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Despite the effectiveness of COVID-19 vaccination in reducing the severity of the disease, the demand for booster is increasing in vulnerable populations like elderly and immunocompromised individuals especially with each new wave of COVID-19 in different countries. There is limited data on the sustained immunity against COVID-19 in patients with liver cirrhosis. The study was aimed to compare the T cell and humoral immune response after 1 year of ChAdOx1nCoV-19 Vaccine in patients with liver cirrhosis and healthy health care workers (HCW). This was a prospective observational study including 36 HCW, 19 liver cirrhosis patients and 10 unvaccinated individuals. Anti-SARS-CoV-2S antibody, neutralizing antibody and memory T cell subsets were evaluated by ELISA and flow cytometry, respectively, in all three groups after 1 year of initial vaccination. Compared to HCW and unvaccinated individuals, liver cirrhosis patients had significantly depleted T cells, although CD4:CD8 + T cell ratio was normal. Both cirrhotic patients and HCW developed memory T cell subset [effector memory RA (P = 0.141, P < 0.001), effector memory (P < 0.001, P < 0.001), central memory (P < 0.001, P < 0.01), stem cell memory (P = 0.009, P = 0.08) and naïve (P < 0.001, P = 0.02)] compared to unvaccinated unexposed individuals of CD4 + T and CD8 + T, respectively. However, among HCW and cirrhotic group no difference was noted on central memory and stem cell memory cells on T cells. Patients with liver cirrhosis developed comparable memory T cells after vaccination which can evoke sustainable immune response on reinfection. Therefore, additional vaccine doses may not be necessary for cirrhosis patients., (© 2024. The Author(s).)

Background: Sudden upsurge in cases of COVID-19 Associated Mucormycosis (CAM) following the second wave of the COVID-19 pandemic was recorded in India. This study describes the clinical characteristics, management and outcomes of CAM cases, and factors associated with mortality., Methods: Microbiologically confirmed CAM cases were enrolled from April 2021 to September 2021 from ten diverse geographical locations in India. Data were collected using a structured questionnaire and entered into a web portal designed specifically for this investigation. Bivariate analyses and logistic regression were conducted using R version 4.0.2., Results: A total of 336 CAM patients were enrolled; the majority were male (n = 232, 69.1%), literate (n = 261, 77.7%), and employed (n = 224, 66.7%). The commonest presenting symptoms in our cohort of patients were oro-facial and ophthalmological in nature. The median (Interquartile Range; IQR) interval between COVID diagnosis and admission due to mucormycosis was 31 (18, 47) days, whereas the median duration of symptoms of CAM before hospitalization was 10 (5, 20) days. All CAM cases received antifungal treatment, and debridement (either surgical or endoscopic or both) was carried out in the majority of them (326, 97.02%). Twenty-three (6.9%) of the enrolled CAM cases expired. The odds of death in CAM patients increased with an increase in HbA1c level (aOR: 1.34, 95%CI: 1.05, 1.72) following adjustment for age, gender, education and employment status., Conclusion: A longer vigil of around 4-6 weeks post-COVID-19 diagnosis is suggested for earlier diagnosis of CAM. Better glycemic control may avert mortality in admitted CAM cases., (© 2023. The Author(s), under exclusive licence to Springer Nature B.V.)

More than six and half million people have died as a result of the COVID-19 pandemic till Dec 2022. Vaccination is the most effective means to prevent mortality and infection attributed to COVID-19. Identifying public attitudes and perceptions on COVID-19 vaccination is essential to strengthening the vaccination programmes. This study aims to identify attitudes and perceptions of twitter users towards COVID-19 vaccinations in four different countries. A sentiment analysis of 663,377 tweets from October 2020 to September 2022 from four different countries (i.e., India, South Africa, UK, and Australia) was conducted. Text mining using roBERTA (Robustly Optimized Bert Pretraining approach) python library was used to identify the polarity of people's attitude as "negative", "positive" or "neutral" based on tweets. A sample of 2000 tweets (500 from each country) were thematically analysed to explore the people's perception concerning COVID-19 vaccines across the countries. The attitudes towards COVID-19 vaccines varied by countries. Negative attitudes were observed to be highest in India (58.48%), followed by United Kingdom (33.22%), Australia (31.42%) and South Africa (28.88%). Positive attitudes towards vaccines were highest in the United Kingdom (21.09%). The qualitative analysis yielded eight themes namely (i) vaccine shortages, (ii) vaccine side-effects, (iii) distrust on COVID-19 vaccines, (iv) voices for vaccine equity, (v) awareness about vaccines, (vi) myth busters, (vii) vaccines work and (viii) vaccines are safe. The twitter discourse reflected the evolving situation of COVID-19 pandemic and vaccination strategies, lacunae and positives in the respective countries studied., (© 2023. Springer Nature Limited.)

Identification of potential therapeutic candidates can be expedited by integrating computational modeling with domain aware machine learning (ML) models followed by experimental validation in an iterative manner. Generative deep learning models can generate thousands of new candidates, however, their physiochemical and biochemical properties are typically not fully optimized. Using our recently developed deep learning models and a scaffold as a starting point, we generated tens of thousands of compounds for SARS-CoV-2 M pro that preserve the core scaffold. We utilized and implemented several computational tools such as structural alert and toxicity analysis, high throughput virtual screening, ML-based 3D quantitative structure-activity relationships, multi-parameter optimization, and graph neural networks on generated candidates to predict biological activity and binding affinity in advance. As a result of these combined computational endeavors, eight promising candidates were singled out and put through experimental testing using Native Mass Spectrometry and FRET-based functional assays. Two of the tested compounds with quinazoline-2-thiol and acetylpiperidine core moieties showed IC[Formula: see text] values in the low micromolar range: [Formula: see text] [Formula: see text]M and 3.41±0.0015 [Formula: see text]M, respectively. Molecular dynamics simulations further highlight that binding of these compounds results in allosteric modulations within the chain B and the interface domains of the M pro . Our integrated approach provides a platform for data driven lead optimization with rapid characterization and experimental validation in a closed loop that could be applied to other potential protein targets., (© 2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Direct-acting antivirals for the treatment of the COVID-19 pandemic caused by the SARS-CoV-2 virus are needed to complement vaccination efforts. Given the ongoing emergence of new variants, automated experimentation, and active learning based fast workflows for antiviral lead discovery remain critical to our ability to address the pandemic's evolution in a timely manner. While several such pipelines have been introduced to discover candidates with noncovalent interactions with the main protease (M pro ), here we developed a closed-loop artificial intelligence pipeline to design electrophilic warhead-based covalent candidates. This work introduces a deep learning-assisted automated computational workflow to introduce linkers and an electrophilic "warhead" to design covalent candidates and incorporates cutting-edge experimental techniques for validation. Using this process, promising candidates in the library were screened, and several potential hits were identified and tested experimentally using native mass spectrometry and fluorescence resonance energy transfer (FRET)-based screening assays. We identified four chloroacetamide-based covalent inhibitors of M pro with micromolar affinities (K I of 5.27 μM) using our pipeline. Experimentally resolved binding modes for each compound were determined using room-temperature X-ray crystallography, which is consistent with the predicted poses. The induced conformational changes based on molecular dynamics simulations further suggest that the dynamics may be an important factor to further improve selectivity, thereby effectively lowering K I and reducing toxicity. These results demonstrate the utility of our modular and data-driven approach for potent and selective covalent inhibitor discovery and provide a platform to apply it to other emerging targets.

