Pulmonary Fibrosis in Hermansky-Pudlak Syndrome
Bibliographic record
Abstract
Hermansky-Pudlak syndrome (HPS) is a rare heterogeneously inherited autosomal recessive group of disorders presenting with oculocutaneous albinism, bleeding diathesis and pulmonary disease. HPS is thought to occur as a consequence of disturbed formation or trafficking of intracellular vesicles, most importantly, melanosomes, platelet dense granules and lysosomes. The latter finding, in particular, contributes much to the morbidity associated with the disease, as ceroid lipofuscin deposits in lysosomes affect many organ systems. This is especially problematic in the lungs where it is often associated with pulmonary fibrosis and premature death. Currently, there are 7 known HPS genes in humans. In the mouse, at least 16 known HPS genes produce HPS-mutant phenotypes. The HPS gene mutation is considered to be one of the most prevalent single-gene disorders in northwest Puerto Rico, home to the largest cohort of known patients. In HPS, interventions addressing the bleeding diathesis and pulmonary fibrosis are often disappointingly ineffectual. Pirfenidone, a novel compound with documented anti-inflammatory, antioxidant and antifibrotic effects, appears to hold promise in delaying or preventing fibrosis. To date, there has been one successful lung transplant performed on a patient with HPS. We present a patient with HPS and review the current literature on our understanding of this rare disorder.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".