Detection of β‐Amyloid by Sialic Acid Coated Bovine Serum Albumin Magnetic Nanoparticles in a Mouse Model of Alzheimer's Disease
Bibliographic record
Abstract
Abstract The accumulation and formation of β‐amyloid (Aβ) plaques in the brain are distinctive pathological hallmarks of Alzheimer's disease (AD). Designing nanoparticle (NP) contrast agents capable of binding with Aβ highly selectively can potentially facilitate early detection of AD. However, a significant obstacle is the blood brain barrier (BBB), which can preclude the entrance of NPs into the brain for Aβ binding. In this work, bovine serum albumin (BSA) coated NPs are decorated with sialic acid (NP‐BSA x ‐Sia) to overcome the challenges in Aβ imaging in vivo. The NP‐BSA x ‐Sia is biocompatible with high magnetic relaxivities, suggesting that they are suitable contrast agents for magnetic resonance imaging (MRI). The NP‐BSA x ‐Sia binds with Aβ in a sialic acid dependent manner with high selectivities toward Aβ deposited on brains and cross the BBB in an in vitro model. The abilities of these NPs to detect Aβ in vivo in human AD transgenic mice by MRI are evaluated without the need to coinject mannitol to increase BBB permeability. T 2 *‐weighted MRI shows that Aβ plaques in mouse brains can be detected as aided by NP‐BSA x ‐Sia, which is confirmed by histological analysis. Thus, NP‐BSA x ‐Sia is a promising new tool for noninvasive in vivo detection of Aβ plaques.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".