Causal associations between type 1 diabetes and COVID-19 infection and prognosis: a two-sample Mendelian randomization study.

INTRODUCTION: It has been suggested that type 1 diabetes was associated with increased COVID-19 morbidity and mortality. However, their causal relationship is still unclear. Herein, we performed a two-sample Mendelian randomization (MR) to investigate the causal effect of type 1 diabetes on COVID-19 infection and prognosis. RESEARCH DESIGN AND METHODS: The summary statistics of type 1 diabetes were obtained from two published genome-wide association studies of European population, one as a discovery sample including 15 573 cases and 158 408 controls, and the other data as a replication sample consisting of 5913 cases and 8828 controls. We first performed a two-sample MR analysis to evaluate the causal effect of type 1 diabetes on COVID-19 infection and prognosis. Then, reverse MR analysis was conducted to determine whether reverse causality exists. RESULTS: MR analysis results showed that the genetically predicted type 1 diabetes was associated with higher risk of severe COVID-19 (OR=1.073, 95% CI: 1.034 to 1.114, pFDR=1.15×10-3) and COVID-19 death (OR=1.075, 95% CI: 1.033 to 1.119, pFDR=1.15×10-3). Analysis of replication dataset showed similar results, namely a positive association between type 1 diabetes and severe COVID-19 (OR=1.055, 95% CI: 1.029 to 1.081, pFDR=1.59×10-4), and a positively correlated association with COVID-19 death (OR=1.053, 95% CI: 1.026 to 1.081, pFDR=3.50×10-4). No causal association was observed between type 1 diabetes and COVID-19 positive, hospitalized COVID-19, the time to the end of COVID-19 symptoms in the colchicine treatment group and placebo treatment group. Reverse MR analysis showed no reverse causality. CONCLUSIONS: Type 1 diabetes had a causal effect on severe COVID-19 and death after COVID-19 infection. Further mechanistic studies are needed to explore the relationship between type 1 diabetes and COVID-19 infection and prognosis.

Active Ingredients of Reduning Injections Maintain High Potency against SARS-CoV-2 Variants.

OBJECTIVE: To investigate the anti-coronavirus potential and the corresponding mechanisms of the two ingredients of Reduning Injection: quercetin and luteolin. METHODS: A pseudovirus system was designed to test the efficacy of quercetin and luteolin to inhibit SARS-CoV-2 infection and the corresponding cellular toxicity. Luteolin was tested for its activities against the pseudoviruses of SARS-CoV-2 and its variants. Virtual screening was performed to predict the binding sites by Autodock Vina 1.1.230 and PyMol. To validate docking results, surface plasmon resonance (SPR) was used to measure the binding affinity of the compounds with various proteins of the coronaviruses. Quercetin and luteolin were further tested for their inhibitory effects on other coronaviruses by indirect immunofluorescence assay on rhabdomyosarcoma cells infected with HCoV-OC43. RESULTS: The inhibition of SARS-CoV-2 pseudovirus by luteolin and quercetin were strongly dose-dependent, with concentration for 50% of maximal effect (EC50) of 8.817 and 52.98 µmol/L, respectively. Their cytotoxicity to BHK21-hACE2 were 177.6 and 405.1 µmol/L, respectively. In addition, luetolin significantly blocked the entry of 4 pseudoviruses of SARS-CoV-2 variants, with EC50 lower than 7 µmol/L. Virtual screening and SPR confirmed that luteolin binds to the S-proteins and quercetin binds to the active center of the 3CLpro, PLpro, and helicase proteins. Quercetin and luteolin showed over 99% inhibition against HCoV-OC43. CONCLUSIONS: The mechanisms were revealed of quercetin and luteolin inhibiting the infection of SARS-CoV-2 and its variants. Reduning Injection is a promising drug for COVID-19.

