Showing papers by "Partha Pratim Roy published in 2021"

An Assessment on Impact of COVID-19 Infection in a Gender Specific Manner.

Abstract: Coronavirus disease 2019 (COVID-19) is caused by novel coronavirus Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It was first time reported in December 2019 in Wuhan, China and thereafter quickly spread across the globe. Till September 19, 2020, COVID-19 has spread to 216 countries and territories. Severe infection of SARS-CoV-2 cause extreme increase in inflammatory chemokines and cytokines that may lead to multi-organ damage and respiratory failure. Currently, no specific treatment and authorized vaccines are available for its treatment. Renin angiotensin system holds a promising role in human physiological system specifically in regulation of blood pressure and electrolyte and fluid balance. SARS-CoV-2 interacts with Renin angiotensin system by utilizing angiotensin-converting enzyme 2 (ACE2) as a receptor for its cellular entry. This interaction hampers the protective action of ACE2 in the cells and causes injuries to organs due to persistent angiotensin II (Ang-II) level. Patients with certain comorbidities like hypertension, diabetes, and cardiovascular disease are under the high risk of COVID-19 infection and mortality. Moreover, evidence obtained from several reports also suggests higher susceptibility of male patients for COVID-19 mortality and other acute viral infections compared to females. Analysis of severe acute respiratory syndrome coronavirus (SARS) and Middle East respiratory syndrome coronavirus (MERS) epidemiological data also indicate a gender-based preference in disease consequences. The current review addresses the possible mechanisms responsible for higher COVID-19 mortality among male patients. The major underlying aspects that was looked into includes smoking, genetic factors, and the impact of reproductive hormones on immune systems and inflammatory responses. Detailed investigations of this gender disparity could provide insight into the development of patient tailored therapeutic approach which would be helpful in improving the poor outcomes of COVID-19. Graphical abstract.

Global footprints of organochlorine pesticides: a pan-global survey.

Abstract: Organochlorine pesticides (OCPs) are ubiquitous environmental contaminants widely used all over the world. These chlorinated hydrocarbons are toxic and often cause detrimental health effects because of their long shelf life and bioaccumulation in the adipose tissues of primates. OCP exposure to humans occurs through skin, inhalation and contaminated foods including milk and dairy products, whereas developing fetus and neonates are exposed through placental transfer and lactation, respectively. In 1960s, OCPs were banned in most developed countries, but because they are cheap and easily available, they are still widely used in most third world countries. The overuse or misuse of OCPs has been rising continuously which pose threats to environmental and human health. This review reports the comparative occurrence of OCPs in human and bovine milk samples around the globe and portrays the negative impacts encountered through the long history of OCP use.

A Survey on Neuromarketing using EEG Signals

Abstract: Neuromarketing is the application of neuroscience to the understanding of consumer preferences towards products and services. As such, it studies the neural activity associated with preference and purchase intent. Neuromarketing is considered an emerging area of research, driven in part by the approximately 400 billion dollars spent annually on advertisement and promotion. Given the size of this market, even a slight improvement in performance can have an immense impact. Traditional approaches to marketing consider a posteriori user feedback in the form of questionnaires, product ratings, or review comments, but these approaches do not fully capture or explain the real-time decision making process of consumers. Various physiological measurement techniques have been proposed to facilitate the recording of this crucial aspect of the decision making process, including brain imaging techniques (Functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), Steady State Topography (SST)) and various biometric sensors. The use of EEG in neuromarketing is especially promising. EEG detects the sequential changes of brain activity, without appreciable time delay, needed to assess both the unconscious reaction and sensory reaction of the customer. Several types of EEG devices are now available in the market, each with its own advantages and disadvantages. Researchers have conducted experiments using many of these devices, across different age groups and different categories of products. Because of the deep insights that can be gained, the field of neuromarketing research is carefully monitored by consumer and research protection groups to ensure that subjects are properly protected. This paper surveys a range of considerations for EEG-based neuromarketing strategies including, the types of information that can be gathered, how marketing stimuli are presented to consumers, how such strategies may affect the consumer in terms of appeal and memory, machine learning techniques applied in the field, and the variety of challenges faced, including ethics, in this emerging field.

