Savitribai Phule Pune University

Klionsky D.J., Abdel-Aziz A.K., Abdelfatah S., Abdellatif M., Abdoli A., Abel S., Abeliovich H., Abildgaard M.H., Abudu Y.P., Acevedo-Arozena A., Adamopoulos I.E., Adeli K., Adolph T.E., Adornetto A., Aflaki E., et. al.

ABSTRACT In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

Fitzmaurice C., Abate D., Abbasi N., Abbastabar H., Abd-Allah F., Abdel-Rahman O., Abdelalim A., Abdoli A., Abdollahpour I., Abdulle A.S., Abebe N.D., Abraha H.N., Abu-Raddad L.J., Abualhasan A., Adedeji I.A., et. al.

Cancer and other noncommunicable diseases (NCDs) are now widely recognized as a threat to global development. The latest United Nations high-level meeting on NCDs reaffirmed this observation and also highlighted the slow progress in meeting the 2011 Political Declaration on the Prevention and Control of Noncommunicable Diseases and the third Sustainable Development Goal. Lack of situational analyses, priority setting, and budgeting have been identified as major obstacles in achieving these goals. All of these have in common that they require information on the local cancer epidemiology. The Global Burden of Disease (GBD) study is uniquely poised to provide these crucial data.To describe cancer burden for 29 cancer groups in 195 countries from 1990 through 2017 to provide data needed for cancer control planning.We used the GBD study estimation methods to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life-years (DALYs). Results are presented at the national level as well as by Socio-demographic Index (SDI), a composite indicator of income, educational attainment, and total fertility rate. We also analyzed the influence of the epidemiological vs the demographic transition on cancer incidence.In 2017, there were 24.5 million incident cancer cases worldwide (16.8 million without nonmelanoma skin cancer [NMSC]) and 9.6 million cancer deaths. The majority of cancer DALYs came from years of life lost (97%), and only 3% came from years lived with disability. The odds of developing cancer were the lowest in the low SDI quintile (1 in 7) and the highest in the high SDI quintile (1 in 2) for both sexes. In 2017, the most common incident cancers in men were NMSC (4.3 million incident cases); tracheal, bronchus, and lung (TBL) cancer (1.5 million incident cases); and prostate cancer (1.3 million incident cases). The most common causes of cancer deaths and DALYs for men were TBL cancer (1.3 million deaths and 28.4 million DALYs), liver cancer (572 000 deaths and 15.2 million DALYs), and stomach cancer (542 000 deaths and 12.2 million DALYs). For women in 2017, the most common incident cancers were NMSC (3.3 million incident cases), breast cancer (1.9 million incident cases), and colorectal cancer (819 000 incident cases). The leading causes of cancer deaths and DALYs for women were breast cancer (601 000 deaths and 17.4 million DALYs), TBL cancer (596 000 deaths and 12.6 million DALYs), and colorectal cancer (414 000 deaths and 8.3 million DALYs).The national epidemiological profiles of cancer burden in the GBD study show large heterogeneities, which are a reflection of different exposures to risk factors, economic settings, lifestyles, and access to care and screening. The GBD study can be used by policy makers and other stakeholders to develop and improve national and local cancer control in order to achieve the global targets and improve equity in cancer care.

Shah V.K., Firmal P., Alam A., Ganguly D., Chattopadhyay S.

After the 1918 flu pandemic, the world is again facing a similar situation. However, the advancement in medical science has made it possible to identify that the novel infectious agent is from the coronavirus family. Rapid genome sequencing by various groups helped in predicting the structure and function of virus, immunogenicity in diverse populations and its preventive measures. Coronavirus attacks the respiratory system causing pneumonia and lymphopenia in infected individuals. Viral components like spike, nucleo-capsid proteins trigger an immune response in the host to eliminate the virus. These viral antigens can be either recognized by B cells or presented by MHC complexes to T cells resulting in antibody production, increased cytokine secretion and cytolytic activity in the acute phase of infection. Association of HLA and its downregulated expression has been correlated with disease severity against influenza and coronaviruses. Studies have reported that infected individuals after recovery can induce strong protective responses by generating a memory T cell pool against SARS-CoV and MERS-CoV. These memory T cells were not persistent for long term and upon reactivation caused local damages due to cross-reactivity. So far the reports suggest that SARS-CoV-2, which is highly contagious shows related symptoms in 3 different stages and develops an exhaustive T cell pool in higher loads of viral infection. As there are no specific treatments available for this novel coronavirus, numerous small molecular drugs which are being used for the treatment of diseases like SARS, MERS, HIV, ebola, malaria and tuberculosis are under clinical trials for controlling COVID-19. A classical immunotherapy of convalescent plasma transfusion from recovered patients has also been initiated for neutralization of viremia in terminally ill COVID-19 patients. Due to the limitations in plasma transfusion, researchers are now focusing on developing neutralizing antibodies against virus particles along with immuno-modulation of cytokines such as IL-6, TNF-α and interferons that could help in combating the infection. This review highlights the similarities of SARS-CoV, MERS-CoV and SARS-CoV-2 in relation to their pathogenicity and immunogenicity and also focuses on various treatment strategies which could be employed for curing COVID-19.

