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Background: The coronavirus disease 2019 (COVID-19) pandemic has exerted a profound influence on humans. Increasing evidence shows that immune response is crucial in influencing the risk of infection and disease severity. Observational studies suggest an association between COVID-19 and immunoglobulin G (IgG) N-glycosylation traits, but the causal relevance of these traits in COVID-19 susceptibility and severity remains controversial., Methods: We conducted a two-sample Mendelian randomization (MR) analysis to explore the causal association between 77 IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity using summary-level data from genome-wide association studies (GWAS) and applying multiple methods including inverse-variance weighting (IVW), MR Egger, and weighted median. We also used Cochran's Q statistic and leave-one-out analysis to detect heterogeneity across each single nucleotide polymorphism (SNP). Additionally, we used the MR-Egger intercept test, MR-PRESSO global test, and PhenoScanner tool to detect and remove SNPs with horizontal pleiotropy and to ensure the reliability of our results., Results: We found significant causal associations between genetically predicted IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity. Specifically, we observed reduced risk of COVID-19 with the genetically predicted increased IgG N-glycan trait IGP45 (OR = 0.95, 95% CI = 0.92-0.98; FDR = 0.019). IGP22 and IGP30 were associated with a higher risk of COVID-19 hospitalization and severity. Two (IGP2 and IGP77) and five (IGP10, IGP14, IGP34, IGP36, and IGP50) IgG N-glycosylation traits were causally associated with a decreased risk of COVID-19 hospitalization and severity, respectively. Sensitivity analyses did not identify any horizontal pleiotropy., Conclusions: Our study provides evidence that genetically elevated IgG N-glycosylation traits may have a causal effect on diverse COVID-19 outcomes. Our findings have potential implications for developing targeted interventions to improve COVID-19 outcomes by modulating IgG N-glycosylation levels., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Long, Xiao, Cui, Wang, Jiang, Tang, Zhang, Liu, Xiang, Zhang, Zhao, Yang, Yan, Wu, Wang, Zhou, Lu, Chen, Li, Jiang, Fan and Zhang.)

The aim of this paper is to examine the return connectedness and multiscale spillovers between the Clean Energy Index and the grain commodity market around COVID-19. Using daily data from January 4, 2017 to July 1, 2022, a time-varying parametric vector autoregressive (TVP-VAR) connectedness approach is first used to reveal connectedness patterns before and during COVID-19. We further used Baruník and Křehlík (2018)'s frequency domain spillover method to assess connectedness in different domain horizons. Our results show spillover effects over time and frequency, with COVID-19 significantly affecting the connectedness of the whole system. Dynamic connectedness peaks significantly after sudden bursts of COVID-19, validating the reported uncertainty. We also documented higher spillover levels in the short term than in the medium and long term. In addition, We find that (i) most clean energy indices are positively affected by the COVID-19 outbreak; (ii) the NASDAQ OMX Bio/Clean Fuels Index and NASDAQ OMX Geothermal Index send spillovers to all grain commodities throughout the sample period, while the WilderHill Clean Energy Index and NASDAQ OMX Wind Energy Index indices are the largest recipients of spillovers from other markets regardless of time horizon; (iii) the OMX Bio/Clean Fuels and OMX Geothermal Energy indices dominate spillover shocks to grain commodity markets. This is the first study to analyse the connectedness and time-frequency dynamics of returns in the green energy index and the grain commodity market. These results provide valuable insights to investors and key policymakers, especially at a time of more significant uncertainty., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

