Elucidation of novel compounds and epitope-based peptide vaccine design against C30 endopeptidase regions of SARS-CoV-2 using immunoinformatics approaches.

There has been progressive improvement in immunoinformatics approaches for epitope-based peptide design. Computational-based immune-informatics approaches were applied to identify the epitopes of SARS-CoV-2 to develop vaccines. The accessibility of the SARS-CoV-2 protein surface was analyzed, and hexa-peptide sequences (KTPKYK) were observed having a maximum score of 8.254, located between amino acids 97 and 102, whereas the FSVLAC at amino acids 112 to 117 showed the lowest score of 0.114. The surface flexibility of the target protein ranged from 0.864 to 1.099 having amino acid ranges of 159 to 165 and 118 to 124, respectively, harboring the FCYMHHM and YNGSPSG hepta-peptide sequences. The surface flexibility was predicted, and a 0.864 score was observed from amino acids 159 to 165 with the hepta-peptide (FCYMHHM) sequence. Moreover, the highest score of 1.099 was observed between amino acids 118 and 124 against YNGSPSG. B-cell epitopes and cytotoxic T-lymphocyte (CTL) epitopes were also identified against SARS-CoV-2. In molecular docking analyses, -0.54 to -26.21 kcal/mol global energy was observed against the selected CTL epitopes, exhibiting binding solid energies of -3.33 to -26.36 kcal/mol. Based on optimization, eight epitopes (SEDMLNPNY, GSVGFNIDY, LLEDEFTPF, DYDCVSFCY, GTDLEGNFY, QTFSVLACY, TVNVLAWLY, and TANPKTPKY) showed reliable findings. The study calculated the associated HLA alleles with MHC-I and MHC-II and found that MHC-I epitopes had higher population coverage (0.9019% and 0.5639%) than MHC-II epitopes, which ranged from 58.49% to 34.71% in Italy and China, respectively. The CTL epitopes were docked with antigenic sites and analyzed with MHC-I HLA protein. In addition, virtual screening was conducted using the ZINC database library, which contained 3,447 compounds. The 10 top-ranked scrutinized molecules (ZINC222731806, ZINC077293241, ZINC014880001, ZINC003830427, ZINC030731133, ZINC003932831, ZINC003816514, ZINC004245650, ZINC000057255, and ZINC011592639) exhibited the least binding energy (-8.8 to -7.5 kcal/mol). The molecular dynamics (MD) and immune simulation data suggest that these epitopes could be used to design an effective SARS-CoV-2 vaccine in the form of a peptide-based vaccine. Our identified CTL epitopes have the potential to inhibit SARS-CoV-2 replication.

Assessment of sustainable green financial environment: the underlying structure of monetary seismic aftershocks of the COVID-19 pandemic.

The study aims to assess a sustainable green financial environment by exploring the underlying structure of monetary seismic aftershocks of the COVID-19 pandemic. This study is qualitative and uses a review of literature, primary data collection methods, and qualitative analysis techniques as the study's overall design. The data is collected by one-to-one interview using a matrix style questionnaire from a panel of experts based on the purposive sampling technique. Interpretive structural modeling (ISM) combined with Matrices' Impacts Cruise's Multiplication Appliquée a UN Classement (MICMAC) is used for assessment, modeling, and analysis of data. The monetary aftershocks, namely, "more cash in hand required," "decreased travel costs," "shift to more certain or fixed revenue streams," "lower rent costs," "more zealous monitoring of cash collection cycle," and "decreased entertainment costs," occupy level I (top of the model being least critical shocks), and "tedious regulations" occupy level VIII (bottom of the model being the most vital). Other aftershocks form the middle of the model being moderate critical. Analysis of MICMAC shows that monetary seismic aftershocks high fees for assistance regarding SOPs, tedious regulations, and more downtime due to pandemic alerts are independent. This study addresses the core issue from within the aftermath of the COVID-19 pandemic. It provides new important information regarding the structure of a sustainable green financial environment that is useful for economists, financial analysts, commercial and central bankers, accountants and finance managers from the organization's public/and private sectors, local and international community, and researchers of the domain. It provides an informative structural model and classification of critical aftershocks. There are specific data/methodological/resource-related limitations of the study. The study's data are collected from a focus group; the study's methodology is qualitative and indicates relations among variables that do not quantify the associations. The study is a typical initiative of academic researchers with limited financial/physical resources; therefore, the generalizability of the study results is accordingly limited. The study is based on original, essential data and innovatively and creatively approaches the problem. It provides a unique model of an unprecedented phenomenon for reverberating the sustainable green financial environment.

