Inflammation Drives Pulmonary Arterial Hypertension
Bibliographic record
Abstract
Patients with pulmonary arterial hypertension (Group 1 pulmonary hypertension, including idiopathic, heritable, connective tissue disease–associated, congenital heart disease–associated pulmonary arterial hypertension, and others) present for noncardiac surgery with an exceptionally high risk of morbidity and mortality even when compared to patients with other forms of pulmonary hypertension.1 Current pulmonary arterial hypertension–specific therapies are primarily pulmonary vasodilators. Yet pulmonary vasoconstriction only partially explains disease pathology, and the development of better therapies necessitates a deeper understanding of pulmonary arterial hypertension pathogenesis. Indeed, evidence links the immune system to pulmonary arterial hypertension pathogenesis (see fig.) and has fundamentally shifted our understanding of the disease mechanism.2In pulmonary arterial hypertension lungs, endothelial apoptosis and the subsequent proliferation of an abnormal, reprogrammed subset of endothelial cells form a neointimal layer (fig. element 1). Smooth muscle cells hypertrophy and proliferate, leading to medial thickening and loss of vascular compliance (fig. element 2). Together, these phenomena narrow the vessel lumen, impeding blood flow. In the adventitia, lymphocytes, macrophages, dendritic cells, and mast cells form tertiary lymphoid tissue, which produces cytokines, autoantibodies, and other soluble mediators (fig. element 3). Release of these mediators from immune cells and activated fibroblasts further recruits immune cells (fig. element 4). These changes perpetuate vascular damage and drive pulmonary arterial hypertension. Protective elements of the immune system, however, including regulatory T cells,3 help dampen this response.Anesthesiologists will welcome agents for perioperative optimization of these high-risk patients.Support was provided from institutional and/or departmental sources, as well as a grant from the International Anesthesia Research Society, San Francisco, California, to Dr. Goldenberg.The authors declare no competing interests.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 0.002 |
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 teacher head, 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".