GITAM University

BACKGROUND: Cancer is one of the major heterogeneous disease with high morbidity and mortality with poor prognosis. Elevated levels of reactive oxygen species (ROS), alteration in redox balance, and deregulated redox signaling are common hallmarks of cancer progression and resistance to treatment. Mitochondria contribute mainly in the generation of ROS during oxidative phosphorylation. Elevated levels of ROS have been detected in cancers cells due to high metabolic activity, cellular signaling, peroxisomal activity, mitochondrial dysfunction, activation of oncogene, and increased enzymatic activity of oxidases, cyclooxygenases, lipoxygenases, and thymidine phosphorylases. Cells maintain intracellular homeostasis by developing an immense antioxidant system including catalase, superoxide dismutase, and glutathione peroxidase. Besides these enzymes exist an important antioxidant glutathione and transcription factor Nrf2 which contribute in balancing oxidative stress. Reactive oxygen species-mediated signaling pathways activate pro-oncogenic signaling which eases in cancer progression, angiogenesis, and survival. Concomitantly, to maintain ROS homeostasis and evade cancer cell death, an increased level of antioxidant capacity is associated with cancer cells. CONCLUSIONS: This review focuses the role of ROS in cancer survival pathways and importance of targeting the ROS signal involved in cancer development, which is a new strategy in cancer treatment.

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region's image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

Hydrogels are promising biomaterials because of their important qualities such as biocompatibility, biodegradability, hydrophilicity and non-toxicity. These qualities make hydrogels suitable for application in medical and pharmaceutical field. Recently, a tremendous growth of hydrogel application is seen, especially as gel and patch form, in transdermal drug delivery. This review mainly focuses on the types of hydrogels based on cross-linking and; secondly to describe the possible synthesis methods to design hydrogels for different pharmaceutical applications. The synthesis and chemistry of these hydrogels are discussed using specific pharmaceutical examples. The structure and water content in a typical hydrogel have also been discussed.

Electrical distribution network reconfiguration is a complex combinatorial optimization process aimed at finding a radial operating structure that minimizes the system power loss while satisfying operating constraints. In this paper, a harmony search algorithm (HSA) is proposed to solve the network reconfiguration problem to get optimal switching combination in the network which results in minimum loss. The HSA is a recently developed algorithm which is conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search which eliminates the need for derivative information. Simulations are carried out on 33- and 119-bus systems in order to validate the proposed algorithm. The results are compared with other approaches available in the literature. It is observed that the proposed method performed well compared to the other methods in terms of the quality of solution.

Solid-lipid nanoparticles and nanostructured lipid carriers are delivery systems for the delivery of drugs and other bioactives used in diagnosis, therapy, and treatment procedures. These nanocarriers may enhance the solubility and permeability of drugs, increase their bioavailability, and extend the residence time in the body, combining low toxicity with a targeted delivery. Nanostructured lipid carriers are the second generation of lipid nanoparticles differing from solid lipid nanoparticles in their composition matrix. The use of a liquid lipid together with a solid lipid in nanostructured lipid carrier allows it to load a higher amount of drug, enhance drug release properties, and increase its stability. Therefore, a direct comparison between solid lipid nanoparticles and nanostructured lipid carriers is needed. This review aims to describe solid lipid nanoparticles and nanostructured lipid carriers as drug delivery systems, comparing both, while systematically elucidating their production methodologies, physicochemical characterization, and in vitro and in vivo performance. In addition, the toxicity concerns of these systems are focused on.

In recent years, the perovskite solar cells have gained much attention because of their ever-increasing power conversion efficiency (PCE), simple solution fabrication process, flyable, light-weight wearable and deployable for ultra-lightweight space and low-cost materials constituents etc. Over the last few years, the efficiency of perovskite solar cells has surpassed 25% due to high-quality perovskite-film accomplished through low-temperature synthesis techniques along with developing suitable interface and electrode-materials. Besides, the stability of perovskite solar cells has attracted much well-deserved attention. In this article we have focused on recent progress of the perovskite solar cells regarding their crystallinity, morphology and synthesis techniques. Also, demonstrated different layers such as electron transport-layers (ETLs), hole transport-layers (HTLs) and buffer-layers utilized in perovskite solar-cells, considering their band gap, carrier mobility, transmittance etc. Outlook of various tin (Sn), carbon and polymer-based perovskite solar cells and their potential of commercialization feasibility has also been discussed.