The hyperinflammatory immune response in severe COVID-19 infection shares features with secondary hemophagocytic lymphohistiocytosis (sHLH) in the form of fever, cytopenia, elevated inflammatory markers, and high mortality. There are contrasting opinions regarding utility of HLH 2004 or HScore in the diagnosis of severe COVID-19-related hyperinflammatory syndrome (COVID-HIS). This was a retrospective study of 47 patients of severe COVID-19 infection, suspected to have COVID-HIS and 22 patients of sHLH to other illnesses, to evaluate the diagnostic utility and limitations of HLH 2004 and/or HScore in context to COVID-HIS and to also evaluate the utility of Temple criteria for predicting severity and outcome in COVID-HIS. Clinical findings, hematological, and biochemical parameters along with the predictor of mortality were compared between two groups. Only 6.4% (3/47) of cases fulfilled ≥5/8 HLH 2004 criteria and only 40.52% (19/47) of patients showed HScore >169 in COVID-HIS group. 65.9% (31/47) satisfied the Temple criteria in COVID-HIS as compared with 40.9% (9/22) in the non-COVID group (p = 0.04). Serum ferritin (p = 0.02), lactate dehydrogenase (p = 0.02), direct bilirubin (p = 0.02), and C-reactive protein (p = 0.03) were associated with mortality in COVID-HIS. Both HScore and HLH-2004 criteria perform poorly for identifying COVID-HIS. Presence of bone marrow hemophagocytosis may help to identify about one-third of COVID-HIS missed by the Temple Criteria.

Background: The second wave of COVID-19 in India was followed by large number of mucormycosis cases. Indiscriminate use of immunosuppressive drugs, underlying diseases such as diabetes, cancers, or autoimmune diseases was thought to be the cause. However, the mortality was not as high as that seen in non-COVID mucormycosis., Objective: To study the detailed characteristics of T-cells for evaluating the underlying differences in the T-cell immune dysfunction in post-COVID and non-COVID mucor patients., Material and Method: The study included histopathologically confirmed cases of mucor (13 post-COVID, 13 non-COVID) and 15 healthy individuals (HI). Expression of T-cell activation (CD44, HLADR, CD69, CD38) and exhaustion (CTLA, PD-1, LAG-3 and TIM-3) markers was evaluated by flow cytometry., Results: All cases showed significant depletion of T-cells compared to HI. Both post-COVID and non-COVID groups showed increased activation and exhaustion as compared to HI. Non-COVID mucor group showed significant activation of CD4+ T cells for HLADR and CD38 (p = .025, p = .054) and marked T-cell exhaustion in form of expression of LAG-3 on both CD4+ T and CD8+ T cells in comparison with post-COVID patients (p = .011, p = .036). Additionally, co-expression of PD-1 & LAG-3 and LAG-3 & TIM-3 on CD8+ T cells was statistically significant in non-COVID mucor patients (p = .016, p = .027)., Conclusion: Immunosuppression in non-COVID mucor showed pronounced exhaustion of T-cells in comparison to post-COVID mucor cases implicating T-cell immune dysfunction is much more severe in non-COVID mucor which are in a state of continuous activation followed by extreme exhaustion leading to poorer outcome., (© 2022 Wiley-VCH GmbH.)

Indian data regarding serious neurological and psychiatric adverse events, following coronavirus disease 2019 (COVID-19) vaccination, are lacking. We, therefore, systematically evaluated cases of post-vaccinal serious neurological and psychiatric adverse reactions published from India. A systematic review of cases published from India, which were archived in PubMed, Scopus, and Google Scholar databases, was performed; pre-print databases along with ahead-of-print contents were searched in addition. Retrieved articles, as on June 27, 2022, were evaluated following PRISMA guidelines. EndNote 20 web tool was used to make a PRISMA flow chart. Individual patients' data were compiled in a tabular form. The protocol of the systematic review was registered with PROSPERO (CRD42022324183). A total of 64 records describing 136 instances of serious neurological and psychiatric adverse events were identified. More than 50% (36/64) reports were from the following four states, namely, Kerala, Uttar Pradesh, New Delhi, and West Bengal. The mean age of persons developing these complications was 44.89 ± 15.77 years. In the majority, adverse events occurred within 2 weeks of administration of the first dose of COVISHIELD vaccine. Immune-mediated central nervous system (CNS) disorders were identified in 54 instances. Guillain-Barre syndrome and other immune-mediated peripheral neuropathies were reported in 21 cases. Post-vaccinal herpes zoster was recorded in 31 vaccine recipients. Psychiatric adverse events were recorded in six patients. In Indian recipients of COVID-19 vaccine, a variety of serious neurological complications were reported. The overall risk appears minuscule. Immune-mediated central and peripheral neuronal demyelinations were the most frequently reported post-vaccinal adverse events. A large number of cases of herpes zoster have also been reported. Immune-mediated disorders responded well to immunotherapy., Competing Interests: None