Genetic characterization of SARS-CoV-2 from cross-border trucks and containers in Kashgar Prefecture, Xinjiang Uygur Autonomous Region, China

Kashgar is a prefecture in Xinjiang Uygur Autonomous Region. China. Kashgar Prefecture (KP) is a land-cargo port connecting China with central Asian countries and Europe. Frequent transportation of cargo has increased the risk of severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) introduction into China, which has increased the pressure on coronavirus disease-2019 (COVID-19) prevention and control. In November 2020, an imported virus-induced COVID-19 outbreak occurred in KP. To investigate the genetic characterization of SARS-CoV-2 that contaminated the trucks and containers, and the potential of border rapid logistics system to serve as carriers for SARS-CoV-2 transmission, thirty-five SARS-CoV-2-positive nucleic-acid samples collected from KP cross-border trucks and containers from 6-10 November 2020 were subjected into SARS-CoV-2 genomic sequencing and comparative analyses. The results showed that the median (minimum to maximum) Ct value of ORF1ab was 37.64 (28.91-39.81) . and that of the N gene was 36.50 (26.35-39.30), and the median (minimum to maximum) of the reads mapping ratio to SARS-CoV-2 was 51.95% (0.86%-99.31%), which indicated low viral loads in these environmental samples. Eighteen of 35 samples had genomic coverage >70%. According to the Pango nomenclature, 18 SARS-CoV-2 sequences belonged to six lineages (B.1, B.I.1, B.1.9. B.1.1.220, B.1.153 and B.1.465), three of which (B.I. B.1.1 and 8.1.153) were found in case samples from the same period of four China-neighboring countries. Analyses of nucleotide mutations and phylogenetic trees showed that the genome sequences of SARS-CoV-2 collected from the same location were similar. Four of 18 sequences were in a sub-lineage with the representative strain of COVID-19 outbreak in KP, one of which had 1 or 2 differences in nucleotide mutation sites with the strain that caused the COVID-19 outbreak in KP, which indicated high homology in the viral genome. We showed that cross-border trucks and containers were contaminated by various genotypes of SARS-CoV-2 from other countries during the outbreak in KP. and in which contained the parental virus of the KP cases. These trucks and containers served as carriers for SARS-CoV-2 introduction from other countries to cause local transmission. Our results provide important references for COVID-19 prevention-and-control strategies in border ports and tracing of outbreak sources in China.

Bi-level artificial intelligence model for risk classification of acute respiratory diseases based on Chinese clinical data

Objective: The high incidence of respiratory diseases has dramatically increased the medical burden under the COVID-19 pandemic in the year 2020. It is of considerable significance to utilize a new generation of information technology to improve the artificial intelligence level of respiratory disease diagnosis. Methods: Based on the semi-structured data of Chinese Electronic Medical Records (CEMRs) from the China Hospital Pharmacovigilance System, this paper proposed a bi-level artificial intelligence model for the risk classification of acute respiratory diseases. It includes two levels. The first level is a dedicated design of the “BiLSTM+Dilated Convolution+3D Attention+CRF” deep learning model that is used for Chinese Clinical Named Entity Recognition (CCNER) to extract valuable information from the unstructured data in the CEMRs. Incorporating the transfer learning and semi-supervised learning technique into the proposed deep learning model achieves higher accuracy and efficiency in the CCNER task than the popular “Bert+BiLSTM+CRF” approach. Combining the extracted entity data with other structured data in the CEMRs, the second level is a customized XGBoost to realize the risk classification of acute respiratory diseases. Results: The empirical study shows that the proposed model could provide practical technical support for improving diagnostic accuracy. Conclusion: Our study provides a proof-of-concept for implementing a hybrid artificial intelligence-based system as a tool to aid clinicians in tackling CEMR data and enhancing the diagnostic evaluation under diagnostic uncertainty.

The Impact of Electroacupuncture Early Intervention on the Brain Lipidome in a Mouse Model of Post-traumatic Stress Disorder.

