The m7G Modification Level and Immune Infiltration Characteristics in Patients with COVID-19

Purpose: The 7-methylguanosine (m7G)-related genes were used to identify the clinical severity and prognosis of patients with coronavirus disease 2019 (COVID-19) and to identify possible therapeutic targets. Patients and Methods: The GSE157103 dataset provides the transcriptional spectrum and clinical information required to analyze the expression of m7G-related genes and the disease subtypes. R language was applied for immune infiltration analysis, functional enrichment analysis, and nomogram model construction. Results: Most m7G-related genes were up-regulated in COVID-19 and were closely related to immune cell infiltration. Disease subtypes were grouped using a clustering algorithm. It was found that the m7G-cluster B was associated with higher immune infiltration, lower mechanical ventilation, lower intensive care unit (ICU) status, higher ventilator-free days, and lower m7G scores. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that differentially expressed genes (DEGs) between m7G-cluster A and B were enriched in viral infection and immune-related aspects, including COVID-19 infection;Th17, Th1, and Th2 cell differentiation, and human T-cell leukemia virus 1 infection. Finally, through machine learning, six disease characteristic genes, NUDT4B, IFIT5, LARP1, EIF4E, LSM1, and NUDT4, were screened and used to develop a nomogram model to estimate disease risk. Conclusion: The expression of most m7G genes was higher in COVID-19 patients compared with that in non-COVID-19 patients. The m7G-cluster B showed higher immune infiltration and milder symptoms. The predictive nomogram based on the six m7G genes can be used to accurately assess risk.

"Home away from home” in pandemic times: how has COVID-19 changed the Airbnb market in Melbourne?

Purpose: This study aims to investigate how the COVID-19 pandemic has impacted and changed Airbnb market in the Greater Melbourne area in terms of its temporal and spatial patterns and identify possible shifts in underlying trends in travel activities. Design/methodology/approach: A panel data set of Airbnb listings in Melbourne is analysed to compare temporal patterns, spatial distribution and lengths of stay of Airbnb users before and after the COVID outbreak. Findings: This study found that the COVID disruption did not fundamentally change the temporal cycle of the Airbnb market. Month-to-month fluctuations peaked at different levels from pre-pandemic times mainly because of lockdowns and other restrictive measures. The impact of COVID-19 disruptions on neighbourhood-level Airbnb revenues is associated with distance to CBD rather than number of COVID cases. Inner city suburbs suffered major loss during the pandemic, whereas outer suburbs gained popularity due to increased domestic travel and long stays. Long stays (28 days or more, as defined by Airbnb) were the fastest growing segment during the pandemic, which indicates the Airbnb market was adapting to increasing demand for purposes like remote working or lifestyle change. After easing of COVID-related restrictions, demand for short-term accommodation quickly recovered, but supply has not shown signs of strong recovery. Spatial distribution of post-pandemic supply recovery shows a similar spatial variation. Neighbourhoods in the inner city have not shown signs of significant recovery, whereas those in the middle and outer rings are either slowly recovering or approaching their pre-COVID levels. Practical implications: The COVID-19 pandemic has significantly impacted short-term rental markets and in particular the Airbnb sector during the phase of its rapid development. This paper helps inform in- and post-pandemic housing policy, market opportunity and investment decision. Originality/value: To the best of the authors' knowledge, this is one of the first attempts to empirically examine both temporal and spatial patterns of the COVID-19 impact on Airbnb market in one of the most severely impacted major cities. It is one of the first attempts to identify shifts in underlying trends in travel based on Airbnb data. © 2022, Emerald Publishing Limited.

Path to sustainable development: Does digital economy matter in manufacturing green total factor productivity?

In the context of the outbreak of the COVID-19 pandemic and China's "digital power” strategy, the realization of a green shift of manufacturing has become a necessary condition to promote the economy, and the digital factor has increasingly become a new driving force. The DEA-Malmquist index and entropy method were used to measure the manufacturing green total factor productivity (GTFP) and the level of digital economy level from 2011 to 2018, respectively. This study then explored the impact of digital economy on manufacturing GTFP based on the system generalized method of moments (GMM) model, as well as the adjustment effects of talent aggregation and financial scale according to the moderating model. This research came to four conclusions. (1) The digital economy can significantly improve the manufacturing GTFP of China, and the influence shows the characteristic of a "marginal increase”;(2) notably, the perspective of manufacturing GTFP decomposition indicates that the digital economy exerts a significant positive effect on manufacturing technical efficiency during the current period but obviously hinders technical progress;(3) interestingly, a mechanistic test showed that the two dimensions of innovation environment—talent aggregation (0.385) and financial scale (0.359)—play critical moderating roles in the influencing process;and (4) the influence has evident regional heterogeneity—it is significantly positive in the east and negative in the central region and west. Finally, corresponding policy suggestions are suggested. © 2022 ERP Environment and John Wiley & Sons Ltd.

