Use of traditional Chinese medicine for the treatment and prevention of COVID-19 and rehabilitation of COVID-19 patients: An evidence mapping study.

Background: The potential effectiveness of traditional Chinese medicine (TCM) against "epidemic diseases" has highlighted the knowledge gaps associated with TCM in COVID-19 management. This study aimed to map the matrix for rigorously assessing, organizing, and presenting evidence relevant to TCM in COVID-19 management. Methods: In this study, we used the methodology of evidence mapping (EM). Nine electronic databases, the WHO International Clinical Trials Registry Platform (ICTRP) Search Portal, ClinicalTrials.gov, gray literature, reference lists of articles, and relevant Chinese conference proceedings, were searched for articles published until 23 March 2022. The EndNote X9, Rayyan, EPPI, and R software were used for data entry and management. Results: In all, 126 studies, including 76 randomized controlled trials (RCTs) and 50 systematic reviews (SRs), met our inclusion criteria. Of these, only nine studies (7.14%) were designated as high quality: four RCTs were assessed as "low risk of bias" and five SRs as "high quality." Based on the research objectives of these studies, the included studies were classified into treatment (53 RCTs and 50 SRs, 81.75%), rehabilitation (20 RCTs, 15.87%), and prevention (3 RCTs, 2.38%) groups. A total of 76 RCTs included 59 intervention categories and 57 efficacy outcomes. All relevant trials consistently demonstrated that TCM significantly improved 22 outcomes (i.e., consistent positive outcomes) without significantly affecting four (i.e., consistent negative outcomes). Further, 50 SRs included nine intervention categories and 27 efficacy outcomes, two of which reported consistent positive outcomes and two reported consistent negative outcomes. Moreover, 45 RCTs and 38 SRs investigated adverse events; 39 RCTs and 30 SRs showed no serious adverse events or significant differences between groups. Conclusion: This study provides evidence matrix mapping of TCM against COVID-19, demonstrating the potential efficacy and safety of TCM in the treatment and prevention of COVID-19 and rehabilitation of COVID-19 patients, and also addresses evidence gaps. Given the limited number and poor quality of available studies and potential concerns regarding the applicability of the current clinical evaluation standards to TCM, the effect of specific interventions on individual outcomes needs further evaluation.

Use of traditional Chinese medicine for the treatment and prevention of COVID-19 and rehabilitation of COVID-19 patients: An evidence mapping study

Background: The potential effectiveness of traditional Chinese medicine (TCM) against "epidemic diseases” has highlighted the knowledge gaps associated with TCM in COVID-19 management. This study aimed to map the matrix for rigorously assessing, organizing, and presenting evidence relevant to TCM in COVID-19 management. Methods: In this study, we used the methodology of evidence mapping (EM). Nine electronic databases, the WHO International Clinical Trials Registry Platform (ICTRP) Search Portal, ClinicalTrials.gov, gray literature, reference lists of articles, and relevant Chinese conference proceedings, were searched for articles published until 23 March 2022. The EndNote X9, Rayyan, EPPI, and R software were used for data entry and management. Results: In all, 126 studies, including 76 randomized controlled trials (RCTs) and 50 systematic reviews (SRs), met our inclusion criteria. Of these, only nine studies (7.14%) were designated as high quality: four RCTs were assessed as "low risk of bias” and five SRs as "high quality.” Based on the research objectives of these studies, the included studies were classified into treatment (53 RCTs and 50 SRs, 81.75%), rehabilitation (20 RCTs, 15.87%), and prevention (3 RCTs, 2.38%) groups. A total of 76 RCTs included 59 intervention categories and 57 efficacy outcomes. All relevant trials consistently demonstrated that TCM significantly improved 22 outcomes (i.e., consistent positive outcomes) without significantly affecting four (i.e., consistent negative outcomes). Further, 50 SRs included nine intervention categories and 27 efficacy outcomes, two of which reported consistent positive outcomes and two reported consistent negative outcomes. Moreover, 45 RCTs and 38 SRs investigated adverse events;39 RCTs and 30 SRs showed no serious adverse events or significant differences between groups. Conclusion: This study provides evidence matrix mapping of TCM against COVID-19, demonstrating the potential efficacy and safety of TCM in the treatment and prevention of COVID-19 and rehabilitation of COVID-19 patients, and also addresses evidence gaps. Given the limited number and poor quality of available studies and potential concerns regarding the applicability of the current clinical evaluation standards to TCM, the effect of specific interventions on individual outcomes needs further evaluation.

