Preliminary Recommendations on the Timing of Lung Surgery after Novel Coronavirus Infection in Patients with Pulmonary Nodules and Lung Cancer.

In recent years, the corona virus disease 2019 (COVID-19) pandemic has had a huge impact on the global medical, political and economic fields. Since the beginning of the COVID-19 epidemic, our understanding of the impact of COVID-19 has grown exponentially. Recently, the COVID-19 epidemic has changed rapidly in China, and there has been controversy over how to carry out surgical operations for patients with lung neoplastic lesions. Some studies have shown that lung cancer patients undergoing surgery are more likely to experience respiratory failure and perioperative death after contract-ing COVID-19 than the general population, however, delays in cancer treatment are also associated with increased mortality among these patients. In particular, the novel coronavirus Omikron variant has a higher transmissibility and may escape the immunity obtained through the previous novel coronavirus infection and vaccination. In order to minimize the risk of novel coronavirus infection in surgical patients, it is necessary to develop new treatment guidelines, expert consensus and preventive measures. However, the current rapid change of the epidemic situation has led to insufficient time and evidence to develop guidelines and consensus. Therefore, thoracic surgeons need to evaluate specific patient populations at higher risk of severe complications before surgery and weigh the benefit of surgical treatment against the risk of novel coronavirus infection. We try to give some recommendations on lung surgery during the current domestic epidemic situation based on the guidelines and consensus of oncology and thoracic surgery organizations in different regions on lung surgery.Copyright © 2023, Chinese Journal of Lung Cancer. All rights reserved.

Preliminary Recommendations on the Timing of Lung Surgery after Novel Coronavirus Infection in Patients with Pulmonary Nodules and Lung Cancer.

In recent years, the corona virus disease 2019 (COVID-19) pandemic has had a huge impact on the global medical, political and economic fields. Since the beginning of the COVID-19 epidemic, our understanding of the impact of COVID-19 has grown exponentially. Recently, the COVID-19 epidemic has changed rapidly in China, and there has been controversy over how to carry out surgical operations for patients with lung neoplastic lesions. Some studies have shown that lung cancer patients undergoing surgery are more likely to experience respiratory failure and perioperative death after contract-ing COVID-19 than the general population, however, delays in cancer treatment are also associated with increased mortality among these patients. In particular, the novel coronavirus Omikron variant has a higher transmissibility and may escape the immunity obtained through the previous novel coronavirus infection and vaccination. In order to minimize the risk of novel coronavirus infection in surgical patients, it is necessary to develop new treatment guidelines, expert consensus and preventive measures. However, the current rapid change of the epidemic situation has led to insufficient time and evidence to develop guidelines and consensus. Therefore, thoracic surgeons need to evaluate specific patient populations at higher risk of severe complications before surgery and weigh the benefit of surgical treatment against the risk of novel coronavirus infection. We try to give some recommendations on lung surgery during the current domestic epidemic situation based on the guidelines and consensus of oncology and thoracic surgery organizations in different regions on lung surgery.Copyright © 2023, Chinese Journal of Lung Cancer. All rights reserved.

Flow and Ebb: Factors affecting SMEs to exit from the DRP market during pandemic

Based on organizational ecology theory, this study demonstrates the exit issue of small- and medium-sized enterprises (SMEs) that grasped the opportunity from the surging demand for disaster relief products (DRPs) while facing uncertainty during the early COVID-19 pandemic. Specifically, we find that higher product complexity and spatial proximity limit DRP SMEs' organizational niches and increase the competition within organizational niches, facilitating their exit. Following the framework of organizational ecology theory, we further clarify the moderating effects of market demands and managerial experience. Our findings extend the firm exit literature by shedding light on the opportunity-seized SMEs during the pandemic and contribute to the organizational ecology literature by expanding its application to the disaster context. © 2022 Elsevier Inc.

KEP-1.0: An Automatic Pipeline to Assist a Rapid Learning of COVID-19 Publications

Toward efficient learning of massive publications during the COVID-19 pandemic, we propose a pipeline, Knowledge Extraction for COVID-19 Publications (KEP), that aims at automatic extraction and representation of key knowledge from user-interested publications. The first version, KEP-1.0, has been developed and published on the Python Package Index (PyPI) (URL: https://pypi.org/project/KEP/). In this first release, knowledge about key topics, disease discussions, and location mentions for each publication is provided. KEP-1.0 not only extracts relevant knowledge but, more importantly, emphasizes the top discussed entities and presents visualizable plots, including bar graphs and word clouds. This allows a rapid preliminary understanding of the main discussions in the publication from these three aspects. Moreover, an enhanced TF-IDF algorithm, the weighted TF-IDF, targeting the publication topic identification purpose, has been proposed and evaluated. The pipeline is fully open-sourced and customizable. KEP-1.0 is ready for use in its current form or to be embedded into existing literature platforms. This pipeline is designed for COVID-related publications, but it has the potential to benefit similar knowledge extraction tasks for other topics of interest with a rapidly increasing number of publications. © 2022 IEEE.

