Concentration, distribution, and influence of aging on the 18 kDa translocator protein in human brain: Implications for brain imaging studies
Bibliographic record
Abstract
Positron emission tomography (PET) imaging of the translocator protein (TSPO) is widely used as a biomarker of microglial activation. However, TSPO protein concentration in human brain has not been optimally quantified nor has its regional distribution been compared to TSPO binding. We determined TSPO protein concentration, change with age, and regional distribution by quantitative immunoblotting in autopsied human brain. Brain TSPO protein concentration (>0.1 ng/µg protein) was higher than those reported by in vitro binding assays by at least 2 to 70 fold. TSPO protein distributed widely in both gray and white matter regions, with distribution in major gray matter areas ranked generally similar to that of PET binding in second-generation radiotracer studies. TSPO protein concentration in frontal cortex was high at birth, declined precipitously during the first three months, and increased modestly during adulthood/senescence (10%/decade; vs. 30% for comparison astrocytic marker GFAP). As expected, TSPO protein levels were significantly increased (+114%) in degenerating putamen in multiple system atrophy, providing further circumstantial support for TSPO as a gliosis marker. Overall, findings show some similarities between TSPO protein and PET binding characteristics in the human brain but also suggest that part of the TSPO protein pool might be less available for radioligand binding.
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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 | 0.000 | 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".