Acupoint stimulation for long COVID: A promising intervention:.

"Long COVID" is a sustained symptom following infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). According to recent statistics, at least 65 million people have long COVID, which poses a long-term threat to human health. The pathogenic mechanisms of coronavirus disease 2019 (COVID-19) are complex and affect multiple organs and systems. Common symptoms include palpitations, breathing difficulties, attention and memory deficits, fatigue, anxiety, and depression. It is difficult to achieve satisfactory treatment effect with a single intervention. Currently, treatment strategies for long COVID are still in the exploratory stage, and there is an urgent need to find appropriate and effective methods for long COVID treatment. Traditional Chinese medicine is effective in treating the various phases of COVID-19. Previous studies have shown that acupoint stimulation therapy is effective in improving palpitations, dyspnea, cognitive impairment, anxiety, depression, and other symptoms in patients. According to previous studies, acupoint stimulation may improve various symptoms related to long COVID. This paper discusses the potential application value of acupoint stimulation in the treatment of long COVID-related symptoms, based on the common sequelae of various systems involved in long COVID, and the effect of acupoint stimulation in the treatment of similar symptoms and diseases in recent years.

An atlas of the blood virome in healthy individuals.

Emerging evidence indicates that gut virome plays a role in human health and disease, however, much less is known about the viral communities in blood. Here we conducted a direct metatranscriptomic sequencing of virus-like-particles in blood from 1200 healthy individuals, without prior amplification to avoid potential amplification bias and with a strictly bioinformatic and manual check for candidate viral reads to reduce false-positive matches. We identified 55 different viruses from 36 viral families, including 24 human DNA, RNA and retroviruses in 70% of the studied pools. The study showed that anelloviruses are widely distributed and dominate the blood virome in healthy individuals. Human herpesviruses and pegivirus-1 are commonly prevalent in asymptomatic humans. We identified the prevalence of RNA viruses often causing acute infection, like HEV, HPIV, RSV and HCoV-HKU1, revealing of a transmissible risk of asymptomatic infection. Several viruses possible related to transfusion safety were identified, including human Merkel cell polyomavirus, papillomavirus, parvovirus B19 and herpesvirus 8 in addition to HBV. In addition, phages in Caudovirales and Microviridae, were commonly found in pools of samples with a very low abundance; a few sequences for invertebrate, plant and giant viruses were found in some of individuals; however, the remaining 31 viruses mostly reflect extensive contamination from commercial reagents and the work environments. In conclusion, this study is the first comprehensive investigation of blood virome in healthy individuals by metatranscriptomic sequencing of VLP in China. Further investigation of potential false positives representing a major challenge for the identification of novel viruses in mNGS, will offer a systemic idea and means to reveal true viral infections of human.

High-throughput screening of SARS-CoV-2 main and papain-like protease inhibitors.

The global COVID-19 coronavirus pandemic has infected over 109 million people, leading to over 2 million deaths up to date and still lacking of effective drugs for patient treatment. Here, we screened about 1.8 million small molecules against the main protease (Mpro) and papain like protease (PLpro), two major proteases in severe acute respiratory syndrome-coronavirus 2 genome, and identified 1851Mpro inhibitors and 205 PLpro inhibitors with low nmol/l activity of the best hits. Among these inhibitors, eight small molecules showed dual inhibition effects on both Mpro and PLpro, exhibiting potential as better candidates for COVID-19 treatment. The best inhibitors of each protease were tested in antiviral assay, with over 40% of Mpro inhibitors and over 20% of PLpro inhibitors showing high potency in viral inhibition with low cytotoxicity. The X-ray crystal structure of SARS-CoV-2 Mpro in complex with its potent inhibitor 4a was determined at 1.8 Å resolution. Together with docking assays, our results provide a comprehensive resource for future research on anti-SARS-CoV-2 drug development.

SARS-CoV-2 Delta and Omicron variants evade population antibody response by mutations in a single spike epitope.

Population antibody response is thought to be important in selection of virus variants. We report that SARS-CoV-2 infection elicits a population immune response that is mediated by a lineage of VH1-69 germline antibodies. A representative antibody R1-32 from this lineage was isolated. By cryo-EM, we show that it targets a semi-cryptic epitope in the spike receptor-binding domain. Binding to this non-ACE2 competing epitope results in spike destruction, thereby inhibiting virus entry. On the basis of epitope location, neutralization mechanism and analysis of antibody binding to spike variants, we propose that recurrent substitutions at 452 and 490 are associated with immune evasion of the identified population antibody response. These substitutions, including L452R (present in the Delta variant), disrupt interactions mediated by the VH1-69-specific hydrophobic HCDR2 to impair antibody-antigen association, enabling variants to escape. The first Omicron variants were sensitive to antibody R1-32 but subvariants that harbour L452R quickly emerged and spread. Our results provide insights into how SARS-CoV-2 variants emerge and evade host immune responses.

