Specific signatures of circulating extracellular vesicles emerged as novel biological tools for the detection of biomarkers for MCI and Alzheimer's disease
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease (AD) is characterized by a series of overlapping pathophysiological cascades, including the aggregation of β‐amyloid plaques and the formation of neurofibrillary tangles derived from hyperphosphorylated tau proteins. Actually, the search for peripheral biomarkers for AD that reflect AD‐related processes in the brain has received considerable attention but the clinical utility of blood‐based biomarkers is not validated. Accumulating evidence supports the central role of exosomes or extracellular vesicles (EVs) in the pathophysiology of AD. In the brain, they can act as vehicles for the cell‐to‐cell transfer of the AD pathogenic proteins. Our objectives were to demonstrate that some proteins cargo in the circulating EVs represent a great potential in harbor disease‐specific molecular signatures of brain disorders, making them an ideal source of biomarkers. Method We have isolated EVs from plasma (pEVs) of control, MCI and AD subjects at different stages of the disease (mild, moderate, severe). Then, their size, shape and density were characterized by transmission electronic microscopy and nanoparticles tracking analysis (NTA). The presence of pEVs specific proteins TSG101, CD63, GAPDH were confirmed by Western Blot and AD‐related proteins were quantified by Luminex assay. Result Our data showed that the levels of these proteins were higher in pEVs than in plasma indicating that the pEVs represent a sensitive tool to screen peripheral biomarkers. We observed a reduction of tTau in pEVs from MCI subjects while the APP concentration was reduced in pEVs from MCI and early AD. Interestingly, pTau‐T181 and APP concentrations in pEVs were correlated with cognitive performances (MMSE , MoCA). Aβ42 and pTau‐T181 levels in pEVs were unchanged in MCI but elevated in moderate AD patients compared to controls. Abnormal levels of APP and pTau‐T181/tTau ratio in pEVs levels demonstrated good accuracy to define MCI and AD staging. Conclusion Taken together, these results demonstrated that the identification of these proteins in pEVs could have great implication in differentiating MCI individuals to AD patients and to monitor the disease progression. This work was supported by the Chaire Louise & André on Alzheimer’s disease, Foundation Armand‐Frappier (CR) and CIHR grant (TF).
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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 | 0.001 | 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".