A double stir casting process was used to fabricate aluminum composites reinforced with various volume fractions of 2, 4, 6, and 8 wt% RHA and SiC particulates in equal proportions. Properties such as hardness, density, porosity and mechanical behavior of the unreinforced and Al/x%RHA/x%SiC (x = 2, 4, 6, and 8 wt%) reinforced hybrid composites were examined. Scanning electron microscope (model JSM-6610LV) was used to study the microstructural characterization of the composites. It was observed that the hardness and porosity of the hybrid composite increased with increasing reinforcement volume fraction and density decreased with increasing particle content. It was also observed that the UTS and yield strength increase with an increase in the percent weight fraction of the reinforcement particles, whereas elongation decreases with the increase in reinforcement. The increase in strength of the hybrid composites is probably due to the increase in dislocation density. A systematic study of the base alloy and composites was done using the Brinell hardness measurement and the corresponding age hardening curves were obtained. It was observed that in comparison to that of the base aluminum alloy, the precipitation kinetics of the composites were accelerated by adding the reinforcement. This effectively reduced the time for obtaining the maximum hardness by the aging heat treatment.

Abstract Environmental degradation due to the carbon emissions from burning fossil fuels has triggered the need for sustainable and renewable energy. Hydrogen has the potential to meet the global energy requirement due to its high energy density; moreover, it is also clean burning. Photoelectrochemical (PEC) water splitting is a method that generates hydrogen from water by using solar radiation. Despite the advantages of PEC water splitting, its applications are limited by poor efficiency due to the recombination of charge carriers, high overpotential, and sluggish reaction kinetics. The synergistic effect of using different strategies with cocatalyst decoration is promising to enhance efficiency and stability. Transition metal-based cocatalysts are known to improve PEC efficiency by reducing the barrier to charge transfer. Recent developments in novel cocatalyst design have led to significant advances in the fundamental understanding of improved reaction kinetics and the mechanism of hydrogen evolution. To highlight key important advances in the understanding of surface reactions, this review provides a detailed outline of very recent reports on novel PEC system design engineering with cocatalysts. More importantly, the role of cocatalysts in surface passivation and photovoltage, and photocurrent enhancement are highlighted. Finally, some challenges and potential opportunities for designing efficient cocatalysts are discussed.

Abstract Organizations adopt blockchain technologies to provide solutions that deliver transparency, traceability, trust, and security to their stakeholders. In a novel contribution to the literature, this study adopts the technology-organization-environment (TOE) framework to examine the technological, organizational, and environmental dimensions for adopting blockchain technology in supply chains. This represents a departure from prior studies which have adopted the technology acceptance model (TAM), technology readiness index (TRI), theory of planned behavior (TPB), united theory of acceptance and use of technology (UTAUT) models. Data was collected through a survey of 525 supply chain management professionals in India. The research model was tested using structural equation modeling. The results show that all the eleven TOE constructs, including relative advantage, trust, compatibility, security, firm’s IT resources, higher authority support, firm size, monetary resources, rivalry pressure, business partner pressure, and regulatory pressure, had a significant influence on the decision of blockchain technology adoption in Indian supply chains. The findings of this study reveal that the role of blockchain technology adoption in supply chains may significantly improve firm performance improving transparency, trust and security for stakeholders within the supply chain. Further, this research framework contributes to the theoretical advancement of the existing body of knowledge in blockchain technology adoption studies.

Patients with Liver disease have been continuously increasing because of excessive consumption of alcohol, inhale of harmful gases, intake of contaminated food, pickles and drugs. Automatic classification tools may reduce burden on doctors. This paper evaluates the selected classification algorithms for the classification of some liver patient datasets. The classification algorithms considered here are Nave Bayes classifier, C4.5, Back propagation Neural Network algorithm, and Support Vector Machines. These algorithms are evaluated based on four criteria: Accuracy, Precision, Sensitivity and Specificity.

ions within the wide concentration range of 30-600 μM with 9.55 μM detection limit.

