Management of epileptic disorders using nanotechnology-based strategies for nose-to-brain drug delivery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it