Evaluating the social outcomes of COVID-19 pandemic: empirical evidence from Pakistan.

The study aims to assess and analyze the social outcomes of the COVID-19 pandemic. The study uses the discourse of comprehensive literature review to identify the outcomes, Interpretive Structural Modeling (ISM) for developing a structural model and Matrices' Impacts Cruise's Multiplication Appliquée a UN Classement (MICMAC) for analysis, classification of societal outcomes, and corroboration of results of ISM. Data from fifteen experts was collected through a survey questionnaire. As a result of the literature review, a list of sixteen outcomes was generated and verified by a panel of experts. Results of ISM revealed that the outcomes, namely, "emotional instability," "mental health self-harm," loneliness reduced recreational activities, obesity, and "increased screen time" come at the top of the model; therefore, they are less vital outcomes, whereas the most significant outcome which is at the bottom of the model is "employment instability"; hence it has a major impact on the society. The remaining outcomes fall in the middle of the model, so they have a moderate-severe impact. Results of MICMAC validate the findings of ISM. Overall findings of the study reveal that "employment instability" is the crucial social outcome of the COVID-19 pandemic. It is an original attempt based on real-time data, which is helpful for society at large, researchers, the international community, and policymakers because this study provides a lot of new information about the phenomenon. The study includes understanding society at large, policymakers, and researchers by illustrating the complex relations and simplifying the connections among a wide range of social outcomes of COVID-19.

Chapter 1 - Natural products and SARS-CoV-2

Natural products have been proven to be the source of many antiviral drugs in the past. History has a bunch of natural products used as traditional medicine, therapies, mixtures, and oils. However, there are many bioactive natural products that need to be evaluated against severe acute respiratory syndrome (SARS-CoV-2) to curb the ongoing pandemic. Several plants and fungal-derived natural products are extensively reported with antiviral activities against SARS-CoV-2. In vitro, preliminary study assays and computational studies revealed several antiviral drugs from natural fungal compounds, including cordycepin isolated from Cordyceps militaris fungi. Polyphenolic compounds isolated from the Broussonetia papyrifera plant showed promising antiviral activity against SARS CoV-2 in in silico studies. Two alkaloid compounds, 10-hydroxyusambarensine and cryptoquindoline isolated from African medicinal plants, inhibited the main protease (Mpro) of SARS CoV-2. At the start of the COVID-19 pandemic, FDA approved the emergency use of chloroquine against SARS CoV-2;chloroquine is a derivative of alkaloid. The development of modern technologies has streamlined the discovery of new drugs from natural products. Gas chromatography–mass spectrometry, infrared radiation, nuclear magnetic resonance, high-performance thin-layer chromatography, and high-performance liquid chromatography and other high output technologies should be available for the structural interpretation and distinguishability of prudent lead molecules

Chapter 12 - Phages and SARS-CoV-2

The emerging human pathogenic viruses, including the recently emerged severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), have markedly affected the human health and have become a challenge for researchers worldwide. Antibiotic therapy and existing vaccines have reduced the pandemic burden to some extent. However, there is still need for efficient treatment, vaccination, and antiviral agents to control the pandemic. This chapter illustrates the role of bacteriophage in bacterial infections, SARS-COV-2 infected patients, biological activities of phages, phage display method, phages as potential inducers of antiviral immunity, phage-based vaccines, CRISPR and phage-based SARS-CoV-2 vaccines, and possible advantages of phage-based vaccines. It is concluded that phages have considerable breadth in the SARS-CoV-2 pandemic and offer many substantial advantages, such as clearing respiratory bacterial infections, which significantly reduce the burden of mortalities. Phage plays a vital role in triggering antiviral immunity by inducing cytokines such as IFN-α and IL-12. It suggests the role in driving antiviral immunity, triggering TLR3-dependent pattern recognition receptors, inhibiting TNF-driving type I IFN, inducing antiviral immunity through upregulation of the expression of defensin in IL-2, and encouraging a marked upregulation of gene hBD2 that induces virucidal effects, thus playing a key role in anti-SARS-COV-2 immunity. Moreover, phages have been presented as an alternative universal adjuvant-free nano-vaccine platform in which single-phage scaffolds are used to incorporate multiple targets.

