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Social media has facilitated the sharing of once isolated testimonies to an extent and with an ease never before possible. The #MeToo movement provides a prime example of how such pooling of individual stories, in large enough numbers, can fuel political movements, fortify a sense of solidarity and community, and compel public reckoning by bringing important issues into mainstream consciousness. In this timely and important study, Helga Lenart-Cheng has uncovered the antecedents of this phenomenon and provided a historical and critical analysis of this seemingly new but in fact deeply rooted tradition. Story Revolutions features a rich variety of case studies, from eighteenth-century memoir collections to contemporary Web 2.0 databases, including memoir contests, digital story-maps, crowd-sourced Covid diaries, and AI-assisted life writing. It spans the Enlightenment, the 1930s, and the twenty-first century—three historical periods marked by a convergence of mass movements and new methods of data collection that led to a boom in activism based in the aggregation and communication of stories. Ultimately, this book offers readers a critical perspective on the concept of community itself, with incisive reflections on what it means to use storytelling to build democracy in the twenty-first century. © 2022 by the Rector and Visitors of the University of Virginia. All rights reserved.
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The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has claimed millions of lives and caused innumerable economic losses worldwide. Unfortunately, state-of-the-art treatments still lag behind the continual emergence of new variants. Key to resolving this issue is developing antivirals to deactivate coronaviruses regardless of their structural evolution. Here, we report an innovative antiviral strategy involving extracellular disintegration of viral proteins with hyperanion-grafted enediyne (EDY) molecules. The core EDY generates reactive radical species and causes significant damage to the spike protein of coronavirus, while the hyperanion groups ensure negligible cytotoxicity of the molecules. The EDYs exhibit antiviral activity down to nanomolar concentrations, and the selectivity index of up to 20,000 against four kinds of human coronavirus, including the SARS-CoV-2 Omicron variant, suggesting the high potential of this new strategy in combating the COVID-19 pandemic and a future "disease X."
ABSTRACT
The current ongoing COVID-19 pandemic caused by the SARS-CoV-2 virus, has severely affected our daily life routines and behavior patterns. According to the World Health Organization, there have been 93 million confirmed cases with more than 1.99 million confirmed death around 235 Countries, areas or territories until 15 January 2021, 11:00 GMT+11. People who are affected with COVID-19 have different symptoms from people to people. When large amounts of patients are affected with COVID-19, it is important to quickly identify the health conditions of patients based on the basic information and symptoms of patients. Then the hospital can arrange reasonable medical resources for different patients. However, existing work has a low recall of 15.7% for survival predictions based on the basic information of patients (i.e., false positive rate (FPR) with 84.3%, FPR: actually survival but predicted as died). There is much room for improvement when using machine learning-based techniques for COVID-19 prediction. In this paper, we propose DeCoP to train a classifier to predict the survival of COVID-19 patients with high recall and F1 score. DeCoP is a deep learning (DL)-based scheme of Bidirectional Long Short-Term Memory (BiLSTM) along with Fuzzy-based Information Decomposition (FID) to predict the survival of patients. First of all, we apply FID oversampling to redistribute the training data of the Open COVID-19 Data Working Group. Then, we employ BiLSTM to learn the high-level feature representations from the redistributed dataset. After that, the high-level feature vector will be used to train the prediction model. Experimental results show that our proposed scheme achieves outstanding performances. Precisely, the improvement achieves about 19% and 18% in terms of recall and F1-measure.
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Many companies are utilizing social media as the primary avenue for customer service during the pandemic. However, how customers' behaviors and interactions with customer service agents on social media are impacted by the lockdowns has not been well understood. In this study, we examine the impact of lockdowns and physical distancing on changes in customers' behaviors, such as emotional expressions in tweets and customers' satisfaction with social media customer service. Using a difference-in-differences research design, we find that with the lockdowns and physical distancing, customers expressed more negative emotions when tweeting the company they were having issues with. Surprisingly, compared to before the pandemic period, customers' emotional expressions became more positive and they were more likely to express their satisfaction after interacting with customer service agents. Interestingly, our findings reveal that gender differences exist in these scenarios. We also discuss the theoretical and practical implications of these findings. © 2023 IEEE Computer Society. All rights reserved.
