Comparative analysis of epidemiological trends of COVID-19 in Shanghai and New York City

ObjectiveTo assess the effects of countermeasures against coronavirus disease 2019 (COVID-19) in Shanghai from March to May 2022 in comparison with epidemiological trend of COVID-19 in New York City. MethodsDaily confirmed cases, asymptomatic SARS-CoV-2 carriers, and daily deaths were obtained in the Shanghai Municipal Health Commission and the Center for Disease Control and Prevention (CDC) of the United States. Descriptive study was conducted by using these data. ResultsFrom March 1 to May 17, the number of daily asymptomatic SARS-CoV-2 infections in Shanghai was up to 58 times as large as that of daily confirmed cases;however, the number of daily confirmed cases in Shanghai was generally less than that in New York in the same time period. At the peak of the COVID-19 epidemic, the growth of daily attack rate in Shanghai was significantly lower than that in New York (P < 0.05). Moreover, the number of daily death was evidently less than that in New York. In addition, the vaccination rate in the elderly (aged 60 years) in Shanghai was evidently lower than that in New York (aged 65 years). ConclusionThe COVID-19 epidemics in Shanghai from March to May 2022 and in New York after December 2021 were both caused by the Omicron variant. Compared with the Delta variant, the Omicron variant has stronger replication ability and infectivity, resulting in challenges to the containment of the epidemic in metropolis such as Shanghai and New York City. The epidemic in New York City remained crucial due to absence of effective countermeasures, while that in Shanghai has been effectively contained with strict countermeasures. The prevention and control strategies may be adjusted along with the continual evolution of SARS-CoV-2 and increasing trend of imported COVID-19 cases.

Estimate the incubation period of coronavirus 2019 (COVID-19).

COVID-19 is an infectious disease that presents unprecedented challenges to society. Accurately estimating the incubation period of the coronavirus is critical for effective prevention and control. However, the exact incubation period remains unclear, as COVID-19 symptoms can appear in as little as 2 days or as long as 14 days or more after exposure. Accurate estimation requires original chain-of-infection data, which may not be fully available from the original outbreak in Wuhan, China. In this study, we estimated the incubation period of COVID-19 by leveraging well-documented and epidemiologically informative chain-of-infection data collected from 10 regions outside the original Wuhan areas prior to February 10, 2020. We employed a proposed Monte Carlo simulation approach and nonparametric methods to estimate the incubation period of COVID-19. We also utilized manifold learning and related statistical analysis to uncover incubation relationships between different age and gender groups. Our findings revealed that the incubation period of COVID-19 did not follow general distributions such as lognormal, Weibull, or Gamma. Using proposed Monte Carlo simulations and nonparametric bootstrap methods, we estimated the mean and median incubation periods as 5.84 (95% CI, 5.42-6.25 days) and 5.01 days (95% CI 4.00-6.00 days), respectively. We also found that the incubation periods of groups with ages greater than or equal to 40 years and less than 40 years demonstrated a statistically significant difference. The former group had a longer incubation period and a larger variance than the latter, suggesting the need for different quarantine times or medical intervention strategies. Our machine-learning results further demonstrated that the two age groups were linearly separable, consistent with previous statistical analyses. Additionally, our results indicated that the incubation period difference between males and females was not statistically significant.

Estimate the incubation period of coronavirus 2019 (COVID-19)

