The enzymatic degradation of hyaluronan is associated with disease progression in experimental pulmonary hypertension
Bibliographic record
Abstract
Hyaluronan (HA) degradation fragments have been linked to inflammation in a wide range of lung diseases. In idiopathic pulmonary arterial hypertension, HA accumulation has been associated with advanced disease. In this study, we investigated the potential role of HA degradation in the early stages of disease by examining HA distribution, molecular mass, synthesis, and enzymatic degradation at different stages of disease progression in a rat model of monocrotaline (MCT)-induced pulmonary hypertension (PH). At 28 days post-MCT, severe PH was associated with increased total lung HA (P = 0.04). In contrast, a significant decrease in total lung HA was observed on day 10, before the onset of PH (P = 0.02). Molecular mass analysis revealed a loss of high molecular mass (HMM) HA at 10 and 24 days post-MCT, followed by an increase in HMM HA at 28 days. Expression of HA synthase 2 (HAS2) was elevated in MCT-challenged animals at 24 and 28 days, consistent with increased synthesis of HMM HA. Analysis by Morgan Elson assay and zymography demonstrated increased hyaluronidase-1 activity in the lungs of MCT-challenged rats, indicating that the observed increases in HAS2 expression and HA synthesis were counterbalanced, in part, by enhanced degradation. The present data demonstrate that, in the MCT model, early-stage PH is associated with enhanced hyaluronidase-1 activity, while both degradation and synthesis are increased at later stages. Thus an early increase in the generation of proinflammatory HA fragments may play a role in the onset and progression of pulmonary arterial hypertension.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".