Narsee Monjee Institute of Management Studies

Drug delivery is the method or process of administering a pharmaceutical compound to achieve a therapeutic effect in humans or animals. For the treatment of human diseases, nasal and pulmonary routes of drug delivery are gaining increasing importance. These routes provide promising alternatives to parenteral drug delivery particularly for peptide and protein therapeutics. For this purpose, several drug delivery systems have been formulated and are being investigated for nasal and pulmonary delivery. These include liposomes, proliposomes, microspheres, gels, prodrugs, cyclodextrins, among others. Nanoparticles composed of biodegradable polymers show assurance in fulfilling the stringent requirements placed on these delivery systems, such as ability to be transferred into an aerosol, stability against forces generated during aerosolization, biocompatibility, targeting of specific sites or cell populations in the lung, release of the drug in a predetermined manner, and degradation within an acceptable period of time.

BACKGROUND: The only conclusive way to diagnose Alzheimer's is to carry out brain autopsy of the patient's brain tissue and ascertain whether the subject had Alzheimer's or any other form of dementia. However, due to the non-feasibility of such methods, to diagnose and conclude the conditions, medical practitioners use tests that examine a patient's mental ability. OBJECTIVE: Accurate diagnosis at an early stage is the need of the hour for initiation of therapy. The cause for most Alzheimer's cases still remains unknown except where genetic distinctions have been observed. Thus, a standard drug regimen ensues in every Alzheimer's patient, irrespective of the cause, which may not always be beneficial in halting or reversing the disease progression. To provide a better life to such patients by suppressing existing symptoms, early diagnosis, curative therapy, site-specific delivery of drugs, and application of hyphenated methods like artificial intelligence need to be brought into the main field of Alzheimer's therapeutics. METHODS: In this review, we have compiled existing hypotheses to explain the cause of the disease, and highlighted gene therapy, immunotherapy, peptidomimetics, metal chelators, probiotics and quantum dots as advancements in the existing strategies to manage Alzheimer's. CONCLUSION: Biomarkers, brain-imaging, and theranostics, along with artificial intelligence, are understood to be the future of the management of Alzheimer's.

Machine learning is one of the fields in the modern computing world. A plenty of research has been undertaken to make machines intelligent. Learning is a natural human behavior which has been made an essential aspect of the machines as well. There are various techniques devised for the same. Traditional machine learning algorithms have been applied in many application areas. Researchers have put many efforts to improve the accuracy of that machinelearning algorithms. Another dimension was given thought which leads to deep learning concept. Deep learning is a subset of machine learning. So far few applications of deep learning have been explored. This is definitely going to cater to solving issues in several new application domains, sub-domains using deep learning. A review of these past and future application domains, sub-domains, and applications of machine learning and deep learning are illustrated in this paper.

Purpose – The purpose of this paper is to accomplish two objectives – to test the functional relationship between adoption readiness (AR), perceived risk (PR) and usage intention for mobile payments in India and to investigate the stability of proposed structural relationships across different customer groups. Design/methodology/approach – The literature concerning major attributes of technology acceptance were systematically reviewed to develop construct of AR. Post that a comprehensive model consisting of AR, personal innovativeness and PR was put together. The model was then empirically tested using structural equation modelling. Findings – On appraising the proposed model, five out of six hypotheses were fully supported while one hypothesis was partially supported. Test of invariance showed significant variance among users and non-users. Research limitations/implications – The results of the study may vary with national context, service offerings, regulatory framework and other customer personal variables (i.e. lifestyle) suggesting future research opportunities. Practical implications – The results facilitate the comprehension of the role of different factors on the mobile payments usage intention among customers. In addition, the results expand the knowledge on consumer behaviour towards financial technological innovations. Originality/value – The results expand one's knowledge on this relationship, propounding interesting empirical evidence of the model invariance among different consumer groups.

Vehicular technology has recently gained increasing popularity, and autonomous driving is a hot topic. To achieve safe and reliable intelligent transportation systems, accurate positioning technologies need to be built to factor in the different types of uncertainties such as pedestrian behavior, random objects, and types of roads and their settings. In this work, we look into the other domains and technologies required to build an autonomous vehicle and conduct a relevant literature analysis. In this work, we look into the current state of research and development in environment detection, pedestrian detection, path planning, motion control, and vehicle cybersecurity for autonomous vehicles. We aim to study the different proposed technologies and compare their approaches. For a car to become fully autonomous, these technologies need to be accurate enough to gain public trust and show immense accuracy in their approach to solving these problems. Public trust and perception of auto vehicles are also explored in this paper. By discussing the opportunities as well as the obstacles of autonomous driving technology, we aim to shed light on future possibilities.

