Faculty publications @ CTUAP

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Design, Synthesis, Antimicrobial and Anticancer Activity of some Novel Benzoxazole-Isatin Conjugates
(2022) PV. Parvati Sai Arun
A series of novel benzoxazole-isatin conjugates were synthesized by treating 2-amino benzoxazole with 5 and 7 substituted isatin derivatives and were screened for in vitro antimicrobial and cytotoxic activities. The results showed that all the synthesized compounds shown mild to potent antibacterial activity. The MIC values were found between 10 and 100 µg/ml against tested bacterial and fungal organisms. Among all the compounds, 3d & 3c showed good antimicrobial. In vitro cytotoxic activities were evaluated by MTT assay of all the test compounds against the different human cancer cell lines. The compounds having substitution with electron-withdrawing groups (halides) at the 5th position on the isatin ring showed the most significant biological activity than substituted at the 7th position. The molecular docking interactions have shown good binding interactions with the protein targets glucosamine-6-phosphate synthase (GlcN-6-P synthase) and telomerase.
Acidic graphene organocatalyst for the superior transformation of wastes into high-added-value chemicals
(2022) Kalidindi, Suresh Babu
Our dependence on finite fossil fuels and the insecure energy supply chains have stimulated intensive research for sustainable technologies. Upcycling glycerol, produced from biomass fermentation and as a biodiesel formation byproduct, can substantially contribute in circular carbon economy. Here, we report glycerol’s solvent-free and room-temperature conversion to high added-value chemicals via a reusable graphene catalyst (G-ASA), functiona lized with a natural amino acid (taurine). Theoretical studies unveil that the superior performance of the catalyst (surpassing even homogeneous, indus trial catalysts) is associated with the dual role of the covalently linked taurine, boosting the catalyst’s acidity and affinity for the reactants. Unlike previous catalysts, G-ASA exhibits excellent activity (7508 mmolg−1 h−1)andselectivity (99.9%) for glycerol conversion to solketal, an additive for improving fuels’ quality and aprecursorofcommodityandfinechemicals.Notably,thecatalyst is also particularly active in converting oils to biodiesel, demonstrating its general applicability.
The recent advances in cobalt-catalyzed C(sp3)–H functionalization reactions
(2022) Kishor Padala
Over the past decades, reactions involving C–H functionalization have become a hot theme in organic transformations because they have a lot of potential for the streamlined synthesis of complex molecules. C(sp3)–H bonds are present in most organic species. Since organic molecules have massive significance in various aspects of life, the exploitation and functionalization of C(sp3)–H bonds hold enormous importance. In recent years, the first-row transition metal-catalyzed direct and selective functionalization of C–H bonds has emerged as a simple and environmentally friendly synthetic method due to its low cost, unique reactivity profiles and easy availability. Therefore, research advancements are being made to conceive catalytic systems that foster direct C(sp3)–H functionalization under benign reaction conditions. Cobalt-based catalysts offer mild and convenient reaction conditions at a reasonable expense compared to conventional 2nd and 3rd-row transition metal catalysts. Consequently, the probing of Co-based catalysts for C(sp3)–H functionalization is one of the hot topics from the outlook of an organic chemist. This review primarily focuses on the literature from 2018 to 2022 and sheds light on the substrate scope, selectivity, benefits and limitations of cobalt catalysts for organic transformations.
Moisturizer and COVID-19: Are We Missing a Trick?
