m 6A RNA Methylation Decreases Atherosclerotic Vulnerable Plaque Through Inducing T Cells
Bibliographic record
Abstract
ABSTRACT Introduction: Knockdown of fat mass and obesity-associated gene (FTO) can induce N6-methyladenosine (m 6A) ribonucleic acid (RNA) methylation. The objective of this study was to explore the effect of m 6A RNA methylation on atherosclerotic vulnerable plaque by FTO knockdown. Methods: A total of 50 New Zealand white rabbits were randomly divided into pure high-fat group, sham operation group, vulnerable plaque group, empty load group, and FTO knockdown group (10 rabbits/group). Results: Flow cytometry showed that helper T (Th) cells in the FTO knockdown group accounted for a significantly higher proportion of lymphocytes than in the vulnerable plaque group and empty load group (P<0.05). Th cells were screened by cell flow. The level of m 6A RNA methylation in the FTO knockdown group was significantly higher than in the vulnerable plaque group and empty load group (P<0.05). The levels of total cholesterol, triglyceride, and low-density lipoprotein C were higher at the 12th week than at the 1st week, but the high-density lipoprotein C level was lower at the 12th week than at the 1st week. At the 12th week, the interleukin-7 level was significantly lower in the adeno-associated virus-9 (AVV9)-FTO short hairpin RNA group than in the control and AVV9-green fluorescent protein groups (P<0.001). Conclusion: After successfully establishing a vascular parkinsonism rabbit model, m 6A RNA methylation can decrease Th cells and vulnerable atherosclerotic plaques.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".