Targeted next-generation sequencing of pathogens reveals the profile of secondary infections in COVID-19 patients (preprint)

PURPOSE: To use targeted next-generation sequencing (tNGS) of pathogens for analysing the etiological distribution of secondary infections in patients with severe and critical novel coronavirus pneumonia (COVID-19), to obtain microbial epidemiological data on secondary infections in patients with COVID-19, and to provide a reference for early empirical antibiotic treatment of such patients. METHODS: Patients with infections secondary to severe and critical COVID-19 and hospitalised at the First Affiliated Hospital of Shandong First Medical University between 1 December 2022 and 30 June 2023 were included in the study. The characteristics and etiological distribution of secondary infections in these patients were analysed using tNGS. RESULTS: A total of 95 patients with COVID-19 secondary infections were included in the study, of whom 87.37% had one or more underlying diseases. Forty-eight pathogens were detected, the most common being HSV-4, Candida albicans, Klebsiella pneumoniae, Enterococcus faecium, HSV-1, Staphylococcus aureus, Aspergillus fumigatus, Acinetobacter baumannii, HSV-5, and Stenotrophomonas maltophilia, with Pneumocystis jirovecii being detected in 14.29% of cases. The majority (76.84%) of COVID-19 secondary infections were mixed infections, with mixed viral-bacterial-fungal infections being the most common (28.42%). CONCLUSION: Most secondary infections in severe and critical COVID-19 patients are mixed, with high rates of viral and fungal infections. In clinical settings, monitoring for reactivation or secondary infections by Herpesviridae viruses is crucial; additionally, these patients have a significantly higher rate of P. jirovecii infection. tNGS testing on bronchoalveolar lavage fluid can help determine the aetiology of secondary infections early in COVID-19 patients and assist in choosing appropriate antibiotics.

A Multivariate Multi-Step LSTM Forecasting Model For Tuberculosis Incidence With Model Explanation In Liaoning Province, China (preprint)

Background: In addition to COVID-19, tuberculosis (TB) is the respiratory infectious disease with the highest incidence in China. We aim to design a series of forecasting models and find the factors that affect the incidence of TB, thereby improving the accuracy of the incidence prediction. Results: : In this paper, we developed a new interpretable prediction system based on the multivariate multi-step Long Short-Term Memory (LSTM) model and SHapley Additive exPlanation (SHAP) method. Moreover, four accuracy measures are introduced into the system: Root Mean Square Error, Mean Absolute Error, Mean Absolute Percentage Error, and symmetric Mean Absolute Percentage Error. Meanwhile, the Autoregressive Integrated Moving Average (ARIMA) model and seasonal ARIMA model are established. The multi-step ARIMA-LSTM model is proposed for the first time to examine the performance of each model in the short, medium, and long term, respectively. Compared with the ARIMA model, each error of the multivariate 2-step LSTM model is reduced by 12.92%, 15.94%, 15.97%, and 14.81% in the short term. The 3-step ARIMA-LSTM model achieved excellent performance, with each error decreased to 15.19%, 33.14%, 36.79%, and 29.76% in the medium and long term. We provide the local and global explanation of the multivariate single-step LSTM model in the field of incidence prediction, pioneering. Conclusions: : The multivariate 2-step LSTM model is suitable for short-term forecasts, and the 3-step ARIMA-LSTM model is appropriate for medium and long-term forecasts. In addition, the prediction effect was better than similar TB incidence forecasting models. The SHAP results indicate that the five most crucial features are maximum temperature, average relative humidity, local financial budget, monthly sunshine percentage, and sunshine hours.

Telehealth readiness and its influencing factors among Chinese clinical nurses: A cross-sectional study.

