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The objective of the present work is to evaluate and establish the potential anti-inflammatory and antioxidant action of the Resveratrol and BioPerineÒ combination capsule available from Healthy Hey. The product responded positively to all the tests for flavonoids and alkaloids, confirming the presence of polyphenol and alkaloids in the capsule. The combination product was subjected to in vitro determination of antioxidant potential in terms of hydrogen donating or radical scavenging ability using the stable free radical DPPH, reducing power using potassium ferrocyanide, hydroxy radical scavenging using Iron-EDTA and ammonium molybdate methods. The IC50 value of the Resveratrol and BioPerineÒ combination against DPPH was found to be 57.01µg/mL. The Resveratrol and BioPerineÒ combination was able to produce a dose and time time-dependent action potassium ferrocyanide to potassium ferricyanide. The maximum reduction occurred at 20 min post-interaction thereafter decreasing in the 30th min. The IC50 value of the Resveratrol and BioPerineÒ combination against the hydroxyl radical was found to be 106.79µg/mL. The IC50 value of the Resveratrol and BioPerineÒ combination was found to be 154.88µg/mL. Resveratrol and BioPerineÒ combination was able to reduce the inhibition after the third hour significantly (p Keywords: Resveratrol, BioPerine, antioxidant, anti-inflammatory, DPPH, Rat paw edema, carrageenan

Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the effectiveness of interventions. Asymptomatic breakthrough infections have been a major problem during the ongoing surge of Delta variant globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines used in the higher-income regions. Here, we show for the first time how statistical and machine learning (ML) approaches can discriminate SARS-CoV-2 infection from immune response to an inactivated whole virion vaccine (BBV152, Covaxin, India), thereby permitting real-world vaccine effectiveness assessments from cohort-based serosurveys in Asia and Africa where such vaccines are commonly used. Briefly, we accessed serial data on Anti-S and Anti-NC antibody concentration values, along with age, sex, number of doses, and number of days since the last vaccine dose for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine (SVM) model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, 724 were classified as infected. Since the vaccine contains wild-type virus and the antibodies induced will neutralize wild type much better than Delta variant, we determined the relative ability of a random subset of such samples to neutralize Delta versus wild type strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, Delta variant, was neutralized more effectively than the wild type, which cannot happen without infection. The fraction rose to 71.8% (28 of 39) in subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6%-80.2%) over the same period.

Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the vaccine effectiveness. Asymptomatic breakthrough infections have been a major problem in assessing vaccine effectiveness in populations globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines since whole virion vaccines generate antibodies against all the viral proteins. Here, we show how a statistical and machine learning (ML) based approach can be used to discriminate between SARS-CoV-2 infection and immune response to an inactivated whole virion vaccine (BBV152, Covaxin). For this, we assessed serial data on antibodies against Spike and Nucleocapsid antigens, along with age, sex, number of doses taken, and days since last dose, for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, our ensemble ML model classified 724 to be infected. For method validation, we determined the relative ability of a random subset of samples to neutralize Delta versus wild-type strain using a surrogate neutralization assay. We worked on the premise that antibodies generated by a whole virion vaccine would neutralize wild type more efficiently than delta strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, neutralization against Delta strain was more effective, indicating infection. We found 71.8% subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6%-80.2%) over the same period. Our approach will help in real-world vaccine effectiveness assessments where whole virion vaccines are commonly used.

