Hybrid Model Teaching in the Postepidemic Period: From Nucleic Acid to Antigen for the Fluorescence Analysis of SARS-CoV-2

Owing to the global spread of the coronavirus disease 2019 (COVID-19), education has shifted to distance online learning, whereas some face-to-face courses have been resumed with the improvement of the outbreak prevention and management situation, including a laboratory course for senior undergraduate students in chemical biology. Here, we present an innovative chemical biology experiment covering COVID-19 topics, which was created for third-year undergraduates. The basic principles of two nucleic-acid- and antigen-based diagnostic techniques for SARS-CoV-2 are demonstrated in detail. These experiments are designed to provide students with comprehensive knowledge of COVID-19 and related diagnoses in daily life. Crucially, the biosafety of this experimental manipulation was ensured by using artificial nucleic acids and recombinant protein. Furthermore, an interactive hybrid online-facing teaching model was designed to cover the key mechanism regarding PCR and serological tests of COVID-19. Finally, a satisfactory evaluation was obtained through a questionnaire, and simultaneously, reasonable improvements to the course design were suggested. The proposed curriculum provides all the necessary information for other instructors to create new courses supported by research.

Changing Pedagogy for Contemporaneity with New Design Platforms

For centuries, the mighty pen dominated classroom pedagogy in academia. In the 1980s, PowerPoint emerged as a significant tool to enhance in-class presentations. For the most part, academic presentations were confined to the four walls of the classroom. In 2019, the Coronavirus pandemic emptied the campuses, causing a pivot in the classroom experience. ZOOM, and other video tools, proliferated as channels for sharing oral and visual information, but there are better communication channels. We posit that academics should utilize graphic design platforms that provide more capability than PowerPoint technology. They exceed the typical slide projection capability, and they allow for more flexibility when integrating course content with social media. Graphic design platforms can be used to create Facebook posts and covers;they can be used to create invitations, cards, resumes, business letterheads, invoices, websites, logos, newsletters, brochures, business proposals, videos, worksheets, and infographics. In this paper we discuss and exhibit two specific communication strategies (that is, live videos and social media postings), and examples are provided using Canva. However, the same examples could have been created using other design platforms such as Stencil, Crello, Picmonkey, Snappa, Easil, Adobe Spark, and Pablo.

Harnessing the CRISPR-Cas13d System for Protein Detection via Dual-aptamer-based Transcription Amplification.

CRISPR-based biosensing technology has been emerging as a revolutionary diagnostics for many diseases-related biomarkers. In particular, RspCas13d, a newly identified RNA-guided Cas13d ribonuclease derived from Ruminococcus sp., has shown great promise for accurate and sensitive detection of RNA due to its RNA sequence-specific recognition and robust collateral trans-cleavage activity. However, its diagnostic utility is limited to detect nucleic-acid-related biomarkers. To address this limitation, we herein present a proof-of-concept demonstration of a target-responsive CRISPR-Cas13d sensing system for protein biomarkers. Such a system is rationally designed by integrating a Dual-Aptamer-based Transcription Amplification Strategy with CRISPR-Cas13d (DATAS-Cas13d), in which the protein binding initiates the in vitro RNA transcription followed by the activation of RspCas13d. Using a short fluorescent ssRNA as the signal reporter and cardiac troponin I (cTnI) as the model analyte, the DATAS-Cas13d system showed a wide linear range, low detection limit and high specificity for the detection of cTnI in buffer and human serum. Thanks to the facile integration of various bioreceptors into the DATAS-Cas13d system, the method could be adapted to detecting a broad range of clinically relevant protein biomarkers, and thus broaden the medical applications of Cas13d-based diagnostics.

Specification testing for ordinary differential equation models with fixed design and applications to COVID-19 epidemic models.

Checking the models about the ongoing Coronavirus Disease 2019 (COVID-19) pandemic is an important issue. Some famous ordinary differential equation (ODE) models, such as the SIR and SEIR models have been used to describe and predict the epidemic trend. Still, in many cases, only part of the equations can be observed. A test is suggested to check possibly partially observed ODE models with a fixed design sampling scheme. The asymptotic properties of the test under the null, global and local alternative hypotheses are presented. Two new propositions about U-statistics with varying kernels based on independent but non-identical data are derived as essential tools. Some simulation studies are conducted to examine the performances of the test. Based on the available public data, it is found that the SEIR model, for modeling the data of COVID-19 infective cases in certain periods in Japan and Algeria, respectively, maybe not be appropriate by applying the proposed test.