Recommendations and guidelines for the diagnosis and management of Coronavirus Disease-19 (COVID-19) associated bacterial and fungal infections in Taiwan

Coronavirus disease-19 (COVID-19) is an emerging infectious disease caused by SARS-CoV-2 that has rapidly evolved into a pandemic to cause over 600 million infections and more than 6.6 million deaths up to Nov 25, 2022. COVID-19 carries a high mortality rate in severe cases. Co-infections and secondary infections with other micro-organisms, such as bacterial and fungus, further increases the mortality and complicates the diagnosis and management of COVID-19. The current guideline provides guidance to physicians for the management and treatment of patients with COVID-19 associated bacterial and fungal infections, including COVID-19 associated bacterial infections (CABI), pulmonary aspergillosis (CAPA), candidiasis (CAC) and mucormycosis (CAM). Recommendations were drafted by the 7th Guidelines Recommendations for Evidence-based Antimicrobial agents use Taiwan (GREAT) working group after review of the current evidence, using the grading of recommendations assessment, development, and evaluation (GRADE) methodology. A nationwide expert panel reviewed the recommendations in March 2022, and the guideline was endorsed by the Infectious Diseases Society of Taiwan (IDST). This guideline includes the epidemiology, diagnostic methods and treatment recommendations for COVID-19 associated infections. The aim of this guideline is to provide guidance to physicians who are involved in the medical care for patients with COVID-19 during the ongoing COVID-19 pandemic. © 2022

The research progress, hotspots, challenges and outlooks of solid-phase denitrification process

Nitrogen pollution is one of the main reasons for water eutrophication. The difficulty of nitrogen removal in low-carbon wastewater poses a huge potential threat to the ecological environment and human health. As a clean biological nitrogen removal process, solid-phase denitrification (SPD) was proposed for long-term operation of low-carbon wastewater. In this paper, the progress, hotspots, and challenges of the SPD process based on different solid carbon sources (SCSs) are reviewed. Compared with synthetic SCS and natural SCS, blended SCSs have more application potential and have achieved pilot-scale application. Differences in SCSs will lead to changes in the enrichment of hydrolytic microorganisms and hydrolytic genes, which indirectly affect denitrification performance. Moreover, the denitrification performance of the SPD process is also affected by the physical and chemical properties of SCSs, pH of wastewater, hydraulic retention time, filling ratio, and temperature. In addition, the strengthening of the SPD process is an inevitable trend. The strengthening measures including SCSs modification and coupled electrochemical technology are regarded as the current research hotspots. It is worth noting that the outbreak of the COVID-19 epidemic has led to the increase of disinfection by-products and antibiotics in wastewater, which makes the SPD process face challenges. Finally, this review proposes prospects to provide a theoretical basis for promoting the efficient application of the SPD process and coping with the challenge of the COVID-19 epidemic. © 2022 Elsevier B.V.

A Dynamic Access Control Model Based on Attributes and Intro VAE

Affected by the COVID-19 pandemic, teleworking is becoming more popular, with the exposed attack surface of the internal network expanding. Once outsiders personate accounts or insiders conduct illegal operations, the data security in teleworking with traditional border protection will be broken. Therefore, it is necessary to implement fine-grained and dynamic access control to protect data from malicious access. Attribute-based access control (ABAC) is ideal, where authorization is performed through attributes and rules. On this basis, risk assessment, context awareness, and machine learning are supplemented for dynamic access control. However, these methods have their limitations due to the requirement of sufficient prior knowledge and massive label-classified data. Moreover, it is challenging to obtain the samples of attack behaviors, and the attack behaviors may change frequently to evade detection. In contrast, the normal behaviors are relatively stable except for the update of network services. We propose a dynamic access control model, ABAC-IntroVAE, to address the above issues. ABAC-IntroVAE judges users' requests through rule matching and behavior analysis based on the attributes of the requests. It first filters out requests against the rules by rule matching. Then, the introspective variational autoencoder (IntroVAE) is used for behavior analysis to realize dynamic access decisions. Requests classified as normal can be authorized for access. ABAC-IntroVAE only needs samples of normal requests for training, avoiding the difficult task of collecting massive and frequently changing samples of attack requests. Meanwhile, the IntroVAE model is updated through continual learning to adapt to new-style normal behaviors due to the update of network services. Our experiment study suggests that our proposed ABAC-IntroVAE can effectively perform dynamic access control. It achieves an accuracy of 97.2% in abnormal detection and maintains an accuracy of over 97% through continual learning, despite the addition of new-style user behavior patterns. © 2022 IEEE.