A strategy to optimize the peptide-based inhibitors against different mutants of the spike protein of SARS-CoV-2.

The SARS-CoV-2 virus has caused high-priority health concerns at a global level. Vaccines have stalled the proliferation of viruses to some extent. Yet, the emergence of newer, potentially more infectious, and dangerous mutants such as Delta and Omicron are among the major challenges in finding a more permanent solution for this pandemic. The effectiveness of antivirals Molnupiravir and Paxlovid, authorized for emergency use by the FDA, are yet to be assessed on a larger population. Patients with a high risk of disease progression have received treatment with antibody-cocktail. Most of the mutations leading to the new lineage of SARS-CoV-2 are found in the spike protein of this virus that plays a key role in facilitating host entry. The current study has investigated how to modify a promising peptide-based inhibitor of spike protein, LCB3, against common mutations, N501Y and K417N in the target protein so that it retains its efficacy against the spike protein. LCB3 being a prototype for protein-based inhibitors is an ideal testing system to learn about protein-based inhibitors. This study proposes the substitutions of amino acid residues of LCB3 inhibitor using a structure-based approach that considers free energy decomposition of residues, the distance between atoms, and the interaction among amino acids. The binding free energy calculations suggest a possible improvement in the binding affinity of existing inhibitor LCB3 to the mutant forms of the S-protein using simple substitutions at specific positions of the inhibitor. This approach, being general, can be used in different inhibitors and other mutations and help in fighting against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

SARS-2 lessons learned so far from Pakistan Perspective (A Review)

The COVID-19 outbreak started in December 2019, first from the China city, Wuhan. The rapid spread of the virus has affected the worldwide population involving 208 countries including UK, USA, Spain, Italy and Pakistan, and has been declared a global pandemic by the WHO. Strict measures have been taken globally to control the COVID-19 outbreak. With limited availability of resources, the government of Pakistan to different measures to prevent the spread, such as establishing screening and testing, isolation and quarantine facilities and training medical professionals and enforcing lockdown. Pakistan ranking 5th in the list of Populated countries and also being a developing country, requires financial aid as well as facilitation to combat the outbreak. Also overviewing the measures, proper hospital facilities are to be established and should be available in every region of the country, whereas the testing and screening facilities are required to be set as per the population of the country. The vaccines being introduced worldwide, its arrival in Pakistan and making it available for the population is also an effort made by the Government to bring back things to normal. Educating citizens and abiding by the safety rules to prevent the spread is still the area that needs to be worked upon. We have highlighted the measures and steps taken by the Pakistani government in last one year, to combat COVID-19 and to ensure the country’s safety and minimize mortality. [ FROM AUTHOR] Copyright of Professional Medical Journal is the property of Professional Medical Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

COVID-19 and metabolic disease: mechanisms and clinical management.

Up to 50% of the people who have died from COVID-19 had metabolic and vascular disorders. Notably, there are many direct links between COVID-19 and the metabolic and endocrine systems. Thus, not only are patients with metabolic dysfunction (eg, obesity, hypertension, non-alcoholic fatty liver disease, and diabetes) at an increased risk of developing severe COVID-19 but also infection with SARS-CoV-2 might lead to new-onset diabetes or aggravation of pre-existing metabolic disorders. In this Review, we provide an update on the mechanisms of how metabolic and endocrine disorders might predispose patients to develop severe COVID-19. Additionally, we update the practical recommendations and management of patients with COVID-19 and post-pandemic. Furthermore, we summarise new treatment options for patients with both COVID-19 and diabetes, and highlight current challenges in clinical management.

Analyzing the Stressors for Frontline Soldiers Fighting Against Coronavirus Disease 2019 Pandemic.

