Reliable and efficient emergency rescue networks: A blockchain and fireworks algorithm-based approach.

In recent years, coronavirus disease 2019 (COVID-19) has been a severe issue the world faces. Emergency rescue networks concerning the distribution of relief materials have gained extensive attention to tackle COVID-19 and related emergency issues. However, it is challenging to establish reliable and efficient emergency rescue networks due to information asymmetry and lack of trust among different rescue stations. In this work, we propose blockchain-based emergency rescue networks to track every transaction of the relief materials reliably and make decisions to deliver relief materials efficiently. More specifically, we propose a hybrid blockchain architecture that employs on-chain data verification to authenticate data records and off-chain data storage to reduce storage overhead. Furthermore, we propose a fireworks algorithm to efficiently calculate the optimal allocation strategies for relief materials. The algorithm provides chaotic random screening and node request guarantee techniques with good convergence. The simulation results show that integrating blockchain technology and the fireworks algorithm can significantly improve relief materials' operation efficiency and distribution quality.

Accelerated ageing is associated with increased COVID-19 severity and differences across ethnic groups may exist.

Background: While increased age is an established risk factor for COVID-19, there is great heterogeneity in outcomes within age groups. This is because chronological age does not reflect health, unlike biological age. We intend to investigate the association between accelerated ageing and COVID-19 outcomes through the lens of three measures, namely phenotypic age acceleration (PhenoAgeAccel), telomere length (Adjusted T/S Ratio) and facial ageing, and to examine whether there are differences across ethnic groups. Methods: Taking participants from the UK Biobank, we associated accelerated ageing with severe COVID-19 outcomes, defined as COVID-related hospitalisation or death. Separate logistic regressions models were created for age and the three accelerated ageing-related variables, adjusting for a variety of covariates in each model. Multivariable logistic regression models were also created within White, Black, Asian and Other ethnic groups to assess for potential differing associations. Forward likelihood ratio logistic regression models were created to evaluate importance of the variables and to assess for patterns of association across the total population and ethnic groups. Results: After adjusting for all covariates, the odds ratio (OR) and 95% confidence interval (95% CI) of COVID-19 severe outcomes for age was 1.080 (1.074-1.086). After further adjusting age for the accelerated ageing variables, the ORs were 1.029 (1.020-1.039) for PhenoAgeAccel and 0.847 (0.772-0.929) for Facial Ageing's "Younger Than You Are" while Adjusted T/S ratio and "Older Than You Are" were statistically insignificant. The OR for age remained similar across ethnic groups. Both PhenoAgeAccel and younger facial ages in the White population and PhenoAgeAccel in the Black population had ORs of 1.031 (1.021-1.042), 0.853 (0.774-0.939), and 1.049 (1.001-1.100), respectively. Both Adjusted T/S Ratio and older facial ages showed statistical insignificance in all ethnicities. In forward logistic regression, age and PhenoAgeAccel were the age-related variables selected most frequently in all models. Interpretation: Accelerated ageing is associated with increased COVID-19 severity. The mechanisms at work here are likely immunosenescence and inflamaging. This association indicates that anti-ageing treatment may improve COVID-19 outcome. The results within ethnic groups and that of telomere length were inconclusive, but point to a need for future, more focused research on the topic.

Diffusion characteristics and risk assessment of respiratory pollutants in high-speed train carriages

Due to the density of people in the cabins of high-speed trains, and the development of the transportation network, respiratory diseases are easily transmitted and spread to various cities. In the context of the epidemic, studying the diffusion characteristics of respiratory pollutants in the cabin and the distribution of passengers is of great significance to the protection of the health of passengers. Based on the theory of computational fluid dynamics (CFD), a high-speed train cabin model with a complete air supply duct is established. For both summer and winter conditions, the characteristics of the flow field and temperature field in the cabin, under full load capacity, and the diffusion characteristics of respiratory pollutants under half load capacity are studied. Taking COVID-19 as an example, the probability of passengers being infected was evaluated. Furthermore, research on the layout of this type of cabin was carried out. The results show that it is not favorable to exhaust air at both ends, as this is likely to cause large-area diffusion of pollutants. The air barrier formed in the aisle can assist the ventilation system, which can prevent pollutants from spreading from one side to the other. Along the length of the train, the respiratory pollutants of passengers almost always spread only forward or backward. Moreover, when the distance between passengers and the infector exceeds one row, the probability of being infected does not decrease significantly. In order to reduce the probability of cross infection, and take into account the passenger efficiency of the railway, passengers in the same row should be separated from each other, and it is best to ride on both sides of the aisle. In the same column, passengers only need to be separated by one row, and it is not recommended to use the middle of the carriage. The number of passengers in the front and back half of the cabin should also be roughly the same.

Death anxiety, self-esteem, and health-related quality of life among geriatric caregivers during the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic has influenced all social spaces and older adults are susceptible to COVID-19. Geriatric caregivers in nursing homes might experience death anxiety when faced with infected older adults and a closed working environment. Death anxiety is a negative and formidable affective state. Yet, little is currently known about the relationships among death anxiety, self-esteem, and health-related quality of life among geriatric caregivers during the COVID-19 pandemic. This study aimed to examine whether self-esteem could moderate the association between death anxiety and health-related quality of life during the pandemic. METHODS: A cross-sectional study was conducted in Jinan and Heze cities, Shandong Province, from November 2020 to January 2021. Participants comprised a convenience sample of 236 geriatric caregivers in nursing homes. Data on sociodemographic variables, death anxiety, self-esteem, and health-related quality of life were collected. Descriptive analysis, Pearson correlation, and moderated analysis were used for statistical analysis. RESULTS: Self-esteem moderated the association between death anxiety and health-related quality of life (death anxiety × self-esteem: B = -0.113, 95% CI: -0.143, -0.018). CONCLUSION: This study revealed that self-esteem played a moderating role between death anxiety and health-related quality of life during the pandemic, which implies that mental health should be given more attention, and that interventions for improving self-esteem need to be carried out.

Community structure of environmental microorganisms associated with COVID-19 affected patients.

To clarify the characteristics and distribution of hospital environmental microbiome associated with confirmed COVID-19 patients. Environmental samples with varying degrees of contamination which were associated with confirmed COVID-19 patients were collected, including 13 aerosol samples collected near eight patients in different wards, five swabs from one patient's skin and his personal belongings, and two swabs from the surface of positive pressure respiratory protective hood and the face shield from a physician who had close contact with one patient. Metagenomic next-generation sequencing (mNGS) was used to analyze the composition of the microbiome. One of the aerosol samples (near patient 4) was detected positive for COVID-19, and others were all negative. The environmental samples collected in different wards possessed protean compositions and community structures, the dominant genera including Pseudomonas, Corynebacterium, Neisseria, Staphylococcus, Acinetobacter, and Cutibacterium. Top 10 of genera accounted for more than 76.72%. Genera abundance and proportion of human microbes and pathogens radiated outward from the patient, while the percentage of environmental microbes increased. The abundance of the pathogenic microorganism of medical supplies is significantly higher than other surface samples. The microbial compositions of the aerosol collected samples nearby the patients were mostly similar to those from the surfaces of the patient's skin and personal belongings, but the abundance varied greatly. The positive rate of COVID-19 RNA detected from aerosol around patients in general wards was quite low. The ward environment was predominantly inhabited by species closely related to admitted patients. The spread of hospital microorganisms via aerosol was influenced by the patients' activity. Supplementary Information: The online version contains supplementary material available at 10.1007/s10453-021-09708-5.