Application of Stem Cell Therapy for Infertility

Abstract: Infertility creates an immense impact on the psychosocial wellbeing of affected couples, leading to poor quality of life. Infertility is now considered to be a global health issue affecting approximately 15% of couples worldwide. It may arise from factors related to the male (30%), including varicocele, undescended testes, testicular cancer, and azoospermia; the female (30%), including premature ovarian failure and uterine disorders; or both partners (30%). With the recent advancement in assisted reproduction technology (ART), many affected couples (80%) could find a solution. However, a substantial number of couples cannot conceive even after ART. Stem cells are now increasingly being investigated as promising alternative therapeutics in translational research of regenerative medicine. Tremendous headway has been made to understand the biology and function of stem cells. Considering the minimum ethical concern and easily available abundant resources, extensive research is being conducted on induced pluripotent stem cells (iPSCs) and mesenchymal stem cells (MSC) for their potential application in reproductive medicine, especially in cases of infertility resulting from azoospermia and premature ovarian insufficiency. However, most of these investigations have been carried out in animal models. Evolutionary divergence observed in pluripotency among animals and humans requires caution when extrapolating the data obtained from murine models to safely apply them to clinical applications in humans. Hence, more clinical trials based on larger populations need to be carried out to investigate the relevance of stem cell therapy, including its safety and efficacy, in translational infertility medicine.

Coronil, a Tri-Herbal Formulation, Attenuates Spike-Protein-Mediated SARS-CoV-2 Viral Entry into Human Alveolar Epithelial Cells and Pro-Inflammatory Cytokines Production by Inhibiting Spike Protein-ACE-2 Interaction

Abstract: Purpose Coronil is a tri-herbal formulation containing extracts from Withania somnifera, Tinospora cordifolia, and Ocimum sanctum. Recently, it was shown that Coronil rescued humanized zebrafish from SARS-CoV-2 induced pathologies. Based on reported computational studies on the phytochemicals present in Coronil, it could be a potential inhibitor of SARS-CoV-2 entry into the host cell and associated cytokines' production. Methods Through an ELISA-based biochemical assay, effects of Coronil on interaction between ACE-2 and different mutants of viral spike (S) protein, crucial for viral invasion of host cell, were evaluated. Additionally, using recombinant pseudoviruses having SARS-CoV-2 spike (S) protein in their envelopes and firefly luciferase reporter in their genomes, effects of Coronil on virus entry into human alveolar epithelial cells were evaluated through luciferase assay. UHPLC profiled Coronil also modulated S-protein mediated production of pro-inflammatory cytokines in A549 cells, like interleukin-6 (IL-6), interleukin-1β (IL-1β), and tumor necrosis factor-α (TNF-α), as evaluated through RT-qPCR and ELISA. Results Coronil effectively inhibited the interaction of ACE-2 not only with the wild-type S protein (SWT) but also with its currently prevalent and more infectious variant (SD614G) and another mutant (SW436R) with significantly higher affinity toward ACE-2. Treatment with Coronil significantly reduced the increased levels of IL-6, IL-1β, and TNF-α in A549 cells incubated with different S-protein variants in a dose-dependent manner. Likewise, it also prevented the SARS-CoV-2 S-protein pseudotyped vesicular stomatitis virus (VSVppSARS-2S) mediated cytokine response in these cells by reducing entry of pseudoviruses into host cells. Conclusion Coronil prevented SARS-CoV-2 S-protein mediated viral entry into A549 cells by inhibiting spike protein-ACE-2 interactions. SARS-CoV-2 S protein induced inflammatory cytokine response in these cells was also moderated by Coronil.