Background: Rhino cerebral mucormycosis is an uncommon opportunistic infection of the nasal sinuses and brain, and a group of saprophytic fungi causes it. During the second wave of COVID-19, India witnessed an unprecedented number of patients with rhino cerebral mucormycosis. Invasion of the cavernous sinus and occlusion of the internal carotid artery in many cases resulted in a stroke. The study aimed to assess the clinical and neuroimaging predictors of stroke in patients with rhino cerebral mucormycosis. We also evaluated the predictors of death in these patients at 90 days., Methods: A prospective study was performed at a tertiary care centre in India between July 2021 and September 2021. We enrolled consecutive microbiologically confirmed patients of rhino cerebral mucormycosis. All patients underwent neuroimaging of the brain. Treatment comprised of anti-fungal drugs and endoscopic nasal/sinus debridement. We followed the patients for 90 days and assessed the predictors of stroke and mortality RESULTS: Forty-four patients with rhino cerebral mucormycosis were enrolled. At inclusion, in 24 patients, the RT-PCR test for SARS-COV-2 was negative. Diabetes mellitus was the most frequent (72.7 %) underlying risk factor; in most, diabetes mellitus was recently discovered. At inclusion or subsequent follow-up, stroke was seen in 11 (25 %) patients. Only seven patients had hemiparesis. Imaging revealed internal carotid artery occlusion in 17 (38.6 %) patients. Hypertension, corticosteroid use, and cavernous sinus thrombosis were independent predictors of stroke. Nine (20.5 %) died during follow-up, and stroke was an independent predictor of death., Conclusion: Stroke indicated poor prognosis among rhino cerebral mucormycosis patients encountered during the second wave of the COVID-19 epidemic., Competing Interests: Declaration of Competing Interest None declared, (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Protein-ligand interactions (PLIs) are essential for biochemical functionality and their identification is crucial for estimating biophysical properties for rational therapeutic design. Currently, experimental characterization of these properties is the most accurate method, however, this is very time-consuming and labor-intensive. A number of computational methods have been developed in this context but most of the existing PLI prediction heavily depends on 2D protein sequence data. Here, we present a novel parallel graph neural network (GNN) to integrate knowledge representation and reasoning for PLI prediction to perform deep learning guided by expert knowledge and informed by 3D structural data. We develop two distinct GNN architectures: [Formula: see text] is the base implementation that employs distinct featurization to enhance domain-awareness, while [Formula: see text] is a novel implementation that can predict with no prior knowledge of the intermolecular interactions. The comprehensive evaluation demonstrated that GNN can successfully capture the binary interactions between ligand and protein's 3D structure with 0.979 test accuracy for [Formula: see text] and 0.958 for [Formula: see text] for predicting activity of a protein-ligand complex. These models are further adapted for regression tasks to predict experimental binding affinities and [Formula: see text] crucial for compound's potency and efficacy. We achieve a Pearson correlation coefficient of 0.66 and 0.65 on experimental affinity and 0.50 and 0.51 on [Formula: see text] with [Formula: see text] and [Formula: see text], respectively, outperforming similar 2D sequence based models. Our method can serve as an interpretable and explainable artificial intelligence (AI) tool for predicted activity, potency, and biophysical properties of lead candidates. To this end, we show the utility of [Formula: see text] on SARS-Cov-2 protein targets by screening a large compound library and comparing the prediction with the experimentally measured data., (© 2022. The Author(s).)

This paper proposes a generic scheme VaCoChain, that fuses blockchain (BC) and unmanned aerial vehicles (UAVs) underlying fifth-generation (5G) communication services for timely vaccine distribution during novel coronavirus (COVID-19) and future pandemics. The scheme offers 5G-tactile internet (5G-TI) based services for UAV communication networks (UAVCN) monitored through ground controller stations (GCs). 5G-TI enabled UAVCN supports real-time dense connectivity at ultra-low round-trip time (RTT) latency of [Formula: see text] and high availability of 99.99999%. Thus, it can support resilient vaccine distributions in a phased manner at government-designated nodal centers (NCs) with reduced round trip delays from vaccine production warehouses (VPW). Further, UAVCNs ensure minimizes human intervention and controls vaccine health conditions due to shorter trip times. Once vaccines are supplied at NCs warehouses, then the BC ensures timestamped documentation of vaccinated persons with chronology, auditability, and transparency of supply-chain checkpoints from VPW to NCs. Through smart contracts (SCs), priority groups can be formed for vaccination based on age, healthcare workers, and general commodities. In the simulation, for UAV efficacy, we have compared the scheme against fourth-generation (4G)-assisted long term evolution-advanced (LTE-A), orthogonal frequency division multiplexing (OFDM) channels, and traditional logistics for round-trip time (RTT) latency, logistics, and communication costs. In the BC setup, we have compared the scheme against the existing 5G-TI delivery scheme (Gupta et al.) for processing latency, packet losses, and transaction time. For example, in communication costs, the proposed scheme achieves an average improvement of 9.13 for block meta-information. For 4000 transactions, the proposed scheme has a communication latency of 16 s compared to 36 s. The packet loss is significantly reduced to 2.5% using 5G-TI compared to 16% in 4G-LTE-A. The proposed scheme has a computation cost of 1.6 ms and a communication cost of 157 bytes, which indicates the scheme efficacy against conventional approaches.

Since it emerged in December of 2019, COVID-19 has placed a huge burden on medical care in countries throughout the world, as it led to a huge number of hospitalizations and mortalities. Many medical centers were overloaded, as their intensive care units and auxiliary protection resources proved insufficient, which made the effective allocation of medical resources an urgent matter. This study describes learned survival prediction models that could help medical professionals make effective decisions regarding patient triage and resource allocation. We created multiple data subsets from a publicly available COVID-19 epidemiological dataset to evaluate the effectiveness of various combinations of covariates-age, sex, geographic location, and chronic disease status-in learning survival models (here, "Individual Survival Distributions"; ISDs) for hospital discharge and also for death events. We then supplemented our datasets with demographic and economic information to obtain potentially more accurate survival models. Our extensive experiments compared several ISD models, using various measures. These results show that the "gradient boosting Cox machine" algorithm outperformed the competing techniques, in terms of these performance evaluation metrics, for predicting both an individual's likelihood of hospital discharge and COVID-19 mortality. Our curated datasets and code base are available at our Github repository for reproducing the results reported in this paper and for supporting future research., (© 2022. The Author(s).)

Despite the recent availability of vaccines against the acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the search for inhibitory therapeutic agents has assumed importance especially in the context of emerging new viral variants. In this paper, we describe the discovery of a novel noncovalent small-molecule inhibitor, MCULE-5948770040, that binds to and inhibits the SARS-Cov-2 main protease (M pro ) by employing a scalable high-throughput virtual screening (HTVS) framework and a targeted compound library of over 6.5 million molecules that could be readily ordered and purchased. Our HTVS framework leverages the U.S. supercomputing infrastructure achieving nearly 91% resource utilization and nearly 126 million docking calculations per hour. Downstream biochemical assays validate this M pro inhibitor with an inhibition constant ( K i ) of 2.9 μM (95% CI 2.2, 4.0). Furthermore, using room-temperature X-ray crystallography, we show that MCULE-5948770040 binds to a cleft in the primary binding site of M pro forming stable hydrogen bond and hydrophobic interactions. We then used multiple μs-time scale molecular dynamics (MD) simulations and machine learning (ML) techniques to elucidate how the bound ligand alters the conformational states accessed by M pro , involving motions both proximal and distal to the binding site. Together, our results demonstrate how MCULE-5948770040 inhibits M pro and offers a springboard for further therapeutic design.

