Perspectives of international multi-center clinical trials on traditional Chinese herbal medicine.

With the introduction of various subjects, such as clinical epidemiology and evidence-based medicine, the qualities and levels of Traditional Chinese Herbal Medicine (TCHM) in China improved substantially, and the processes of internationalization of Traditional Chinese Medicine (TCM) are further accelerated. Since, a variety of drug products in China have been approved for marketing in other countries, and approximately 10 products have submitted the IND application to FDA of United States, of which various Chinese herbal preparations such as compound Danshen dripping pills, Xingling granules, and HMPL-004 have been approved to be investigated in phase III clinical trials. In general, multi-center studies of TCHM are increasing with years, but most of the studies are performed in some certain country, and the actual international multi-center clinical trials are very rare. Number of SCI literatures on multi-center clinical trials of TCHM that published in the recent decade also showed increasing tendency with years, despite the evident reduction in the past 2 years due to the influence of COVID-19 pandemic. Of the multi-center clinical trials of TCHM that performed by mainland China and other oversees regions, except for Taiwan, China, nearly 70% were focused on classic Chinese medicinal formulae and Chinese patent medicine, while the other 30% were on dietary supplements and plant extracts. Facing the future, the "human experience" has attracted close attentions from researchers throughout the world. Effectively utilizing the historic "human experience" is an important method to vitalize potential of original scientific and technological resources of TCHM. Performing multi-center clinical trials with high qualities is still an essential method for TCHM in accessing the mainstream medicine market. In addition, it is also required to further improve the evaluation techniques and methods that not only meet the international standards but also meet the characteristics of TCHM. Furthermore, we should also focus on the TCHM specific clinical values and scientific reports.

Chest computed tomography radiomics to predict the outcome for patients with COVID-19 at an early stage

PURPOSE: Early monitoring and intervention for patients with novel coronavirus disease-2019 (COVID-19) will benefit both patients and the medical system. Chest computed tomography (CT) radiomics provide more information regarding the prognosis of COVID-19. METHODS: A total of 833 quantitative features of 157 COVID-19 patients in the hospital were extracted. By filtering unstable features using the least absolute shrinkage and selection operator algorithm, a radiomic signature was built to predict the prognosis of COVID-19 pneumonia. The main outcomes were the area under the curve (AUC) of the prediction models for death, clinical stage, and complications. Internal validation was performed using the bootstrapping validation technique. RESULTS: The AUC of each model demonstrated good predictive accuracy [death, 0.846; stage, 0.918; complication, 0.919; acute respiratory distress syndrome (ARDS), 0.852]. After finding the optimal cut-off for each outcome, the respective accuracy, sensitivity, and specificity were 0.854, 0.700, and 0.864 for the prediction of the death of COVID-19 patients; 0.814, 0.949, and 0.732 for the prediction of a higher stage of COVID-19; 0.846, 0.920, and 0.832 for the prediction of complications of COVID-19 patients; and 0.814, 0.818, and 0.814 for ARDS of COVID-19 patients. The AUCs after bootstrapping were 0.846 [95% confidence interval (CI): 0.844-0.848] for the death prediction model, 0.919 (95% CI: 0.917-0.922) for the stage prediction model, 0.919 (95% CI: 0.916-0.921) for the complication prediction model, and 0.853 (95% CI: 0.852-0.0.855) for the ARDS prediction model in the internal validation. Based on the decision curve analysis, the radiomics nomogram was clinically significant and useful. CONCLUSION: The radiomic signature from the chest CT was significantly associated with the prognosis of COVID-19. A radiomic signature model achieved maximum accuracy in the prognosis prediction. Although our results provide vital insights into the prognosis of COVID-19, they need to be verified by large samples in multiple centers.

The global economic policy uncertainty spillover analysis: In the background of COVID-19 pandemic.

Combining the spillover index approach and LASSO-VAR method, we construct the spillover network of 19 specific countries' economic policy uncertainty (EPU). Then we deconstruct the constructed network into four blocks by the block models, the impacts of COVID-19 on EPU spillover effects between each country and blocks is analyzed gradually. The results reveal that: (1) The transnational contagion of EPU is significant, and the spillover network of policy uncertainty is time-varying. (2) EPU networks can be divided into four different blocks by block models. The role of blocks and the spatial spillover transmission path between blocks are different in different periods. (3) The new infection cases and deaths of COVID-19 have a significant effect on reception and transmission directional EPU spillovers, while there is no significant impact on net spillovers. The international movement restrictions during the period of COVID-19 significantly increase the directional and net EPU spillovers. Our findings have some implications for policy-makers and market regulators in the context of the COVID-19 pandemic.

Comparative effectiveness and safety of 32 pharmacological interventions recommended by guidelines for coronavirus disease 2019: a systematic review and network meta-analysis combining 66 trials.

