Shared genetic architecture between type 2 diabetes and COVID-19 severity.

PURPOSE: Patients with type 2 diabetes (T2D) have demonstrated a higher risk for developing more severe cases of COVID-19, but the complex genetic mechanism between them is still unknown. The aim of the present study was to untangle this relationship using genetically based approaches. METHODS: By leveraging large-scale genome-wide association study (GWAS) summary statistics of T2D and COVID-19 severity, linkage disequilibrium score regression and Mendelian randomization (MR) analyses were utilized to quantify the genetic correlations and causal relationships between the two traits. Gene-based association and enrichment analysis were further applied to identify putative functional pathways shared between T2D and COVID-19 severity. RESULTS: Significant, moderate genetic correlations were detected between T2D and COVID-19 hospitalization (rg = 0.156, SE = 0.057, p = 0.005) or severe disease (rg = 0.155, SE = 0.057, p = 0.006). MR analysis did not support evidence for a causal effect of T2D on COVID-19 hospitalization (OR 1.030, 95% CI 0.979, 1.084, p = 0.259) or severe disease (OR 0.999, 95% CI 0.934, 1.069, p = 0.982). Genes having pgene < 0.05 for both T2D and COVID-19 severe were significantly enriched for biological pathways, such as response to type I interferon, glutathione derivative metabolic process and glutathione derivative biosynthetic process. CONCLUSIONS: Our findings further confirm the comorbidity of T2D and COVID-19 severity, but a non-causal impact of T2D on severe COVID-19. Shared genetically modulated molecular mechanisms underlying the co-occurrence of the two disorders are crucial for identifying therapeutic targets.

Identification and development of a five-gene signature to improve the prediction of mechanical ventilator-free days for patients with COVID-19.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) is a highly contagious infectious disease caused by the newly discovered severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Severe COVID-19 infection causes complications in the respiratory tract, which results in pulmonary failure, thus requiring prolonged mechanical ventilation (MV). An increase in the number of patients with COVID-19 poses numerous challenges to the healthcare system, including the shortage of MV facilities. Despite continued efforts to improve COVID-19 diagnosis and treatment, no study has established a reliable predictive model for the risk assessment of deteriorating COVID-19 cases. MATERIALS AND METHODS: We extracted the expression profiles and clinical data of the GSE157103, GSE116560 and GSE21802 cohorts from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified as the intersection of the resulting differential genes as analysed via limma, edgeR and DESeq2 R packages. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the R package 'clusterProfiler'. Variables closely related to MV were examined using univariate Cox regression analysis, and significant variables were subjected to least absolute shrinkage and selection operator regression (LASSO) analysis for the construction of a risk model. Kaplan-Meier analysis and receiver operating characteristic (ROC) curves were generated to verify the predictive values of the risk model. RESULTS: We identified 198 unigenes that were differentially expressed in COVID-19 samples. Moreover, a five-gene signature (BTN3A1, GPR35, HAAO, SLC2A6 and TEX2) was constructed to predict the ventilator-free days of patients with COVID-19. In our study, we used the five-gene signature to calculate the risk score (MV score) for each patient. The results revealed a statistical correlation between the MV score and the scores of the Acute Physiology and Chronic Health Evaluation and Sequential Organ Failure Assessment of patients with COVID-19. Kaplan-Meier analysis revealed that the number of ventilator-free days was significantly reduced in the low-MVscore group compared to the high-MVscore group. The ROC curves revealed that our model had a good performance, and the areas under the ROC curve were 0.93 (3-week ROC) and 0.97 (4-week ROC). The 'Limma' package analysis revealed 71 upregulated genes and 59 downregulated genes in the high-MV score group compared to the low-MV score group. These DEGs were mainly enriched in cytokine signalling in immune system and cellular response to cytokine stimulus. CONCLUSIONS: This study identified a five-gene signature that can predict the length of ventilator-free days for patients with COVID-19.

