Models for erectile dysfunction and their importance to novel drug discovery
Bibliographic record
Abstract
INTRODUCTION: Erectile dysfunction (ED) affects quality of life and is a barometer for vascular health. The pathophysiology is complex and multifactorial. Animal models have been critical in elucidating an improved comprehension of erectile function. They provide experimental platforms where desired physiologic, and non-physiologic perturbations can be performed. Results have led to the development of novel therapeutic targets. AREAS COVERED: The current article provides an overview of history of animal models in ED research as well as a review of the current roles in the study of ED. The authors highlight the advantages and disadvantages of each model while illustrating the similarities to the human condition and summarizing the major preclinical studies investigating novel therapeutic targets in the treatment of ED. EXPERT OPINION: Animal models have been instrumental in the discovery of the current therapeutic agents. Advances in molecular biology and proteomics have uncovered many novel potential targets including tissue regeneration and stem cell applications. Rodent models are the current animal model of choice for ED research due to lower cost, well-established modeling protocol, and the ability to manipulate genetically. Future clinical trials should directly assess the translatability of these animal models to humans as well as the safety risks and long-term efficacy that the results generate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".