Detection of COVID-19 DNA using Förster Resonance Energy Transfer between upconversion and gold nanoparticles

FRET is ascribed to the spectral overlapping of upconversion luminescence and the absorption of AuNPs. This experiment enables early-stage coronavirus detection. The results show a sensitivity of 100 fM for the detection of COVID-19 DNA. © 2022 The Author(s)

Healthcare-associated COVID-19 outbreaks: a nationwide population-based cohort study.

BACKGROUND: Healthcare-associated coronavirus disease 2019 (COVID-19) has significant implications for patients, their companions and healthcare workers (HCWs). Controlling transmission in healthcare settings is critical to reduce deaths due to COVID-19. AIM: To describe the epidemiology and characteristics of healthcare-associated COVID-19 outbreaks and outbreak-related cases. METHODS: The investigation data for each healthcare-associated outbreak that occurred between 15th January 2020 and 31st July 2021 in Taiwan were analysed retrospectively. Confirmed outbreak-associated cases were categorized as HCW cases, patient companion cases or patient cases, and the characteristics of the confirmed cases were compared between these categories. FINDINGS: In total, 54 healthcare-associated COVID-19 outbreaks including 512 confirmed cases were reported. The median number of affected cases per outbreak was six [interquartile range (IQR) 2-12], and the median outbreak duration was 12 days (IQR 4.3-17.0). Only 5.7% and 0.2% of all confirmed cases were partially and fully vaccinated, respectively. Most outbreaks (90%, 48/54) occurred in May and June 2021. HCW cases, companion cases and patient cases accounted for 19.5%, 41.2% and 39.3% of the total cases. Patient cases were significantly older (median age 72 years, IQR 61-83) and had higher 30-day all-cause mortality (37.4%) than HCW cases (median age 41 years, IQR 28-58, 0%) and companion cases (median age 52 years; IQR 42-62, 1%). CONCLUSION: Healthcare-associated COVID-19 outbreaks have a critical impact on patients. Nevertheless, two-thirds of cases in the healthcare-associated outbreaks in this study comprised HCWs and companions. In order to effectively mitigate COVID-19 transmission in healthcare settings, multi-pronged infection prevention and control measures should be implemented and tailored for these three groups.

PMH4 The Psychological and Behavioral Responses to Covid-19 Epidemic in Pregnant Women in China: A Nationwide Survey

Objectives: To investigate the psychological and behavioral responses of pregnant women to COVID-19 epidemic. Methods: A population-based cross-sectional web-based survey was carried out between Feb 13-16, 2020, where 1908 pregnant women responded. Participants were pregnant women who had registered with the Banmi Online Maternity School, one of the largest national online platforms for maternity college in China. This study used linear and logistic regression to evaluate the influence of demographic factors on psychological and behavioral responses of pregnant women in China to COVID-19 outbreak, and used structural equation modeling (SEM) to evaluate the relative strength of associations between psychological and behavioral responses assessed by PCL-C, EPDS and, stress level as well as preventive behavioral adjustment scales in a sample of 1908 pregnant women in China. Results: Among the 1908 respondents, 1099 met criteria for a positive screening for postpartum depression, and 287 met the criteria for a positive screening for PTSD, where 264 women exceeds the cut-off points for both. We found that women with lower educational level tended to have higher scores of PCL-C, and EPDS scales as well as stress level and behavioral adjustment;and more were regarded as suspected PTSD and probable PPD. Moreover, the SEM analysis showed the highest effect of psychological responses on behavioral responses in the pregnant women was exerted on stress (coefficient =0.376, P<0.001), and Fear of infection (coefficient =-0.747, P<0.001). Conclusions: The psychological states of pregnant women under the COVID-19 epidemic was lower-estimated, and psychoeducation as well as other psychological intervention may be needed to equip both the affected pregnant women and family members with healthy problem-solving and communication skills and provide education and resources about the mental health condition that the pregnant women is experiencing.