AIM/OBJECTIVE: This study aimed to assess telehealth readiness among clinical nurses in China and explore the factors that affect their telehealth readiness and the relationships of telehealth readiness and telehealth practice-related variables. BACKGROUND: Telehealth is a new service model that uses information and communication technology to provide professional health care services for resource-poor areas. With the global spread of COVID-19, nurses urgently need to adapt and apply telehealth technology to replace conventional face-to-face treatment. However, nurse-led telehealth services in China are currently only in the pilot phase and the readiness of clinical nurses needs to be assessed to facilitate successful telehealth implementation. DESIGN: A cross-sectional, multicentre study was undertaken with the questionnaire survey method. METHODS: Data were collected in October-December 2020 used online questionnaires. A convenience sample of 3386 nurses from 19 hospitals in China completed the Chinese version of Telehealth Readiness Assessment Tools. RESULTS: The mean score of the telehealth readiness was in the category between 61 and 80 points (mean 61.23, SD 11.61). The percentages of nurses meeting the following levels of telehealth readiness were as follows: low (49.9%), moderate (42.0%) and high (8.1%). Significantly higher domain scores were recorded for nurses in the unmarried, head of responsible nursing group. Moreover, there were positive correlations between telehealth readiness level and service experience, service willingness, mode cognition, manpower allocation and policy guidance. CONCLUSIONS: There are still many factors hindering the successful implementation of telehealth. Nursing educators should formulate telehealth education curriculum and service standards to improve the telehealth readiness of nurses.

Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT

Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infection severity and forecasting disease progression. A novel LesionEncoder framework is proposed to detect lesions in chest CT scans and to encode lesion features for automatic severity assessment and progression prediction. The LesionEncoder framework consists of a U-Net module for detecting lesions and extracting features from individual CT slices, and a recurrent neural network (RNN) module for learning the relationship between feature vectors and collectively classifying the sequence of feature vectors. Chest CT scans of two cohorts of COVID-19 patients from two hospitals in China were used for training and testing the proposed framework. When applied to assessing severity, this framework outperformed baseline methods achieving a sensitivity of 0.818, specificity of 0.952, accuracy of 0.940, and AUC of 0.903. It also outperformed the other tested methods in disease progression prediction with a sensitivity of 0.667, specificity of 0.838, accuracy of 0.829, and AUC of 0.736. The LesionEncoder framework demonstrates a strong potential for clinical application in current COVID-19 management, particularly in automatic severity assessment of COVID-19 patients. This framework also has a potential for other lesion-focused medical image analyses.

Analysis of the similarities and differences of COVID-19 symptoms description in North and South China from the perspective of integrated Chinese and western medicine

Objective: To reveal the similarities and differences of the Corona Virus Disease 2019 (COVID-19) symptoms in North and South China, and to promote the prevention and treatment of COVID- 19 with integrated Chinese and Western medicine (ICWM).

Testing Green Financing for Greater Renewable Electricity Generation and Energy Efficiency: Implications for Post COVID-19 Time (preprint)

There is a possibility that worldwide expenditures in renewable energy and energy efficiency projects could fall much further in 2017 and 2018. This may jeopardize the Sustainable Development Goals (SDGs) and the Paris climate change agreement. Lack of access to private financing slows the development of green initiatives. Now that sustainable energy isn't about science and technology, it's all about getting financing. Therefore, recent study intended to investigate the role of green financing for maximum renewable electricity generation and efficiency of energy in United States of America (USA). Our study suggested to value environmental initiatives, like other infrastructure initiatives, for greater electricity generation and energy efficiency in USA. Such infrastructural projects need long-term financing and capital-intensiveness. Our findings suggest that to sustain growth, development, and energy poverty reduction, around $26 trillion would be required, in terms of green financing, in the USA alone by the year 2030 to enhance energy efficiency. To achieve energy sustainability goals in USA, recent research suggested some policy implication considering the post COVID-19 time. If suggested policy implications are implemented successfully there are chances that green financing would make energy generation and energy efficiency as effective.

