Karnataka State Women's University

The global nutraceutical sector is undergoing a paradigm shift from conventional, resource-intensive analytical practices to sustainable, green approaches that align with environmental, economic, and ethical imperatives. This review examines the application of Green Analytical Chemistry (GAC) principles in nutraceutical analysis, focusing on innovative extraction methods, miniaturized and solvent-free techniques, AI-assisted workflows, and blockchain-enabled transparency. It critically examines the limitations of classical chromatographic techniques (HPLC, GC-MS, TLC) in terms of solvent consumption, waste generation, and energy demand, while showcasing greener alternatives such as FTIR, NIR, Raman spectroscopy, ultrasound-assisted extraction, supercritical fluid extraction, and natural deep eutectic solvents (NaDES). Analytical challenges, ranging from matrix complexity, bioavailability issues, and compound stability to regulatory validation and scalability, are addressed through advancements in chemometrics, portable devices, and real-time monitoring platforms. The review emphasizes the growing demand for clean-label, traceable nutraceuticals and the role of digital innovations, including AI-driven optimization and digital twin simulations, in reducing waste and trial costs. Cross-sector collaboration between academia, industry, and regulatory bodies (EFSA, FDA, FSSAI) is advocated to mainstream sustainable practices, establish ISO-certified green methodologies, and harmonize global standards. By moving beyond chromatograms to environmentally conscious, technologically advanced analytics, the nutraceutical industry can ensure product efficacy, consumer safety, and long-term ecological balance.

Advanced cancer poses significant challenges that extend beyond the patient to include family caregivers, who play a vital role in treatment adherence, symptom management, emotional support, and palliative care. This article presents a narrative review of existing literature along with findings from an experimental study conducted among caregivers of patients with advanced cancer. The study aimed to evaluate the effectiveness of structured caregiver involvement on patient outcomes and caregiver burden. A total of 50 caregivers were included and divided into experimental and control groups. Results demonstrated significant improvement in patient symptom control, treatment adherence, quality of life, and a reduction in caregiver burden in the experimental group. The findings highlight the importance of integrating caregiver-centered interventions into advanced cancer care.

The study outlines the design and implementation of a Home Security and Intrusion Detection System based on IoT with the goal of supplying homeowners with real-time monitoring, intelligent threat detection, and immediate alert systems. The suggested system will combine several Internet of Things (IoT) devices, including PIR and ultrasonic sensors, camera modules, and microcontrollers (Arduino/Raspberry Pi) and Wi-Fi modules to create a smart, autonomous, and connected surveillance network. The microcontroller processes data of the sensing layer and sends it via MQTT/HTTP protocols and displays on a mobile or web-based platform so that the user can immediately respond. To test the system in different scenarios of intrusion, a variety of intrusion conditions were applied to test the accuracy of detecting, the time of responding to alerts, and the power consumption of the system. The experimental results had shown a high success rate of detection of 98 % and low false alarms and low power consumption and the networks had constant connectivity. Evaluation on ease of set-up, accuracy, and reliability of the system was also done by the users who expressed more than 96 % satisfaction. Comprehensively, the study has served to advance smart home technologies due to the ability to offer an efficient, scalable, and affordable IoT-based security system that can protect in real-time and expand in the future through the inclusion of AI and cloud.