An Efficient Sign Language Recognition (SLR) System Using Camshift Tracker and Hidden Markov Model (HMM)

Abstract: An efficient Sign Language Recognition (SLR) system could facilitate communication with hearing impaired persons by identifying the sign gestures Similar to regional spoken languages, different regions have developed their own sign gesture representations (for example, American Sign Language (ASL), German Sign Language (GSL), Indian Sign Language (ISL), etc) Such variations in the hand shapes and movements add many challenges in the recognition process The overall SLR process can be divided into a number of modules such as hand and face detection, hand tracking, features extraction and gesture recognition In this paper, we propose a novel end-to-end SLR system from RGB video-sequences After detecting the skin color from video frames, Camshift tracker is employed to extract the trajectories of hand motion Next, Hidden Markov Model (HMM) based sequence classification is used to recognize the gestures A novel approach identifying double and single hand gestures is also proposed Furthermore new features, from skin region and hand trajectories, are proposed to improve the gesture classification performance We tested our system on dataset proposed by American Sign Language Linguistic Research Project (ASLLRP) [25], which consists of isolated signs The experiment results are encouraging

Quadbox: Quadrilateral Bounding Box Based Scene Text Detection Using Vector Regression

Abstract: Scene text appears with a wide range of sizes and arbitrary orientations. For detecting such text in the scene image, the quadrilateral bounding boxes provide a much tight bounding box compared to the rotated rectangle. In this work, a vector regression method has been proposed for text detection in the wild to generate a quadrilateral bounding box. The bounding box prediction using direct regression requires predicting the vectors from each position inside the quadrilateral. It needs to predict four-vectors, and each varies drastically in its length and orientation. It makes the vector prediction a difficult problem. To overcome this, we have proposed a centroid-centric vector regression by utilizing the geometry of quadrilateral. In this work, we have added the philosophy of indirect regression to direct regression by shifting all points within the quadrilateral to the centroid and afterward performed vector regression from shifted points. The experimental results show the improvement of the quadrilateral approach over the existing direct regression approach. The proposed method shows good performance on many existing public datasets. The proposed method also demonstrates good results on the unseen dataset without getting trained on it, which validates the approach’s generalization ability.

End-to-end Triplet Loss based Emotion Embedding System for Speech Emotion Recognition

Abstract: In this paper, an end-to-end neural embedding system based on triplet loss and residual learning has been proposed for speech emotion recognition. The proposed system learns the embeddings from the emotional information of the speech utterances. The learned embeddings are used to recognize the emotions portrayed by given speech samples of various lengths. The proposed system implements Residual Neural Network architecture. It is trained using softmax pretraining and triplet loss function. The weights between the fully connected and embedding layers of the trained network are used to calculate the embedding values. The embedding representations of various emotions are mapped onto a hyperplane, and the angles among them are computed using the cosine similarity. These angles are utilized to classify a new speech sample into its appropriate emotion class. The proposed system has demonstrated 91.67% and 64.44% accuracy while recognizing emotions for RAVDESS and IEMOCAP dataset, respectively.

Subject-Specific Cognitive Workload Classification Using EEG-Based Functional Connectivity and Deep Learning.

Abstract: Cognitive workload is a crucial factor in tasks involving dynamic decision-making and other real-time and high-risk situations. Neuroimaging techniques have long been used for estimating cognitive workload. Given the portability, cost-effectiveness and high time-resolution of EEG as compared to fMRI and other neuroimaging modalities, an efficient method of estimating an individual’s workload using EEG is of paramount importance. Multiple cognitive, psychiatric and behavioral phenotypes have already been known to be linked with “functional connectivity”, i.e., correlations between different brain regions. In this work, we explored the possibility of using different model-free functional connectivity metrics along with deep learning in order to efficiently classify the cognitive workload of the participants. To this end, 64-channel EEG data of 19 participants were collected while they were doing the traditional n-back task. These data (after pre-processing) were used to extract the functional connectivity features, namely Phase Transfer Entropy (PTE), Mutual Information (MI) and Phase Locking Value (PLV). These three were chosen to do a comprehensive comparison of directed and non-directed model-free functional connectivity metrics (allows faster computations). Using these features, three deep learning classifiers, namely CNN, LSTM and Conv-LSTM were used for classifying the cognitive workload as low (1-back), medium (2-back) or high (3-back). With the high inter-subject variability in EEG and cognitive workload and recent research highlighting that EEG-based functional connectivity metrics are subject-specific, subject-specific classifiers were used. Results show the state-of-the-art multi-class classification accuracy with the combination of MI with CNN at 80.87%, followed by the combination of PLV with CNN (at 75.88%) and MI with LSTM (at 71.87%). The highest subject specific performance was achieved by the combinations of PLV with Conv-LSTM, and PLV with CNN with an accuracy of 97.92%, followed by the combination of MI with CNN (at 95.83%) and MI with Conv-LSTM (at 93.75%). The results highlight the efficacy of the combination of EEG-based model-free functional connectivity metrics and deep learning in order to classify cognitive workload. The work can further be extended to explore the possibility of classifying cognitive workload in real-time, dynamic and complex real-world scenarios.