COVID-19 is an infectious disease that presents unprecedented challenges to society. Accurately estimating the incubation period of the coronavirus is critical for effective prevention and control. However, the exact incubation period remains unclear, as COVID-19 symptoms can appear in as little as 2 days or as long as 14 days or more after exposure. Accurate estimation requires original chain-of-infection data, which may not be fully available from the original outbreak in Wuhan, China. In this study, we estimated the incubation period of COVID-19 by leveraging well-documented and epidemiologically informative chain-of-infection data collected from 10 regions outside the original Wuhan areas prior to February 10, 2020. We employed a proposed Monte Carlo simulation approach and nonparametric methods to estimate the incubation period of COVID-19. We also utilized manifold learning and related statistical analysis to uncover incubation relationships between different age and gender groups. Our findings revealed that the incubation period of COVID-19 did not follow general distributions such as lognormal, Weibull, or Gamma. Using proposed Monte Carlo simulations and nonparametric bootstrap methods, we estimated the mean and median incubation periods as 5.84 (95% CI, 5.42–6.25 days) and 5.01 days (95% CI 4.00–6.00 days), respectively. We also found that the incubation periods of groups with ages greater than or equal to 40 years and less than 40 years demonstrated a statistically significant difference. The former group had a longer incubation period and a larger variance than the latter, suggesting the need for different quarantine times or medical intervention strategies. Our machine-learning results further demonstrated that the two age groups were linearly separable, consistent with previous statistical analyses. Additionally, our results indicated that the incubation period difference between males and females was not statistically significant.

Comorbidities prolonged viral shedding of patients infected with SARS-CoV-2 omicron variant in Shanghai: A multi-center, retrospective, observational study.

BACKGROUND: As the omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) surges amid the coronavirus disease 2019 (COVID-19) pandemic, there is limited comorbidities data associated with viral shedding time (VST). We aimed to investigate the effect of comorbidities on VST in asymptomatic and mild patients with omicron. METHODS: A multi-center, retrospective, observational study was conducted from March 12, 2022 to May 24, 2022 in Shanghai. The analysis was adjusted for patients' baseline demographic, using log-rank test and logistic regression model. RESULTS: The study enrolled 198,262 subjects. The median duration of viral shedding time (VST) was 8.29 days. The number of cumulative viral shedding events was significantly lower in the chronic obstructive pulmonary disease (COPD), hyperlipidemia, diabetes, urinary system disease, and cardiocerebrovascular disease than in the no corresponding comorbidities group. Patients with comorbidities had a lower incidence of viral shedding, and the most significant independent risk factor is COPD (aOR 1.78, 95% CI: 1.53-2.08, p < 0.001). Across different age ranges, the comorbidities affecting viral shedding also differ, with the greatest risk factors for viral shedding being hyperlipidemia (aOR 2.23, 95% CI: 1.50-3.31, p < 0.001) and COPD (aOR 1.85, 95% CI: 1.50-2.28, p < 0.001) between ages of 18-39 and 40-64, and thyroid dysfunction (aOR 2.36, 95% CI: 1.60-3.47, p < 0.001) above age 64. CONCLUSIONS: Omicron-infected patients with comorbidities might prolong the VST. The independent risk factors also differ across age ranges, suggesting that providing targeted effective prevention and control guidance and allocating appropriate resources to different populations should be a crucial strategy.

Real-world effectiveness and protection of SARS-CoV-2 vaccine among patients hospitalized for COVID-19 in Xi'an, China, December 8, 2021, to January 20, 2022: A retrospective study.

Introduction: In December 2021, a large-scale epidemic broke out in Xi'an, China, due to SARS-CoV-2 infection. This study reports the effect of vaccination on COVID-19 and evaluates the impact of different vaccine doses on routine laboratory markers. Methods: The laboratory data upon admission, of 231 cases with COVID-19 hospitalized from December 8, 2021 to January 20, 2022 in Xi'an, including blood routine, lymphocyte subtypes, coagulative function tests, virus specific antibodies and blood biochemical tests were collected and analyzed. Results: Of the 231 patients, 21 were not vaccinated, 158 were vaccinated with two doses and 52 with three doses. Unvaccinated patients had a higher proportion of moderate and severe symptoms than vaccinated patients, while two-dose vaccinated patients had a higher proportion than three-dose vaccinated patients. SARS-CoV-2 specific IgG levels were significantly elevated in vaccinated patients compared with unvaccinated patients. Particularly, unvaccinated patients had lower counts and percentages of lymphocytes, eosinophils and CD8+ T-lymphocytes, and elevated coagulation-related markers. In addition, vaccination had no effect on liver and kidney function. Conclusions: Vaccination against SARS-CoV-2, inducing high IgG level and increased CD8+ T cells and eosinophils, and regulating coagulation function, can significantly attenuate symptoms of COVID-19, suggesting that the vaccine remains protective against SARS-CoV-2.

