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Background: The pathophysiology of viral-infections is highly complex and involves host immunocompetence, host genetics, and gene-environment interactions. We hypothesized that polymorphic variants in host genes, blood group and previous vaccination status against H1N1 may affect the clinical course of covid-19 infection. Method(s): A total of 202 subjects who were RT-PCR negative after Covid-19 infection were recruited. We investigated association between Covid-19 infection (Severity and recovery period) and multiple factors including ABO and Rh blood groups, H1N1 vaccination, polymorphism in Viral susceptibility genes (ACE2 G8790A), and polymorphism in host response genes (ACE I/D rs4646994, IL6- 174G/C, GSTT1/GSTM1 I/D and GSTP1 Ile 105 Val). Result(s): B-ve and O-ve ABO and Rh blood groups had significantly higher Covid-19 recovery period applied on one-vs.-all in a nonparametric t-test (p<0.05). Subjects who had vaccinated themselves against H1N1 presented with a lower recovery-period (p<0.05). Both variables (blood group and H1N1 vaccination) were not however associated with Covid-19 severity. Out of the studied polymorphisms, ACE2 G8790A and GSTT1/GSTM1 were significantly associated with covid-19 infection. Our results indicated that G/G genotype of ACE2 G8790A (OR 3.52, P 0.007) and GSTT1/ GSTM1 null (M1 - / - OR = 3.98, P = 0.0004;T1 - / - OR 3.84, P = 0.004) and double null (M1 - / - /T1 - / - OR = 9.66, P = 0.001) are likely to be associated with an increased risk for severe-critical outcomes in individuals with COVID-19. Other polymorphisms analyzed in this study were found to have no significant association with Covid-19 outcome. Conclusion(s): This study suggests that outcome of Covid-19 infection is affected by both clinical and genetic factors. Thus it seems plausible to utilize these factors as prediction and susceptibility markers in the prognosis of COVID-19, which may help to personalize the treatment.
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Coronavirus disease 2019 (COVID-19), first reported in Wuhan, China, has rapidly spread worldwide. Traditional Chinese medicine (TCM) has been used to prevent and treat viral epidemics and plagues for over 2,500 years. In the guidelines on fighting against COVID-19, the National Health Commission of the People's Republic of China has recommended certain TCM formulas, namely Jinhua Qinggan granule (JHQGG), Lianhua Qingwen granule (LHQWG), Qingfei Paidu decoction (QFPDD), Xuanfei Baidu granule (XFBD), Xuebijing injection (XBJ), and Huashi Baidu granule (HSBD) for treating COVID-19 infected individuals. Among these six TCM formulas, JHQGG and LHQWG effectively treated mild/moderate and severe COVID-19 infections. XFBD therapy is recommended for mild COVID-19 infections, while XBJ and HSBD effectively treat severe COVID-19 infections. The internationalization of TCM faces many challenges due to the absence of a clinical efficacy evaluation system, insufficient research evidence, and a lack of customer trust across the globe. Therefore, evidence-based research is crucial in battling this infectious disease. This review summarizes SARS-CoV-2 pathogenesis and the history of TCM used to treat various viral epidemics, with a focus on six TCM formulas. Based on the evidence, we also discuss the composition of various TCM formulas, their underlying therapeutic mechanisms, and their role in curing COVID-19 infections. In addition, we evaluated the roles of six TCM formulas in the treatment and prevention of other influenza diseases, such as influenza A (H1N1), severe acute respiratory syndrome (SARS), and Middle East respiratory syndrome (MERS). Furthermore, we highlighted the efficacy and side effects of single prescriptions used in TCM formulas.Copyright © 2021
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It is crucial to study air quality and its impact on human health, as it can leave not only short-term effects but also have long-term effects, especially on people suffering from cardiovascular and lung diseases. During the COVID-19 pandemic, a major lockdown of almost 70 days in four different phases was announced in India. Due to this exercise, many visually observed a drastic change in air quality;however, actual quantifications were limited. Therefore, there is a need to quantify how air quality changed from before to during and post-lockdown scenarios. This study quantifies the COVID-19 India lockdown impact on air quality by analyzing the change in major air pollutants such as SO2, NO2, CO, O3, PM2.5, and PM10. The major objectives of this study are to quantify the change in major air pollutants across India during the lockdown and to identify their trends and respective hotspots. In order to achieve these objectives, air quality estimates are obtained from Sentinel 5P satellite, while PM2.5 and PM10 values are taken from Central Pollution Control Broad's ground monitoring stations. For temporal analysis, different time intervals starting from before the lockdown (i.e., March 1, 2020) till the end of the fourth lockdown (i.e., May 31, 2020) were analyzed across India. Results state that (1) There was a significant decline of − 48.11% and − 11.56% in concentrations of SO2 and NO2, respectively, after averaging values at their respective hotspots (2) A decrease of − 6.78% and − 0.42% was observed in O3 and CO concentration during the lockdown period in the year 2020 compared with the same period in the year 2019. (3) For PM2.5, Kolkata had the maximum drop of − 83.28%, while Bengaluru had the least drop of − 38.86%, whereas, for PM10, Kolkata had the maximum drop again of − 80.53%, while Delhi, on the other hand, had an increment of 13.42% at the end of the fourth lockdown. The results indicate the indirect benefit of the COVID-19 lockdown on air quality. It also provides a better understanding of hotspots and trends that can aid the government and the policy-makers to identify precautionary measures to reduce air pollution and prioritize hotspots. © 2022, Indian Society of Remote Sensing.