Background: Postmarketing surveillance of COVID-19 vaccination reveals that the COVID-19 vaccine administration is associated with several rare but serious neurological complications., Case Report: We report a case of new-onset tumefactive demyelinating brain lesion that developed after administration of an adenovector-based COVID-19 vaccine. A middle-aged female presented with recent right hemiparesis, which was noticed 2 days after she received the first dose of the vaccine. Magnetic resonance imaging (MRI) revealed a large subcortical T2/FLAIR hyperintensities involving corpus callosum as well. The patient responded to oral methylprednisolone. At 4 weeks, a follow-up MRI revealed a reduction in size of the lesion., Conclusion: To conclude, adenovector-based COVID-19 vaccination may be associated with a tumefactive demyelinating lesion., Competing Interests: None

Coronavirus pandemic has caused a vast number of deaths worldwide. Thus creating an urgent need to develop effective counteragents against novel coronavirus disease (COVID-19). Many antiviral drugs have been repurposed for treatment but implicated minimal recovery, which further advanced the need for clearer insights and innovation to derive effective therapeutics. Strategically, Noscapine, an approved antitussive drug with positive effects on lung linings may show favorable outcomes synergistically with antiviral drugs in trials. Hence, we have theoretically examined the combinatorial drug therapy by culminating the existing experimental results with in silico analyses. We employed the antitussive noscapine in conjugation with antiviral drugs (Chloroquine, Umifenovir, Hydroxychloroquine, Favlplravir and Galidesivir). We found that Noscapine-Hydroxychloroquine (Nos-Hcq) conjugate has strong binding affinity for the main protease (Mpro) of SARS-CoV-2, which performs key biological function in virus infection and progression. Nos-Hcq was analyzed through molecular dynamics simulation. The MD simulation for 100 ns affirmed the stable binding of conjugation unprecedentedly through RMSD and radius of gyration plots along with critical reaction coordinate binding free energy profile. Also, dynamical residue cross-correlation map with principal component analysis depicted the stable binding of Nos-Hcq conjugate to Mpro domains with optimal secondary structure statistics of complex dynamics. Also, we reveal the drugs with stable binding to major domains of Mpro can significantly improve the work profile of reaction coordinates, drug accession and inhibitory regulation of Mpro. The designed combinatorial therapy paves way for further prioritized in vitro and in vivo investigations for drug with robust binding against Mpro of SARS-CoV-2.

The prerequisite of therapeutic drug design and discovery is to identify novel molecules and developing lead candidates with desired biophysical and biochemical properties. Deep generative models have demonstrated their ability to find such molecules by exploring a huge chemical space efficiently. An effective way to generate new molecules with desired target properties is by constraining the critical fucntional groups or the core scaffolds in the generation process. To this end, we developed a domain aware generative framework called 3D-Scaffold that takes 3D coordinates of the desired scaffold as an input and generates 3D coordinates of novel therapeutic candidates as an output while always preserving the desired scaffolds in generated structures. We demonstrated that our framework generates predominantly valid, unique, novel, and experimentally synthesizable molecules that have drug-like properties similar to the molecules in the training set. Using domain specific data sets, we generate covalent and noncovalent antiviral inhibitors targeting viral proteins. To measure the success of our framework in generating therapeutic candidates, generated structures were subjected to high throughput virtual screening via docking simulations, which shows favorable interaction against SARS-CoV-2 main protease (Mpro) and nonstructural protein endoribonuclease (NSP15) targets. Most importantly, our deep learning model performs well with relatively small 3D structural training data and quickly learns to generalize to new scaffolds, highlighting its potential application to other domains for generating target specific candidates.

The coronavirus disease 2019 (COVID-19) pandemic is an issue of global significance that has taken the lives of many across the world. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus responsible for its pathogenesis. The pulmonary manifestations of COVID-19 have been well described in the literature. Initially, it was thought to be limited to the respiratory system; however, we now recognize that COVID-19 also affects several other organs, including the nervous system. Two similar human coronaviruses (CoV) that cause severe acute respiratory syndrome (SARS-CoV-1) and Middle East respiratory syndrome (MERS-CoV) are also known to cause disease in the nervous system. The neurological manifestations of SARS-CoV-2 infection are growing rapidly, as evidenced by several reports. There are several mechanisms responsible for such manifestations in the nervous system. For instance, post-infectious immune-mediated processes, direct virus infection of the central nervous system (CNS), and virus-induced hyperinflammatory and hypercoagulable states are commonly involved. Guillain-Barré syndrome (GBS) and its variants, dysfunction of taste and smell, and muscle injury are numerous examples of COVID-19 PNS (peripheral nervous system) disease. Likewise, hemorrhagic and ischemic stroke, encephalitis, meningitis, encephalopathy acute disseminated encephalomyelitis, endothelialitis, and venous sinus thrombosis are some instances of COVID-19 CNS disease. Due to multifactorial and complicated pathogenic mechanisms, COVID-19 poses a large-scale threat to the whole nervous system. A complete understanding of SARS-CoV-2 neurological impairments is still lacking, but our knowledge base is rapidly expanding. Therefore, we anticipate that this comprehensive review will provide valuable insights and facilitate the work of neuroscientists in unfolding different neurological dimensions of COVID-19 and other CoV associated abnormalities., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.)

Coronavirus disease 2019 (COVID-19) has been shown to be associated with a lot of neurological complications, of whom Guillain-Barre syndrome (GBS) is an important post-infectious consequentiality. More than 220 patients with GBS have been reported thus far. We intend to share our experience with five patients of GBS where one of them had severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the cerebrospinal fluid (CSF). This is the first-ever report demonstrating the presence of SARS-CoV-2 in the CSF of an adult patient; a similar occurrence has recently been described in a pediatric patient. We wish to emphasize the fact that commonly GBS occurs as a result of a post-infectious process but in a few cases where the symptoms of COVID-19 and GBS occur concurrently, corresponding to the viremic phase, separate pathogenesis needs to be thought of. This para-infectious nature is exemplified by the presence of virus in the cerebrospinal fluid of one of our patients. We review the neuroinvasive potential of SARS-Cov-2 in this regard and draw parallels with Cytomegalovirus, Zika virus, and Human Immunodeficiency virus-associated occurrences of GBS., (© 2021 Wiley Periodicals LLC.)

Despite the intensive research efforts towards antiviral drug against COVID-19, no potential drug or vaccines has not yet discovered. Initially, the binding site of COVID-19 main protease was predicted which located between regions 2 and 3. Structure-based virtual screening was performed through a hierarchal mode of elimination technique after generating a grid box. This led to the identification of five top hit molecules that were selected on the basis of docking score and visualization of non-bonding interactions. The docking results revealed that the hydrogen bonding and hydrophobic interactions are the major contributing factors in the stabilization of complexes. The docking scores were found between -7.524 and -6.711 kcal/mol indicating strong ligand-protein interactions. Amino acid residues Phe140, Leu141, Gly143, Asn142, Thr26, Glu166 and Thr190 (hydrogen bonding interactions) and Phe140, Cys145, Cys44, Met49, Leu167, Pro168, Met165, Val42, Leu27 and Ala191 (hydrophobic interactions) formed the binding pocket of COVID-19 main protease. From identified hits, ZINC13144609 and ZINC01581128 were selected for atomistic MD simulation and density functional theory calculations. MD simulation results confirm that the protein interacting with both hit molecules is stabilized in the chosen POPC lipid bilayer membrane. The presence of lowest unoccupied molecular orbital (LUMO) and highest occupied molecular orbital (HOMO) in the hydrophobic region of the hit molecules leads to favorable ligand-protein contacts. The calculated pharmacokinetic descriptors were found to be in their acceptable range and therefore confirming their drug-like properties. Hence, the present investigation can serve as the basis for designing and developing COVID-19 inhibitors. Communicated by Ramaswamy H. Sarma.

