Stage Migration in Renal Malignancies in COVID Era: A Single-Center Analysis.

The COVID-19 disease, caused by SARS-CoV-2 virus, attained the status of a pandemic by March 2020. There was apprehension among patients suffering from renal malignancies about balancing cancer treatment and preventing COVID-19 infection transmission. We analyzed 184 patients with renal malignancies retrospectively, who presented to our institute over 2 years: 91 patients of renal malignancies in pre-COVID era (March 2019-Feb 2020) and 93 patients in COVID era (March 2020-Feb 2021). The parameters analyzed were age, tumor size, clinical presentation, clinical stage, pathological stage, nuclear grade, and presence of metastasis. Level of significance was kept at 95%, and p value <0.05 was considered significant. The age of patients was comparable in both groups (p: 0.381). Clinical presentation was also similar in both groups whereas there were more cases diagnosed during routine evaluation in pre-COVID era (p: 0.022). Tumor size was 5.84 ± 3.03cm vs. 7.10±3.83cm (p: 0.017) in pre-COVID vs. COVID era, respectively. Patients in COVID era had significantly higher clinical stage (p = 0.041), pathological stage (p =0.027), nuclear grade (p = 0.007), and presence of metastasis (p = 0.005) as compared to pre-COVID era. Patients, who underwent Nephron-sparing surgery, also had higher pathological stage in COVID era. COVID overshadowed the management of renal malignancies. There was a clear shift and stage migration in patients of renal malignancies in COVID era as compared to pre-COVID era, probably because of less routine health check-ups and patients deferring hospital visits due to fear of contracting COVID infection.

A Single Multiomics Transistor for Electronic Detection of SARS‐Cov2 Variants Antigen and Viral RNA Without Amplification

The SARS‐CoV‐2 pandemic caused a public health crisis throughout the world and highlighted the need for rapid and sensitive testing as a countermeasure. A sensitive and specific biosensor platform is developed for the detection of antigen and RNA of SARS‐CoV‐2, and its variant (B1.1.529). The demonstrated biosensor platform combines unique protein catalyzed capture bioreceptors (PCCs) for antigen capture and a chimeric (RNA‐DNA) probe for RNA detection using LwaCas13a collateral cleavage activity atop graphene field effect transistors (gFETs). The reported biosensor is able to differentiate unprocessed 104 pfu m−1 samples of SARS‐CoV‐2 from Influenza and Rhinovirus. The limit of detection (LOD) calculated for SARS‐CoV‐2 antigen is 103 in buffer and 104 PFU mL−1 in 10% saliva, while LOD of ≈65 am calculated for viral RNA isolate without amplification. To provide a high reliability of detection, the role of internal and external factors with respect to gate voltage is further analyzed by Principal Component Analysis (PCA). Based on PCA analysis, the authors are able to classify the samples as pathogen positive or negative (Y > 0: Positive for pathogen, Y < 0: Negative for pathogen). The reported platform can be quickly adapted for multi‐omics and multiplexed diagnosis of continuously evolving biothreats and global pandemics. [ABSTRACT FROM AUTHOR] Copyright of Advanced Materials Technologies is the property of John Wiley & Sons, Inc. 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 abstract 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 abstract. (Copyright applies to all Abstracts.)

Spinal cord involvement in COVID-19: A review.