Ligand substituent effect on the cytotoxicity activity of two new copper(II) complexes bearing 8-hydroxyquinoline derivatives: validated by MTT assay and apoptosis in MCF-7 cancer cell line (human breast cancer)

Abstract: In this study, we have examined the effect of ligand substituent on the structure–cytotoxicity relationships of the MCF-7 cancer cell line (human breast cancer), by two copper(II) complexes {[Cu(qmbn)(Hqmba)(q)]·NO3·2H2O} (1) and {[Cu(Hqmba)2(q)]·NO3·2H2O} (2) (where, qmbn = 2-(quinolin-8-yloxy)(methyl) benzonitrile (L1); Hqmba = 2-((quinolin-8-yloxy)methyl)benzoic acid (L2) and q = quinolin-8-olate). The structural analysis reveals that both the complexes exhibit distorted octahedral (CuN3O3) configuration which is further corroborated by density functional theory (DFT) calculations. The cytotoxicity impact of ligands (L1 and L2) and complexes (1 and 2) was screened against the MCF-7 cell line (human breast cancer). The MTT assay uptake indicated that the presence of –COOH functionality in complex 2 leads to higher cytotoxicity (lower IC50) than that observed for complex 1 containing a –CN group. This could be due to the strong H-bonding forming propensity of the carboxylic acids. Incubation of MCF-7 cancer cells with IC50 concentrations of 1 and 2 promoted cellular detachments via nuclear condensation and membrane destabilization followed by apoptosis as a result of metal-assisted generation of reactive oxygen species. Flow cytometry analysis showed that 1 and 2 might prompt early apoptosis in MCF-7 cells as the maximum percentage of cells appeared in the LR quadrant. Furthermore, mRNA expression analysis confirmed that both the complexes induced apoptosis in MCF-7 cells. Comparative mRNA expression analysis of complexes with their respective ligands also confirmed the enhanced apoptotic behavior of complexes. Furthermore, molecular docking studies of the complexes have also been performed with the active site of EGFR kinase receptors (major target for any cancer causing agent) due to similar analogues with FDA-approved EGFR inhibitors in order to rationalize its promising cytotoxicity activity.

Decellularized xenogenic cartilage extracellular matrix (ECM) scaffolds for the reconstruction of osteochondral defects in rabbits

Abstract: The use of decellularized native allogenic or xenogenic cartilaginous extracellular matrix (ECM) biomaterials is widely expanding in the fields of tissue engineering and regenerative medicine. In this study, we aimed to develop an acellular, affordable, biodegradable, easily available goat conchal cartilaginous ECM derived scaffolding biomaterial for repair and regeneration of osteochondral defects in rabbits. Cartilages harvested from freshly collected goat ears were decellularized using chemical agents, namely, hypotonic-hypertonic (HH) buffer and Triton X-100 solution, separately. The morphologies and ultrastructure orientations of the decellularized cartilages remained unaltered in spite of complete cellular loss. Furthermore, when the acellular cartilaginous ECMs were cultured with murine mesenchymal stem cells (MSCs) (C3H10T1/2 cells), cellular infiltration and proliferation were thoroughly monitored using SEM, DAPI and FDA stained images, whereas the MTT assay proved the biocompatibility of the matrices. The increasing amounts of secreted ECM proteins (collagen and sGAG) indicated successful chondrogenic differentiation of the MSCs in the presence of the treated cartilage samples. In vivo biocompatibility studies showed no significant immune response or tissue rejection in the treated samples but tissue necrosis in control samples after 3 months. Upon implantation of the constructs in rabbits' osteochondral defects for 3 months, the histological and micro-CT evaluation revealed significant enhancement and regeneration of neocartilage and subchondral bony tissues. The IGF-1 loaded cartilaginous constructs showed comparatively better healing response after 3 months. Our results showed that decellularized xenogenic cartilaginous biomaterials preserved the bioactivity and integrity of the matrices that also favored in vitro stem cell proliferation and chondrogenic differentiation and enabled osteochondral regeneration, thus paving a new way for articular cartilage reconstruction.