The neuroprotective effect of electroacupuncture (EA) treatment has been well studied; growing evidence suggests that changes in lipid composition may be involved in the pathogenesis of post-traumatic stress disorder (PTSD) and may be a target for treatment. However, the influence of early EA intervention on brain lipid composition in patients with PTSD has never been investigated. Using a modified single prolonged stress (mSPS) model in mice, we assessed the anti-PTSD-like effects of early intervention using EA and evaluated changes in lipid composition in the hippocampus and prefrontal cortex (PFC) using a mass spectrometry-based lipidomic approach. mSPS induced changes in lipid composition in the hippocampus, notably in the content of sphingolipids, glycerolipids, and fatty acyls. These lipid changes were more robust than those observed in the PFC. Early intervention with EA after mSPS ameliorated PTSD-like behaviors and partly normalized mSPS-induced lipid changes, notably in the hippocampus. Cumulatively, our data suggest that EA may reverse mSPS-induced PTSD-like behaviors due to region-specific regulation of the brain lipidome, providing new insights into the therapeutic mechanism of EA.

Contrastive Attention Network with Dense Field Estimation for Face Completion

Most modern face completion approaches adopt an autoencoder or its variants to restore missing regions in face images. Encoders are often utilized to learn powerful representations that play an important role in meeting the challenges of sophisticated learning tasks. Specifically, various kinds of masks are often presented in face images in the wild, forming complex patterns, especially in this hard period of COVID-19. It’s difficult for encoders to capture such powerful representations under this complex situation. To address this challenge, we propose a self-supervised Siamese inference network to improve the generalization and robustness of encoders. It can encode contextual semantics from full-resolution images and obtain more discriminative representations. To deal with geometric variations of face images, a dense correspondence field is integrated into the network. We further propose a multi-scale decoder with a novel dual attention fusion module (DAF), which can combine the restored and known regions in an adaptive manner. This multi-scale architecture is beneficial for the decoder to utilize discriminative representations learned from encoders into images. Extensive experiments clearly demonstrate that the proposed approach not only achieves more appealing results compared with state-of-the-art methods but also improves the performance of masked face recognition dramatically.

Cancer Communication and User Engagement on Chinese Social Media: Content Analysis and Topic Modeling Study.

BACKGROUND: Cancer ranks among the most serious public health challenges worldwide. In China-the world's most populous country-about one-quarter of the population consists of people with cancer. Social media has become an important platform that the Chinese public uses to express opinions. OBJECTIVE: We investigated cancer-related discussions on the Chinese social media platform Weibo (Sina Corporation) to identify cancer topics that generate the highest levels of user engagement. METHODS: We conducted topic modeling and regression analyses to analyze and visualize cancer-related messages on Weibo and to examine the relationships between different cancer topics and user engagement (ie, the number of retweets, comments, and likes). RESULTS: Our results revealed that cancer communication on Weibo has generally focused on the following six topics: social support, cancer treatment, cancer prevention, women's cancers, smoking and skin cancer, and other topics. Discussions about social support and cancer treatment attracted the highest number of users and received the greatest numbers of retweets, comments, and likes. CONCLUSIONS: Our investigation of cancer-related communication on Weibo provides valuable insights into public concerns about cancer and can help guide the development of health campaigns in social media.

Occupant-centric robotic air filtration and planning for classrooms for Safer school reopening amid respiratory pandemics.

Coexisting with the current COVID-19 pandemic is a global reality that comes with unique challenges impacting daily interactions, business, and facility maintenance. A monumental challenge accompanied is continuous and effective disinfection of shared spaces, such as office/school buildings, elevators, classrooms, and cafeterias. Although ultraviolet light and chemical sprays are routines for indoor disinfection, they irritate humans, hence can only be used when the facility is unoccupied. Stationary air filtration systems, while being irritation-free and commonly available, fail to protect all occupants due to limitations in air circulation and diffusion. Hence, we present a novel collaborative robot (cobot) disinfection system equipped with a Bernoulli Air Filtration Module, with a design that minimizes disturbance to the surrounding airflow and maneuverability among occupants for maximum coverage. The influence of robotic air filtration on dosage at neighbors of a coughing source is analyzed with derivations from a Computational Fluid Dynamics (CFD) simulation. Based on the analysis, the novel occupant-centric online rerouting algorithm decides the path of the robot. The rerouting ensures effective air filtration that minimizes the risk of occupants under their detected layout. The proposed system was tested on a 2 × 3 seating grid (empty seats allowed) in a classroom, and the worst-case dosage for all occupants was chosen as the metric. The system reduced the worst-case dosage among all occupants by 26% and 19% compared to a stationary air filtration system with the same flow rate, and a robotic air filtration system that traverses all the seats but without occupant-centric planning of its path, respectively. Hence, we validated the effectiveness of the proposed robotic air filtration system.