(2023) Tantravahi, Srinivasan
: Corona Virus Disease 2019 (COVID-19) is reported to be transmitted predominantly by respiratory droplets and fomites. The regular use of a mask can mitigate the airborne transmission of the Severe Acute Respiratory Syndrome Corona Virus-2 (SARS-CoV-2), but comprehensive prevention of the virus is possible only when the contact spread of the virus is also addressed. The recommended use of soap and hand sanitizer (alcoholic hand rub) is effective only until subsequent contact with the virus. Furthermore, regular and repeated application of these disinfectants is impractical and harmful to the skin. The damage to the outermost epidermal layers of the skin exposes the Angiotensin-converting enzyme 2 (ACE2) receptor rich keratinocytes, enhancing the scope for percutaneous transmission of virus. Moisturizers, composed of fatty acids, fatty alcohols, mineral oils, petrolatum, etc., are generally considered cosmetics used to maintain and enhance skin condition. At very low concentrations, several of these components are found to neutralize enveloped viruses, indicating their potential antiviral activity. Soaps also generally contain many of these constituents, making them effective against viruses. Petrolatum, a key component of occlusive moisturizers, is also said to enhance innate immunity. Additionally, moisturizers also alleviate inflammation and prevent skin dryness and damage. The periodic and regular application of an appropriate moisturizer on hand and palm can play a significant role in curtailing the transmission of infectious agents, including (SARS-CoV-2), and could act as an extra line of defense against microbial infections
A study of sleep disorders, mental distress, and depression among students during COVID pandemic
(2023) Mandala, Gangu Naidu
The present study has attempted to study the effects of depression, mental distress, and sleep disorders among students during the COVID-19 pandemic. Stress, mental distress, depression, sleep disorders, headaches, loneliness, screen fatigue, and high distress levels are common symptoms observed across the population. The study focused on the students of higher education who have been attending online classes since the inception of COVID-19 virus. The detailed questionnaire was circulated online to 450 students, out of which 323 responded. After filtering the incomplete responses, 286 sample sizes were taken into consideration. The data were analysed using SPSS software, and hypotheses and model testing were performed using the AMOS software. A signifi cant relationship was found between depression, distress, sleep disorders, and student behaviour. Loneliness, lack of physical interaction, and overexposure to screens were found to be major trigger elements affecting students’ mental health. To dilute the effect on students’ behaviour and enhance their mental health, the authors recommend taking precautionary measures by the concerned stakeholders.
An exfoliated redox active imide covalent organic framework for metal free hydrogen gas sensing†
(2023) Kalidindi, Suresh Babu
A two dimensional (2D) redox active donor–acceptor COF made of triphenylamine (TPA) and naphthalenediimide (NDI) acted as an efficient hydrogen chemisresistor and performed better than traditional metal oxides. Calculations have shown that the charge transfer interaction between H2 and an NDI linker through a carbonyl functionality enables a change in the resistance of the material upon exposure to H2 gas.
Editorial: Climate change and stress mitigation strategy in plants
(2023) Kumar, Anirudh
Prospects for developing allergen-depleted food crops
(2023) Kumar, Anirudh
In addition to the challenge of meeting global demand for food production, there are increasing concerns about food safety and the need to protect consumer health from the negative effects of foodborne allergies. Certain bio-molecules (usually proteins) present in food can act as allergens that trigger unusual immunological reactions, with potentially life-threatening consequences. The relentless working lifestyles of the modern era often incorporate poor eating habits that include readymade prepackaged and processed foods, which contain additives such as peanuts, tree nuts, wheat, and soy-based products, rather than traditional home cooking. Of the predominant allergenic foods (soybean, wheat, fish, peanut, shellfish, tree nuts, eggs, and milk), peanuts (Arachis hypogaea) are the best characterized source of allergens, followed by tree nuts (Juglans regia, Prunus amygdalus, Corylus avellana, Carya illinoinensis, Anacardium occidentale, Pistacia vera, Bertholletia excels), wheat (Triticum aestivum), soybeans (Glycine max), and kidney beans (Phaseolus vulgaris). The prevalence of food allergies has risen significantly in recent years including chance of accidental exposure to such foods. In contrast, the standards of detection, diagnosis, and cure have not kept pace and unfortunately are often suboptimal. In this review, we mainly focus on the prevalence of allergies associated with peanut, tree nuts, wheat, soybean, and kidney bean, highlighting their physiological properties and functions as well as considering research directions for tailoring allergen gene expression. In particular, we discuss how recent advances in molecular breeding, genetic engineering, and genome editing can be used to develop potential low allergen food crops that protect consumer health.
H2O2‑Mediated Synthesis of a Quinazolin-4(3H)‑one Scaffold: A Sustainable Approach
(American Chemical Society, 2023) Kishor Padala
A quinazolin-4(3H)-one ring system is a privileged heterocyclic moiety with distinctive biological properties. From this perspective, the development of an efficient strategy for the synthesis of quinazolin-4(3H)-one has always been in demand for the synthetic chemistry community. In this report, we envisaged an efficient protocol for the synthesis of quinazolin-4(3H)-one using substituted 2-amino benzamide with dimethyl sulfoxide (DMSO) as a carbon source and H2O2 as an effective oxidant. Mechanistically, the reaction proceeds through the radical approach with DMSO as one carbon source. To further substantiate the synthetic claim, the synthetic protocol has been extended to the synthesis of the anti-endotoxic active compound 3-(2- carboxyphenyl)-4-(3H)-quinazolinone.