BackgroundCardiovascular disease (CVD) remains a leading cause of mortality worldwide, with a disproportionate burden in low- and middle-income countries (LMICs). While malnutrition is a recognized clinical issue, its role as a contributing factor to coronary artery disease (CAD) is often overlooked. This study aimed to assess the prevalence of malnutrition and its association with significant coronary artery stenosis (CAS) using routinely available nutritional indices, in combination with body mass index (BMI), in a real-world Japanese patient cohort.MethodsThis retrospective cross-sectional study included 1,107 patients who underwent coronary angiography (CAG) at Kagawa University Hospital, Kagawa, Japan, between January 2006 and December 2023. Nutritional status was assessed using various indices: controlling nutritional status (CONUT), prognostic nutritional index (PNI), and geriatric nutritional risk index/nutritional risk index (GNRI/NRI). Malnutrition was defined using standard clinical thresholds. CAS was defined as 75% or greater luminal narrowing in at least one coronary artery. Multivariable logistic regression models were developed to assess the association between nutritional indices and CAS, adjusting for age, sex, smoking status, and lipid levels. Interaction terms were included to evaluate the modifying effect of BMI (continuous). Additional risk phenotypes were created by combining nutritional indices and BMI based on the 75th percentile.ResultsSignificant CAS was identified in 45.4% of the study population. Malnutrition prevalence using standard clinical cutoffs ranged from 26.8% using CONUT to over 70% with GNRI/NRI and PNI, with poor inter-index agreement (Fleiss’ κ = 0.227). Interaction analysis showed that severe-risk GNRI/NRI scores were positively associated with CAS at higher BMI levels, while mild-risk scores showed inverse associations. Several phenotype-based combinations were also significantly associated with increased CAS risk, including high CONUT + normal BMI (Odds ratio, OR = 1.88), low CONUT + high BMI (OR = 1.75), and high PNI + high BMI (OR = 1.92). Among predictive models, the CONUT + BMI combination demonstrated the best overall discriminative performance (AUC = 0.71; sensitivity = 78.6%), while the PNI + BMI model had the highest positive predictive value (66.7%).ConclusionMalnutrition is prevalent among patients undergoing CAG and is significantly associated with CAS, particularly when assessed in combination with BMI. Our findings suggest that GNRI/NRI, when interacted with BMI as a continuous variable, provides a sensitive gradient of risk, while percentile-based combinations using CONUT or PNI offer clear categorical phenotypes for risk stratification. These accessible, routinely used biomarkers and EMR-based indices can serve as practical tools for early risk identification, especially in resource-limited LMIC settings. Prospective studies are warranted to determine whether targeted nutritional interventions based on these indices can improve cardiovascular outcomes.

The increasing adoption of electric vehicles (EVs) has intensified the need for efficient management of charging infrastructure. This project addresses the challenge of predicting the availability status of EV charging stations — classifying whether a slot is available or not available — using a deep learning model and enhancing its interpretability through a decision-tree- based Explainability approach. A sequential LSTM-GRU model was developed to predict the availability status of charging slots at multiple stations in Paris, incorporating temporal, spatial, and contextual features such as time of day, day of the week, location coordinates, and trend indicators. To ensure the transparency and reliability of the deep learning model’s predictions, a Decision Tree classifier was employed as an interpretable surrogate model. By analyzing the feature importance’s derived from the Decision Tree, the study identified ’Longitude’ and ’Latitude’ as the most significant factors influencing charger availability, highlighting a strong spatial dependency in EV infrastructure usage patterns. The integration of interpretable models alongside deep learning models enhances decision-making confidence and provides actionable insights for urban mobility planners and infrastructure managers.

Abstract- The most prevalent kind of cancer worldwide is skin cancer. Early detection is crucial since failure to discover it in the primary stage could be fatal. Even though there are distinctions within the class and a lot of parallels between classes, it is too hard to tell with the naked eye. Due of the disease's widespread occurrence, several automated deep learning-based algorithms have been developed to date to assist physicians in spotting skin lesions early on. We trained VGG19 on the HAM10000 dataset by fine-tuning the Convolutional Neural Networks (CNNs) and using pre-trained ImageNet weights. With FT, the best performance was noted. With an overall accuracy of 82.4±1.9 percent, the developed model outperformed the one employed in transfer learning. This performance could save morbidity and treatment costs by providing a second opinion and bolstering the clinician's diagnosis.

This study presents an optimized inventory management model for deteriorating items, focusing on quality-dependent demand patterns within a generalized neutrosophic environment. Recognizing the challenges of degradation, defective products, fluctuating demand, and uncertain cost parameters, the model leverages a newly developed ranking and defuzzification approach. The study integrates a delay in payment policy, quality-linked demand, and preservation technology investment to optimize cycle time and minimize total inventory costs. The Particle Swarm Optimization (PSO) algorithm is applied to find the optimal solutions, with a sensitivity analysis exploring impacts on various inventory parameters. Results demonstrate the model's adaptability to real-world uncertainties, enhancing decision-making for sustainable inventory systems.