeling with a suitable topic number and generated corresponding keywords and topic names. We then divided these topics into different themes by plotting them into a 2D plane via multidimensional scaling. Results After removing duplications and irrelevant reports, our search identified 7791 relevant news reports. We listed the number of articles published per day. According to the coherence value, we chose 20 as the number of topics and generated the topics’ themes and keywords. These topics were categorized into nine main primary themes based on the topic visualization figure. The top three most popular themes were prevention and control procedures, medical treatment and research, and global or local social and economic influences, accounting for 32.57% (n=2538), 16.08% (n=1258), and 11.79% (n=919) of the collected reports, respectively. Conclusions Topic modeling of news articles can produce useful information about the significance of mass media for early health communication. Comparing the number of articles for each day and the outbreak development, we noted that mass media news reports in China lagged behind the development of COVID-19. The major themes accounted for around half the content and tended to focus on the larger society rather than on individuals. The COVID-19 crisis has become a worldwide issue, and society has become concerned about donations and support as well as mental health among others. We recommend that future work addresses the mass media’s actual impact on readers during the COVID-19 crisis through sentiment analysis of news data.

Background:Preoperative chemoradiotherapy has become the current standard regimen for locally advanced rectal cancer (LARC). However, the additional benefit of oxaliplatin to preoperative chemotherapy was still controversial. On one hand, oxaliplatin may improve the tumor response rate of even prolong the survival time. On the other hand, it can bring a series of adverse effects. Opinions vary from studies to studies. We aim to perform a meta-analysis to evaluate the efficacy, safety, and long-term survival of oxaliplatin in preoperative chemoradiotherapy for LARC.Method:To identify clinical trials fusing oxaliplatin in preoperative chemoradiotherapy for LARC published until December 2015, we searched PubMed, the Cochrane Library, and the Springer Link databases by combining various key words. We also search for relevant ASCO conferences. Data were extracted from every study to perform a meta-analysis using STATA 12.0 software.Result:Eleven articles or ASCO abstracts from 8 studies with a total of 5597 patients were included. Adding oxaliplatin to preoperative chemoradiotherapy can significantly improve the ypCR rate [risk ratio (RR) = 1.208, 95% confidence interval (95% CI): 1.070–1.364, P = 0.002, I2 = 14.5%], and decrease the preoperative metastasis (RR = 0.494, 95% CI: 0.256–0.954, P = 0.036, I2 = 53.9%) and local recurrence rate (RR = 0.761, 95% CI: 0.616–0.941, P = 0.012, I2 = 26.1%). What's more, oxaliplatin can prolong the disease-free survival (DFS) [hazard ratio (HR) = 0.867, 95% CI: 0.741–0.992, P = 0.000, I2 = 16.3%]. However, oxaliplatin can increase the chemoradiotherapy-related toxicities (RR = 1.858, 95% CI 1.427–2.419, P = 0.000, I2 = 84.7%). There was no significant difference between the groups with and without oxaliplatin in operation rate, R0 resection rate, sphincter preservation rate, permanent stoma rate, postoperative complication, mortality, and overall survival.Conclusion:Preoperative chemoradiotherapy with oxaliplatin bring both advantage and disadvantage to LARC. Whether to use oxaliplatin should be decided by patient's general condition and tolerance. Although oxaliplatin can prolong the DFS, survival benefit should be proved by further data.

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BackgroundIn December 2019, some COVID-19 cases were first reported and soon the disease broke out.As this dreadful disease spreads rapidly, the mass media has been active in community education on COVID-19 by delivering health information about this novel coronavirus. MethodsWe adopted the Huike database to extract news articles about coronavirus from major press media, between January 1 st , 2020, to February 20 th , 2020. The data were sorted and analyzed by Python software and Python package Jieba. We sought a suitable topic number using the coherence number. We operated Latent Dirichlet Allocation (LDA) topic modeling with the suitable topic number and generated corresponding keywords and topic names. We divided these topics into different themes by plotting them into two-dimensional plane via multidimensional scaling. FindingsAfter removing duplicates, 7791 relevant news reports were identified. We listed the number of articles published per day. According to the coherence value, we chose 20 as our number of topics and obtained their names and keywords. These topics were categorized into nine primary themes based on the topic visualization figure. The top three popular themes were prevention and control procedures, medical treatment and research, global/local social/economic influences, accounting for 32·6%, 16·6%, 11·8% of the collected reports respectively.

