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The global pandemic of coronavirus disease 2019 (COVID-19) has brought great challenges to the traditional medical model. During the outbreak of COVID-19 in Shanghai, China, from March to May, 2022, there was a significant increase in the number of pediatric cases due to high transmissibility, immune escape, and vaccine breakthrough capacity of Omicron variants. The designated hospitals for children with COVID-19 served as a connecting link between children's specialized hospitals and mobile cabin hospitals. From April 7 to June 2, 2022, a total of 871 children with COVID-19 were admitted to Renji Hospital, Shanghai Jiao Tong University School of Medicine (South Branch), a designated hospital for children with COVID-19. Among these patients, 568 (65.2%) were children under 3 years old, 870 (99.9%) were mild or moderate, and 1 was severe. This article reports the experience in the management of pediatric cases in this designated hospital, which included the following aspects: establishing an optimal case-admission process; strengthening multidisciplinary standardized diagnosis and treatment; optimizing the management, warning, and rescue system for severe COVID-19; implementing family-centered nursing care; formulating an individualized traditional Chinese medicine treatment regimen; optimizing the discharge process and strengthening bed turnover; implementing strict whole-process control to reduce the risk of nosocomial infection; constructing a structured medical record system and using information platforms to adapt to the work mode of large-volume cases; conducting scientific research and sharing the experience in diagnosis and treatment.

Establishing and evaluating physician-pharmacist collaborative clinics to manage patients with type 2 diabetes in primary hospitals in Hunan province: study protocol of a multi-site randomized controlled trial in the era of COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic has exerted an unprecedented and universal impact on global health system, resulting in noticeable challenges in traditional chronic disease care, of which diabetes was reported to be most influenced by the reduction in healthcare resources in the pandemic. China has the world's largest diabetes population, and current diabetes management in China is unsatisfactory, particularly in rural areas. Studies in developed countries have demonstrated that physician-pharmacist collaborative clinics are efficient and cost-effective for diabetes management, but little is known if this mode could be adapted in primary hospitals in China. The aim of this proposed study is to develop and evaluate physician-pharmacist collaborative clinics to manage type 2 diabetes mellitus (T2DM) in primary hospitals in Hunan province. METHODS: A multi-site randomized controlled trial will be conducted to evaluate the effectiveness and cost-effectiveness of the physician-pharmacist collaborative clinics compared with usual care for Chinese patients with T2DM. Six primary hospitals will participate in the study, which will recruit 600 eligible patients. Patients in the intervention group will receive services from both physicians and pharmacists in the collaborative clinics, while the control group will receive usual care from physicians. Patients will be followed up at the 3rd, 6th, 9th and 12th month. Comparison between the two groups will be conducted by assessing the clinical parameters, process indicators and costs on diabetes. A satisfaction survey will also be carried out at the end of the study. DISCUSSION: If effective, the physician-pharmacist collaborative clinics can be adapted and used in primary hospitals of China to improve glycemic control, enhance medication adherence, decrease incidence of complications and reduce patients' dependence on physicians. Findings from the present study are meaningful for developing evidence-based diabetes care policy in rural China, especially in the COVID-19 pandemic era. TRIAL REGISTRATION: Chinese Clinical Trial Registry, ChiCTR2000031839 , Registered 12 April 2020.

[Impacts of Meteorology and Emission Variations on PM2.5 Concentration Throughout the Country During the 2020 Epidemic Period].

This study analyzed the impacts of meteorological conditions and changes in air pollutant emissions on PM2.5 across the country during the first quarter of 2020 based on the WRF-CMAQ model. Results showed that the variations in meteorological conditions led to a national PM2.5 concentration decreased of 1.7% from 2020-01 to 2020-03, whereas it increased by 1.6% in January and decreased by 1.3% and 7.9% in February and March, respectively. The reduction of pollutants emissions led to a decrease of 14.1% in national PM2.5 concentration during the first quarter of 2020 and a decrease of 4.0%, 25.7%, and 15.0% in January, February, and March, respectively. Compared to the same period last year, the PM2.5 concentration measured in Wuhan City decreased more than in the entire country. This was caused by improved meteorological conditions and a higher reduction of pollutant emissions in Wuhan City. PM2.5 in Beijing increased annually before the epidemic outbreak and during the strict control period, mainly due to unfavorable meteorological conditions. However, the decrease in PM2.5 in Beijing compared to March 2019 was closely related to the substantial reduction of emissions. The measured PM2.5 in the "2+26" cities, the Fenwei Plain and the Yangtze River Delta (YRD) decreased during the first quarter of 2020, with the largest drop occurring in the Yangtze River Delta due to higher YRD emissions reductions. The meteorological conditions of "2+26" cities and Fenwei Plain were unfavorable before the epidemic outbreak and greatly improved during the strict control period, whereas the Yangtze River Delta had the most favorable meteorological conditions in March. The decrease in PM2.5 concentration caused by the reduction of pollutant emissions in the three key areas was highest during the strict control period.