CONTEXT: Recent literature points towards myelitis, like encephalitis, as a common central nervous system complication of COVID-19. This review elaborates on disorders of the spinal cord caused by the SARS-CoV-2 virus. OBJECTIVES: To review the published data about SARS-CoV-2-associated spinal cord disorders and assess their clinical, neuroimaging, treatment, and prognostic aspects. METHODS: The PubMed and Google Scholar databases were searched for published cases using the search items "COVID-19 OR SARS-CoV-2 AND myelitis", "COVID-19 OR SARS-CoV-2 AND myelopathy", and "COVID-19 OR SARS-CoV-2 AND spinal cord". RESULTS: Thirty-three isolated cases were included in the present review, of which 14 were aged 60 years and above (range: 3-70 years). Eighteen patients had lung abnormalities on chest imaging. Eight patients had developed either an areflexic paraparesis or quadriparesis. In 17 patients, neuroimaging demonstrated longitudinally extensive transverse myelitis, while 3 cases showed neuroimaging changes in the spinal cord as a part of acute disseminated encephalomyelitis syndrome. Cerebrospinal fluid (CSF) examinations revealed inflammatory changes in 18 patients. However, the SARS-CoV-2 virus in the CSF was discovered in 2 patients. In 2 patients, anti-SARS-CoV-2 antibodies were demonstrated in the CSF. Following treatment, 13 patients were able to walk. CONCLUSIONS: A variety of COVID-19-related spinal cord manifestations, such as acute transverse myelitis, acute necrotizing myelitis, SARS-CoV-2 myelitis, acute disseminated encephalomyelitis, neuromyelitis optica spectrum disorder, hypoxic myelopathy, MOG antibody-associated myelitis, spinal cord infarction, and spinal epidural abscess, have been reported. The possible mechanisms of this involvement being direct invasion, cytokine storm, coagulopathy, and an autoimmune response. However, response to treatment has been generally unsatisfactory, with many patients having residual weakness necessitating long-term rehabilitation.

TUT4/7-mediated uridylation of a coronavirus subgenomic RNAs delays viral replication.

Coronaviruses are positive-strand RNA viruses with 3' polyadenylated genomes and subgenomic transcripts. The lengths of the viral poly(A) tails change during infection by mechanisms that remain poorly understood. Here, we use a splint-ligation method to measure the poly(A) tail length and poly(A) terminal uridylation and guanylation of the mouse hepatitis virus (MHV) RNAs. Upon infection of 17-CL1 cells with MHV, a member of the Betacoronavirus genus, we observe two populations of terminally uridylated viral transcripts, one with poly(A) tails ~44 nucleotides long and the other with poly(A) tails shorter than ~22 nucleotides. The mammalian terminal uridylyl-transferase 4 (TUT4) and terminal uridylyl-transferase 7 (TUT7), referred to as TUT4/7, add non-templated uracils to the 3'-end of endogenous transcripts with poly(A) tails shorter than ~30 nucleotides to trigger transcript decay. Here we find that depletion of the host TUT4/7 results in an increased replication capacity of the MHV virus. At late stages of infection, the population of uridylated subgenomic RNAs with tails shorter than ~22 nucleotides is reduced in the absence of TUT4/7 while the viral RNA load increases. Our findings indicate that TUT4/7 uridylation marks the MHV subgenomic RNAs for decay and delays viral replication.

New-age vaccine adjuvants, their development, and future perspective.

In the present scenario, immunization is of utmost importance as it keeps us safe and protects us from infectious agents. Despite the great success in the field of vaccinology, there is a need to not only develop safe and ideal vaccines to fight deadly infections but also improve the quality of existing vaccines in terms of partial or inconsistent protection. Generally, subunit vaccines are known to be safe in nature, but they are mostly found to be incapable of generating the optimum immune response. Hence, there is a great possibility of improving the potential of a vaccine in formulation with novel adjuvants, which can effectively impart superior immunity. The vaccine(s) in formulation with novel adjuvants may also be helpful in fighting pathogens of high antigenic diversity. However, due to the limitations of safety and toxicity, very few human-compatible adjuvants have been approved. In this review, we mainly focus on the need for new and improved vaccines; the definition of and the need for adjuvants; the characteristics and mechanisms of human-compatible adjuvants; the current status of vaccine adjuvants, mucosal vaccine adjuvants, and adjuvants in clinical development; and future directions.

Improving performance of deep learning predictive models for COVID-19 by incorporating environmental parameters.

