Lipopolysaccharide Induced Opening of the Blood Brain Barrier on Aging 5XFAD Mouse Model
Bibliographic record
Abstract
The development of neurotherapeutics for many neurodegenerative diseases has largely been hindered by limited pharmacologic penetration across the blood-brain barrier (BBB). Previous attempts to target and clear amyloid-β (Aβ) plaques, a key mediator of neurodegenerative changes in Alzheimer's disease (AD), have had limited clinical success due to low bioavailability in the brain because of the BBB. Here we test the effects of inducing an inflammatory response to disrupt the BBB in the 5XFAD transgenic mouse model of AD. Lipopolysaccharide (LPS), a bacterial endotoxin recognized by the innate immune system, was injected at varying doses. 24 hours later, mice were injected with either thioflavin S, a fluorescent Aβ-binding small molecule or 30 nm superparamagnetic iron oxide (SPIO) nanoparticles, both of which are unable to penetrate the BBB under normal physiologic conditions. Our results showed that when pretreated with 3.0 mg/kg LPS, thioflavin S can be found in the brain bound to Aβ plaques in aged 5XFAD transgenic mice. Following the same LPS pretreatment, SPIO nanoparticles could also be found in the brain. However, when done on wild type or young 5XFAD mice, limited SPIO was detected. Our results suggest that the BBB in aged 5XFAD mouse model is susceptible to increased permeability mediated by LPS, allowing for improved delivery of the small molecule thioflavin S to target Aβ plaques and SPIO nanoparticles, which are significantly larger than antibodies used in clinical trials for immunotherapy of AD. Although this approach demonstrated efficacy for improved delivery to the brain, LPS treatment resulted in significant weight loss even at low doses, resulting from the induced inflammatory response. These findings suggest inducing inflammation can improve delivery of small and large materials to the brain for improved therapeutic or diagnostic efficacy. However, this approach must be balanced with the risks of systemic inflammation.
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How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".