ABSTRACT
Artificial intelligence is a future and valuable tool for early disease recognition and support in patient condition monitoring. It can increase the reliability of the cure and decision making by developing useful systems and algorithms. Healthcare workers, especially nurses and physicians, are overworked due to a massive and unexpected increase in the number of patients during the coronavirus pandemic. In such situations, artificial intelligence techniques could be used to diagnose a patient with life-threatening illnesses. In particular, diseases that increase the risk of hospitalization and death in coronavirus patients, such as high blood pressure, heart disease and diabetes, should be diagnosed at an early stage. This article focuses on diagnosing a diabetic patient through data mining techniques. If we are able to diagnose diabetes in the early stages of the disease, we can force patients to stay home and care for their health, so the risk of being infected with the coronavirus would be reduced. The proposed method has three steps: preprocessing, feature selection and classification. Several combinations of Harmony search algorithm, genetic algorithm, and particle swarm optimization algorithm are examined with K-means for feature selection. The combinations have not examined before for diabetes diagnosis applications. K-nearest neighbor is used for classification of the diabetes dataset. Sensitivity, specificity, and accuracy have been measured to evaluate the results. The results achieved indicate that the proposed method with an accuracy of 91.65% outperformed the results of the earlier methods examined in this article.
ABSTRACT
PURPOSE: The newly emerged coronavirus (SARS-CoV-2) continues to infect humans, and no completely efficient treatment has yet been found. Antibody therapy is one way to control infection caused by COVID-19, but the use of classical antibodies has many disadvantages. Heavy chain antibodies (HCAbs) are single-domain antibodies derived from the Camelidae family. The variable part of these antibodies (Nanobodies or VHH) has interesting properties such as small size, identify criptic epitopes, stability in harsh conditions, good tissue permeability and cost-effective production causing nanobodies have become a good candidate in the treatment and diagnosis of viral infections. METHODS: Totally 157 records (up to November 10, 2021), were recognized to be reviewed in this study. 62 studies were removed after first step screening due to their deviation from inclusion criteria. The remaining 95 studies were reviewed in details. After removing articles that were not in the study area, 45 remaining studies met the inclusion criteria and were qualified to be included in the systematic review. RESULTS: In this systematic review, the application of nanobodies in the treatment and detection of COVID-19 infection was reviewed. The results of this study showed that extensive and sufficient studies have been performed in the field of production of nanobodies against SARS-CoV-2 virus and the obtained nanobodies have a great potential for use in patients infected with SARS-CoV-2 virus. CONCLUSION: According to the obtained results, it was found that nanobodies can be used effectively in the treatment and diagnosis of SARS-CoV-2 virus.