mRNA vaccines have emerged rapidly in recent years as a prophylactic and therapeutic agent against various diseases including cancer and infectious diseases. Improvements of mRNA vaccines have been underway, among which boosting of efficacy is of great importance. Pam2Cys, a simple synthetic metabolizable lipoamino acid that signals through Toll-like receptor (TLR) 2/6 pathway, eliciting both humoral and cellular adaptive immune responses, is an interesting candidate adjuvant. To investigate the enhancement of the efficacies of mRNA vaccines by Pam2Cys, the adjuvant was incorporated into mRNA-lipid nanoparticles (LNPs) to achieve co-delivery with mRNA. Immunization with the resulting mRNA-LNPs (Pam2Cys) shaped up the immune milieu in the draining lymph nodes (dLNs) through the induction of IL-12 and IL-17, among other cytokines. Antigen presentation was carried out mainly by migratory and dLN-resident conventional type 2 DCs (cDC2s) and significantly more potent antitumor responses were triggered in both prophylactic and therapeutic tumor models in a CD4 + and CD8 + T cell-dependent fashion. Accompanying memory antitumor immunity was also established. Moreover, the vaccine also stimulated much more robust humoral and cellular immunity in a surrogate COVID-19 prophylactic model. Last but not the least, the new vaccines exhibited good preliminary safety profiles in murine models. These facts warrant future development of Pam2Cys-incorporated mRNA vaccines or relevant mRNA therapeutics for clinical application., (© 2023. The Author(s).)

Background : Coronavirus disease 2019 (COVID-19) is a severe infectious illness. It has been reported that COVID-19 has an effect on thyroid function. However, the association between thyroid function and prognosis of COVID-19 is still unclear. Methods : This retrospective study included patients with COVID-19 admitted to Tongji Hospital in Wuhan from January 28 to April 4, 2020. Demographic, epidemiological, clinical, laboratory, treatment, and outcome data were collected from patients with laboratory-confirmed COVID-19. Patients without history of thyroid disease who had a thyroid function test at admission were enrolled in the final analysis. Risk factors of in-hospital death were explored using univariable and multivariable Cox regression analyses. Survival differences were assessed with Kaplan-Meier curves and log-rank test. Results : A total of 127 patients were included in this study, with 116 survivors and 11 non-survivors. The serum levels of thyroid stimulating hormone (TSH) [0.8 (0.5-1.7) vs . 1.9 (1.0-3.1) μIU/mL, P = .031] and free triiodothyronine (FT 3 ) [2.9 (2.8-3.1) vs . 4.2 (3.5-4.7) pmol/L, P < .001] were lower in non-survivors than in survivors, and a low FT 3 state (defined as FT 3  < 3.1 pmol/L) at admission accounted for a higher proportion in non-survivors than in survivors (72.7% vs . 11.2%, P < .001). Univariate Cox regression analysis showed that FT 3 level (HR 0.213, 95% CI: 0.101-0.451, P < .001) and the low FT 3 state (HR 14.607, 95% CI: 3.873-55.081, P < .001) were negatively and positively associated with the risk of in-hospital death, respectively. Furthermore, multivariate Cox regression analysis revealed that a low FT 3 state was associated with an increased risk of in-hospital death after adjusting for confounding factors (HR 13.288, 95% CI: 1.089-162.110, P = .043). Moreover, Kaplan-Meier curves indicated a lower survival probability in COVID-19 patients with a low FT 3 status. Conclusion : Serum FT 3 level is lower in non-survivors among moderate-to-critical patients with COVID-19, and the low FT 3 state is associated with an increased risk of in-hospital mortality of COVID-19.

Background: Diabetes is associated with poor coronavirus disease 2019 (COVID-19) outcomes. However, little is known on the impact of undiagnosed diabetes in the COVID-19 population. We investigated whether diabetes, particularly undiagnosed diabetes, was associated with an increased risk of death from COVID-19., Methods: This retrospective study identified adult patients with COVID-19 admitted to Tongji Hospital (Wuhan) from January 28 to April 4, 2020. Diabetes was determined using patients' past history (diagnosed) or was newly defined if the hemoglobin A1c (HbA1c) level at admission was ≥6.5% (48 mmol/mol) (undiagnosed). The in-hospital mortality rate and survival probability were compared between the non-diabetes and diabetes (overall, diagnosed, and undiagnosed diabetes) groups. Risk factors of mortality were explored using Cox regression analysis., Results: Of 373 patients, 233 were included in the final analysis, among whom 80 (34.3%) had diabetes: 44 (55.0%) reported a diabetes history, and 36 (45.0%) were newly defined as having undiagnosed diabetes by HbA1c testing at admission. Compared with the non-diabetes group, the overall diabetes group had a significantly increased mortality rate (22.5% vs. 5.9%, p <  0.001). Moreover, the overall, diagnosed, and undiagnosed diabetes groups displayed lower survival probability in the Kaplan-Meier survival analysis (all p <  0.01). Using multivariate Cox regression, diabetes, age, quick sequential organ failure assessment score, and D-dimer ≥1.0 μg/mL were identified as independent risk factors for in-hospital death in patients with COVID-19., Conclusions: The prevalence of undiagnosed pre-existing diabetes among patients with COVID-19 is high in China. Diabetes, even newly defined by HbA1c testing at admission, is associated with increased mortality in patients with COVID-19. Screening for undiagnosed diabetes by HbA1c measurement should be considered in adult Chinese inpatients with COVID-19.

