Correlation between Brain 18F-AV45 and 18F-FDG PET Distribution Characteristics and Cognitive Function in Patients with Mild and Moderate AD
Bibliographic record
Abstract
Abstract Background The purpose of this study is to investigate the characteristics of amyloid beta (Aβ) load and fluorodeoxyglucose (FDG) metabolism and analyze their correlation with cognitive impairment in the cerebral cortex of Alzheimer's disease (AD) patients using 18F-florbetapir (18F-AV45) and 18F-FDG PET technology. Methods 27 patients with AD were enrolled. All AD patients underwent detailed clinical and imaging examinations of the nervous system, completed the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) tests, and finished 18F-AV45 and 18F-FDG PET scans. NeuroQ software was used to analyze PET images. Results 81.48% (22 out of 27) AD patients had significantly increased Aβ load and 96.30% (26 out of 27) had significantly reduced FDG metabolism. Moderate AD patients had more brain areas of reduced FDG metabolism with more severe reduction in some brain regions compared with mild AD patients, despite there was no differences of Aβ load between these patients. The range of reduced FDG metabolism was negatively correlated with the total scores of MMSE and MoCA, and the degree of FDG metabolism in some brain regions was positively correlated with the total score of MMSE and MoCA. Conclusion Brain 18F-AV45 and 18F-FDG imaging may be potential biomarkers of AD, and 18F-FDG imaging is correlated with the degree of cognitive impairment in AD patients.
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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".