Wheatgrass inhibits the lipopolysaccharide-stimulated inflammatory effect in RAW 264.7 macrophages.

Abstract: Inflammation is a multifaceted set of cellular communications generated against foreign infection, toxic influence or autoimmune injury. The present study investigates the anti-inflammatory effect of wheatgrass extract against the harmful impact of lipopolysaccharide (LPS) in macrophage cells, i.e., RAW 264.7 cells. Our results indicate that 5- and 7- days old wheatgrass extracts inhibit the LPS-stimulated production of nitric oxide. Moreover, wheatgrass extract significantly downregulates the mRNA expression of LPS-stimulated various pro-inflammatory markers, tumor necrosis factor-α, interleukin-6, interleukin-1β, AP-1 and also iNOS-2 and COX-2. Our flow cytometry analyses confirmed that wheatgrass extract prevents the generation of reactive oxygen species in LPS-stimulated RAW 264.7 cells, thus arresting oxidative stress in cells. The immunoblot analyses also confirmed a significant reduction in the expression of inflammatory proteins, namely, iNOS-2 and COX-2, in wheatgrass extract-treated cells, compared to LPS-stimulated condition. The NF-κB transactivation assay further confirmed the inhibitory effect of wheatgrass extracts on the LPS-stimulated expression of NF-κB. Molecular docking based studies showed the plausible binding of two significant wheatgrass constituents, i.e., apigenin and myo-inositol with COX-2 protein, with binding energies of −10.59 kcal/mol and −7.88 kcal/mol, respectively. Based on the above results, wheatgrass may be considered as a potential therapeutic candidate for preventing inflammation.

Trajectory-Based Scene Understanding Using Dirichlet Process Mixture Model

Abstract: Appropriate modeling of a surveillance scene is essential for the detection of anomalies in road traffic. Learning usual paths can provide valuable insight into road traffic conditions and thus can help in identifying unusual routes taken by commuters/vehicles. If usual traffic paths are learned in a nonparametric way, manual interventions in road marking can be avoided. In this paper, we propose an unsupervised and nonparametric method to learn the frequently used paths from the tracks of moving objects in $\Theta (kn)$ time, where $k$ denotes the number of paths and $n$ represents the number of tracks. In the proposed method, temporal dependencies of the moving objects are considered to make the clustering meaningful using temporally incremental gravity model (TIGM). In addition, the distance-based scene learning makes it intuitive to estimate the model parameters. Further, we have extended the TIGM hierarchically as a dynamically evolving model (DEM) to represent notable traffic dynamics of a scene. The experimental validation reveals that the proposed method can learn a scene quickly without prior knowledge about the number of paths ( $k$ ). We have compared the results with various state-of-the-art methods. We have also highlighted the advantages of the proposed method over the existing techniques popularly used for designing traffic monitoring applications. It can be used for administrative decision making to control traffic at junctions or crowded places and generate alarm signals, if necessary.

