[<sup>18</sup>F]MK6240 and [<sup>18</sup>F]PI2620 autoradiography on postmortem human brain tissue in AD
Bibliographic record
Abstract
Abstract Background To compare the post mortem uptake of [18F]MK6240 and [18F]PI2620 in the brain hemisphere, of A‐T‐ (CN) and A+T+ tissue Method Autoradiography (ARG) imaging using [18F]MK6240 and [18F]PI2620 was used to assess uptake of CN and AD tissues. One study used sections of whole‐hemisphere of one CN tissue and one AD tissue. The uptake value of [18F]MK6240 was calculated from a regions of interest (ROI) drawn manually from the area of cortex gray matter (GM), cortex white matter (WM), and background (BG). Other study was 24 tissues from the prefrontal cortex (Pfc) (12CN, 12AD), hippocampus (Hip) (12 CN, 12 AD) and cerebellum (Cer) (12 CN, 12 AD) were evaluated with [18F]MK6240, and [18F]PI2620. The uptake value of [18F]MK6240 and [18F]PI2620 was calculated from ROI drawn manually from the area of pfc GM, hip GM and cer GM. Result The ARG showed greater [18F]MK6240 uptake in AD than in CN tissue. There were significant differences between CN and AD tissue in terms of cortex gray GM distribution and cortex WM, but no significant differences in BG. These results also, there is no correlation between the specific activity of [18F]MK6240 and the uptake value in CN and AD tissues. In a second experiment, based on our findings there was a significant difference between CN and AD groups when assessed by [18F]MK6240 as well as [18F]PI2620 in the pfc_GM and hip_GM. Furthermore, we found no difference between AD and CN tissues when assessed by [18F]MK6240 and [18F]PI2620 in cerebellum GM. Ratio values Pfc/Cer and Hip/Cer assessed by [18F]MK6240 as well as there were significant differences between CN and AD. [18F]MK6240 and [18F]PI2620 were highly correlated in AD but not in CN brain tissues. Conclusion We also found that [18F]PI2620 and [18F]MK6240 have similar brain uptake in post‐mortem tissue.
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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) | 0.000 | 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) | 0.003 | 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".