Evaluation of Four Rapid Antigen Tests for the Detection of SARS-CoV-2 Infection with Nasopharyngeal Swabs.

Owing to the high transmissibility of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, the capacity of testing systems based on the gold standard real-time reverse transcription-polymerase chain reaction (rRT-PCR) is limited. Rapid antigen tests (RATs) can substantially contribute to the prevention of community transmission, but their further assessment is required. Here, using 1503 nasopharyngeal swabs, we compared the diagnostic performance of four RAT kits (Abbott Panbio™ COVID-19 Ag Rapid Test, SD Biosensor Standard™ Q COVID-19 Ag Test, Humasis COVID-19 Ag Test, and SG Medical Acrosis COVID-19 Ag Test) to the cycle threshold (Ct) values obtained from rRT-PCR. The precision values, area under the curve values, SARS-CoV-2 variant detection ability, and non-SARS-CoV-2 specificity of all four kits were similar. An assay using the Acrosis kit had a significantly better positive detection rate with a higher recall value and cut-off value than that using the other three RAT kits. During the current COVID-19 pandemic, the Acrosis kit is an effective tool to prevent the spread of SARS-CoV-2 in communities.

Developing a supervised learning-based social media business sentiment index.

The fast-growing digital data generation leads to the emergence of the era of big data, which become particularly more valuable because approximately 70% of the collected data in the world comes from social media. Thus, the investigation of online social network services is of paramount importance. In this paper, we use the sentiment analysis, which detects attitudes and emotions toward issues of society posted in social media, to understand the actual economic situation. To this end, two steps are suggested. In the first step, after training the sentiment classifiers with several big data sources of social media datasets, we consider three types of feature sets: feature vector, sequence vector and a combination of dictionary-based feature and sequence vectors. Then, the performance of six classifiers is assessed: MaxEnt-L1, C4.5 decision tree, SVM-kernel, Ada-boost, Naïve Bayes and MaxEnt. In the second step, we collect datasets that are relevant to several economic words that the public use to explicitly express their opinions. Finally, we use a vector auto-regression analysis to confirm our hypothesis. The results show the statistically significant relationship between public sentiment and economic performance. That is, "depression" and "unemployment" lead to KOSPI. Also, it shows that the extracted keywords from the sentiment analysis, such as "price," "year-end-tax" and "budget deficit," cause the exchange rates.