Evaluation of Instance-Based Learning and Q-Learning Algorithms in Dynamic Environments

Abstract: Reinforcement learning is an unsupervised learning algorithm, where learning is based upon feedback from the environment. Prior research has proposed cognitive (e.g., Instance-based Learning or IBL) and statistical (Q-learning) reinforcement learning algorithms. However, an evaluation of these algorithms in a single dynamic environment has not been explored. In this paper, a comparison between the statistical Q-learning algorithm and the cognitive IBL algorithm is presented. A well-known environment, “Frozen Lake,” is used to train, generalize, and scale Q-learning and IBL algorithms. For generalizing, the Q-learning and IBL agents were trained on one version of the Frozen Lake and tested on a permuted version of the same environment. For scaling, the two algorithms were tested on a larger version of the Frozen Lake environment. Results revealed that the IBL algorithm used less training time and generalized better to different environment variants. The IBL algorithm was also able to show scalability by retaining its superior performance in the larger environment. These results indicate that the IBL algorithm could be proposed as an alternative to the standard reinforcement learning algorithms based on dynamic programming such as Q-learning. The inclusion of human factors (such as memory) in the IBL algorithm makes it suitable for robust learning in complex and dynamic environments.

A Review on Automated Cancer Detection in Medical Images using Machine Learning and Deep Learning based Computational Techniques: Challenges and Opportunities

Abstract: Cancer is one of the most deadly diseases diagnosed among the population across the globe so far. The number of cases is increasing at a high pace each year that subsequently leads to the advancement in different diagnosis tools and technologies to handle this pandemic. Significant increase in the mortality rate worldwide leads tremendous scope to device and implement latest computer aided diagnostic systems for its early detection. The one among such techniques is machine learning coupled with medical imaging modalities. This combination has proven to be efficient in diagnosing various medical conditions in cancer diagnosis. Current study presents a review of different machine learning techniques applied on imaging modalities for cancer diagnosis from 2008 to 2019. This study focuses on diagnosis of five most prevalent and deadly cancers i.e., cervical cancer, oral cancer, breast cancer, brain cancer and skin cancer. Extensive and exhaustive review was carried out after going through different research papers, research articles and book chapters published by reputed international and national publishers such as Springer Link, Science Direct, IEEE Xplore Digital library and PubMed. A number of conference proceedings have also been included subject to the fulfilling of our quality evaluation criteria. This review article provides a comprehensive overview of machine learning approaches using image modalities for cancer detection and diagnosis with main focus on challenges being faced during their research. Majority of the challenges are identified based on the use of potential machine learning based approaches, image modalities, features and evaluation metrics. This review not only identified challenges but also ear mark and present the new research opportunities for researchers working in this field. It has been widely observed that traditional machine learning algorithms Like SVM, GMM performed excellent in classification whereas the deep learning has dominated the field of medical image analysis to a greater extent. It is evident from the literature survey that the researchers have achieved the accuracies of 100% in classification of cancerous and normal tissue images using different machine learning techniques. This article will provide an insight to the researchers working in this domain to identify which machine learning technique work best on what type of data set, selection of features, various challenges and their proposed solutions in solving this complex problem. Limitations and future research opportunities in the field of implementing different machine learning techniques in cancer diagnosis and classification is also presented at the end of this review article.

EEG-Based Cognitive State Assessment Using Deep Ensemble Model and Filter Bank Common Spatial Pattern

Abstract: Electroencephalography (EEG) is the most used physiological measure to evaluate the cognitive state of a user efficiently. As EEG inherently suffers from a poor spatial resolution, features extracted from each EEG channel may not be efficiently used for the cognitive state assessment. In this paper, the EEG-based cognitive state assessment has been performed during the mental arithmetic experiment, which includes two cognitive states (task and rest) of a user. To obtain the temporal as well as the spatial resolution of the EEG signal, we combined the Filter Bank Common Spatial Pattern (FBCSP) method and Long Short-Term Memory (LSTM)-based deep ensemble model for classifying the cognitive state of a user. Subject-wise data distribution has been performed due to the execution of a large volume of data in a low computing environment. In the FBCSP method, the input EEG is decomposed into multiple equal-sized frequency bands, and spatial features of each frequency bands are extracted using the Common Spatial Pattern (CSP) algorithm. Next, a feature selection algorithm has been applied to identify the most informative features for classification. The proposed deep ensemble model consists of multiple similar structured LSTM networks that work in parallel. The output of the ensemble model (i.e., the cognitive state of a user) is computed using the average weighted combination of the individual model prediction. This proposed model achieves 87% classification accuracy, and it can also effectively estimate the cognitive state of a user in a low computing environment.

