Flap Reconstruction of the Oropharyngeal Defect After Tumor Resection via Combined Transcervical and Transoral Approach in Patients With HPV-Positive and -Negative Oropharyngeal Squamous Cell Carcinoma.

Objective: To investigate a novel surgical approach of combined transcervical parapharyngeal space (PPS) with the transoral approach to dissect oropharyngeal cancer. Methods: 31 patients who were pathologically diagnosed with oropharyngeal cancer and had undergone surgical treatment in Beijing Tongren Hospital during June 2018 and December 2020 were enrolled. All patients were squamous cell carcinoma patients. There were 25 males and 6 females, and the age ranged between 44 and 70 years old. The number of patients with T1, T2, T3, and T4 stage disease was 8, 15, 8, and 0, respectively, according to the American Joint Committee on Cancer staging method, 8th edition. After the dissection of the submandibular and cervical lymph nodes, the parapharyngeal space was exposed, and the parapharyngeal space lymph node and the outer borderline of the tumor were dissected, and then the inner borderline of the tumor was dissected via a transoral approach; the tumor was dissected en bloc, and the defects were reconstructed with the flap from the neck through the parapharyngeal space. Results: Among the patients enrolled, 21 were HPV positive and 10 were HPV negative. 8 patients were free of lymph node metastasis. The tumor resection margins were negative in all 31 patients. Safe and sufficient excision of tumors was feasible by this new surgical approach, avoiding complications associated with mandibulotomy or lip-splitting. All patients had no obvious dysfunctions of swallowing and voice. By the time of this follow-up, none died caused by OPSCC, and only two patients suffered from local recurrence. The 3-year survival rate is 100%, and the 3-year recurrence-free survival rate is 84.58%. Conclusion: The surgical approach of combined transcervical parapharyngeal space with the transoral approach was effective and safe. On this basis, this approach has the advantage of fewer postoperative complications and better functional results.

Systematic review and meta-analysis of the effectiveness and safety of hydroxychloroquine in treating COVID-19 patients.

BACKGROUND: Since COVID-19 outbreak, hydroxychloroquine (HCQ) has been tested for effective therapies, and the relevant researches have shown controversial results. METHODS: Systematic review and meta-analysis were conducted after a thorough search of relevant studies from databases. Trials that have evaluated HCQ for COVID-19 treatment were recruited for statistical analysis with fixed- and random-effect models. RESULTS: Nine trials involving 4112 patients were included in present meta-analysis. It was seen that HCQ-azithromycin (HCQ-AZI) combination regimen increased the mortality rate in COVID-19 (odds ratio [OR], 2.34; 95% confidence interval [CI], 1.63-3.36) patients; however, it also showed benefits associated with the viral clearance in patients (OR, 27.18; 95% CI, 1.29-574.32). HCQ-alone when used as a therapy in COVID-19 did not reveal significant changes in mortality rate, clinical progression, viral clearance, and cardiac QT prolongation. Subsequent subgroup analysis showed that HCQ treatment could decrease mortality rate and progression to severe illness in severely infected COVID-19 patients (OR, 0.27; 95% CI, 0.13-0.58). A lower risk of mortality rate was also noted in the stratified group of >14 days follow-up period (OR, 0.27; 95% CI, 0.13-0.58) compared to ≤14 days follow-up period group that conversely showed an increased mortality rate (OR, 2.09; 95% CI, 1.41-3.10). CONCLUSION: Our results indicated that HCQ-AZI combination treatment increased mortality rate in patients with COVID-19, but it also showed benefits associated with viral clearance in patients. HCQ-alone used for treatment has revealed benefits in decreasing the mortality rate among severely infected COVID-19 group and showed potential to be used for COVID-19 treatment in long-term follow-up period group. Accordingly, more rigorous, large-scale, and long follow-up period studies in patients with COVID-19 are needed.

Building an Open Resources Repository for COVID-19 Research.

