Development of a novel angiotensin converting enzyme 2 stimulator with broad implications in SARS-CoV2 and type 1 diabetes.

Angiotensin-converting enzyme 2 (ACE2) is protective in cardiovascular disease, lung injury and diabetes yet paradoxically underlies our susceptibility to SARs-CoV2 infection and the fatal heart and lung disease it can induce. Furthermore, diabetic patients have chronic, systemic inflammation and altered ACE2 expression resulting in increased risk of severe COVID-19 and the associated mortality. A drug that could increase ACE2 activity and inhibit cellular uptake of severe acute respiratory syndrome coronavirus 2 (SARs-CoV2), thus decrease infection, would be of high relevance to cardiovascular disease, diabetes and SARs-CoV2 infection. While the need for such a drug lead was highlighted over a decade ago receiving over 600 citations, 1 to date, no such drugs are available. 2 Here, we report the development of a novel ACE2 stimulator, designated '2A'(international PCT filed), which is a 10 amino acid peptide derived from a snake venom, and demonstrate its in vitro and in vivo efficacy against SARs-CoV2 infection and associated lung inflammation. Peptide 2A also provides remarkable protection against glycaemic dysregulation, weight loss and disease severity in a mouse model of type 1 diabetes. No untoward effects of 2A were observed in these pre-clinical models suggesting its strong clinical translation potential.

Hybrid Artificial Intelligence-Based Models for Prediction of Death Rate in India Due to COVID-19 Transmission

COVID-19 prediction models are highly welcome and necessary for authorities to make informed decisions. Traditional models, which were used in the past, were unable to reliably estimate death rates due to procedural flaws. The genetic algorithm in association with an artificial neural network (GA-ANN) is one of the suitable blended AI strategies that can foretell more correctly by resolving this difficult COVID-19 phenomena. The genetic algorithm is used to simultaneously optimise all of the ANN parameters. In this work, GA-ANN and ANN models were performed by applying historical daily data from sick, recovered, and dead people in India. The performance of the designed hybrid GA-ANN model is validated by comparing it to the standard ANN and MLR approach. It was determined that the GA-ANN model outperformed the ANN model. When compared to previous examined models for predicting mortality rates in India, the hypothesized hybrid GA-ANN model is the most competent. This hybrid AI (GA-ANN) model is suggested for the prediction due to reasonably better performance and ease of implementation.

An Attitude-Behavioral Model to Understand People's Behavior Towards Tourism during COVID-19 Pandemic

The impact of pandemics on the tourism industry should be explored from the perspective of those who will travel, go to the tourist places on vacation, and avail services from tourism and hospitality-related organizations. This study has aimed to identify the reasons for the changed human psychology towards tourism during the COVID-19 Pandemic to develop an attitude-behavioral model. This investigation thus conducted an extensive empirical study among tourists to capture their social, emotional, and financial beliefs. The research then examined the measurement model through confirmatory factor analysis (CFA) before investigating the cause-effect relationship through the structural model. Analysis revealed that the negative effect of attitude on behavioral intention toward this new equilibrium is controlled by the emotional aspect of attitude. Furthermore this paper made several contributions to the literature on human psychology, crisis management, human behavior, marketing, and tourism.

Tourism sustainability during COVID-19: developing value chain resilience

The aim of this study is to evaluate the perceptions of prospective tourists through parameters by which the tourism and hospitality service sector can withstand the widespread implications to the sector as a result of the current pandemic. In turn this will lead to weighing up the means for recovery. The identified parameters are then classified, categorized and linked up with supply chain drivers to obtain a holistic picture that can feed into strategic planning from which the tourism and hospitality service sector could utilize to establish a resilient supply chain. This data can provide deep insight for both theorists and practitioners to utilize. It was found that reforming six supply chain drivers, whilst at the same time developing core competencies, is the central essence of a resilient supply chain within the tourism and hospitality business sector (who are at present working hard to counterbalance the many threats and consequent risks posed due to the pandemic).

An attitude-behavioral model to understand people's behavior towards tourism during COVID-19 pandemic.

The impact of pandemics on the tourism industry should be explored from the perspective of those who will travel, go to the tourist places on vacation, and avail services from tourism and hospitality-related organizations. This study has aimed to identify the reasons for the changed human psychology towards tourism during the COVID-19 Pandemic to develop an attitude-behavioral model. This investigation thus conducted an extensive empirical study among tourists to capture their social, emotional, and financial beliefs. The research then examined the measurement model through confirmatory factor analysis (CFA) before investigating the cause-effect relationship through the structural model. Analysis revealed that the negative effect of attitude on behavioral intention toward this new equilibrium is controlled by the emotional aspect of attitude. Furthermore this paper made several contributions to the literature on human psychology, crisis management, human behavior, marketing, and tourism.