Cuong H.N., Pansambal S., Ghotekar S., Oza R., Thanh Hai N.T., Viet N.M., Nguyen V.

Copper oxide nanoparticles (CuO NPs) are one of the most widely used nanomaterials nowadays. CuO NPs have numerous applications in biological processes, medicine, energy devices, environmental remediation, and industrial fields from nanotechnology. With the increasing concern about the energy crisis and the challenges of chemical and physical approaches for preparing metal NPs, attempts to develop modern alternative chemistry have gotten much attention. Biological approaches that do not produce toxic waste and therefore do not require purification processes have been the subject of numerous studies. Plants may be extremely useful in the study of biogenic metal NP synthesis. This review aims to shed more light on the interactions between plant extracts and CuO NP synthesis. The use of living plants for CuO NPs biosynthesis is a cost-effective and environmentally friendly process. To date, the findings have revealed many aspects of plant physiology and their relationships to the synthesis of NPs. The current state of the art and potential challenges in the green synthesis of CuO NPs are described in this paper. This study found a recent increase in the green synthesis of CuO NPs using various plant extracts. As a result, a thorough explanation of green synthesis and stabilizing agents for CuO NPs made from these green sources is given. Additionally, the multifunctional applications of CuO NPs synthesized with various plant extracts in environmental remediation, sensing, catalytic reduction, photocatalysis, diverse biological activities, energy storage, and several organic transformations such as reduction, coupling, and multicomponent reactions were carefully reviewed. We expect that this review could serve as a useful guide for readers with a general interest in the plant extract mediated biosynthesis of CuO NPs and their potential applications.

Pillai R., Sivathanu B., Dwivedi Y.K.

Artificial Intelligence (AI) is transforming the way retail stores operate. AI-Powered Automated Retail Stores are the next revolution in physical retail. Consumers are facing fully automated technology in these retail stores. Therefore, it is necessary to scrutinize the antecedents of consumers' intention to shop at AI-Powered Automated Retail Stores. This study delves into this area to find the predictors of consumers’ intention to shop at AI-Powered Automated Retail Stores. It extends the technology readiness and acceptance model by the addition of AI context-specific constructs such as Perceived Enjoyment, Customization and Interactivity from the present literature. The proposed model is tested by surveying 1250 consumers & the data is analyzed using the PLS-SEM technique and empirically validated. The outcome of the study reveals that Innovativeness and Optimism of consumers affect the perceived ease and perceived usefulness. Insecurity negatively affects the perceived usefulness of AI-powered automated retail stores. Perceived ease of use, perceived usefulness, perceived enjoyment, customization and interactivity are significant predictors of shopping intention of consumers in AI-powered automated stores. This research presents insightful academic and managerial implications in the domain of retailing and technology in retail.

Mahajan S., Jagtap S.

The development of metal-oxide semiconductor (MOS) based gas sensor has engrossed demanding research interest in the last several decades because of their potential for the selectivity, high sensitivity and fast detection of various toxic gases. Carbon monoxide (CO) is odorless, colorless and one of the most toxic gases. In this review recent progress on the development of different MOS based nanostructure for CO gas sensor is summarized. The commonly investigated classes of materials are MOSs (notably, zinc oxide and tin oxide) and yttrium stabilized zirconium. Recently, many metal oxide semiconductor including tin oxide, zinc oxide, titanium oxide, indium oxide, tungsten oxide, cerium oxide, copper oxide, composite MOS etc have been tested for CO gas sensing. The main focus is on new approaches for the synthesis of CO gas sensor by various researchers to improve the sensing performance like selectivity, sensitivity, response and recovery time with different materials and catalysts used.

Khan A., Vibhute A.D., Mali S., Patil C.H.

The globe's population is increasing day by day, which causes the severe problem of organic food for everyone. Farmers are becoming progressively conscious of the need to control numerous essential factors such as crop health, water or fertilizer use, and harmful diseases in the field. However, it is challenging to monitor agricultural activities. Therefore, precision agriculture is an important decision support system for food production and decision-making. Several methods and approaches have been used to support precision agricultural practices. The present study performs a systematic literature review on hyperspectral imaging technology and the most advanced deep learning and machine learning algorithm used in agriculture applications to extract and synthesize the significant datasets and algorithms. We reviewed legal studies carefully, highlighted hyperspectral datasets, focused on the most methods used for hyperspectral applications in agricultural sectors, and gained insight into the critical problems and challenges in the hyperspectral data processing. According to our study, it has been found that the Hyperion hyperspectral, Landsat-8, and Sentinel 2 multispectral datasets were mainly used for agricultural applications. The most applied machine learning method was support vector machine and random forest. In addition, the deep learning-based Convolutional Neural Networks (CNN) model is mainly used for crop classification due to its high performance with hyperspectral datasets. The present review will be helpful to the new researchers working in the field of hyperspectral remote sensing for agricultural applications with a machine and deep learning methods. • Hyperspectral image data and deep/machine learning methods have been synthesized. • The most used hyper and multispectral data is EO-1 Hyperion, Landsat and Sentinel. • The most used machine learning methods are SVM, random forest, and neural networks. • The most widely used deep learning methods are convolutional neural networks. • Python is the most preferred language for developing deep learning models.