Purpose: Paroxysmal sympathetic hyperactivity (PSH) occurs in around 15-33% patients of traumatic brain injury. Due to presence of non-specific symptoms, it's always difficult to differentiate between paroxysmal sympathetic storm and cytokine storm syndrome and hence can delay specific treatment., Case Report: We report a clinical case of 19-year-old male tested COVID 19 positive with diffuse axonal injury presented with features of paroxysmal sympathetic storm and cytokine storm syndrome. The patient showed the signs clinical improvement when we treated both these conditions., Conclusion: We suggest that clinicians need to have a high degree of suspicion of paroxysmal sympathetic storm in patients of traumatic brain injury and consider its diagnosis. Also, if patient is COVID 19 positive, early identification of signs of developing cytokine storm with monitoring of biomarkers is important for its timely management.

The current scenario, an ongoing pandemic of COVID-19, places a dreadful burden on the healthcare system worldwide. Subsequently, there is a need for a rapid, user-friendly, and inexpensive on-site monitoring system for diagnosis. The early and rapid diagnosis of SARS-CoV-2 plays an important role in combating the outbreak. Although conventional methods such as PCR, RT-PCR, and ELISA, etc., offer a gold-standard solution to manage the pandemic, they cannot be implemented as a point-of-care (POC) testing arrangement. Moreover, surface-enhanced Raman spectroscopy (SERS) having a high enhancement factor provides quantitative results with high specificity, sensitivity, and multiplex detection ability but lacks in POC setup. In contrast, POC devices such as lateral flow immunoassay (LFIA) offer rapid, simple-to-use, cost-effective, reliable platform. However, LFIA has limitations in quantitative and sensitive analyses of SARS-CoV-2 detection. To resolve these concerns, herein we discuss a unique modality that is an integration of SERS with LFIA for quantitative analyses of SARS-CoV-2. The miniaturization ability of SERS-based devices makes them promising in biosensor application and has the potential to make a better alternative of conventional diagnostic methods. This review also demonstrates the commercially available and FDA/ICMR approved LFIA kits for on-site diagnosis of SARS-CoV-2.

Development of effective counteragents against the novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains, requires clear insights and information for understanding the immune responses associated with it. This global pandemic has pushed the healthcare system and restricted the movement of people and succumbing of the available therapeutics utterly warrants the development of a potential vaccine to contest the deadly situation. In the present study, highly efficacious, immunodominant cytotoxic T-lymphocyte (CTL) epitopes were predicted by advanced immunoinformatics assays using the spike glycoprotein of SARS-CoV2, generating a robust and specific immune response with convincing immunological parameters (Antigenicity, TAP affinity, MHC binder) engendering an efficient viral vaccine. The molecular docking studies show strong binding of the CTL construct with MHC-1 and host membrane specific TLR2 receptors. The molecular dynamics simulation in an explicit system confirmed the stable and robust binding of CTL epitope with TLR2. Steep magnitude RMSD variation and compelling residual fluctuations existed in terminal residues and various loops of the β linker segments of TLR2-epitope (residues 105-156 and 239-254) to about 0.4 nm. The reduced R g value (3.3 nm) and stagnant SASA analysis (275 nm/S 2 /N after 8 ns and 5 ns) for protein surface and its orientation in the exposed and buried regions suggests more compactness due to the strong binding interaction of the epitope. The CTL vaccine candidate establishes a high capability to elicit the critical immune regulators, like T-cells and memory cells as proven by the in silico immunization assays and can be further corroborated through in vitro and in vivo assays.

Coronavirus disease (COVID-19) is an emerging and highly infectious disease making global public health concern and socio-economic burden. It is caused due to Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2). It has the tendency to spread rapidly through person-to-person. Currently, several molecular diagnostic platforms such as PCR, qRT-PCR, reverse transcription loop-mediated isothermal amplification (RT-LAMP), CRISPR are utilized for the diagnosis of SARS-CoV-2. These conventional techniques are costly, time consuming and require sophisticated instrumentation facility with well trained personnel for testing. Hence, it is tough to provide testing en-masse to the people in developing countries. On the other hand, several serological biosensors such as lateral flow immunosensor, optical, electrochemical, microfluidics integrated electrochemical/fluorescence is currently utilized for the diagnosis of SARS-CoV-2. In current pandemic situation, there is an urgent need of rapid and efficient diagnosis on mass scale of SARS-CoV-2 for early stage detection. Early monitoring of viral infections can help to control and prevent the spreading of infections in large chunk of population. In this review, the SARS-CoV-2 and their biomarkers in biological samples, collection of samples and recently reported potential electrochemical immunosensors for the rapid diagnosis of SARS-CoV-2 are discussed.

Background: Itolizumab downregulates the synthesis of proinflammatory cytokines and adhesion molecules by inhibiting CD6 leading to lower levels of interferon-γ, interleukin-6, and tumor necrotic factor-α and reduced T-cell infiltration at inflammatory sites. This study aims to compare the effects of tocilizumab and itolizumab in the management of severe coronavirus disease 2019 (COVID-19). Methods: The study population was adults (>18 years) with severe COVID-19 pneumonia admitted to the intensive care unit receiving either tocilizumab or itolizumab during their stay. The primary outcome was clinical improvement (CI), defined as a two-point reduction on a seven-point ordinal scale in the status of the patient from initiating the drug or live discharge. The secondary outcomes were time until CI, improvement in PO2/FiO2 ratio, best PO2/FiO2 ratio, need for mechanical ventilation after administration of study drugs, time to discharge, and survival days. Results: Of the 126 patients included in the study, 92 received tocilizumab and 34 received itolizumab. CI was seen in 46.7% and 61.7% of the patients in the tocilizumab and itolizumab groups, respectively and was not statistically significant (P=0.134). The PO2/FiO2 ratio was significantly better with itolizumab compared to tocilizumab (median [interquartile range]: 315 [200–380] vs. 250 [150–350], P=0.043). The incidence of serious adverse events due to the study drugs was significantly higher with itolizumab compared to tocilizumab (14.7% vs. 3.3%, P=0.032). Conclusions: The CI with itolizumab is similar to tocilizumab. Better oxygenation can be achieved with itolizumab and it can be a substitute for tocilizumab in managing severe COVID-19. [ABSTRACT FROM AUTHOR]

The number of affirmed COVID-19 cases showed an enormous increase in the recent past throughout the globe. Keeping in view the catastrophic destruction of this devastating virus, there is a must-need situation to maximize the use of existing healthcare technologies such as the healthcare Internet of Things (H-IoT). In healthcare, patient wearable devices are widely recognized as a dormant technology with enormous capabilities to assess and combat various diseases, e.g., cough, seizure, temperature, heartbeat, and so on. As we know, in the H-IoT, patient-wearable devices are dispersed in an infrastructure-free environment that exposes them to several private and public coercion while accumulating and transmitting high sensitive data over the wireless communication channel. Therefore, security is the main concern of these applications, and thus, the primary focus of this article to outline the limitations and challenges in the present literature from 2019 to 2021, to identify the requirements of H-IoT applications used in the context of COVID-19. Following this, we will move one step ahead to explore the current security techniques adopted in these applications. Consequently, we will identify the network architectural, cryptographic, protocols, and operational security challenges during our study to recommend viable research directions and opportunities, which could be helpful and capable to minimize the network architecture, deployment, and maintenance cost with more productive outcomes. [ABSTRACT FROM AUTHOR]

