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At the beginning of 2020, the World Health Organization (WHO) started a coordinated global effort to counterattack the potential exponential spread of the SARS-Cov2 virus, responsible for the coronavirus disease, officially named COVID-19. This comprehensive initiative included a research roadmap published in March 2020, including nine dimensions, from epidemiological research to diagnostic tools and vaccine development. With an unprecedented case, the areas of study related to the pandemic received funds and strong attention from different research communities (universities, government, industry, etc.), resulting in an exponential increase in the number of publications and results achieved in such a small window of time. Outstanding research cooperation projects were implemented during the outbreak, and innovative technologies were developed and improved significantly. Clinical and laboratory processes were improved, while managerial personnel were supported by a countless number of models and computational tools for the decision-making process. This chapter aims to introduce an overview of this favorable scenario and highlight a necessary discussion about ethical issues in research related to the COVID-19 and the challenge of low-quality research, focusing only on the publication of techniques and approaches with limited scientific evidence or even practical application. A legacy of lessons learned from this unique period of human history should influence and guide the scientific and industrial communities for the future. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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There are several techniques to support simulation of time series behavior. In this chapter, the approach will be based on the Composite Monte Carlo (CMC) simulation method. This method is able to model future outcomes of time series under analysis from the available data. The establishment of multiple correlations and causality between the data allows modeling the variables and probabilistic distributions and subsequently obtaining also probabilistic results for time series forecasting. To improve the predictor efficiency, computational intelligence techniques are proposed, including a fuzzy inference system and an Artificial Neural Network architecture. This type of model is suitable to be considered not only for the disease monitoring and compartmental classes, but also for managerial data such as clinical resources, medical and health team allocation, and bed management, which are data related to complex decision-making challenges. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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The COVID-19 pandemic spread generated an urgent need for computational systems to model its behavior and support governments and healthcare teams to make proper decisions. There are not many cases of global pandemics in history, and the most recent one has unique characteristics, which are tightly connected to the current society's lifestyle and beliefs, creating an environment of uncertainty. Because of that, the development of mathematical/computational models to forecast the pandemic behavior since its beginning, i.e., with a restricted amount of data collected, is necessary. This chapter focuses on the analysis of different data mining techniques to allow the pandemic prediction with a small amount of data. A case study is presented considering the data from Wuhan, the Chinese city where the virus was first detected, and the place where the major outbreak occurred. The PNN + CF method (Polynomial Neural Network with Corrective Feedback) is presented as the technique with the best prediction performance. This is a promising method that might be considered in future eventual waves of the current pandemic or event to have a suitable model for future epidemic outbreaks around the world. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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The application of different tools for predicting COVID19 cases spreading has been widely considered during the pandemic. Comparing different approaches is essential to analyze performance and the practical support they can provide for the current pandemic management. This work proposes using the susceptible-exposed-asymptomatic but infectious-symptomatic and infectious-recovered-deceased (SEAIRD) model for different learning models. The first analysis considers an unsupervised prediction, based directly on the epidemiologic compartmental model. After that, two supervised learning models are considered integrating computational intelligence techniques and control engineering: the fuzzy-PID and the wavelet-ANN-PID models. The purpose is to compare different predictor strategies to validate a viable predictive control system for the COVID19 relevant epidemiologic time series. For each model, after setting the initial conditions for each parameter, the prediction performance is calculated based on the presented data. The use of PID controllers is justified to avoid divergence in the system when the learning process is conducted. The wavelet neural network solution is considered here because of its rapid convergence rate. The proposed solutions are dynamic and can be adjusted and corrected in real time, according to the output error. The results are presented in each subsection of the chapter. