Electronic Biometric Detector and body composition index in predicting disease risk

According to the World Health Organization, COVID-19 has killed 14.9 million people worldwide by 2021. According to statistics from the World Obesity Alliance, if the domestic obese population exceeds 50% (three high diseases), the country's COVID-19 mortality rate will be 10 times higher. There seems to be a close relationship between obesity and the risk of hospitalization and treatment. In this study, the electronic biological information detector was used to collect the response values of the body cells of adults in Taiwan. Through the comparison and analysis of big data, the functional status of each organ system in the human body was calculated and compared with the body composition index. It was found that there is a high correlation between the two. The results of this study may provide feasibility of different health risk assessments. © The 2023 International Conference on Artificial Life and Robotics (ICAROB2023).

COVID-19-related Versus Non-Viral Acute Respiratory Distress Syndrome: Comparison of Upper Airway Molecular Pathway and Drug Discovery Design based on Systems Biology and Deep Learning Methods

The coronavirus disease 2019 (COVID-19) epidemic is currently raging around the world with a rapid speed. Among the COVID-19 patients, SARS-CoV-2 associated acute respiratory distress syndrome (ARDS) is the main contribution to the high ratio of morbidity and mortality. However, clinical manifestations between SARS-CoV-2-caused-ARDS and non-SARS-CoV-2-caused-ARDS are quite common and their therapy is limited owing to the intricated pathophysiology are not fully understood. In this study, we constructed a candidate host-pathogen interspecies genome-wide genetic and epigenetic network (HPI-GWGEN) via database mining at first. With the help of host-pathogen microarray data, real HPI-GWGEN of COVID-19-ARDS and Non-Viral-ARDS were obtained by system modeling, system identification and Akaike information criterion (AIC) of model order selection method to delete the false positives in candidate HPI-GWGEN. Afterwards, principal network projection (PNP) approach is utilized to extract core HPI-GWGEN and their core signaling pathways of COVID-19-ARDS and Non-Viral-ARDS annotated by KEGG pathways. In order to design multiple-molecule drugs of COVID-19-ARDS and Non-Viral-ARDS, we identified essential biomarkers of pathogenesis by comparing the core signal pathways between COVID-19-ARDS and Non-Viral-ARDS. The deep neural network of drug-target interaction model (DNN-DTI) would be trained by drug-target interaction databases in advance to predict candidate drugs for the identified biomarkers. We further narrowed down these predicted drug candidates as potential multiple-molecule drug by filters of drug design specifications, including regulation ability, sensitivity, excretion, toxicity and drug-likeness. Taken together, we not only enlighten the etiologic mechanisms under COVID-19-ARDS but also provided novel therapeutic options for COVID-19-ARDS and Non-viral-ARDS. © 2021 IEEE