MicroRNA profiling reveals novel biomarkers for cardiovascular and psychological health in plateau psycho CVD
Bibliographic record
Abstract
This study aimed to explore the expression characteristics of miRNAs in cardiovascular diseases (CVD) and depression within a plateau environment, to better understand their potential role in Plateau Psycho-CVD. A prospective study design was employed to analyze circulating small RNAs from 20 subjects using high-throughput sequencing technology. Participants were divided into four groups (C, PPC, PP, PC) for comparative analysis. Differentially expressed miRNAs were selected for further functional enrichment analysis. The findings revealed that hsa-miR-1976 and hsa-miR-4685-3p were significantly upregulated in patients with Plateau Psycho-CVD and mental health issues. These miRNAs were closely associated with key pathways relevant to cardiovascular and mental health, including the PI3K-Akt and neurotrophin signaling pathways. Additionally, the downregulated miRNAs in the PPC group were linked to increased expression of AKT1 and STAT3, genes associated with bipolar disorder and inflammatory pathways, indicating a potential impact on neural function. This study identifies hsa-miR-1976 and hsa-miR-4685-3p as novel biomarkers for plateau stress dual heart disease, with AKT1 and STAT3 emerging as potential therapeutic targets. These insights pave the way for further research and clinical applications in related fields.
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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 | 0.001 | 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".