Cope with the COVID-19 pandemic: Dynamic bed allocation and patient subsidization in a public healthcare system.

In many countries and territories, public hospitals play a major role in coping with the COVID-19 pandemic. For public hospital managers, on the one hand, they must best utilize their hospital beds to serve the COVID-19 patients immediately. On the other hand, they need to consider the need of bed resources from non-COVID-19 patients, including emergency and elective patients. In this work, we consider two control mechanisms for public hospital managers to maximize the overall utility of patients. One is the dynamic allocation of bed resources according to the evolution process of the COVID-19 pandemic. The other is the usage of a subsidy scheme to move elective patients from the public to private hospitals. We develop a dynamic programming model to study the allocation of isolation and ordinary beds and the effect of the subsidy policy in serving three types of patients, COVID-19, emergency, and elective-care. We first show that the dynamic allocation between isolation and ordinary beds can provide a better utilization of bed resources, by cutting down at least 33.5% of the total cost compared with the static policy (i.e., keeping a fixed number of isolation beds) when facing a medium pandemic alert. Our results further show that subsidizing elective patients and referring them to private hospitals is an efficient way to ease the overcrowded situation in public hospitals. Our results demonstrate that, by dynamically conducting bed allocation and subsidy scheme in different phases of the COVID-19 pandemic, patient overall utility can be greatly improved.

Study on Collaborative Emission Reduction in Green-House and Pollutant Gas Due to COVID-19 Lockdown in China

In recent years, as China’s peaking carbon dioxide emissions and air pollution control projects have converged, scholars have begun to focus on the synergistic mechanisms of greenhouse gas and pollution gas reduction. In 2020, the unprecedented coronavirus pandemic, which led to severe nationwide blockade measures, unexpectedly provided a valuable opportunity to study the synergistic reduction in greenhouse gases and polluting gases. This paper uses a combination of NO2, O3, and CO2 column concentration products from different satellites and surface concentrations from ground-based stations to investigate potential correlations between these monitoring indicators in four Chinese representative cities. We found that XCO2 decreased in March to varying degrees in different cities. It was witnessed that the largest decrease in CO2, −1.12 ppm, occurred in Wuhan, i.e., the first epicenter of COVID-19. We also analyzed the effects of NO2 and O3 concentrations on changes in XCO2. First, in 2020, we used a top-down approach to obtain the conclusion that the change amplitude of NO2 concentration in Beijing, Shanghai, Guangzhou, and Wuhan were −24%, −18%, −4%, and −39%, respectively. Furthermore, the O3 concentration increments were 5%, 14%, 12%, and 14%. Second, we used a bottom-up approach to obtain the conclusion that the monthly averaged NO2 concentrations in Beijing, Shanghai, and Wuhan in March had the largest changes, changing to −39%, −40%, and −61%, respectively. The corresponding amounts of changes in monthly averaged O3 concentrations were −14%, −2%, and 9%. However, the largest amount of change in monthly averaged NO2 concentration in Guangzhou was found in December 2020, with a value of −40%. The change in O3 concentration was −12% in December. Finally, we analyzed the relationship of NO2 and O3 concentrations with XCO2. Moreover, the results show that the effect of NO2 concentration on XCO2 is positively correlated from the point of the satellite (R = 0.4912) and the point of the ground monitoring stations (R = 0.3928). Surprisingly, we found a positive (in satellite observations and R = 0.2391) and negative correlation (in ground monitoring stations and R = 0.3333) between XCO2 and the O3 concentrations. During the epidemic period, some scholars based on model analysis found that Wuhan’s carbon emissions decreased by 16.2% on average. Combined with satellite data, we estimate that Wuhan’s XCO2 fell by about 1.12 ppm in February. At last, the government should consider reducing XCO2 and NO2 concentration at the same time to make a synergistic reduction.