Ovarian Cancer Stem Cells: Characterization and Role in Tumorigenesis.

Abstract: Ovarian cancer is a heterogenous disease with variable clinicopathological and molecular mechanisms being responsible for tumorigenesis. Despite substantial technological improvement, lack of early diagnosis contributes to its highest mortality. Ovarian cancer is considered to be the most lethal female gynaecological cancer across the world. Conventional treatment modules with platinum- and Taxane-based chemotherapy can cause an initial satisfactory improvement in ovarian cancer patients. However, approximately 75–80% patients of advanced stage ovarian cancer, experience relapse and nearly 40% have overall poor survival rate. It has been observed that a subpopulation of cells referred as cancer stem cells (CSCs), having self renewal property, escape the conventional chemotherapy because of their quiescent nature. Later, these CSCs following its interaction with microenvironment and release of various inflammatory cytokines, chemokines and matrix metalloproteinases, induce invasion and propagation to distant organs of the body mainly peritoneal cavity. These CSCs can be enriched by their specific surface markers such as CD44, CD117, CD133 and intracellular enzyme such as aldehyde dehydrogenase. This tumorigenicity is further aggravated by the epithelial to mesenchymal transition of CSCs and neovascularisation via epigenetic reprogramming and over-expression of various signalling cascades such as Wnt/β-catenin, NOTCH, Hedgehog, etc. to name a few. Hence, a comprehensive understanding of various cellular events involving interaction between cancer cells and cancer stem cells as well as its surrounding micro environmental components would be of unmet need to achieve the ultimate goal of better management of ovarian cancer patients. This chapter deals with the impact of ovarian cancer stem cells in tumorigenesis which would help in the implementation of basic research into the clinical field in the form of translational research in order to reduce the morbidity and mortality in ovarian cancer patients through amelioration of diagnosis and impoverishment of therapeutic resistance.

A highly transparent tri-polymer complex in situ hydrogel of HA, Collagen and 4-arm-PEG as potential vitreous substitute

Abstract: There is a requirement of removal and replacement of vitreous for various ophthalmic diseases, e.g., retinopathy and retinal detachment. Clinical tamponades, e.g., silicone oil and fluorinated gases are used but limited due to their toxicity and some complications. A lot of polymer-based materials have been tested and proposed as vitreous substitute, but till date, there is no ideal vitreous substitute available. Thus, it requires to develop an improved vitreous substitute which will be highly suitable for vitreous replacement. We have developed tri-polymer complex in situ hydrogels by crosslinking among hyaluronic acid (HA), collagen (Coll) and 4-arm-polyethylene glycol (PEG). All the developed hydrogels are biocompatible with NIH 3T3 mouse fibroblast cells, having pH in the range 7-7.44 and refractive index in the range 1.333-1.345. The developed hydrogels are highly transparent, showing transmittance >97%. FTIR study shows that the hydrogel was crosslinked by amide bond formation between HA and PEG, and between collagen and PEG. The rheological study shows that all the developed hydrogels exhibit viscoelastic behavior and all the hydrogels have storage modulus values (>100 pa) which is greater than loss modulus values—indicating sufficient elasticity for vitreous application. The elastic nature of the hydrogel increases with the increase in PEG concentration. The gel is formed in between 2 to 3 min—indicating its application in situ. The viscosity of the developed hydrogels shows shear thinning behavior. The pre-gel solution of the hydrogel is injectable through a 22 G needle —indicating its application in situ through vitrectomy surgery. All the hydrogels are hydrophilic and have water content of 96% approximately. Thus, the results show the positive properties for its application as a potential vitreous substitute.

Physiological Relevance of Angiotensin Converting Enzyme 2 As a Metabolic Linker and Therapeutic Implication of Mesenchymal Stem Cells in COVID-19 and Hypertension.

