Extending the Record of Terpenes from Soft Coral Sarcophyton mililatensis.

In the present study, a new polyoxygenated cembranoid named sarcomililatol H (1) as well as six known terpenes 2-7 with different skeletons were isolated from South China Sea soft coral Sarcophyton mililatensis. Based on the comprehensive analyses of 1D and 2D NMR spectroscopic data, the structure of the new compound 1 was established. This new cembranoid was characterized by the presence of the rarely encountered tetrahydropyran ring with the ether linkage across C-2 and C-12. By applying the time-dependent density functional theory electronic circular dichroism (TDDFT ECD) approach, the absolute configuration of sarcomililatol H (1) was determined. All of the isolates were subjected to the anti-inflammatory and anti-tumor bioassays. However, none of them was active in these evaluations. Additionally, the preliminary virtual screening of inhibitory against SARS-CoV-2 by molecular docking showed that diterpene 1 could be regarded as a SARS-CoV-2 main protease (Mpro ) inhibitor (binding energy: -7.63 kcal/mol). The discovery of these terpenes has expanded the chemical diversity and complexity of terpenes from the species S. mililatensis.

Factors affecting prolonged SARS-CoV-2 infection and development and validation of predictive nomograms.

Prolonged severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has received much attention since it is associated with mortality and is hypothesized as the cause of long COVID-19 and the emergence of a new variant of concerns. However, a prediction model for the accurate prediction of prolonged infection is still lacking. A total of 2938 confirmed patients with COVID-19 diagnosed by positive reverse transcriptase-polymerase chain reaction tests were recruited retrospectively. This study cohort was divided into a training set (70% of study patients; n = 2058) and a validation set (30% of study patients; n = 880). Univariate and multivariate logistic regression analyses were utilized to identify predictors for prolonged infection. Model 1 included only preadmission variables, whereas Model 2 also included after-admission variables. Nomograms based on variables of Model 1 and Model 2 were built for clinical use. The efficiency of nomograms was evaluated by using the area under the curve, calibration curves, and concordance indexes (C-index). Independent predictors of prolonged infection included in Model 1 were: age ≥75 years, chronic kidney disease, chronic lung disease, partially or fully vaccinated, and booster. Additional independent predictors in Model 2 were: treated with nirmatrelvir/ritonavir more than 5 days after diagnosis and glucocorticoid. The inclusion of after-admission variables in the model slightly improved the discriminatory power (C-index in the training cohort: 0.721 for Model 1 and 0.737 for Model 2; in the validation cohort: 0.699 for Model 1 and 0.719 for Model 2). In our study, we developed and validated predictive models based on readily available variables of preadmission and after-admission for predicting prolonged SARS-CoV-2 infection of patients with COVID-19.

A practical scoring model to predict the occurrence of critical illness in hospitalized patients with SARS-CoV-2 omicron infection.

Background: The variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have emerged repeatedly, especially the Omicron strain which is extremely infectious, so early identification of patients who may develop critical illness will aid in delivering proper treatment and optimizing use of resources. We aimed to develop and validate a practical scoring model at hospital admission for predicting which patients with Omicron infection will develop critical illness. Methods: A total of 2,459 patients with Omicron infection were enrolled in this retrospective study. Univariate and multivariate logistic regression analysis were performed to evaluate predictors associated with critical illness. Moreover, the area under the receiver operating characteristic curve (AUROC), continuous net reclassification improvement, and integrated discrimination index were assessed. Results: The derivation cohort included 1721 patients and the validation cohort included 738 patients. A total of 98 patients developed critical illness. Thirteen variables were independent predictive factors and were included in the risk score: age > 65, C-reactive protein > 10 mg/L, lactate dehydrogenase > 250 U/L, lymphocyte < 0.8*10^9/L, white blood cell > 10*10^9/L, Oxygen saturation < 90%, malignancy, chronic kidney disease, chronic cardiac disease, chronic obstructive pulmonary disease, diabetes, cerebrovascular disease, and non-vaccination. AUROC in the derivation cohort and validation cohort were 0.926 (95% CI, 0.903-0.948) and 0.907 (95% CI, 0.860-0.955), respectively. Moreover, the critical illness risk scoring model had the highest AUROC compared with CURB-65, sequential organ failure assessment (SOFA) and 4C mortality scores, and always obtained more net benefit. Conclusion: The risk scoring model based on the characteristics of patients at the time of admission to the hospital may help medical practitioners to identify critically ill patients and take prompt measures.