Abstract Green innovation could become a valuable firm resource for establishing competitive advantage while simultaneously contributing towards sustainable development; in other words, green innovation has the potential to address the dilemma between consuming available resources and preserving them for the future. However, there is a dearth of studies systematically examining the present structure and future scope of research on green innovation as a firm resource. Seeking to explain the sustainable development dilemma of green innovations through the theoretical perspective of the resource‐based view of the firm, we address this gap with a comprehensive bibliometric analysis of 951 relevant articles. The key contributors to the extant literature are recognised with bibliographic coupling, citation analysis and co‐authorship analysis. A co‐citation analysis identifies four major thematic areas of research: green supply chain management, green product design, corporate environmental responsibilities and social sustainability. Further, a dynamic co‐citation analysis tracks the progression of these thematic areas. Content analysis of the thematic areas provides insights into the status of the research domain. This study also contributes to the extant literature by identifying prestigious articles on green innovation as a firm resource, analysing the co‐occurrence of keywords and suggesting future research agendas.

Aging, often considered a result of random cellular damage, can be accurately estimated using DNA methylation profiles, the foundation of pan-tissue epigenetic clocks. Here, we demonstrate the development of universal pan-mammalian clocks, using 11,754 methylation arrays from our Mammalian Methylation Consortium, which encompass 59 tissue types across 185 mammalian species. These predictive models estimate mammalian tissue age with high accuracy (r > 0.96). Age deviations correlate with human mortality risk, mouse somatotropic axis mutations and caloric restriction. We identified specific cytosines with methylation levels that change with age across numerous species. These sites, highly enriched in polycomb repressive complex 2-binding locations, are near genes implicated in mammalian development, cancer, obesity and longevity. Our findings offer new evidence suggesting that aging is evolutionarily conserved and intertwined with developmental processes across all mammals.

Inflammation is one of the common events in the majority of acute as well as chronic debilitating diseases and represent a chief cause of morbidity in today's era of modern lifestyle. If unchecked, inflammation leads to development of rheumatoid arthritis, diabetes, cancer, Alzheimer's disease, and atherosclerosis along with pulmonary, autoimmune and cardiovascular diseases. Inflammation involves a complex network of many mediators, a variety of cells, and execution of multiple pathways. Current therapy for inflammatory diseases is limited to the steroidal and non-steroidal anti-inflammatory agents. The chronic use of these drugs is reported to cause severe adverse effects like gastrointestinal, cardiovascular, and renal abnormalities. There is a massive need to explore new anti-inflammatory agents with selective action and lesser toxicity. Plants and isolated phytoconstituents are promising and interesting sources of new anti-inflammatories. However, drug development from natural sources has been linked with hurdles like the complex nature of extracts, difficulties in isolation of pure phytoconstituents, and the yield of isolated compounds in minute quantities that is insufficient for subsequent lead development. Although various in-vivo and in-vitro models for anti-inflammatory drug development are available, judicious selection of appropriate animal models is a vital step in the early phase of drug development. Systematic evaluation of phytoconstituents can facilitate the identification and development of potential anti-inflammatory leads from natural sources. The present review describes various techniques of anti-inflammatory drug screening with its advantages and limitations, elaboration on biological targets of phytoconstituents in inflammation and biomarkers for the prediction of adverse effects of anti-inflammatory drugs. The systematic approach proposed through present article for anti-inflammatory drug screening can rationalize the identification of novel phytoconstituents at the initial stage of drug screening programs.

Diabetes is a metabolic disorder affecting large percentage of population worldwide. NF-κβ plays key role in pathogenesis of vascular complications of diabetes. Persistent hyperglycemia activates NF-κβ that triggers expression of various cytokines, chemokines and cell adhesion molecules. Over-expression of TNF-α, interleukins, TGF-β, Bcl2 and other pro-inflammatory proteins and pro-apoptotic genes by NF-κβ is key risk factor in vascular dysfunction. NF-κβ over-expression also triggers calcification of endothelial cells leading to endothelial dysfunction and further vascular complications. Inhibition of NF-κβ pro-inflammatory pathway is upcoming novel target for management of vascular complications of diabetes. Various natural and synthetic inhibitors of NF-κβ have been studied in management of diabetic complications. Recent preclinical and clinical studies validate NF-κβ as promising target in the management of vascular complications of diabetes.