YouTube as a platform to better understand the treatment of lymphoma using ibrutinib: a cross-sectional study.

Background: Ibrutinib, one Food and Drug Administration-approved, orally available, small-molecule Bruton tyrosine kinase (BTK) inhibitor, is an effective targeted therapy for patients with chronic lymphocytic leukemia (CLL) and mantle cell lymphoma (MCL). YouTube is increasingly used for health purposes. However, videos for ibrutinib on YouTube have not been previously evaluated. This study assessed the accuracy and quality of YouTube videos on ibrutinib, to better understand the information shown on a dominant media platform. Methods: The first 150 video results returned by the YouTube search engine in response to the keyword "ibrutinib" were included (up to June 27, 2022). Typically used predefined inclusion and exclusion criteria were applied to screen the videos based on our needs. A 5-point Global Quality Scale (GQS) determined whether the videos would be useful to patients or not, and the quality of content was analyzed by five content-specific items. The quality of the included videos was classified as "low", "moderate", or "excellent" according to GQS and content score. The median and interquartile range were used to describe the values and Kruskal-Wallis test were used in the analysis. Results: A total of 99 videos with a median of 237 views met the inclusion criteria. The videos were categorized into educational videos (n=6, 6.07%), personal experience and blog (n=3, 3.03%) and interviews videos (n=90, 90.9%). Almost half of the videos were classified as moderate (n=51, 51.51%), followed by excellent (n=25, 25.26%) and low (n=23, 23.23%). Between the groups, no statistically significant differences were observed in the numbers of dislikes, comments, posted days, percentage positivity and viewing rate (P>0.05). There were marked differences in the length, likes, views, viewers' interaction and likeability (P<0.05). Conclusions: YouTube could be an effective source for different groups of people to obtain helpful information about ibrutinib. The physicians, pharmacists, nurses and healthcare organizations should prepare and upload more comprehensible and reliable videos with evidence-based information.

Antimicrobial and anticancer activities of Hainan dry noni fruit alcoholic extracts and their novel compounds identification using UPLC-Q-Exactive Obitrap-MS/MS.

Morinda citrifolia Linn (noni) is an important plant in the Pacific Asian region. The fruit has been used as a food source and has shown therapeutical benefits for health. Recently, it has become a source for bioactive compounds. In this study, we investigated the antimicrobial and anticancer activities of alcoholic extracts of Hainan dry noni fruit with machinery assistance and identified their novel compounds by UPLC-Q-Exactive Obitrap-MS/MS. By IE extractor aided method, the extraction of both NFE (Noni Fruit Ethanol) and NFM (Noni Fruit Methanol) solvent crude sample extracts were obtained with recovery yields of 98.48% and 71.65%, respectively. The antimicrobial effect of the crude extracts was subjected to disc diffusion test screening against two microbial strains bacterium SA (Staphylococcus aureus) and, fungal CA (Candida albicans). The MIC values of SA and CA were 35.34 and 47.80 mg/mL for NFE, 117.40 and 108.01 mg/mL for NFM, respectively. Further on, cell viability assay showed that IC50 values of extract NFE and NFM on human UMUC-3 bladder carcinogenic cells were 865.1 and 789.1 µg/mL with less effect to human SVHUC-1 normal cell line for 72hr incubation. Using UPLC-Q-exactive Orbitrap-MS/MS, ten compounds were identified in the noni extracts and confirmed from the HMDB and FooDB. Five known bioactive compounds had been used for treatments in anti-cancer, anti-obesity, and Covid-19 patients. The remaining five compounds were found novel in noni fruit. They were Cyanidin 3-(2 G-xylosylrutinoside), Inulobiose, Clausarinol, Pectachol, and 4,7-Megastigmadien-9-ol. The potential bioactivities of these novel compounds will be studied in the near future. These findings form a basis on screening natural medicinal plant extracts for beneficial use as a food and health source.

