Methylglyoxal and Glyoxal as Potential Peripheral Markers for MCI Diagnosis and Their Effects on the Expression of Neurotrophic, Inflammatory and Neurodegenerative Factors in Neurons and in Neuronal Derived-Extracellular Vesicles
Bibliographic record
Abstract
Methylglyoxal (MG) and glyoxal (GO) are suggested to be associated with the development of neurodegenerative pathologies. However, their peripheral levels in relation to cognitive decline and their effects on key factors in neuronal cells are poorly investigated. The aim of this study was to determine their serum levels in MCI (mild cognitive impairment) and Alzheimer's disease (AD) patients, to analyze their effects on the neurotrophic and inflammatory factors, on neurodegenerative markers in neuronal cells and in neuronal derived-extracellular vesicles (nEVs). Our results show that MG and GO levels in serum, determined by HPLC, were higher in MCI. ROC (receiver-operating characteristic curves) analysis showed that the levels of MG in serum have higher sensitivity to differentiate MCI from controls but not from AD. Meanwhile, serum GO levels differentiate MCI from control and AD groups. Cells and nEVs levels of BDNF, PRGN, NSE, APP, MMP-9, ANGPTL-4, LCN2, PTX2, S100B, RAGE, Aβ peptide, pTau T181 and alpha-synuclein were quantified by luminex assay. Treatment of neuronal cells with MG or GO reduced the cellular levels of NSE, PRGN, APP, MMP-9 and ANGPTL-4 and the nEVs levels of BDNF, PRGN and LCN2. Our findings suggest that targeting MG and GO may be a promising therapeutic strategy to prevent or delay the progression of AD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".