Positron Emission Tomography Imaging of Fibrillar Parenchymal and Vascular Amyloid-β in TgCRND8 Mice
Bibliographic record
Abstract
Few quantitative diagnostic and monitoring, tools are available to clinicians treating patients with Alzheimer's disease. Further, many of the promising quantitative imaging tools under development lack clear specificity toward different types of Amyloid-β (Aβ) pathology such as vascular or oligomeric species. Antibodies offer an opportunity to image specific types of Aβ pathology because of their excellent specificity. In this study, we developed a method to translate a panel of anti-Aβ antibodies, which show excellent histological performance, into live animal imaging contrast agents. In the TgCRND8 mouse model of Alzheimer's disease, we tested two antibodies, M64 and M116, that target parenchyma aggregated Aβ plaques and one antibody, M31, that targets vascular Aβ. All three antibodies were administered intravenously after labeling with both poly(ethylene glycol) to enhance circulation and (64)Cu to allow detection via positron emission tomography (PET) imaging. We were clearly able to differentiate TgCRND8 mice from wild type controls by PET imaging using either M116, the anti-Aβ antibody targeting parenchymal Aβ or M31, the antivascular Aβ antibody. To confirm the validity of the noninvasive imaging of specific Aβ pathology, brains were examined after imaging and showed clear evidence of binding to 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".