Bring the life stages into the domain of basic and clinical pharmacology.

Completely distinct physiological conditions and immune responses exist among different human life stages. Age is not always consistent with the life stage. We proposed to incorporate the concept of the life stages into basic and clinical pharmacology, including clinical trials, drug labels, and drug usage in clinical practice. Life-stage-based medical treatment is the application of medicine according to life stages such as prepuberty, reproductive, and aging. A large number of diseases are life-stage-dependent. Many medications and therapy have shown various age effects but not been recognized as life-stage-dependent. The same dosage and drug applications used in different life stages lead to divergent outcomes. Incorporating life stages in medicine and drug usage will enhance the efficacy and precision of the medication in disease treatment.

Association between population density and infection rate suggests the importance of social distancing and travel restriction in reducing the COVID-19 pandemic.

Currently, 2019-nCoV has spread to most countries of the world. Understanding the environmental factors that affect the spread of the disease COVID-19 infection is critical to stop the spread of the disease. The purpose of this study is to investigate whether population density is associated with the infection rate of the COVID-19. We collected data from official webpages of cities in China and in the USA. The data were organized on Excel spreadsheets for statistical analyses. We calculated the morbidity and population density of cities and regions in these two countries. We then examined the relationship between morbidity and other factors. Our analysis indicated that the population density in cities in Hubei province where the COVID-19 was severe was associated with a higher percentage of morbidity, with an r value of 0.62. Similarly, in the USA, the density of 51 states and territories is also associated with morbidity from COVID-19 with an r value of 0.55. In contrast, as a control group, there is no association between the morbidity and population density in 33 other regions of China, where the COVID-19 epidemic is well under control. Interestingly, our study also indicated that these associations were not influenced by the first case of COVID-19. The rate of morbidity and the number of days from the first case in the USA have no association, with an r value of - 0.1288. Population density is positively associated with the percentage of patients with COVID-19 infection in the population. Our data support the importance of such as social distancing and travel restriction in the prevention of COVID-19 spread.

An alternative approach-combination of lockdown and open in fighting COVID-19 pandemics.

As the COVID-19 pandemic enters its third year and the omicron variant becomes dominant, we propose an alternative strategy for dealing with COVID-19, called hybrid lockdown, that is, the combination of lockdown (the centralized and organized lockdown of the high-risk population) and free mobility (normal mobility) of the low-risk population. Such an approach will enable a country or region, especially with a high population density, to achieve significant prevention and control the effects of the COVID-19 pandemic at the least cost.

Mortality in Four Waves of COVID-19 Is Differently Associated with Healthcare Capacities Affected by Economic Disparities.

BACKGROUND: The greatest challenges are imposed on the overall capacity of disease management when the cases reach the maximum in each wave of the pandemic. METHODS: The cases and deaths for the four waves of COVID-19 in 119 countries and regions (CRs) were collected. We compared the mortality across CRs where populations experience different economic and healthcare disparities. FINDINGS: Among 119 CRs, 117, 112, 111, and 55 have experienced 1, 2, 3, and 4 waves of COVID-19 disease, respectively. The average mortality rates at the disease turning point were 0.036, 0.019. 0.017, and 0.015 for the waves 1, 2, 3, and 4, respectively. Among 49 potential factors, income level, gross national income (GNI) per capita, and school enrollment are positively correlated with the mortality rates in the first wave, but negatively correlated with the rates of the rest of the waves. Their values for the first wave are 0.253, 0.346 and 0.385, respectively. The r value for waves 2, 3, and 4 are -0.310, -0.293, -0.234; -0.263, -0.284, -0.282; and -0.330, -0.394, -0.048, respectively. In high-income CRs, the mortality rates in waves 2 and 3 were 29% and 28% of that in wave 1; while in upper-middle-income CRs, the rates for waves 2 and 3 were 76% and 79% of that in wave 1. The rates in waves 2 and 3 for lower-middle-income countries were 88% and 89% of that in wave 1, and for low-income countries were 135% and 135%. Furthermore, comparison among the largest case numbers through all waves indicated that the mortalities in upper- and lower-middle-income countries is 65% more than that of the high-income countries. INTERPRETATION: Conclusions from the first wave of the COVID-19 pandemic do not apply to the following waves. The clinical outcomes in developing countries become worse along with the expansion of the pandemic.

Variations of COVID-19 mortality are affected by economic disparities across countries.

