Management of epileptic disorders using nanotechnology-based strategies for nose-to-brain drug delivery
Bibliographic record
Abstract
INTRODUCTION: Epilepsy, a major neurological disorder affects about 1% of the Indian population. The discovery of noninvasive strategies for epilepsy presents a challenge for the scientists. Different types of nose-to-brain dosage-forms have been studied for epilepsy management. It aims to give new perspectives for developing new and existing anti-epileptic drugs. Combining nanotechnology with nose-to-brain approach can help in promoting the treatment efficacy by site-specific delivery. Also, it will minimize the side-effects and patient noncompliance observed in conventional administration routes. Peptide delivery can be an interesting approach for the management of epilepsy. Drug-loaded intranasal nanoformulations exhibit diverse prospective potentials in the management of epilepsy. Considering that, nanotherapy using nose-to-brain delivery as a prospective technique for the efficient management of epilepsy is reviewed. AREAS COVERED: The authors have compiled all recently available data pertaining to the nose-to-brain delivery of therapeutics using nanotechnological strategies. The fundamental mechanism of nose-to-brain delivery, claims for intranasal delivery and medical devices for epilepsy are discussed. EXPERT OPINION: Drug-loaded intranasal nanoformulations exhibit different prospective potentials in the management of epilepsy. Considering the foregoing research done in the field of nanotechnology, globally, authors propose nose-to-brain delivery of nanoformulations as a potential technique for the efficient management of epilepsy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".