BackgroundDeath anxiety is a common phenomenon in all societies. Older adults may be more prone to death anxiety than their younger counterparts; however, death anxiety among older adults is not well understood. This study explores the relationship between meaning in life, self-esteem, and death anxiety in senior citizens in China.MethodsA total of 283 older adults participated in this study; data were collected via the Meaning in Life Questionnaire, the Rosenberg Self-Esteem Scale, and the Death Anxiety Scale.ResultsResults show that the dimensions of meaning in life, presence of meaning (r = − 0.43, p < 0.01), search for meaning (r = − 0.31, p < 0.01), and self-esteem (r = − 0.54, p < 0.01) were each negatively correlated with death anxiety. Regression analysis reveals that meaning in life significantly predicted self-esteem and death anxiety (F = 45.70, p < 0.01; R2 = 0.33). Path analysis indicated that self-esteem either completely or partially mediated the effects of meaning in life on death anxiety in older adults.ConclusionsOverall, meaning in life appears to be significantly correlated with death anxiety in older adults, and self-esteem can mediate this effect.

With the rapid development of COVID-19 around the world, people are requested to maintain "social distance" and "stay at home". In this scenario, extensive social interactions transfer to cyberspace, especially on social media platforms like Twitter and Sina Weibo. People generate posts to share information, express opinions and seek help during the pandemic outbreak, and these kinds of data on social media are valuable for studies to prevent COVID-19 transmissions, such as early warning and outbreaks detection. Therefore, in this paper, we release a novel and finegrained large-scale COVID-19 social media dataset collected from Sina Weibo, named Weibo-COV 1 , contains more than 40 million posts ranging from December 1, 2019 to April 30, 2020. Moreover, this dataset includes comprehensive information nuggets like post-level information, interactive information, location information, and repost network. We hope this dataset can promote studies of COVID-19 from multiple perspectives and enable better and rapid researches to suppress the spread of this pandemic.

Background The COVID-19 outbreak has increased challenges associated with health management, especially cancer management. In an effort to provide continuous pharmaceutical care to cancer patients, Sun Yat-sen University Cancer Center (SYSUCC) implemented a remote pharmacy service platform based on its already existing web-based hospital app known as Cloud SYSUCC. Objective The aim of this study was to investigate the characteristics, acceptance, and initial impact of the Cloud SYSUCC app during a COVID-19 outbreak in a tertiary cancer hospital in China. Methods The total number of online prescriptions and detailed information on the service were obtained during the first 6 months after the remote service platform was successfully set up. The patients’ gender, age, residence, primary diagnosis, drug classification, weekly number of prescriptions, and prescribed drugs were analyzed. In addition, a follow-up telephonic survey was conducted to evaluate patients’ satisfaction in using the remote prescription service. Results A total of 1718 prescriptions, including 2022 drugs for 1212 patients, were delivered to 24 provinces and municipalities directly under the Central Government of China between February 12, 2020, and August 11, 2020. The majority of patients were female (841/1212, 69.39%), and 90.18% (1093/1212) of them were aged 31-70 years old. The top 3 primary diagnoses for which remote medical prescriptions were made included breast cancer (599/1212, 49.42%), liver cancer (249/1212, 20.54%), and thyroid cancer (125/1212, 10.31%). Of the 1718 prescriptions delivered, 1435 (83.5%) were sent to Guangdong Province and 283 (16.5%) were sent to other provinces in China. Of the 2022 drugs delivered, 1012 (50.05%) were hormonal drugs. The general trend in the use of the remote prescription service declined since the 10th week. A follow-up telephonic survey found that 88% (88/100) of the patients were very satisfied, and 12% (12/100) of the patients were somewhat satisfied with the remote pharmacy service platform. Conclusions The remote pharmacy platform Cloud SYSUCC is efficient and convenient for providing continuous pharmaceutical care to patients with cancer during the COVID-19 crisis. The widespread use of this platform can help to reduce person-to-person transmission as well as infection risk for these patients. Further efforts are needed to improve the quality and acceptance of the Cloud SYSUCC platform, as well as to regulate and standardize the management of this novel service.

The noisy labeling problem has been one of the major obstacles for distant supervised relation extraction. Existing approaches usually consider that the noisy sentences are useless and will harm the model's performance. Therefore, they mainly alleviate this problem by reducing the influence of noisy sentences, such as applying bag-level selective attention or removing noisy sentences from sentence-bags. However, the underlying cause of the noisy labeling problem is not the lack of useful information, but the missing relation labels. Intuitively, if we can allocate credible labels for noisy sentences, they will be transformed into useful training data and benefit the model's performance. Thus, in this paper, we propose a novel method for distant supervised relation extraction, which employs unsupervised deep clustering to generate reliable labels for noisy sentences. Specifically, our model contains three modules: a sentence encoder, a noise detector and a label generator. The sentence encoder is used to obtain feature representations. The noise detector detects noisy sentences from sentence-bags, and the label generator produces high-confidence relation labels for noisy sentences. Extensive experimental results demonstrate that our model outperforms the state-of-the-art baselines on a popular benchmark dataset, and can indeed alleviate the noisy labeling problem.

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