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Vaccines are the most economical and effective means to protect against SARS-CoV-2 infection. Adjuvants are able to enhance antigen-specific immune responses, reduce antigen doses and vaccination times, and reshape adaptive immune responses, which are crucial to improving the protective efficacy of vaccines. In this article, we review the mechanisms and advantages/disadvantages of the adjuvants used in COVID-19 vaccines that have been authorized for emergency use or are undergoing clinical trials around the world, and analyze the issues that must be considered in the application of adjuvants to COVID-19 vaccines. In addition, we put forward suggestions for future research strategies for COVID-19 vaccine adjuvants: (1) adjuvants for immunocompromised people, (2) adjuvants for inducing T cell responses, (3) novel mucosal immune adjuvants, and (4) the rational design of combination adjuvant. © 2022 Editorial Office of Journal of Third Military Medical University. All rights reserved.
ABSTRACT
Vaccines are the most economical and effective means to protect against SARS-CoV-2 infection. Adjuvants are able to enhance antigen-specific immune responses, reduce antigen doses and vaccination times, and reshape adaptive immune responses, which are crucial to improving the protective efficacy of vaccines. In this article, we review the mechanisms and advantages/disadvantages of the adjuvants used in COVID-19 vaccines that have been authorized for emergency use or are undergoing clinical trials around the world, and analyze the issues that must be considered in the application of adjuvants to COVID-19 vaccines. In addition, we put forward suggestions for future research strategies for COVID-19 vaccine adjuvants: (1) adjuvants for immunocompromised people, (2) adjuvants for inducing T cell responses, (3) novel mucosal immune adjuvants, and (4) the rational design of combination adjuvant. © 2022 Editorial Office of Journal of Third Military Medical University. All rights reserved.
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The present study aimed to examine work environment related factors and frontline primary healthcare profes-sionals' mental-emotional wellbeing during the COVID-19 pandemic in school communities of Hong Kong. A total of 61 (20%) school health nurses (frontline primary healthcare professionals) participated in a cross-sec-tional online survey from March to June 2020. Outcomes of mental-emotional health were measured using the Mental Health Continuum-Short Form (14-item scale with three subscales related to emotional, social and psychological wellbeing);the Perceived Stress Scale (10-item scale with two subscales related to perceived help-lessness and lack of self-efficacy;and the Coping Orientation to Problems Experienced Inventory (Brief COPE), a 28-item inventory with two subscales related to adaptive and maladaptive strategies. Almost half (42.6%) of participants experienced mental health problems. Those employed in government subsidized schools had significantly lower scores in mental health wellbeing than those who worked in private schools. Factors relating to increased mental health problems included lack of emotional support, inadequate training relating to infection prevention and control measures, disengagement and self-blame. A variety of factors influencing school health nurses' social, emotional and psychological wellbeing in their work environment during the COVID-19 pandemic were also reported. The mental-emotional wellbeing of school nurses may relate to their subjective feeling of loneliness as participants were the sole frontline primary healthcare professional working in the school community during the COVID-19 pandemic. Study findings provide relevant evidence for management teams to build a culture of psychological and social support into workplace policies and procedures. Continuous staff development and adequate social support are important to promote the mental-emotional wellbeing of primary healthcare professionals in school communities as they play a significant role in safeguarding resources during pandemics. © 2023, Tech Science Press. All rights reserved.
