P4‐164: Mapping neuroinflammation <i>in vivo</i> in healthy aging and Alzheimer's disease: A PET study using a novel translocator protein 18kDA (TSPO) radioligand, [18F]‐FEPPA
Bibliographic record
Abstract
Increased density of activated microglia is one of the cellular markers of neuroinflammation. Microglia activation is characterized by the overexpression of the 18kDA Translocator Protein (TSPO), which can be quantified in-vivo using a TSPO PET targeting radioligand. The overexpression of TSPO has been reported in many age-related disorders including the Alzheimer's disease (AD), however studies of neuroinflammation in healthy aging are limited. The aim of the present study is to examine neuroinflammation by quantifying TSPO expression using a novel TSPO PET radioligand, [18 F]-FEPPA in both healthy and AD individuals. We hypothesized that (1) neuroinflammation is related to healthy aging, and (2) neuroinflammation is elevated in AD patients. So far, 2 AD patients (age= 54 + 9 years) and 19 healthy volunteers (mean age 51 + 16 years) were included in the study. To assess the effect of age on neuroinflammation, the healthy volunteers were divided into two groups: young (n = 10, age <55 years) and old (n = 9, age > 55 years). Dynamical [18 F]-FEPPA-PET scan with full arterial sampling was acquired on HRRT-PET camera and analyzed using the 2-tissue compartment model as previously described (Rusjan 2011). A repeated measure ANOVA analysis was performed to compare [18 F]-FEPPA total volumes of distribution (V T) in the hippocampus (HC), temporal (TC), prefrontal cortex (PFC) between the (1) young (n = 10) and old group (n = 9), and (2) the AD patients (n = 2) and healthy controls (n = 19). We found no significant difference in regional [18 F]-FEPPA V T between the young and old healthy groups (F (1,17)= 0.953; P = 0.343. The AD patients showed a significantly greater [18 F]-FEPPA V T in the HC, TC, and PFC (F (1,19)= 10.761, P = 0.004). Post-hoc analysis revealed the greatest difference in the mean [18 F]-FEPPA V T was found in the TC (F (1,19) = 14.37, P = 0.001), followed by the HC (F(1,19)= 8.72, P = 0.008) and PFC (F (1,19)= 7.33, P = 0.014).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".