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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A significant number of people infected by COVID19 do not get sick immediately but become carriers of the disease. These patients might have a certain incubation period. However, the classical compartmental model, SEIR, was not originally designed for COVID19. We used the simple, commonly used SEIR model to retrospectively analyse the initial pandemic data from Singapore. Here, the SEIR model was combined with the actual published Singapore pandemic data, and the key parameters were determined by maximizing the nonlinear goodness of fit R2 and minimizing the root mean square error. These parameters served for the fast and directional convergence of the parameters of an improved model. To cover the quarantine and asymptomatic variables, the existing SEIR model was extended to an infectious disease model with a greater number of population compartments, and with parameter values that were tuned adaptively by solving the nonlinear dynamics equations over the available pandemic data, as well as referring to previous experience with SARS. The contribution presented in this paper is a new model called the adaptive SEAIRD model;it considers the new characteristics of COVID19 and is therefore applicable to a population including asymptomatic carriers. The predictive value is enhanced by tuning of the optimal parameters, whose values better reflect the current pandemic. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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Objective To investigate the mental health status of military healthcare workers in shelter hospitals in Shanghai during the epidemic caused by severe acute respiratory syndrome coronavirus 2 omicron variant and its influencing factors. Methods A total of 540 military healthcare workers in shelter hospitals in Shanghai were investigated with patient health questionnaire-9 (PHQ-9), generalized anxiety disorder-7 (GAD-7) and Athens insomnia scale (AIS) to explore their mental health status, and logistic regression was used to analyze the influencing factors. Results A total of 536 valid questionnaires were collected, with an effective rate of 99.3% (536/540). The incidence of depression, anxiety and insomnia among military healthcare workers in shelter hospitals in Shanghai was 45.5% (244/536), 26.1% (140/536) and 59.5% (319/536), respectively. Logistic regression analysis showed that whether people resided in Shanghai, the proportion of negative information in daily browsing information and diet status in shelter hospitals were the influencing factors of depression, anxiety and insomnia (all P<0.05);age and confidence in the future of Shanghai were the influencing factors of depression and insomnia (all P<0.05);and the time spent daily on epidemic-related information was an influencing factor of insomnia (P=0.021). Conclusion The incidence of depressive, anxiety and insomnia among military healthcare workers in shelter hospitals in Shanghai is high during the epidemic caused by severe acute respiratory syndrome coronavirus 2 omicron variant. Psychological consequences of the epidemic should be monitored regularly and continuously to promote the mental health of military healthcare workers.Copyright © 2022, Second Military Medical University Press. All rights reserved.
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Objective To explore the clinical effect and safety of Suhexiang Pills () in the treatment of patients infected with SARS-CoV-2. Methods A total of 192 patients infected with SARS-CoV-2 admitted to 17 hospitals including Beijing Hospital of Traditional Chinese Medicine Affiliated to Capital Medical University from December 2022 to January 2023 were randomly divided into control group and treatment group, with 89 patients in the treatment group and 103 in the control group. The patients in control group received basic treatment according to the Diagnosis and Treatment Protocol for COVID-19 (Trial Version 10). The patients in treatment group were oral administered with Suhexiang Pills on the basis of the control group, one pill each time, twice day. The patients in two groups were treated for 5 d. The clinical efficacy of the two groups after treatment was compared. The differences in scores of headache, chest pain, limb pain and inflammatory indexes before and after treatment were compared. Results After treatment, the total clinical effective rate of the treatment group was 95.51%, which was significantly higher than that of the control group (81.55%, P < 0.05). After treatment, headache, chest pain and limb pain scores were significantly decreased in both groups (P < 0.05), the headache score of the treatment group was significantly lower than that of the control group from the first day of treatment (P < 0.05), the chest pain score of the treatment group was significantly lower than that of the control group on the fifth day of treatment (P < 0.05), the limb pain score of the treatment group was significantly lower than that of the control group from the third day of treatment (P < 0.05). After treatment, the levels of C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6) in the two groups were decreased significantly (P < 0.05) and the levels of CRP and IL-6 in the treatment group were significantly lower than those of the control group (P < 0.05). There was no significant difference in the incidence of adverse events between the two groups. Conclusion Suhexiang Pills have a certain effect on headache, chest pain and limb pain, inhibiting the inflammatory response in patients infected with SARS-CoV-2, with good safety.Copyright © 2023 Editorial Office of Chinese Traditional and Herbal Drugs. All rights reserved.