This study aimed to analyze stressors to which medical staff is vulnerable due to the coronavirus disease 2019 (COVID-19) pandemic. It also imposes a hierarchy on complex relations among stressors for excavating underlying structure and builds a model of interrelationships contrasting reality. The design of this study comprises a literature survey, data collection from primary sources, and analysis. Stressors have been explored from within current published/unpublished literature and validated by experts through approval vote. Data were collected from the focus group (panel of experts), and interpretive structural modeling (ISM) was used as the research methodology. Findings of ISM are avowed through "cross-impact matrix multiplication applied to classification" (MICMAC) analysis. As a result of the literature survey, a list of stressors was generated, and a total of 19 stressors qualified as representative of the phenomenon. The results of ISM show that two stressors (i.e., "unavailability of proper personal protective equipment (PPE)" and "lack of proper communication") emerged as the most critical stressors since they occupy the bottom of the model, whereas, four stressors (i.e., "anxious about isolation/quarantine," "subject to violent crimes," "feeling frustrated and powerless," and "exhausting shifts/hours without clear end") are relatively less critical since they occupy the top of the model. The rest of the stressors occupy the middle of the model and therefore, have moderate-severe effects on frontline soldiers. The results of MICMAC show that the stressor "subject to violent crimes" is classified in the dependent cluster and the remaining fall in the linkage cluster but no stressor falls in independent and autonomous. Overall results indicate that all stressors are relevant to the phenomenon under this study, but they are currently not settled. This study is invaluable for policymakers, frontline soldiers, researchers, the international community, and society since it provides a lot of new information that is helpful in refining strategies and combating influential stressors.

Structural Modeling on the Determinants of Effectiveness of SOPs Containing COVID-19 in Mass Gatherings.

The study is aimed to analyze the determinants of the effectiveness of SOPs in mass gatherings for containing COVID-19. The overall design of the study involves a literature review, data collection by field survey, structural modeling, and analysis. The study is built on the experts' opinion of a focus group (representing people who recently participated in and are responsible for mass gatherings). The study uses the discussion of the literature review to identify the determinants, interpretive structural modeling (ISM) for developing and analyzing a structural model, and Matrice d'Impacts Croises Multiplication Appliquée a un Classement (MICMAC) for corroboration of results of the ISM/classification of determinants. From the literature review, a list of determinants is generated and verified by a panel of experts. The results of the ISM revealed that the determinants "legal environment of the country," "practicability of SOPs," "perceived benefit of adapting SOPs," and "possibilities of avoiding gathering" occupied the top of the model, therefore, they are less critical determinants, whereas "nature of gathering" occupied the bottom of the model, and is thus the most critical determinant. The remaining determinants form the middle of the model, and are therefore moderately severe. The results of MICMAC show that the determinant "perceived benefit of adapting SOPs" is dependent, "nature of gathering" is independent, and all others are linked. The results of MICMAC implicitly substantiate the findings of the ISM. The overall results of the study show that "nature of gathering" is the key determinant. This research does not require a priori theory since it is a theory-building study that uses an inductive approach. It is based on real data and it is useful for local authorities, organizers, participants (attendees/visitors) of mass gatherings, health officials/regulators, researchers, and the community at large. This study has fundamental importance for planning and preparing for such events while ensuring the minimum risk of COVID-19 transmission.

Designing Short Peptides to Block the Interaction of SARS-CoV-2 and Human ACE2 for COVID-19 Therapeutics.

To date, the current COVID-19 pandemic caused by SARS-CoV-2 has infected 99.2 million while killed 2.2 million people throughout the world and is still spreading widely. The unavailability of potential therapeutics against this virus urges to search and develop new drugs. SARS-CoV-2 enters human cells by interacting with human angiotensin-converting enzyme 2 (ACE2) receptor expressed on human cell surface through utilizing receptor-binding domain (RBD) of its spike glycoprotein. The RBD is highly conserved and is also a potential target for blocking its interaction with human cell surface receptor. We designed short peptides on the basis of our previously reported truncated ACE2 (tACE2) for increasing the binding affinity as well as the binding interaction network with RBD. These peptides can selectively bind to RBD with much higher affinities than the cell surface receptor. Thus, these can block all the binding residues required for binding to cell surface receptor. We used selected amino acid regions (21-40 and 65-75) of ACE2 as scaffold for the de novo peptide design. Our designed peptide Pep1 showed interactions with RBD covering almost all of its binding residues with significantly higher binding affinity (-13.2 kcal mol-1) than the cell surface receptor. The molecular dynamics (MD) simulation results showed that designed peptides form a stabilized complex with RBD. We suggest that blocking the RBD through de novo designed peptides can serve as a potential candidate for COVID-19 treatment after further clinical investigations.