Background: The surgical approach (transthoracic or transabdominal) for patients with Siewert type II adenocarcinoma of the esophagogastric junction (AEG) still remains controversial.Methods: Data of patients with Siewert type II AEG were collected in the Guangdong General Hospital from 2004 to 2014 and we compared their clinicopathological outcome and prognosis in regard to the transthoracic (TT) and transabdominal (TA) approach.Results: A total of 158 patients with Siewert type II AEG were analyzed and our results demonstrated that their overall medium survival was 52 months. Also, their 5-year overall survival rate was 39.1%, which was comparable between the TT and TA group (35.1% vs. 43.2%, P>0.05), while more lymph nodes were dissected in TA group (23.7±0.2 vs. 18.1±0.3, P<0.05), with less postoperative complications (14.3% vs.28.4%, P<0.05) and shorten hospital stay (12±4 vs. 15±7 d, P<0.05).Conclusions: For patients with Siewert type II AEG, the TA approach is more suitable to achieve an optimal extent of lymph node dissection, reduction in the incidence of complication, shorten hospital stay, and to promote the recovery.

Background Gestational diabetes mellitus (GDM) can cause adverse consequences to both mothers and their newborns. However, pregnant women living in low- and middle-income areas or countries often fail to receive early clinical interventions at local medical facilities due to restricted availability of GDM diagnosis. The outstanding performance of artificial intelligence (AI) in disease diagnosis in previous studies demonstrates its promising applications in GDM diagnosis. Objective This study aims to investigate the implementation of a well-performing AI algorithm in GDM diagnosis in a setting, which requires fewer medical equipment and staff and to establish an app based on the AI algorithm. This study also explores possible progress if our app is widely used. Methods An AI model that included 9 algorithms was trained on 12,304 pregnant outpatients with their consent who received a test for GDM in the obstetrics and gynecology department of the First Affiliated Hospital of Jinan University, a local hospital in South China, between November 2010 and October 2017. GDM was diagnosed according to American Diabetes Association (ADA) 2011 diagnostic criteria. Age and fasting blood glucose were chosen as critical parameters. For validation, we performed k-fold cross-validation (k=5) for the internal dataset and an external validation dataset that included 1655 cases from the Prince of Wales Hospital, the affiliated teaching hospital of the Chinese University of Hong Kong, a non-local hospital. Accuracy, sensitivity, and other criteria were calculated for each algorithm. Results The areas under the receiver operating characteristic curve (AUROC) of external validation dataset for support vector machine (SVM), random forest, AdaBoost, k-nearest neighbors (kNN), naive Bayes (NB), decision tree, logistic regression (LR), eXtreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT) were 0.780, 0.657, 0.736, 0.669, 0.774, 0.614, 0.769, 0.742, and 0.757, respectively. SVM also retained high performance in other criteria. The specificity for SVM retained 100% in the external validation set with an accuracy of 88.7%. Conclusions Our prospective and multicenter study is the first clinical study that supports the GDM diagnosis for pregnant women in resource-limited areas, using only fasting blood glucose value, patients’ age, and a smartphone connected to the internet. Our study proved that SVM can achieve accurate diagnosis with less operation cost and higher efficacy. Our study (referred to as GDM-AI study, ie, the study of AI-based diagnosis of GDM) also shows our app has a promising future in improving the quality of maternal health for pregnant women, precision medicine, and long-distance medical care. We recommend future work should expand the dataset scope and replicate the process to validate the performance of the AI algorithms.

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BackgroundThe tumor location-modified Lauren classification (mLC) has been proposed recently, but its clinical significance remains under debate. This study aimed to elucidate the clinical relevance of mLC and evaluate its superiority to the Lauren classification (LC) for gastric cancer patients with gastrectomy.MethodsThis study retrospectively evaluated 2764 consecutive gastric cancer patients from three comprehensive medical institutions. The patients were categorized into training, inner-validation, and independent validation sets. The relationships between mLC and other clinicopathologic factors were analyzed, and independent prognostic factors were identified. Survival prognostic discriminatory ability and predictive accuracy were compared between mLC and LC using the concordance index (C-index) and Akaike’s information criterion (AIC), and a nomogram based on mLC was constructed to compare its prognostic improvement with the tumor-node metastasis (TNM) staging system.ResultsA significant association between mLC and gender, age, histologic type, T stage, N stage, and M stage was found. The findings showed that mLC, not LC, is an independent prognostic factor, with a smaller AIC and a higher C-index than LC. The nomogram based on mLC showed a better predictive ability than TNM alone.ConclusionsCompared with LC, mLC, which could be considered a more reliable prognostic factor, may improve the prognostic discriminatory ability and predictive accuracy for gastric cancer patients with gastrectomy.Electronic supplementary materialThe online version of this article (10.1245/s10434-018-6654-8) contains supplementary material, which is available to authorized users.