Clinically approved IVIg delivered to the hippocampus with focused ultrasound promotes neurogenesis in a model of Alzheimer’s disease
Bibliographic record
Abstract
Preclinical and clinical data support the use of focused ultrasound (FUS), in the presence of intravenously injected microbubbles, to safely and transiently increase the permeability of the blood-brain barrier (BBB). FUS-induced BBB permeability has been shown to enhance the bioavailability of administered intravenous therapeutics to the brain. Ideal therapeutics candidates for this mode of delivery are those capable of inducing benefits peripherally following intravenous injection and in the brain at FUS-targeted areas. In Alzheimer's disease, intravenous immunoglobulin (IVIg), a fractionated human blood product containing polyclonal antibodies, act as immunomodulator peripherally and centrally, and it can reduce amyloid pathology in the brain. Using the TgCRND8 mouse model of amyloidosis, we tested whether FUS can improve the delivery of IVIg, administered intravenously (0.4 g/kg), to the hippocampus and reach an effective dose to reduce amyloid plaque pathology and promote neurogenesis. Our results show that FUS-induced BBB permeability is required to deliver a significant amount of IVIg (489 ng/mg) to the targeted hippocampus of TgCRN8 mice. Two IVIg-FUS treatments, administered at days 1 and 8, significantly increased hippocampal neurogenesis by 4-, 3-, and 1.5-fold in comparison to saline, IVIg alone, and FUS alone, respectively. Amyloid plaque pathology was significantly reduced in all treatment groups: IVIg alone, FUS alone, and IVIg-FUS. Putative factors promoting neurogenesis in response to IVIg-FUS include the down-regulation of the proinflammatory cytokine TNF-α in the hippocampus. In summary, FUS was required to deliver an effective dose of IVIg to promote hippocampal neurogenesis and modulate the inflammatory milieu.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".