Structural implications of SARS-CoV-2 Surface Glycoprotein N501Y mutation within receptor-binding domain [499-505]–computational analysis of the most frequent Asn501 polar uncharged amino acid mutations

The aim of this study was to evaluate the impact of the most frequent Asn501 polar uncharged amino acid mutations upon important structural properties of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) Surface Glycoprotein RBD–hACE2 (human angiotensin-converting enzyme 2) heterodimer. Mutations N501Y, N501T and N501S were considered and their impact upon complex solubility, secondary motifs formation and intermolecular hydrogen bonding interface was analyzed. Results and findings are reported based on 50 ns run in Gromacs molecular dynamics simulation software. Special attention is paid on the biomechanical shifts in the receptor-binding domain (RBD) [499-505]: ProThrAsn(Tyr)GlyValGlyTyr, having substituted Asparagine to Tyrosine at position 501. The main findings indicate that the N501S mutation increases SARS-CoV-2 S-protein RBD–hACE2 solubility over N501T, N501 (wild type): (Formula presented.), (Formula presented.). The N501Y mutation shifts (Formula presented.) -helix S-protein RBD [366-370]: SerValLeuTyrAsn into π-helix for t > 38.5 ns. An S-protein RBD [503-505]: ValGlyTyr shift from (Formula presented.) -helix into a turn is observed due to the N501Y mutation in t > 33 ns. An empirical proof for the presence of a Y501-binding pocket, based on RBD [499-505]: PTYGVGY (Formula presented.) 's RMSF peak formation is presented. There is enhanced electrostatic interaction between Tyr505 (RBD) phenolic -OH group and Glu37 (hACE2) side chain oxygen atoms due to the N501Y mutation. The N501Y mutation shifts the (Formula presented.) hydrogen bond into permanent polar contact;(Formula presented.);(Formula presented.). © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Machine learning and data analysis-based study on the health issues post-pandemic.

The COVID-19 pandemic has had significant impacts on the health of individuals and communities around the world. While the immediate health impacts of the virus itself are well-known, there are also a number of post-pandemic health issues that have emerged as a result of the pandemic. The pandemic has caused increased levels of anxiety, depression, and other mental health issues among people of all ages. The isolation, uncertainty, and grief caused by the pandemic have taken a toll on people's mental well-being, and there is a growing concern that the long-term effects of the pandemic on mental health could be severe. Many people have delayed or avoided medical care during the pandemic, which could lead to long-term health problems. Additionally, people who have contracted COVID-19 may experience ongoing symptoms, such as fatigue, shortness of breath, and muscle weakness, which could impact their long-term health. Machine learning (ML) can be a powerful tool to analyze the health impact of the post-pandemic period. With the vast amounts of data available from electronic health records, public health databases, and other sources, this article is making use of ML methods which can help identify patterns and insights to conclude the study. The proposed ML models can analyze health data to identify trends and patterns that may indicate future health problems. By monitoring patterns in medical records and public health data, the proposed ML model can help public health officials detect and respond to outbreaks more quickly. The survey outcome reveals that the level of physical activities has been decreased by 22% during COVID-19-outbreak. The variance is shown at 49% during COVID-19 outbreak. The absence of physical activity (PA) and perceived stress (PS) are observed to be suggestively correlated with the QoL (quality of life) of adults. Deteriorated mental health also disrupts the normal lives and impacts the sleeping quality of people. The analysis of the data is performed using statistical analytical tools to depict the consequences of pandemic on the health of individuals aged between 50 to 80 years.

Computational analysis of a new biomimetic active ventilation paradigm for indoor spaces

Purpose: Ventilation of indoor spaces is required for the delivery of fresh air rich in oxygen and the removal of carbon dioxide, pollutants and other hazardous substances. The COVID-19 pandemic brought the topic of ventilating crowded indoors to the front line of health concerns. This study developed a new biologically inspired concept of biomimetic active ventilation (BAV) for interior environments that mimics the mechanism of human lung ventilation, where internal air is continuously refreshed with the external environment. The purpose of this study is to provide a detailed proof-of-concept of the new BAV paradigm using computational models. Design/methodology/approach: This study developed computational fluid dynamic models of unoccupied rooms with two window openings on one wall and two BAV modules that periodically translate perpendicular to or rotate about the window openings. This study also developed a time-evolving spatial ventilation efficiency metric for exploring the accumulated refreshment of the interior space. The authors conducted two-dimensional (2D) simulations of various BAV configurations to determine the trends in how the working parameters affect the ventilation and to generate initial estimates for the more comprehensive three-dimensional (3D) model. Findings: Simulations of 2D and 3D models of BAV for modules of different shapes and working parameters demonstrated air movements in most of the room with good air exchange between the indoor and outdoor air. This new BAV concept seems to be very efficient and should be further developed. Originality/value: The concept of ventilating interior spaces with periodically moving rigid modules with respect to the window openings is a new BAV paradigm that mimics human respiration. The computational results demonstrated that this new paradigm for interior ventilation is efficient while air velocities are within comfortable limits. © 2023, Emerald Publishing Limited.