The Coronavirus disease 2019 (COVID-19) pandemic has severely crippled the economy on a global scale. Effective and accurate forecasting models are essential for proper management and preparedness of the healthcare system and resources, eventually aiding in preventing the rapid spread of the disease. With the intention to provide better forecasting tools for the management of the pandemic, the current research work analyzes the effect of the inclusion of environmental parameters in the forecasting of daily COVID-19 cases. Three univariate variants of the long short-term memory (LSTM) model (basic/vanilla, stacked, and bi-directional) were employed for the prediction of daily cases in 9 cities across 3 countries with varying climatic zones (tropical, sub-tropical, and frigid), namely India (New Delhi and Nagpur), USA (Yuma and Los Angeles) and Sweden (Stockholm, Skane, Uppsala and Vastra Gotaland). The results were compared to a basic multivariate LSTM model with environmental parameters (temperature (T) and relative humidity (RH)) as additional inputs. Periods with no or minimal lockdown were chosen specifically in these cities to observe the uninhibited spread of COVID-19 and explore its dependence on daily environmental parameters. The multivariate LSTM model showed the best overall performance; the mean absolute percentage error (MAPE) showed an average of 64% improvement from other univariate models upon the inclusion of the above environmental parameters. Correlation with temperature was generally positive for the cold regions and negative for the warm regions. RH showed mixed correlations, most likely driven by its temperature dependence and effect of allied local factors. The results suggest that the inclusion of environmental parameters could significantly improve the performance of LSTMs for predicting daily cases of COVID-19, although other positive and negative confounding factors can affect the forecasting power.

Is safe distance enough to prevent COVID-19? Dispersion and tracking of aerosols in various artificial ventilation conditions using OpenFOAM.

The current COVID-19 pandemic has underlined the importance of learning more about aerosols and particles that migrate through the airways when a person sneezes, coughs and speaks. The coronavirus transmission is influenced by particle movement, which contributes to the emergence of regulations on social distance, use of masks and face shield, crowded assemblies, and daily social activity in domestic, public, and corporate areas. Understanding the transmission of aerosols under different micro-environmental conditions, closed, or ventilated, has become extremely important to regulate safe social distances. The present work attempts to simulate the airborne transmission of coronavirus-laden particles under different respiratory-related activities, i.e., coughing and speaking, using CFD modelling through OpenFOAM v8. The dispersion coupled with the Discrete Phase Method (DPM) has been simulated to develop a better understanding of virus carrier particles transmission processes and their path trailing under different ventilation scenarios. The preliminary results of this study with respect to flow fields were in close agreement with published literature, which was then extended under varied ventilation scenarios and respiratory-related activities. The study observed that improper wearing of mask leads to escape of SARS-CoV-2 containminated aerosols having a smaller aerodynamic diameter from the gap between face mask and face, infecting different surfaces in the vicinity. It was also observed that aerosol propagation infecting the area through coughing is a faster phenomenon compared to the propagation of coronavirus-laden particles during speaking. The study's findings will help decision-makers formulate common but differentiated guidelines for safe distancing under different micro-environmental conditions.

Test of a Homeopathic Algorithm for COVID-19: the Importance of a Broad Perspective.