The host immune system is highly compromised in case of viral infections and relapses are very common. The capacity of the virus to destroy the host cell by liberating its own DNA or RNA and replicating inside the host cell poses challenges in the development of antiviral therapeutics. In recent years, many new technologies have been explored for diagnosis, prevention, and treatment of viral infections. Nanotechnology has emerged as one of the most promising technologies on account of its ability to deal with viral diseases in an effective manner, addressing the limitations of traditional antiviral medicines. It has not only helped us to overcome problems related to solubility and toxicity of drugs, but also imparted unique properties to drugs, which in turn has increased their potency and selectivity toward viral cells against the host cells. The initial part of the paper focuses on some important proteins of influenza, Ebola, HIV, herpes, Zika, dengue, and corona virus and those of the host cells important for their entry and replication into the host cells. This is followed by different types of nanomaterials which have served as delivery vehicles for the antiviral drugs. It includes various lipid-based, polymer-based, lipid-polymer hybrid-based, carbon-based, inorganic metal-based, surface-modified, and stimuli-sensitive nanomaterials and their application in antiviral therapeutics. The authors also highlight newer promising treatment approaches like nanotraps, nanorobots, nanobubbles, nanofibers, nanodiamonds, nanovaccines, and mathematical modeling for the future. The paper has been updated with the recent developments in nanotechnology-based approaches in view of the ongoing pandemic of COVID-19.Graphical abstract.

Microwaves are utilized for extraction of Phytoconstituents from complex herbal sample as a result of incredible research. Conventional extraction strategies are tedious and need more solvents and are no more relevant for thermal sensitive plant components. This review emphasize on the working and significance of microwave extraction technology in herbal research and medical field. The extraction step must be more yielding; quick, particular, not more solvent consuming, ensuring stability of thermolabile components and these features are available with microwave extraction method. In this nonconventional technology heat is created utilizing microwave energy. The important parameters that influence extraction efficiency are solvent properties, volume, duration of exposure, microwave control, system attributes, temperature and application were discussed in this article. The microwave assisted extraction, as green technology is contrasted with other extraction technique. This review is intended to discuss this green extraction technique along with its critical parameters for extracting bioactive compounds from complex plant matrices.

Purpose Due to COVID-19 pandemic, the government around the world has closed all the educational institutions to control the spread of disease, which is creating a direct impact on students, educators and institutions. The sudden shift from the physical classroom to virtual space is creating a disruption among students. The purpose of this study was to analyze the perception of academic stress experienced by students during current online education and coping strategies using emotional intelligence adopted by them. Design/methodology/approach Using a purposive sampling method, data were collected on a sample of 94 students pursuing undergraduation and postgraduation from two Indian cities, Ahmedabad, and Mumbai. The survey was conducted using two online questionnaires, Perceptions of Academic Stress Scale and Emotional Intelligence Scale and analyzed using descriptive statistics with chi-square analysis. A telephonic discussion was also conducted with some respondents to understand different coping strategies used by them to handle the stress. Findings The findings indicated significant differences were observed between the fear of academic failure and online and home environment among male and female students. Many of them have started diverting themselves to various creative activities and taking up courses that are helping them to learn new technical skills. By using emotional intelligence and distancing from boredom and depressive thoughts, students were trying to cope with negative effects arising from the current pandemic situation. Research limitations/implications This research study will be beneficial to educators, scholars, students, parents and will add a contribution to its field. However, the key factors studied were limited to a small sample from selected institutions and cities, which cannot be used to generalize to a large population. Practical implications The findings of this paper will be useful to assess the key challenges of online education especially at the time when it is the only option. Social implications The findings of this paper will be beneficial to understand the academic stress experienced by students and how a cultural and educational modification will be implemented. Originality/value This research study was conducted during the lockdown in India (April–May 2020), and the results derived through it are original in nature.

