Nanomaterial-based smart coatings for antibacterial, antifungal, and antiviral activities

Infectious diseases caused by different pathogens (parasites, protozoa, bacteria, viruses, and fungi) have affected the world at various times in the form of epidemics and pandemics. The coronavirus has also directly affected the world's economy and public health. Various drugs such as antibiotics, antimicrobials, antifungals, and antivirals have been investigated to combat these diseases. However, these fatal infections are still a major concern because of their transmission through contaminated surfaces, human-to-human contact, airborne diffusion, and microbial resistance. Therefore, considerable efforts are required to suppress the transmission of these pathogens. Smart coatings are able to sense their environment and adapt their properties according to the stimulus. Furthermore, various parameters of coating technology can be controlled on a molecular level to influence the morphology. Nanomaterial (NM)-based smart coatings are 99.99% effective against bacteria, viruses, and fungi because of the unique properties of NMs involved. Moreover, NM-based smart coatings are 1000-fold more efficient than traditional coating technologies. Besides their antifungal, antiviral, and antibacterial application, they are anticorrosive and self-cleaning. This chapter summarizes various NM-based smart coatings (organic, inorganic, and carbon) implemented in antibacterial, antifungal, and antiviral applications. Furthermore, the application of these coatings in various fields and their associated challenges will be discussed. © 2023 Elsevier Inc. All rights reserved.

Analytical Modelling and Simulation of Graphene Based Biosensor to Detect SARS-COV-2 from Aerosal Particles

The health sector is focusing on the wellness of the society, is advancing in the phases of diagnosis and treatment. Biosensors based devices are used to diagnose a variety of human diseases. Recently, there was a sudden hike in the human mortality rate by chronic diseases caused by mutants of SARS-COV-2, on global scale. It is important to detect these kinds of diseases on an early stage to reduce the risk of spreading. For the analysis of Covid-19 influenza, tests such as Rapid Antigen Test (RAT), True NAT, CBNAAT and the commonly done RPT PCR were utilised. This proposal describes a non-invasive, quick and practical method for sensing the at-risk or infected persons with SARS-COV-2, aiming at controlling the epidemic. The proposed method employs a breath sensing device consisting of a Graphene Field Effect Transistor biosensor which can identify disease-specific biomarkers from exhaled sniff, hence allowing speedy and precise detection. This test aids screening of large populations as it is simple and quick and emerges as a promising candidate for SARS-COV-2 tests due to a high sensitivity. This work justifies the accurate diagnosis of Severe Acute Respiratory Syndrome COV 2 from aerosol particles by GFET Biosensor.

Lung Opacity Detection Using Transfer Learning

In the present day medical scenario, lung related diseases are growing in tremendous scale. There is a large leap of death rates due to lung related diseases in recent years. Since covid and other viruses are seriously causing a large variety of lung diseases including pneumonia, lung opacity, and other nostalgic diseases. Due to large amount of people visiting the hospitals everyday it becomes quite tough to evaluate reports of every patient manually. Thus, it requires automation of report analysis. With the advancement in artificial intelligence, it is now possible to analyse the scan of the patient and to produce appropriate result. This automation not only help the management but also helps the patient by reducing the costs. With the help of this project, we will introduce a platform where the user can submit their scan or lung X-ray and can get the appropriate result. In this project we are concentrating on the lung related disease called lung opacity. It is a disease where the lung of patient is filled with pus leading to inconvenience in breathing and in few severe cases it even leads to death. In order to help the hospital managements in automation and patients in early identification of diseases we are proposing a brilliant model that can tackle this problem and provide best automation solution.

FDA approved five-membered ring fused pyrimidine-based derivatives and their biological properties

Pyrimidines-based drugs are one of the most important drugs for novel and recurring viruses, including the coronavirus. This chapter deals with 41 FDA-approved five-membered ring fused pyrimidine-based drugs, their synthetic strategies, and pharmacological activities. © 2023 Elsevier Ltd. All rights reserved.