BACKGROUND/OBJECTIVE: Most of the symptoms of coronavirus disease 2019 (COVID-19) are covered by large repertory rubrics and hence many remedies have been proposed as "genus epidemicus". The aim of this study was to combine the information from various data collections to prepare a COVID-19 Bayesian mini-repertory/an algorithm-based application (app) and test it. METHODS: In July 2021, 1,161 COVID-19 cases from 100 practitioners globally were combined. These data were used to calculate "condition-confined" likelihood ratios (LRs) for 59 symptoms of COVID-19. Out of these, 35 symptoms of the 11 medicines that had at least 20 cases each were considered. The information was entered in a spreadsheet (algorithm) to calculate combined LRs of specific combinations of symptoms. The algorithm contained the medicines Arsenicum album, Belladonna, Bryonia alba, Camphora, Gelsemium sempervirens, Hepar sulphuris, Mercurius solubilis, Nux vomica, Phosphorus, Pulsatilla and Rhus toxicodendron. To test concordance, the doctors were then invited to re-enter the symptoms of their cases into this algorithm. RESULTS: The algorithm was re-tested on 358 cases, and concordance was seen in 288 cases. On analysis of the data, bias was noticed in the Merc group, which was therefore excluded from the algorithm. The remaining 10 medicines, representing 81.8% of all cases, were included in the preparation of the next version of the homeopathic mini-repertory and app. CONCLUSION: The Bayesian mini-repertory and app is based on qualitative clinical experiences of various doctors in COVID-19 and gives indications for specific medicines for common COVID-19 symptoms. It is freely available [English: https://hpra.co.uk/; Spanish: https://hpra.co.uk/es ] for further testing and utilization by the profession.

Homeopathic Medicines in Second Wave of COVID-19: Prognostic Factor Research.

BACKGROUND/OBJECTIVE: The clinical profile and course of COVID-19 evolved perilously in a second wave, leading to the use of various treatment modalities that included homeopathy. This prognostic factor research (PFR) study aimed to identify clinically useful homeopathic medicines in this second wave. METHODS: This was a retrospective, multi-centred observational study performed from March 2021 to May 2021 on confirmed COVID-19 cases who were either in home isolation or at COVID Care Centres in Delhi, India. The data were collected from integrated COVID Care Centres where homeopathic medicines were prescribed along with conventional treatment. Only those cases that met a set of selection criteria were considered for analysis. The likelihood ratio (LR) was calculated for the frequently occurring symptoms of the prescribed medicines. An LR of 1.3 or greater was considered meaningful. RESULTS: Out of 769 confirmed COVID-19 cases reported, 514 cases were selected for analysis, including 467 in home isolation. The most common complaints were cough, fever, myalgia, sore throat, loss of taste and/or smell, and anxiety. Most cases improved and there was no adverse reaction. Certain new symptoms, e.g., headache, dryness of mouth and conjunctivitis, were also seen. Thirty-nine medicines were prescribed, the most frequent being Bryonia alba followed by Arsenicum album, Pulsatilla nigricans, Belladonna, Gelsemium sempervirens, Hepar sulphuris, Phosphorus, Rhus toxicodendron and Mercurius solubilis. By calculating LR, the prescribing indications of these nine medicines were ascertained. CONCLUSION: Add-on use of homeopathic medicines has shown encouraging results in the second wave of COVID-19 in integrated care facilities. Further COVID-related research is required to be undertaken on the most commonly prescribed medicines.

Pollution free UV-C radiation to mitigate COVID-19 transmission.

The high rate of transmission of the COVID-19 virus has brought various types of disinfection techniques, for instance, hydrogen peroxide vaporization, microwave generating steam, UV radiation, and dry heating, etc. to prevent the further transmission of the virus. The chemical-based techniques are predominantly used for sanitization of hands, buildings, hospitals, etc. However, these chemicals may affect the health of humans and the environment in unexplored aspects. Furthermore, the UV lamp-based radiation sanitization technique had been applied but has not gained larger acceptability owing to its limitation to penetrate different materials. Therefore, the optical properties of materials are especially important for the utilization of UV light on such disinfection applications. The germicidal or microorganism inactivation application of UV-C has only been in-use in a closed chamber, due to its harmful effect on human skin and the eye. However, it is essential to optimize UV for its use in an open environment for a larger benefit to mitigate the virus spread. In view of this, far UV-C (222nm) based technology has emerged as a potential option for the sanitization in open areas and degradation of microorganisms present in aerosol during the working conditions. Hence, in the present review article, efforts have been made to evaluate the technical aspects of UV (under the different spectrum and wavelength ranges) and the control of COVID 19 virus spread in the atmosphere including the possibilities of the human body sanitization in working condition.