The COVID-19 outbreak is a global pandemic declared by the World Health Organization, with rapidly increasing cases in most countries. A wide range of research is urgently needed for understanding the COVID-19 pandemic, such as transmissibility, geographic spreading, risk factors for infections, and economic impacts. Reliable data archive and sharing are essential to jump-start innovative research to combat COVID-19. This research is a collaborative and innovative effort in building such an archive, including the collection of various data resources relevant to COVID-19 research, such as daily cases, social media, population mobility, health facilities, climate, socioeconomic data, research articles, policy and regulation, and global news. Due to the heterogeneity between data sources, our effort also includes processing and integrating different datasets based on GIS (Geographic Information System) base maps to make them relatable and comparable. To keep the data files permanent, we published all open data to the Harvard Dataverse (https://dataverse.harvard.edu/dataverse/2019ncov), an online data management and sharing platform with a permanent Digital Object Identifier number for each dataset. Finally, preliminary studies are conducted based on the shared COVID-19 datasets and revealed different spatial transmission patterns among mainland China, Italy, and the United States.

Globalism or Nationalism? The Paradox of Chinese Official Discourse in the Context of the COVID-19 Outbreak.

By employing discourse-historical approach and corpus linguistics, this paper examines media reports to analyze the Chinese official discourse in the context of the COVID-19 outbreak. The results demonstrate that a paradox of globalism and nationalism has been simultaneously reflected when reporting the global pandemic. Based on a polarizing discursive construction of positive "self" and negative "others," on many occasions, the globalist and nationalist arguments have been closely intertwined and complement each other to reinforce the legitimacy of the ruling party at home and the international reputation of China under the leadership of the ruling party.

COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word Mining

With the spread and development of new epidemics, it is of great reference value to identify the changing trends of epidemics in public emotions. We designed and implemented the COVID-19 public opinion monitoring system based on time series thermal new word mining. A new word structure discovery scheme based on the timing explosion of network topics and a Chinese sentiment analysis method for the COVID-19 public opinion environment are proposed. Establish a "Scrapy-Redis-Bloomfilter" distributed crawler framework to collect data. The system can judge the positive and negative emotions of the reviewer based on the comments, and can also reflect the depth of the seven emotions such as Hopeful, Happy, and Depressed. Finally, we improved the sentiment discriminant model of this system and compared the sentiment discriminant error of COVID-19 related comments with the Jiagu deep learning model. The results show that our model has better generalization ability and smaller discriminant error. We designed a large data visualization screen, which can clearly show the trend of public emotions, the proportion of various emotion categories, keywords, hot topics, etc., and fully and intuitively reflect the development of public opinion.

Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions.

BACKGROUND: The coronavirus disease 2019 (COVID-19) outbreak originating in Wuhan, Hubei province, China, coincided with chunyun, the period of mass migration for the annual Spring Festival. To contain its spread, China adopted unprecedented nationwide interventions on January 23 2020. These policies included large-scale quarantine, strict controls on travel and extensive monitoring of suspected cases. However, it is unknown whether these policies have had an impact on the epidemic. We sought to show how these control measures impacted the containment of the epidemic. METHODS: We integrated population migration data before and after January 23 and most updated COVID-19 epidemiological data into the Susceptible-Exposed-Infectious-Removed (SEIR) model to derive the epidemic curve. We also used an artificial intelligence (AI) approach, trained on the 2003 SARS data, to predict the epidemic. RESULTS: We found that the epidemic of China should peak by late February, showing gradual decline by end of April. A five-day delay in implementation would have increased epidemic size in mainland China three-fold. Lifting the Hubei quarantine would lead to a second epidemic peak in Hubei province in mid-March and extend the epidemic to late April, a result corroborated by the machine learning prediction. CONCLUSIONS: Our dynamic SEIR model was effective in predicting the COVID-19 epidemic peaks and sizes. The implementation of control measures on January 23 2020 was indispensable in reducing the eventual COVID-19 epidemic size.