Exploratory Study of Prognostic Plasma Biomarkers in Patients with Pulmonary Arterial Hypertension
Bibliographic record
Abstract
Pulmonary arterial hypertension (PAH) is characterized by progressive pulmonary vascular lumen occlusion, ultimately leading to right ventricular failure and death. Risk stratification is essential for the management of patients with PAH. So far, B-type natriuretic peptide and its N-terminal pro-form are the only circulating biomarkers used as part of composite PAH risk assessment tools. Identification of other biomarkers of vascular or systemic origin may be valuable to provide additional information on disease severity and prognosis. Using proximity extension assay, >700 proteins related to oncology and neurology were measured in the plasma of 60 patients with PAH and 28 age- and sex-matched controls. Among the 114 proteins significantly up-regulated in patients with PAH, 14 were independently associated with death/lung transplantation after adjustment for the 2015 European Society of Cardiology/European Respiratory Society, the Registry to Evaluate Early and Long-Term PAH Disease Management (REVEAL) 2.0 risk scores, and the refined four-stratum risk assessment model. Among them, ectodysplasin A2 receptor (EDA2R), WAP four-disulfide core domain 2 (WFDC2), and tumor necrosis factor receptor superfamily member 10B (TNFRSF10B) displayed incremental prognostic value on top of these predictive models. Combining previously published proteomic data sets generated from different panels with the same cohort, a set of 23 proteins was identified, many of which are strongly associated with chronological age, that predict outcome of patients with PAH after adjusting for risk assessment tools. In conclusion, proteins likely involved in the pathophysiology of the disease and potential candidates for prognostic enrichment were identified in this study.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".