Project description:Initial studies found a bidirectional interaction between coronavirus disease 2019 (COVID-19) and diabetes. In this article we intend to review the association and consequences of COVID-19 in the future of diabetes in case of prevalence and treatment. There is substantial evidence that COVID-19 may result in the new-onset of hyperglycaemia. Therefore, it seems that COVID-19 will change the predictions for the prevalence of diabetes. Moreover, it raises numerous challenges for the clinical management of diabetes. In COVID-19 patients, new-onset hyperglycemia is associated with a poor prognosis. Diabetes mellitus and other comorbidities should be strictly controlled. But, with the emergence of COVID-19 disease, the future of diabetes mellitus in terms of prevalence and treatment will change and policymakers should consider developing new management strategies in this regard.Supplementary informationThe online version contains supplementary material available at 10.1007/s40200-022-00994-5.
Project description:Estimating rates of COVID-19 infection and associated mortality is challenging due to uncertainties in case ascertainment. We perform a counterfactual time series analysis on overall mortality data from towns in Italy, comparing the population mortality in 2020 with previous years, to estimate mortality from COVID-19. We find that the number of COVID-19 deaths in Italy in 2020 until September 9 was 59,000-62,000, compared to the official number of 36,000. The proportion of the population that died was 0.29% in the most affected region, Lombardia, and 0.57% in the most affected province, Bergamo. Combining reported test positive rates from Italy with estimates of infection fatality rates from the Diamond Princess cruise ship, we estimate the infection rate as 29% (95% confidence interval 15-52%) in Lombardy, and 72% (95% confidence interval 36-100%) in Bergamo.
Project description:In this article we want to show the potential of an evolutionary algorithm called Topological Weighted Centroid (TWC). This algorithm can obtain new and relevant information from extremely limited and poor datasets. In a world dominated by the concept of big (fat?) data we want to show that it is possible, by necessity or choice, to work profitably even on small data. This peculiarity of the algorithm means that even in the early stages of an epidemic process, when the data are too few to have sufficient statistics, it is possible to obtain important information. To prove our theory, we addressed one of the most central issues at the moment: the COVID-19 epidemic. In particular, the cases recorded in Italy have been selected. Italy seems to have a central role in this epidemic because of the high number of measured infections. Through this innovative artificial intelligence algorithm, we have tried to analyze the evolution of the phenomenon and to predict its future steps using a dataset that contained only geospatial coordinates (longitude and latitude) of the first recorded cases. Once the coordinates of the places where at least one case of contagion had been officially diagnosed until February 26th, 2020 had been collected, research and analysis was carried out on: outbreak point and related heat map (TWC alpha); probability distribution of the contagion on February 26th (TWC beta); possible spread of the phenomenon in the immediate future and then in the future of the future (TWC gamma and TWC theta); how this passage occurred in terms of paths and mutual influence (Theta paths and Markov Machine). Finally, a heat map of the possible situation towards the end of the epidemic in terms of infectiousness of the areas was drawn up. The analyses with TWC confirm the assumptions made at the beginning.
Project description:The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the COVID-19 pandemic. Since its first report in December 2019, despite great efforts made in almost every country worldwide, this disease continues to spread globally, especially in most parts of Europe, Iran, and the United States. Here, we update the recent understanding in clinical characteristics, diagnosis strategies, as well as clinical management of COVID-19 in China as compared to Italy, with the purpose to integrate the China experience with the global efforts to outline references for prevention, basic research, treatment as well as final control of the disease. Being the first two countries we feel appropriate to evaluate the evolution of the disease as well as the early result of the treatment, in order to offer a different baseline to other countries. It is also interesting to compare two countries, with a very significant difference in population, where the morbidity and mortality has been so different, and unrelated to the size of the country.
Project description:Bitter taste receptor TAS2R38 is expressed in the respiratory tract and can respond to quorum-sensing molecules produced by pathogens, stimulating the release of nitric oxide, with biocidal activity. TAS2R38 presents two main high-frequency haplotypes: the "taster" PAV and the "non-taster" AVI. Individuals carrying the AVI allele could be at greater risk of infections, including SARS-CoV-2. The aim of this study was to assess the frequency of PAV and AVI alleles in COVID-19 patients with severe or non-severe symptoms compared to healthy subjects to further corroborate, or not, the hypothesis that the PAV allele may act as a protecting factor towards SARS-CoV-2 infection while the AVI one may represent a risk factor. After careful selection, 54 individuals were included in the study and underwent genetic analysis and PROP phenotype assessment. Our investigation could not point out at a significant relationship between single nucleotide polymorphisms responsible for PROP bitterness and presence/severity of SARS-CoV-2 infection, as previous studies suggested. Our results uncouple the direct genetic contribution of rs10246939, rs1726866 and rs713598 on COVID-19, calling for caution when proposing a treatment based on TAS2R38 phenotypes.