Unmanned Air Vehicles (UAVs) are becoming popular in real-world scenarios due to current advances in sensor technology and hardware platform development. The applications of UAVs in the medical field are broad and may be shared worldwide. With the recent outbreak of COVID-19, fast diagnostic testing has become one of the challenges due to the lack of test kits. UAVs can help in tackling the COVID-19 by delivering medication to the hospital on time. In this paper, to detect the number of COVID-19 cases in a hospital, we propose a deep convolution neural architecture using transfer learning, classifying the patient into three categories as COVID-19 (positive) and normal (negative), and pneumonia based on given X-ray images. The proposed deep-learning architecture is compared with state-of-the-art models. The results show that the proposed model provides an accuracy of 94.92%. Further to offer time-bounded services to COVID-19 patients, we have proposed a scheme for delivering emergency kits to the hospitals in need using an optimal path planning approach for UAVs in the network. [ABSTRACT FROM AUTHOR]

The hyperinflammatory immune response in severe COVID-19 infection shares features with secondary hemophagocytic lymphohistiocytosis (sHLH) in the form of fever, cytopenia, elevated inflammatory markers, and high mortality. There are contrasting opinions regarding utility of HLH 2004 or HScore in the diagnosis of severe COVID-19-related hyperinflammatory syndrome (COVID-HIS). This was a retrospective study of 47 patients of severe COVID-19 infection, suspected to have COVID-HIS and 22 patients of sHLH to other illnesses, to evaluate the diagnostic utility and limitations of HLH 2004 and/or HScore in context to COVID-HIS and to also evaluate the utility of Temple criteria for predicting severity and outcome in COVID-HIS. Clinical findings, hematological, and biochemical parameters along with the predictor of mortality were compared between two groups. Only 6.4% (3/47) of cases fulfilled ≥5/8 HLH 2004 criteria and only 40.52% (19/47) of patients showed HScore >169 in COVID-HIS group. 65.9% (31/47) satisfied the Temple criteria in COVID-HIS as compared with 40.9% (9/22) in the non-COVID group (p = 0.04). Serum ferritin (p = 0.02), lactate dehydrogenase (p = 0.02), direct bilirubin (p = 0.02), and C-reactive protein (p = 0.03) were associated with mortality in COVID-HIS. Both HScore and HLH-2004 criteria perform poorly for identifying COVID-HIS. Presence of bone marrow hemophagocytosis may help to identify about one-third of COVID-HIS missed by the Temple Criteria. [ABSTRACT FROM AUTHOR]

Since the outbreak of COVID-19 pandemic in 2020, a dramatic loss of human life has occurred and this trend presents an unprecedented challenge to public health, economic systems and social operations. Hence, it is urgent for us to take some countermeasures to restrain and dispel epidemic diffusion to the uttermost. Data freshness plays an inevitable role in timely infestor determination during this process. However, existing works pay little attention to optimizing this indicator in health monitoring. To make up this research gap, in this paper, we propose a mixed game-based Age of Information (AoI) optimization scheme, where the edge-based wireless technologies and AI-empowered diagnostic bots are adopted. Firstly, we establish the system model for Epidemic Prevention and Control Center (EPCC)-based health state monitoring network, where ultimate biosensing data is transmitted from AI bots via edge servers. Then, upon deriving AoI expression with a closed form, the minimization goal between edge servers and bots is specified. Simultaneously, we reformulate the AoI optimization problem from the mixed game viewpoint (i.e., coalition formation game and ordinary potential game), and then propose two algorithms for cooperative order-based bot deployment and stochastic learning-based channel selection. Finally, compared with the typical baselines, the experiment result shows our scheme can reach the lower AoI value for biosensing data transmission under different parameter settings. [ABSTRACT FROM AUTHOR]

Construction project delays are costly to stakeholders. Therefore, extensive research has been carried out in various countries covering different types of construction projects to identify delay factors and mitigation strategies. However, the literature has not documented the impact of a once-in-a-century event such as the COVID-19 pandemic on construction projects' schedules and costs. The present study uses a case study approach to examine the effect of the COVID-19 pandemic on three lift irrigation projects' construction programs and the financial implications for the contractor in the Indian construction industry. The findings reveal that the main factors responsible for delays due to the COVID-19 pandemic were fear of an outbreak, imposition of a nationwide lockdown, late return of workers, resource shortages, and changes in operating procedures. Furthermore, the study found that the contract conditions and government directives allowed for time extensions in construction projects by recognizing the pandemic as a force majeure. However, such provisions did not protect contractors' financial interests. This study evaluates the financial implications of the COVID-19 pandemic for the contractor in terms of worker compensation during the lockdown, idling of resources, overhead costs, cost escalation, and liquidity. The public health crisis from the COVID-19 pandemic led to a series of preventive measures from the government and organizations worldwide that caused severe disruptions in construction projects and disturbed the construction supply chain. This study recommends rethinking the scope and coverage of force majeure and excusable delay clauses in standard construction contracts beyond provisions for an extension of time to protect the financial interests of different stakeholders. The vagueness in assessing and compensating for the losses from unprecedented events such as the COVID-19 pandemic could expose construction organizations to significant financial risks and threaten their existence in a highly competitive industry. Therefore, construction organizations should carefully review the contract provisions of future projects for proper risk allocations to accommodate unprecedented events and pandemics to help them recover successfully from various disruptions and associated financial losses. Moreover, proper communication and negotiation, a supportive and collaborative project culture, and early government interventions are crucial in dealing with such events and avoiding disputes among project stakeholders. [ABSTRACT FROM AUTHOR]

Rationale: The impact of COVID-19 in patients with autoimmune liver disease treated with immunosuppressive therapy has not been described so far. This case report describes the clinical course of a patient with autoimmune hepatitis (AIH) who developed COVID-19 and the features of cytokine syndrome leading to its deterioration in our intensive care unit. Patient's Concern: A 28-year-old male presented with generalized anasarca for two weeks and chronic liver disease for 8 months. Diagnosis: AIH and Covid-19 with features of cytokine storm syndrome. Interventions: Intravenous furosemide, mannitol, syrup lactulose, steroids (prednisolone 40 mg), azathioprine 1 mg/kg body weight, rifaximin, vitamin K, and blood products. Outcomes: The patient had hepatic encephalopathy and AIH and died on the 10th day after admission despite ventilatory support, sustained low-efficiency hemodialysis, and resuscition. Lessons: The dramatic release of cytokines and the inflammatoryimmune responses not only alter the pathophysiology but also affects the onset and severity of disease progression in patients with AIH. [ABSTRACT FROM AUTHOR]