COVID-19 detection in chest X-ray images using deep boosted hybrid learning.

The new emerging COVID-19, declared a pandemic disease, has affected millions of human lives and caused a massive burden on healthcare centers. Therefore, a quick, accurate, and low-cost computer-based tool is required to timely detect and treat COVID-19 patients. In this work, two new deep learning frameworks: Deep Hybrid Learning (DHL) and Deep Boosted Hybrid Learning (DBHL), is proposed for effective COVID-19 detection in X-ray dataset. In the proposed DHL framework, the representation learning ability of the two developed COVID-RENet-1 & 2 models is exploited individually through a machine learning (ML) classifier. In COVID-RENet models, Region and Edge-based operations are carefully applied to learn region homogeneity and extract boundaries features. While in the case of the proposed DBHL framework, COVID-RENet-1 & 2 are fine-tuned using transfer learning on the chest X-rays. Furthermore, deep feature spaces are generated from the penultimate layers of the two models and then concatenated to get a single enriched boosted feature space. A conventional ML classifier exploits the enriched feature space to achieve better COVID-19 detection performance. The proposed COVID-19 detection frameworks are evaluated on radiologist's authenticated chest X-ray data, and their performance is compared with the well-established CNNs. It is observed through experiments that the proposed DBHL framework, which merges the two-deep CNN feature spaces, yields good performance (accuracy: 98.53%, sensitivity: 0.99, F-score: 0.98, and precision: 0.98). Furthermore, a web-based interface is developed, which takes only 5-10s to detect COVID-19 in each unseen chest X-ray image. This web-predictor is expected to help early diagnosis, save precious lives, and thus positively impact society.

Evaluating the Immediate Response of Country-Wide Health Systems to the Covid-19 Pandemic: Applying the Gray Incidence Analysis Model.

The purpose of the study is to evaluate county-wide health systems using the data set of the first wave of the COVID-19 pandemic. The overall design of study comprises a literature review, secondary data, and a mathematical analysis. It is a cross-sectional quantitative study following a deductive approach. It uses the data of the first wave of the COVID-19 pandemic taken from the website of Worldometer as of April 8, 2020. The study uses a gray incidence analysis model (commonly known as Gray Relational Analysis, i.e., GRA) as its research methodology. On the basis of the results of GRA, a classification has been made under a predetermined scheme of ensigns: much better, better, somewhat better, fair, poor, somewhat worse, and worse health systems. There are a total 211 countries that have been divided into the seven aforementioned categories. Findings of the study show that Southern Africa Development Community (SADC) countries fall predominantly under the much better ensign, whereas Organization for Economic Co-operation and Development (OECD), Schengen Area (SA), and/or European Union (EU) countries fall under the worse ensign. Pakistan falls under the ensign of poor. It is an original attempt to evaluate the response of health systems based on real data using a scientific methodology. The study provides valuable information about the health systems of the countries for forming an informed opinion about the health systems herein. The study provides useful new information for stakeholders and a new framework for future research.

Analysis of crisis communication by the Prime Minister of Australia during the COVID-19 pandemic.

Leadership and communication capabilities of federal leaders during crises are imperative to support and guide citizens' behaviors and emotions. The following content analysis examines crisis communication delivered by the Australian Prime Minister (PM), Scott Morrison during the COVID-19 pandemic. Communication delivered over seven months starting from the first reported case of COVID-19 in Australia, was analyzed through a process of coding to identify central organizing crisis communication frames and themes and measured against eleven main themes based on principles of Crisis and Emergency Risk Communication (CERC) recommended by the WHO and US Centers for Disease Control and Prevention. Transcripts were sourced from the PM's official website and 91 communiques were analyzed. Key epidemiological indicators and public health measures were reviewed over timeframe to examine changes in communication over the pandemic. Findings indicated that PM Morrison included many features of CERC within his official messaging. Our analysis revealed that the original framework was limited in its scope to encompass certain messages and thus the allocation of new frames,'public health and medical advice' and 'assuring and commending the public and institutions', allowed for a more thorough analysis of communication during a novel global health pandemic. The temporal analysis demonstrated that the government's policy and communication temporally followed case numbers and relative threat of the virus. This study has provided an in-depth review of CERC during the first phase of the COVID-19 pandemic. New frames and themes for the current CERC framework are suggested which can be transferable to other crises in Australia and other countries.

