MicroRNAs in tear fluids predict underlying molecular changes associated with Alzheimer’s disease
Bibliographic record
Abstract
Extracellular circulating microRNAs (miRNAs) have been discussed as potential biomarkers for Alzheimer's disease (AD) diagnosis. As the retina is a part of the CNS, we hypothesize that miRNAs expression levels in the brain, particularly neocortex-hippocampus, eye tissues, and tear fluids are similar at different stages of AD progression. Ten miRNA candidates were systematically investigated in transgenic APP-PS1 mice, noncarrier siblings, and C57BL/6J wild-type controls at young and old ages. Relative expression levels of tested miRNAs revealed a similar pattern in both APP-PS1 mice and noncarrier siblings when compared with age- and sex-matched wild-type controls. However, the differences seen in expression levels between APP-PS1 mice and noncarrier siblings could possibly have resulted from underlying molecular etiology of AD. Importantly, miRNAs associated with amyloid beta (Aβ) production (-101a, -15a, and -342) and proinflammation (-125b, -146a, and -34a) showed significant up-regulations in the tear fluids with disease progression, as tracked by cortical Aβ load and reactive astrogliosis. Overall, for the first time, the translational potential of up-regulated tear fluid miRNAs associated with AD pathogenesis was comprehensively demonstrated.
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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) | 0.001 | 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".