This paper focuses on consumer electronics products and observes the comparative effect of celebrity vis-à-vis expert influencers on consumers' online purchase intentions. The mediating role played by brand admiration and brand attitude between influencer marketing and online purchase intentions are tested. The moderating role played by message involvement between influencer marketing and brand attitude is also observed. The survey method was employed to conduct this research, and data were collected from 438 respondents. The proposed hypotheses were tested using structural equation modeling, hierarchical regression analysis, and Hayes process method. The results submit that there is a definite advantage in choosing an expert influencer over an attractive celebrity influencer while planning the marketing communications of consumer electronics products. The mediating role of brand attitude and brand admiration is empirically evident. The moderating effect of involvement is also established.

Purpose The purpose of this paper is to study the influence of factors such as financial literacy on a consumer’s investment decisions, particularly in the stock market. Based on two empirical studies, the theory of planned behaviour (TPB) was used to understand stock market participation (SMP) in India while developing a model to represent the relationships between the various factors. Consumer financial literacy was conceptualised to be a part of perceived behavioural control and included in the TPB. Design/methodology/approach A mixed methods research was followed where qualitative research preceded a quantitative survey-based study. In-depth interviews were conducted with investors and experts, results of which, when combined with the literature review, revealed seven variables including financial literacy which were pooled into three distinct groups based on the TPB. Responses obtained from 506 retail investors from four cities in India were analysed. Structural equation modelling was used to test the models and arrive at a final empirical model. Findings Results of the study indicated that investment intention predicts actual investments in the stock market (which represented behaviour). Financial literacy – both subjective and objective – were also found to be significant influencers on intention while only objective financial literacy seemed to affect behaviour. Three variables – perception of regulator, risk avoidance, and hassle factor – were combined to form a second-order construct which was named “Attitude to Investment Behaviour”. This had a negative impact on intention to invest in the equity markets. Financial well-being seemed to have a negative impact on intention while having a positive relationship with behaviour. Practical implications The results present significant investor behaviour and policy implications for financial services marketing. Some interventions, especially in the area of consumer financial literacy, are more likely than others to help consumers bridge the gap between non-participation and participation in the stock market. Originality/value The study makes a contribution to investor behaviour theory in the form of a comprehensive model to explain SMP in an emerging market. This can be further tested across geographies.

An efficient use of energy is the pre-condition for economic development. But excessive use of fossil fuel harms the environment. As renewable energy emits no or low greenhouse gases, more countries are trying to increase the use of energies from renewable sources. At the same time, no matter developed or developing, nations have to maintain economic growth. By collecting SCI/SSCI indexed peer-reviewed journal articles, this article systematically reviews the consumption nexus of renewable energy and economic growth. A total of 46 articles have been reviewed following the PRISMA guidelines from 2010 to 2021. Our review research shows that renewable energy does not hinder economic growth for both developing and developed countries, whereas, there is little significance of consuming renewable energy (threshold level) on economic growth for developed countries.

Abstract Mesoporous carbon nitrides (MCN) with C 3 N 4 stoichiometry could find applications in fields ranging from catalysis, sensing, and adsorption–separation to biotechnology. The extension of the synthesis of MCN with different nitrogen contents and chemical structures promises access to a wider range of applications. Herein we prepare mesoporous C 3 N 5 with a combined triazole and triazine framework via a simple self‐assembly of 5‐amino‐1 H ‐tetrazole (5‐ATTZ). We are able to hybridize these nanostructures with graphene by using graphene–mesoporous‐silica hybrids as a template to tune the electronic properties. DFT calculations and spectroscopic analyses clearly demonstrate that the C 3 N 5 consists of 1 triazole and 2 triazine moieties. The triazole‐based mesoporous C 3 N 5 and its graphene hybrids are found to be highly active for oxygen reduction reaction (ORR) with a higher diffusion‐limiting current density and a decreased overpotential than those of bulk g‐C 3 N 4 .

The Internet of Things is an emerging technology across the world, which helps to connect sensors, vehicles, hospitals, industries and consumers through internet connectivity. This type of architecture leads to Smart Cities, Smart home, Smart agriculture and Smart World. Architecture of IoT is very complex because of the large number of devices, link layer technology and services that are involved in this system. However, security in IoT is the most important parameter. In this paper, we give an overview of the architecture of IoT with the help of Smart World. In the second phase of this paper, we discuss the security challenges in IoT followed by the security measures in IoT. Finally, these challenges, which are discussed in the paper, could be research direction for future work in security for IoT.

