Daily Archives: May 31, 2025

Authors: Prabhu Prasad

Abstract: The integration of artificial intelligence (AI) with nanotechnology is rapidly transforming the landscape of drug delivery systems, enabling the creation of smart, responsive platforms capable of adapting to dynamic biological environments. Responsive drug delivery systems use nanocarriers that can detect specific physiological cues and release therapeutic agents accordingly, improving efficacy and minimizing side effects. This paper delves into the role of AI in designing and optimizing these nanocarriers, discussing machine learning models for predicting carrier behavior, AI-driven synthesis, and personalized drug release strategies. It also examines biomedical applications, challenges, ethical considerations, and future directions, highlighting how this synergy paves the way for precision medicine tailored to individual patients' needs.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.552

 

 

Published by: vikaspatanker

Authors: Dr. Shafiq Ruslan

Abstract: The integration of artificial intelligence (AI) with nanotechnology has led to the emergence of AI-powered nanodevices capable of real-time monitoring of physiological parameters. These innovative devices offer unprecedented sensitivity, accuracy, and miniaturization, enabling continuous health monitoring at the molecular and cellular levels. This paper explores the development, functioning, and biomedical applications of AI-enabled nanodevices designed to monitor vital physiological signals in real time. It further discusses the challenges, recent advancements, and future directions in the field, emphasizing the transformative potential of these technologies in personalized healthcare and disease management.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.551

 

 

Published by: vikaspatanker

Authors: Dr. Nazrin Hidayat

Abstract: The rapid advancement of nanotechnology has led to the widespread development and application of nanomaterials in diverse fields, including medicine, electronics, and environmental science. Despite their numerous benefits, nanomaterials pose potential risks to human health and the environment due to their unique physicochemical properties. Accurate assessment of nanomaterial toxicity is therefore crucial to ensure safe usage and regulatory compliance. Machine learning (ML), a subset of artificial intelligence, offers powerful predictive modeling techniques that can analyze complex datasets to forecast nanomaterial toxicity effectively. This paper explores the role of machine learning in predicting the toxicological effects of nanomaterials, reviews common ML algorithms employed, discusses data challenges, and highlights future prospects for integrating ML-driven toxicity prediction into nanomaterial safety assessment frameworks.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.550

 

 

Published by: vikaspatanker

Authors: Dr. Hafizul Ramzee

Abstract: Nanorobotics, a cutting-edge field at the crossroads of nanotechnology and robotics, is poised to revolutionize minimally invasive surgery by enabling interventions at a scale previously unimaginable. The integration of artificial intelligence (AI) with nanorobotics significantly enhances the capability of these tiny machines to navigate complex biological environments, perform precise therapeutic actions, and adapt to dynamic physiological conditions. This paper provides a comprehensive exploration of how AI supports the development, control, and application of nanorobots for minimally invasive surgical procedures. It discusses current state-of-the-art technologies, specific biomedical applications, inherent challenges, ethical considerations, and future research directions. The convergence of AI and nanorobotics represents a paradigm shift towards highly personalized, safer, and more effective surgical techniques, potentially transforming patient care and outcomes in the years to come.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.549

 

 

Published by: vikaspatanker

Authors: Dr. Enobi Qwama

Abstract: Nanoparticles have become a cornerstone in the field of biomedicine due to their unique physicochemical properties and ability to interact at the cellular and molecular levels. Efficient synthesis of nanoparticles with precise control over size, shape, and surface characteristics is critical for their successful application in drug delivery, imaging, and therapeutic interventions. Artificial intelligence (AI), particularly machine learning and deep learning techniques, has emerged as a powerful tool to optimize nanoparticle synthesis processes by analyzing complex experimental data and predicting ideal synthesis parameters. This paper explores how AI-driven methodologies enhance nanoparticle synthesis, discusses current applications in biomedicine, and addresses challenges and future perspectives for integrating AI into nanomanufacturing workflows.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.548

 

 

