Levels of Receptor for Advanced Glycation End Products and Glyoxalase-1 in the Total Circulating Extracellular Vesicles from Mild Cognitive Impairment and Different Stages of Alzheimer’s Disease Patients
Bibliographic record
Abstract
BACKGROUND: Growing evidence supports that receptor for advanced glycation end products (RAGE) and glyoxalase-1 (GLO-1) are implicated in the pathophysiology of Alzheimer's disease (AD). Extracellular vesicles (EVs) are nanovesicles secreted by almost all cell types, contribute to cellular communication, and are implicated in AD pathology. Recently, EVs are considered as promising tools to identify reliable biomarkers in AD. OBJECTIVE: The aim of our study was to determine the levels of RAGE and GLO-1 in circulating EVs from mild cognitive impairment (MCI) and AD patients and to analyze their correlation with the clinical Mini-Mental State Examination and Montreal Cognitive Assessment scores. We have studied the possibility that neuronal cells could release and transfer GLO-1 through EVs. METHODS: RAGE and GLO-1 levels were measured in circulating EVs, respectively, by Luminex assay and western blot. Released-EVs from SK-N-SH neuronal cells were isolated and GLO-1 levels were determined by western blot. RESULTS: Our data showed higher levels of RAGE in EVs from late AD patients while GLO-1 levels in EVs from early AD were lower as compared to control and MCI patients. Interestingly, levels of RAGE and GLO-1 in EVs were correlated with the cognitive scores regardless of age. For the first time, we demonstrated that GLO-1 was released from neuronal cells through EVs. CONCLUSION: Although more samples will be needed, our preliminary results support the use of peripheral EVs cargo as new tools for the discovery of peripheral AD biomarkers.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".