Computational Analysis of Receptor-Binding Domains of SARS-CoV-2 to Reveal the Mechanism of Immune Escape

Covid-19 has become a world pandemic for years. With the appearance of mutations, immune escape has become a problem, reducing the effectiveness of vaccines and antibodies. To reveal the mechanism of immune escape, we analyze the geometrical properties of the receptor-binding domain in the SARS-CoV-2 spike protein, which plays a vital role in the immune reaction. Several important variants are taken as examples, and the wild type model is prepared as a reference. The computational method is applied to simulate the behaviors of the models, and alpha shape algorithm is employed to extract geometrical data of the protein surface. Average moving distance of the surface atoms is used to quantify their activity. Our results show that the mutations changed the properties of the protein. The variants have different distributions of active sites, which may change the specific antigenicity and influence the binding abilities of drugs and antibodies. This study explains the mechanism of immune escape of SARS-CoV-2, and provides a geometrical method to find potential new target sites for the design of drugs and vaccines. © 2022 IEEE.

Urine-based multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS.

BACKGROUND: Acute respiratory distress syndrome (ARDS), a life-threatening condition during critical illness, is a common complication of COVID-19. It can originate from various disease etiologies, including severe infections, major injury, or inhalation of irritants. ARDS poses substantial clinical challenges due to a lack of etiology-specific therapies, multisystem involvement, and heterogeneous, poor patient outcomes. A molecular comparison of ARDS groups holds the potential to reveal common and distinct mechanisms underlying ARDS pathogenesis. METHODS: We performed a comparative analysis of urine-based metabolomics and proteomics profiles from COVID-19 ARDS patients (n = 42) and bacterial sepsis-induced ARDS patients (n = 17). To this end, we used two different approaches, first we compared the molecular omics profiles between ARDS groups, and second, we correlated clinical manifestations within each group with the omics profiles. RESULTS: The comparison of the two ARDS etiologies identified 150 metabolites and 70 proteins that were differentially abundant between the two groups. Based on these findings, we interrogated the interplay of cell adhesion/extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis through a multi-omic network approach. Moreover, we identified a proteomic signature associated with mortality in COVID-19 ARDS patients, which contained several proteins that had previously been implicated in clinical manifestations frequently linked with ARDS pathogenesis. CONCLUSION: In summary, our results provide evidence for significant molecular differences in ARDS patients from different etiologies and a potential synergy of extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis. The proteomic mortality signature should be further investigated in future studies to develop prediction models for COVID-19 patient outcomes.

Optimal Control Strategy to Combat the Spread of Covid-19 in Absence of Effective Vaccine

Many regions of the world are now facing the second wave of boomed cases of COVID-19. This time, the second wave of this highly infectious disease (COVID-19) is becoming more devastating. To control the existing situation, more mass testing, and tracing of COVID-19 positive individuals are required. Furthermore, practicing to wear a face mask and maintenance of physical distancing are strongly recommended for everyone. Taking all these into consideration, an optimal control problem has been reformulated in terms of nonlinear ordinary differential equations in this paper. The aim of this study is to explore the control strategy of coronavirus-2 disease (COVID-19) and thus, minimize the number of symptomatic, asymptomatic and infected individuals as well as cost of the controls measures. The optimal control model has been analyzed analytically with the help of the necessary conditions of very well-known Pontryagin's maximum principle. Numerical simulations of the optimal control problem are also performed to illustrate the results.

