Analysis the molecular similarity of least common amino acid sites in ACE2 receptor to predict the potential susceptible species for SARS-CoV-2 (preprint)

This research offers a bioinformatics approach to forecasting both domestic and wild animals' likelihood of being susceptible to SARS-CoV-2 infection. Genomic sequencing can resolve phylogenetic relationships between the virus and the susceptible host. The genome sequence of SARS-CoV-2 is highly interactive with the specific sequence region of the ACE2 receptor of the host species. We further evaluate this concept to identify the most important SARS-CoV-2 binding amino acid sites in the ACE2 receptor sequence through the common similarity of the last common amino acid sites (LCAS) in known susceptible host species. Therefore, the SARS-CoV-2 viral genomic interacting key amino acid region in the ACE2 receptor sequence of known susceptible human host was summarized and compared with other reported known SARS-CoV-2 susceptible host species. We identified the 10 most significant amino acid sites for interaction with SARS-CoV-2 infection from the ACE2 receptor sequence region based on the LCAS similarity pattern in known sensitive SARS-CoV-2 hosts. The most significant 10 LCAS were further compared with ACE2 receptor sequences of unknown species to evaluate the similarity of the last common amino acid pattern (LCAP). We predicted the probability of SARS-CoV-2 infection risk in unknown species through the LCAS similarity pattern. This method can be used as a screening tool to assess the risk of SARS-CoV-2 infection in domestic and wild animals to prevent outbreaks of infection.

The reliability and integrity of overall survival data based on follow-up records only and potential solutions to the challenges

Summary Overall survival (OS) is considered the standard clinical endpoint to support effectiveness claims in new drug applications globally, particularly for lethal conditions such as cancer. However, the source and reliability of OS in the setting of clinical trials have seldom been doubted and discussed. This study first raised the common issue that data integrity and reliability are doubtful when we collect OS information or other time-to-event endpoints based solely on simple follow-up records by investigators without supporting material, especially since the 2019 COVID-19 pandemic. Then, two rounds of discussions with 30 Chinese experts were held and 12 potential source scenarios of three methods for obtaining the time of death of participants, including death certificate, death record and follow-up record, were sorted out and analysed. With a comprehensive assessment of the 12 scenarios by legitimacy, data reliability, data acquisition efficiency, difficulty of data acquisition, and coverage of participants, both short-term and long-term recommended sources, overall strategies and detailed measures for improving the integrity and reliability of death date are presented. In the short term, we suggest integrated sources such as public security systems made available to drug inspection centres appropriately as soon as possible to strengthen supervision. Death certificates provided by participants’ family members and detailed standard follow-up records are recommended to investigators as the two channels of mutual compensation, and the acquisition of supporting materials is encouraged as long as it is not prohibited legally. Moreover, we expect that the sharing of electronic medical records and the legal disclosure of death records in established health registries can be realized with the joint efforts of the whole industry in the long-term. The above proposed solutions are mainly based on the context of China and can also provide reference for other countries in the world.

Prediction of electricity consumption during epidemic period based on improved particle swarm optimization algorithm

A prediction method of electricity consumption is developed in order to address the problems of big change and imbalance in electricity consumption caused by COVID-19. In this method, BP (Back Propagation) neural network and improved particle swarm optimization (IPSO) algorithm are combined and applied. Firstly, Pearson correlation coefficient approach is utilized to conduct data correlation analysis. Then, the BP neural network prediction model is built, and IPSO algorithm is used to optimize the neural network’s initial weights and thresholds. Considering the medical data, public opinion data, policy data and historical data of electricity consumption during epidemic period, the electricity consumption of each industry in the future is predicted. The findings suggest that the proposed model performs well in terms of prediction. The Mean Absolute Percentage Error (MAPE) for each industry’s evaluation index is 1.41%, 1.70 %, and 1.37 %, respectively. Compared with other models, the prediction accuracy is higher. By exploring the predicted results of electricity consumption during epidemic period, it is hoped that a basis prediction method of electricity consumption for power grid companies in the event of a sudden outbreak will be provided.