Topic Relevance of Public Health Emergencies Influence on Internet Public Opinion Resonance: Simulation Based on Langevin’s Equation

Public health emergencies occurred frequently, which usually result in the negative Internet public opinion events. In the complex network information ecological environment, multiple public opinion events may be aggregated to generate public opinion resonance due to the topic category, the mutual correlation of the subject involved, and the compound accumulation of specific emotions. In order to reveal the phenomenon and regulations of the public opinion resonance, we firstly analyze the influence factors of the Internet public opinion events in the public health emergencies. Then, based on Langevin’s equation, we propose the Internet public opinion stochastic resonance model considering the topic relevance. Furthermore, three exact public health emergencies in China are provided to reveal the regulations of evoked events “revival” caused by original events. We observe that the Langevin stochastic resonance model considering topic relevance can effectively reveal the resonance phenomenon of Internet public opinion caused by public health emergencies. For the original model without considering the topic relevance, the new model is more sensitive. Meanwhile, it is found that the degree of topic relevance between public health emergencies has a significant positive correlation with the intensity of Internet public opinion resonance.

Identifying potential treatments of COVID-19 from Traditional Chinese Medicine (TCM) by using a data-driven approach.

ETHNOPHARMACOLOGICAL RELEVANCE: Traditional Chinese Medicine (TCM) has been widely used as an approach worldwide. Chinese Medicines (CMs) had been used to treat and prevent viral infection pneumonia diseases for thousands of years and had accumulated a large number of clinical experiences and effective prescriptions. AIM OF THE STUDY: This research aimed to systematically excavate the classical prescriptions of Chinese Medicine (CM), which have been used to prevent and treat Pestilence (Wenbing, Wenyi, Shiyi or Yibing) for long history in China, to obtain the potential prescriptions and ingredients to alternatively treat COVID-19. MATERIALS AND METHODS: We developed the screening system based on data mining, molecular docking and network pharmacology. Data mining and association network were used to mine the high-frequency herbs and formulas from ancient prescriptions. Virtual screening for the effective components of high frequency CMs and compatibility Chinese Medicine was explored by a molecular docking approach. Furthermore, network pharmacology method was used to preliminarily uncover the molecule mechanism. RESULTS: 574 prescriptions were obtained from 96,606 classical prescriptions with the key words to treat "Warm diseases (Wenbing)", "Pestilence (Wenyi or Yibing)" or "Epidemic diseases (Shiyi)". Meanwhile, 40 kinds of CMs, 36 CMs-pairs, 6 triple-CMs-groups existed with high frequency among the 574 prescriptions. Additionally, the key targets of SARS-COV-2, namely 3CL hydrolase (Mpro) and angiotensin-converting enzyme 2(ACE2), were used to dock the main ingredients from the 40 kinds by the LigandFitDock method. A total of 66 compounds components with higher frequency were docked with the COVID-19 targets, which were distributed in 26 kinds of CMs, among which Gancao (Glycyrrhizae Radix Et Rhizoma), HuangQin (Scutellariae Radix), Dahuang (Rhei Radix Et Rhizome) and Chaihu (Bupleuri Radix) contain more potential compounds. Network pharmacology results showed that Gancao (Glycyrrhizae Radix Et Rhizoma) and HuangQin (Scutellariae Radix) CMs-pairs could also interact with the targets involving in immune and inflammation diseases. CONCLUSIONS: These results we obtained probably provided potential candidate CMs formulas or active ingredients to overcome COVID-19. Prospectively, animal experiment and rigorous clinic studies are needed to confirm the potential preventive and treat effect of these CMs and compounds.