Severe acute respiratory syndrome coronavirus-2: An era of struggle and discovery leading to the emergency use authorization of treatment and prevention measures based on computational analysis

Severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2), a novel betacoronavirus, has surprised the world with its disease spread and mortality rate. SARS-CoV-2 is a positive-sense, enveloped RNA virus that can infect various organs of the body, potentially leading to multiple organ dysfunction and eventual death. While various medications have received emergency use authorizations (EUAs) for the treatment of Coronavirus disease-2019 (COVID-19), as of April 30, 2021, only one drug has been Food and Drug Administration (FDA)-approved: remdesivir. Currently, three vaccines have received EUAs in the United States, but none are FDA-approved. This shortage of treatments and prevention measures is extremely problematic. Thus computational approaches would provide important data about drug resistance and variants. Such data will be useful for the development of drugs and vaccines. This chapter is a synopsis of SARS-CoV-2 clinical presentation, COVID-19 symptomology, treatment, prevention mechanisms, and SARS-CoV-2 variants using computational analysis. © 2022 Elsevier Inc. All rights reserved.

Community implications for gun violence prevention during co-occurring pandemics; a qualitative and computational analysis study.

This study provides insight into New York City residents' perceptions about violence after the outbreak of Coronavirus disease (COVID-19) based on information from communities in New York City Housing Authority (NYCHA) buildings. In this novel analysis, we used focus group and social media data to confirm or reject findings from qualitative interviews. We first used data from 69 in-depth, semi-structured interviews with low-income residents and community stakeholders to further explore how violence impacts New York City's low-income residents of color, as well as the role of city government in providing tangible support for violence prevention during co-occurring health (COVID-19) and social (anti-Black racism) pandemics. Residents described how COVID-19 and the Black Lives Matter movement impacted safety in their communities while offering direct recommendations to improve safety. Residents also shared recommendations that indirectly improve community safety by addressing long term systemic issues. As the recruitment of interviewees was concluding, researchers facilitated two focus groups with 38 interviewees to discuss similar topics. In order to assess the degree to which the themes discovered in our qualitative interviews were shared by the broader community, we developed an integrative community data science study which leveraged natural language processing and computer vision techniques to study text and images on public social media data of 12 million tweets generated by residents. We joined computational methods with qualitative analysis through a social work lens and design justice principles to most accurately and holistically analyze the community perceptions of gun violence issues and potential prevention strategies. Findings indicate valuable community-based insights that elucidate how the co-occurring pandemics impact residents' experiences of gun violence and provide important implications for gun violence prevention in a digital era.

Computational and Experimental Approaches to Study the RNA Secondary Structures of RNA Viruses.

Most pandemics of recent decades can be traced to RNA viruses, including HIV, SARS, influenza, dengue, Zika, and SARS-CoV-2. These RNA viruses impose considerable social and economic burdens on our society, resulting in a high number of deaths and high treatment costs. As these RNA viruses utilize an RNA genome, which is important for different stages of the viral life cycle, including replication, translation, and packaging, studying how the genome folds is important to understand virus function. In this review, we summarize recent advances in computational and high-throughput RNA structure-mapping approaches and their use in understanding structures within RNA virus genomes. In particular, we focus on the genome structures of the dengue, Zika, and SARS-CoV-2 viruses due to recent significant outbreaks of these viruses around the world.

An Insight Based on Computational Analysis of the Interaction between the Receptor-Binding Domain of the Omicron Variants and Human Angiotensin-Converting Enzyme 2.

Concerns have been raised about the high number of mutations in the spike protein of the new emergence of the highly transmissible Omicron variant (B.1.1529 lineage) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This variant's extraordinary ability to evade antibodies would significantly impair the current vaccination program. This present study aimed to computationally analyze the interaction between the receptor-binding domain (RBD) in the spike protein of Omicron variants and human angiotensin-converting enzyme 2 (hACE2). The docking results indicated that Omicron BA.2 has exceptionally strong interactions with hACE2 in comparison to Omicron BA.1, Delta, and wild-type, as indicated by various parameters such as salt bridge, hydrogen bond, and non-bonded interactions. The results of the molecular dynamics simulation study corroborate these findings, indicating that Omicron BA.2 has a strong and stable interaction with hACE2. This study provides insight into the development of an effective intervention against this variant.

Structural interactions of phytoconstituent(s) from cinnamon, bay leaf, oregano, and parsley with SARS-CoV-2 nucleocapsid protein: A comparative assessment for development of potential antiviral nutraceuticals.

