Volume 13 | Issue 6 |

 Abstract

Multidrug-resistant (MDR) infections and chronic wound healing complications remain critical challenges in healthcare. Nanotechnology-enabled drug delivery systems, particularly nanoparticle-loaded chitosan hydrogels, offer an effective solution by enhancing antimicrobial efficacy and providing sustained drug release. This review focuses on the integration of silver nanoparticles (AgNPs) and ciprofloxacin into chitosan hydrogels and their evaluation as potential topical antimicrobial agents. Chitosan serves as a biocompatible and biodegradable polymer matrix, while AgNPs and ciprofloxacin provide broad-spectrum antibacterial activity. The formulation demonstrates favorable physicochemical properties, controlled drug release, and potent antibacterial action against Staphylococcus aureus and Escherichia coli. Advanced characterization using FTIR, SEM, and XRD confirmed structural integrity and nanoparticle dispersion. The synergistic system exhibits promising applications for wound healing and infection control.

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 Keywords

Chitosan hydrogel, silver nanoparticles, ciprofloxacin, antimicrobial activity, controlled release, wound healing, nanotechnology, topical drug delivery

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Kerala state is vulnerable to a multitude of disasters and categorised as a multi-hazard prone state. Kerala is divided into three levels of prone states: 14.5% of the state is flood prone, 14.4% is land slide, and 55.5% Coastline prone to Coastal Hazards and other is man-made kind of disasters. The state falls in zone III in the national seismic zonation map. Kerala has a high density of Population, an urban character, a long coastline, and is vulnerable to the potential impacts of climate change, exotic infectious diseases, and terrorism-related hazards. With this view, the Kerala State Disaster Management Authority has formulated the "Kerala State Disaster Management Policy (KSDMP)". It has followed the National Guideline in Disaster Management

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Disaster Management(DM), Kerala State Disaster Management Policy(KSDMP)

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Portfolio optimization plays a vital role in financial decision-making by strategically allocating assets to maximize returns while minimizing risk. Conventional approaches, such as Markowitz's mean-variance model, face challenges in managing complex, high-dimensional, and multimodal financial markets. To overcome these limitations, nature-inspired metaheuristic algorithms like the Puma Optimizer provide effective alternatives. The Puma Optimizer is a novel metaheuristic optimization algorithm inspired by the behavior and intelligence of pumas (also known as cougars or mountain lions). It is specifically designed for global optimization tasks and demonstrates high performance across various benchmark functions and machine learning problems. Two sets of portfolio consisting of 15 and 30 stocks have been considered. Efficient frontiers in two cases have been plotted and the results obtained are satisfactory

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Portfolio Optimization, Puma Optimizer, Financial Markets

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Devices known as biosensors are those that take in biological signals and transform them into electrical signals that may be detected. DNA, RNA, proteins, and enzymes are among the biological entities that are combined with electrochemical transducers to identify and certain bio analytes, such as immune-antigen interaction. Many kinds of biodetection has effectively used in the disciplines of architecture, biomedicine, and nuetriments sectors to identify and eliminate some pollutants, weather lifeless or inalive creatures. Commonly used sensors nowadays include electrochemical detection, visual, surface localized surface Plasmon resonance, enzymes, DNA, coliphage, microorganism sensors. These biodetections can be used for the detection of the broad spectrum of biotic analytes and has shown more comperhensive and success in medical laboratories, fare bioanalysis, microbial sensing etc. In this literature, we review on bio materials used in bio sensors and their various applications.

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 Keywords

Nanomaterials, Biosensors, Graphene, Carbon Nanotubes, Engyme, Applications

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Rishikulshala: Ek Anootha Prakalp

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Rishikulshala: Ek Anootha Prakalp

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Keywords: ICU, Length of Stay, Critical Illness, Nursing Interventions, Mechanical Ventilation, Sepsis, Physiotherapy, Care Bundles, India, Tertiary Care

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Agronomy is a vital sector in India, and Tamil Nadu's diverse agro-climatic conditions make it a significant contributor to the nation's agricultural output. Among the various farming practices, organic farming and contract farming have gained considerable attention due to their potential benefits in sustainability, economic viability, and environmental conservation. Organic farming emphasises long-term environmental health and sustainability but faces challenges such as high initial costs, lower yields, and limited market access. In contrast, contract farming provides financial stability and guaranteed markets but often results in the loss of farmer autonomy and dependence on corporate entities. This study compares the impacts of both farming models in Tamil Nadu, analysing their respective benefits, challenges, and implications for the future of the state's agricultural landscape. The paper accomplishes that a balanced approach integrating both models could foster a more sustainable and economically viable agricultural sector.