Patel S., Alam A., Pant R., Chattopadhyay S.

Wnt signaling is one of the central mechanisms regulating tissue morphogenesis during embryogenesis and repair. The pivot of this signaling cascade is the Wnt ligand which binds to receptors belonging to Frizzled family or ROR1/ROR2 and RYK family. This interaction governs the downstream signaling cascade (canonical/non-canonical) ultimately extending its effect on cellular cytoskeleton, transcriptional control of proliferation and differentiation and organelle dynamics. Anomalous Wnt signaling has been associated with several cancers, most prominently colorectal, breast, lung, oral, cervical and hematopoietic malignancies. It extends its effect on tumorigenesis by modulating the tumor microenvironment via fine-crosstalk between transformed cells and infiltrating immune cells like leukocytes. This review is an attempt to highlight the latest developments in the understanding of Wnt signaling in the context of tumors and their microenvironment. A dynamic process known as immunoediting governs the fate of tumor progression based on correlation of various signalling pathways in the tumor microenvironment and immune cells. Cancer cells also undergo a series of mutations in the tumor suppressor genes which favours tumorigenesis. Wnt signaling and its crosstalk with various immune cells has both negative as well as positive effects on tumor progression. On one hand, it helps in the maintenance and renewal of the leucocytes whereas on the other hand, it promotes immune tolerance limiting the antitumor response. Wnt signaling also plays a role in epithelial-mesenchymal transition (EMT), thereby promoting Cancer Stem Cells (CSCs) maintenance. Further, we summarize the ongoing strategies used to target aberrant Wnt signaling as a novel therapeutic intervention to combat various cancers and their limitations.

Joshi R.S., Jagdale S.S., Bansode S.B., Shankar S.S., Tellis M.B., Pandya V.K., Chugh A., Giri A.P., Kulkarni M.J.

Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has resulted in the current COVID-19 pandemic. Worldwide this disease has infected over 2.5 million individuals with a mortality rate ranging from 5 to 10%. There are several efforts going on in the drug discovery to control the SARS-CoV-2 viral infection. The main protease (MPro) plays a critical role in viral replication and maturation, thus can serve as the primary drug target. To understand the structural evolution of MPro, we have performed phylogenetic and Sequence Similarity Network analysis, that depicted divergence of Coronaviridae MPro in five clusters specific to viral hosts. This clustering was corroborated with the comparison of MPro structures. Furthermore, it has been observed that backbone and binding site conformations are conserved despite variation in some of the residues. These attributes can be exploited to repurpose available viral protease inhibitors against SARS-CoV-2 MPro. In agreement with this, we performed screening of ∼7100 molecules including active ingredients present in the Ayurvedic anti-tussive medicines, anti-viral phytochemicals and synthetic anti-virals against SARS-CoV-2 MPro as the primary target. We identified several natural molecules like δ-viniferin, myricitrin, taiwanhomoflavone A, lactucopicrin 15-oxalate, nympholide A, afzelin, biorobin, hesperidin and phyllaemblicin B that strongly binds to SARS-CoV-2 MPro. Intrestingly, these molecules also showed strong binding with other potential targets of SARS-CoV-2 infection like viral receptor human angiotensin-converting enzyme 2 (hACE-2) and RNA dependent RNA polymerase (RdRp). We anticipate that our approach for identification of multi-target-directed ligand will provide new avenues for drug discovery against SARS-CoV-2 infection. Communicated by Ramaswamy H. Sarma

Virnodkar S.S., Pachghare V.K., Patil V.C., Jha S.K.

The remote sensing (RS) technique is less cost- and labour- intensive than ground-based surveys for diverse applications in agriculture. Machine learning (ML), a branch of artificial intelligence (AI), provides an effective approach to construct a model for regression and classification of a multivariate and non-linear system. Without being explicitly programmed, machine learning models learn from training data, i.e., past experience. Machine learning, when applied to remotely sensed data, has the potential to evolve a real-time farm-specific management system to reinforce farmers' ability to make appropriate decisions. Recently, the use of machine learning techniques combined with RS data has reshaped precision agriculture in many ways, such as crop identification, yield prediction and crop water stress assessment, with better accuracy than conventional RS methods. As agriculture accounts for approximately 70% of the worldwide water withdrawals, it must be used in the most efficient way to obtain maximum yields and food production. The use of water management and irrigation based on plant water stress have been demonstrated to not only save water but also increase yield. To date, RS and ML-based results have encouraged farmers and decision-makers to adopt this technology to meet global food demands. This phenomenon has led to the much-needed interest of researchers in using ML to improve agriculture outcomes. However, the use of ML for the potential evaluation of water stress continues to be unexplored and the existing methods can still be greatly improved. This study aims to present an overall review of the widely used methods for crop water stress monitoring using remote sensing and machine learning and focuses on future directions for researchers.