Update in Pulmonary Vascular Disease 2016 and 2017
Bibliographic record
Abstract
Since the last update in this series was published in 2015, pulmonary vascular medicine has seen important advances from the molecular to population levels (1). Basic, translational, and epidemiologic science have rapidly advanced the understanding of how genetic, endocrine, immune, and metabolic factors contribute to pulmonary vascular disease. Preclinical and early-stage clinical efforts highlight promising treatments addressing emerging targets in the pathophysiology of pulmonary arterial hypertension (PAH). Prior work exploring the molecular basis of sex bias in PAH, namely increased female susceptibility and survival in PAH, has been focused on the regulation and activity of estrogen in women. The importance of sex hormone activity was recently extended to men with PAH, in whom worse hemodynamic and functional parameters correlate with higher concentrations of estradiol and lower concentrations of dehydroepiandrosterone sulfate (2). Indeed, the observed low concentrations of dehydroepiandrosterone sulfate associated with pulmonary hypertension (PH) risk have been reproduced (3). In mice carrying a pathogenic BMPR2 (bone morphogenetic protein receptor type 2) mutant gene, inhibition of estrogen activity mitigated experimental PH and normalized critical metabolic signaling axes (4). The aryl hydrocarbon receptor regulates downstream estrogen production in pulmonary vascular cells via both CYP1A1 (cytochrome P450 family 1 subfamily A member 1) and aromatase. The aryl hydrocarbon receptor can be targeted in BMPR2-overexpressing mice (5), increasing microRNA (miR)-29, which recapitulates elevated miR-29 found in heritable pulmonary arterial hypertension (HPAH) lung tissue. By contrast, anti–miR-29 attenuates experimental PH and restores PPAR-γ (peroxisome proliferator-activated receptor-γ) (6). Finally, the Y chromosome appears to protect against experimental PH (7), offering a novel genetic explanation for sex bias in PAH.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.103 | 0.065 |
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".