Background: India has been amongst the most affected nations during the SARS CoV2 pandemic, with sparse data on country wide spread of asymptomatic infections and antibody persistence. This longitudinal cohort study was aimed to evaluate SARS CoV2 seropositivity rate as a marker of infection and evaluate temporal persistence of antibodies with neutralization capability and to infer possible risk factors for infection. Methods: Council of Scientific and Industrial Research, India (CSIR) with its more than 40 laboratories and centers in urban and semi urban settings spread across the country piloted the pan country surveillance. 10427 adult individuals working in CSIR laboratories and their family members based on voluntary participation were assessed for antibody presence and stability was analyzed over 6 months utilizing qualitative Elecysys SARS CoV2 specific antibody kit and GENScript cPass SARS CoV2 Neutralization Antibody Detection Kit. Along with demographic information, possible risk factors were evaluated through self to be filled online forms with data acquired on blood group type, occupation type, addiction and habits including smoking and alcohol, diet preferences, medical history and transport type utilized. Symptom history and information on possible contact and compliance with COVID 19 universal precautions was also obtained. Findings:1058 individuals (10.14%) had antibodies against SARS CoV2. A follow up on 346 seropositive individuals after three months revealed stable to higher antibody levels against SARS CoV2 but declining plasma activity for neutralizing SARS CoV2 receptor binding domain and ACE2 interaction. A repeat sampling of 35 individuals, at six months, revealed declining antibody levels while the neutralizing activity remained stable compared to three months. Majority of seropositive individuals (75%) did not recall even one of nine symptoms since March 2020. Fever was the most common symptom with one fourth reporting loss of taste or smell. Significantly associated risks for seropositivity (Odds Ratio, 95% CI, p value) were observed with usage of public transport (1.79, 1.43 to 2∙24, 2.81561x10-6), occupational responsibilities such as security, housekeeping personnel etc. (2.23, 1.92 to 2.59, 6.43969x10-26), non smokers (1.52, 1.16 to 1.99, 0.02) and non vegetarianism (1.67, 1.41 to 1.99, 3.03821x10-8). An iterative regression analysis was confirmatory and led to only modest changes to estimates. Predilections for seropositivity was noted with specific ABO blood groups; O was associated with a lower risk. Interpretation: In a first of its kind study from India, we report the seropositivity in a country wide cohort and identify variable susceptible associations for contacting infection. Serology and Neutralizing Antibody response provides much sought for general insights on the immune response to the virus among Indians and will be an important resource for designing vaccination strategies. Funding: Council of Scientific and Industrial Research, India (CSIR)

Due to the exponential growth of image data there is a dire need for innovative tools which can easily manage, retrieve images and images from the large image database. The most common approach which is being used is Content-Based Image Retrieval (CBIR) system. CBIR is the popular image retrieval system by which reterived the targetted image can be retrieved by matching the features of the given image. The goal of this paper is to develop an image retrieval based on content properties such as shape, color, texture etc. usually encoded into feature vectors. One of the main advantages of the CBIR approach is the possibility of an automatic retrieval process instead of the traditional keyword-based approach. The CBIR technology has been used in several applications such as fingerprint identification, biodiversity information systems, digital libraries, medicine and historical research among others. This paper aims to develop a new efficient tool for CBIR based on above mention parameters using MATLAB.

India is a country of stark contrasts and contradictions. Traditional norms are increasingly meshed with influences of modernity. Gandhian values deeply rooted in the echelons of Indian consumer society are set against a backdrop of Western consumerism. A complex web of influences is transforming Indian society and is creating a new identity. This is New India. The implications are widespread and significant. A number of international lifestyle and luxury brands have entered the Indian market with great optimism, only to exit confused and frustrated. The allure of a Western logo has not always been sufficient to win over the luxury rupee. Luxury brand strategies, that may have been successful in other emerging markets, are not always transferable to the context of India. The challenge of decoding the DNA of India’s market is forcing luxury marketers to take a step back and rethink their tactics and strategies. This chapter sets out to guide luxury brand marketers on how to connect with the Indian luxury consumer and avoid the pitfalls of this complex market.

In this fast media age, luxury brands face a paradox in how to communicate to the emerging class of luxury consumers in India. The dilemma facing luxury brand marketers is determining the optimal trade-off between accessibility and exclusivity. Do we target communication to the mass and aspirational consumers and risk losing some of the mystique and aura of luxury? Or do we favour the mystique of luxury and risk disconnecting with a potentially lucrative customer segment?