The Latest in Animal Models of Pulmonary Hypertension and Right Ventricular Failure
Bibliographic record
Abstract
Pulmonary hypertension (PH) describes heterogeneous population of patients with a mean pulmonary arterial pressure >20 mm Hg. Rarely, PH presents as a primary disorder but is more commonly part of a complex phenotype associated with comorbidities. Regardless of the cause, PH reduces life expectancy and impacts quality of life. The current clinical classification divides PH into 1 of 5 diagnostic groups to assign treatment. There are currently no pharmacological cures for any form of PH. Animal models are essential to help decipher the molecular mechanisms underlying the disease, to assign genotype-phenotype relationships to help identify new therapeutic targets, and for clinical translation to assess the mechanism of action and putative efficacy of new therapies. However, limitations inherent of all animal models of disease limit the ability of any single model to fully recapitulate complex human disease. Within the PH community, we are often critical of animal models due to the perceived low success upon clinical translation of new drugs. In this review, we describe the characteristics, advantages, and disadvantages of existing animal models developed to gain insight into the molecular and pathological mechanisms and test new therapeutics, focusing on adult forms of PH from groups 1 to 3. We also discuss areas of improvement for animal models with approaches combining several hits to better reflect the clinical situation and elevate their translational value.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".