A Geo-AI-based ensemble mixed spatial prediction model with fine spatial-temporal resolution for estimating daytime/nighttime/daily average ozone concentrations variations in Taiwan.

High levels of ground level ozone (O3) are associated with detrimental health concerns. Most of the studies only focused on daily average and daytime trends due to the presence of sunlight that initiates its formation. However, atmospheric chemical reactions occur all day, thus, nighttime concentrations should be given equal importance. In this study, geospatial-artificial intelligence (Geo-AI) which combined kriging, land use regression (LUR), machine learning, an ensemble learning, was applied to develop ensemble mixed spatial models (EMSMs) for daily, daytime, and nighttime periods. These models were used to estimate the long-term O3 spatio-temporal variations using a two-decade worth of in-situ measurements, meteorological parameters, geospatial predictors, and social and season-dependent factors. From the traditional LUR approach, the performance of EMSMs improved by 60% (daytime), 49% (nighttime), and 57% (daily). The resulting daily, daytime, and nighttime EMSMs had a high explanatory power with and adjusted R2 of 0.91, 0.91, and 0.88, respectively. Estimation maps were produced to examine the changes before and during the implementation of nationwide COVID-19 restrictions. These results provide accurate estimates and its diurnal variation that will support pollution control measure and epidemiological studies.

The persuasive role of the past: Policy feedback and citizens' acceptance of information communication technologies during the COVID‐19 pandemic in China

How can the enforcement of policies in the past influence a society's future adoption of information communication technologies (ICTs)? In this paper, we tackle this question by exploring how past e‐governance policies influence citizens' willingness to use the health QR code, which is a COVID‐19 tracing app widely used in China's pandemic control. Past policies regarding smart‐city development in China involve two aspects: the construction of electronic infrastructure and the applications of specific technologies. Empirical analysis based on a nationwide dataset in China suggests that past policies exhibit persuasive effects and influence citizens' acceptance of the health QR code. Specifically, e‐governance applications in cities significantly enhance citizens' acceptance through the demonstration of their usefulness. However, the construction of e‐governance infrastructure per se does not have the same impact on citizens' acceptance. By connecting citizens' acceptance of new technology with past e‐governance policies, the study illustrates a nuanced policy feedback mechanism through which past policies can substantially reshape public opinion by policy outcomes.

The persuasive role of the past: Policy feedback and citizens' acceptance of information communication technologies during the COVID-19 pandemic in China.

How can the enforcement of policies in the past influence a society's future adoption of information communication technologies (ICTs)? In this paper, we tackle this question by exploring how past e-governance policies influence citizens' willingness to use the health QR code, which is a COVID-19 tracing app widely used in China's pandemic control. Past policies regarding smart-city development in China involve two aspects: the construction of electronic infrastructure and the applications of specific technologies. Empirical analysis based on a nationwide dataset in China suggests that past policies exhibit persuasive effects and influence citizens' acceptance of the health QR code. Specifically, e-governance applications in cities significantly enhance citizens' acceptance through the demonstration of their usefulness. However, the construction of e-governance infrastructure per se does not have the same impact on citizens' acceptance. By connecting citizens' acceptance of new technology with past e-governance policies, the study illustrates a nuanced policy feedback mechanism through which past policies can substantially reshape public opinion by policy outcomes.

¿Cómo puede la aplicación de políticas en el pasado influir en la futura adopción de tecnologías de la información y la comunicación (TIC) en una sociedad? En este documento, abordamos esta pregunta explorando cómo las políticas de gobierno electrónico anteriores influyen en la voluntad de los ciudadanos de usar el código QR de salud, que es una aplicación de rastreo de COVID­19 ampliamente utilizada en el control de la pandemia en China. Las políticas anteriores con respecto al desarrollo de ciudades inteligentes en China involucran dos aspectos: la construcción de infraestructura electrónica y las aplicaciones de tecnologías específicas. El análisis empírico basado en un conjunto de datos a nivel nacional en China sugiere que las políticas anteriores exhiben efectos persuasivos e influyen en la aceptación del código QR de salud por parte de los ciudadanos. Específicamente, las aplicaciones de gobierno electrónico en las ciudades mejoran significativamente la aceptación de los ciudadanos a través de la demostración de su utilidad. Sin embargo, la construcción de infraestructura de gobierno electrónico per se no tiene el mismo impacto en la aceptación de los ciudadanos. Al conectar la aceptación de las nuevas tecnologías por parte de los ciudadanos con las políticas anteriores de gobierno electrónico, el estudio ilustra un mecanismo matizado de retroalimentación de políticas a través del cual las políticas anteriores pueden remodelar sustancialmente la opinión pública mediante los resultados de las políticas.

