Phosphoproteome Microarray Analysis of Extracellular Particles as a Tool to Explore Novel Biomarker Candidates for Alzheimer’s Disease
Bibliographic record
Abstract
Phosphorylation plays a key role in Alzheimer's disease (AD) pathogenesis, impacting distinct processes such as amyloid-beta (Aβ) peptide production and tau phosphorylation. Impaired phosphorylation events contribute to senile plaques and neurofibrillary tangles' formation, two major histopathological hallmarks of AD. Blood-derived extracellular particles (bdEP) can represent a disease-related source of phosphobiomarker candidates, and hence, in this pilot study, bdEP of Control and AD cases were analyzed by a targeted phosphoproteomics approach using a high-density microarray that featured at least 1145 pan-specific and 913 phosphosite-specific antibodies. This approach, innovatively applied to bdEP, allowed the identification of 150 proteins whose expression levels and/or phosphorylation patterns were significantly altered across AD cases. Gene Ontology enrichment and Reactome pathway analysis unraveled potentially relevant molecular targets and disease-associated pathways, and protein-protein interaction networks were constructed to highlight key targets. The discriminatory value of both the total proteome and the phosphoproteome was evaluated by univariate and multivariate approaches. This pilot experiment supports that bdEP are enriched in phosphotargets relevant in an AD context, holding value as peripheral biomarker candidates for disease diagnosis.
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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 | 0.001 |
| 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) | 0.001 | 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".