A statistical analysis of tweets on covid-19 vaccine hesitancy utilizing opinion mining: an Indian perspective.

The world witnessed the emergence of a deadly virus in December 2019, later named COVID-19. The virus was found to be highly contagious, and so people across the world were highly prone to be affected by the virus. Being a virus-borne disease, developing a vaccine was one of the most promising remedies. Thus, research organizations across the globe started working on developing the vaccine. However, it was later found by many researchers that a large number of people were hesitant to receive the vaccine. This paper aims to study the acceptance and hesitancy levels of people in India and compares them with the acceptance and hesitancy levels of people from the UK, the USA, and the rest of the world by analyzing their tweets on Twitter. For this study, 2,98,452 tweets were fetched from January 2020 to March 2022 from Twitter, and 1,84,720 tweets from 1,22,960 unique users were selected based on their country of origin. Machine learning based Sentiment analysis is then used to evaluate and analyze the tweets. The paper also proposes an NLP-based algorithm to perform opinion mining on Twitter data. The study found the public sentiment of the Indian population to be 63% positive, 28% neutral, and 9% negative. While the worldwide sentiment distribution is 45% positive, 34% neutral, and 21% negative, the USA has 42% positive, 34% neutral, and 23% negative and the UK has 50% positive, 29% neutral, and 21% negative. Also, sentiment analysis for individual vaccines in Indian context resulted in "Covaxin" with the highest positive sentiment at 43% followed by "Covishield" at 36%. The outcome of this work yields an insight into the public perception of the COVID-19 vaccine and thus can be used to formulate policies for existing and future vaccine campaigns. This study becomes more relevant as it is the consolidated opinion of Indian people, which is versatile in nature.

Circular economy based approach for green energy transitions and climate change benefits

Concerning rapid resource depletion and the negative effects of climate change, the adaptation of Circular Economy (CE) strategies in the environmental sector is gaining global recognition and application. This study forecasts the energy demand and emissions scenarios for a circular economy-dependent green energy transition, based on learnings from the forced situation caused by the COVID-19 pandemic. This study focuses on the industrial, domestic, and transportation sectors of the National Capital Territory (NCT) of India i.e., Delhi. Implementation of circular economy strategies helps in limiting the impacts of rising energy demand by shifting towards green fuels and biofuels. The data related to energy demand, consumption, percentage share and growth, etc., was gathered and incorporated in Low Emission Analysis Platform model to generate three scenarios i.e., business as usual, circular economy (CE), and pandemic scenario to compare the outputs of energy demand and emissions in terms of CO2 and particulate matter. The results for the CE scenario, for the year 2020 to 2040 shows that there will be a reduction of 158.2 kt PM2.5 emissions (24.3%) and 540 Mt CO2 emissions (49%) as well as a reduction of 203 Mtoe total energy usage (49.3%) as compared to the business-as-usual scenario. The COVID-19 pandemic had a significant impact on societal and commercial activities in the concerned city. Activities came to a standstill during the COVID-19 pandemic, but the same had significantly improved the environment. This unusual forced situation of COVID-19 lockdown produced a unique scenario which shows that the total extra reduction in energy demand of 46%, CO2 and PM2.5 emissions of 45–60% could be achieved by 2040, as compared to CE scenario. Graphical Supplementary Information The online version contains supplementary material available at 10.1007/s43538-022-00137-7.

Air pollution aggravating COVID-19 lethality? Exploration in Asian cities using statistical models.