Development of RNA-Based Assay for Rapid Detection of SARS-CoV-2 in Clinical Samples.

INTRODUCTION: The ongoing spread of pandemic coronavirus disease-19 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is of growing concern. Rapid diagnosis and management of SARS-CoV-2 are crucial for controlling the outbreak in the community. Here, we report the development of a first rapid-colorimetric assay capable of detecting SARS-CoV-2 in the human nasopharyngeal RNA sample in less than 30 min. METHOD: We utilized a nanomaterial-based optical sensing platform to detect RNA-dependent RNA polymerase gene of SARS-CoV-2, where the formation of oligo probe-target hybrid led to salt-induced aggregation and change in gold-colloid color from pink to blue visibility range. Accordingly, we found a change in colloid color from pink to blue in assay containing nasopharyngeal RNA sample from the subject with clinically diagnosed COVID-19. The colloid retained pink color when the test includes samples from COVID-19 negative subjects or human papillomavirus-infected women. RESULTS: The results were validated using nasopharyngeal RNA samples from positive COVID-19 subjects (n = 136). Using real-time polymerase chain reaction as gold standard, the assay was found to have 85.29% sensitivity and 94.12% specificity. The optimized method has detection limit as little as 0.5 ng of SARS-CoV-2 RNA. CONCLUSION: We found that the developed assay rapidly detects SARS-CoV-2 RNA in clinical samples in a cost-effective manner and would be useful in pandemic management by facilitating mass screening.

TiO2 based Photocatalysis membranes: An efficient strategy for pharmaceutical mineralization.

Among the various emerging contaminants, pharmaceuticals (PhACs) seem to have adverse effects on the quality of water. Even the smallest concentration of PhACs in ground water and drinking water is harmful to humans and aquatic species. Among all the deaths reported due to COVID-19, the mortality rate was higher for those patients who consumed antibiotics. Consequently, PhAC in water is a serious concern and their removal needs immediate attention. This study has focused on the PhACs' degradation by collaborating photocatalysis with membrane filtration. TiO2-based photocatalytic membrane is an innovative strategy which demonstrates mineralization of PhACs as a safer option. To highlight the same, an emphasis on the preparation and reinforcing properties of TiO2-based nanomembranes has been elaborated in this review. Further, mineralization of antibiotics or cytostatic compounds and their degradation mechanisms is also highlighted using TiO2 assisted membrane photocatalysis. Experimental reactor configurations have been discussed for commercial implementation of photoreactors for PhAC degradation anchored photocatalytic nanomembranes. Challenges and future perspectives are emphasized in order to design a nanomembrane based prototype in future for wastewater management.

Bilateral Cystoid Macular Edema and Corneal Endothelial Graft Rejection following Influenza and Varicella-Zoster Vaccinations.

PURPOSE: Multiple cases of corneal graft rejection after various vaccinations have been reported over the past decades. Here we described a case of bilateral cystoid macular edema (CME) and endothelial rejection in a DSAEK patient following influenza and varicella vaccines. CASE REPORT: A 72-year-old woman with bilateral Fuch's endothelial dystrophy received bilateral DSAEK surgeries. She received an influenza vaccination and her left visual acuity (VA) decreased due to CME. Half a year later, the patient received a varicella-zoster virus vaccine. 11 days later, she was found to have signs of endothelial graft rejection in both eyes. Unfortunately, her vision further deteriorated despite intensive topical steroid treatment. CONCLUSIONS: In view of the current worldwide efforts on mass vaccination against the COVID-19 pandemic, we suggest an increased use of topical corticosteroids both before and after vaccination may be helpful in reducing this risk.

Biochemical Predictors of Acute Kidney Injury in Critically Ill COVID-19 Patients.