SARS-CoV-2 Omicron Variant: Epidemiological Features, Biological Characteristics, and Clinical Significance.

The SARS-CoV-2 Omicron (B.1.1529) variant was designated as a variant of concern (VOC) by the World Health Organization (WHO) on November 26, 2021. Within two months, it had replaced the Delta variant and had become the dominant circulating variant around the world. The Omicron variant possesses an unprecedented number of mutations, especially in the spike protein, which may be influencing its biological and clinical aspects. Preliminary studies have suggested that increased transmissibility and the reduced protective effects of neutralizing antibodies have contributed to the rapid spread of this variant, posing a significant challenge to control the coronavirus disease 2019 (COVID-19) pandemic. There is, however, a silver lining for this wave of the Omicron variant. A lower risk of hospitalization and mortality has been observed in prevailing countries. Booster vaccination also has ameliorated a significant reduction in neutralization. Antiviral drugs are minimally influenced. Moreover, the functions of Fc-mediated and T-cell immunity have been retained to a great extent, both of which play a key role in preventing severe disease.

How do China's lockdown and post-COVID-19 stimuli impact carbon emissions and economic output? Retrospective estimates and prospective trajectories.

This paper develops a multi-sector and multi-factor structural gravity model that allows an analytical and quantitative decomposition of the emission and output changes into composition and technique effects. We find that the negative production shock of China's containment policy propagates globally via supply chains, with the carbon-intensive sectors experiencing the greatest carbon emission shocks. We further reveal that China's current stimulus package in 2021-2025 is consistent with China's emission intensity-reduction goals for 2025, but further efforts are required to meet China's carbon emissions-peaking target in 2030 and Cancun 2°C goal. Short-term changes in carbon emissions resulting from lockdowns and initial fiscal stimuli in "economic rescue" period have minor long-term effects, whereas the transitional direction of future fiscal stimulus exerts more predominant impact on long-term carbon emissions. The efficiency improvement effects are more important than the sectoral structure effects of the fiscal stimulus in achieving greener economic growth.

Incidence and Risk of Fatal Vehicle Crashes Among Professional Drivers: A Population-Based Study in Taiwan.

Fatal vehicle crashes (FVCs) are among the leading causes of death worldwide. Professional drivers often drive under dangerous conditions; however, knowledge of the risk factors for FVCs among professional drivers remain scant. We investigated whether professional drivers have a higher risk of FVCs than non-professional drivers and sought to clarify potential risk factors for FVCs among professional drivers. We analyzed nationwide incidence rates of FVCs as preliminary data. Furthermore, by using these data, we created a 1:4 professionals/non-professionals preliminary study to compare with the risk factors between professional and non-professional drivers. In Taiwan, the average crude incidence rate of FVCs for 2003-2016 among professional drivers was 1.09 per 1,000 person-years; professional drivers had a higher percentage of FVCs than non-professional drivers among all motor vehicle crashes. In the 14-year preliminary study with frequency-matched non-professional drivers, the risk of FVCs among professional drivers was significantly associated with a previous history of involvement in motor vehicle crashes (adjustment odds ratio [OR] = 2.157; 95% confidence interval [CI], 1.896-2.453), previous history of benzodiazepine use (adjustment OR = 1.385; 95% CI, 1.215-1.579), and speeding (adjustment OR = 1.009; 95% CI, 1.006-1.013). The findings have value to policymakers seeking to curtail FVCs.

Hospital factors that predict intention of health care workers to leave their job during the COVID-19 pandemic.

PURPOSE: To identify factors responsible for hospital health care workers' intention to leave their job during the COVID-19 pandemic. DESIGN: A cross-sectional study was performed. METHODS: A self-administered questionnaire was delivered to solicit hospital health care workers' demographics, intention to leave, workplace environment, and changes related to COVID-19 from July to November 2020 in Taiwan. Principal component analysis was performed to compare group-related factors. Multiple logistic regression was used to determine the risk factors for the intention of health care workers to leave their job. FINDINGS: Among the 1209 health care workers (mean age, 36.3 years) who participated in the study, intention to leave the job was found to be related to factors relating to COVID-19, including perceived risk, affected social relationships, and increased workload and job stress, after adjustment for demographic and work factors. Supportive administration/management were protective factors against leaving the job. These results were supported by sensitivity analyses. CONCLUSIONS: Our findings suggest that the intention of health care workers to leave their job during a pandemic is related to potentially modifiable factors relating to the infection itself and work environment. CLINICAL RELEVANCE: High perceived risk of COVID-19, affected social relationaops, and increased workload and job stress were positively associated with the intention of health care workers to leave their job, whereas supportive administration and management were protective factors against leaving the job. Development of workplace strategies is important to help mitigate these above factors, improve psychological wellbeing, and promote workforce stability.