The role of interferon a on antiviral immunity in upper respiratory tract

Upper respiratory tract is directly connected with the external environment, and its natural immune system is the first line of defense against pathogens. In antiviral infection, interferon (IFN) is the main component of the antiviral natural immune system and IFN-a is a newly discovered immune effector molecule that is mainly produced in the mucosal barrier. IFN-a exerts a biological role through Janus kinase (JAK) and signal transducer and activator of transcription (STAT) signaling pathway, and plays an important part in regulating innate and acquired immunity of respiratory mucosa. IFN-a principally expresses on the mucosal barrier with a long-lasting antiviral impact and controls immune-inflammatory damage, which is becoming a new focus of antiviral immunity research in the upper respiratory tract, especially in fighting against 2019 novel coronavirus diseases (COVID-19). Thus, we summarize the research progress of IFN-a antiviral immunity in the upper respiratory tract to provide new insight in the prevention and treatment of viral infection in the upper respiratory tract.

Effects of Organizational Atmosphere and Organizational Practice on Knowledge, Attitude, and Practice Toward Diffusion and Utilization of Hepatic Contrast-Enhanced Ultrasound Among Physicians.

Background: Promoting technology diffusion and utilization is a key measure to address the great disparity in technical capacity within integrated health systems. However, even the effectiveness and appropriateness regarding technology has been widely recognized, its diffusion and utilization are still stagnant. The mechanisms that influence the technology from being recognized to being widely applied in practice remain largely unknown. Purpose: Taking hepatic contrast-enhanced ultrasound (CEUS) as an example, this study aimed to investigate the comprehensive influencing mechanism of organizational atmosphere and organizational practice on the knowledge, attitude, and practice toward diffusion and utilization of hepatic CEUS in the medical alliance. Methods: Based on the integration of organizational ready for change (ORC) and knowledge-attitude-practice (KAP), a structured questionnaire was developed. A multistage random sampling method was applied to investigate physicians who directly use CEUS working at the liver disease-related departments of sampled health institutions. Structural equation modeling (SEM) was used to verify the proposed hypotheses, and determine the relationship between the factors. Results: In total, 292 physicians were included. SEM results demonstrated that knowledge influenced both attitude and practice, while attitude positively predicted practice. Organizational practice and organizational atmosphere associated positively with each other. Organizational atmosphere positively affected the physicians' attitude toward CEUS diffusion and utilization (ß = 0.425, p < 0.001), while organizational practice positively affected corresponding knowledge (ß = 0.423, p < 0.001) and practice (ß = 0.275, p < 0.001). Additionally, there was a partial mediating effect between organizational practice and physicians' CEUS diffusion and utilization behavior. Conclusion: By verifying the influencing mechanism of organizational atmosphere and organizational practice on the physicians' KAP of hepatic CEUS diffusion and utilization, this study benefit tailoring strategies for promoting technology diffusion and utilization within medical alliance. It is recommended to develop an organizational atmosphere of advocating technology innovation, establish organizational support mechanism (SM) with multiple concrete supporting countermeasures, and so on.

Clinical value of laboratory indicators for predicting disease progression and death in patients with COVID-19: a retrospective cohort study.

