Plasma glucose levels and diabetes are independent predictors for mortality in patients with COVID-19.

This study is performed to figure out how the presence of diabetes affects the infection, progression and prognosis of 2019 novel coronavirus disease (COVID-19), and the effective therapy that can treat the diabetes-complicated patients with COVID-19. A multicentre study was performed in four hospitals. COVID-19 patients with diabetes mellitus (DM) or hyperglycaemia were compared with those without these conditions and matched by propensity score matching for their clinical progress and outcome. Totally, 2444 confirmed COVID-19 patients were recruited, from whom 336 had DM. Compared to 1344 non-DM patients with age and sex matched, DM-COVID-19 patients had significantly higher rates of intensive care unit entrance (12.43% vs. 6.58%, P = 0.014), kidney failure (9.20% vs. 4.05%, P = 0.027) and mortality (25.00% vs. 18.15%, P < 0.001). Age and sex-stratified comparison revealed increased susceptibility to COVID-19 only from females with DM. For either non-DM or DM group, hyperglycaemia was associated with adverse outcomes, featured by higher rates of severe pneumonia and mortality, in comparison with non-hyperglycaemia. This was accompanied by significantly altered laboratory indicators including lymphocyte and neutrophil percentage, C-reactive protein and urea nitrogen level, all with correlation coefficients >0.35. Both diabetes and hyperglycaemia were independently associated with adverse prognosis of COVID-19, with hazard ratios of 10.41 and 3.58, respectively.

The Spatial Effect of Shared Mobility on Urban Traffic Congestion: Evidence from Chinese Cities

This paper explores the spatial spillover effect of shared mobility on urban traffic congestion by constructing spatial econometric models. Based on panel data of 94 Chinese cities from 2016 to 2019, this study analyses the spatial correlation of shared mobility enterprise layout and geographical correlation of urban transport infrastructure and examines their influence mechanism. From the perspective of geographic spatial distribution, congestion has positive spatial correlation among Chinese cities, and it has different directions and centripetal forces across regions. The shared mobility enterprises in a region have same direction distribution with traffic congestion, but the centripetal forces of the aggregation effect are different. The econometric results include the fact that bike-sharing has reduced congestion significantly, but the overall impact of car-sharing is not clear. Neither bike-sharing nor car-sharing can offset the traffic congestion caused by economic activities and income growth. From the perspective of spillover effects, congestion has been influenced by bike-sharing, economic development, population, and public passengers in surrounding areas. In terms of spatial heterogeneity, bike-sharing relieves congestion in the Pearl River Delta region while having no significant effect in other regions. Meanwhile, car-sharing has aggravated congestion in the Yangtze River Delta but eased traffic jams in the Pearl River Delta.

Screening of a Small Molecule Compound Library Identifies Toosendanin as an Inhibitor Against Bunyavirus and SARS-CoV-2.

Severe fever with thrombocytopenia syndrome virus (SFTSV) is an emerging tick-borne virus causing serious infectious disease with a high case-fatality of up to 50% in severe cases. Currently, no effective drug has been approved for the treatment of SFTSV infection. Here, we performed a high-throughput screening of a natural extracts library for compounds with activities against SFTSV infection. Three hit compounds, notoginsenoside Ft1, punicalin, and toosendanin were identified for displaying high anti-SFTSV efficacy, in which, toosendanin showed the highest inhibition potency. Mechanistic investigation indicated that toosendanin inhibited SFTSV infection at the step of virus internalization. The anti-viral effect of toosendanin against SFTSV was further verified in mouse infection models, and the treatment with toosendanin significantly reduced viral load and histopathological changes in vivo. The antiviral activity of toosendanin was further expanded to another bunyavirus and the emerging SARS-CoV-2. This study revealed a broad anti-viral effect of toosendanin and indicated its potential to be developed as an anti-viral drug for clinical use.

Neurological manifestations in COVID-19 patients and their application in predicting fatal disease: A retrospective cohort study.