A Systematic Review and Meta-analysis of the Association Between SARS-CoV-2 Vaccination and Myocarditis or Pericarditis.

INTRODUCTION: There have been reports of potential negative cardiovascular effects from the COVID-19 vaccine, such as myocarditis or pericarditis. This study sought to ascertain the risk of myocarditis/pericarditis after COVID-19 vaccination by conducting an extensive meta-analysis of published cases. METHODS: A systematic literature search was conducted in 7 online databases by March 31, 2022. Heterogeneity was tested by I2 index. RR and 95% CI were pooled through either random-effect or fixed-effect models. Sensitivity analysis and publication bias were also conducted. RESULTS: A total of 11 studies with 58,620,611 subjects were included. COVID-19 vaccination correlated with an increased risk of myocarditis or pericarditis (RR=2.04; 95% CI=1.33, 3.14). In addition, an increased risk of myocarditis or pericarditis in people who received the second dose of COVID-19 vaccine compared with that in those who received only the first dose of COVID-19 vaccine was also found (RR=4.06; 95% CI=2.08, 7.92). An increased incidence of pericarditis or myocarditis was noted predominantly in those who received BNT162b2 and mRNA-1273 vaccines (RR=2.19; 95% CI=1.46, 3.29 and RR=4.15; 95% CI=1.87, 9.22, respectively). DISCUSSION: Study results indicate that a higher incidence of myocarditis or pericarditis was found after COVID-19 vaccination. In addition, the risk of developing myocarditis or pericarditis was greater after the second dose than after the first dose. Nevertheless, the risks of myocarditis and pericarditis in COVID-19 vaccine recipients are still significantly lower than the health risks observed in patients with COVID-19. Therefore, the benefits and harms must be carefully assessed to determine the best management option for patients who are in the high-risk group of myocarditis or pericarditis.

Progress in the treatment of coronavirus pneumonia

Coronavirus pneumonia is caused by eoronavirus infection. Severe acute respiratory syndrome (SARS) in 2003, Middle east respiratory syndrome (MERS) in 2012, and coronavirus disease 2019 (COV1D-19) in 2019 were all caused by coronavirus. The prevalence of these diseases has caused great disaster to people all over the world. However, there is no specific drug for the treatment of coronavirus pneumonia. In View of the current worldwide epidemic situation of covid-19, it is particularly urgent to develop effective antiviral drugs. This article reviews the progress in the treatment of new coronavirus pneumonia, hoping to provide help for the prevention and control of the disease.

Sensitive electrochemical assay of acetaminophen based on 3D-hierarchical mesoporous carbon nanosheets.

Acetaminophen plays a key role in first-line Covid-19 cure as a supportive therapy of fever and pain. However, overdose of acetaminophen may give rise to severe adverse events such as acute liver failure in individual. In this work, 3D-hierarchical mesoporous carbon nanosheet (hMCNS) microspheres with superior properties were fabricated using simple and quick strategy and applied for sensitive quantification of acetaminophen in pharmaceutical formulation and rat plasmas after administration. The hMCNS microspheres are prepared via chemical etching of zinc oxide (ZnO) nanoparticles from a zinc-gallic acid precursor composite (Zn-GA) synthesized by high-temperature anaerobic pyrolysis. The obtained hMCNS could enhance analytes accessibility and accelerate proton transfer in the interface, hence increasing the electrochemical performance. Under optimized experimental conditions, the proposed electrochemical sensor achieves a detection limit of 3.5 nM for acetaminophen. The prepared electrochemical sensor has been successfully applied for quantification of acetaminophen in pharmaceutical formulations and the rat plasma samples before and after administration. Meanwhile, this sensor is compared with high-performance liquid chromatography (HPLC) as a reference technology, showing an excellent accuracy. Such an electrochemical sensor has great potential and economic benefits for applications in the fields of pharmaceutical assay and therapeutic drug monitoring (TDM).

Toward a Deeper Understanding of Gut Microbiome in Depression: The Promise of Clinical Applicability.

