Innovations in Nanotechnology-Enhanced Proniosomal Systems for Targeted Drug Delivery
DOI:
https://doi.org/10.37547/Keywords:
Nanotechnology, Proniosomal systems, Drug deliveryAbstract
Proniosomes are a promising drug delivery system, and their integration with nanotechnology offers significant advantages. This article reviews the current state of nanotechnology integration in proniosomal drug delivery systems, highlighting the benefits, challenges, and future directions of this combined approach.
Downloads
References
1. Singh, P., et al. (2025). General introduction to different neurodegenerative diseases. In Neurodegenerative Diseases (pp. 1-19). CRC Press.
2. Mauro, D., et al. (2024). The role of early treatment in the management of axial spondyloarthritis: Challenges and opportunities. Rheumatology and Therapy, 11(1), 19-34.
3. Valarmathi, P., et al. (2025). Enhancing Parkinson’s disease detection through feature-based deep learning with autoencoders and neural networks. Scientific Reports, 15(1), 8624.
4. De Giorgi, R., et al. (2024). 12-month neurological and psychiatric outcomes of semaglutide use for type 2 diabetes: A propensity-score matched cohort study. EClinicalMedicine, 74.
5. Gabrani, G., et al. (2024). Revolutionizing healthcare: Impact of artificial intelligence in disease diagnosis, treatment, and patient care. In Handbook on Augmenting Telehealth Services (pp. 17-31). CRC Press.
6. Khalifa, M., M. Albadawy, & U. Iqbal. (2024). Advancing clinical decision support: The role of artificial intelligence across six domains. Computer Methods and Programs in Biomedicine Update, 5, 100142.
7. Chand, J. & G. Subramanian. (2025). Neurodegenerative disorders: Types, classification, and basic concepts. In Multi-Factorial Approach as a Therapeutic Strategy for the Management of Alzheimer’s Disease (pp. 31-40). Springer.
8. Proskauer Pena, S.L. (2025). Cellular mechanisms of segregation and consolidation of memory in a rat model of Alzheimer’s disease.
9. Gupta, A. & B. Sharma. (2025). Neurodegenerative diseases (ND): An introduction. In Synaptic Plasticity in Neurodegenerative Disorders (pp. 3-20). CRC Press.
10. Kunwar, O.K. & S. Singh. (2025). Neuroinflammation and neurodegeneration in Huntington’s disease: Genetic hallmarks, role of metals, and organophosphates. Neurogenetics, 26(1), 1-15.
11. Gadhave, D.G., et al. (2024). Neurodegenerative disorders: Mechanisms of degeneration and therapeutic approaches with their clinical relevance. Ageing Research Reviews, 102357.
12. Chudzik, A., A. Śledzianowski, & A.W. Przybyszewski. (2024). Machine learning and digital biomarkers can detect early stages of neurodegenerative diseases. Sensors, 24(5), 1572.
13. Hatami-Fard, G. & S. Anastasova-Ivanova. (2024). Advancements in cerebrospinal fluid biosensors: Bridging the gap from early diagnosis to the detection of rare diseases. Sensors, 24(11), 3294.
14. Marques, M., A. Almeida, & H. Pereira. (2024). The medicine revolution through artificial intelligence: Ethical challenges of machine learning algorithms in decision-making. Cureus, 16(9).
15. Sarker, I.H. (2021). Machine learning: Algorithms, real-world applications, and research directions. SN Computer Science, 2(3), 160.
16. Cen, X., et al. (2024). Towards interpretable imaging genomics analysis: Methodological developments and applications. Information Fusion, 102, 102032.
17. Ogwu, M.C. & S.C. Izah. (2025). Artificial intelligence and machine learning in tropical disease management. In Technological Innovations for Managing Tropical Diseases (pp. 155-182). Springer.
18. Rashid, M. & M. Sharma. (2025). AI-assisted diagnosis and treatment planning: A discussion of how AI can assist healthcare professionals in making more accurate diagnoses and treatment plans for diseases. In AI in Disease Detection: Advancements and Applications (pp. 313-336).