It is estimated that 50% of patients with coronavirus disease 2019 (COVID-19) have varying degrees of renal involvement. In this clinical biomarker development research, we examined in a retrospective study design the temporal changes in biochemical laboratory parameters in relation to the development of acute kidney injury (AKI). In a sample of 399 patients admitted from May 2020 to May 2021 to a tertiary health care intensive care unit (ICU), the incidence of AKI was 27.3%, and the median time to AKI was on 7th day of ICU admission. Most common etiology of AKI was kidney hypoperfusion. Within 72 h of developmental of low blood pressure, 63.76% developed AKI. The likelihood of AKI was higher in those with elevated serum ferritin, aspartate transaminase, and thrombocytopenia (low platelet count). A cutoff value of 750.3 ng/mL [area under the ROC curve (AUC) = 0.777] for serum ferritin, and 40.05 U/L for alanine aminotransferase (AUC = 0.677) 1 day before development of AKI displayed, respectively, a sensitivity of 76.2% and 64.3%, whereas the specificity was 69.5% and 64.1%, respectively, for these two biochemical predictors. A cutoff value of platelets (152.50 × 109/L [AUC = 0.75]) measured 4 days before development of AKI, displayed 83.3% sensitivity and 16.4% specificity. Taken together, our study thoroughly examined the temporal association of various clinical and laboratory parameters with AKI and prediction models were developed as per results of the time series data. These observations in a tertiary health care setting contribute to ongoing efforts for biomarker discovery and development using routine biochemical tests so as to forecast AKI in patients with COVID-19.

Detection of pathogenic viruses in the urban wastewater in Kuwait-implications for monitoring viral disease outbreaks.

Effective surveillance for epidemic-prone viral diseases is essential for emergency preparedness to respond to threats and occurrences of pandemics. While it is difficult and expensive to conduct health facility-based surveillance, there is a growing interest in conducting sewage-based epidemiological studies to monitor the health of the urban population because of the relative ease of sample collection and the availability of advanced molecular techniques for the detection of pathogens in the sewage. Sewage samples offer unique means to study the aggregate health of the population as opposed to the monitoring of the health of any individual by traditional methods. We worked together with the Ministry of Public Works in Kuwait and developed a platform for the collection and testing of sewage samples from different regions of Kuwait for studying population health. In this report, we describe the results of a cross-sectional study conducted between 16 and 23 September 2019 in an attempt to detect influenza, Noro, Rota, hepatitis A, and hepatitis E viruses in urban sewage samples collected in Kuwait. All five targeted viruses were detected in the samples collected from urban wastewater in Kuwait using reverse-transcriptase quantitative PCR (RT-qPCR). We recently checked for the presence of SARS-CoV-2 in the stored cDNA samples and confirmed the absence of SARS-CoV-2 in them. This is the first report that demonstrates the preparedness in Kuwait for using sewage samples for the detection and monitoring of many pathogenic viruses which may greatly increase the capacity of the country to deal with a viral disease outbreak in the future.

COMBATdb: a database for the COVID-19 Multi-Omics Blood ATlas.

Advances in our understanding of the nature of the immune response to SARS-CoV-2 infection, and how this varies within and between individuals, is important in efforts to develop targeted therapies and precision medicine approaches. Here we present a database for the COvid-19 Multi-omics Blood ATlas (COMBAT) project, COMBATdb (https://db.combat.ox.ac.uk). This enables exploration of multi-modal datasets arising from profiling of patients with different severities of illness admitted to hospital in the first phase of the pandemic in the UK prior to vaccination, compared with community cases, healthy controls, and patients with all-cause sepsis and influenza. These data include whole blood transcriptomics, plasma proteomics, epigenomics, single-cell multi-omics, immune repertoire sequencing, flow and mass cytometry, and cohort metadata. COMBATdb provides access to the processed data in a well-defined framework of samples, cell types and genes/proteins that allows exploration across the assayed modalities, with functionality including browse, search, download, calculation and visualisation via shiny apps. This advances the ability of users to leverage COMBAT datasets to understand the pathogenesis of COVID-19, and the nature of specific and shared features with other infectious diseases.

An Understanding of Coronavirus and exploring the Molecular Dynamics Simulations to Find Promising Candidates against the Mpro of nCoV to Combat the COVID-19: A Systematic Review

The first infection case of new coronavirus was reported at the end of 2019 and after then, the cases are reported in all nations in a very short period. Further, the regular news of mutations in the virus has made life restricted with appropriate behavior. To date, a new strain (Omicron and its new subvariant Omicron XE) has brought fear amongst us due to a higher trajectory of increase in the number of cases. The researchers thus started giving attention to this viral infection and discovering drug-like candidates to cure the infections. Finding a drug for any viral infection is not an easy task and takes plenty of time. Therefore, computational chemistry/bioinformatics is followed to get promising molecules against viral infection. Molecular dynamics (MD) simulations are being explored to get drug candidates in a short period. The molecules are screened via molecular docking, which provides preliminary information which can be further verified by MD simulations. To understand the change in structure, MD simulations generated several trajectories such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), hydrogen bonding, and radius of gyration for the main protease (Mpro) of the new coronavirus (nCoV) in the presence of small molecules. Additionally, change in free energy for the formation of complex of Mpro of nCoV with the small molecule can be determined by applying molecular mechanics with generalized born and surface area solvation (MM-GBSA). Thus, the promising molecules can be further explored for clinical trials to combat COVID-19. Graphical

