N-Chromosome Royal Jelly, Propolis and Bee Pollen Supplementation Improve the Clinical Conditions of COVID-19 Patients: A Randomized Controlled Trial

Royal jelly, propolis, and bee pollen are used for different purposes all around the world according to their anti-inflammatory, antioxidant, and antimicrobial activities. Given that Coronavirus 2019 (COVID-19) is a viral condition accompanied by a dysregulated inflammatory response in the body, we intend to evaluate the effects of natural supplementations on the disease course. A randomized, open-label, controlled trial was conducted among 50 definitive cases of COVID-19. These patients were randomly assigned into control and intervention groups. Royal Jelly, propolis, and bee pollen were prescribed to patients in the intervention group (n = 24) in addition to conventional treatment; while the control group only received the standard treatment (n = 26). At the end of the study, functional class improved in both groups, but this change was more pronounced in the intervention group (p < 0.05). Moreover, total symptoms duration and the time to return to work were significantly reduced in the intervention group (p < 0.05). Although royal jelly, propolis, and bee pollen are not definitive treatments in COVID-19 patients, they can be used as an adjuvant treatment to limit disease symptoms and virus propagation.

Approbation of the stochastic group virus protection model

The article discusses the implementation in Java of the stochastic collaborative virus defense model developed within the framework of the Distributed Object-Based Stochastic Hybrid Systems (DOBSHS) model and its analysis. The goal of the work is to test the model in conditions close to the real world on the way to introducing its use in the practical environment. We propose a method of translating a system specification in the SHYMaude language, intended for the specification and analysis of DOBSHS models in the rewriting logic framework, into the corresponding Java implementation. The resulting Java system is deployed on virtual machines, the virus and the group virus alert system are modeled stochastically. To analyze the system we use several metrics, such as the saturation time of the virus propagation, the proportion of infected nodes upon reaching saturation and the maximal virus propagation speed. We use Monte Carlo method with the computation of confidence intervals to obtain estimates of the selected metrics. We perform analysis on the basis of the sigmoid virus propagation graph over time in the presence of the defense system. We implemented two versions of the system using two protocols for transmitting messages between nodes, TCP/IP and UDP. We measured the influence of the protocol type and the associated costs on the defense system effectiveness. To assess the potential of cost reduction associated with the use of different message transmission protocols, we performed analysis of the original DOBSHS model modified to model message transmission delays. We measured the influence of other model parameters important for the next steps towards the practical use of the model. To address the system scalability, we propose a hierarchical approach to the system design to make possible its use with a large number of nodes.

Sentiment Evolution Analysis and Association Rule Mining for COVID-19 Tweets

This article presents a data mining study carried out on social media users in the context of COVID-19 and offers four main contributions. The first one consists in the construction of a COVID-19 dataset composed of tweets posted by users during the first stages of the virus propagation. The second contribution offers a sample of the interactions between users on topics related to the pandemic. The third contribution is a sentiment analysis, which explores the evolution of emotions throughout time, while the fourth one is an association rule mining task. The indicators determined by statistics and the results obtained from sentiment analysis and association rule mining are eloquent. For instance, signs of an upcoming worldwide economic crisis were clearly detected at an early stage in this study. Overall results are promising and can be exploited in the prediction of the aftermath of COVID-19 and similar crisis in the future.

SARS-CoV-2 detection in multi-sample pools in a real pandemic scenario: a screening strategy of choice for active surveillance

Background The current COVID-19 pandemic has overloaded the diagnostic capacity of laboratories by the gold standard method rRT-PCR. This disease has a high spread rate and almost a quarter of infected individuals never develop symptoms. In this scenario, active surveillance is crucial to stop the virus propagation. Methods Between July 2020 and April 2021, 11580 oropharyngeal swab samples collected in closed and semi-closed institutions were processed for SARS-CoV-2 detection in pools, implementing this strategy for the first time in Cordoba, Argentina. Five-sample pools were constituted before nucleic acid extraction and amplification by rRT-PCR. Comparative analysis of cycle threshold (Ct) values from positive pools and individual samples along with a cost-benefit report of the whole performance of the results was performed. Results From 2314 5-sample pools tested, 158 were classified as positive (6.8%), 2024 as negative (87.5%), and 132 were categorized as indeterminate (5.7%). The Ct value shift due to sample dilution showed an increase in Ct of 2.6 ±1.53 cycles for N gene and 2.6 ±1.78 for ORF1ab gene. Overall, 290 pools were disassembled and 1450 swabs were analyzed individually. This strategy allowed correctly identifying 99.8% of the samples as positive (7.6%) or negative (92.2%), avoiding the execution of 7,806 rRT-PCR reactions which represents a cost saving of 67.5%. Conclusion This study demonstrates the feasibility of pooling samples to increase the number of tests performed, helping to maximize molecular diagnostic resources and reducing the work overload of specialized personnel during active surveillance of the COVID-19 pandemic.