19. Alam, T., et al. (2025). Machine learning in healthcare: Key applications and insights from recent studies.
20. Shah, H.H. (2021). Early disease detection through data analytics: Turning healthcare intelligence. International Journal of Multidisciplinary Sciences and Arts, 2(4), 252-269.
21. Rawat, A.S., J. Rajendran, & S.S. Sikarwar. (2025). Introduction to AI in disease detection—An overview of the use of AI in detecting diseases, including the benefits and limitations of the technology. In AI in Disease Detection: Advancements and Applications (pp. 1-26).
22. Singh, L., et al. (2025). Ethical and regulatory compliance challenges of generative AI in human resources. In Generative Artificial Intelligence in Finance: Large Language Models, Interfaces, and Industry Use Cases to Transform Accounting and Finance Processes (pp. 199-214).
23. Alabi, M. (2025). AI-assisted medical diagnosis using deep learning and computer vision.
24. Rashid, Z., et al. (2025). The paradigm of digital health: AI applications and transformative trends. Neural Computing and Applications, 1-32.
25. Ali, H. (2022). AI in neurodegenerative disease research: Early detection, cognitive decline prediction, and brain imaging biomarker identification. Int J Eng Technol Res Manag, 6(10), 71.
26. Zhang, J. (2022). Mining imaging and clinical data with machine learning approaches for the diagnosis and early detection of Parkinson’s disease. npj Parkinson’s Disease, 8(1), 13.
27. Wasilewski, T., W. Kamysz, & J. Gębicki. (2024). AI-assisted detection of biomarkers by sensors and biosensors for early diagnosis and monitoring. Biosensors, 14(7), 356.
28. Ahmed, H., et al. (2021). Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: Opportunities and hurdles. PeerJ Computer Science, 7, e697.
29. Naik, A., A.A. Kale, & J.M. Rajwade. (2024). Sensing the future: A review on emerging technologies for assessing and monitoring bone health. Biomaterials Advances, 214008.
30. Sharma, D. & P. Kaushik. (2025). Applications of AI in neurological disease detection—A review of specific ways in which AI is being used to detect and diagnose neurological disorders, such as Alzheimer’s and Parkinson’s. In AI in Disease Detection: Advancements and Applications (pp. 167-189).
31. Barker, M.S., et al. (2025). Excessive emotional reactivity in a case of behavioral variant frontotemporal dementia with amyotrophic lateral sclerosis. Psychiatry Research Case Reports, 4(1), 100247.
32. Valerio, J.E., et al. (2025). Advancing early identification of clinical trials in neurosurgical interventions for Parkinson’s disease: The critical role of AI-driven platforms and technological innovation.
33. Mirabian, S., et al. (2025). The potential role of machine learning and deep learning in differential diagnosis of Alzheimer’s disease and FTD using imaging biomarkers: A review. The Neuroradiology Journal, 19714009251313511.
34. Mehrotra, S., et al. (2025). Advances and challenges in the diagnosis of leishmaniasis. Molecular Diagnosis & Therapy, 1-18.
35. Sedano, R., et al. (2025). Artificial intelligence to revolutionize IBD clinical trials: A comprehensive review. Therapeutic Advances in Gastroenterology, 18, 17562848251321915.
36. Yousra, K. (2025). Intelligent monitoring of an industrial system using image classification. Ministry of Higher Education.
37. Mishra, A., S.K. Sahu, & S. Mistry. (2025). Role of computational biology in the diagnosis of neurodegenerative disorders. In Computational Intelligence for Genomics Data (pp. 167-179). Elsevier.
38. Faiyazuddin, M., et al. (2025). The impact of artificial intelligence on healthcare: A comprehensive review of advancements in diagnostics, treatment, and operational efficiency. Health Science Reports, 8(1), e70312.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Dr. Maria S. Gonzalez, Dr. Javid K. Malik

This work is licensed under a Creative Commons Attribution 4.0 International License.