BACKGROUND: When the COVID-19 case number reaches a maximum in a country, its capacity and management of health system face greatest challenge. METHODS: We performed a cross-sectional study on data of turning points for cases and deaths for the first three waves of COVID-19 in countries with more than 5000 cumulative cases, as reported by Worldometers and WHO Coronavirus (COVID-19) Dashboard. We compared the case fatality rates (CFRs) and time lags (in unit of day) between the turning points of cases and deaths among countries in different development stages and potential influence factors. As of May 10, 2021, 106 out of 222 countries or regions (56%) reported more than 5000 cases. Approximately half of them have experienced all the three waves of COVID-19 disease. The average mortality rate at the disease turning point was 0.038 for the first wave, 0.020 for the second wave, and 0.023 for wave 3. In high-income countries, the mortality rates during the first wave are higher than that of the other income levels. However, the mortality rates during the second and third waves of COVID-19 were much lower than those of the first wave, with a significant reduction from 5.7% to 1.7% approximately 70%. At the same time, high-income countries exhibited a 2-fold increase in time lags during the second and the third waves compared to the first wave, suggesting that the periods between the cases and deaths turning point extended. High rates in the first wave in developed countries are associated to multiple factors including transportation, population density, and aging populations. In upper middle- and lower middle-income countries, the decreasing of mortality rates in the second and third waves were subtle or even reversed, with increased mortality during the following waves. In the upper and lower middle-income countries, the time lags were about 50% of the durations observed from high-income countries. INTERPRETATION: Economy and medical resources affect the efficiency of COVID-19 mitigation and the clinical outcomes of the patients. The situation is likely to become even worse in the light of these countries' limited ability to combat COVID-19 and prevent severe outcomes or deaths as the new variant transmission becomes dominant.

Oral SARS-CoV-2 Spike Protein Recombinant Yeast Candidate Prompts Specific Antibody and Gut Microbiota Reconstruction in Mice.

A recent study showed that patients with coronavirus disease 2019 (COVID-19) have gastrointestinal symptoms and intestinal flora dysbiosis. Yeast probiotics shape the gut microbiome and improve immune homeostasis. In this study, an oral candidate of yeast-derived spike protein receptor-binding domain (RBD) and fusion peptide displayed on the surface of the yeast cell wall was generated. The toxicity and immune efficacy of oral administration were further performed in Institute of Cancer Research (ICR) mice. No significant difference in body weights, viscera index, and other side effects were detected in the oral-treated group. The detectable RBD-specific immunoglobulin G (IgG) and immunoglobulin A (IgA) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and more complex microbiota were detected from oral administration mice compared with those of the control group. Interestingly, the recombinant yeast was identified in female fetal of the high-dose group. These results revealed that the displaying yeast could fulfill the agent-driven immunoregulation and gut microbiome reconstitution. The findings will shed light on new dimensions against SARS-CoV-2 infection with the synergistic oral agents as promising non-invasive immunization and restoring gut flora.

Graphical Trajectory Comparison to Identify Errors in Data of COVID-19: A Cross-Country Analysis.

Data from the early stage of a novel infectious disease outbreak provide vital information in risk assessment, prediction, and precise disease management. Since the first reported case of COVID-19, the pattern of the novel coronavirus transmission in Wuhan has become the interest of researchers in epidemiology and public health. To thoroughly map the mechanism of viral spreading, we used the patterns of data at the early onset of COVID-19 from seven countries to estimate the time lag between peak days of cases and deaths. This study compared these data with those of Wuhan and estimated the natural history of disease across the infected population and the time lag. The findings suggest that comparative analyses of data from different regions and countries reveal the differences between peaks of cases and deaths caused by COVID-19 and the incomplete and underestimated cases in Wuhan. Different countries may show different patterns of cases peak days, deaths peak days, and peak periods. Error in the early COVID-19 statistics in Brazil was identified. This study provides sound evidence for policymakers to understand the local circumstances in diagnosing the health of a population and propose precise and timely public health interventions to control and prevent infectious diseases.

Democracy and case fatality rate of COVID-19 at early stage of pandemic: a multicountry study.