Published by: vikaspatanker

Authors: Dr. Zimora Kaldu

Abstract: Personalized medicine, also known as precision medicine, seeks to tailor medical treatment to the individual characteristics of each patient, including their genetic makeup, lifestyle, and environment. Nanotechnology provides innovative tools such as nanocarriers, nanosensors, and nanorobots that enable targeted drug delivery, sensitive diagnostics, and real-time monitoring. Deep learning, a subset of artificial intelligence, has demonstrated remarkable success in analyzing complex biomedical data and extracting meaningful insights. The integration of deep learning with nanotechnology holds great promise for advancing personalized medicine by optimizing therapeutic strategies, enhancing diagnostic accuracy, and improving patient outcomes. This paper explores the convergence of these fields, reviewing current applications, challenges, and future prospects in developing personalized healthcare solutions.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.547

 

 

Published by: vikaspatanker

Authors: Dr. Zirika Temba

Abstract: Early detection of diseases significantly improves patient outcomes by enabling timely intervention and effective treatment. Smart nanosensors, leveraging advances in nanotechnology, offer remarkable sensitivity and specificity in detecting biomarkers associated with various diseases at their earliest stages. However, the complexity of the signals generated by these sensors and the vast amount of data involved require advanced computational techniques for accurate interpretation. Artificial intelligence (AI), particularly machine learning and deep learning, plays an increasingly vital role in processing nanosensor data, identifying patterns, and enhancing diagnostic accuracy. This paper reviews the integration of AI with nanosensor technology for early disease detection, discusses key design considerations, presents notable applications, and explores the challenges and future opportunities in this interdisciplinary field.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.546

 

 

Published by: vikaspatanker

Authors: Mitali Vashishtha

Abstract: This paper explores Unified Payments Interface (UPI), a digital payment system developed in India. It examines how it connects with banks, reduces environmental impact through digital transactions, and promotes financial inclusion. The paper examines the role of the Unified Payments Interface (UPI) in promoting sustainability in finance. It highlights how UPI has transformed digital payments by integrating seamlessly with banks, fostering financial inclusion, and reducing reliance on physical infrastructure. The research explores its contributions to economic sustainability, by lowering transaction costs and enabling cashless ecosystems, and environmental sustainability , through paperless transactions and reduced carbon footprints. Additionally, the paper discusses UPI’s integration with the banking ecosystem, its challenges, and its potential as a model for sustainable digital finance.

Published by: vikaspatanker

Authors: Dr. Halifu Zenbe

Abstract: Nanoparticles play a pivotal role in modern biomedical applications, particularly in targeted drug delivery, imaging, and diagnostics. Understanding the complex interactions between nanoparticles and cellular systems is crucial to ensure efficacy, minimize toxicity, and enhance the overall performance of nanomedicine. However, the multifaceted nature of nanoparticle-cell interactions, influenced by numerous physicochemical parameters and cellular heterogeneity, poses a significant challenge for traditional experimental approaches. Machine learning (ML), a subset of artificial intelligence, provides powerful tools for analyzing complex datasets and predicting biological responses to nanoparticles. This paper explores various machine learning methodologies applied to predict nanoparticle-cell interactions, discusses key applications and case studies, addresses the challenges in data acquisition and model validation, and outlines future perspectives to improve predictive accuracy and accelerate nanomedicine development.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.545

 

Published by: vikaspatanker

Authors: Tando Kulesi

Abstract: Targeted drug delivery represents a transformative approach in modern therapeutics, aiming to precisely deliver pharmaceutical agents to specific tissues, cells, or intracellular compartments. This approach significantly improves therapeutic efficacy while minimizing off-target side effects commonly associated with conventional systemic drug administration. Nanocarriers—engineered nanoscale vehicles such as liposomes, polymeric nanoparticles, dendrimers, and metallic nanostructures—have become central to targeted drug delivery due to their tunable physicochemical properties and ability to navigate complex biological environments. Despite their promise, designing nanocarriers that achieve optimal targeting, stability, and controlled release remains a challenging task involving multifaceted biological and physicochemical interactions. Artificial Intelligence (AI), especially through machine learning and deep learning, is revolutionizing this design process by enabling the analysis and interpretation of complex datasets, predicting nanocarrier behavior in biological systems, and optimizing their design parameters for improved performance. This paper thoroughly reviews the current advances in applying AI for the design of nanocarriers, explores successful case studies, discusses inherent challenges, and envisions future directions that could dramatically accelerate nanomedicine development and personalized healthcare.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.544

 

Published by: vikaspatanker