Facial emotion recognition using geometrical features based deeplearning techniques
(2023) Bonthu Kotaiah N
In recent years, intelligent emotion recognition is active research in computer vision to understand the dynamic communication between machines and humans. As a result, automatic emotion recognition allows the machine to assess and acquire the human emotional state to predict the intents based on the facial expression. Researchers mainly focus on speech features and body motions; identifying affect from facial expressions remains a less explored topic. Hence, this paper proposes novel approach for intelligent facial emotion recognition using optimal geometrical features from facial landmarks using VGG-19s (FCNN). Here, we utilize Haarcascade to detect the subject face and determine the distance and angle measurements. The entire process is to classify the facial ex-pressions based on extracting relevant features with the normalized angle and distance measures. The experimental analysis shows high accuracy on the MUG dataset of 94.22% and 86.45% on GEMEP datasets, respectively
Optimization of Dynamic Pricing in E-Commerce Platform with Demand Side Management using Fuzzy Logic System
(2023) Mandala, Gangu Naidu
In e-commerce platforms, dynamic pricing has drawn a lot of attention as a potent tactic to boost sales and improve consumer happiness. But monitoring and comprehending client demand is crucial to the success of dynamic pricing. This study suggests an optimization framework for demand-side management that incorporates fuzzy logic into dynamic pricing in e-commerce platforms. To represent and capture the uncertainty and imprecision present in client demand, the suggested framework makes use of fuzzy logic. Fuzzy logic makes it possible to represent and work with linguistic variables, which makes decision-making more adaptable and natural. The approach takes into account the influence of customer behavior and preferences on pricing decisions by incorporating demand-side management. There are two primary steps in the optimization process. First, a fuzzy demand model is created to estimate consumer demand based on a variety of inputs, including pricing, product qualities, and customer traits. This model offers a quantitative knowledge of consumer behavior under various pricing conditions. Second, a pricing plan that maximizes platform profit while accounting for customer happiness and demand changes is determined using an optimization algorithm. By providing personalized pricing based on consumer preferences, the optimized pricing strategy increases revenue while also enhancing customer happiness. Demand-side management and fuzzy logic are combined to improve decision-making and help e-commerce platforms adjust to shifting consumer preferences and market conditions. © 2023 IEEE.
IoT-based Smart Home Automation Systems for Energy Conservation
(2023) Suryanarayana N.V.S.
This research study explores the numerous components of smart home automation systems, including actuators, sensors, and controllers, and the integration of these components with Internet of Things (IoT) technology. To demonstrate the system's ability to reduce energy consumption, this study involves an experimental analysis of a prototype system. In order to scientifically evaluate the system's performance, the test data must be statistically analysed. The results of this research study have the potential to contribute towards the enhancement of energy efficiency and sustainability of the proposed system by offering insights for the construction of sustainable residential settings. This study investigates the effects of the residential environments. The use of complex and innovative systems has the potential to significantly improve the efforts to save energy and promote a sustainable future. © 2023 IEEE.
Deep learning based Identification of Solid Waste Management in Smart Cities through Garbage Separation and Monitoring
(2023) Suryanarayana N.V.S.
The solid waste management is the process of proper decomposition of waste materials within a period of time. This includes the collection of garbage's and then proceeded through certain measures for decomposition. There are various methods adopted in the garbage separation process. This includes the artificial intelligence techniques for the estimation and determination of the solid waste through automatic detection and separation of the garbage waste using control and sensing units. They are integrated with the internet of things to enable the two way communication system. This helps to visualize the functioning of the system adopting the digital platform. The proposed system is implemented through the smart dust bin held in every household that automatically senses the non-biodegradable and biodegradable waste materials. The classification of the waste materials are identified through the image processing techniques. © 2023 IEEE.