OBJECTIVES: As early prediction of severe illness and death for patients with coronavirus disease 2019 (COVID-19) is important, we aim to explore the clinical value of laboratory indicators in evaluating the progression and prognosis of patients with COVID-19. DESIGN: Retrospective cohort study. SETTING: Hospital-based study in China. PARTICIPANTS: Adult patients with COVID-19 from December 15, 2019 to March 15, 2020. END POINT: Disease severity and mortality. METHODS: Clinical data of 638 patients with COVID-19 were collected and compared between severe and non-severe groups. The predictive ability of laboratory indicators in disease progression and prognosis of COVID-19 was analysed using the receiver operating characteristic curve. The survival differences of COVID-19 patients with different levels of laboratory indicators were analysed utilising Kaplan-Meier analysis. RESULTS: 29.8% (190/638) of patients with COVID-19 progressed to severe. Compared with patients with no adverse events, C reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR) and D-dimer were significantly higher in severe patients with adverse events, such as acute myocardial injury, respiratory failure, acute kidney injury, mechanical ventilation, intensive care unit admission, multiple organ dysfunction syndromes and death (all p<0.05). The multivariate logistic analysis suggested that CRP, NLR and D-dimer were independent risk factors for the disease progression of COVID-19 (all p<0.05). The model combining all of them owned the highest area under the receiver operating characteristic curve (AUC) predicting disease progression and death of COVID-19, with AUC of 0.894 (95% CI 0.857 to 0.931) and 0.918 (95% CI 0.873 to 0.962), respectively. Survival analysis suggested that the patients with a high level of CRP, NLR or D-dimer performed shorter overall survival time (all p<0.05). CONCLUSIONS: The combination of CRP, NLR and D-dimer could be an effective predictor for the aggravation and death in patients with COVID-19. The abnormal expression of these indicators might suggest a strong inflammatory response and multiple adverse events in patients with severe COVID-19.

Technical framework for wastewater-based epidemiology of SARS-CoV-2.

Wastewater-based epidemiology (WBE) is expected to become a powerful tool to monitor the dissemination of SARS-CoV-2 at the community level, which has attracted the attention of scholars all over the world. However, there is not yet a standard protocol to guide its implementation. In this paper, we proposed a comprehensive technical and theoretical framework of relative quantification via qPCR for determining the virus abundance in wastewater and estimating the infection ratio in corresponding communities, which is expected to achieve horizontal and vertical comparability of the data using a human-specific biomarker as the internal reference. Critical factors affecting the virus detectability and the estimation of infection ratio include virus concentration methods, lag-period, per capita virus shedding amount, sewage generation rate, temperature-related decay kinetics of virus/biomarker in wastewater, and hydraulic retention time (HRT), etc. Theoretical simulation shows that the main factors affecting the detectability of virus in sewage are per capita virus shedding amount and sewage generation rate. While the decay of SARS-CoV-2 RNA in sewage is a relatively slow process, which may have limited impact on its detection. Under the ideal condition of high per capita virus shedding amount and low sewage generation rate, it is expected to detect a single infected person within 400,000 people.

Using the spike protein feature to predict infection risk and monitor the evolutionary dynamic of coronavirus.

BACKGROUND: Coronavirus can cross the species barrier and infect humans with a severe respiratory syndrome. SARS-CoV-2 with potential origin of bat is still circulating in China. In this study, a prediction model is proposed to evaluate the infection risk of non-human-origin coronavirus for early warning. METHODS: The spike protein sequences of 2666 coronaviruses were collected from 2019 Novel Coronavirus Resource (2019nCoVR) Database of China National Genomics Data Center on Jan 29, 2020. A total of 507 human-origin viruses were regarded as positive samples, whereas 2159 non-human-origin viruses were regarded as negative. To capture the key information of the spike protein, three feature encoding algorithms (amino acid composition, AAC; parallel correlation-based pseudo-amino-acid composition, PC-PseAAC and G-gap dipeptide composition, GGAP) were used to train 41 random forest models. The optimal feature with the best performance was identified by the multidimensional scaling method, which was used to explore the pattern of human coronavirus. RESULTS: The 10-fold cross-validation results showed that well performance was achieved with the use of the GGAP (g = 3) feature. The predictive model achieved the maximum ACC of 98.18% coupled with the Matthews correlation coefficient (MCC) of 0.9638. Seven clusters for human coronaviruses (229E, NL63, OC43, HKU1, MERS-CoV, SARS-CoV, and SARS-CoV-2) were found. The cluster for SARS-CoV-2 was very close to that for SARS-CoV, which suggests that both of viruses have the same human receptor (angiotensin converting enzyme II). The big gap in the distance curve suggests that the origin of SARS-CoV-2 is not clear and further surveillance in the field should be made continuously. The smooth distance curve for SARS-CoV suggests that its close relatives still exist in nature and public health is challenged as usual. CONCLUSIONS: The optimal feature (GGAP, g = 3) performed well in terms of predicting infection risk and could be used to explore the evolutionary dynamic in a simple, fast and large-scale manner. The study may be beneficial for the surveillance of the genome mutation of coronavirus in the field.

