Abstract 070: ONE UP-REGULATED NOVEL MICRORNA AND 4 DOWN-REGULATED MRNA TARGETS WERE IDENTIFIED IN PERIPHERAL BLOOD MONONUCLEAR CELLS OF HYPERTENSIVE PATIENTS WITH METABOLIC SYNDROME
Bibliographic record
Abstract
Introduction: Hypertension is associated with target organ damage such as kidney injury. The immune system plays a role in hypertension and target organ damage. Activation of T cells has been reported among peripheral blood mononuclear cells (PBMCs) of patients with HTN. MicroRNAs are crucial post-transcriptional regulators of immune cell development and function. Hypothesis: MicroRNAs play a role in the activation of immune cells in hypertension with target organ damage in humans. Methods: Normotensive subjects (NTN) and patients with hypertension (HTN) associated or not with at least 2 other features of the metabolic syndrome (MetS) or chronic kidney disease (CKD) were studied (n=15-16). PBMCs were isolated from blood, RNA extracted, small and total RNA sequencing (RNA-seq) using an Illumina HiSeq-2500 and data were analyzed using a systems biology approach. Differentially expressed (DE) microRNAs and mRNAs were identified with fold change (FC) >2 and >1.5, respectively, and P <0.005. DE miRNAs with RNA-seq count number (CN) >500, and predicted targets by TargetScan with CN>300 were validated by reverse transcription-quantitative PCR (RT-qPCR) in PBMCs. Results: RNA-seq identified DE microRNAs and mRNAs in HTN (22 and 19), MetS (57 and 401) and CKD (6 and 26) compared to NTN. RT-qPCR validated a novel miRNA (miR-pl-86 [2-fold up]) and 4 predicted mRNA targets (WD repeat domain 89 [ WDR89 , 51% down], Dmx like 1 [ DMXL1 , 52% down], zinc finger protein 600 [ ZNF600 , 63% down] and NOC3 like DNA replication regulator [ NOC3L , 61% down]) in MetS vs NTN ( P <0.05). RNA-seq results were correlated with RT-qPCR data for miR-pl-86 (R 2 =0.37, P <5.5E-07, n=56) , WDR89 (R 2 =0.29, P <1.4E-05, n=57) , DMXL1 (R 2 =0.33, P <2.7E-06, n=57) , ZNF600 (R 2 =0.25, P <7.9E-05, n=57) , and NOC3L (R 2 =0.27, P <3.5E-05, n=57). Conclusion: This study identified one up-regulated novel microRNA and 4 down-regulated mRNA targets in PBMCs of patients with HTN and MetS.
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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) | 0.003 | 0.001 |
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".