Many studies have evaluated factors that influence the course of the COVID-19 pandemic in different countries. This multicountry study assessed the influence of democracy and other factors on the case fatality rate of COVID-19 during the early stage of the pandemic. We accessed the World Health Organization, World Bank, and the Democracy Index 2019 databases for data from the 148 countries. Multiple analyses were conducted to examine the association between the Democracy Index and case fatality rate of COVID-19. Within 148 countries, the percentage of the population aged 65 years and above (p = 0.0193), and health expenditure as a percentage of GDP (p = 0.0237) were positively associated with countries' case fatality rates. By contrast, hospital beds per capita helped to reduce the case fatality rates. In particular, the Democracy Index was positively associated with case fatality rates in a subgroup of 47 high-income countries. This study suggests that enhancing the health system with increased hospital beds and healthcare workforce per capita should reduce case fatality rate. The findings suggest that a higher Democracy Index is associated with more deaths from COVID-19 at the early stage of the pandemic, possibly due to the decreased ability of the government.

The Data set for Patient Information Based Algorithm to Predict Mortality Cause by COVID-19.

The data of COVID-19 disease in China and then in South Korea were collected daily from several different official websites. The collected data included 33 death cases in Wuhan city of Hubei province during early outbreak as well as confirmed cases and death toll in some specific regions, which were chosen as representatives from the perspective of the coronavirus outbreak in China. Data were copied and pasted onto Excel spreadsheets to perform data analysis. A new methodology, Patient Information Based Algorithm (PIBA) [1], has been adapted to process the data and used to estimate the death rate of COVID-19 in real-time. Assumption is that the number of days from inpatients to death fall into a pattern of normal distribution and the scores in normal distribution can be obtained by observing 33 death cases and analysing the data [2]. We selected 5 scores in normal distribution of these durations as lagging days, which will be used in the following estimation of death rate. We calculated each death rate on accumulative confirmed cases with each lagging day from the current data and then weighted every death rate with its corresponding possibility to obtain the total death rate on each day. While the trendline of these death rate curves meet the curve of current ratio between accumulative death cases and confirmed cases at some points in the near future, we considered that these intersections are within the range of real death rates. Six tables were presented to illustrate the PIBA method using data from China and South Korea. One figure on estimated rate of infection and patients in serious condition and retrospective estimation of initially occurring time of CORID-19 based on PIBA.

Impact of COVID-19 on air quality in the Yangtze River Delta, China.

To investigate the effect of nationwide restrictions due to COVID-19 on air quality in the Yangtze River Delta (YRD), China, we defined four periods named period I (January 1 to 23, 2020), period II (January 24 to February 23), period III (February 24 to April 7), and period IV (April 8 to May 31), which indicated normal period, lockdown period, regional work resumption period, and nationwide work resumption period, respectively. Hourly PM2.5, PM10, NO2, SO2, CO, and O3 in 41 cities in the YRD region were analyzed. Compared to period I, NO2 decreased by 58% during period II and increased in periods III and IV. SO2 remained constant during the four periods (7-8 µg/m3). Higher PM2.5 concentration was monitored during period II (41 µg/m3) when compared to period III (35 µg/m3), which was resulted from the enhanced secondary formation. Spatial distribution analysis further indicated that PM2.5 in the northern YRD during period II was higher than that during period III, whereas PM2.5 in the southern YRD in the period II was similar to that in period III. The results demonstrated that PM2.5 shows a nonlinear response to the reduction of its precursors, and this phenomenon varies in different areas. Compared to periods I (36 µg/m3) and III (64 µg/m3), relatively higher O3 during period II (64 µg/m3) was probably resulted from less NO emission and hence weakened NO titration effect. The study suggested that coordinated and balanced measures are needed to improve air quality.

A cost-effective plan for global testing - an infection rate stratified, algorithm guided, multiple-level, continuously pooled testing strategy.

The most effective measure to prevent or stop the spread of infectious diseases is the early identification and isolation of infected individuals through comprehensive screening. At present, in the COVID-19 pandemic, such screening is often limited to isolated regions as determined by local governments. Screening of potentially infectious individuals should be conducted through coordinated national or global unified actions. Our current research focuses on using resources to conduct comprehensive national and regional regular testing with a risk rate based, algorithmic guided, multiple-level, pooled testing strategy. Here, combining methodologies with mathematical logistic models, we present an analytic procedure of an overall plan for coordinating state, national, or global testing. The proposed plan includes three parts 1) organization, resource allocation, and distribution; 2) screening based on different risk levels and business types; and 3) algorithm guided, multiple level, continuously screening the entire population in a region. This strategy will overcome the false positive and negative results in the polymerase chain reaction (PCR) test and missing samples during initial tests. Based on our proposed protocol, the population screening of 300,000,000 in the US can be done weekly with between 15,000,000 and 6,000,000 test kits. The strategy can be used for population screening for current COVID-19 and any future severe infectious disease when drugs or vaccines are not available.

