Abstract 16926: First Global Assessment of Circulating Plasma MicroRNAs as Potential Biomarkers for Pulmonary Arterial Hypertension
Bibliographic record
Abstract
Background: Circulating extracellular microRNAs (miRNAs) have been identified as possible biomarkers in cancer and some cardiovascular diseases, but as yet their levels have not been examined in the plasma of patients with pulmonary arterial hypertension (PAH). Hypothesis: Altered levels of circulating miRNAs will provide insight into underlying mechanisms of PAH and serve as disease-specific biomarkers. Methods: A global screen of 1066 different miRNAs was performed with a discovery cohort of 4 treatment-naive patients with idiopathic (I) PAH and 3 healthy controls (age and sex matched). Total RNA was extracted from plasma and profiled with a miRNA (RT)-qPCR array platform (Qiagen). A separate cohort of 13 healthy controls and 14 PAH patients (8 associated (A) and 6 IPAH) was used for validation purposes. MicroRNA expression levels were normalized with a mean-centering restricted method in the discovery cohort, and 2 novel internal reference genes were used in the validation cohort. Results: 436±45 miRNAs were detectable (PCR Cq cutoff Conclusion: This first global assessment of circulating miRNAs in IPAH supports their utility as non-invasive biomarkers for disease activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".