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Background: Within the coronavirus 2019 (COVID-19) pandemic, literature has found worsened patient outcomes and increased virus transmissibility associated with reduced air quality. This factor, a structural social determinant of health (SDOH), has shown great promise as a link between air quality and patient outcomes during the COVID-19 pandemic. Researching SDOH within our patient populations is often difficult and limited by poor documentation or extensive questionnaires or surveys. The use of demographic data derived from the electronic health record (EHR) to more accurately represent SDOH holds great promise. The use of area-level determinants of health outcomes has been shown to serve as a good surrogate for individual exposures. We posit that an area level measure of air quality, the county-level Air Quality Index (AQI), will be associated with disease worsening in intensive care unit (ICU) patients being treated for COVID-19. Method(s): We will calculate AQI using a combination of open-source records available via the United States Environmental Protection Agency (EPA) and manual calculations using geospatial informatics systems (GIS) methods. Subjects will be identified as adult (> 18 years) patients admitted to Vanderbilt University Medical Center's ICUs between January 1, 2020, and March 31, 2022 with a positive SARS-CoV-2 laboratory analysis result. We will exclude patients without a home address listed. Patient demographic and hospital data from ICU admission to 28 days following admission will include: age, sex, home address, race, insurance type, primary language, employment status, highest level of education, and hospital course data. Together these will be collated to produce our primary outcome variable of WHO Clinical Progression Scale score. These validated scores range from 0 (uninfected) to 10 (dead) to track clinically meaningful progression of COVID-19 infected patients. Our AQI variable will be obtained from the EPA available county-level monitoring station spatial data combined with open-source state/county center point spatial data. These data contain historic cataloguing to determine air quality at both specific time points and averages over time. Where a county's average yearly AQI is not available due to lack of a monitoring station, we will use spatial data tools to calculate an average based on data from nearby stations. We will utilize yearly averages of AQI in the year prior to COVID-19 diagnosis to describe overall impact of air quality on patients' respiratory outcomes as opposed to single day exposures. Linkage of patient data to AQI database will be performed using patient addresses. Discussion(s): By combining area level data with electronic health record (EHR) data, we will be positioned to understand the contribution of environmental and social determinants of health on patient outcomes. Our long-term goal is to elucidate which social and environmental determinants of health are associated with worse outcomes from COVID-19 and other respiratory viruses, using data extracted from the EHR.
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Objective To investigate the mental health status of military healthcare workers in shelter hospitals in Shanghai during the epidemic caused by severe acute respiratory syndrome coronavirus 2 omicron variant and its influencing factors. Methods A total of 540 military healthcare workers in shelter hospitals in Shanghai were investigated with patient health questionnaire-9 (PHQ-9), generalized anxiety disorder-7 (GAD-7) and Athens insomnia scale (AIS) to explore their mental health status, and logistic regression was used to analyze the influencing factors. Results A total of 536 valid questionnaires were collected, with an effective rate of 99.3% (536/540). The incidence of depression, anxiety and insomnia among military healthcare workers in shelter hospitals in Shanghai was 45.5% (244/536), 26.1% (140/536) and 59.5% (319/536), respectively. Logistic regression analysis showed that whether people resided in Shanghai, the proportion of negative information in daily browsing information and diet status in shelter hospitals were the influencing factors of depression, anxiety and insomnia (all P<0.05);age and confidence in the future of Shanghai were the influencing factors of depression and insomnia (all P<0.05);and the time spent daily on epidemic-related information was an influencing factor of insomnia (P=0.021). Conclusion The incidence of depressive, anxiety and insomnia among military healthcare workers in shelter hospitals in Shanghai is high during the epidemic caused by severe acute respiratory syndrome coronavirus 2 omicron variant. Psychological consequences of the epidemic should be monitored regularly and continuously to promote the mental health of military healthcare workers.Copyright © 2022, Second Military Medical University Press. All rights reserved.