This case series of four cases of pregnancy with rheumatic heart disease with COVD-19 disease reports on the management of delivery and complications of heart disease with COVID-19, high-lighting the presentation, severity, delivery concerns, and clinical management with the maternal and fetal outcomes. Of the four fullterm deliveries, one delivered normally, one instrumental delivery and two by cesarean section. All four required oxygen support post-delivery, 2/4 (50%) were transferred for intensive care unit (ICU) care, 1/4 (25%) required mechanical ventilation, 1/4 (25%) had postpartum hemorrhage, 1/4 (25%) had COVID related sepsis and received convalescent plasma therapy, and 2/4 (50%) received antiviral remdesivir. The most severe disease (COVID sepsis and ICU stay) was seen in patient of heart disease with COVID with preclampsia. All neonates tested SARS-CoV-2 negative, with one early neonatal death. All four mothers were discharged in stable condition of COVID and heart status. COVID-19 in cardiac disease pregnancies has increased rates of complications, oxygen, and ICU requirements than other pregnancies with COVID, requiring multidisciplinary team for intensive monitoring of intrapartum and postpartum period. [ABSTRACT FROM AUTHOR]

Rationale: The mechanism of sudden cardiac death in COVID-19 can be multifactorial. Cardiac hypersensitivity to 5-ASA therapy leading to myocarditis has been reported in some cases. Cytokine storm syndrome and idiosyncratic reaction with mesalazine use may lead to sudden cardiac death in COVID-19. Use of immunosuppressants in hospitalized COVID-19 patients should be continued with caution, especially in patients with inflammatory bowel disease. Patient's concern: A 75-year-old man who was tested positive for SARS-CoV-2 was admitted with a history of shortness of breath for the last two days. He was a known case of Crohn's disease treated with mesalazine. Diagnosis: COVID-19 pneumonia with underlying Crohn's disease leading to sudden cardiac death. Intervention: Remdesivir, antibiotics, steroids, low molecular weight heparin, tablet zinc, tab vitamin C, and other supportive treatment were started. Because of increased inflammatory markers, itolizumab was given to the patient on the 2nd day. Outcome: On the 5th day of the intensive care unit, the patient complained of sudden chest pain with respiratory distress leading to bradycardia and asystole and could not be resuscitated. Lessons: Causes for sudden cardiac death in COVID-19 pneumonia patients with Crohn's disease is multifactorial. Although mesalazine may be a safe and effective drug in the management of inflammatory bowel disease, it can induce sytokine strom syndrome and idiosyncratic reactions that could be one of the reasons of sudden cardic death. Therefore, we should be aware of its serious and potentially life-threatening complications, especially in COVID-19 infected patients. [ABSTRACT FROM AUTHOR]

Existing deep learning technologies generally learn the features of chest X-ray data generated by Generative Adversarial Networks (GAN) to diagnose COVID-19 pneumonia. However, the above methods have a critical challenge: data privacy. GAN will leak the semantic information of the training data which can be used to reconstruct the training samples by attackers, thereby this method will leak the privacy of the patient. Furthermore, for this reason, that is the limitation of the training data sample, different hospitals jointly train the model through data sharing, which will also cause privacy leakage. To solve this problem, we adopt the Federated Learning (FL) framework, a new technique being used to protect data privacy. Under the FL framework and Differentially Private thinking, we propose a Federated Differentially Private Generative Adversarial Network (FedDPGAN) to detect COVID-19 pneumonia for sustainable smart cities. Specifically, we use DP-GAN to privately generate diverse patient data in which differential privacy technology is introduced to make sure the privacy protection of the semantic information of the training dataset. Furthermore, we leverage FL to allow hospitals to collaboratively train COVID-19 models without sharing the original data. Under Independent and Identically Distributed (IID) and non-IID settings, the evaluation of the proposed model is on three types of chest X-ray (CXR)images dataset (COVID-19, normal, and normal pneumonia). A large number of truthful reports make the verification of our model can effectively diagnose COVID-19 without compromising privacy. [ABSTRACT FROM AUTHOR]

Rationale: The gastrointestinal manifestations of COVID-19 include anorexia, nausea, vomiting, abdominal pain, and diarrhea. However, pancreatitis as the presentation of COVID-19 is rarely reported. Patient's Concern: A 63-year-old COVID-19 patient presented with complaints of abdominal pain and difficulty breathing for 5 d. Diagnosis: Contrast-enhanced computed tomography of the abdomen suggested acute interstitial pancreatitis without any biliary tract obstruction. Interventions: The patient was resuscitated with intravenous fluids based on dynamic parameters of fluid responsiveness. The patient was started on enteral feeding, analgesics, antibiotics, dexamethasone, low molecular weight heparin, and supportive therapy. Outcomes: The patient developed severe acute respiratory distress syndrome and died 6 days after admission. Lessons: Management of COVID-19 in the presence of pancreatitis is challenging. Adequate early fluid resuscitation is an important aspect of medical management for COVID-19 patients with pancreatitis and restrictive strategies must be followed. Increased liver enzymes and renal dysfunction in acute pancreatitis can also limit the use of specific therapies like remdesivir. Dexamethasone, even though it has shown a beneficial effect in treating COVID-19, can have an additive effect in causing hyperglycemia in these cases. Clinicians should be aware of this atypical presentation of COVID-19 with pancreatitis and adjust their management strategies, keeping in mind the considerations for both diseases. [ABSTRACT FROM AUTHOR]

Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection affects the cardiovascular system at many levels. It initially infects endothelial cells, inducing marked endothelial damage and inflammation. However, there was no empirical evidence of functional compromise of arterial walls. Aims and objective: Our primary objective was to study functional arterial damage in coronavirus disease 2019 (COVID-19) and establish the noninvasive measurement of arterial stiffness as an independent marker of disease severity. Materials and methods: We recorded the arterial stiffness of 23 mild, 21 moderate, and 20 severe COVID-19 patients grouped on the latest National Institute of Health (NIH) severity criteria. We observed arterial stiffness of COVID-19 patients with standard parameters like noninvasive estimated carotid--femoral pulse wave velocity (cfPWV), age-normalized increase in cfPWV (ANI&lowbar;cfPWV), age-normalized increase in aortic augmentation pressure (ANI&lowbar;AugP), and heart rate-normalized augmentation index (HRN_ AIx). All the parameters were also corrected for statistically significant confounding factors. Results: Moderate and severe COVID-19 patients have extremely significantly elevated arterial stiffness than mild patients. In mild patients, cfPWV (829.1 ± 139.2 cm/second) was significantly lower than both moderate (1067 ± 152.5 cm/second, p <0.0001) and severe (1416 ± 253.9 cm/second, p <0.0001) patients. ANI&lowbar;cfPWV in moderate and severe patients was significantly higher than mild patients (mild: 101.2 ± 126.1 cm/second; moderate: 279 ± 114.4 cm/second; severe: 580.1 ± 216.4 cm/second; intergroup p <0.0001). The results even after correction for significant confounding factors did not show any considerable change in the increasing trend of arterial stiffness. Conclusion: This study establishes the functional deterioration of arteries in proportion to the severity of COVID-19. [ABSTRACT FROM AUTHOR]