BACKGROUND: The global pandemic coronavirus disease 2019 (COVID-19) has become a major public health problem and presents an unprecedented challenge. However, no specific drugs were currently proven. This study aimed to evaluate the comparative efficacy and safety of pharmacological interventions in patients with COVID-19. METHODS: Medline, Embase, the Cochrane Library, and clinicaltrials.gov were searched for randomized controlled trials (RCTs) in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)/SARS-CoV. Random-effects network meta-analysis within the Bayesian framework was performed, followed by the Grading of Recommendations Assessment, Development, and Evaluation system assessing the quality of evidence. The primary outcome of interest includes mortality, cure, viral negative conversion, and overall adverse events (OAEs). Odds ratio (OR) with 95% confidence interval (CI) was calculated as the measure of effect size. RESULTS: Sixty-six RCTs with 19,095 patients were included, involving standard of care (SOC), eight different antiviral agents, six different antibiotics, high and low dose chloroquine (CQ_HD, CQ_LD), traditional Chinese medicine (TCM), corticosteroids (COR), and other treatments. Compared with SOC, a significant reduction of mortality was observed for TCM (OR = 0.34, 95% CI: 0.20-0.56, moderate quality) and COR (OR = 0.84, 95% CI: 0.75-0.96, low quality) with improved cure rate (OR = 2.16, 95% CI: 1.60-2.91, low quality for TCM; OR = 1.17, 95% CI: 1.05-1.30, low quality for COR). However, an increased risk of mortality was found for CQ_HD vs. SOC (OR = 3.20, 95% CI: 1.18-8.73, low quality). TCM was associated with decreased risk of OAE (OR = 0.52, 95% CI: 0.38-0.70, very low quality) but CQ_HD (OR = 2.51, 95% CI: 1.20-5.24) and interferons (IFN) (OR = 2.69, 95% CI: 1.02-7.08) vs. SOC with very low quality were associated with an increased risk. CONCLUSIONS: COR and TCM may reduce mortality and increase cure rate with no increased risk of OAEs compared with standard care. CQ_HD might increase the risk of mortality. CQ, IFN, and other antiviral agents could increase the risk of OAEs. The current evidence is generally uncertain with low-quality and further high-quality trials are needed.

Two Cases of Well Controlled Chronic Spontaneous Urticaria Triggered by the Moderna COVID-19 Vaccine.

Chronic spontaneous urticaria (CSU, chronic idiopathic urticaria) is a clinical diagnosis characterized by recurrent urticaria of unknown origin, with or without angioedema, that occurs for six weeks or longer. Management of CSU includes a second-generation H1 antihistamine and/or elimination of exacerbating factors. If initial treatment is unsuccessful, trials of first generation H1 antihistamine, H2 blocking antihistamine, leukotriene-receptor antagonist, anti-inflammatory or immunosuppressive agents may be administered. Exacerbating factors include stress, environmental conditions, medications, physical stimuli, and infections. We report the first two cases of a COVID-19 vaccine triggered relapse of CSU that was previously well controlled on therapy.

Incorporation of Urinary Neutrophil Gelatinase-Associated Lipocalin and Computed Tomography Quantification to Predict Acute Kidney Injury and In-Hospital Death in COVID-19 Patients.

BACKGROUND: The prevalence of acute kidney injury (AKI) in COVID-19 patients is high, with poor prognosis. Early identification of COVID-19 patients who are at risk for AKI and may develop critical illness and death is of great importance. OBJECTIVE: The aim of this study was to develop and validate a prognostic model of AKI and in-hospital death in patients with COVID-19, incorporating the new tubular injury biomarker urinary neutrophil gelatinase-associated lipocalin (u-NGAL) and artificial intelligence (AI)-based chest computed tomography (CT) analysis. METHODS: A single-center cohort of patients with COVID-19 from Wuhan Leishenshan Hospital were included in this study. Demographic characteristics, laboratory findings, and AI-assisted chest CT imaging variables identified on hospital admission were screened using least absolute shrinkage and selection operator (LASSO) and logistic regression to develop a model for predicting the AKI risk. The accuracy of the AKI prediction model was measured using the concordance index (C-index), and the internal validity of the model was assessed by bootstrap resampling. A multivariate Cox regression model and Kaplan-Meier curves were analyzed for survival analysis in COVID-19 patients. RESULTS: One hundred seventy-four patients were included. The median (±SD) age of the patients was 63.59 ± 13.79 years, and 83 (47.7%) were men.u-NGAL, serum creatinine, serum uric acid, and CT ground-glass opacity (GGO) volume were independent predictors of AKI, and all were selected in the nomogram. The prediction model was validated by internal bootstrapping resampling, showing results similar to those obtained from the original samples (i.e., 0.958; 95% CI 0.9097-0.9864). The C-index for predicting AKI was 0.955 (95% CI 0.916-0.995). Multivariate Cox proportional hazards regression confirmed that a high u-NGAL level, an increased GGO volume, and lymphopenia are strong predictors of a poor prognosis and a high risk of in-hospital death. CONCLUSIONS: This model provides a useful individualized risk estimate of AKI in patients with COVID-19. Measurement of u-NGAL and AI-based chest CT quantification are worthy of application and may help clinicians to identify patients with a poor prognosis in COVID-19 at an early stage.