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Background: Previous studies have demonstrated promising serologic responses in PLWH receiving a third dose of vaccine against SARS-CoV-2. However, real-world clinical effectiveness, especially during the pandemic caused by B.1.1.529 variant, remains less investigated. Method(s): PLWH seeking HIV care at our hospital from 2021/6 to 2022/6 were included and advised to receive the third dose of COVID-19 vaccine. Individuals were excluded from this study if they had been previously diagnosed with COVID-19. Different types of COVID-19 vaccines were available in the vaccination program, including BNT162b2, mRNA-1273 (either 50 or 100 mug), MVC-COV1901 and NVX-CoV2373 vaccines. PLWH were screening for the occurrence of COVID-19 through the reporting system of notifiable diseases of Taiwan CDC, and were tested for anti-nucleocapsid (anti-N) IgG every 1 to 3 months. Participants were followed for 180 days until the fourth dose of COVID-19 vaccination, occurrence of SARS-CoV-2 infection, seroconversion of anti-N IgG, death, or loss to follow-up, whichever occurred first. Result(s): 1,496 PLWH were included: 631 (42.2%) receiving 100 mug mRNA-1273 vaccine, 468 (31.3%) 50 mug mRNA-1273 vaccine, and 328 (21.9%) BNT162b2 vaccine, 65 (4.3%) MVC-COV1901 vaccine, and 4 (0.3%) NVX-CoV2373 vaccine for the third dose of SARS-CoV-2 vaccination. 297 (19.9%) PLWH were diagnosed with COVID-19 during the follow-up period, including 92 (14.6%) who received 100 mug mRNA-1273, 111 (23.7%) 50 mug mRNA-1273, 79 (24.1%) BNT162b2 and 15 (21.7%) either MVC-COV1901 or NVX-CoV2373;in addition, 98 PLWH had seroconversion of anti-N IgG during follow-up, including 23, 50, 19 and 6 PLWH who received 100 mug mRNA-1273, 50 mug mRNA-1273, BNT162b2, and either MVC-COV1901 or NVX-CoV2373, respectively. Similar rates of new infection with SARS-CoV-2 or seroconversion of anti-N IgG were demonstrated regardless the vaccine type of the third dose (log-rank test, p=0.46). Factors associated with a diagnosis of SARS-CoV-2 infection and seroconversion of anti-N IgG included an age >50 years (aOR, 0.67;95% CI, 0.49-0.91) and newly infected with hepatitis C virus (HCV) (aOR, 1.41;95% CI, 1.09-1.83). Conclusion(s): Our study demonstrated that clinical effectiveness of the third dose of different vaccines available to PLWH was similar in preventing SARSCoV- 2 infection or seroconversion of anti-N IgG Taiwan. PLWH aged less than 50 years and those with newly diagnosed HCV infection were at higher risk of acquiring COVID-19. Kaplan-Meier survival curve for acquiring COVID-19 or seroconversion of anti-N IgG in PLWH receiving different COVID-19 vaccination of the third dose (log-rank test, 4 groups, p = 0.46).
