A3978 miR-338–3p down-regulation was identified in small arteries of hypertensive patients with chronic kidney disease
Bibliographic record
Abstract
Objectives: Hypertension (HTN) and chronic kidney disease (CKD) are global health disorders that are epidemiologically associated. Vascular injury is an early manifestation in HTN and contributes to CKD. It is characterized by vascular dysfunction and remodeling and gene expression changes. MicroRNAs (miRs) are important non-coding RNA gene expression regulators, but their implication in vascular injury remains unclear. We aimed to identify differentially expressed (DE) miRs in small arteries of HTN and CKD human subjects to get insight into pathophysiological molecular mechanisms in these conditions. Methods: Normotensive, HTN (systolic blood pressure (BP) >135 mmHg or diastolic BP of 85–115 mmHg with BpTRU) and CKD subjects (estimated glomerular filtration rate < 60 mL/min/m2) (n = 15–16) were studied. Small arteries were isolated from subcutaneous gluteal biopsies and RNA extracted for small and total RNA sequencing using Illumina HiSeq-2500. EdgeR was used for differential expression analysis, TargetScan to predict DE miR targets in the DE mRNAs and reverse transcription-quantitative PCR (RT-qPCR) to confirm RNA differential expression and find vascular cells expressing these RNAs. Results: DE miRs were identified (P < 0.05) uniquely associated with HTN (3↑ and 6↓) and CKD (42↑ and 39↓) and in both groups (2↑). Correlation between RNA-sequencing and RT-qPCR data was demonstrated for 3 miRs (of 14 tested) including the down-regulated miR-338–3p uniquely associated with CKD (r = 0.91, P < 10–16). ACER2, CCL14 and GPX3 were predicted targets for this miR. miR-338–3p and its predicted targets were found to be expressed in endothelial cells. Conclusion: miR-338–3p down-regulation was found in small arteries uniquely associated with CKD.
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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 | 0.000 |
| 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".