SARS-CoV-2 has been responsible for causing 6,218,308 deaths globally till date and has garnered worldwide attention. The lack of effective preventive and therapeutic drugs against SARS-CoV-2 has further worsened the scenario and has bolstered research in the area. The N-terminal and C-terminal RNA binding domains (NTD and CTD) of SARS-CoV-2 nucleocapsid protein represent attractive therapeutic drug targets. Naturally occurring compounds are an excellent source of novel drug candidates due to their structural diversity and safety. Ten major bioactive compounds were identified in ethanolic extract (s) of Cinnamomum zeylanicum, Cinnamomum tamala, Origanum vulgare, and Petroselinum crispum using HPLC and their cytotoxic potential was determined against cancer and normal cell lines by MTT assay to ascertain their biological activity in vitro. To evaluate their antiviral potential, the binding efficacy to NTD and CTD of SARS-CoV-2 nucleocapsid protein was determined using in silico biology tools. In silico assessment of the phytocomponents revealed that most of the phytoconstituents displayed a druglike character with no predicted toxicity. Binding affinities were in the order apigenin > catechin > apiin toward SARS-CoV-2 nucleocapsid NTD. Toward nucleocapsid CTD, the affinity decreased as apigenin > cinnamic acid > catechin. Remdesivir displayed lesser affinity with NTD and CTD of SARS-CoV-2 nucleocapsid proteins than any of the studied phytoconstituents. Molecular dynamics (MD) simulation results revealed that throughout the 100 ns simulation, SARS-CoV-2 nucleocapsid protein NTD-apigenin complex displayed greater stability than SARS-CoV-2 nucleocapsid protein NTD-cinnamic acid complex. Hence, apigenin, catechin, apiin and cinnamic acid might prove as effective prophylactic and therapeutic candidates against SARS-CoV-2, if examined further in vitro and in vivo. PRACTICAL APPLICATIONS: Ten major bioactive compounds were identified in the extract(s) of four medicinally important plants viz. Cinnamomum zeylanicum, Cinnamomum tamala, Origanum vulgare and Petroselinum crispum using HPLC and their biological activity was also evaluated against cancer and normal cell lines. Interestingly, while all extract(s) wielded significant cytotoxicity against cancer cells, no significant toxicity was found against normal cells. The outcome of the results prompted evaluation of the antiviral potential of the ten bioactive compounds using in silico biology tools. The present study emphasizes on the application of computational approaches to understand the binding interaction and efficacy of the ten bioactive compounds from the above plants with SARS-CoV-2 nucleocapsid protein N-terminal and C-terminal RNA binding domains in preventing and/or treating COVID-19 using in silico tools. Druglikeness and toxicity profiles of the compounds were carried out to check the therapeutic application of the components. Additionally, molecular dynamics (MD) simulation was performed to check the stability of ligand-protein complexes. The results provided useful insights into the structural binding interaction(s) that can be exploited for the further development of potential antiviral agents targeting SARS-CoV-2 especially since no specific therapy is still available to combat the rapidly evolving virus and the existing treatment is more or less symptomatic which makes search for novel antiviral agents all the more necessary and crucial.

Interaction of compounds derived from the Chinese medicinal formula Huangqi Guizhi Wuwu Tang with stroke-related numbness and weakness targets: An in-silico docking and molecular dynamics study.

Huangqi Guizhi Wuwu Tang (HGWT) is a traditional Chinese herbal formula used for managing post-stroke symptoms. Existing research have supported the use of this formula particularly for stroke-related numbness and weakness (SRNW); however, their mechanisms of actions are not fully understood. This study aims to investigate the molecular mechanisms of components from HGWT targeting specific proteins related to numbness and weakness through computational docking and molecular dynamics (MD) simulations. A total of 786 compounds from HGWT were retrieved from a herbal compound database and docked against a candidate SRNW target protein, with the asernestioside B (HQ068)-mitogen-activated protein kinase 3 (MAPK3) complex predicted to exhibit the highest binding affinity (-10.4 kcal/mol) and number of ligand-receptor contacts. Subsequent molecular dynamics (MD) simulations were performed in triplicate on the apo-MAPK3 protein and asernestioside B -bound form in a solvated system for 200 ns per trajectory to ascertain the stability of the enzyme-ligand complex, and to determine the structural impact of ligand binding. The stability of the complex and overall tertiary structural changes were characterized using root-mean-square deviation (RMSD), radius of gyration (Rg), root-mean-square fluctuation (RMSF) calculations Differences in the RMSF of apo and ligand-bound MAPK3 were most prominent in three major regions: (a) activation loop Asp184:Pro213 (b) MAPK3 insertion site Gly262:Ala291 and (c) loop region at the C-terminus Tyr334:Pro356. Lower values of RMSF for the HQ068-bound protein at the activation loop suggest that HQ068 binding stabilizes MAPK3 in a different conformation in this region compared to the apo protein. Free energy calculations of the asernestioside B-MAPK3 complex revealed key residues contributing to the interaction, which include Pro264, Gln 266, Asp268 and Thr288. These key residues may play an integral role in the binding of selective modulators or substrates of extracellular signal-regulated kinase (ERK) within the MAPK cascade. Overall, this study provides a mechanistic overview of compounds from HGWT. Modelling predicted that asernestioside B may act with high potency against MAPK3, while exhibiting a favourable ADMET profile, and this compound should be explored as a potential agent to alleviate SRNW-related symptoms in future studies.