Current and prospective impacts of digital marketing on the small agricultural stakeholders in the developing countries

Artificial intelligence and big data technology have been recognized as a new path to address global industrial and social problems. Because of the COVID-19 pandemic, the digital agricultural market (DAM) is projected to grow rapidly in the coming years. This industry is expected to grow from $5.6 billion in 2020 to $6.2 and $11 billion by the ends of 2021 and 2025, respectively, which will create a good opportunity for the growth or establishment of a new DAM business. This will guide the local and international companies working in the DAM industry to increase their investments in the local or international business in the future, which requires identifying and analyzing this industry’s current and perspective actions on social welfare and the environment. This chapter covers the following items: DAM before, during, and what is expected after the COVID-19 pandemic in developing countries and its role in mitigating the negative impacts of uncertainty (price fluctuations, climate change, crises, and biological risks) on small stakeholders. The expected opportunities and risks of investment in the DAM industry in the future are also highlighted, in addition to estimates of its impacts on social welfare and the environment. Market segmentation of the DAM in selected developing countries and its expected growth rate and share including hardware, software, and services are also outlined. The DMA value chain stakeholders in developing countries, particularly in Africa, are identified. The role of the mobile banking system to accelerate the agricultural transformation in developing countries is described, while recognizing the impacts of DAM on food security, poverty reduction, and reducing food losses and waste. The role of agricultural digitalization in achieving the sustainable development goals 2030 is presented. Finally, suggest recommendations for policymakers and investors in developing countries for the design of effective strategies for establishing electronic agricultural businesses through adopting digitalization and public–private partnership investments are described. © 2022 Elsevier Inc. All rights reserved.

[Correlation analysis of smell and taste loss with COVID-19 outbreak trend based on big data of internet].

Objective: To analyze the correlation between loss of smell/taste and the number of real confirmed cases of coronavirus disease 2019 (COVID-19) worldwide based on Google Trends data, and to explore the guiding role of smell/taste loss for the COVID-19 prevention and control. Methods: "Loss of smell" and "loss of taste" related keywords were searched in the Google Trends platform, the data were obtained from Jan. 1 2019 to Jul. 11 2021. The daily and newly confirmed COVID-19 case number were collected from World Health Organization (WHO) since Dec. 30 2019. All data were statistically analyzed by SPSS 23.0 software. The correlation was finally tested by Spearman correlation analysis. Results: A total of data from 80 weeks were collected. The retrospective analysis was performed on the new trend of COVID-19 confirmed cases in a total of 186 292 441 cases worldwide. Since the epidemic of COVID-19 was recorded on the WHO website, the relative searches related to loss of smell/taste in the Google Trends platform had been increasing globally. The global relative search volumes of "loss of smell" and "loss of taste" on Google Trends was 10.23±2.58 and 16.33±2.47 before the record of epidemic while 80.25±39.81 and 80.45±40.04 after (t value was 8.67, 14.43, respectively, both P<0.001). In the United States and India, the relative searches for "loss of smell" and "loss of taste" after the record of epidemic were also much higher than before (all P<0.001). The correlation coefficients between the trend of weekly new COVID-19 cases and the Google Trends of "loss of smell" in the global, United States, and India was 0.53, 0.76, and 0.82 respectively (all P<0.001), the correlation coefficients with Google Trends of "loss of taste" was 0.54, 0.78, and 0.82 respectively (all P<0.001). The lowest and highest point of loss of smell/taste search curves of Google Trends in different periods appeared 7 to 14 days earlier than that of the weekly newly COVID-19 confirmed cases curves, respectively. Conclusions: There is a significant positive correlation between the number of newly confirmed cases of COVID-19 worldwide and the amount of keywords, such as "loss of smell" and "loss of taste", retrieved in Google Trends. The trend of big data based on Google Trends might predict the outbreak trend of COVID-19 in advance.

Cross-lingual COVID-19 Fake News Detection

The COVID-19 pandemic poses a great threat to global public health. Meanwhile, there is massive misinformation associated with the pandemic which advocates unfounded or unscientific claims. Even major social media and news outlets have made an extra effort in debunking COVID-19 misinformation, most of the fact-checking information is in English, whereas some unmoderated COVID-19 misinformation is still circulating in other languages, threatening the health of less-informed people in immigrant communities and developing countries. In this paper, we make the first attempt to detect COVID-19 misinformation in a low-resource language (Chinese) only using the fact-checked news in a high-resource language (English). We start by curating a Chinese realfake news dataset according to existing fact-checking information. Then, we propose a deep learning framework named CrossFake to jointly encode the cross-lingual news body texts and capture the news content as much as possible. Empirical results on our dataset demonstrate the effectiveness of CorssFake under the cross-lingual setting and it also outperforms several monolingual and cross-lingual fake news detectors. The dataset is available at https://github.com/YingtongDou/CrossFake. © 2021 IEEE.