There have been giant leaps in the field of education in the past 1-2 years.. Schools and colleges are transitioning online to provide more resources to their students. The COVID-19 pandemic has provided students more opportunities to learn and improve themselves at their own pace. Online proctoring services (part of assessment) are also on the rise, and AI-based proctoring systems (henceforth called as AIPS) have taken the market by storm. Online proctoring systems (henceforth called as OPS), in general, makes use of online tools to maintain the sanctity of the examination. While most of this software uses various modules, the sensitive information they collect raises concerns among the student community. There are various psychological, cultural and technological parameters need to be considered while developing AIPS. This paper systematically reviews existing AI and non-AI-based proctoring systems. Through the systematic search on Scopus, Web of Science and ERIC repositories, 43 paper were listed out from the year 2015 to 2021. We addressed 4 primary research questions which were focusing on existing architecture of AIPS, Parameters to be considered for AIPS, trends and Issues in AIPS and Future of AIPS. Our 360-degree analysis on OPS and AIPS reveals that security issues associated with AIPS are multiplying and are a cause of legitimate concern. Major issues include Security and Privacy concerns, ethical concerns, Trust in AI-based technology, lack of training among usage of technology, cost and many more. It is difficult to know whether the benefits of these Online Proctoring technologies outweigh their risks. The most reasonable conclusion we can reach in the present is that the ethical justification of these technologies and their various capabilities requires us to rigorously ensure that a balance is struck between the concerns with the possible benefits to the best of our abilities. To the best of our knowledge, there is no such analysis on AIPS and OPS. Our work further addresses the issues in AIPS in human and technological aspect. It also lists out key points and new technologies that have only recently been introduced but could significantly impact online education and OPS in the years to come.

Diabetes is a chronic disease that continues to be a significant and global concern since it affects the entire population's health. It is a metabolic disorder that leads to high blood sugar levels and many other problems such as stroke, kidney failure, and heart and nerve problems. Several researchers have attempted to construct an accurate diabetes prediction model over the years. However, this subject still faces significant open research issues due to a lack of appropriate data sets and prediction approaches, which pushes researchers to use big data analytics and machine learning (ML)-based methods. Applying four different machine learning methods, the research tries to overcome the problems and investigate healthcare predictive analytics. The study's primary goal was to see how big data analytics and machine learning-based techniques may be used in diabetes. The examination of the results shows that the suggested ML-based framework may achieve a score of 86. Health experts and other stakeholders are working to develop categorization models that will aid in the prediction of diabetes and the formulation of preventative initiatives. The authors perform a review of the literature on machine models and suggest an intelligent framework for diabetes prediction based on their findings. Machine learning models are critically examined, and an intelligent machine learning-based architecture for diabetes prediction is proposed and evaluated by the authors. In this study, the authors utilize our framework to develop and assess decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction, which are the most widely used techniques in the literature at the time of writing. It is proposed in this study that a unique intelligent diabetes mellitus prediction framework (IDMPF) is developed using machine learning. According to the framework, it was developed after conducting a rigorous review of existing prediction models in the literature and examining their applicability to diabetes. Using the framework, the authors describe the training procedures, model assessment strategies, and issues associated with diabetes prediction, as well as solutions they provide. The findings of this study may be utilized by health professionals, stakeholders, students, and researchers who are involved in diabetes prediction research and development. The proposed work gives 83% accuracy with the minimum error rate.

Influencers are people on social media that distinguish themselves by the high number of followers and the ability to influence other users. While influencers are a long-standing phenomenon in social media, virtual influencers have made their appearance on such platforms only recently: they are CGI characters that act like and resemble humans, even if they do not physically exist in the real world. This recent phenomenon has sparked interest in society, and several questions arise regarding their evolution, opinions, ethics, purpose in marketing, and future perspective. In this article, we conduct an exhaustive review of the virtual influencer phenomenon. Through an extensive study of the literature, press articles, social platforms data, blogs, and interviews, we give a comprehensive reflection on virtual influencers. Starting from their evolution, we analyze their opportunities and threats. We provide detailed information about the most popular ones and their marketing collaborations, with a comparative analysis of virtual and real (human) influencers. Moreover, we conducted an online survey to grasp people's perspectives. From the 360 participants' answers, we draw conclusions about virtual influencers' ethics, importance, overall feelings, and future. Results show controversial opinions on this recent phenomenon.