Astraea: Anonymous and Secure Auditing Based on Private Smart Contracts for Donation Systems

Many regions are in urgent need of facial masks for slowing down the spread of COVID-19. To fight the pandemic, people are contributing masks through donation systems. Most existing systems are built on a centralized architecture which is prone to the single point of failure and lack of transparency. Blockchain-based solutions neglect fundamental privacy concerns (donation privacy) and security attacks (collusion attack, stealing attack). Moreover, current auditing solutions are not designed to achieve donation privacy, thus not appropriate in our context. In this work, we design a decentralized, anonymous, and secure auditing framework Astraea based on private smart contracts for donation systems. Specifically, we integrate a Distribute Smart Contract (DiSC) with an SGX Enclave to distribute donations, prove the integrity of donation number (intention) and donation sum while preserving donation privacy. With DiSC, we design a Donation Smart Contract to refund deposits and defend against the stealing attack the collusion attack from malicious collector and transponder. We formally define and prove the privacy and security of Astraea by using security reduction. We build a prototype of Astraea to conduct extensive performance analysis. Experimental results demonstrate that Astraea is practically efficient in terms of both computation and communication. IEEE

Management of renin-angiotensinaldosterone system blockade in patients admitted in hospital with confirmed coronavirus disease (Covid-19) infection: The mcgill raas-covid-19 randomized controlled trial

Background: More data is needed on the cardiovascular impact of discontinuing versus continuing renin-angiotensin aldosterone system inhibitors (RAASi) among patients hospitalized with a severe acute respiratory syndrome coronavirus 2 infection (COVID-19). Methods: The McGill RAAS-COVID-19 trial was a randomized, open label trial in adult patients hospitalized with COVID-19, who were previously treated with RAASi (angiotensin converting enzyme inhibitors [ACEi]/angiotensin receptor blocker [ARB]) (NCT04508985;10/2020-03/2021). Participants were randomized 1:1 to discontinue or continue RAASi. The primary outcome was a global rank score calculated from baseline to day 7 (or discharge) incorporating clinical events and biomarker changes. Global rank scores were compared between groups using the Wilcoxon test statistic and the negative binomial test (using incident rate ratio [IRR]). All analyses were conducted using the intention-to-treat principle. Results: Overall, 21 participants were randomized to discontinue RAASi and 25 to continue. Patients' mean age was 71.5 years and 43.5% were female. Discontinuation of RAASi, versus continuation, resulted in a similar mean global rank score (discontinuation 6 [standard deviation [SD] 6.3] vs continuation 3.8 (SD 2.5);p= 0.60), but the negative binomial analysis identified that discontinuation increased the risk of adverse outcomes (IRR 1.7 [95% CI 1.1 to 2.6];p=0.03). Particularly, RAASi discontinuation increased brain natriuretic peptide (BNP) levels (% change from baseline: +16.7% vs.-27.5%;p= 0.02) and increased the incidence of acute heart failure (33% vs. 4.2%, p=0.03). Conclusion: Discontinuation of RAASi increased BNP levels and risk of acute heart failure in participants hospitalized with COVID-19;where possible, RAASi should be continued.

Quality can improve as productivity increases: Machining as proof

Increasing productivity is a crucial task, especially during a crisis when a shortage of products occurs. In addition, a major concern related to higher productivity is the loss of quality that may result in an even worse situation than when there is no product. However, this general common sense is not always true. Beyond their roles during a crisis, quality and productivity are key players after the crisis for competitive advantages and economic recovery. It was initially thought that quality and productivity are opposite to each other and need to be compromised. Once an economic crisis occurred in the 1970s, a novel perspective on the quality-productivity relationship was proposed and played a prominent role in the recovery from that crisis. Researchers such as Deming proposed that productivity can in fact increase as quality increases. This perspective helped people in that crisis and has so far been continually explored in industry and academia. Now, herein, we propose a new perspective that is the reverse of the aforementioned perspective and has its own benefits. We examine it in machining that is a process used in almost all industrial activities. We consider the cutting speed as a productivity (and throughput) measure and the workpiece temperature as a quality metric. We show that why the workpiece temperature may decrease at higher cutting speeds. From various experimental data in the literature, we also realize that the behavior of the workpiece temperature versus the cutting speed is steeper before the peak than that after the peak. We analytically try to explain this observation as well. Then, we analyze our general perspective from three aspects. First, we propose and implement a volume partition analysis. Second, we introduce a new dimension in Taguchi's view of quality. Third, we introduce the circuit model in the realm of quality and productivity, based on which we develop the mutual interactions between quality and productivity. The proposed perspective in this study is expected to be a helpful mindset change in manufacturing both during and after the COVID-19 crisis. © 2021 The Authors. Published by Elsevier B.V.

