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Blockchain technology enables the creation of a distributed and tamper-proof ledger, even in the presence of untrusted agents. While much financial resources and attention are devoted to blockchain tools, the underlying technology is not well understood by the general population. This paper presents a newly developed online tool that allows users to learn and create their own blockchain, with a graphical user interface and code. The module is freely available on nanoHUB.org and describes all components of the blockchain, including the SHA256, Proof of Work, and other features that enable the blockchain to function as a tamper-proof ledger. This tool has been utilized to instruct students without prior knowledge of blockchain technology, and the survey of students’ responses demonstrates that this tool is an effective way of teaching the general population about blockchain technology. 

Due to the COVID-19 pandemic, many undergraduate students have been given no other option but to take their classes remotely. This has provided many challenges for both students and instructors, especially in the STEM field due to the required laboratory coursework. For this reason, alternative methods of distance learning are needed to optimize student laboratory experiences. The sudden transition to a remote format and adjusting to a new learning environment has proven to be difficult for both students and faculty. It has also been established throughout the pandemic that students perform substantially worse in on-line coursework compared with traditional, in-person classes. Students in a general chemistry course were introduced to innovative asynchronous lab modules that could be performed at home with the additional opportunity of conducting statistical analysis tests. These modules utilize discussion boards, graphing assessments, and labs to teach students how to perform different statistical tests and to familiarize students with the DataClassroom, Google Sheets, and Microsoft Excel platforms. This asynchronous learning format will promote both overall student engagement in STEM courses and student understanding of statistical analysis, thus exhibiting the potential to implement these modules in future undergraduate STEM coursework. 

Background . New York City (NYC) experienced an initial surge and gradual decline in the number of SARS-CoV-2-confirmed cases in 2020. A change in the pattern of laboratory test results in COVID-19 patients over this time has not been reported or correlated with patient outcome. Methods . We performed a retrospective study of routine laboratory and SARS-CoV-2 RT-PCR test results from 5,785 patients evaluated in a NYC hospital emergency department from March to June employing machine learning analysis. Results . A COVID-19 high-risk laboratory test result profile (COVID19-HRP), consisting of 21 routine blood tests, was identified to characterize the SARS-CoV-2 patients. Approximately half of the SARS-CoV-2 positive patients had the distinct COVID19-HRP that separated them from SARS-CoV-2 negative patients. SARS-CoV-2 patients with the COVID19-HRP had higher SARS-CoV-2 viral loads, determined by cycle threshold values from the RT-PCR, and poorer clinical outcome compared to other positive patients without the COVID12-HRP. Furthermore, the percentage of SARS-CoV-2 patients with the COVID19-HRP has significantly decreased from March/April to May/June. Notably, viral load in the SARS-CoV-2 patients declined, and their laboratory profile became less distinguishable from SARS-CoV-2 negative patients in the later phase. Conclusions . Our longitudinal analysis illustrates the temporal change of laboratory test result profile in SARS-CoV-2 patients and the COVID-19 evolvement in a US epicenter. This analysis could become an important tool in COVID-19 population disease severity tracking and prediction. In addition, this analysis may play an important role in prioritizing high-risk patients, assisting in patient triaging and optimizing the usage of resources. 

Abstract Background Accurate diagnostic strategies to identify SARS-CoV-2 positive individuals rapidly for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PCR from nasopharyngeal swabs specimens, however the results are not promptly obtainable in all patient care locations. Routine laboratory testing, in contrast, is readily available with a turn-around time (TAT) usually within 1-2 hours. Method We developed a machine learning model incorporating patient demographic features (age, sex, race) with 27 routine laboratory tests to predict an individual’s SARS-CoV-2 infection status. Laboratory testing results obtained within 2 days before the release of SARS-CoV-2 RT-PCR result were used to train a gradient boosting decision tree (GBDT) model from 3,356 SARS-CoV-2 RT-PCR tested patients (1,402 positive and 1,954 negative) evaluated at a metropolitan hospital. Results The model achieved an area under the receiver operating characteristic curve (AUC) of 0.854 (95% CI: 0.829-0.878). Application of this model to an independent patient dataset from a separate hospital resulted in a comparable AUC (0.838), validating the generalization of its use. Moreover, our model predicted initial SARS-CoV-2 RT-PCR positivity in 66% individuals whose RT-PCR result changed from negative to positive within 2 days. Conclusion This model employing routine laboratory test results offers opportunities for early and rapid identification of high-risk SARS-CoV-2 infected patients before their RT-PCR results are available. It may play an important role in assisting the identification of SARS-CoV-2 infected patients in areas where RT-PCR testing is not accessible due to financial or supply constraints.