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Background: Previous studies had demonstrated that patients with hematologic malignancies had suboptimal antibody response after receiving COVID-19 vaccines, especially among those having previously treated with anti- CD20 monoclonal antibodies. Method(s): Adult patients with non-Hodgkin's lymphoma or chronic lymphocytic leukemia (CLL) were enrolled before receiving the second dose of SARS-CoV-2 vaccine. Determinations of anti-SARS-CoV-2 spike and nucleocapsid IgG titers were performed every 1-3 months, after they received the second and the third dose of SARS-CoV-2 vaccine, respectively. Patients were excluded from analysis if they were diagnosed with COVID-19. All serum samples were tested for anti-nucleocapsid antibody and those tested positive were excluded from subsequent analyses. Result(s): A total of 85 participants were enrolled, including 42 (49.4%) with diffused large B-cell lymphoma, and 13 (15.3) with follicular lymphoma and 9 with CLL. 72 (84.7%) participants had received anti-CD20 monoclonal antibodies, with a median interval of 24 months between last anti-CD20 treatment and the second dose of vaccine, and 21 (24.7%) had HIV infection. Factors associated with failure to achieve an anti-spike IgG titer >141 BAU/ mL within 12 weeks after the second dose of vaccine included HIV infection (adjusted odds ratio [aOR], 0.14;95% CI, 0.04-0.51), active hematologic disease (aOR, 5.50;95% CI 1.42-21.32), receipt of anti-CD20 monoclonal antibodies (aOR, 6.65;95% CI 1.52-29.07), and receipt of two doses of homologous mRNA vaccination (aOR, 0.17;95% CI 0.05-0.56). In the participants having previously treated with anti-CD20 regimen, only 8.6% achieved an antibody response ( >141 BAU/mL) in the first year, while 78.3% achieved anti-spike IgG titer > 141 BAU/mL after two years post B-cell depleting treatment. After the third dose of SARS-CoV-2 vaccine, 53.6% achieved an antispike IgG titer > 141 BAU/mL in the first year post anti-CD20 treatment. Conclusion(s): Our study demonstrated that previous treatment with anti-CD20 monoclonal antibodies was associated a lower antibody response among patients with lymphoproliferative disorders receiving two doses of SARS-CoV-2 vaccine. While two doses of SARS-CoV-2 vaccines might not be sufficient even one year apart from the last dose of rituximab, a third dose of vaccine may boost anti-spike IgG particularly in the subset of recent exposure to rituximab. Anti-spike IgG determined 1-3 months after the second (A) / third (B) dose of COVID-19 vaccine, stratified by the interval between last anti-CD20 regimen and the second / third dose of COVID-19 vaccine. (Figure Presented).
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Since the breakout of Corona Virus Disease 2019 (COVID-19), the global fight against influenza has begun. Various technologies have been developed to support the fast-growing contactless service market, and hence contactless services are rapidly becoming a new growth strategy. In particular, the retail service industry most urgently needs contactless service technology. A representative technical case is the self-checkout machine, which can reduce labor costs and provide customer satisfaction. We present a solution in this article. We propose a hand gesture recognition contactless self-checkout system, which is a hand gesture recognition model based on YOLOv5s. The hand gesture recognition mAP (0.5) value reaches 0.995, the mAP (0.5:0.95) value reaches 0.865, and the F1 score is 0.96, together with the accuracy and recall rate is close to 1. Compared with the excellent algorithm YOLOx-s, the FPS value of YOLOv5s can reach 123 (YOLOx-s is 108). In addition, the model can be used to detect recorded static and dynamic hand gestures in real-time. Practical results show that the YOLOv5s can effectively recognize hand gestures and realize the contactless checkout process.
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Building an effective resilient supply chain system (RSCS) is critical and necessary to reduce the risk of supply chain disruptions in unexpected scenarios such as COVID-19 pandemic and trade wars. To overcome the impact of insufficient raw material supply on the supply chain in mass disruption scenarios, this study proposes a novel RSCS considering product design changes (PDC). An RSCS domain model is first developed from the perspective of PDC based on a general conceptual framework, i.e., function-context-behavior-principle-state-structure (FCBPSS), which can portray complex systems under unpredictable situations. Specifically, the interaction among the structure, state and behavior of the infrastructure system and substance system is captured, and then a quantitative analysis of the change impact process is presented to evaluate the resilience of both the product and supply chain. Next, a case study is conducted to demonstrate the PDC strategy and to validate the feasibility and effectiveness of the RSCS domain model. The results show that the restructured RSCS based on the proposed strategy and model can remedy the huge losses caused by the unavailability of raw materials.
