Statistical analysis supports UTR (untranslated region) deletion theory in SARS-CoV-2.

It was noticed that the mortality rate of SARS-CoV-2 infection experienced a significant declination in the early stage of the epidemic. We suspect that the sharp deterioration of virus toxicity is related to the deletion of the untranslated region (UTR) of the virus genome. It was found that the genome length of SARS-CoV-2 engaged a significant truncation due to UTR deletion after a mega-sequence analysis. Sequence similarity analysis further indicated that short UTR strains originated from its long UTR ancestors after an irreversible deletion. A good correlation was discovered between genome length and mortality, which demonstrated that the deletion of the virus UTR significantly affected the toxicity of the virus. This correlation was further confirmed in a significance analysis of the genetic influence on the clinical outcomes. The viral genome length of hospitalized patients was significantly more extensive than that of asymptomatic patients. In contrast, the viral genome length of asymptomatic was considerably longer than that of ordinary patients with symptoms. A genome-level mutation scanning was performed to systematically evaluate the influence of mutations at each position on virulence. The results indicated that UTR deletion was the primary driving force in alternating virus virulence in the early evolution. In the end, we proposed a mathematical model to explain why this UTR deletion was not continuous.

On the dynamical model for COVID-19 with vaccination and time-delay effects: A model analysis supported by Yangzhou epidemic in 2021.

The 2019 novel coronavirus (COVID-19) emerged at the end of 2019 has a great influence on the health and lives of people all over the world. The spread principle is still unclear. This paper considers a novel evolution model of COVID-19 in terms of an integral-differential equation, involving vaccination effect and the incubation of COVID-19. The proposed mathematical model is rigorously analyzed on its asymptotic behavior with new probability functions, showing the final spread tendency. Moreover, our model is also verified numerically by the practical epidemic data of COVID-19 in Yangzhou from July to August 2021.

Intermittent Hypoxic Preconditioning: A Potential New Powerful Strategy for COVID-19 Rehabilitation.

COVID-19 is a highly infectious respiratory virus, which can proliferate by invading the ACE2 receptor of host cells. Clinical studies have found that the virus can cause dyspnea, pneumonia and other cardiopulmonary system damage. In severe cases, it can lead to respiratory failure and even death. Although there are currently no effective drugs or vaccines for the prevention and treatment of COVID-19, the patient's prognosis recovery can be effectively improved by ameliorating the dysfunction of the respiratory system, cardiovascular systems, and immune function. Intermittent hypoxic preconditioning (IHP) as a new non-drug treatment has been applied in the clinical and rehabilitative practice for treating chronic obstructive pulmonary disease (COPD), diabetes, coronary heart disease, heart failure, hypertension, and other diseases. Many clinical studies have confirmed that IHP can improve the cardiopulmonary function of patients and increase the cardiorespiratory fitness and the tolerance of tissues and organs to ischemia. This article introduces the physiological and biochemical functions of IHP and proposes the potential application plan of IHP for the rehabilitation of patients with COVID-19, so as to provide a better prognosis for patients and speed up the recovery of the disease. The aim of this narrative review is to propose possible causes and pathophysiology of COVID-19 based on the mechanisms of the oxidative stress, inflammation, and immune response, and to provide a new, safe and efficacious strategy for the better rehabilitation from COVID-19.

The dynamical model for COVID-19 with asymptotic analysis and numerical implementations.

The 2019 novel coronavirus (COVID-19) emerged at the end of 2019 has a great impact on China and all over the world. The transmission mechanism of COVID-19 is still unclear. Except for the initial status and the imported cases, the isolation measures and the medical treatments of the infected patients have essential influences on the spread of COVID-19. In this paper, we establish a mathematical model for COVID-19 transmission involving the interactive effect of various factors for the infected people, including imported cases, isolating rate, diagnostic rate, recovery rate and also the mortality rate. Under the assumption that the random incubation period, the cure period and the diagnosis period are subject to the Weibull distribution, the quantity of daily existing infected people is finally governed by a linear integral-differential equation with convolution kernel. Based on the asymptotic behavior and the quantitative analysis on the model, we rigorously prove that, for limited external input patients, both the quantity of infected patients and its variation ratio will finally tend to zero, if the infected patients are sufficiently isolated or the infection rate is small enough. Finally, numerical performances for the proposed model as well as the comparisons between our simulations and the clinical data of the city Wuhan and Italy are demonstrated, showing the validity of our model with suitably specified model parameters.