What Drives the Influence of Health Science Communication Accounts on TikTok? A Fuzzy-Set Qualitative Comparative Analysis.

Medical institutions face a variety of challenges as they seek to enhance their reputation and increase the influence of their social media accounts. Becoming a social media influencer in the health field in today's complex online environment requires integrated social and technical systems. However, rather than holistically investigating the mechanism of account influence, studies have focused on a narrow subset of social and technical conditions that drive online influence. We attribute this to the mismatch between complex causality problems and traditional symmetric regression methods. In this study, we adopted an asymmetric configurational perspective that allowed us to test a causally complex model of the conditions that create strong and not-strong account influence. We used fuzzy-set qualitative comparative analysis (fsQCA) to detect the effects of varying configurations of three social system characteristics (i.e., an oncology-related attribute, a public attribute, and comment interaction) and two technical system characteristics (i.e., telepresence and video collection) on the TikTok accounts of 63 elderly Chinese doctors (60 to 92 years old). Our results revealed two pathways associated with distinct sociotechnical configurations to strong account influence and three pathways associated with distinct sociotechnical configurations to not-strong account influence. Furthermore, the results confirmed that a single antecedent condition cannot, on its own, produce an outcome, i.e., account influence. Multiple inter-related conditions are required to produce an influential account. These results offer a more holistic picture of how health science communication accounts operate and reconcile the scattered results in the literature. We also demonstrate how configurational theory and methods can be used to analyze the complexities of social media platforms.

Dynamic Patient Admission Control With Time-Varying and Uncertain Demands in COVID-19 Pandemic

In the coronavirus epidemic, many Chinese hospitals have established buffer zones to prevent the spread and transmission of the virus. The buffer zone is a monitored and separate area where the patients who need hospitalizations after the quick treatments in the emergency department can temporarily wait for the Covid-19 test and receive some healthcare services to stabilize their conditions. Because the beds in the buffer zones are limited, the managers face the patient admission control problem for the buffer zone. This management and control problem is challenging since the patient arrivals are uncertain, and the patients’ conditions are different. In this paper, we build the infinite- and finite-horizon Markov decision process (MDP) models for this problem. We use the uniformization method to discretize the patient flow. We propose various iteration algorithms to solve the MDP models and obtain the optimal and threshold policies. Numerical experiments validate the advantages of the policies obtained by the algorithms in this paper over the current policies of hospitals. Note to Practitioners—The ongoing COVID-19 pandemic has been causing enormous damage to people’s health, jobs, and well-being. COVID-19 has affected almost all countries globally and has changed the operation mode of the healthcare system, especially the hospitals. The hospitals are the frontlines of healthcare service and the battle with the COVID-19 pandemic. This article is motivated by our collaborations with hospitals in Shanghai, China. In China, many hospitals establish buffer zones: a monitored area where the patients who need hospitalizations after the quick treatments in the emergency department can temporarily wait for the Covid-19 test and receive some healthcare services to stabilize their conditions. Because the zone’s capacity is limited, the managers must make dynamic patient admission control decisions according to multiple factors, such as patients’ health conditions and the usage of beds in the zone. We propose two MDP models to solve this complex problem. Several iteration algorithms are designed to solve the MDP models and obtain the optimal and threshold policies. Based on hospitals’ real-life data, we show the methods presented in this paper can help hospital managers make more reasonable decisions. Although we focus on the hospital’s buffer zone in China, the methodology and approach for this problem can be extended to other practical hospital management scenarios in the coronavirus pandemic. For example, For example, some hospitals have admission control problems for coronavirus patients due to hospital capacity limitations. The hospital has to decide if a patient is accepted as an inpatient or suggested to home quarantine. In such a case, the admission control problem can also be solved by the methodologies in the paper. [ FROM AUTHOR] Copyright of IEEE Transactions on Automation Science & Engineering is the property of IEEE and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The Physician Scheduling of Fever Clinic in the COVID-19 Pandemic

