Increased circulating TREM2+ microglial extracellular vesicles in aged APP/PS1 Alzheimer's disease rats
Bibliographic record
Abstract
Abstract TREM2 is a microglial marker important in Alzheimer’s disease (AD) risk and pathogenesis, but current methods to detect microglial TREM2 expression in vivo are limited. Circulating extracellular vesicles (EVs) show promise as potential biomarkers for AD, and microglial EVs (MEVs) may offer valuable insight into brain TREM2 activity. Here, we investigated plasma-derived TREM2+ MEVs as a potential biomarker of brain microglial TREM2 activity and cognition in a rat model of aging and AD. TMEM119+/TREM2+ EVs were fluorescently labelled and assessed using nanoscale flow cytometry directly in plasma collected from wildtype and APP/PS1 rats aged to 3-, 9-, and 15-months-old. Molecular and histological assays were used to assess microglial markers in rat brain tissue, and a radial arm water maze task was employed to evaluate spatial working and reference memory. We demonstrated that TMEM119+/TREM2+ EVs can be detected in the systemic circulation and were increased in 15-month APP/PS1 rats. Further, the amount of TMEM119+/TREM2+ EVs associated with the severity of cognitive impairment in aged rats, while TREM2 brain expression varied by anatomical region, age, transgene, and assay. Collectively, this study provides the first assessment of TMEM119+/TREM2+ EVs as a biomarker of brain microglial expression and cognition in a rat model of aging and AD.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".