The present work estimates the increased risk of coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 by establishing the linkage between the mortality rate in the infected cases and the air pollution, specifically Particulate Matters (PM) with aerodynamic diameters ≤ 10 µm and ≤ 2.5 µm. Data related to nine Asian cities are analyzed using statistical approaches, including the analysis of variance and regression model. The present work suggests that there exists a positive correlation between the level of air pollution of a region and the lethality related to COVID-19, indicating air pollution to be an elemental and concealed factor in aggravating the global burden of deaths related to COVID-19. Past exposures to high level of PM2.5 over a long period, is found to significantly correlate with present COVID-19 mortality per unit reported cases (p < 0.05) compared to PM10, with non-significant correlation (p = 0.118). The finding of the study can help government agencies, health ministries and policymakers globally to take proactive steps by promoting immunity-boosting supplements and appropriate masks to reduce the risks associated with COVID-19 in highly polluted areas.

Co-Yudh: A Convolutional Neural Network (CNN)-Inspired Platform for COVID Handling and Awareness.

The COVID-19 pandemic has been a menace to the World. According to WHO, a mortality rate of 1.99% is reported as of 28th November 2021. The need of the hour is to implement certain safety measures that may not eradicate but at least put a restriction on the rising number of COVID-19 cases all over the World. To ensure that the COVID-19 protocols are being abided by, a Convolutional Neural Network (CNN)-based framework "Co-Yudh" is being developed that comprises features like detecting face masks and social distancing, tracking the number of COVID-19 cases, and providing an online medical consultancy. The paper proposes two algorithms based on CNN for implementing the above features such as real-time face mask detection using the Transfer Learning approach in which the MobileNetV2 model is used which is trained on the Simulated Masked Face Dataset (SMFD). Further, the trained model is evaluated on the novel dataset-Mask Evaluation Dataset (MED). Additionally, the YOLOv4 model is used for detecting social distancing. It also uses web scraping for tracking the number of COVID-19 cases which updates on a daily basis. This is an easy-to-use framework that can be installed in various workplaces and can serve all the purposes to keep a check on the COVID-19 protocols in the area. Our preliminary results are quite satisfactory when tested against different environmental variables and show promising avenues for further exploration of the technique. The proposed framework is a more improved version of the existing works done so far.

Understanding COVID-19 transmission through Bayesian probabilistic modeling and GIS-based Voronoi approach: a policy perspective.

Originating from Wuhan, China, COVID-19 is spreading rapidly throughout the world. The transmission rate is reported to be high for this novel strain of coronavirus, called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as compared to its predecessors. Major strategies in terms of clinical trials of medicines and vaccines, social distancing, use of personal protective equipment (PPE), and so on are being implemented in order to control the spread. The current study concentrates on lockdown and social distancing policy followed by the Indian Government and evaluates its effectiveness using Bayesian probability model (BPM). The change point analysis (CPA) done through the above approach suggests that the states which implemented the lockdown before the exponential rise of cases are able to control the spread of the disease in a much better and efficient way. The analysis has been done for states of Maharashtra, Gujarat, Madhya Pradesh, Rajasthan, Tamil Nadu, West Bengal, Uttar Pradesh, and Delhi as union territory. The highest value of Δ (delta) is reported for Gujarat and Madhya Pradesh with a value of 9.6 weeks, while the lowest value is 4.7, evidently for Maharashtra which is the worst affected. All of the states indicate a significant correlation (p < 0.05, tstat > tcritical) for Δ, i.e., the difference in the time period of CPA and lockdown with cases per population (CPP) and cases per unit area (CPUA), while weak correlation (p < 0.1 and tstat < tcritical) is exhibited by delta and cases per unit population density (CPD). For both CPP and CPUA, tstat > tcritical indicating a significant correlation, while Pearson's correlation indicates the direction to be negative. Further analysis in terms of identification of high-risk areas has been studied from the Voronoi approach of GIS based on the inputs from BPM. All the states follow the above pattern of high population, high case scenario, and the boundaries of risk zones can be identified by Thiessen polygon (TP) constructed therein. The findings of the study help draw strategic and policy-driven response for India, toward tackling COVID-19 pandemic.