The emergence of the coronavirus disease 2019 pandemic has dramatically increased the global prevalence of depression. Unfortunately, antidepressant drugs benefit only a small minority of patients. Thus, there is an urgent need to develop new interventions. Accumulating evidence supports a causal relationship between gut microbiota dysbiosis and depression. To advance microbiota-based diagnostics and therapeutics of depression, a comprehensive overview of microbial alterations in depression is presented to identify effector microbial biomarkers. This procedure generated 215 bacterial taxa from humans and 312 from animal models. Compared to controls, depression shows significant differences in ß-diversity, but no changes in microbial richness and diversity. Additionally, species-specific microbial changes are identified like increased Eggerthella in humans and decreased Acetatifactor in rodent models. Moreover, a disrupted microbiome balance and functional changes, characterized by an enrichment of pro-inflammatory bacteria (e.g., Desulfovibrio and Escherichia/Shigella) and depletion of anti-inflammatory butyrate-producing bacteria (e.g., Bifidobacterium and Faecalibacterium) are consistently shared across species. Confounding effects of geographical region, depression type, and intestinal segments are also investigated. Ultimately, a total of 178 species and subspecies probiotics are identified to alleviate the depressive phenotypes. Current findings provide a foundation for developing microbiota-based diagnostics and therapeutics and advancing microbiota-oriented precision medicine for depression.

Does aspirin have an effect on risk of death in patients with COVID-19? A meta-analysis.

PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic has shown unprecedented impact world-wide since the eruption in late 2019. Importantly, emerging reports suggest an increased risk of thromboembolism development in patients with COVID-19. Meanwhile, it is found that aspirin reduced mortality in critically ill patients with non-COVID-19 acute respiratory distress syndrome. Therefore, a meta-analysis was performed to investigate the effects of aspirin on COVID-19 mortality. METHODS: A systematic literature search was conducted in 10 electronic databases and 4 registries. Random effects models were used to calculate pooled relative risks (RRs) with 95% confidence intervals (Cis) to estimate the effect of aspirin on COVID-19 mortality. Relevant subgroup analyses and sensitivity analyses were also performed. RESULTS: The results showed that aspirin use was associated with a reduction in COVID-19 mortality (adjusted RR 0.69; 95% CI 0.50-0.95; P < 0.001). Subgroup analysis found that the low-dose group was associated with a reduced COVID-19 mortality (adjusted RR 0.64; 95% CI 0.48-0.85; P < 0.01). Aspirin use was associated with reduced COVID-19 mortality in Europe and America (crude RR 0.71; 95% CI 0.52-0.98; P = 0.04), and results from cohort studies suggested that aspirin use was a protective factor for COVID-19 mortality (adjusted RR 0.73; 95% CI 0.52-0.99; P = 0.04). Meanwhile, aspirin use was not associated with bleeding risk (crude RR 1.22; 95% CI 0.80-1.87; P = 0.96). CONCLUSIONS: This meta-analysis found that aspirin use was associated with a reduction in mortality in patients with COVID-19 and not with an increased risk of bleeding.

Dynamic Impact of the Perceived Value of Public on Panic Buying Behavior during COVID-19

In this paper, an evolutionary game model for the development of panic buying events in COVID-19 is constructed by studying the dynamic process of the public and the government adjusting their strategic choices and playing a continuous game. This paper uses regret theory to depict the public's perceived value of the items in the panic buying situation, describes the characteristics of each stage of the rumors spreading process and the evolution process of panic buying events, and introduces the variable of public critical ability to measure the public's panic buying willingness. The results show that the government's intervention measures according to the characteristics of different stages can effectively control the continuous fermentation time and influence the scope of panic buying events. The implementation of the government's rumor-refutation strategy will also significantly affect the volume of public panic buying, which will help the government timely understand the public's epidemic prevention needs in COVID-19, relieve public panic, and provide a basis for the effective management and scheduling of emergency supplies.

Reliability and Validity of General Health Questionnaire-12 in Chinese Dental Healthcare Workers During the COVID-19 Pandemic.