Added value of the measles-rubella supplementary immunization activity in reaching unvaccinated and under-vaccinated children, a cross-sectional study in five Indian districts, 2018-20.

INTRODUCTION: Supplementary immunization activities (SIAs) aim to interrupt measles transmission by reaching susceptible children, including children who have not received the recommended two routine doses of MCV before the SIA. However, both strategies may miss the same children if vaccine doses are highly correlated. How well SIAs reach children missed by routine immunization is a key metric in assessing the added value of SIAs. METHODS: Children aged 9 months to younger than 5 years were enrolled in cross-sectional household serosurveys conducted in five districts in India following the 2017-2019 measles-rubella (MR) SIA. History of measles containing vaccine (MCV) through routine services or SIA was obtained from documents and verbal recall. Receipt of a first or second MCV dose during the SIA was categorized as "added value" of the SIA in reaching un- and under-vaccinated children. RESULTS: A total of 1,675 children were enrolled in these post-SIA surveys. The percentage of children receiving a 1st or 2nd dose through the SIA ranged from 12.8% in Thiruvananthapuram District to 48.6% in Dibrugarh District. Although the number of zero-dose children prior to the SIA was small in most sites, the proportion reached by the SIA ranged from 45.8% in Thiruvananthapuram District to 94.9% in Dibrugarh District. Fewer than 7% of children remained measles zero-dose after the MR SIA (range: 1.1-6.4%) compared to up to 28% before the SIA (range: 7.3-28.1%). DISCUSSION: We demonstrated the MR SIA provided considerable added value in terms of measles vaccination coverage, although there was variability across districts due to differences in routine and SIA coverage, and which children were reached by the SIA. Metrics evaluating the added value of an SIA can help to inform the design of vaccination strategies to better reach zero-dose or undervaccinated children.

Predilection Of Indian Portfolio Framework In COVID-19 Infodemic- An Analysis

Investors are confronted with plethora of investment alternatives, to make their money work for them, based upon desired risk and return availability. An investment portfolio is a manifest of individual's investment behaviour towards various investment avenues. At this junction of Coronavirus pandemic, the financial portfolio has been acclimatized to maintain balance and profitability. Therefore, the study proposes to assess the influence of socio-economic profile during these turbulent times on the investor's awareness, motivation, risk bearing capacity and attitude towards risk and non-risk mapped financial instruments. A structured questionnaire was administered to a sample of 168 Indian investors using convenience sampling technique and parametric & non-parametric statistics was used for delineating the influence of infodemic on behavioural finance. With worries about a potential stagflation growing, the investors focussed on liquidity instead of growth and invested more in debt or income schemes.

VLSI Implementation of Optimized CNN based COVID-19 Lung Infection Segmentation and Classification from CT Image

The outbreak of COVID-19 challenged the existence of human life on earth. The diagnosis and treatment of this disease is highly crucial in current scenario. Since there is low difference in the intensity of normal cells and affected cells, Computed Tomography (CT) is an efficient tool for the diagnosis of lung infections caused due to COVID-19. In order to train a deep neural network, there is a requirement for huge number of labelled images. There is a necessity to develop an efficient neural network that requires less number of training images. To overcome these problems a novel network is developed for lung CT segmentation (SqueezeNet). For the extraction of energy values from the segmented image, Discrete Wavelet Transform (DWT) with lifting scheme is incorporated in the framework. These energy values are used for training the classifier (ResNet). The entire framework is implemented on hardware using Virtex 2 Pro FPGA. The performance of the proposed system is evaluated using Mean Absolute Error (MAE) and Specificity. The MAE of the system is found to be 0.099, which is very low compared to existing classifiers. The specificity of the system is 0.978, which is higher than that of existing classifiers. © 2022 IEEE.

Previous SARS-CoV-2 Infection Status Among the Current RT-PCR-Positive Individuals Affected During the Second Wave of COVID-19 Infections in Chennai, India.

