UNRAVELING THE LUNG VASCULAR REMODELING IN PULMONARY HYPERTENSION USING A QUANTITATIVE DIGITAL PATHOLOGY SOFTWARE
Bibliographic record
Abstract
ABSTRACT Pulmonary arterial hypertension (PAH) is a rare chronic life-threatening disorder, characterized by the elevation of the mean pulmonary arterial pressure above 20 mmHg at rest. Histologically, PAH induces lung vascular remodeling, with the thickening of vessel wall. The conventional histological analysis commonly used in non-clinical models to assess lung vascular remodeling relies on manual measurements of representative lung vessels and is time-consuming. We have developed a fully automated reader-independent software (MorphoQuant-Lung) to both specifically detect vessels and measure vascular wall components from a-SMA rat lung sections. Analysis was performed on monocrotaline-and Sugen/hypoxia-induced PH rat models, treated or not with Sildenafil. The software requires 3-5 minutes to detect up to 1500 vessels per section, classify them per size, quantify intima, media and wall thicknesses, and calculate their level of occlusion. A comparison of our digital analysis results with those of the pathologist’s conventional visual analysis was performed for wall thickness and lumen radius showing a strong correlation between the two techniques (r: 0.80 and r: 0.88) regardless of the rat model. In addition, the occlusion estimated by automated analysis also strongly correlated with the mean pulmonary arterial pressure and the pulmonary vascular resistance (r ranging from 0.71 to 0.83) in both rat models. The added value of the present digital analysis paves the way for a more in-depth understanding of the PAH physiopathology in preclinical research and provides a robust and reliable tool for efficient therapeutic drug development.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".