Optical biosensors - Illuminating the path to personalized drug dosing.

Optical biosensors are low-cost, sensitive and portable devices that are poised to revolutionize the medical industry. Healthcare monitoring has already been transformed by such devices, with notable recent applications including heart rate monitoring in smartwatches and COVID-19 lateral flow diagnostic test kits. The commercial success and impact of existing optical sensors has galvanized research in expanding its application in numerous disciplines. Drug detection and monitoring seeks to benefit from the fast-approaching wave of optical biosensors, with diverse applications ranging from illicit drug testing, clinical trials, monitoring in advanced drug delivery systems and personalized drug dosing. The latter has the potential to significantly improve patients' lives by minimizing toxicity and maximizing efficacy. To achieve this, the patient's serum drug levels must be frequently measured. Yet, the current method of obtaining such information, namely therapeutic drug monitoring (TDM), is not routinely practiced as it is invasive, expensive, time-consuming and skilled labor-intensive. Certainly, optical sensors possess the capabilities to challenge this convention. This review explores the current state of optical biosensors in personalized dosing with special emphasis on TDM, and provides an appraisal on recent strategies. The strengths and challenges of optical biosensors are critically evaluated, before concluding with perspectives on the future direction of these sensors.

Analyzing the Underlying Structure of Online Teaching During the COVID-19 Pandemic Period: An Empirical Investigation of Issues of Students.

The aim of the study is to reveal the underlying structure of issues of university students taking online classes during the COVID-19 pandemic period. The overall design of the study includes a review of contemporary literature and field survey for data collection and analysis. Discourse of literature coupled with expert opinion has been employed for identification of issues. Interpretive Structural Modeling (ISM) is used for the determination of intra-issue relationships and analyzing the underlying structure. Cross impact matrix multiplication applied to classification (MICMAC analysis) is used as a technique for classifying issues on the basis of driving-dependence power. Results of the literature show that there are 21 major issues faced by the students taking online classes. ISM shows that lack of institutional guidelines, lack of regulators' guidelines, stress of pandemic situation, and abrupt (not planned) start of online classes are the most critical issues. MICMAC analysis reveals that there is no autonomous issue, 4 (i.e., connectivity issue, shy to use technology, lack of institutional guidelines, and stress of pandemic situation) are independent, 6 other issues are dependent, and the remaining 11 are linking. This is a valuable study having practical implications for regulators, students, parents, and society to understand the current problem. It is an original attempt that contributes toward literature in the form of a structural model and a diagram of classification of issues.

Psychometric Analysis for fear of COVID-19 Scale (FCV-19S) and its association with depression in patients with diabetes: A cross sectional study from a Tertiary Care Centre in Karachi, Pakistan.

BACKGROUND AND AIMS: To assess the psychometric properties of the Fear of COVID-19 (FCV-19S) scale and to determine its associated factors among the Pakistani patients with diabetes. METHODS: This observational study was conducted in 24-h helpline service, a department of Baqai Institute of Diabetology and Endocrinology (BIDE). Study duration was from August to September 2020. The target population was registered adult patients with type 2 diabetes aged >16 years. Baseline demographic details were obtained from hospital management system of BIDE. Forward-backward translation method was used to translate the existing Fear scale (FCV-19S). Symptoms of depressive disorder were assessed through Patient Health Questionnaire (PHQ9). RESULTS: Total of 380 participants with mean age 51.93 ± 12.03 years contributed in the study. Three factors loading and item correlation of fear COVID-19 explained 96% of total variance having unidimensional Cronbach's alpha of 0.881. All demographic indicators that showed significance in univariate model were included in multivariate model. Females had more fear for COVID-19 compared to males (OR = 1.73, 95% CI (1.15-2.6)), whereas current smokers had also showed 4 times more fear than non-smokers (OR = 4.19, 95% CI (1.18-14.83). Depression assessed by PHQ9 showed maximum fear of COVID-19 in participants with moderate depression. CONCLUSION: FCV-19S had adequate psychometric properties for assessing effects of pandemic in people with diabetes attending tertiary care center.