Stereologic measures of beta-amyloid load in postmortem autosomal dominant Alzheimer disease brain validate PiB-PET as a useful biomarker
Bibliographic record
Abstract
In vivo positron emission tomography (PET) using [C11]-labeled Pittsburgh Compound B ([C11]PiB) has previously been shown to detect amyloid-β (Aβ) in late-onset Alzheimer disease (LOAD) brain; however, the sensitivity of this technique for detecting β-amyloidosis in autosomal dominant Alzheimer disease (ADAD) has not been systematically investigated. To validate [C11]PiB PET as a useful biomarker of β-amyloidosis, we measured the cortical and regional standardized uptake value ratios (SUVRs) in 16 ADAD and 15 LOAD cases and compared them with histopathologic measures of β-amyloidosis in postmortem brain. The PiB-PET data were obtained between 40–70 min after bolus injection of ∼15 mCi of [11C]PiB. MRI and PiB-PET images were co-registered and SUVRs were generated for several brain regions. Using Aβ immunohistochemistry (10D5, Eli Lilly), the burden of Aβ plaques was quantified in 16 regions of interest using an area fraction fractionator probe (Stereo Investigator, MicroBrightfield, VT). There were regional variations in Aβ plaque burden with highest densities observed in the neocortical areas and the striatum. On spearman correlations, in vivo PiB-PET correlated with postmortem Aβ plaque burden in both LOAD and ADAD, with strongest correlations seen in neocortical areas. In summary, [C11]PiB-PET has utility as a biomarker in both ADAD and LOAD. LEARNING OBJECTIVES This presentation will enable the learner to: 1. Discuss how PET-PiB beta-amyloid imaging is used as a potential biomarker of Alzheimer disease (AD) 2. Correlate postmortem neuropathologic evidence of beta-amyloidosis with PET-PiB data, and learn that PET-PiB is a potentially useful tool to detect beta-amyloidosis in presymptomatic and symptomatic individuals
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".