Intranasal Drug Delivery by Nanotechnology: Advances in and Challenges for Alzheimer’s Disease Management
Bibliographic record
Abstract
Alzheimer's disease, a progressive neurodegenerative condition, is characterized by a gradual decline in cognitive functions. Current treatment approaches primarily involve the administration of medications through oral, parenteral, and transdermal routes, aiming to improve cognitive function and alleviate symptoms. However, these treatments face limitations, such as low bioavailability and inadequate permeation. Alternative invasive methods, while explored, often entail discomfort and require specialized assistance. Therefore, the development of a non-invasive and efficient delivery system is crucial. Intranasal delivery has emerged as a potential solution, although it is constrained by the unique conditions of the nasal cavity. An innovative approach involves the use of nano-carriers based on nanotechnology for intranasal delivery. This strategy has the potential to overcome current limitations by providing enhanced bioavailability, improved permeation, effective traversal of the blood-brain barrier, extended retention within the body, and precise targeting of the brain. The comprehensive review focuses on the advancements in designing various types of nano-carriers, including polymeric nanoparticles, metal nanoparticles, lipid nanoparticles, liposomes, nanoemulsions, Quantum dots, and dendrimers. These nano-carriers are specifically tailored for the intranasal delivery of therapeutic agents aimed at combatting Alzheimer's disease. In summary, the development and utilization of intranasal delivery systems based on nanotechnology show significant potential in surmounting the constraints of current Alzheimer's disease treatment strategies. Nevertheless, it is essential to acknowledge regulatory as well as toxicity concerns associated with this route; meticulous consideration is required when engineering a carrier. This comprehensive review underscores the potential to revolutionize Alzheimer's disease management and highlights the importance of addressing regulatory considerations for safe and effective implementations. Embracing this strategy could lead to substantial advancements in the field of Alzheimer's disease treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".