Stigma of sickle cell disease among Indian tribal population: A multi-centric qualitative study
(2023) Parikipandla, Sridevi
Background: Sickle Cell Disease (SCD) is the most prevalent hemoglobinopathy, impacting around 5% of the global population. The Indian tribal population, which has been a key focus of the Indian SCD program, can experience health-related stigma due to the multidimensional impact of the disease. This preliminary qualitative inquiry delves into the lived experiences of individuals and synthesizes domains to identify the sources of stigma. Methodology: The study's framework for developing the stigma tool was rooted in Bronfenbrenner's Ecology of Human Development. The study was implemented in five tribal-dominated districts of India and involved in-depth interviews with sickle cell disease (SCD) patients and their caregivers to explore their stigmatizing experiences. Results: The analysis revealed four overarching themes and several subthemes explaining the type of stigma, its source, and factors contributing to stigmatization. First, the study focused on elements associated with perceived stigma, such as disclosure, self-isolation/refusal to participate, and self-judgment. The second theme pertained to the internalization of stigma. The third theme addressed experienced stigma concerning the disease's impact on day-to-day events, and the fourth theme explored the support system patients needed. The framework highlighted the varying degrees of stigmatizing components within different aspects of patients' ecology. Conclusion: Our study highlights the importance of addressing stigma at various levels. Policies, programs, and healthcare interventions must target stigma across these levels. Culturally adaptive tools for identifying stigma, implementing appropriate interventions, and improving healthcare participation are essential for enhancing the quality of life and reducing the disease burden.
The isolation-biological activities (2014–2022), bio, semi, total synthesis (1978–2022) and SAR studies of a potential naturally engineered scaffold aristolactam
(2023) Kishor Padala
Aristolactams are a small group of aporphinoid alkaloids containing the phenanthrene chromophore that were first isolated from Aristolochia argentina (Aristolochiaceae), which is the richest source of this family of alkaloids. Plants containing aristolactam alkaloids have also been used in traditional medicine to treat various diseases. Thus, in this review, we compile and summarise the recent structures, isolation and biological activities of new and known aristolactam alkaloids from 2014 to 2022, and the total synthetic approaches to the natural products bearing an aristolactam motif from 1978 to 2022. Focus has been given to ingenious strategies to functionalize the aristolactam moiety at multiple positions. In addition, the SAR studies of a potential naturally engineered aristolactam scaffold are discussed.
TMSOTf-Promoted Synthesis of Quinazolin-4(3H)-one Utilizing DMSO as a Carbon Source
(2023)
An efficient TMSOTf-promoted multicomponent reaction has been developed for the one-pot synthesis of quinazolin-4 (3H)-ones. Using the TMSOTf as a Lewis acid promoter and DMSO as a carbon source, the reaction of isatoic anhydride, primary amines yielded a variety of quinazolines-4 (3H)-ones. Additionally, TMSOTf promoted the reaction of 2-amino-N-substituted benzamide with DMSO yielding the same scaffolds in high yields. However, the use of DMSO-d6 as a solvent in the reaction enabled the incorporation of the −CD moiety in quinazolines-4 (3H)-one skeleton. This proves that DMSO plays a twin role as a C1 source and solvent. Various functional groups containing a wide range of quinazolin-4 (3H)-ones and other heterocycles were developed employing this methodology. Also, the synthetic methodology has been extended for the synthesis of 3-(2-carboxyphenyl)-4-(3H)-quinazolinone, as an anti-endotoxic drug.
Strengthening Health System and Community Mobilization for Sickle Cell Disease Screening and Management among Tribal Populations in India: An Interventional Study
(2023) Parikipandla, Sridevi
Sickle cell disease (SCD) affects 5% of the global population, with over 300,000 infants born yearly. In India, 73% of those with the sickle hemoglobin gene belong to indigenous tribes in remote regions lacking proper healthcare. Despite the prevalence of SCD, India lacked state-led public health programs until recently, leaving a gap in screening and comprehensive care. Hence, the Indian Council of Medical Research conducted implementation research to address this gap. This paper discusses the development and impact of the program, including screening and treatment coverage for SCD in tribal areas. With a quasi-experimental design, this study was conducted in six tribal-dominated districts in three phases – formative, intervention, and evaluation. The intervention included advocacy, partnership building, building the health system’s capacity and community mobilization, and enabling the health systems to screen and manage SCD patients. The capacity building included improving healthcare workers’ skills through training and infrastructure development of primary healthcare (PHC) facilities. The impact of the intervention is visible in terms of people’s participation (54%, 76% and 93% of the participants participated in some intervention activities, underwent symptomatic screening and demanded the continuity of the program, respectively), and improvement in SCD-related knowledge of the community and health workers (with more than 50% of net change in many of the knowledge-related outcomes). By developing screening and treatment models, this intervention model demonstrated the feasibility of SCD care at the PHC level in remote rural areas. This accessible approach allows the tribal population in India to routinely seek SCD care at their local PHCs, offering great convenience. Nevertheless, additional research employing rigorous methodology is required to fine-tune the model. National SCD program may adopt this model, specifically for community-level screening and management of SCD in remote and rural areas.
