[ <sup>11</sup> C]PBR28 PET Imaging is Sensitive to Neuroinflammation in the Aged Rat
Bibliographic record
Abstract
Neuroinflammation in the aging rat brain was investigated using [(11)C]PBR28 microPET (positron emission tomography) imaging. Normal rats were studied alongside LRRK2 p.G2019S transgenic rats; this mutation increases the risk of Parkinson's disease in humans. Seventy [(11)C]PBR28 PET scans were acquired. Arterial blood sampling enabled tracer kinetic modeling and estimation of VT. In vitro autoradiography was also performed. PBR28 uptake increased with age, without differences between nontransgenic and transgenic rats. In 12 months of aging (4 to 16 months), standard uptake value (SUV) increased by 56% from 0.44 to 0.69 g/mL, whereas VT increased by 91% from 30 to 57 mL/cm(3). Standard uptake value and VT were strongly correlated (r = 0.52, 95% confidence interval (CI) = 0.31 to 0.69, n = 37). The plasma free fraction, fp, was 0.21 ± 0.03 (mean ± standard deviation, n = 53). In vitro binding increased by 19% in 16 months of aging (4 to 20 months). The SUV was less variable across rats than VT; coefficients of variation were 13% (n = 27) and 29% (n = 12). The intraclass correlation coefficient for SUV was 0.53, but was effectively zero for VT. These data show that [(11)C]PBR28 brain uptake increases with age, implying increased microglial activation in the aged 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".