India witnessed a very strong second wave of coronavirus disease 2019 (COVID-19) during March and June 2021. Newly emerging variants of concern can escape immunity and cause reinfection. We tested newly diagnosed COVID-19 cases during the second wave in Chennai, India for the presence of Immunoglobulin G (IgG) antibodies to estimate the extent of re-infection. Of the 902 unvaccinated COVID-19 positive individuals, 53 (26.5%) were reactive for IgG antibodies and non-reactive for Immunogobulin M (IgM) antibodies. Among the 53 IgG-positive individuals, the interval between symptom onset (or last contact with the known case in case of asymptomatic) was <5 days in 29 individuals, ≥5 days in 11 individuals, while 13 asymptomatic individuals did not know their last contact with a positive case. The possible re-infections ranged between 3.2% (95% CI: 2.2-4.5%) and 4.3% (95% CI: 3.4-6.2%). The findings indicate that re-infection was not a major reason of the surge in cases during second wave. The IgG seropositivity among recently diagnosed unvaccinated COVID-19 patients could provide early indications about the extent of re-infections in the area.

Effectiveness of ChAdOx1 nCoV-19 Corona Virus Vaccine (CovishieldTM) in preventing SARS-CoV2 infection, Chennai, Tamil Nadu, India, 2021 (preprint)

Background: India experienced the second wave of the COVID-19 pandemic in March 2021, driven by the delta variant. Apprehensions around the usefulness of vaccines against delta variant posed a risk to the vaccination program. Therefore, we estimated the effectiveness of two doses of the ChAdOx1 nCoV-19 (Covishield) vaccine against COVID-19 infection among individuals [≥]45 years in Chennai, India. Methods: A community-based cohort study was conducted from May to September 2021 in a selected geographic area in Chennai, Tamil Nadu. The estimated sample size was 10,232. We enumerated individuals from all eligible households and periodically updated vaccination and COVID-19 infection data. We computed vaccine effectiveness with its 95% confidence interval for two doses of the Covishield vaccine against any COVID-19 infection. Results: We enrolled 69,435 individuals, of which 21,793 were above 45 years. Two dose coverage of Covishield in the 18+ and 45+ age group was 18% and 31%, respectively. The overall incidence of COVID-19 infection was 1099 per 100,000 population. The vaccine effectiveness against COVID-19 disease in the [≥]45 age group was 61.3% (95% CI: 43.6 - 73.4) at least two weeks after receiving the second dose of Covishield. Genomic analysis of 74 (28 with two doses, 15 with one dose, and 31 with zero dose) out of the 90 aliquots collected from the 303 COVID-19 positive individuals in the 45+ age group showed delta variants and their sub-lineages. Conclusion: We demonstrated the effectiveness of two doses of the ChAdOx1 vaccine against the delta variant in the general population of Chennai. We recommend similar future studies considering emerging variants and newer vaccines. Two-dose vaccine coverage could be ensured to protect against COVID-19 infection.

Monitoring the trend of SARS-CoV-2 seroprevalence in Chennai, India, July and October 2020

Background: The first serosurvey conducted in Chennai, India in July 2020 reported sudden acute respiratory syndrome coronavirus 2 (SARS-CoV-2) prevalence of 18.4%. The aim of this study was to estimate the seroprevalence in the month of October 2020. Methods: We conducted a survey in 153 streets covering 51 wards and all 15 zones of the city and enrolled from each street 40 individuals >= 10 y of age. We collected 3-5 ml of venous blood and tested for anti-nucleocapsid (N) immunoglobulin (IgG) antibodies using a SARS-CoV-2 IgG assay. We estimated the weighted seroprevalence of SARS-CoV-2 infection and adjusted for test characteristics. Results: Of the 6366 sera tested, 2052 were positive for anti-N IgG antibodies. The weighted seroprevalence after adjusting for test characteristics was 30.1% (95% confidence interval [CI] 24.7 to 36.1). There was wide variation in the seroprevalence between wards, ranging from 11.0% (95% CI 5.6 to 16.4) to 48.1% (95% CI 39.5 to 56.7). Conclusions: The seroprevalence of SARS-CoV-2 infection in Chennai nearly doubled between July and October 2020.