Captopril alleviates lung inflammation in SARS-CoV-2-infected hypertensive mice.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiologic agent responsible for the global coronavirus disease 2019 (COVID-19) pandemic. Numerous studies have demonstrated that cardiovascular disease may affect COVID-19 progression. In the present study, we investigated the effect of hypertension on viral replication and COVID-19 progression using a hypertensive mouse model infected with SARS-CoV-2. Results revealed that SARS-CoV-2 replication was delayed in hypertensive mouse lungs. In contrast, SARS-CoV-2 replication in hypertensive mice treated with the antihypertensive drug captopril demonstrated similar virus replication as SARS-CoV-2-infected normotensive mice. Furthermore, antihypertensive treatment alleviated lung inflammation induced by SARS-CoV-2 replication (interleukin (IL)-1ß up-regulation and increased immune cell infiltration). No differences in lung inflammation were observed between the SARS-CoV-2-infected normotensive mice and hypertensive mice. Our findings suggest that captopril treatment may alleviate COVID-19 progression but not affect viral replication.

Efficacy of Xuebijing injection for the treatment of coronavirus disease 2019 via network pharmacology

Background: To evaluate the mechanism of Chinese patent drug Xuebijing (XBJ) injection in the treatment of a new coronavirus disease 2019 (COVID-19) based on network pharmacology and molecular docking technology.

Prevalence of depression, anxiety, and insomnia symptoms among patients with COVID-19: A meta-analysis of quality effects model.

BACKGROUND: Evidence from previous virus epidemics has shown that infected patients are at risk for developing psychiatric and mental health disorders, such as depression, anxiety, and insomnia. Hence, to collect high-quality data on the impact of COVID-19 pandemic on the prevalence of depression, anxiety, and insomnia symptoms among patients infected with SARS-CoV-2 should be the immediate priority. METHODS: A comprehensive search of Medline, Embase, Web of Science, and PsycINFO databases was conducted from January 1, 2020 to December 26, 2020 for eligible studies reporting on the prevalence of depression, anxiety, and insomnia symptoms in patients with COVID-19. Studies meeting the following criteria were included in the analysis: (1) included patients with COVD-19; (2) recorded the prevalence of depression, anxiety, or insomnia symptom; (3) sample size ≥30; (4) with validated screening tools; and (5) passed through the international peer-review process. Data extraction and quality assessment was independently performed by two reviewers. The quality effects meta-analysis was conducted further to calculate the pooled prevalence. RESULTS: Twenty-two studies were included for analysis with a total of 4318 patients. The pooled prevalence of depression, anxiety and insomnia symptoms was 38% (95% CI = 25-51), 38% (95% CI = 24-52), and 48% (95% CI = 11-85), respectively. Neither subgroup analysis nor sensitivity analysis can explain the source of high heterogeneity. In addition, the prevalence estimates of depression, anxiety and insomnia symptoms varied based on different screening tools. CONCLUSIONS: The present systematic review and meta-analysis suggest that depression, anxiety, and insomnia symptoms are prevalent in a considerable proportion of patients with COVID-19. Thus, early detection and properly intervention for mental illness in this population are of great significance. Additionally, the quality of included studies to date has been variable, and ongoing surveillance is essential.

Analysis of the positive rate of 4254 cases of COVID-19 nucleic acid tests in different aites in Wuhan, China.