Recent Advances on Metal Oxide Based Nano-Photocatalysts as Potential Antibacterial and Antiviral Agents

Photocatalysis, a unique process that occurs in the presence of light radiation, can potentially be utilized to control environmental pollution, and improve the health of society. Photocatalytic removal, or disinfection, of chemical and biological species has been known for decades;however, its extension to indoor environments in public places has always been challenging. Many efforts have been made in this direction in the last two–three years since the COVID-19 pandemic started. Furthermore, the development of efficient photocatalytic nanomaterials through modifications to improve their photoactivity under ambient conditions for fighting with such a pandemic situation is a high research priority. In recent years, several metal oxides-based nano-photocatalysts have been designed to work efficiently in outdoor and indoor environments for the photocatalytic disinfection of biological species. The present review briefly discusses the advances made in the last two to three years for photocatalytic viral and bacterial disinfections. Moreover, emphasis has been given to the tailoring of such nano-photocatalysts in disinfecting surfaces, air, and water to stop viral/bacterial infection in the indoor environment. The role of such nano-photocatalysts in the photocatalytic disinfection of COVID-19 has also been highlighted with their future applicability in controlling such pandemics.

Lycopene: a therapeutic strategy against coronavirus disease 19 (COVID- 19).

Lycopene is a group of phytochemicals found in nature, primarily in fruits and vegetables. Lycopene is thought to protect against a variety of diseases attributed to its antioxidant capabilities. Lycopene has anti-inflammatory, anti-cancer, and immunity-boosting qualities, among other biological and pharmacological benefits. COVID-19 (coronavirus disease 19) is an infectious disease caused by the SARS-CoV-2 virus, which has recently emerged as one of the world's leading causes of death. Patients may be asymptomatic or show signs of respiratory, cytokine release syndrome, gastrointestinal, or even multiple organ failure, all of which can lead to death. In COVID-19, inflammation, and cytokine storm are the key pathogenic mechanisms, according to SARS-CoV-2 infection symptoms. ARDS develops in some vulnerable hosts, which is accompanied by an inflammatory "cytokine syndrome" that causes lung damage. Immunological and inflammatory markers were linked to disease severity in mild and severe COVID-19 cases, implying that inflammatory markers, including IL-6, CRP, ESR, and PCT were significantly linked with COVID-19 severity. Patients with severe illness have reduced levels of several immune subsets, including CD4 + T, NK, and CD8 + cells. As a result, lycopene can be commended for bolstering physiological defenses against COVID-19 infections.

Diagnostic Tools for Rapid Screening and Detection of SARS-CoV-2 Infection.

The novel coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has severely impacted human health and the health management system globally. The ongoing pandemic has required the development of more effective diagnostic strategies for restricting deadly disease. For appropriate disease management, accurate and rapid screening and isolation of the affected population is an efficient means of containment and the decimation of the disease. Therefore, considerable efforts are being directed toward the development of rapid and robust diagnostic techniques for respiratory infections, including SARS-CoV-2. In this article, we have summarized the origin, transmission, and various diagnostic techniques utilized for the detection of the SARS-CoV-2 virus. These higher-end techniques can also detect the virus copy number in asymptomatic samples. Furthermore, emerging rapid, cost-effective, and point-of-care diagnostic devices capable of large-scale population screening for COVID-19 are discussed. Finally, some breakthrough developments based on spectroscopic diagnosis that could revolutionize the field of rapid diagnosis are discussed.

Cases of acute coronary syndrome and presumed cardiac death prior to arrival at an urban tertiary care hospital in Pakistan during the COVID-19 pandemic.