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Understanding the residents’routine and repetitive behavior patterns is important for city planners and strategic partners to enact appropriate city management policies. However, the existing approaches reported in smart city management areas often rely on clustering or machine learning, which are ineffective in capturing such behavioral patterns. Aiming to address this research gap, this article proposes an analytical framework, adopting sequential and periodic pattern mining techniques, to effectively discover residents’routine behavior patterns. The effectiveness of the proposed framework is demonstrated in a case study of American public behavior based on a large-scale venue check-in dataset. The dataset was collected in 2020 (during the global pandemic due to COVID-19) and contains 257 561 check-in data of 3995 residents. The findings uncovered interesting behavioral patterns and venue visit information of residents in the United States during the pandemic, which could help the public and crisis management in cities. IEEE
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Objective To explore the clinical effect and safety of Suhexiang Pills () in the treatment of patients infected with SARS-CoV-2. Methods A total of 192 patients infected with SARS-CoV-2 admitted to 17 hospitals including Beijing Hospital of Traditional Chinese Medicine Affiliated to Capital Medical University from December 2022 to January 2023 were randomly divided into control group and treatment group, with 89 patients in the treatment group and 103 in the control group. The patients in control group received basic treatment according to the Diagnosis and Treatment Protocol for COVID-19 (Trial Version 10). The patients in treatment group were oral administered with Suhexiang Pills on the basis of the control group, one pill each time, twice day. The patients in two groups were treated for 5 d. The clinical efficacy of the two groups after treatment was compared. The differences in scores of headache, chest pain, limb pain and inflammatory indexes before and after treatment were compared. Results After treatment, the total clinical effective rate of the treatment group was 95.51%, which was significantly higher than that of the control group (81.55%, P < 0.05). After treatment, headache, chest pain and limb pain scores were significantly decreased in both groups (P < 0.05), the headache score of the treatment group was significantly lower than that of the control group from the first day of treatment (P < 0.05), the chest pain score of the treatment group was significantly lower than that of the control group on the fifth day of treatment (P < 0.05), the limb pain score of the treatment group was significantly lower than that of the control group from the third day of treatment (P < 0.05). After treatment, the levels of C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6) in the two groups were decreased significantly (P < 0.05) and the levels of CRP and IL-6 in the treatment group were significantly lower than those of the control group (P < 0.05). There was no significant difference in the incidence of adverse events between the two groups. Conclusion Suhexiang Pills have a certain effect on headache, chest pain and limb pain, inhibiting the inflammatory response in patients infected with SARS-CoV-2, with good safety.Copyright © 2023 Editorial Office of Chinese Traditional and Herbal Drugs. All rights reserved.
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Several synthetic routes of nirmatrelvir (the ingredient of a new drug to treat COVID-19 made by Pfizer) have been reported. We focused on a second route to improve the synthetic method of nirmatrelvir with a methodology that included different steps. The first step was an analysis of reaction byproducts using acetonitrile as a solvent of the condensation reaction to improve the inversion rate. Then, we used isobutyl acetate as a crystalline solvent to obtain the key intermediate as a solvate, which was a stable crystal product with high purity. Complementarily, we also used trifluoroacetic anhydride as the primary-amide dehydrating agent, and 2-methyl tetrahydrofuran as the solvent to prepare nirmatrelvir, which led to an overall yield of 48% via four steps and a purity of 99.5% according to high-performance liquid chromatography. We also investigated the crystal form of nirmatrelvir: the single-crystal features and transformation from a crystal form to nirmatrelvir were dependent upon temperature. Our data have great value for study of the synthetic method and crystal stability of nirmatrelvir. © 2023 The Royal Society of Chemistry.