This article addresses a weekly physician scheduling problem in Covid-19. This problem has arisen in fever clinics in two collaborative hospitals located in Shanghai, China. Because of the coronavirus pandemic, the hospitals must consider some specific constraints in the scheduling problem. For example, due to social distance limitation, the patient queue lengths are much longer in the coronavirus pandemic, even with the same waiting patients. Thus, the hospitals must consider the maximum queue length in the physician scheduling problem. Moreover, the fever clinic’s scheduling rules are different from those in the common clinic, and some specific regulatory constraints have to be considered in the epidemic. We first build a mathematical model for this problem, in which a pointwise stationary fluid flow approximation method is used to compute the queue length. Some linearization techniques are designed to make the problem can be solved by commercial solvers, such as Gurobi. We find that solving this model from practical applications of the hospital within an acceptable computation time is challenging. Consequently, we develop an efficient two-phase approach to solve the problem. A staffing model and a branch-and-price algorithm are proposed in this approach. The performances of our models and approaches are discussed. The effectiveness of the proposed algorithms for real-life data from collaborative hospitals is validated. Note to Practitioners—This article is motivated by our collaborations with two hospitals in Shanghai, China. Covid-19 has swept the world since 2019 and is still raging in many regions, posing an unprecedented challenge to healthcare systems in countries worldwide. The hospitals are the frontlines of healthcare service, and the physicians are the most critical resource in the battles to coronavirus pandemic. In China, many large-scale hospitals establish fever clinics to serve fever patients. The physician scheduling for such clinics is different and complicated in the Covid-19 due to many specific constraints. We find that the managers are tough to give high-quality schedules to physicians. Thus, we propose a set of algorithms to solve this problem. Especially, a two-phase approach that consists of a staffing standard and a branch-and-price algorithm is designed. Based on hospitals’ real-life data, we show that the methods presented in this article can be used to help hospital managers obtain more reasonable scheduling solutions that can improve the service quality without increasing the workloads of physicians. [ FROM AUTHOR] Copyright of IEEE Transactions on Automation Science & Engineering is the property of IEEE and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

CRISPR-Cas12a-mediated label-free electrochemical aptamer-based sensor for SARS-CoV-2 antigen detection.

Serological antigen testing has emerged as an important diagnostic paradigm in COVID-19, but often suffers from potential cross-reactivity. To address this limitation, we herein report a label-free electrochemical aptamer-based sensor for the detection of SARS-CoV-2 antigen by integrating aptamer-based specific recognition with CRISPR-Cas12a-mediated signal amplification. The sensing principle is based on the competitive binding of antigen and the preassembled Cas12a-crRNA complex to the antigen-specific aptamer, resulting in a change in the collateral cleavage activity of Cas12a. To further generate an electrochemical signal, a DNA architecture was fabricated by in situ rolling circle amplification on a gold electrode, which serves as a novel substrate for Cas12a. Upon Cas12a-based collateral DNA cleavage, the DNA architecture was degraded, leading to a significant decrease in impedance that can be measured spectroscopically. Using SARS-CoV-2 nucleocapsid antigen as the model, the proposed CRISPR-Cas12a-based electrochemical sensor (CRISPR-E) showed excellent analytical performance for the quantitative detection of nucleocapsid antigen. Since in vitro selection can obtain aptamers selective for many SARS-CoV-2 antigens, the proposed strategy can expand this powerful CRISPR-E system significantly for quantitative monitoring of a wide range of COVID-19 biomarkers.

New insights into genetic characteristics between multiple myeloma and COVID-19: An integrative bioinformatics analysis of gene expression omnibus microarray and the cancer genome atlas data.

BACKGROUND: Multiple myeloma (MM) is a hematological malignancy. Coronavirus disease 2019 (COVID-19) infection correlates with MM features. This study aimed to identify MM prognostic biomarkers with potential association with COVID-19. METHODS: Differentially expressed genes (DEGs) in five MM data sets (GSE47552, GSE16558, GSE13591, GSE6477, and GSE39754) with the same expression trends were screened out. Functional enrichment analysis and the protein-protein interaction network were performed for all DEGs. Prognosis-associated DEGs were screened using the stepwise Cox regression analysis in the cancer genome atlas (TCGA) MMRF-CoMMpass cohort and the GSE24080 data set. Prognosis-associated DEGs associated with COVID-19 infection in the GSE164805 data set were also identified. RESULTS: A total of 98 DEGs with the same expression trends in five data sets were identified, and 83 DEGs were included in the protein-protein interaction network. Cox regression analysis identified 16 DEGs were associated with MM prognosis in the TCGA cohort, and only the cytochrome c oxidase subunit 6C (COX6C) gene (HR = 1.717, 95% CI 1.231-2.428, p = .002) and the nucleotide-binding oligomerization domain containing 2 (NOD2) gene (HR = 0.882, 95% CI 0.798-0.975, p = .014) were independent factors related to MM prognosis in the GSE24080 data set. Both of them were downregulated in patients with mild COVID-19 infection compared with controls but were upregulated in patients with severe COVID-19 compared with patients with mild illness. CONCLUSIONS: The NOD2 and COX6C genes might be used as prognostic biomarkers in MM. The two genes might be associated with the development of COVID-19 infection.