Discovery of rhodomyrtone as a broad-spectrum antiviral inhibitor with anti-SARS-CoV-2 activity (preprint)

The outbreak of new viruses, such as serve acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as well as the emerging of drug-resistance viruses highlight the urgent need for the development of broad-spectrum antiviral drugs. Herein, we report the discovery of a plant-derived small molecule, 6,8-dihydroxy-9-isobutyl-2,2,4,4-tetramethyl-7-(3-methylbutanoyl)-4,9-dihydro-1H- xanthene-1,3(2H)-dione (rhodomyrtone, RDT), which exhibited potent broad-spectrum antiviral activities against several RNA and DNA viruses, including SARS-CoV-2, respiratory syncytial virus (RSV), herpes simplex virus type 1 (HSV-1), herpes simplex virus type 2 (HSV-2), varicella-zoster virus (VZV), human cytomegalovirus (HCMV), and Kaposi's sarcoma-associated herpesvirus (KSHV). RDT can significantly suppress viral gene expression and show the low possibility to elicit drug-resistant variants. Mechanistic study implied that RDT inhibited viral infection by disturbing the cellular factors that essential for viral gene expression. Our results suggested that RDT might be a promising lead compound for the development of broad-spectrum antiviral drugs.

The common personal behavior and preventive measures among 42 uninfected travelers from the Hubei province, China during COVID-19 outbreak: a cross-sectional survey in Macao SAR, China.

BACKGROUND: The novel coronavirus diseases 2019 (COVID-19) caused over 1.7 million confirmed cases and cumulative mortality up to over 110,000 deaths worldwide as of 14 April 2020. A total of 57 Macao citizens were obligated to stay in Hubei province, China, where the highest COVID-19 prevalence was noted in the country and a "lockdown" policy was implemented for outbreak control for more than one month. They were escorted from Wuhan City to Macao via a chartered airplane organized by Macao SAR government and received quarantine for 14 days with none of the individual being diagnosed with COVID-19 by serial RNA tests from the nasopharyngeal specimens and sera antibodies. It was crucial to identify common characteristics among these 57 uninfected individuals. METHODS: A questionnaire survey was conducted to extract information such as behavior, change of habits and preventive measures. RESULTS: A total of 42 effective questionnaires were analyzed after exclusion of 14 infants and children with age under fifteen as ineligible for the survey and missing of one questionnaire, with a response rate of 97.7% (42 out of 43). The proportion of female composed more than 70% of this group of returners. The main reason for visiting Hubei in 88.1% of respondents was to visit relatives. Over 88% of respondents did not participate in high-risk activities due to mobility restriction. All (100%) denied contact with suspected or confirmed COVID-19 cases. Comparison of personal hygiene habits before and during disease outbreak showed a significant increase in practice including wearing a mask when outdoor (16.7% and 95.2%, P < 0.001) and often wash hands with soap or liquid soap (85.7% and 100%, P = 0.031).

Real-time estimation and prediction of mortality caused by COVID-19 with patient information based algorithm.

The global COVID-19 outbreak is worrisome both for its high rate of spread, and the high case fatality rate reported by early studies and now in Italy. We report a new methodology, the Patient Information Based Algorithm (PIBA), for estimating the death rate of a disease in real-time using publicly available data collected during an outbreak. PIBA estimated the death rate based on data of the patients in Wuhan and then in other cities throughout China. The estimated days from hospital admission to death was 13 (standard deviation (SD), 6 days). The death rates based on PIBA were used to predict the daily numbers of deaths since the week of February 25, 2020, in China overall, Hubei province, Wuhan city, and the rest of the country except Hubei province. The death rate of COVID-19 ranges from 0.75% to 3% and may decrease in the future. The results showed that the real death numbers had fallen into the predicted ranges. In addition, using the preliminary data from China, the PIBA method was successfully used to estimate the death rate and predict the death numbers of the Korean population. In conclusion, PIBA can be used to efficiently estimate the death rate of a new infectious disease in real-time and to predict future deaths. The spread of 2019-nCoV and its case fatality rate may vary in regions with different climates and temperatures from Hubei and Wuhan. PIBA model can be built based on known information of early patients in different countries.