In this paper, the dynamic relationship between the volatilities of both the Indian equity market and six major commodity markets is analyzed during the COVID-19 pandemic. We employ the time-varying parameters vector autoregression model and wavelet coherence approach to assessing market connectedness. The results reveal a significant increase in volatility correlation and spillovers between the Indian equity market and the six major commodity markets after the outbreak of COVID-19. Furthermore, it was found that after the COVID-19 outbreak, the volatility from the commodity markets rapidly spilled over to the Indian equity market. Similar evidence is also suggested by the wavelet coherence and confirms that the high level of contagion mainly occurs in the medium and long term. We further incorporate our evidence with hedging ratio and minimum portfolio connectedness weight. The results uncover that copper and crude oil are useful assets for Indian equity investors to conduct hedging and construct the portfolio. [ABSTRACT FROM AUTHOR]

Objective To analyze the spatial distribution characteristics of coronavirus disease 2019 (COVID-19) epidemic of Yunnan province, and we provide targeted prevention and control advice. Methods From 23 January to 22 February 2020, geographic information system was established based on the data of COVID-19 epidemic and demographic information from online reporting system, local indication of spatial auto correlation (LISA) were conducted by GeoDa1.14.0 and Mapinfo 15.0 software. Results Except for Diqing and Nujiang in 16 prefectures (cities) of the province, cases were reported in 14 other prefectures (cities). Among them, the confirmed cases were mainly reported in economically developed regions and regions with more developed tourism industry. The epidemic situation of COVID-19 in Yunnan Province first increased sharply and then decreased slowly. There was no statistical difference in global autocorrelation in incidence, but there were some areas aggregated, local clusters appeared only in the early stages of the epidemic. There were 3 L-L clusters (Dehong, Banna and Zhaotong), 1 H-L cluster(Kunming), 4 L-H clusters(Yuxi, Qujing, Chuxiong and Honghe) of early outbreak. Spatial aggregation gradually disappears over time. Conclusion The previous epidemic situation can be prevented and controlled, and there is no space aggregation, but before the epidemic situation is completely extinguished, we still need to improve our alertness, with scientific and correct methods to prevention and control, and avoid the new outbreak and epidemic. [ABSTRACT FROM AUTHOR]

To explore the possibility of predicting the clinical types of Corona-Virus-Disease-2019 (COVID-19) pneumonia by analyzing the non-focus area of the lung in the first chest CT image of patients with COVID-19 by using automatic machine learning (Auto-ML). 136 moderate and 83 severe patients were selected from the patients with COVID-19 pneumonia. The clinical and laboratory data were collected for statistical analysis. The texture features of the Non-focus area of the first chest CT of patients with COVID-19 pneumonia were extracted, and then the classification model of the first chest CT of COVID-19 pneumonia was constructed by using these texture features based on the Auto-ML method of radiomics, The area under curve(AUC), true positive rate(TPR), true negative rate (TNR), positive predictive value(PPV) and negative predictive value (NPV) of the operating characteristic curve (ROC) were used to evaluate the accuracy of the first chest CT image classification model in patients with COVID-19 pneumonia. The TPR, TNR, PPV, NPV and AUC of the training cohort and test cohort of the moderate group and the control group, the severe group and the control group, the moderate group and the severe group were all greater than 95% and 0.95 respectively. The non-focus area of the first CT image of COVID-19 pneumonia has obvious difference in different clinical types. The AUTO-ML classification model of Radiomics based on this difference can be used to predict the clinical types of COVID-19 pneumonia. [ABSTRACT FROM AUTHOR]