Abstract: Severe acute respiratory syndrome corona virus − 2 (SARS-CoV-2) is a single stranded RNA virus and responsible for infecting human being. In many cases the individual may remain asymptomatic. Some recently reported studies revealed that individuals of elderly age group and with pre-existing medical conditions such as hypertension, diabetes mellitus had severe consequences, even may lead to death. However, it is not clearly delineated whether hypertension itself or associated comorbidities or antihypertensive therapy contributes to the grave prognosis of COVID-19 infections. This review is aimed to decipher the exact mechanisms involved at molecular level from existing evidence and as reported. It has been reported that SARS-CoV-2 enters into the host cell through interaction between conserved residues of viral spike protein and angiotensin converting enzyme 2 (ACE2) receptor which is highly expressed in host’s cardiac and pulmonary cells and finally transmembrane protease, serine-2 (TMPRSS2), helps in priming of the surface protein. Subsequently, symptom related to multi organ involvement is primarily contributed by cytokine storm. Although various clinical trials are being conducted on renin- angiotensin- system inhibitor, till to date there is no standard treatment protocol approved for critically ill COVID-19 positive cases with pre-existing hypertension. Recently, several studies are carried out to document the safety and efficacy outcome of mesenchymal stem cell transplantation based on its immunomodulatory and regenerative properties. Therefore, identification of future novel therapeutics in the form of mesenchymal stem cell either alone or in combination with pharmacological approach could be recommended for combating SARS-CoV-2 which might be dreadful to debilitating elderly people.

Black pepper and piperine induce anticancer effects on leukemia cell line.

Abstract: The black pepper, most commonly used in Indian cuisines for ages, is considered as "king of spices." The present study evaluates the anticancer potential of black pepper and its main constituent, i.e. alkaloid piperine, against human leukemia cell line, K-562 cells. Gas chromatography-mass spectrometry (GC-MS) analysis confirmed the presence of piperine in black pepper extract. The methanolic extract of black pepper (BP-M) and pure piperine (PIP) showed a strong cytotoxic effect against this cell line. Both BP-M and PIP generated apoptotic bodies in K-562 cells and caused nuclear condensation as visualized by fluorescent microscopy, which was further confirmed by flow cytometry analysis. BP-M and PIP also generated reactive oxygen species in K-562 cells as established by flow cytometry. The translation of Bax, caspase-3 and caspase-9 genes was found to be upregulated with subsequent downregulation of Bcl-2 gene. The anti-proliferative effect of both BP-M and PIP was also observed by trypan blue staining and was further confirmed by the downregulated expression of proliferating cell nuclear antigen (PCNA). The molecular docking studies showed the binding of PIP with PCNA and Bcl-2 and supported the in vitro findings. The docking studies also proposed the binding of PIP to ADP binding pocket of Apaf-1 protein. Taken together, these findings signify the anticancer potential of both black pepper and PIP, thus proposing black pepper as a potent nutraceutical for preventing the progression of chronic myeloid leukemia.

Volatile components in papaya fruits are the non-invasive biomarkers to monitor the ripening stage and the nutritional value

Abstract: Papaya fruits have great nutritional and economical values. The ripening stage of papaya fruits at the time of harvesting massively impacts the quality and shelf-life of fruits. Volatile organic compounds (VOCs) emitting from the papaya fruits during the ripening process could be used as a real-time non-invasive biomarker to characterize the ripening stage. This paper reports identification of VOCs as non-invasive ripening biomarkers of papaya fruits based on solid-phase micro-extraction coupled with gas chromatography–mass spectrometry (GC–MS) analysis of VOCs emitted from the papaya fruit cv. ‘Red Lady’. Three ripening stages were studied, viz., green unripe (UR), yellowish-green intermediate ripe (IR), and yellow full ripe (FR). GC–MS analyses and the subsequent statistical studies identified a total of 35 VOCs. Partial Least Squares Discriminant Analysis (PLS-DA) of VOCs from the three ripening stages identified six biomarker VOCs, which can efficiently distinguish between ripening stages. Among the six ripening biomarkers, three VOCs (methyl hexanoate, 3-carene and longifolene) showed a remarkable correlation with the ripening-associated changes in the fruit nutritional profile. The biomarkers reported here could be used as a viable technology for non-invasive monitoring of ripening stages and the nutritional value of papaya fruits.