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The COVID-19 pandemic continues to negatively impact people's mental health worldwide. Due to the rise in unemployment, loss of income, and lack of social interaction, people are now more likely to feel lonely, go on fewer outings, and dread the unexpected nature of viral transmission. Meanwhile, Public Health authorities are interested in monitoring people's mental and emotional well-being. In this paper, natural language processing is used to analyze human sentiments concerning the COVID-19 pandemic that has been dangerously affecting individuals' mental and physical well-being for more than two years now. Even though several waves of COVID-19 have passed, of which the first and third waves i.e., the initial pandemic period from 20th March 2020 and the rise of the Delta variant from January 2020 had the most impact on the mental health of individuals, this is further evident by the results of this paper. This research focuses on how severely this virus has affected people's mental health and emotions. After processing the data i.e., cleaning, formatting, and removing irregularities from the data, feature engineering models are applied to acquire the results. The results through VADER (valence-aware dictionary and sentiment reasoning) indicate an increase in overall negative sentiments between two mentioned periods. Additionally, the NRC-EIL (National Research Council of Canada - Emotion Intensity Lexicon) analysis showed that 'fear' and 'sadness' occurred during those times. © 2022 IEEE.
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Objective: This study aimed to categorize and analyze the public response toward third/booster shots of COVID-19 on Twitter. Methods: We downloaded the COVID-19 vaccine booster shots related Tweets using the Twitter API. The collected Tweets were pre-processed to prepare them for analysis by (1) removing non-English language tweets, retweets, emojis, emoticons, non-printable characters, the punctuation marks, and the prepositions, (2) anonymizing the identity of the users, and (3) normalizing various forms of the same words. We used the state-of-the-art BertTopic modeling library to identify the most popular topics. Results: Of 165,048 Tweets collected, 36,908 Tweets were analyzed in this study. From these tweets, we identified 9 topics, which were about Biden administration, Pfizer & BioNTech, Moderna, Johnson & Johnson, eligibility for booster shots, side effects, Donald Trump, variants of the Novel Coronavirus, and conspiracy theory & propaganda. The mean of sentiment was positive in all topics. The lowest and highest mean of sentiments were for the Donald Trump topic (0.0097) and the Johnson & Johnson topic (0.1294), respectively. Conclusions: The topics identified in this study not only accurately reflect the contemporary COVID-19 discussion, but also the high degree of politicization in the USA. While the latter might be a result of our rejection of non-English tweets, it is reassuring to see our fully automated, unsupervised pipeline reliably extract such global features in the data at scale. We, therefore, believe that the methodology presented in this study is mature and useful for other infoveillance studies on a wide variety of topics. © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.
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Background: Despite a marked decline in worldwide under-five mortality over the past 30 years, the largest proportion of these deaths remain neonates. In 2018, 4 million (75% of all under-five deaths) infants died worldwide [1]. The neonatal mortality rate in Ethiopia was 28.1 per 100 live births, compared to 17.7 globally and 27.2 in Africa, ranking 23rd for highest neonatal mortality [2]. The Ethiopian Pediatric Society (EPS), entered a virtual partnership with the American Academy of Pediatrics (AAP) in 2020 to provide educational and practical support to clinicians in Ethiopia to perform quality improvement (QI) research. QI training for clinicians at eight Ethiopian sites is modeled on Project ECHO (Extension for Community Healthcare Outcomes) [3]. ECHO improves capacity for specialists to deliver care to underserved communities via collaboration with local clinicians using video teleconferencing, which has been a particularly advantageous tool to continue global health work during the COVID-19 pandemic. The goal of this project is to support local Ethiopian providers in designing, implementing, and assessing a QI intervention. Methods: We adapt the Project ECHO model to have U.S. neonatology faculty-fellow teams mentor clinicians from 8 hospitals in Ethiopia. Using video conferencing and regular contact through mobile devices, teams work to identify gaps in care, create SMART aim statements, identify key stakeholders and barriers to change, and implement interventions. Interventions are measured with a phone application, Liveborn, which allows instant transmission of data internationally. Sites provide monthly progress updates to the EPS. AAP/EPS leadership, U.S.-based faculty-fellow teams, and QI team leaders meet monthly via video conference for didactic sessions on QI methodology followed by teams presenting their progress, which promotes discussion and collaboration between the sites, AAP/EPS leadership, and U.S. mentorship teams to address any barriers. Results: Teams identified compliance with delayed cord clamping (DCC) and skin-to-skin (STS) after delivery as a gap by independently collecting local data. QI teams are currently still implementing interventions and measuring improvement. Teams have identified several barriers, the most common cited being stakeholder buy-in and logistic challenges in implementation. During monthly meetings, teams continually discuss and brainstorm ways to address barriers overall and among individual sites. Conclusion: Via the ECHO model, the AAP and EPS are supporting Ethiopian QI teams in designing and implementing QI interventions despite the limitations imposed by the COVID-19 pandemic. Challenges to this mentorship process include communication barriers (language, internet network) and perception of lack of need by Ethiopian clinicians for U.S. mentorship. Using the ECHO model, all teams have defined SMART aims, begun to implement interventions, and are now collecting compliance data.
