Binding of the Positron Emission Tomography Tracer Pittsburgh Compound-B Reflects the Amount of Amyloid-β in Alzheimer's Disease Brain But Not in Transgenic Mouse Brain
Bibliographic record
Abstract
During the development of in vivo amyloid imaging agents, an effort was made to use micro-positron emission tomography (PET) imaging in the presenilin-1 (PS1)/amyloid precursor protein (APP) transgenic mouse model of CNS amyloid deposition to screen new compounds and further study Pittsburgh Compound-B (PIB), a PET tracer that has been shown to be retained well in amyloid-containing areas of Alzheimer's disease (AD) brain. Unexpectedly, we saw no significant retention of PIB in this model even at 12 months of age when amyloid deposition in the PS1/APP mouse typically exceeds that seen in AD. This study describes a series of ex vivo and postmortem in vitro studies designed to explain this low retention. Ex vivo brain pharmacokinetic studies confirmed the low in vivo PIB retention observed in micro-PET experiments. In vitro binding studies showed that PS1/APP brain tissue contained less than one high-affinity (K(d) = 1-2 nm) PIB binding site per 1000 molecules of amyloid-beta (Abeta), whereas AD brain contained >500 PIB binding sites per 1000 molecules of Abeta. Synthetic Abeta closely resembled PS1/APP brain in having less than one high-affinity PIB binding site per 1000 molecules of Abeta, although the characteristics of the few high-affinity PIB binding sites found on synthetic Abeta were very similar to those found in AD brain. We hypothesize that differences in the time course of deposition or tissue factors present during deposition lead to differences in secondary structure between Abeta deposited in AD brain and either synthetic Abeta or Abeta deposited in PS1/APP brain.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".