A Stroke is a health condition that causes damage by tearing the blood vessels in the brain. It can also occur when there is a halt in the blood flow and other nutrients to the brain. According to the World Health Organization (WHO), stroke is the leading cause of death and disability globally. Most of the work has been carried out on the prediction of heart stroke but very few works show the risk of a brain stroke. With this thought, various machine learning models are built to predict the possibility of stroke in the brain. This paper has taken various physiological factors and used machine learning algorithms like Logistic Regression, Decision Tree Classification, Random Forest Classification, K-Nearest Neighbors, Support Vector Machine and Naïve Bayes Classification to train five different models for accurate prediction. The algorithm that best performed this task is Naïve Bayes that gave an accuracy of approximately 82%.

Artificial intelligence (AI) is a branch of computer science that allows machines to work efficiently, can analyze complex data. The research focused on AI has increased tremendously, and its role in healthcare service and research is emerging at a greater pace. This review elaborates on the opportunities and challenges of AI in healthcare and pharmaceutical research. The literature was collected from domains such as PubMed, Science Direct and Google scholar using specific keywords and phrases such as ‘Artificial intelligence’, ‘Pharmaceutical research’, ‘drug discovery’, ‘clinical trial’, ‘disease diagnosis’, etc. to select the research and review articles published within the last five years. The application of AI in disease diagnosis, digital therapy, personalized treatment, drug discovery and forecasting epidemics or pandemics was extensively reviewed in this article. Deep learning and neural networks are the most used AI technologies; Bayesian nonparametric models are the potential technologies for clinical trial design; natural language processing and wearable devices are used in patient identification and clinical trial monitoring. Deep learning and neural networks were applied in predicting the outbreak of seasonal influenza, Zika, Ebola, Tuberculosis and COVID-19. With the advancement of AI technologies, the scientific community may witness rapid and cost-effective healthcare and pharmaceutical research as well as provide improved service to the general public.

Natural coagulants have been the focus of research of many investigators through the last decade owing to the problems caused by the chemical coagulants. Optimization of process parameters is vital for the effectiveness of coagulation process. In the present study optimization of parameters like pH, dose of coagulant and mixing speed were studied using natural coagulants sago and chitin in comparison with alum. Jar test apparatus was used to perform the coagulation. The results showed that the removal of turbidity was up to 99 % by both alum and chitin at lower doses of coagulant, i.e., 0.1–0.3 g/L, whereas sago has shown a reduction of 70–100 % at doses of 0.1 and 0.2 g/L. The optimum conditions observed for sago were 6 and 7 whereas chitin was stable at all pH ranges, lower coagulant doses, i.e., 0.1–0.3 g/L and mixing speed—rapid mixing at 100 rpm for 10 min and slow mixing 20 rpm for 20 min. Hence, it can be concluded that sago and chitin can be used for treating water even with large seasonal variation in turbidity.

BACKGROUND: Terminalia chebula (Combretaceae) has been widely used in Ayurveda for the treatment of diabetes. In the present investigation, the chloroform extract of T. chebula seed powder was investigated for its antidiabetic activity in streptozotocin-induced diabetic rats using short term and long term study protocols. The efficacy of the extract was also evaluated for protection of renal functions in diabetic rats. METHODS: The blood glucose lowering activity of the chloroform extract was determined in streptozotocin-induced (75 mg/kg, i.p.; dissolved in 0.1 M acetate buffer; pH 4.5) diabetic rats, after oral administration at the doses of 100, 200 and 300 mg/kg in short term study. Blood samples were collected from the eye retro-orbital plexus of rats before and also at 0.5, 1, 2, 4, 6, 8 and 12 h after drug administration and the samples were analyzed for blood glucose by using glucose-oxidase/peroxidase method using a visible spectrophotometer. In long term study, the extract (300 mg/kg) was administered to streptozotocin-induced diabetic rats, daily for 8 weeks. Blood glucose was measured at weekly intervals for 4 weeks. Urine samples were collected before the induction of diabetes and at the end of 8 weeks of treatments and analyzed for urinary protein, albumin and creatinine levels. The data was compared statistically using one-way ANOVA with post-hoc Dunnet's t-test. RESULTS: The chloroform extract of T. chebula seeds produced dose-dependent reduction in blood glucose of diabetic rats and comparable with that of standard drug, glibenclamide in short term study. It also produced significant reduction in blood glucose in long term study. Significant renoprotective activity is observed in T. chebula treated rats. The results indicate a prolonged action in reduction of blood glucose by T. chebula and is probably mediated through enhanced secretion of insulin from the beta-cells of Langerhans or through extra pancreatic mechanism. The probable mechanism of potent renoprotective actions of T. chebula has to be evaluated. CONCLUSION: The present studies clearly indicated a significant antidiabetic and renoprotective effects with the chloroform extract of T. chebula and lend support for its traditional usage. Further investigations on identification of the active principles and their mode of action are needed to unravel the molecular mechanisms involved in the observed effects.