There's an outbreak of coronavirus diesase 2019 (COVID-19) since December 2019, first in Wuhan. It has caused huge medical challenges to Hubei Province with currently more than 67 thousand confirmed cases till 8th March 2020. Identification, there is no clinically effective drug. Isolation and masks are essential to limit human-to-human transmission initially. The nucleic acid test (NAT) of COVID-19 currently was the most reliable established laboratory diagnosis method in clinical. From 8th February to 7th March 2020, 4254 cases were collected for analysis at six nucleic acid collection sites in the community medical team of Hubei Provincial Hospital of Traditional Chinese Medicine, which cover almost all groups who need NAT in Wuhan. Distribution of positive rates in different sites by genders, ages, or occupations were compared. The positive rates of different sites from high to low were: hospital wards (24.71%) > fever clinics (16.57%) > nursing homes (5.51%) > isolation hotels (5.30%) > rehabilitation stations (1.36%) >close contact sites (0.17%). The confirmed patients in isolation hotels, hospital ward, and fever clinical were mainly middle-aged and elderly, and most of them were women. The positive rate in isolation hotels and fever clinics gradually decreased over time. There were no significant differences between genders among those six nucleic acid collection sites (P < .05). The hospital wards have the highest positive rate; however, close contact sites have lowest one. Patients who are discharged from hospitals may still have potential risks. Middle-aged and older people remain the focus of epidemic prevention and control.

Characteristics and potential sources of wintertime air pollution in Linfen, China.

Linfen in China's Shanxi Province suffers severe air pollution in winter. Understanding the characteristics of air pollution and providing scientific support to mitigate such pollution are urgent matters. This study investigated the variations of PM2.5, PM10, NO2, SO2, O3, and CO in Linfen between December 1, 2019 and February 29, 2020. The mean concentrations of PM2.5, PM10, NO2, SO2, MDA8 (the maximum daily 8-h average) O3, and CO were 106.2, 139.4, 47.2, 41.0, 57.0 µg m-3, and 1.8 mg m-3, respectively. Large amounts of pollutants emitted by coal burning, industry, vehicles, and residents contributed to air pollution. Unfavorable meteorological conditions, such as lower temperature, weaker wind, higher relative humidity, and reduced planetary boundary layer height, made the situation worse. Fireworks and firecrackers set off to celebrate traditional Chinese festivals caused the concentration of PM pollutants to spike, with the maximum daily mean concentration of PM2.5 reached 314 µg m-3 and the peak hourly value reached 378.0 µg m-3. Suspensions of commercial and social activities due to COVID-19 reduced anthropogenic emissions, mainly from industry and transportation, which decreased the level of air pollutants other than O3. Analyses involving backward trajectory cluster, the potential source contribution function, and concentration weighted trajectory demonstrated that PM2.5 pollution mainly came from local emissions in Shanxi Province and regional transport from Inner Mongolia, Shaanxi, Hebei, Henan, and Gansu provinces. Shanxi and its surrounding provinces should adopt measures such as tightening environmental management standards, promoting the use of renewable energy, and adjusting the transportation structure to reduce regional emissions. This study will help policy-makers draft plans and policies to reduce air pollution in Linfen.

A hybrid multi-objective optimizer-based model for daily electricity demand prediction considering COVID-19.

Electricity consumption has been affected due to worldwide lockdown policies against COVID-19. Many countries have pointed out that electricity supply security during the epidemic is critical to ensuring people's livelihood. Accurate prediction of electricity demand would act a more important role in ensuring energy security for all the countries. Although there have been many studies on electricity forecasting, they did not consider the pandemic, and many works only considered the prediction accuracy and ignored the stability. Driven by the above reasons, it is necessary to develop an electricity consumption prediction model that can be well applied in the pandemic. In this work, a hybrid prediction system is proposed with data processing, modelling, and optimization. An improved complete ensemble empirical mode decomposition with adaptive noise is used for data preprocessing, which overcomes the shortcomings of the original method; a multi-objective optimizer is adopted for ensuring the accuracy and stability; support vector machine is used as the prediction model. Taking daily electricity demand of US as an example, the results prove that the proposed hybrid models are superior to benchmark models in both prediction accuracy and stability. Moreover, selection of input parameters is discussed, and the results indicate that the model considering the daily infections has the highest prediction accuracy and stability, and it is proved that the proposed model has great potential in real-world applications.