Recent research for COVID-19 drugs and therapies

The sudden outbreak of COVID-19 is causing a severe health crisis to China and the world. The rapid spread coronavirus worldwide highlights the urgent need to find effective drugs and therapies to fight the COVID-19 and related diseases such as acute respiratory disease. Based on the current knowledge on the structure, pathogenic mechanism and pharmacy of coronavirus and previous understanding and experience in treating similar diseases, researchers are testing and screening the approved broad-spectrum antiviral drugs and compounds, and investigating new specific drugs aiming at curing COVID-19. Facing the coronavirus pandemic, it is essential methods and strategies to review existing broad-spectrum antiviral drugs and screen possible effective medicines through some compounds database or repository of structured medical information. Till now, scientists have proposed hundreds of possible drug candidates in a short period of time. This article summarized the potential drugs and treatments proposed by scientists, pharmaceutical industries, and clinicians through literature survey on PubMed-a medical academic database, two clinical trials databases, and internet information published before February 16th, 2020. Then a system review and analysis on the development situations and trends about COVID-19 drugs and therapies were carried out. According to the documents published, scientists and doctors have focused on several possible categories of drugs, including: (1) Chemical compounds, such as nucleoside analogues, protease inhibitors, and other antiviral drugs with multiple mechanisms;(2) biologics, such as monoclonal antibodies, interferon, peptides, plasma of recovered patients and some new kinds of therapies;(3) traditional Chinese medicines, such as some syndrome differentiation therapies. Now, nearly 100 agents or therapies have entered into the clinic research processes and some of them have arrived at the IV phase of clinical trials. Due to the considerable previous experiences and understands that have been accumulated in the researches on SARS-nCoV, Ebola, MERS, and AIDS, with the help of advanced biotechnology and medical technology, scientists can respond faster than ever before, and have investigated a variety of potential treatment options. For example, Remdesivir, which was originally targeted at Ebola virus, Kaletra, which has been used for the treatment of AIDS, Chloroquine, a typical drug used as antimalarial long time ago, glucocorticoids, a common therapies method used to combat SARS, washed microbiota transplantation and Mesenchymal Stem Cell, emerging biological therapies, and combination of multiple drugs or combination of traditional Chinese medicines and modern medicines, are bringing hope to treat COVID-19. Now scientists have proposed several drug candidates (e.g., Remdesivir, Kaletra, Arbidol and Chloroquine) that could be the potential anti-coronavirus agents. All of these drugs are under study in the late phases of clinical trials. While Remdesivir and Kaletra have already shown positive effect on COVID-19 related disease at personalized level. Traditional Chinese Medicines treatment with syndrome differentiation combined with modern medicines also shows a good effect. A considerable number of candidate drugs have shown good results in vitro cell tests on COVID-19. Currently, the follow-up animal tests, toxicology tests, and human clinical trials are in progress at various stages. After a period of clinical application research and further validation research on the clinical effect, safety, dosage and indications of these candidate drugs, it is expected that more drugs with significant efficacy will emerge. Finally, we will defeat COVID-19, and the public will live in a healthy and peaceful world.