Development of Exhaled Breath Diagnosis Sensors for Rapid Identification of COVID-19 Patients

The novel coronavirus pneumonia, a global pandemic disease named as coronavirus disease 2019, has caused enormous losses on the health and economies of people all over the world, while there is still a lack of quick and sensitive diagnostic method and effective therapy. Developing rapid diagnostic method for coronavirus disease 2019 has become exceptional urgent. Herein we report a rapid diagnostic method for the novel coronavirus through monitoring the volatile biomarkers in human exhaled breath. The breath volatile biomarkers are derived from the metabolism of novel coronavirus, including acetoin, 2,4,6-trimethylpyridine, 3-methyl tridecane, tetradecane, isooctyl alcohol, pentadecane, hexadecane, 1-methylene-1H-indene. By comparing the types and concentrations of the volatile biomarkers in human exhaled breath combined with SERS sensor, we could distinguish between the healthy person and the patients with coronavirus disease 2019. This work confirms that various volatile organic compounds metabolized by novel coronavirus can be employed for rapidly screening of patients with coronavirus disease 2019, and has broad application prospects in the prevention and control of the epidemic.

Evaluation of prognostic indicators for COVID⁃19 patients with diabetes

Objective: To retrospectively explore the prognostic indicators of COVID‑19 patients with diabetes. Methods: The clinical data of COVID‑19 patients with diabetes admitted to Huoshenshan Hospital in Wuhan from February 2020 to April 2020 was collected. Patients were divided into survival group and death group according to their outcomes. The differences of various indexes between the two groups were compared and the prognostic factors were screened by multivariate logistic regression analysis. The receiver operation characteristic (ROC) curve was used to evaluate the prognostic value of these factors. Results: Of the 80 patients, 49 were males and 31 were females, with an average age of 65.2 years. There were 61 cases discharged after treatment and 19 cases died. Age, leukocyte count, lymphocyte count, platelet count, C‑reactive protein (CRP), D‑dimer, lactate dehydrogenase(LDH), urea nitrogen, serum albumin, and serum sodium were significantly different between the survival group and the death group ( P <0.05). Multivariate logistic regression analysis showed that CRP ( OR =1.030, 95% CI =1.011‑1.048, P =0.002) and LDH ( OR =1.015, 95% CI =1.006‑1.025, P =0.002) correlated significantly with the mortality risk of the patients. The ROC curve analysis showed that the area under curve ( AUC ) of CRP was 0.884, when the optimal cut‑off value in predicting death events was 40.335 mg/L, the sensitivity, specificity, and accuracy was 78.9%, 82.0%, and 81.3%, respectively. The AUC of lactate dehydrogenase was 0.930, when the optimal cut‑off value in predicting death events was 273.450 U/L, the sensitivity, specificity, and accuracy was 94.7%, 86.9%, and 88.8%, respectively. Conclusion: Both CRP and LDH at admission has obvious correlation with the prognosis of COVID‑19 patients with diabetes and could be used to predict their prognosis. © 2021, Editorial Board of Medical Journal of Wuhan University. All right reserved.

A magnetic nanoparticle labeled immunochromatography kit for sars-cov-2 infection diagnosis

The novel coronavirus disease (COVID-19) is breaking out and spreading rapidly around the world. There is an urgent need for an accurate and rapid detection method to quickly find infected patients and asymptomatic carriers in order to prevent the spread of the severe acute respiratory syndrome coronavirus [SARS-CoV-2]. In this paper, we designed a test strip which used the principle of double antigen sandwich. Fe3O4 magnetic nanobeads are firstly coupled with specific antibodies, and the S protein of the new coronavirus is used as the coating antigen to capture specific antibodies against the new coronavirus, which is used to detect the virus nucleoprotein of specific antibodies in clinical samples. At the same time, Fe3O4 magnetic nanobeads have unique magnetic properties, which can be used to generate different types of detection signals and simplify the detection process. These results can be judged by color changes and magnetic changes at the test and control lines. Compared with the traditional method, this test strip of Fe3O4 magnetic nanobeads has high sensitivity and can qualitatively detect samples within 15 minutes. The magnetic performance of the magnetic nanobeads can be used to improve the sensitivity of the strip in our further research and product development.

Designing a novel nano-vaccine against SARS-CoV-2

The new coronavirus SARS-CoV-2 has become a global pandemic, which has had a huge impact on the lives of people around the world and has caused huge impacts and losses on global economic development. To now, there is still no effective drug or therapy against coronavirus. A large number of studies have shown that vaccines are the ultimate weapon to eliminate major infectious diseases. The development of new vaccines against new coronaviruses is the best way to prevent new coronavirus infections. In this study, we developed a new vaccine against the new coronavirus by combining our self-developed nano adjuvant loaded with carnosine graphene oxide adjuvant with loaded with CpG molecule and RBD protein antigen. Our results showed that this vaccine can produce high titer anti-SARS-CoV-2 RBD antibody neutralizing SARS-CoV-2 in mice within 2 weeks.