Integration of Digital Economy and Circular Economy: Current Status and Future Directions

Circular economy (CE) is a concept actively advocated by the European Union (EU), China, Japan, and the United Kingdom. At present, CE is considered to grant the most traction for companies to achieve sustainable development. However, CE is still rarely adopted by enterprises. As the backbone of the fourth industrial revolution, the digital economy (DE) is considered to have a disruptive effect. Studies have shown that digital technology has great potential in promoting the development of CE. Especially during the COVID-19 epidemic that has severely negatively affected the global economy, environment, and society, CE and DE are receiving high attention from policy makers, practitioners, and scholars around the world. However, the integration of CE and digital technology is a small and rapidly developing research field that is still in its infancy. Although there is a large amount of research in the fields of CE and DE, respectively, there are few studies that look into integrating these two fields. Therefore, the purpose of this paper is to explore the research progress and trends of the integration of CE and DE, and provide an overview for future research. This paper adopts a bibliometric research method, employs the Web of Science database as its literature source, and uses VOSviewer visual software to carry out keyword co-occurrence analysis, which focuses on publication trends, journal sources, keyword visualization, multidisciplinary areas, life cycle stages, and application fields.

Mapping the Accessibility of Medical Facilities of Wuhan during the COVID-19 Pandemic

In December 2019, the coronavirus disease 2019 (COVID-19) pandemic attacked Wuhan, China. The city government soon strictly locked down the city, implemented a hierarchical diagnosis and treatment system, and took a series of unprecedented pharmaceutical and non-pharmaceutical measures. The residents’ access to the medical resources and the consequently potential demand–supply tension may determine effective diagnosis and treatment, for which travel distance and time are key indicators. Using the Application Programming Interface (API) of Baidu Map, we estimated the travel distance and time from communities to the medical facilities capable of treating COVID-19 patients, and we identified the service areas of those facilities as well. The results showed significant differences in service areas and potential loading across medical facilities. The accessibility of medical facilities in the peripheral areas was inferior to those in the central areas;there was spatial inequality of medical resources within and across districts;the amount of community healthcare centers was insufficient;some communities were underserved regarding walking distance;some medical facilities could be potentially overloaded. This study provides reference, in the context of Wuhan, for understanding the spatial aspect of medical resources and residents’ relevant mobility under the emergency regulation, and re-examining the coordination of emergency to improve future planning and utilization of medical facilities at various levels. The approach can facilitate policymakers to assess potential loading of medical facilities, identify low-accessibility areas, and deploy new medical facilities. It also implies that the accessibility analysis can be rapid and relevant even only with open-source data.

An Antioxidant Enzyme Therapeutic for COVID-19 (preprint)

The COVID-19 pandemic has taken a significant toll on people worldwide, and there are currently no specific antivirus drugs or vaccines. We report herein a therapeutic based on catalase, an antioxidant enzyme that can effectively breakdown hydrogen peroxide and minimize the downstream reactive oxygen species, which are excessively produced resulting from the infection and inflammatory process. Catalase assists to regulate production of cytokines, protect oxidative injury, and repress replication of SARS-CoV-2, as demonstrated in human leukocytes and alveolar epithelial cells, and rhesus macaques, without noticeable toxicity. Such a therapeutic can be readily manufactured at low cost as a potential treatment for COVID-19.

A Predictive Model of the Temperature-Dependent Inactivation of Coronaviruses (preprint)

The COVID-19 pandemic has stressed healthcare systems and supply lines, forcing medical doctors to risk infection by decontaminating and reusing single-use medical personal protective equipment. The uncertain future of the pandemic is compounded by limited data on the ability of the responsible virus, SARS-CoV-2, to survive across various climates, preventing epidemiologists from accurately modeling its spread. However, a detailed thermodynamic analysis of experimental data on the inactivation of SARS-CoV-2 and related coronaviruses can enable a fundamental understanding of their thermal degradation that will help model the COVID-19 pandemic and mitigate future outbreaks. This paper introduces a thermodynamic model that synthesizes existing data into an analytical framework built on first principles, including the rate law and the Arrhenius equation, to accurately predict the temperature-dependent inactivation of coronaviruses. The model provides much-needed thermal decontamination guidelines for personal protective equipment, including masks. For example, at 70 °C, a 3-log (99.9%) reduction in virus concentration can be achieved in ≈ 3 minutes and can be performed in most home ovens without reducing the efficacy of typical N95 masks. The model will also allow for epidemiologists to incorporate the lifetime of SARS-CoV-2 as a continuous function of environmental temperature into models forecasting the spread of coronaviruses across different climates and seasons.