The Impacts of Social Media Use and Online Racial Discrimination on Asian American Mental Health: Cross-sectional Survey in the United States During COVID-19.

BACKGROUND: During the COVID-19 pandemic, increased social media usage has led to worsened mental health outcomes for many people. Moreover, due to the sociopolitical climate during the pandemic, the prevalence of online racial discrimination has contributed to worsening psychological well-being. With increases in anti-Asian hate, Asian and Asian American social media users may experience the negative effects of online racial discrimination in addition to the reduced psychological well-being resulting from exposure to online COVID-19 content. OBJECTIVE: This study aims to investigate the impact of COVID-19-related social media use and exposure to online racial discrimination during the pandemic on the mental health outcomes (ie, anxiety, depression, and secondary traumatic stress [STS]) of Asian Americans compared with those of non-Asian Americans. In addition, this study explores the mediating role of negative affect and the moderating role of racial/ethnic identification. METHODS: An online survey was conducted through Amazon Mechanical Turk and a university-wide research portal from March 3 to March 15, 2021. A total of 1147 participants took the survey. Participants' social media usage related to COVID-19 and exposure to 2 online forms of racial discrimination (individual and vicarious), mental health outcomes (anxiety, depression, and STS), racial/ethnic identification, negative affect, and demographics were assessed. RESULTS: Our results showed that COVID-19-related social media use, individual discrimination, and vicarious discrimination were predictors of negative mental health outcomes (anxiety, depression, and STS). Asian Americans reported higher vicarious discrimination than Latinx and White Americans, but Asian Americans' mental health outcomes did not differ substantially from those of the other racial/ethnic groups. Racial/ethnic identification moderated the relationship between both types of discrimination and STS, and negative affect served as a mediator between both types of discrimination and all 3 mental health outcomes. CONCLUSIONS: These results suggest that social media exposure continues to have a dire effect on mental health during the COVID-19 pandemic. This study helps to contextualize the rise of anti-Asian American hate and its impact on mental health outcomes in the United States.

Understanding the Association between Online Social Support Obtainment and Coping during a Public Crisis.

This study aims to examine how the process of online support obtainment may affect cognitive and behavioral coping during a public crisis. A cross-sectional online survey (N = 555) was conducted during the early stage of the COVID-19 pandemic in the U.S. Our findings revealed that informational support, obtained primarily through passive and private online involvement, led to increased risk perceptions of COVID-19; emotional support, obtained mainly via private online involvement, appeared to elicit higher perceived efficacy to cope with the crisis. People's engagement in preventive behaviors was found to be affected by efficacy perceptions, but not by risk perceptions. The results suggested that online social support functioned as a double-edged sword to affect people's coping with a public crisis.

Synthesis and Biochemical Evaluation of 8H-Indeno[1,2-d]thiazole Derivatives as Novel SARS-CoV-2 3CL Protease Inhibitors.

The COVID-19 pandemic caused by SARS-CoV-2 is a global burden on human health and economy. The 3-Chymotrypsin-like cysteine protease (3CLpro) becomes an attractive target for SARS-CoV-2 due to its important role in viral replication. We synthesized a series of 8H-indeno[1,2-d]thiazole derivatives and evaluated their biochemical activities against SARS-CoV-2 3CLpro. Among them, the representative compound 7a displayed inhibitory activity with an IC50 of 1.28 ± 0.17 µM against SARS-CoV-2 3CLpro. Molecular docking of 7a against 3CLpro was performed and the binding mode was rationalized. These preliminary results provide a unique prototype for the development of novel inhibitors against SARS-CoV-2 3CLpro.

Multiplicity and dynamics of social representations of the COVID-19 pandemic on Chinese social media from 2019 to 2020.

Documenting the emergent social representations of COVID-19 in public communication is necessary for critically reflecting on pandemic responses and providing guidance for global pandemic recovery policies and practices. This study documents the dynamics of changing social representations of the COVID-19 pandemic on one of the largest Chinese social media, Weibo, from December 2019 to April 2020. We draw on the social representation theory (SRT) and conceptualize topics and topic networks as a form of social representation. We analyzed a dataset of 40 million COVID-19 related posts from 9.7 million users (including the general public, opinion leaders, and organizations) using machine learning methods. We identified 12 topics and found an expansion in social representations of COVID-19 from a clinical and epidemiological perspective to a broader perspective that integrated personal illness experiences with economic and sociopolitical discourses. Discussions about COVID-19 science did not take a prominent position in the representations, suggesting a lack of effective science and risk communication. Further, we found the strongest association of social representations existed between the public and opinion leaders and the organizations' representations did not align much with the other two groups, suggesting a lack of organizations' influence in public representations of COVID-19 on social media in China.