The Occurrence Birth-Death Process for Combined-Evidence Analysis in Macroevolution and Epidemiology.

Phylodynamic models generally aim at jointly inferring phylogenetic relationships, model parameters, and more recently, the number of lineages through time, based on molecular sequence data. In the fields of epidemiology and macroevolution, these models can be used to estimate, respectively, the past number of infected individuals (prevalence) or the past number of species (paleodiversity) through time. Recent years have seen the development of "total-evidence" analyses, which combine molecular and morphological data from extant and past sampled individuals in a unified Bayesian inference framework. Even sampled individuals characterized only by their sampling time, that is, lacking morphological and molecular data, which we call occurrences, provide invaluable information to estimate the past number of lineages. Here, we present new methodological developments around the fossilized birth-death process enabling us to (i) incorporate occurrence data in the likelihood function; (ii) consider piecewise-constant birth, death, and sampling rates; and (iii) estimate the past number of lineages, with or without knowledge of the underlying tree. We implement our method in the RevBayes software environment, enabling its use along with a large set of models of molecular and morphological evolution, and validate the inference workflow using simulations under a wide range of conditions. We finally illustrate our new implementation using two empirical data sets stemming from the fields of epidemiology and macroevolution. In epidemiology, we infer the prevalence of the coronavirus disease 2019 outbreak on the Diamond Princess ship, by taking into account jointly the case count record (occurrences) along with viral sequences for a fraction of infected individuals. In macroevolution, we infer the diversity trajectory of cetaceans using molecular and morphological data from extant taxa, morphological data from fossils, as well as numerous fossil occurrences. The joint modeling of occurrences and trees holds the promise to further bridge the gap between traditional epidemiology and pathogen genomics, as well as paleontology and molecular phylogenetics. [Birth-death model; epidemiology; fossils; macroevolution; occurrences; phylogenetics; skyline.].

Simple manipulative of gross organization of skeletal muscle: enhancing learning among students offline and online.

Understanding the gross organization of skeletal muscle is critical to understanding the mechanism of action of muscle physiology. Due to coronavirus disease-19 (COVID-19), many colleges have had to discontinue or curtail teaching and laboratory activities. Whether students are in the classroom or learning online, it is important for them to understand the basics of skeletal muscle organization that allows for movement. Manipulatives have been shown to enhance student learning and understanding in many fields, including physiology. This gives instructors an easy-to-follow tool for making a manipulative that allows students to see the organization of the skeletal muscle. Students can make this manipulative themselves from supplies commonly found in the home or office.

A novel G-quadruplex aptamer-based spike trimeric antigen test for the detection of SARS-CoV-2.

The recent SARS-CoV-2 outbreak has been declared a global health emergency. It will take years to vaccinate the whole population to protect them from this deadly virus, hence the management of SARS-CoV-2 largely depends on the widespread availability of an accurate diagnostic test. Toward addressing the unmet need of a reliable diagnostic test in the current work by utilizing the power of Systematic Evolution of Ligands by EXponential enrichment, a 44-mer G-quadruplex-forming DNA aptamer against spike trimer antigen of SARS-CoV-2 was identified. The lead aptamer candidate (S14) was characterized thoroughly for its binding, selectivity, affinity, structure, and batch-to-batch variability by utilizing various biochemical, biophysical, and in silico techniques. S14 has demonstrated a low nanomolar KD, confirming its tight binding to a spike antigen of SARS-CoV-2. S14 can detect as low as 2 nM of antigen. The clinical evaluation of S14 aptamer on nasopharyngeal swab specimens (n = 232) has displayed a highly discriminatory response between SARS-CoV-2 infected individuals from the non-infected one with a sensitivity and specificity of ∼91% and 98%, respectively. Importantly, S14 aptamer-based test has evinced a comparable performance with that of RT-PCR-based assay. Altogether, this study established the utility of aptamer technology for the detection of SARS-CoV-2.