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Background: Hypertension is the most important modifiable cause of cardiovascular (CV) disease and all-cause mortality worldwide. Numerous epidemiological studies and pharmacological intervention trials have demonstrated that lower and lowering blood pressures (BP) are associated with fewer CV events and lower mortality. Despite the positive correlations between BP levels and later CV events are continuous since BP levels as low as 90/60 mmHg in almost all large-scale epidemiological studies, the diagnostic criteria of hypertension and BP thresholds and targets of antihypertensive therapy have largely remained at the level of 140/90 mmHg in the past 30 years (since the release of the Fifth Report of the Joint National Committee [JNC 5] on high BP in 1993). The publication of both the SPRINT and the STEP trials (comprising >8,500 Caucasian/African and Chinese participants, respectively) provides enough evidence to shake this 140/90 mmHg dogma. In both trials, lowering systolic BP (SBP) to <130 mmHg, compared to the traditional SBP target of <140 (130-139) mmHg, was consistently associated with a 25-30% relative risk reduction in CV events. Another dogma regarding hypertension management is "office (or clinic) BP measurements" Although standardized office BP measurement has been widely recommended, the practice of office BP measurements is hard to be or has never been ideal in real-world practice. Further, the debate regarding the numerical equivalence between automated office BP (AOBP) measurements adopted in the SPRINT trial and office BP measurements has never been settled. The variations of office BP readings and the differences between office BP and home BP readings bewilder not only patients, but also healthcare professionals. On the other hand, out-of-office BP monitoring receives growing attention in contemporary hypertension guidelines. Home BP monitoring (HBPM) and ambulatory BP monitoring (ABPM) are two recognized approaches to obtaining out-of-office BP. HBPM is easy-to-use, more likely to be free of environmental and/or emotional stress (such as white-coat effect), feasible to document long-term BP variations, of good reproducibility and reliability, and more correlated with hypertension-mediated organ damage (HMOD) and CV events. Methods/Results: The Taiwan Hypertension Society (THS) and the Taiwan Society of Cardiology (TSOC) jointly issued the Consensus Statement on HBPM in 2020. The "722" protocol to standardize HBPM has been advocated by both Societies and widely accepted by healthcare professionals. In the 2022 Taiwan Hypertension Guidelines, we break the dogma of "office BP-based management strategy" and further expand the role of HBPM to the whole hypertension management process, from diagnosis to long-term follow-up. The Task Force considers that, to improve the quality of long-term management of all chronic diseases including hypertension, patients themselves should take an active role and HBPM is the right tool to achieve this goal, regardless of many other advantages of HBPM. This approach is of particularly importance in the post-COVID era and can bridge the management with artificial intelligence technologies. Conclusion(s): To facilitate implementation of the guidelines, a series of flowcharts to encompass assessment, adjustment, and HBPM-guided hypertension management are provided.
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The approved worldwide use of two messenger RNA (mRNA) vaccines (BNT162b2 and mRNA-1273) in late 2020 has proven the remarkable success of mRNA therapeutics together with lipid nanoformulation technology in protecting people against coronaviruses during COVID-19 pandemic. This unprecedented and exciting dual strategy with nanoformulations and mRNA therapeutics in play is believed to be a promising paradigm in targeted cancer immunotherapy in future. Recent advances in nanoformulation technologies play a prominent role in adapting mRNA platform in cancer treatment. In this review, we introduce the biologic principles and advancements of mRNA technology, and chemistry fundamentals of intriguing mRNA delivery nanoformulations. We discuss the latest promising nano-mRNA therapeutics for enhanced cancer immunotherapy by modulation of targeted specific subtypes of immune cells, such as dendritic cells (DCs) at peripheral lymphoid organs for initiating mRNA cancer vaccine-mediated antigen specific immunotherapy, and DCs, natural killer (NK) cells, cytotoxic T cells, or multiple immunosuppressive immune cells at tumor microenvironment (TME) for reversing immune evasion. We highlight the clinical progress of advanced nano-mRNA therapeutics in targeted cancer therapy and provide our perspectives on future directions of this transformative integrated technology toward clinical implementation.
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As one of the most polluted provinces in China, air pollution events occur frequently in Shandong. Based on the hourly (or daily) concentrations of six air pollutants (PM2.5, PM10, O3, NO2, SO2 and CO), the situations of air quality improvement in three kinds of cities (key cities, coastal cities and general cities) are assessed comprehensively during 2014-2020. Contrary to the daily maximum 8-h average ozone (MDA8 O3), the annual average concentrations of other pollutants show the downward trends during 2014-2020. Therein, the improvement rates of annual average concentrations of air pollutants in key cities are highest. By 2020, the day proportions of O3 as the primary pollutant are up to 38% in three kinds of cities. Besides, due to the impact of COVID-19, the monthly average concentrations of PM2.5, PM10, NO2, SO2 and CO in February 2020 decrease by 32.1-49.5% year-on-year. There are still about 50% of population exposed to high-risk regions (R i > 2), which are mainly concentrated in main urban areas and industrial areas. Thus, the adjustment of industrial structure and energy composition in the context of carbon peak and carbon neutrality should be implemented in the future. Supplementary Information: The online version contains supplementary material available at 10.1007/s13762-022-04651-5.
