A Careless Whisper Between Genius Hour and Schooling Under/After the COVID-19 Pandemic

One of the most popular educational trends in 2021, Genius Hour, is an innovative educational technology that allows students to spend an hour a day independently completing self-paced and optional tasks, originated from the policy of Google Inc. using 20% of working hours on "the tasks that are not related to work, but are of interest to oneself". In 2018, the Ministry of Education promulgated the 12-year National Basic Education Curriculum Guidelines of Integrated Activity, in which the learning performance of the self-directed learning and management projects in the second and third learning stages of the Integrated Activity field coincides with the connotation of the so-called "Genius Hour". However, according to the current number of learning periods in the primary and secondary school curriculum syllabus, there will be limitations for the implementation of "genius time" in public primary and secondary schools. This article is based on the examples of "Genius Time" in practical teaching, the expectations for self-directed learning in the field of integrated activities in the curriculum, and the dialog records from the informal online interviews with 12 primary and secondary school principals, directors and teachers on "Genius Hour" to provide a possible imagination for the implementation of "Genius Hour" in the primary and secondary schools in countries where the COVID-19 pandemic still prevail.

Using the Kano model to associate the number of confirmed cases of COVID-19 in a population of 100,000 with case fatality rates: An observational study.

BACKGROUND: An important factor in understanding the spread of COVID-19 is the case fatality rate (CFR) for each county. However, many of research reported CFRs on total confirmed cases (TCCs) rather than per 100,000 people. The disparate definitions of CFR in COVID-19 result in inconsistent results. It remains uncertain whether the incident rate and CFR can be compared to identify countries affected by COVID-19 that are under (or out of) control. This study aims to develop a diagram for dispersing TCC and CFR on a population of 100,000 (namely, TCC100 and CFR100) using the Kano model, to examine selected countries/regions that have successfully implemented preventative measures to keep COVID-19 under control, and to design an app displaying TCC100 and CFR100 for all infected countries/regions. METHODS: Data regarding confirmed cases and deaths of COVID-19 in countries/regions were downloaded daily from the GitHub website. For each country/region, 3 values (TCC100, CFR100, and CFR) were calculated and displayed on the Kano diagram. The lower TCC100 and CFR values indicated that the COVID-19 situation was more under control. The app was developed to display both CFR100/CFR against TCC100 on Google Maps. RESULTS: Based on 286 countries/regions, the correlation coefficient (CC) between TCC100 and CFR100 was 0.51 (t = 9.76) in comparison to TCC100 and CFR with CC = 0.02 (t = 0.3). As a result of the traditional scatter plot using CFR and TCC100, Andorra was found to have the highest CFR100 (=6.62%), TCC100 (=935.74), and CFR (=5.1%), but lower CFR than New York (CFR = 7.4%) and the UK (CFR = 13.5%). There were 3 representative countries/regions that were compared: Taiwan [TCC100 (=1.65), CFR100 (=2.17), CFR (=1%)], South Korea [TCC100 (=20.34), CFR100 (=39.8), CFR (=2%), and Vietnam [TCC100 (=0.26), CFR100 (=0), CFR (=0%)]. CONCLUSION: A Kano diagram was drawn to compare TCC100 against CFT (or CFR100) to gain a better understanding of COVID-19. There is a strong association between a higher TCC100 value and a higher CFR100 value. A dashboard was developed to display both CFR100/CFR against TCC100 for countries/regions.

Using the Kano model to associate the number of confirmed cases of COVID-19 in a population of 100,000 with case fatality rates: An observational study

Background: An important factor in understanding the spread of COVID-19 is the case fatality rate (CFR) for each county. However, many of research reported CFRs on total confirmed cases (TCCs) rather than per 100,000 people. The disparate definitions of CFR in COVID-19 result in inconsistent results. It remains uncertain whether the incident rate and CFR can be compared to identify countries affected by COVID-19 that are under (or out of) control. This study aims to develop a diagram for dispersing TCC and CFR on a population of 100,000 (namely, TCC100 and CFR100) using the Kano model, to examine selected countries/regions that have successfully implemented preventative measures to keep COVID-19 under control, and to design an app displaying TCC100 and CFR100 for all infected countries/regions. Methods: Data regarding confirmed cases and deaths of COVID-19 in countries/regions were downloaded daily from the GitHub website. For each country/region, 3 values (TCC100, CFR100, and CFR) were calculated and displayed on the Kano diagram. The lower TCC100 and CFR values indicated that the COVID-19 situation was more under control. The app was developed to display both CFR100/CFR against TCC100 on Google Maps. Results: Based on 286 countries/regions, the correlation coefficient (CC) between TCC100 and CFR100 was 0.51 (t = 9.76) in comparison to TCC100 and CFR with CC = 0.02 (t = 0.3). As a result of the traditional scatter plot using CFR and TCC100, Andorra was found to have the highest CFR100 (=6.62%), TCC100 (=935.74), and CFR (=5.1%), but lower CFR than New York (CFR = 7.4%) and the UK (CFR = 13.5%). There were 3 representative countries/regions that were compared: Taiwan [TCC100 (=1.65), CFR100 (=2.17), CFR (=1%)], South Korea [TCC100 (=20.34), CFR100 (=39.8), CFR (=2%), and Vietnam [TCC100 (=0.26), CFR100 (=0), CFR (=0%)]. Conclusion: A Kano diagram was drawn to compare TCC100 against CFT (or CFR100) to gain a better understanding of COVID-19. There is a strong association between a higher TCC100 value and a higher CFR100 value. A dashboard was developed to display both CFR100/CFR against TCC100 for countries/regions.

Discovery of baicalin and baicalein as novel, natural product inhibitors of SARS-CoV-2 3CL protease in vitro (preprint)

Human infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cause coronavirus disease 19 (COVID-19) and there is currently no cure. The 3C-like protease (3CLpro), a highly conserved protease indispensable for replication of coronaviruses, is a promising target for development of broad-spectrum antiviral drugs. To advance the speed of drug discovery and development, we investigated the inhibition of SARS-CoV-2 3CLpro by natural products derived from Chinese traditional medicines. Baicalin and baicalein were identified as the first non-covalent, non-peptidomimetic inhibitors of SARS-CoV-2 3CLpro and exhibited potent antiviral activities in a cell-based system. Remarkably, the binding mode of baicalein with SARS-CoV-2 3CLpro determined by X-ray protein crystallography is distinctly different from those of known inhibitors. Baicalein is perfectly ensconced in the core of the substrate-binding pocket by interacting with two catalytic residues, the crucial S1/S2 subsites and the oxyanion loop, acting as a "shield" in front of the catalytic dyad to prevent the peptide substrate approaching the active site. The simple chemical structure, unique mode of action, and potent antiviral activities in vitro, coupled with the favorable safety data from clinical trials, emphasize that baicalein provides a great opportunity for the development of critically needed anti-coronaviral drugs.