BACKGROUND: A reduction in overall acute coronary syndrome (ACS) cases, increases in the severity of ACS presentation, and increased rates of out-of-hospital cardiac arrest (OHCA) have been reported from multiple countries during the COVID-19 pandemic. The attributed factors include COVID-19 infection, fear of COVID-19 and resultant avoidance of health care facilities, and restrictions on mobility. Pakistan, a country with a high burden of cardiovascular disease (CVD) and challenges related to health care access, will be expected to demonstrate these same findings. Therefore, we compared ACS hospitalization, ACS severity, and patients who have already died (dead on arrival, or DOA) due to presumed OHCA at a tertiary cardiac hospital during pre-pandemic and intra-pandemic periods in Pakistan. METHODS: Standardized data elements were extracted from the charts of patients with ACS, and telephonic verbal autopsies (VA) using a validated tool were conducted for patients who were arrived DOA. As a comparison, cases during the same months prior to the COVID-19 were analyzed for respective waves. Events were counted, and proportions and frequencies are reported for each time period. RESULTS: A total of 4,480 ACS cases were reviewed; 1,216 cases during March-July 2019, 804 cases in the same months of 2020 (33.8% decrease); 1,304 cases in August 2019-January 2020 and 1,157 in the corresponding months of 2020 and 2021 (11.2% decrease). There was no observed change in the baseline characteristics of patients with ACS or their symptom-to-door time, and in-hospital mortality was unchanged across all time periods. There were 218 DOA cases in pre-pandemic months and 360 cases during the pandemic. The pre-pandemic rate of DOA was 12/1000 emergency patients (95% CI 10-13) compared to 22/1000 (95% CI 22-27) during the pandemic (30/1000in the 1st wave and 17/1000 during 2nd wave). On VA, CVD was found to be the major cause of death during both time periods. CONCLUSION: At a cardiac hospital in Pakistan, the COVID-19 pandemic was associated with a reduction in ACS hospitalization and an increased DOA rate.

Theaflavin 3-gallate inhibits the main protease (Mpro) of SARS-CoV-2 and reduces its count in vitro.

The main protease (Mpro) of SARS-CoV-2 has been recognized as an attractive drug target because of its central role in viral replication. Our previous preliminary molecular docking studies showed that theaflavin 3-gallate (a natural bioactive molecule derived from theaflavin and found in high abundance in black tea) exhibited better docking scores than repurposed drugs (Atazanavir, Darunavir, Lopinavir). In this study, conventional and steered MD-simulations analyses revealed stronger interactions of theaflavin 3-gallate with the active site residues of Mpro than theaflavin and a standard molecule GC373 (a known inhibitor of Mpro and novel broad-spectrum anti-viral agent). Theaflavin 3-gallate inhibited Mpro protein of SARS-CoV-2 with an IC50 value of 18.48 ± 1.29 µM. Treatment of SARS-CoV-2 (Indian/a3i clade/2020 isolate) with 200 µM of theaflavin 3-gallate in vitro using Vero cells and quantifying viral transcripts demonstrated reduction of viral count by 75% (viral particles reduced from Log106.7 to Log106.1). Overall, our findings suggest that theaflavin 3-gallate effectively targets the Mpro thus limiting the replication of the SARS-CoV-2 virus in vitro.

Improving performance of classifiers for diagnosis of critical diseases to prevent COVID risk.

The risk of developing COVID-19 and its variants may be higher in those with pre-existing health conditions such as thyroid disease, Hepatitis C Virus (HCV), breast tissue disease, chronic dermatitis, and other severe infections. Early and precise identification of these disorders is critical. A huge number of patients in nations like India require early and rapid testing as a preventative measure. The problem of imbalance arises from the skewed nature of data in which the instances from majority class are classified correct, while the minority class is unfortunately misclassified by many classifiers. When it comes to human life, this kind of misclassification is unacceptable. To solve the misclassification issue and improve accuracy in such datasets, we applied a variety of data balancing techniques to several machine learning algorithms. The outcomes are encouraging, with a considerable increase in accuracy. As an outcome of these proper diagnoses, we can make plans and take the required actions to stop patients from acquiring serious health issues or viral infections.

PCA-Based Incremental Extreme Learning Machine (PCA-IELM) for COVID-19 Patient Diagnosis Using Chest X-Ray Images.

Novel coronavirus 2019 has created a pandemic and was first reported in December 2019. It has had very adverse consequences on people's daily life, healthcare, and the world's economy as well. According to the World Health Organization's most recent statistics, COVID-19 has become a worldwide pandemic, and the number of infected persons and fatalities growing at an alarming rate. It is highly required to have an effective system to early detect the COVID-19 patients to curb the further spreading of the virus from the affected person. Therefore, to early identify positive cases in patients and to support radiologists in the automatic diagnosis of COVID-19 from X-ray images, a novel method PCA-IELM is proposed based on principal component analysis (PCA) and incremental extreme learning machine. The suggested method's key addition is that it considers the benefits of PCA and the incremental extreme learning machine. Further, our strategy PCA-IELM reduces the input dimension by extracting the most important information from an image. Consequently, the technique can effectively increase the COVID-19 patient prediction performance. In addition to these, PCA-IELM has a faster training speed than a multi-layer neural network. The proposed approach was tested on a COVID-19 patient's chest X-ray image dataset. The experimental results indicate that the proposed approach PCA-IELM outperforms PCA-SVM and PCA-ELM in terms of accuracy (98.11%), precision (96.11%), recall (97.50%), F1-score (98.50%), etc., and training speed.