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Objective: To investigate the clinical value of outpatient screening in department of general surgery during the Corona Virus Disease 2019 (COVID-19) outbreak. Method(s): The retrospective and descriptive study was conducted. The clinical data of 57 patients who visited surgery clinic and emergency department of Union Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology between February 1st and 26th in 2020 were collected. There were 30 males and 27 females, aged (53+/-16)years, with a range from 17 to 87 years. All the 57 patients were measured score of outpatient screening in department of general surgery. The score >=3 indicated high risk and the score < 3 indicated low risk. Observation indicators: (1) clinical data of patients;(2) score of outpatient screening for COVID-19 of patients. Measurement data with normal distribution were represented as Mean+/-SD, and comparison between groups was analyzed by the t test. Measurement data with skewed distribution were described as M (IQR), and comparison between groups was analyzed by the rank sum test. Count data were described as absolute numbers, and comparison between groups was analyzed using the chi-square test. Result(s): (1) Clinical data of patients: of the 57 patients, there were 12 males and 14 females of the 26 confirmed or suspected cases, versus 18 males and 13 females of the 31 non-infection cases, showing no significant difference between the two groups (chi2=0.805, P>0.05). The 26 confirmed or suspected cases of COVID-19 had an age of (57+/-16)years, and 31 non-infection cases had an age of (50+/-16) years, with no significant difference between the two groups (t=-1.646, P>0.05). (2) Score of outpatient screening for COVID-19 of patients: the score of outpatient screening for COVID-19 of the 26 confirmed or suspected cases was 3.0(4.0), versus 1.0(1.0) of the 31 non-infection cases, showing a significant difference between the two groups (Z=-3.695, P<0.05). There were 17 and 9 of the 26 confirmed or suspected cases with high risks and low risks, respectively, versus 3 and 28 of the 31 non-infection cases, with a significant difference between the two groups (chi2=19.266, P<0.05). Conclusion(s): During the COVID-19 outbreak, outpatient screening in department of general surgery can effectively screen out high-risk patients.Copyright © 2020 by the Chinese Medical Association.
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Objective To evaluate the role of live webcast as a new medium in the propaganda and education of liver transplant recipients. Methods According to the contents of live webcast propaganda and education meeting for liver transplant recipients, relevant data of the live webcast meeting were counted and analyzed, including baseline data of participants, participation pattern, viewing frequency and duration, etc. The characteristics between live webcast and traditional propaganda and education meetings were compared. Results By the end of the live webcast meeting, 273 participants were registered, including 2 oversea participants and 271 from China. These domestic participants were from 26 provinces, autonomous regions and municipalities in China. The total number of views was 1 526. Participants attended the meeting by clicking direct link (n=243), WeChat group access (n=22), WeChat chat access (n=7) and Dingding App access (n=1). The viewing duration was (68±5) min. Compared with the traditional method, the number and places of registers of the live webcast propaganda and education meeting were increased. The questioning methods and filling out follow-up information were more convenient. Participants could attend the meeting free from charge anywhere, and saved more time. The live webcast propaganda and education meeting was not affected by the COVID-19 pandemic, and data statistical method was optimized. Conclusions Live webcast as a new medium, has a wide range of advantages, which provides a novel form of propaganda and education for the recipients after liver transplantation. It is of significance to improve the long-term survival rate and to enhance the quality of life of recipients after liver transplantation. © 2020 The authors.
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As social media such as Twitter has become an important medium for disseminating information, it is essential to understand how the information diffusion on social media influences public adoption of vaccines. Based on the innovation diffusion theory, we construct a user and information quality indicator system for early adopters of COVID-19 vaccination by identifying their creation of user-generated content on social media. Machine learning approaches and text analysis methods are used to perform topic clustering and sentiment analysis on vaccination-related tweets on Twitter. Based on each country's vaccination data in January 2021, the study examines the relationship between the quality of social media early adopters, and the quality of the information they publish with vaccine adoption by using the OSL regression model. The empirical results show that the total number of tests, the number of new COVID-19 cases, and the human development index have a significantly positive influence on vaccine adoption. Neutral emotions and offensive language of early adopters on social media have a significantly negative relationship with vaccine adoption. These interesting findings can help governments and public health officials understand early adopters' perceptions of vaccines and play an important role in targeted policy interventions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.