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Aim To elucidate the effective components of Ganoderma applanatum and its mechanism of preventing the coronavirus disease 2019(COVID-19).Methods To begin with, UHPLC-Q-Exactive-Orbitrap-MS was established to identify the main chemical constituents of G.applanatum.Then, the predicted targets of G.applanatum were selected by Swiss Target Prediction.GO analysis and KEGG analysis of core target genes were performed using the DAVID database.Finally, to explore the potential mechanism of G.applanatum against COVID-19, core functional components-core target-metabolism path network diagram was constructed using Cytoscape 3.8.0, and molecular docking was used to analyze the binding force of the core effective compounds with angiotensin-converting enzyme II(ACE2)and three SARS CoV-2 proteins, nonstructural protein-15 Endoribonuclease(NSP15), the receptor-binding domain of spike protein(RBD of S protein), and main protease(Mpro/3CLpro).Results Sixty-two components were identified from G.applanatum by UHPLC-Q-Exactive-Orbitrap-MS study;30 active components were closely associated with 32 core targets including IL6, PTGS2, and MAPK1;KEGG analysis showed that it might treat COVID-19 through signaling pathways, such as PI3K-Akt signaling pathway, TNF signaling pathway, tuberculosis, and so on;molecular docking analysis showed that 1,4-Dihydroxy-2-naphthoic acid, parthenolide, 7,8-Dihydroxycoumarin, and other vital compounds had a certain degree of affinity with ACE2 and three SARS CoV-2 proteins.Conclusion This study clarifies the chemical composition and the potential mechanism of G.applanatum, providing a scientific basis for screening the effective ingredients of G.applanatum. Copyright © 2022 Publication Centre of Anhui Medical University. All rights reserved.
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The outbreak of the COVID-19 pandemic has greatly impacted patients with non-small cell lung cancer (NSCLC), making the fear of cancer recurrence (FCR) more pronounced. We explored the effects of FCR on immunotherapy efficacy and quality of life during the COVID-19 pandemic in China among the 124 NSCLC patients enrolled in this study. Quality of life and immunotherapy efficacy were compared between high- and low-FCR groups after completing 4-6 courses of treatment or cancer progression. Worse immunotherapy efficacy and quality of life were reported for the high-FCR group than for the low-FCR group. These findings emphasize the need to pay close attention to the level of FCR in NSCLC patients. Efforts should be taken to alleviate FCR levels among NSCLC patients. Moreover, research is needed to investigate the possible link between immunotherapy efficacy and FCR. Copyright © 2022 E-Century Publishing Corporation. All rights reserved.
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Introduction The management of teenagers with diabetes during the COVID-19 pandemic has become more challenging with the negative psychosocial impact brought upon by the pandemic. Methodology We embarked on a cross-sectional study to identify the factors influencing glycaemic control (HbA1c) among teenagers with diabetes during the COVID-19 pandemic. Interviews regarding lifestyle changes were conducted among teenagers with type 1 (T1DM) and type 2 diabetes mellitus (T2DM), followed by the administration of the Depression, Anxiety, and Stress Scale (DASS-21). Results A total of 59 adolescents with T1DM (32 males, 54.2%) and 31 patients with T2DM (10 males, 32.3%) were recruited. Overall, the HbA1c worsened from 9.13% before the COVID-19 pandemic to 9.33% during the pandemic (p-value 0.039). Significant factors which negatively influenced glycaemic control were male sex, puberty, prolonged screen time, presence of symptoms of anxiety/stress, and T2DM. However, skipping breakfast, sleep adequacy and physical activity did not directly influence the HbA1c. About one-third of the participants suffered from some form of mental disturbance (31.1% of patients had depressive symptoms, 38.9% of patients had anxiety symptoms, and 23.3% of patients experienced stress). The incidence of depression was higher among participants with T2DM, while anxiety and stress were higher among those with T1DM. Male gender, good glycaemic control pre-pandemic, and prepubertal status were associated with depressive symptoms during the pandemic. Conclusion Besides the disruption of daily routine, glycaemic control worsened among diabetic adolescents during the COVID-19 pandemic. A holistic management plan is needed to address the psychosocial concerns of this group to ensure optimal mental well-being and appropriate glycaemic control.