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Aim: To examine the effect of remdesivir for treating non-hospitalized patients with COVID-19. Study design: Retrospective study Place and duration of study: Department of Medicine, Bolan Medical College, Quetta from 1st April 2021 to 30th September 2021. Methodology: One hundred patients were enrolled and divided into group A and group B within the age of 24-70 years. Group A was receiving remdesivir while group B is not receiving remdesivir. The clinical variables, BMI, comorbidities, duration of disease severity and viral load were determined. RT-PCR was conducted to determine viral load. Results: The mean age of study participants was 50±15 years with greater number of males. Diabetes was the major comorbidity. The time duration was decreased in group A upto 5 days and 8 days in group B. The viral load was decreased by mean value 6.32±1.76 to 6.2±1.78 in group B then group A respectively. Conclusion: Remdesivir is effective in COVID-19 treatment.
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BACKGROUND The ongoing coronavirus disease 2019 (COVID-19) pandemic infiltrates every aspect of our life, including the psychological impact. China has experienced the first wave of this epidemic, and it is now affecting the global population. AIM This study aimed to investigate the prevalence and associated factors of posttrauma tic stress disorder (PTSD) among the general Chinese population. METHODS A detailed questionnaire, comprising of 38 questions designed in both English and Chinese, was developed. The survey was conducted via WeChat, a multipurpose messaging, social media, and mobile payment app, which is widely used by the Chinese population. RESULTS In total, 1082 individuals from 31 provinces, autonomous regions, and municipalities participated in the survey by filling the questionnaires through the WeChat app. 97.8% of the participants had an Impact of Event -Scale-Revised (IES-R) total score above 20, which is an indicator of PTSD. The IES-R total and all the three subscales, including intrusion, avoidance, and hyperarousal, are significantly correlated with age. In addition, age, profession, marital status, and level of education are significantly correlated with the degree of PTSD symptoms. CONCLUSION The COVID-19 epidemic has widely caused PTSD among the general Chinese population. These results bear important implications for regions struggling with the pandemic to implement effective interventions to cope with these mental health problems.
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This study aims to determine the plans and strategies adopted by the hotel industry during the COVID-19 crisis. In order to conduct this study, a qualitative research methodology was used where semi-structured interviews were conducted from three countries: Austria, Pakistan, and Indonesia. Interviews were conducted online with the help of applications such as Skype, WhatsApp, and Zoom. A thematic analysis was conducted in which codes, categories, and final themes were generated. The final themes are the study's findings, which are strategies for employees, strategies for customers, strategies for SME hotels, and strategies for the staff. Strategies for employees indicate the planning and programs of the SME Hotels. The study's findings show that strategies adopted by the hotel industries vary from country to country while depending on the contextual factors and role of the government. Most Small and Medium Enterprises (SMEs) hotels in developing countries are less familiar with how to react and survive in the conditions of the crises. Thus, this study can benefit the different stakeholders in the developed and developing countries for adopting strategies and maintain their business during the crises of the COVID-19.
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Purpose: The purpose of this paper is to investigate university students’ WeChat usage during the COVID-19 pandemic lockdown in relation to the mediating role of online self-disclosure on their quality of friendship and well-being. A model is proposed to explain how students’ interactions occur during the lockdown and the mediatory role which self-disclosure plays in influencing their socio-psychological markup. Design/methodology/approach: The research model was tested empirically through a survey conducted online with 600 research participants, comprising of university students in China. Findings: Results in structural equation modeling show that WeChat interaction significantly correlates with the quality of friendship, online self-disclosure but not significantly correlates with well-being, but an indirect relationship was found out in the mediation analysis. There is also a significant relationship between online self-disclosure, quality of friendship and well-being. Mediation analysis shows that online self-disclosure mediates the relationship between interactions on WeChat and quality of friendship;it also mediates the relationship between WeChat interaction and well-being. In all, the results achieved in this study will significantly help provide more insights in comprehending the nuances attached to some socio-psychological aspects of WeChat and how its usage affects people during the period of crisis. Originality/value: Theoretically based investigation of WeChat usage among university students and its relationship with online self-disclosure, quality of friendship and well-being is still quite scarce, thereby underscoring the needs and significance of a theoretically based study in this regard. This study tested the credibility and validity of the proposed model in the context of the recent COVID-19 pandemic lockdown in China, which is one of the first in recent times. © 2021, Emerald Publishing Limited.