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Keywords: Agriculture, organic farming, contract farming, Tamil Nadu

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Conduct Disorder (CD) is a serious behavioral and emotional disorder in which children or adolescents consistently exhibit behaviors that violate societal norms, the rights of others, and the expectations of their family and school. The diagnostic criteria for Conduct Disorder are outlined in both the DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, 4th edition) and the DSM-5 (5th edition), OBJECTIVES:-1 To assess the knowledge regarding early identification and management of conduct disorder in children among primary school teachers at selected schools of Jaipur.2. To find out the association between the knowledge regarding early identification and management of conduct disorder in children among primary school teachers at selected schools of Jaipur and their selected demographic variables. 3. To develop an information booklet regarding early identification and management of conduct disorder METHODOLOGY a quantitative research approach with a descriptive research design was a structured knowledge questionnaire was used to assess the knowledge regarding early identification and management of conduct disorder among primary school teachers total Enumerations sampling technique was use to select the subject which include a sample of 100 primary school teachers RESULTS The data was analysed by both descriptive and inferential statistics. The result showed that,most of the participants have.22% (22)teachers have good knowledge, 32% (32)have average knowledge and 46% (46) have poor knowledge regarding conduct disorder The computed chi-square test revealed that, the socio-demographic variables, education qualification, professional qualification teaching experience were significantly associated with knowledge at a 0.05 significance. The calculated chi-square test also revealed that, the socio-demographic variables Age, gender number of children marital status previous training attained were not significantly associated with knowledgeat a 0.05 significance level regarding early identification and management of conduct disorder.

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Conduct Disorder, Children, assessment

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The global trade environment in 2025 is marked by heightened uncertainty, protectionist policy shifts, and evolving geopolitical alignments. This research paper synthesizes recent empirical studies and official data to analyze the causes and consequences of trade disruptions, focusing on tariff escalations, supply chain reconfigurations, and the implications for both advanced and developing economies. The findings reveal a contraction in global trade growth, increased trade policy uncertainty, and a growing divergence in economic outcomes across regions. The paper includes a focused analysis of India's adaptive strategies, highlighting its evolving trade policy and resilience amid global uncertainty. Policy recommendations are provided to mitigate the risks of persistent fragmentation and promote resilient, inclusive global trade.

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Trade policy uncertainty, protectionism, geoeconomic fragmentation, global supply chains, tariffs, industrial policy, trade imbalances

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Given its prominence as a leading cause of deaths worldwide, careful detection of this condition is still very important and current heart disease diagnosis methods are heavily dependent on experts, who however, need time to process patients and show the potential possibilities of human error. The research project is aiming to develop a deep learning-based system for heart disease prediction in order to make it efficient and accurate. To do such complex patterns extraction the system uses Convolutional Neural Networks and Long Short-Term Memory (LSTM) networks, which work on the blood pressure readings (t) together with patient's age (l) and cholesterol levels (b) as well as electrocardiogram (ECG) result. Feature selection methods are used to find acceptable information characteristics to reveal important predictive variables, improving the system understanding and the system performance at operation. Using a comprehensive dataset, the model completes training and its accuracy works to the optimal levels for it to be able to identify the patients within the appropriate risk groups. Robustness and reliability of the performance of the model has been established through the use of precision, recall, F1- score and ROC-AUC evaluation metrics. The system is designed to be working as a diagnosis tool that helps healthcare personnel to detect the heart diseases in a timely and convenient way. The proposed solution tries to minimize diagnosis delays and improve patient well-being and also assist in preventive healthcare operations through automation of predictions. A system for implementation within clinical operations could do this and change the direction of heart disease strategies by providing quick and reliable early diagnosis options to healthcare professionals.

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heart disease prediction, deep learning, CNN, LSTM, neural networks, medical diagnosis, feature selection, healthcare analytics, patient risk assessment, ROC-AUC, precision, recall.

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Creative Commons Attribution 4.0 and The Open Definition