Towards a Mathematical Model for the Viral Progression in the Pharynx

In this work, a comprehensive model for the viral progression in the pharynx has been developed. This one-dimension model considers both Fickian diffusion and convective flow coupled with chemical reactions, such as virus population growth, infected and uninfected cell accumulation as well as virus clearance. The effect of a sterilizing agent such as an alcoholic solution on the viral progression in the pharynx was taken into account and a parametric analysis for the effect of kinetic rate parameters on virus propagation was made. Moreover, different conditions caused by further medical treatment, such as a decrease in virus yield per infected cell, were examined. It is shown that the infection fails to establish by decreasing the virus yield per infected cell. It is believed that this work could be used to further investigate the medical treatment of viral progression in the pharynx.

Effects of population mobility on the COVID-19 spread in Brazil

This article proposes a study of the SARS-CoV-2 virus spread and the efficacy of public policies in Brazil. Using both aggregated (from large Internet companies) and fine-grained (from Departments of Motor Vehicles) mobility data sources, our work sheds light on the effect of mobility on the pandemic situation in the Brazilian territory. Our main contribution is to show how mobility data, particularly fine-grained ones, can offer valuable insights into virus propagation. For this, we propose a modification in the SENUR model to add mobility information, evaluating different data availability scenarios (different information granularities), and finally, we carry out simulations to evaluate possible public policies. In particular, we conduct a case study that shows, through simulations of hypothetical scenarios, that the contagion curve in several Brazilian cities could have been milder if the government had imposed mobility restrictions soon after reporting the first case. Our results also show that if the government had not taken any action and the only safety measure taken was the population’s voluntary isolation (out of fear), the time until the contagion peak for the first wave would have been postponed, but its value would more than double.

A Reliable numerical scheme for a computer virus propagation model

In this paper, we apply the Mickens’methodology to construct a dynamically consistentnonstandard finite difference (NSFD) scheme for acomputer virus propagation model. It is proved thatthe constructed NSFD scheme correctly preservesessential mathematical features of the continuous-timemodel, which are positivity, boundedness and asymptotic stability. Consequently, we obtain an effectivenumerical scheme that can provide reliable approximations for the computer virus propagation model.Meanwhile, some typical standard finite differenceschemes fail to preserve the essential properties ofthe computer virus propagation model; hence, theycan generate numerical approximations which arenot only negative but also unstable. Finally, a setof numerical experiments is performed to supportthe theoretical results as well as to demonstrate theadvantage of the NSFD scheme over standard ones.As we expected, there is a good agreement betweenthe numerical results and theoretical assertions.

A Comprehensive Roadmap Towards Generation of Influenza B Reporter Assay Using a Single DNA Polymerase Based Cloning of Reporter RNA Construct

Mini-genome reporter assay is a key tool for conducting RNA virus research. But, procedural complications and lack of adequate literature pose major challenge towards developing these assay systems. Here we present a novel yet generic and simple cloning strategy for construction of influenza B virus reporter RNA template and describe extensive standardization of the reporter RNP/polymerase activity assay for monitoring viral RNA synthesis in infection free setting. Using this assay system, we, for the first time showed the effect of viral protein NS1 and host protein PKC-Delta upon influenza B virus RNA synthesis. Additionally, the assay system showed promising results in evaluating the efficacy of antiviral drugs targeting viral RNA synthesis and virus propagation. Together, this work offers a detailed protocol for standardization of influenza virus mini-genome assay and an excellent tool for screening of host factors and antivirals in a fast, user friendly and high throughput manner.