N6-methyladenosine (m6A) modification and its clinical relevance in cognitive dysfunctions
Bibliographic record
Abstract
BACKGROUND: N6 adenosine methylation (m6A) is the most abundant internal RNA modification in eukaryotic cells. Dysregulation of m6A has been associated with the perturbations of cell proliferation and cell death in different diseases. However, the roles of m6A in the neurodegenerative process and cognitive dysfunction are unclear. METHODS: We systematically investigated the molecular alterations of m6A regulators and their clinical relevance with cognitive dysfunctions using published datasets of Alzheimer's Disease (AD), vascular dementia, and mild cognitive impairment (MCI). FINDINGS: The expressions of m6A regulators vary in different tissues and closely correlate with neurodegenerative pathways. We identified co-expressive m6A regulators SNRPG and SNRPD2 as potential biomarkers to predict transformation from MCI to AD. Moreover, we explored correlations between Apolipoprotein E4 and m6A methylations. INTERPRETATION: Collectively, these findings suggest that m6A methylations as potential biomarkers and therapeutic targets for cognitive dysfunction. FUNDING: This work was supported by the National Natural Science Foundation of China (81871040) and the Shanghai Health System Talent Training Program (2018BR29).
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".