Government responsiveness and public acceptance of big-data technology in urban governance: Evidence from China during the COVID-19 pandemic.

The COVID-19 global pandemic has posed unprecedented challenges to nations and cities worldwide. Governments have adopted Information and Communication Technologies (ICTs) to rapidly control the spread of a novel coronavirus. As an innovative but controversial ICT-based tool, health QR code plays a vital role by assisting rapid contact tracing. Yet, whether and how citizens accept this policy tool remains an unknown theoretical and empirical question. In this paper, we study the sources that determine citizens' acceptance of health QR code in city governance. Based on a nation-wide online survey covering 28 major provincial-capital cities in China, we find that individual experiences and political identities affect citizens' acceptance of QR code. Even though public opinion regarding this issue is diverse, the government's responses to citizens' requests play a critical role in enhancing their acceptance of using QR code both in the current and future stages. Specifically, as the citizens perceive a higher level of city government responsiveness, they are less worried about privacy leaks and more likely to perceive the effectiveness of health QR code in improving public health, thus resulting in a higher acceptance. The results offer broad policy implications for smart cities and urban governance.

Impact of the VAT reduction policy on local fiscal pressure in China in light of the COVID-19 pandemic: A measurement based on a computable general equilibrium model.

Value added tax (VAT) reduction is an important tool for cultivating new driving forces for economic development, and it has had a notable impact on local fiscal pressure. Taking the 2018-2019 VAT rate reduction in China as an example, this paper uses a CGE model to estimate the impact of the VAT reduction policy on local fiscal pressure in China in light of the COVID-19 pandemic. The results show that local fiscal pressure increased from 0.342 to 0.435, an increase of 27.08%. The study provides policy implications on optimizing tax structure and alleviating local fiscal pressure.

Novel insight from the first lung transplant of a COVID-19 patient.

BACKGROUND: To reveal detailed histopathological changes, virus distributions, immunologic properties and multi-omic features caused by SARS-CoV-2 in the explanted lungs from the world's first successful lung transplantation of a COVID-19 patient. MATERIALS AND METHODS: A total of 36 samples were collected from the lungs. Histopathological features and virus distribution were observed by optical microscope and transmission electron microscope (TEM). Immune cells were detected by flow cytometry and immunohistochemistry. Transcriptome and proteome approaches were used to investigate main biological processes involved in COVID-19-associated pulmonary fibrosis. RESULTS: The histopathological changes of the lung tissues were characterized by extensive pulmonary interstitial fibrosis and haemorrhage. Viral particles were observed in the cytoplasm of macrophages. CD3+ CD4- T cells, neutrophils, NK cells, γ/δ T cells and monocytes, but not B cells, were abundant in the lungs. Higher levels of proinflammatory cytokines iNOS, IL-1ß and IL-6 were in the area of mild fibrosis. Multi-omics analyses revealed a total of 126 out of 20,356 significant different transcription and 114 out of 8,493 protein expression in lung samples with mild and severe fibrosis, most of which were related to fibrosis and inflammation. CONCLUSIONS: Our results provide novel insight that the significant neutrophil/ CD3+ CD4- T cell/ macrophage activation leads to cytokine storm and severe fibrosis in the lungs of COVID-19 patient and may contribute to a better understanding of COVID-19 pathogenesis.

Big Data Analytics in the Fight against Major Public Health Incidents (Including COVID-19): A Conceptual Framework.

Major public health incidents such as COVID-19 typically have characteristics of being sudden, uncertain, and hazardous. If a government can effectively accumulate big data from various sources and use appropriate analytical methods, it may quickly respond to achieve optimal public health decisions, thereby ameliorating negative impacts from a public health incident and more quickly restoring normality. Although there are many reports and studies examining how to use big data for epidemic prevention, there is still a lack of an effective review and framework of the application of big data in the fight against major public health incidents such as COVID-19, which would be a helpful reference for governments. This paper provides clear information on the characteristics of COVID-19, as well as key big data resources, big data for the visualization of pandemic prevention and control, close contact screening, online public opinion monitoring, virus host analysis, and pandemic forecast evaluation. A framework is provided as a multidimensional reference for the effective use of big data analytics technology to prevent and control epidemics (or pandemics). The challenges and suggestions with respect to applying big data for fighting COVID-19 are also discussed.