Novel thermal treatment model to decontaminate airborne SARS Cov-2 virus for residential and commercial buildings

In the COVID-19 pandemic, control of airborne virus transmission is exceptionally challenging as it is attached to suspended particles in the air and stays for an extended time. Air contaminated with airborne viruses holds a substantial risk for household transmission. In this study, a novel thermal treatment system is modeled based on porous heating for the decontamination of airborne SARS-Cov-2. The model includes an air heating domain, insulated chamber, buffer tank and heat exchanger. The airborne SARS-Cov-2 is decontaminated when passing through a porous heat pipe and the insulated chamber for an anticipated dwelling period of more than 5?min at 105°C and further stored in a buffer tank for natural cooling. The obligatory decontaminated air is allowed in the residential space under ambient conditions passing through a heat exchanger. The numerical investigation of the porous pipe model at different L/D ratios with altered porosities aims to establish the best-performing porous domain. Besides this, the buffer tank is intended to maintain buffer storage of the treated air and significant natural cooling before passing to the heat exchanger. A solar PV module is proposed to meet the prerequisite energy requirements of the equipped devices.

A computational comparative analysis of the binding mechanism of molnupiravir's active metabolite to RNA-dependent RNA polymerase of wild-type and Delta subvariant AY.4 of SARS-CoV-2.

The antiviral drug molnupiravir targets the SARS-CoV-2 RNA-dependent RNA polymerase (RdRP) enzyme. Early treatment with molnupiravir reduced the risk of hospitalization or death in at-risk, unvaccinated adults with COVID-19, according to phase 3 clinical trials. Many mutations have occurred within this virus as a result of its widespread distribution. The current study sought to determine whether mutations in the RdRP of Delta subvariant AY.4 (D-AY.4 RdRP) influence the interaction of the enzyme with molnupiravir triphosphate (MTP), the active metabolite of molnupiravir. The interactions between the wild-type (WT) RdRP and D-AY.4 RdRP with MTP were evaluated based on molecular docking and dynamic simulation (MD) studies. The results show that the MTP interaction is stronger and more stable with D-AY.4 RdRP than with WT RdRP. This study provides insight into the potential significance of administering MTP to patients infected with D-AY.4 RdRP, which may have a more favorable chance of alleviating the illness. According to the findings of this study, MTP has a high likelihood of becoming widely used as an anti-SARS-CoV-2 agent. The fact that MTP is not only cytotoxic but also mutagenic to mammalian cells, as well as the possibility that it may cause DNA damage in the host, have all been raised as potential concerns.

A review on molecular docking analysis of phytocompounds against SARS-CoV-2 druggable targets

The novel beta-coronavirus, SARS-CoV-2, responsible for the coronavirus disease 2019 (COVID-19) emerged in China in December 2019. Due to its high transmission and infection rate, it has spread around the world and has transformed into a ravaging global pandemic with enormously unprecedented impacts globally on human, social, and economic health. Just like SARS-CoV and MERS-CoV, there is no specific antiviral drug for its treatment. The only available therapeutics are supportive and symptom-based. Thus, scientists are harnessing various strategies to expedite drug development. One such approach is drug repurposing through computational screening of phytocompounds, which leverages proteins that are essential for the entry, replication, pathogenesis, assembly, and release of SARS-CoV-2. Here, we review the available literature on molecular docking of phytoligands against SARS-CoV-2 integral proteins, in a bid to update our current knowledge and identify the most promising molecules. The overwhelming majority of the promising lead compounds are either phenolics or terpenoids. Furthermore, of the elucidated SARS-CoV-2 targets, the main protease (3CL(pro)) appears as one of the most attractive druggable targets. Notably, compounds such as rutin, quercetin, luteolin, neoandrographolide, curcumin, and others with evident anti-inflammatory benefits, in addition to their predicted anti-SARS-CoV-2 properties, deserve further studies to validate their activity.