Even before the onslaught of COVID-19 pandemic could settle, the unprecedented rise in cases with COVID-19 associated mucormycosis pushed the medical health to the fringe. Hyperglycaemia and corticosteroids appear to be the most consistent associations leading to the commonest manifestation of mucormycosis, Rhino-Orbito-Cerebral Mucormycosis. To address challenges right from categorisation and staging of the disease to the management of relentless progression, a multi-disciplinary expert committee was formed to handle the task in an evidence-based format to enforce best practices. The report of the committee on one hand attempts to succinctly present the currently available evidence while at the other also attempts to bridge the evidence-deficient gaps with the specialty-specific virtuosity of experts. [ABSTRACT FROM AUTHOR]

Background and Aims: The Nutrition Risk in Critically ill (NUTRIC) score is an appropriate nutritional assessment tool in mechanically ventilated patients. We retrospectively observed the applicability of the NUTRIC score for predicting outcomes in coronavirus disease (COVID)-19 acute respiratory distress syndrome (ARDS) patients. Methods: All adult COVID-19 ARDS patients admitted to the intensive care unit and requiring various forms of oxygen therapy were included in the study. The demographic characteristics and clinical information about the patients were obtained from the hospital's medical records department. The nutritional risk for each patient was assessed using the NUTRIC score at 72 hours of ICU admission. The discriminating power and ability of NUTRIC score, Sequential Organ Failure Assessment (SOFA) score, age and Acute Physiology and Chronic Health Evaluation (APACHE) II to predict the 28-day mortality and need for mechanical ventilation (MV) was calculated using receiver operating characteristic curves and area under this curve. Results: A total of 80 COVID-19 ARDS patients fitted into the inclusion criteria. Among non-survivors, the median Glasgow Coma Score, APACHE II score, NUTRIC score and SOFA score were 10, 16, 6 and 4, respectively. The cut-off values for NUTRIC score, SOFA, and APACHE II to predict 28-day mortality and need for MV was obtained as 3.5, 3.5 and 11.5, respectively. These cut-off values of NUTRIC score, SOFA score, and APACHE II have a sensitivity of 62%, 72.5% and 75.5%, respectively, and specificity of 95%, 72% and 83% for predicting mortality. Conclusions: Most COVID-19 ARDS patients requiring MV in the ICU are at nutritional risk, and a high NUTRIC score is associated with higher mortality. [ABSTRACT FROM AUTHOR]

Effective screening of COVID-19 cases has been becoming extremely important to mitigate and stop the quick spread of the disease during the current period of COVID-19 pandemic worldwide. In this article, we consider radiology examination of using chest X-ray images, which is among the effective screening approaches for COVID-19 case detection. Given deep learning is an effective tool and framework for image analysis, there have been lots of studies for COVID-19 case detection by training deep learning models with X-ray images. Although some of them report good prediction results, their proposed deep learning models might suffer from overfitting, high variance, and generalization errors caused by noise and a limited number of datasets. Considering ensemble learning can overcome the shortcomings of deep learning by making predictions with multiple models instead of a single model, we propose EDL-COVID, an ensemble deep learning model employing deep learning and ensemble learning. The EDL-COVID model is generated by combining multiple snapshot models of COVID-Net, which has pioneered in an open-sourced COVID-19 case detection method with deep neural network processed chest X-ray images, by employing a proposed weighted averaging ensembling method that is aware of different sensitivities of deep learning models on different classes types. Experimental results show that EDL-COVID offers promising results for COVID-19 case detection with an accuracy of 95%, better than COVID-Net of 93.3%. [ABSTRACT FROM AUTHOR]

Currently, drones represent a promising technology for combating Coronavirus disease 2019 (COVID‐19) due to the transport of goods, medical supplies to a given target location in the quarantine areas experiencing an epidemic outbreak. Drone missions will increasingly rely on drone collaboration, which requires the drones to reduce communication complexity and be controlled in a decentralized fashion. Blockchain technology becomes a must in industrial applications because it provides decentralized data, accessibility, immutability, and irreversibility. Therefore, Blockchain makes data public for all drones and enables drones to log information concerning world states, time, location, resources, delivery data, and drone relation to all neighbors drones. This paper introduces decentralized independent multi‐drones to accomplish the task collaboratively. Improving blockchain with a consensus algorithm can improve network partitioning and scalability in order to combat COVID‐19. The multi‐drones task is to combat COVID‐19 via monitoring and detecting, social distancing, sanitization, data analysis, delivering goods and medical supplies, and announcement while avoiding collisions with one another. We discuss End to End (E2E) delivery application of combination blockchain and multi‐drone in combating COVID‐19 and beyond future pandemics. Furthermore, the challenges and opportunities of our proposed framework are highlighted. [ABSTRACT FROM AUTHOR]

Background: Gargling had been reported to have some roles in the prevention and treatment of respiratory tract infections. The purpose of this study was to assess the ability of regular gargling using 7.5% sodium bicarbonate to eliminate SARS-CoV-2 in the oropharynx and nasopharynx. Materials and methods: This pilot, open-labeled, nonrandomized, parallel single-center study. The effect of 30 seconds, three times per day gargling using 7.5% sodium bicarbonate solution--25 mL on SARS-CoV-2 viral clearance among coronavirus disease-2019 (COVID-19) patients in a dedicated COVID hospital at All India Institute of Medical Sciences, Patna, Bihar, India. We monitored the progress on by days 0, 1, 2, 3, 4, 5, 6, and 7 by observing variables like clinical category, P/F ratio, neutrophil/lymphocyte ratio (NLR) ratio, platelet count, ferritin, lactate dehydrogenase (LDH), CRP, procalcitonin, d-dimer, INR, APTT, and sequential organ function assessment (SOFA) score. We have also done repeat reverse transcription-polymerase chain reaction (RT-PCR) testing on day 5 and day 7. Results: A total of 10 patients (7 males and 3 females) were included in our study after confirmed COVID positivity. The age range was from 30 to 61 years. Based on clinical severity and P/F ratio, 7 patients were included in the milder group as their ratio was more than 200 and the rest 3 patients were included in the moderate group as P/F ratio was less than 200. Two respondents had comorbidities, which were non-Hodgkin's lymphoma and ovarian carcinoma. Viral clearance was achieved at day 7 in 3 of 10 patients. However, the analysis of using 7.5% sodium bicarbonate 25 mL gargle statistically showed nonsignificant p-value for all of our studied variables. However, the PCR results were negative on 24 hours apart, i.e., on day 5 and day 7. Conclusions: This is only a preliminary study which showed that gargling with 7.5% sodium bicarbonate may not be effective in achieving early SARS-CoV-2 viral clearance among mild COVID-19 patients. However, still larger studies are required to ascertain the benefit of gargling for different stages of COVID-19 patients with keeping in mind the important variables suggestive of viremia clearance. [ABSTRACT FROM AUTHOR]