Cargo proteins of plasma astrocyte‐derived exosomes in Alzheimer's disease
Bibliographic record
Abstract
Efficient intercellular transfer of RNAs, proteins, and lipids as protected exosomal cargo has been demonstrated in the CNS, but distinct physiologic and pathologic roles have not been well defined for this pathway. The capacity to isolate immunochemically human plasma neuron-derived exosomes (NDEs), containing neuron-specific cargo, has permitted characterization of CNS-derived exosomes in living humans. Constituents of the amyloid β-peptide (Aβ)42-generating system now are examined in 2 distinct sets of human neural cells by quantification in astrocyte-derived exosomes (ADEs) and NDEs, enriched separately from plasmas of patients with Alzheimer's disease (AD) or frontotemporal dementia (FTD) and matched cognitively normal controls. ADE levels of β-site amyloid precursor protein-cleaving enzyme 1 (BACE-1), γ-secretase, soluble Aβ42, soluble amyloid precursor protein (sAPP)β, sAPPα, glial-derived neurotrophic factor (GDNF), P-T181-tau, and P-S396-tau were significantly (3- to 20-fold) higher than levels in NDEs for patients and controls. BACE-1 levels also were a mean of 7-fold higher in ADEs than in NDEs from cultured rat type-specific neural cells. Levels of BACE-1 and sAPPβ were significantly higher and of GDNF significantly lower in ADEs of patients with AD than in those of controls, but not significantly different in patients with FTD than in controls. Abundant proteins of the Aβ42 peptide-generating system in ADEs may sustain levels in neurons. ADE cargo proteins may be useful for studies of mechanisms of cellular interactions and effects of BACE-1 inhibitors in AD.-Goetzl, E. J., Mustapic, M., Kapogiannis, D., Eitan, E., Lobach, I. V., Goetzl, L., Schwartz, J. B., Miller, B. L. Cargo proteins of plasma astrocyte-derived exosomes in Alzheimer's disease.
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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 | 0.001 | 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) | 0.000 | 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".