BACKGROUND: To explore the development of central nervous system (CNS) symptoms and clinical application in predicting the clinical outcomes of SARS-COV-2 patients. METHODS: A retrospective cohort study was performed on the hospitalized patients with SARS-COV-2 recruited from four hospitals in Hubei Province, China from 18 January to 10 March 2020. The patients with CNS symptoms were determined. Data regarding clinical symptoms and laboratory tests were collected from medical records. RESULTS: Of 1268 patients studied, 162 (12.8%) had CNS symptoms, manifested as unconsciousness (71, 5.6%), coma (69, 5.4%), dysphoria (50, 3.9%), somnolence (34, 2.7%) and convulsion (3, 0.2%), which were observed at median of 14 (interquartile range 9-18) days after symptom onset and significantly associated with older age (OR = 5.71, 95% confidence interval [CI] 2.78-11.73), male (OR = 1.73, 95% CI 1.22-2.47) and preexisting hypertension (OR = 1.78, 95% CI 1.23-2.57). The presence of CNS symptoms could be predicted by abnormal laboratory tests across various clinical stages, including by lymphocyte counts of <0.93 × 109/L, LDH≥435 U/L and IL-6≥28.83 pg/L at 0-10 days post disease; by lymphocyte count<0.86 × 109/L, IL-2R ≥ 949 U/L, LDH≥382 U/L and WBC≥8.06 × 109/L at 11-20 days post disease. More patients with CNS symptoms developed fatal outcome compared with patients without CNS symptoms (HR = 33.96, 95% CI 20.87-55.16). CONCLUSION: Neurological symptoms of COVID-19 were related to increased odds of developing poor prognosis and even fatal infection.

A systematic meta-analysis of immune signatures in patients with COVID-19.

Currently severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission has been on the rise worldwide. Predicting outcome in COVID-19 remains challenging, and the search for more robust predictors continues. We made a systematic meta-analysis on the current literature from 1 January 2020 to 15 August 2020 that independently evaluated 32 circulatory immunological signatures that were compared between patients with different disease severity was made. Their roles as predictors of disease severity were determined as well. A total of 149 distinct studies that evaluated ten cytokines, four antibodies, four T cells, B cells, NK cells, neutrophils, monocytes, eosinophils and basophils were included. Compared with the non-severe patients of COVID-19, serum levels of Interleukins (IL)-2, IL-2R, IL-4, IL-6, IL-8, IL-10 and tumor necrosis factor α were significantly up-regulated in severe patients, with the largest inter-group differences observed for IL-6 and IL-10. In contrast, IL-5, IL-1ß and Interferon (IFN)-γ did not show significant inter-group difference. Four mediators of T cells count, including CD3+ T, CD4+ T, CD8+ T, CD4+ CD25+ CD127- Treg, together with CD19+ B cells count and CD16+ CD56+ NK cells were all consistently and significantly depressed in severe group than in non-severe group. SARS-CoV-2 specific IgA and IgG antibodies were significantly higher in severe group than in non-severe group, while IgM antibody in the severe patients was slightly lower than those in the non-severe patients, and IgE antibody showed no significant inter-group differences. The combination of cytokines, especially IL-6 and IL-10, and T cell related immune signatures can be used as robust biomarkers to predict disease severity following SARS-CoV-2 infection.

Epidemiological parameters of COVID-19 and its implication for infectivity among patients in China, 1 January to 11 February 2020.

BackgroundThe natural history of disease in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remained obscure during the early pandemic.AimOur objective was to estimate epidemiological parameters of coronavirus disease (COVID-19) and assess the relative infectivity of the incubation period.MethodsWe estimated the distributions of four epidemiological parameters of SARS-CoV-2 transmission using a large database of COVID-19 cases and potential transmission pairs of cases, and assessed their heterogeneity by demographics, epidemic phase and geographical region. We further calculated the time of peak infectivity and quantified the proportion of secondary infections during the incubation period.ResultsThe median incubation period was 7.2 (95% confidence interval (CI): 6.9‒7.5) days. The median serial and generation intervals were similar, 4.7 (95% CI: 4.2‒5.3) and 4.6 (95% CI: 4.2‒5.1) days, respectively. Paediatric cases < 18 years had a longer incubation period than adult age groups (p = 0.007). The median incubation period increased from 4.4 days before 25 January to 11.5 days after 31 January (p < 0.001), whereas the median serial (generation) interval contracted from 5.9 (4.8) days before 25 January to 3.4 (3.7) days after. The median time from symptom onset to discharge was also shortened from 18.3 before 22 January to 14.1 days after. Peak infectivity occurred 1 day before symptom onset on average, and the incubation period accounted for 70% of transmission.ConclusionThe high infectivity during the incubation period led to short generation and serial intervals, necessitating aggressive control measures such as early case finding and quarantine of close contacts.