Synthesis and Biochemical Evaluation of 8H-Indeno[1,2-d]thiazole Derivatives as Novel SARS-CoV-2 3CL Protease Inhibitors

The COVID-19 pandemic caused by SARS-CoV-2 is a global burden on human health and economy. The 3-Chymotrypsin-like cysteine protease (3CLpro) becomes an attractive target for SARS-CoV-2 due to its important role in viral replication. We synthesized a series of 8H-indeno[1,2-d]thiazole derivatives and evaluated their biochemical activities against SARS-CoV-2 3CLpro. Among them, the representative compound 7a displayed inhibitory activity with an IC50 of 1.28 ±0.17 μM against SARS-CoV-2 3CLpro. Molecular docking of 7a against 3CLpro was performed and the binding mode was rationalized. These preliminary results provide a unique prototype for the development of novel inhibitors against SARS-CoV-2 3CLpro.

A Class of Shark-Derived Single-Domain Antibodies can Broadly Neutralize SARS-Related Coronaviruses and the Structural Basis of Neutralization and Omicron Escape.

The identification of a novel class of shark-derived single domain antibodies, named vnarbodies that show picomolar affinities binding to the receptor binding domain (RBD) of Wuhan and Alpha, Beta, Kappa, Delta, Delta-plus, and Lambda variants, is reported. Vnarbody 20G6 and 17F6 have broad neutralizing activities against all these SARS-CoV-2 viruses as well as other sarbecoviruses, including Pangolin coronavirus and Bat coronavirus. Intranasal administration of 20G6 effectively protects mice from the challenges of SARS-CoV-2 Wuhan and Beta variants. 20G6 and 17F6 contain a unique "WXGY" motif in the complementary determining region 3 that binds to a hidden epitope on RBD, which is highly conserved in sarbecoviruses through a novel ß-sheet interaction. It is found that the S375F mutation on Omicron RBD disrupts the structure of ß-strand, thus impair the binding with 20G6. The study demonstrates that shark-derived vnarbodies offer a prophylactic and therapeutic option against most SARS-CoV-2 variants and provide insights into antibody evasion by the Omicron variant.

Discovery of highly potent SARS-CoV-2 Mpro inhibitors based on benzoisothiazolone scaffold.

The COVID-19 pandemic has drastically impacted global economies and public health. Although vaccine development has been successful, it was not sufficient against more infectious mutant strains including the Delta variant indicating a need for alternative treatment strategies such as small molecular compound development. In this work, a series of SARS-CoV-2 main protease (Mpro) inhibitors were designed and tested based on the active compound from high-throughput diverse compound library screens. The most efficacious compound (16b-3) displayed potent SARS-CoV-2 Mpro inhibition with an IC50 value of 116 nM and selectivity against SARS-CoV-2 Mpro when compared to PLpro and RdRp. This new class of compounds could be used as potential leads for further optimization in anti COVID-19 drug discovery.

Longitudinal Peripheral Blood Transcriptional Analysis Reveals Molecular Signatures of Disease Progression in COVID-19 Patients.

Coronavirus disease 2019 (COVID-19) is caused by a novel coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with some patients developing severe illness or even death. Disease severity has been associated with increased levels of proinflammatory cytokines and lymphopenia. To elucidate the atlas of peripheral immune response and pathways that might lead to immunopathology during COVID-19 disease course, we performed a peripheral blood RNA sequencing analysis of the same patient's samples collected from symptom onset to full recovery. We found that PBMCs at different disease stages exhibited unique transcriptome characteristics. We observed that SARS-CoV-2 infection caused excessive release of inflammatory cytokines and lipid mediators as well as an aberrant increase of low-density neutrophils. Further analysis revealed an increased expression of RNA sensors and robust IFN-stimulated genes expression but a repressed type I IFN production. SARS-CoV-2 infection activated T and B cell responses during the early onset but resulted in transient adaptive immunosuppression during severe disease state. Activation of apoptotic pathways and functional exhaustion may contribute to the reduction of lymphocytes and dysfunction of adaptive immunity, whereas increase in IL2, IL7, and IL15 may facilitate the recovery of the number and function of lymphocytes. Our study provides comprehensive transcriptional signatures of peripheral blood response in patients with moderate COVID-19.

Graphene biosensors for bacterial and viral pathogens.

The infection and spread of pathogens (e.g., COVID-19) pose an enormous threat to the safety of human beings and animals all over the world. The rapid and accurate monitoring and determination of pathogens are of great significance to clinical diagnosis, food safety and environmental evaluation. In recent years, with the evolution of nanotechnology, nano-sized graphene and graphene derivatives have been frequently introduced into the construction of biosensors due to their unique physicochemical properties and biocompatibility. The combination of biomolecules with specific recognition capabilities and graphene materials provides a promising strategy to construct more stable and sensitive biosensors for the detection of pathogens. This review tracks the development of graphene biosensors for the detection of bacterial and viral pathogens, mainly including the preparation of graphene biosensors and their working mechanism. The challenges involved in this field have been discussed, and the perspective for further development has been put forward, aiming to promote the development of pathogens sensing and the contribution to epidemic prevention.