[The role of large-scale testing platform in the prevention and control of the COVID-19 pandemic: an empirical study based on a novel numerical model].

Objective: China adopted an unprecedented province-scale quarantine since January 23rd 2020, after the novel coronavirus (COVID-19) broke out in Wuhan in December 2019. Responding to the challenge of limited testing capacity, large-scale (>20 000 tests per day) standardized and fully-automated laboratory (Huo-Yan) was built as an ad-hoc measure. There is so far no empirical data or mathematical model to reveal the impact of the testing capacity improvement since quarantine. Methods: Based on the suspected case data released by the Health Commission of Hubei Province and the daily testing data of Huo-Yan Laboratory, the impact of detection capabilities on the realization of "clearing" and "clearing the day" of supected cases was simulated by establishing a novel non-linear and competitive compartments differential model. Results: Without the establishment of Huo-Yan, the suspected cases would increase by 47% to 33 700, the corresponding cost of quarantine would be doubled, the turning point of the increment of suspected cases and the achievement of "daily settlement" (all newly discovered suspected cases are diagnosed according to the nucleic acid testing result) would be delayed for a whole week and 11 days. If the Huo-Yan Laboratory could ran at its full capacity, the number of suspected cases could start to decrease at least a week earlier, the peak of suspected cases would be reduced by at least 44%, and the quarantine cost could be reduced by more than 72%. Ideally, if a daily testing capacity of 10 500 tests was achieved immediately after the Hubei lockdown, "daily settlement" for all suspected cases could be achieved. Conclusions: Large-scale, standardized clinical testing platform, with nucleic acid testing, high-throughput sequencing, and immunoprotein assessment capabilities, need to be implemented simultaneously in order to maximize the effect of quarantine and minimize the duration and cost of the quarantine. Such infrastructure, for both common times and emergencies, is of great significance for the early prevention and control of infectious diseases.

[Analysis of adenovirus infection in acute respiratory tract infection cases in Shanghai from 2015 to 2019].

Objective: To study the epidemiological characteristics and mixed infection of adenovirus in acute respiratory tract infections in Shanghai from 2015 to 2019, and to provide scientific basis for the prevention and control of adenovirus. Methods: Acute respiratory tract infections were collected from 3 hospitals in Shanghai from 2015 to 2019. Relevant information was registered and respiratory specimens were sampled for detection of respiratory pathogens by multiplex PCR. Results: A total of 1 543 cases of acute respiratory tract infection were included. The positive rate of adenovirus was 2.92%(45/1 543), the positive rates of influenza like illness (ILI) and severe acute respiratory illness (SARI) were 2.74%(29/1 058) and 3.30%(16/485), respectively. The positive rate of ILI during January-May 2019 was 5.43%(7/129), higher than that in the same period of 2015- 2018 (0.52%-4.48%) (Fisher's exact test value=8.92, P=0.036). The incidence of adenovirus-positive cases was mainly distributed in the first and second quarters, accounting for 62.22% (28/45). The difference of the incidence of adenovirus-positive cases in each quarter was significant (χ(2)= 12.52, P=0.006). The positive rate in the second quarter was highest (6.03%), which was higher than that in other quarters (1.89%-2.93%). There were significant differences among different age groups (χ(2)=16.94, P=0.001), and the positive rate decreased with age (χ(2)=10.16, P=0.001). The positive rate of 13-19 years old group (9.43%) was higher than that of other age groups (1.48%-4.81%). The positive rate of student group (12.07%) was higher than that of other occupations (2.61%). The difference was systematic (χ(2)=11.53, P=0.001). Mixed infection accounted for 31.11% (14/45) of 45 adenovirus positive cases. The mixed infection rates of ILI and SARI were 34.48% (10/29) and 25.00% (4/16), respectively. Among 14 cases of mixed infection, the main mixed infection pathogens of adenovirus were influenza A virus and coronavirus. Conclusion: Adenovirus surveillance should be further strengthened in adolescents with a focus on students and other key groups in the second quarter.