A Survey on Cryptocurrency Price Prediction using Hybrid Approaches of Deep Learning Models
(2023) Bonthu Kotaiah N
Deep-learning and machine-learning algorithms have recently become a prominent research topic for forecasting the price of cryptocurrencies. Some research indicates that deep learning models are incapable of accurately and promptly predicting daily cryptocurrency prices, whereas other research compares the efficacy of various models. Such techniques include machine learning, deep learning, and statistical models, among others. Several studies have devised hybrid approaches that combine novel methodologies in an effort to enhance the accuracy of bitcoin price forecasts. Complex models of deep learning and interdependent relationships are examples of these modern methods. To further improve the quality of survey data, there are additional datasets that making frequent errors. The search results indicate that efforts are being made to better bitcoin price estimations using deep learning and hybrid methods. © 2023 IEEE.
A Comprehensive Review of Course Recommendation Systems for MOOCs
(2024) Bonthu Kotaiah N
In recent years, many students have accepted Massive Open Online Courses (MOOCs) as a means of education. Due to the enormous number of courses available through MOOC, students need help in identifying and selecting an appropriate course based on their profile and interests. To address this issue, MOOCs incorporate a course recommendation system that generates a list of courses based on the student’s prerequisites. This literature review attempts to detect and assess trends, processes employed, and developments in MOOC course RS through an exhaustive analysis of academic literature published between January 1, 2016, and November 31, 2023. The study includes the various methodologies employed, the datasets used for evaluations, the performance measures used, and the many issues encountered by Recommendation Systems. Literature published in ScienceDirect, Wiley, Springer, ACM, and IEEE, were chosen for review. After applying inclusion and exclusion criteria, 76 articles from the aforementioned databases, including journals, conferences, and book chapters, were selected. The investigation found that methods from Machine Learning and Deep Learning were widely deployed. Traditional approaches like ”content-based filtering, collaborative filtering, and hybrid filtering” were frequently employed in conjunction with other algorithms for more accurate and precise suggestions. It also underlines the need to take data sparsity, the cold start problem, data overload, and user preferences into account when designing a course recommendation system. This paper contributes to examining the cutting-edge course Recommendation System in depth, examining recent developments, difficulties, and future work in this field.
Advances in nano silver-based biomaterials and their biomedical applications
(2024) Birudu Ravi Babu
Silver nanoparticles are among the most widely researched and used for nanotechnology-derived structures due to their extraordinary inherent optical properties, chemical stability, catalytic activity, and high conductivity. These idiosyncratic properties can be attributed to their unique physico-chemical characteristics, such as ultrafine sizes, high surface area, diverse shapes, and strong localized surface plasmon resonance. These distinctive features can be tailored using various physical, chemical, and biological synthesis methods. Various physical techniques are viable for producing silver nanoparticles on a large scale, but they suffer from drawbacks such as high-power consumption, expensive set-up, and limited control over nanoparticle size distribution. Chemical methods provide benefits like high yield, consistent shape and size distribution, and cost efficiency, but the residual toxicity of the chemicals involved hinders their biological applications. Biological synthesis approaches effectively overcome the limitations of both physical and chemical methods by eliminating the need for hazardous chemicals, requiring less energy, enabling diverse nanoparticle morphologies, and offering eco-friendliness and exceptional biocompatibility. The novel and promising properties of nanosilver-based biomaterials have been demonstrated to be suitable for a wide range of pharmacological and therapeutic biomedical applications. Their extensive application in wound healing, dentistry, cardiovascular disease treatment, nerve tissue engineering, cancer treatment, and biosensing can be attributed to their inherent antimicrobial and antibiofilm activity, antithrombotic properties, potential for nerve regeneration, photothermal conversion efficiency and sensitivity, respectively. This review discusses the different methods employed for synthesising silver nanoparticles and focuses on using nanosilverbased biomaterials for various biomedical applications.

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