Nose-to-brain delivery of lithium via a sprayable in situ-forming hydrogel composed of chelating starch nanoparticles
Bibliographic record
Abstract
While bipolar disorder patients can benefit from lithium therapy, high levels of lithium in the serum can induce undesirable systemic side effects. Intranasal (IN) lithium delivery offers a potential solution to this challenge given its potential to facilitate improved lithium transport to brain when delivered to the olfactory mucosa. Herein, a sprayable, in situ forming nanoparticle network hydrogel (NNH) based on Schiff base interactions between chelator-functionalized oxidized starch nanoparticles (SNPs) and carboxymethyl chitosan (CMCh) is reported that can be deployed within the nasal cavity to release ultra-small penetrative SNPs over time. Chelating functional groups including citrate, ethylenediaminetetraacetic acid, and pentetic acid are shown to bind a variety of cations including lithium, magnesium, and calcium, with chelation directly linked to enhancements in the gel mechanics even for monovalent lithium. The hydrogels show high in vitro cytocompatibility with mouse striatal neuron and human primary nasal cell lines. Effective IN delivery of lithium to the brain is demonstrated for the first time, with both solution-based and hydrogel-loaded lithium showing in vivo efficacy in an amphetamine-induced pre-clinical rat bipolar manic phase model; specifically, IN-delivered NNHs can maintain successful attenuation of locomotor activity for up to 6 h while all other tested treatments (drug-only IN or conventional intraperitoneal delivery) failed to retain attenuation for more than two hours at the same lithium dose. As such, in situ-gelling and ion-chelating NNHs represent a new material that can effectively enable metal ion management in biomedical applications.
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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".