Internet Use, Risk Awareness, and Demographic Characteristics Associated With Engagement in Preventive Behaviors and Testing: Cross-Sectional Survey on COVID-19 in the United States.

BACKGROUND: During the coronavirus disease (COVID-19) pandemic, engagement in preventive behaviors and getting tested for the virus play a crucial role in protecting people from contracting the new coronavirus. OBJECTIVE: This study aims to examine how internet use, risk awareness, and demographic characteristics are associated with engagement in preventative behaviors and testing during the COVID-19 pandemic in the United States. METHODS: A cross-sectional survey was conducted on Amazon Mechanical Turk from April 10, 2020, to April 14, 2020. Participants' internet use (in terms of the extent of receiving information pertaining to COVID-19), risk awareness (whether any immediate family members, close friends or relatives, or people in local communities tested positive for COVID-19), demographics (sex, age, ethnicity, income, education level, marital status, and employment status), as well as their engagement in preventative behaviors and testing were assessed. RESULTS: Our data included 979 valid responses from the United States. Participants who received more COVID-19-related health information online reported more frequent effort to engage in all types of preventive behaviors: wearing a facemask in public (odds ratio [OR] 1.55, 95% CI 1.34-1.79, P<.001), washing hands (OR 1.58, 95% CI 1.35-1.85, P<.001), covering nose and mouth when sneezing and coughing (OR 1.78, 95% CI 1.52-2.10, P<.001), keeping social distance with others (OR 1.41, 95% CI 1.21-1.65, P<.001), staying home (OR 1.40, 95% CI 1.20-1.62, P<.001), avoiding using public transportation (OR 1.57, 95% CI 1.32-1.88, P<.001), and cleaning frequently used surfaces (OR 1.55, 95% CI 1.34-1.79, P<.001). Compared with participants who did not have positive cases in their social circles, those who had immediate family members (OR 1.48, 95% CI 8.28-26.44, P<.001) or close friends and relatives (OR 2.52, 95% CI 1.58-4.03, P<.001) who tested positive were more likely to get tested. Participants' sex, age, ethnicity, marital status, and employment status were also associated with preventive behaviors and testing. CONCLUSIONS: Our findings revealed that the extent of receiving COVID-19-related information online, risk awareness, and demographic characteristics including sex, ethnicity, age, marital status, and employment status are key factors associated with US residents' engagement in various preventive behaviors and testing for COVID-19.

Using Reports of Symptoms and Diagnoses on Social Media to Predict COVID-19 Case Counts in Mainland China: Observational Infoveillance Study.

BACKGROUND: Coronavirus disease (COVID-19) has affected more than 200 countries and territories worldwide. This disease poses an extraordinary challenge for public health systems because screening and surveillance capacity is often severely limited, especially during the beginning of the outbreak; this can fuel the outbreak, as many patients can unknowingly infect other people. OBJECTIVE: The aim of this study was to collect and analyze posts related to COVID-19 on Weibo, a popular Twitter-like social media site in China. To our knowledge, this infoveillance study employs the largest, most comprehensive, and most fine-grained social media data to date to predict COVID-19 case counts in mainland China. METHODS: We built a Weibo user pool of 250 million people, approximately half the entire monthly active Weibo user population. Using a comprehensive list of 167 keywords, we retrieved and analyzed around 15 million COVID-19-related posts from our user pool from November 1, 2019 to March 31, 2020. We developed a machine learning classifier to identify "sick posts," in which users report their own or other people's symptoms and diagnoses related to COVID-19. Using officially reported case counts as the outcome, we then estimated the Granger causality of sick posts and other COVID-19 posts on daily case counts. For a subset of geotagged posts (3.10% of all retrieved posts), we also ran separate predictive models for Hubei province, the epicenter of the initial outbreak, and the rest of mainland China. RESULTS: We found that reports of symptoms and diagnosis of COVID-19 significantly predicted daily case counts up to 14 days ahead of official statistics, whereas other COVID-19 posts did not have similar predictive power. For the subset of geotagged posts, we found that the predictive pattern held true for both Hubei province and the rest of mainland China regardless of the unequal distribution of health care resources and the outbreak timeline. CONCLUSIONS: Public social media data can be usefully harnessed to predict infection cases and inform timely responses. Researchers and disease control agencies should pay close attention to the social media infosphere regarding COVID-19. In addition to monitoring overall search and posting activities, leveraging machine learning approaches and theoretical understanding of information sharing behaviors is a promising approach to identify true disease signals and improve the effectiveness of infoveillance.