Hesitancy in the time of coronavirus: Temporal, spatial, and sociodemographic variations in COVID-19 vaccine hesitancy.

Leveraging nationally representative survey data on 443,680 respondents from January to March 2021, this study examines the temporal, spatial, and sociodemographic variations in COVID-19 vaccine hesitancy in the U.S. Findings reveal multidimensional determinants of vaccination intentions involving confidence, complacency, and circumspection factors. Using descriptive analyses and multilevel mixed-effects regression models, we find persistent partisan divide across states and significant racial disparities, with Blacks more likely to develop vaccine hesitancy due to confidence and circumspection than Whites. Vaccine hesitancy among Blacks declines dramatically across time but varies little across states, indicating new directions to effectively address inequalities in vaccination. Results also show nuanced gender differences, with women more likely to develop hesitancy due to circumspection and men more likely to have hesitancy due to complacency. Moreover, we find important intersection between race, gender, and education that calls for efforts to adequately address the concerns of the most vulnerable and disadvantaged groups.

Modified reference based imputation and tipping point analysis in the presence of missing data due to COVID-19.

In longitudinal clinical trials, missing data are inevitable due to intercurrent events (ICEs) such as treatment interruption or premature discontinuation for different reasons. The COVID-19 pandemic has had substantial impact on clinical trials since early 2020 as it may result in missing data due to missed visits and premature discontinuations. The missing data due to COVID-19 can reasonably be assumed as missing at random (MAR). We propose a combined hypothetical strategy for sensitivity analyses to handle missing data due to both COVID-19 and non-COVID reasons. We modify the commonly used missing not at random (MNAR) methods, reference based imputation (RBI) and tipping point analysis, under this strategy. We propose the standard multiple imputation approach and derive an analytic likelihood based approach to implement the proposed methods to improve efficiency in applications. The proposed strategy and methods are applicable to a more general scenario when there are missing data due to both MAR and MNAR reasons.

Translating daily COVID-19 screening into a simple glucose test: a proof of concept study.

Home testing is an attractive emerging strategy to combat the COVID-19 pandemic and prevent overloading of healthcare resources through at-home isolation, screening and monitoring of symptoms. However, current diagnostic technologies of SARS-CoV-2 still suffer from some drawbacks because of the tradeoffs between sensitivity, usability and costs, making the test unaffordable to most users at home. To address these limitations, taking advantage of clustered regularly interspaced short palindromic repeats (CRISPRs) and a portable glucose meter (PGM), we present a proof-of-concept demonstration of a target-responsive CRISPR-PGM system for translating SARS-CoV-2 detection into a glucose test. Using this system, a specific N gene, N protein, and pseudo-viruses of SARS-CoV-2 have been detected quantitatively with a PGM. Given the facile integration of various bioreceptors into the CRISPR-PGM system, the proposed method provides a starting point to provide patients with a single-device solution that can quantitatively monitor multiple COVID-19 biomarkers at home.

A serological aptamer-assisted proximity ligation assay for COVID-19 diagnosis and seeking neutralizing aptamers.

Rapid and accurate diagnosis of COVID-19 plays an essential role in the current epidemic prevention and control. Despite the promise of nucleic acid and antibody tests, there is still a great challenge to reduce the misdiagnosis, especially for asymptomatic individuals. Here we report a generalizable method for highly specific and ultrasensitive detection of serum COVID-19-associated antigens based on an aptamer-assisted proximity ligation assay. The sensor is based on binding two aptamer probes to the same protein target that brings the ligation DNA region into close proximity, thereby initiating ligation-dependent qPCR amplification. Using this system, serum nucleocapsid protein has been detected quantitatively by converting protein recognition into a detectable qPCR signal using a simple, homogeneous and fast detection workflow in ∼2 hours. In addition, this system has also been transformed into a universal platform for measuring specific interactions between spike S1 and its receptor ACE2, and more importantly demonstrated the feasibility for screening and investigation of potential neutralizing aptamers. Since in vitro selection can obtain aptamers selective for many COVID-19-associated antigens, the method demonstrated here will serve as an important tool for the diagnosis and therapeutics of COVID-19.

Transcriptional and proteomic insights into the host response in fatal COVID-19 cases.