Abstract Piezoelectric materials have the merits of fast response, speed, and high precision. These materials have been widely investigated for functional device applications such as sensors, actuators, microelectromechanical systems (MEMS), nanoelectromechanical systems (NEMS), 3D‐printing etc. This paper offers a review on recent advances in piezoelectric materials and their limitations. In addition, different piezoelectric materials such as lead‐based, lead‐free piezoelectric materials etc. are discussed, and their applications in the catalysis, energy harvesting, sensors, biomedical engineering, and additive manufacturing are addressed.

AIDS (acquired immune deficient syndrome) is a deadly human viral infectious disease caused by HIV (human immune-deficient virus) infection. Almost every AIDS patient losses his/her life before mid 1990s. AIDS was once the 1st disease killer in US (1993). After one decade hard work, antiviral drug cocktails-high active anti-retroviral therapy (HAART) have been invented for almost all HIV infection treatments. Due to the invention of HAART, 80-90% HIV/AIDS patients still effectively response to HAART for deadly AIDS episode controls and life saving. Yet, this type of HIV therapeutics is incurable. HIV/AIDS patients need to take HAART medications regularly and even life-long. To counteract this therapeutic drawback, more revolutionary efforts (different angles of therapeutic modes/attempts) are urgently needed. In this article, the major progresses and drawbacks of HIV/AIDS chemotherapy (HAART) to HIV/AIDS patients have been discussed. Future trends (updating pathogenesis study, next generations of drug developments, new drug target discovery, different scientific disciplinary and so on) are highlighted.

Lymph node metastasis in breast cancer may be accurately predicted using a DenseNet-169 model. However, the current system for identifying metastases in a lymph node is manual and tedious. A pathologist well-versed with the process of detection and characterization of lymph nodes goes through hours investigating histological slides. Furthermore, because of the massive size of most whole-slide images (WSI), it is wise to divide a slide into batches of small image patches and apply methods independently on each patch. The present work introduces a novel method for the automated diagnosis and detection of metastases from whole slide images using the Fast AI framework and the 1-cycle policy. Additionally, it compares this new approach to previous methods. The proposed model has surpassed other state-of-art methods with more than 97.4% accuracy. In addition, a mobile application is developed for prompt and quick response. It collects user information and models to diagnose metastases present in the early stages of cancer. These results indicate that the suggested model may assist general practitioners in accurately analyzing breast cancer situations, hence preventing future complications and mortality. With digital image processing, histopathologic interpretation and diagnostic accuracy have improved considerably.

Active learning pedagogies play an important role in enhancing higher order cognitive skills among the student community. In this work, a laboratory course for first year engineering chemistry is designed and executed using an inquiry-based learning pedagogical approach. The goal of this module is to promote higher order thinking skills in chemistry. Laboratory exercises are designed based on Bloom's taxonomy and a just-in-time facilitation approach is used. A pre-laboratory discussion outlining the theory of the experiment and its relevance is carried out to enable the students to analyse real-life problems. The performance of the students is assessed based on their ability to perform the experiment, design new experiments and correlate practical utility of the course module with real life. The novelty of the present approach lies in the fact that the learning outcomes of the existing experiments are achieved through establishing a relationship with real-world problems.

Active and passive realization of Fractance device of order<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:mrow></mml:math>is presented. The crucial point in the realization of fractance device is finding the rational approximation of its impedance function. In this paper, rational approximation is obtained by using continued fraction expansion. The rational approximation thus obtained is synthesized as a ladder network. The results obtained have shown considerable improvement over the previous techniques.