BACKGROUND: The General Health Questionnaire-12 (GHQ-12) is a widely used instrument to assess mental health status. However, little is known about its applicability in Chinese healthcare workers. This study aimed to evaluate the reliability and validity of the GHQ-12 in Chinese dental healthcare workers. METHODS: Dental healthcare workers participated in the first occupational survey in China conducted by the Chongqing Stomatological Association from February 2021 to March 2021 by filling out GHQ-12. The reliability and validity of GHQ-12 were then tested. RESULTS: A total of 3,020 valid electronic questionnaires were acquired. The positive detection rate of self-reported mental health status was 23.80% (719/3,020). The Cronbach's α coefficient of the GHQ-12 was 0.892, and the Cronbach's α coefficient was 0.877-0.888 after the deletion of individual items, and the split-half reliability was 0.843. The correlation coefficient between the item-total score ranged from 0.465 to 0.762 (P<0.05). The exploratory factor analysis found 2 common factors with a factor load of 0.564-0.818. The confirmatory factor analysis showed that the factor load on the specified items was 0.480-0.790. CONCLUSIONS: The two-factor model of GHQ-12 featured good reliability and validity, which could be used to assess the mental health status of Chinese dental healthcare workers.

Functional Connectome Prediction of Anxiety Related to the COVID-19 Pandemic.

OBJECTIVE: Increased anxiety in response to the COVID-19 pandemic has been widely noted. The purpose of this study was to test whether the prepandemic functional connectome predicted individual anxiety induced by the pandemic. METHODS: Anxiety scores from healthy undergraduate students were collected during the severe and remission periods of the pandemic (first survey, February 22-28, 2020, N=589; second survey, April 24 to May 1, 2020, N=486). Brain imaging data and baseline (daily) anxiety ratings were acquired before the pandemic. The predictive performance of the functional connectome on individual anxiety was examined using machine learning and was validated in two external undergraduate student samples (N=149 and N=474). The clinical relevance of the findings was further explored by applying the connectome-based neuromarkers of pandemic-related anxiety to distinguish between individuals with specific mental disorders and matched healthy control subjects (generalized anxiety disorder, N=43; major depression, N=536; schizophrenia, N=72). RESULTS: Anxiety scores increased from the prepandemic baseline to the severe stage of the pandemic and remained high in the remission stage. The prepandemic functional connectome predicted pandemic-related anxiety and generalized to the external sample but showed poor performance for predicting daily anxiety. The connectome-based neuromarkers of pandemic-related anxiety further distinguished between participants with generalized anxiety and healthy control subjects but were not useful for diagnostic classification in major depression and schizophrenia. CONCLUSIONS: These findings demonstrate the feasibility of using the functional connectome to predict individual anxiety induced by major stressful events (e.g., the current global health crisis), which advances our understanding of the neurobiological basis of anxiety susceptibility and may have implications for developing targeted psychological and clinical interventions that promote the reduction of stress and anxiety.

Assisting scalable diagnosis automatically via CT images in the combat against COVID-19.

The pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that the application of deep learning (DL) to 3D CT images could help identify COVID-19 infections. Using data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 pneumonia patients, COVIDNet achieved an accuracy rate of 94.3% and an area under the curve of 0.98. As of March 23, 2020, the COVIDNet system had been used 11,966 times with a sensitivity of 91.12% and a specificity of 88.50% in six hospitals with PCR confirmation. Application of DL to CT images may improve both efficiency and capacity of case detection and long-term surveillance.

Severe COVID-19: A Review of Recent Progress With a Look Toward the Future.

The novel coronavirus disease 2019 (COVID-19) is an acute infectious disease caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Currently, the World Health Organization has confirmed that COVID-19 is a global infectious disease pandemic. This is the third acute infectious disease caused by coronavirus infection in this century, after sudden acute respirator syndrome and Middle East respiratory syndrome. The damage mechanism of SARS-CoV-2 is still unclear. It is possible that protein S binds to angiotensin-converting enzyme 2 receptors and invades alveolar epithelial cells, causing direct toxic effects and an excessive immune response. This stimulates a systemic inflammatory response, thus forming a cytokine storm, which leads to lung tissue injury. In severe cases, the disease can lead to acute respiratory distress syndrome, septic shock, metabolic acidosis, coagulation dysfunction, and multiple organ dysfunction syndromes. Patients with severe COVID-19 have a relatively high mortality rate. Currently, there are no specific antiviral drugs for the treatment of COVID-19. Most patients need to be admitted to the intensive care unit for intensive monitoring and supportive organ function treatments. This article reviews the epidemiology, pathogenesis, clinical manifestations, diagnosis, and treatment methods of severe COVID-19 and puts forward some tentative ideas, aiming to provide some guidance for the diagnosis and treatment of severe COVID-19.