Intranasal, siRNA Delivery to the Brain by TAT/MGF Tagged PEGylated Chitosan Nanoparticles
Bibliographic record
Abstract
Neurodegeneration is characterized by progressive loss of structure and function of neurons. Several therapeutic methods and drugs are available to alleviate the symptoms of these diseases. The currently used delivery strategies such as implantation of catheters, intracarotid infusions, surgeries, and chemotherapies are invasive in nature and pose a greater risk of postsurgical complications, which can have fatal side effects. The current study utilizes a peptide (TAT and MGF) tagged PEGylated chitosan nanoparticle formulation for siRNA delivery, administered intranasally, which can bypass the blood brain barrier. The study investigates the optimal dose, duration, biodistribution, and toxicity, of the nanoparticle-siRNA formulation, in-vivo. The results indicate that 0.5 mg/kg of siRNA is delivered successfully to the hippocampus, thalamus, hypothalamus, and Purkinje cells in the cerebellum after 4 hrs of post intranasal delivery. The results indicate maximum delivery to the brain in comparison to other tissues with no cellular toxic effects. This study shows the potential of peptide-tagged PEGylated chitosan nanoparticles to be delivered intranasally and target brain tissue for the treatment of neurological disorders.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".