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The current ongoing COVID-19 pandemic caused by the SARS-CoV-2 virus, has severely affected our daily life routines and behavior patterns. According to the World Health Organization, there have been 93 million confirmed cases with more than 1.99 million confirmed death around 235 Countries, areas or territories until 15 January 2021, 11:00 GMT+11. People who are affected with COVID-19 have different symptoms from people to people. When large amounts of patients are affected with COVID-19, it is important to quickly identify the health conditions of patients based on the basic information and symptoms of patients. Then the hospital can arrange reasonable medical resources for different patients. However, existing work has a low recall of 15.7% for survival predictions based on the basic information of patients (i.e., false positive rate (FPR) with 84.3%, FPR: actually survival but predicted as died). There is much room for improvement when using machine learning-based techniques for COVID-19 prediction. In this paper, we propose DeCoP to train a classifier to predict the survival of COVID-19 patients with high recall and F1 score. DeCoP is a deep learning (DL)-based scheme of Bidirectional Long Short-Term Memory (BiLSTM) along with Fuzzy-based Information Decomposition (FID) to predict the survival of patients. First of all, we apply FID oversampling to redistribute the training data of the Open COVID-19 Data Working Group. Then, we employ BiLSTM to learn the high-level feature representations from the redistributed dataset. After that, the high-level feature vector will be used to train the prediction model. Experimental results show that our proposed scheme achieves outstanding performances. Precisely, the improvement achieves about 19% and 18% in terms of recall and F1-measure. IEEE
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Aim: Occupational contact dermatitis (OCD) is common amongst healthcare workers (HCW). This retrospective study describes the causes of allergic contact dermatitis in HCW in New Zealand and reviews the current literature review on OCD in HCW during the COVID-19 pandemic. Material and Methods: All HCW undergoing patch testing between July 2008 and January 2020 at a public hospital patch-test clinic, and between June 2019 and January 2020 at a private dermatology clinic were included. Data collected included patient demographics, occupation, results of patch testing and pre and post-patch test diagnoses. Literature search was performed on Pubmed with keywords: healthcare workers, occupational, allergic contact dermatitis (ACD), irritant contact dermatitis (ICD), COVID-19. Results: Out of 837 patients patch tested during the study period, 67 were HCW. The mean age of HCW was 41 years (standard deviation 14) and 58 (87%) were female. The most common occupations were nurses (40%), allied health (22%) and doctors (18%). Forty-six (69%) patients had a background of atopic dermatitis. Hand dermatitis was the most common presentation (49%), followed by facial/neck dermatitis (25%). The most common relevant positive reactions were to rubber accelerators (24%), fragrances (16%), perservatives (15%) and topical steroids (9%). Literature review reflects that the incidence of ICD increased significantly due to increased frequency of hand washing and use of personal protective equipment during the COVID-19 pandemic. Contemporary data regarding ACD is limited. Conclusion: The most common allergens in HCW are rubber chemicals, fragrances and preservatives. The COVID- 19 pandemic has highlighted the incidence of OCD amongst HCWs. While rates of ICD have risen, data does not yet suggest increased rates of ACD.
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Growing surge of misinformation among COVID-19 can post great hindrance to truth, it can magnify distrust in policy makers and/or degrade authorities' credibility, and it can even harm public health. Classification of textual context on social media data relating COVID-19 is an effective tool to combat misinformation on social media platforms. We leveraged Twitter data in developing classification methods to detect misinformation and to identify tweet sentiment. Six fusion-based classification models were built fusing three classical machine learning algorithms: multinomial nave Bayes, logistic regression, and support vector classifier. The best performing models were selected to detect misinformation and to classify sentiment on tweets that were created during early outbreak of COVID-19 pandemic and the fifth month into pandemic. We found that majority of the public held positive sentiment toward all six types of misinformation news on Twitter social media platform. Except political or biased news, general public expressed more positively toward unreliable, conspiracy, clickbait, unreliable with political/biased, and clickbait with political/biased news later in the summer month than earlier during the outbreak. The results provide decision or policy makers valuable knowledge gain in public opinion towards various types of misinformation spreading over social media.