Coronavirus disease 2019 (COVID-19), the global pandemic caused by SARS-CoV-2, has resulted thus far in greater than 933,000 deaths worldwide; yet disease pathogenesis remains unclear. Clinical and immunological features of patients with COVID-19 have highlighted a potential role for changes in immune activity in regulating disease severity. However, little is known about the responses in human lung tissue, the primary site of infection. Here we show that pathways related to neutrophil activation and pulmonary fibrosis are among the major up-regulated transcriptional signatures in lung tissue obtained from patients who died of COVID-19 in Wuhan, China. Strikingly, the viral burden was low in all samples, which suggests that the patient deaths may be related to the host response rather than an active fulminant infection. Examination of the colonic transcriptome of these patients suggested that SARS-CoV-2 impacted host responses even at a site with no obvious pathogenesis. Further proteomics analysis validated our transcriptome findings and identified several key proteins, such as the SARS-CoV-2 entry-associated protease cathepsins B and L and the inflammatory response modulator S100A8/A9, that are highly expressed in fatal cases, revealing potential drug targets for COVID-19.

Nanopore Targeted Sequencing for the Accurate and Comprehensive Detection of SARS-CoV-2 and Other Respiratory Viruses.

The ongoing global novel coronavirus pneumonia COVID-19 outbreak has engendered numerous cases of infection and death. COVID-19 diagnosis relies upon nucleic acid detection; however, currently recommended methods exhibit high false-negative rates and are unable to identify other respiratory virus infections, thereby resulting in patient misdiagnosis and impeding epidemic containment. Combining the advantages of targeted amplification and long-read, real-time nanopore sequencing, herein, nanopore targeted sequencing (NTS) is developed to detect SARS-CoV-2 and other respiratory viruses simultaneously within 6-10 h, with a limit of detection of ten standard plasmid copies per reaction. Compared with its specificity for five common respiratory viruses, the specificity of NTS for SARS-CoV-2 reaches 100%. Parallel testing with approved real-time reverse transcription-polymerase chain reaction kits for SARS-CoV-2 and NTS using 61 nucleic acid samples from suspected COVID-19 cases show that NTS identifies more infected patients (22/61) as positive, while also effectively monitoring for mutated nucleic acid sequences, categorizing types of SARS-CoV-2, and detecting other respiratory viruses in the test sample. NTS is thus suitable for COVID-19 diagnosis; moreover, this platform can be further extended for diagnosing other viruses and pathogens.

Spatial Heterogeneity of Housing Space Consumption in Urban China: Locals vs. Inter- and Intra-Provincial Migrants

The relationships between migration and housing congestion have attracted attention in engaging the public against the COVID-19 pandemic and some other public health crises. In recent years in China, promoting the citizenization ("shimin hua") of migrants and improving the quality of urbanization have become the focus of attention in the new-type urbanization today. The housing space consumption of migrants is one of the important indices to look into regarding their real living status in the receiving cities: how do the housing consumption behavior and residential quality vary between the local, inter-and intra-provincial migratory patterns? This article uses the micro household data of the 1% population sampling survey conducted in 2015 by the National Bureau of Statistics of China to look into the spatial variance of the aggregate housing space consumption behaviors of the local and non-local population at the prefectural level and above in urban China. This study finds that: (a) the longer migratory pattern indicates a thriftier housing space consumption that implies a higher probability of residential overcrowding among the inter-provincial migrants;at the same time, the locals enjoy the greater living comfort than their migrant peers;(b) the spatial variance in terms of housing space consumption can be attributed to a series of destination city contexts, such as the geological background, city administrative rank, areal location, local-nonlocal demography, municipal economic growth, and the local residential development levels. The results show that the more "targeted" housing policies are needed to solve the housing difficulties with migrant workers for a goal of human-centered urbanization development. Although we lack the more detailed data-sets to examine the correlation between public health risks (like the COVID-19 pandemic) and housing congestion problems (especially with the population on the move), this research is still illuminating in terms of how to cut down the public health risk in a highly mobile and rapidly urbanizing context like China.

Promising methods for detection of novel coronavirus SARS-CoV-2

Abstract A very recent outbreak of the novel coronavirus, COVID-19, in the city of Wuhan, China, in December 2019 and its subsequent spread within and across China have resulted in several deaths and infections. Presently, nucleic acid amplification test is essential for the confirmation of COVID infection. In this report, we summarized the six promising methods, including whole-genome sequencing, real-time reverse transcription polymerase chain reaction, nanopore target sequencing, antibody-based immunoassay techniques, use of paper-based biomolecular sensors, and the clustered regularly interspaced short palindromic repeats-Cas system-based technology, which can also be deployed for the detection of SARS-CoV-2. We further introduced the principles of these methods, discussed the scope and practicability of application of the available products and methods, and highlighted the potential approaches to develop additional products and techniques for early diagnosis of COVID-19.