Data on multiple SARS-CoV-2 surface glycoprotein alignments.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) surface glycoproteins deposited to the NCBI GenBank from Europe, by the mid of April 2021 (12.04.2021) were analysed. At least one amino acid mutation relative to YP_009724390.1 referent SARS-CoV-2 surface glycoprotein: Wuhan-Hu-1 complete genome (NCBI accession: NC_045512) was found in 788 SARS-CoV-2 surface glycoproteins. Data was computed by NCBI Cobalt multiple alignment tool [1] (one country by another) and structured by special purpose application developed in Visual Studio 2019. Advanced data structures were used to organize computed data. Linked dataset lists program output: SARS-CoV-2 surface glycoprotein mutations per processed protein.

Multiplex serological assay for establishing serological profiles of polymorphic, closely related peptide antigens.

Profiling of serological responses to establish the landscape of antibody specificities in individuals exposed to pathogens or vaccines is crucial for (a) revealing humoral immune correlates of protection; (b) uncovering markers of pathogen exposure; and (c) identifying antigens and epitopes associated with disease vs. protection. Establishing the antigenic profile of serological responses requires either expensive microarrays or labor- and time-intensive ELISA assays. Multiplex assay platforms are increasingly being evaluated for their usefulness for high-throughput testing of sera or plasma. The methodology described here utilizes a plate-based assay that allows the simultaneous detection of up to ten antigens per well in a 96 well format using an electrochemiluminescence immunoassay (ECLIA).•The newly developed protocol outlines high-throughput profiling of serological responses using a multiplex testing platform with subsequent computational analysis.•The protocol is a modification of the basic assay development manual from the manufacturer of the MESO QuickPlex SQ 120 instrument (MSD, Gaithersburg, MD) and can be used for synthetic peptides as well as full length proteins.•The protocol can be applied to map serological responses to pathogens or pathogen-derived antigens to establish serological profiles in search for biomarkers or immune correlates.

Anti-malarial Drugs are Not Created Equal for SARS-CoV-2 Treatment: A Computational Analysis Evidence.

BACKGROUND: The evolution of the pandemic has burdened the national healthcare systems worldwide and at present, there is no preferred antiviral treatment for COVID-19. Recently, the SARS-Cov-2 protease structure was released that may be exploited in in-silico studies in order to conduct molecular docking analysis. METHODS: In particular, we compared the binding of twoantimalarial drugs, already in use, (i.e. chloroquine and hydroxychloroquine), which showed some potential clinical effects on COVID-19 patients, using ritonavir, lopinavir and darunavir as positive control tree antiviral recognized compounds. RESULTS: Our results showed that hydroxychloroquine but not chloroquine exhibited a significant binding activity to the main protease similar to that possessed by protease inhibitors tested for other viral infections. CONCLUSION: Our data suggest that hydroxychloroquine may exert additional direct antiviral activity compared to chloroquine. In the absence of clinical studies comparing the efficacy of these two compounds, hydroxychloroquine may offer additional effects and may be considered as the first choice.

Raman spectroscopy-based detection of RNA viruses in saliva: A preliminary report.

Several non-invasive Raman spectroscopy-based assays have been reported for rapid and sensitive detection of pathogens. We developed a novel statistical model for the detection of RNA viruses in saliva, based on an unbiased selection of a set of 65 Raman spectral features that mostly attribute to the RNA moieties, with a prediction accuracy of 91.6% (92.5% sensitivity and 88.8% specificity). Furthermore, to minimize variability and automate the downstream analysis of the Raman spectra, we developed a GUI-based analytical tool "RNA Virus Detector (RVD)." This conceptual framework to detect RNA viruses in saliva could form the basis for field application of Raman Spectroscopy in managing viral outbreaks, such as the ongoing COVID-19 pandemic. (http://www.actrec.gov.in/pi-webpages/AmitDutt/RVD/RVD.html).