Bibliographic record
Abstract
Introduction: Understanding of the pathophysiology of Peyronie's disease is limited.A mechanistic Peyronie's disease model remains elusive, with currently available animal models representing induced penile fibrosis, and thus not replicating the actual disease state.This study set out to develop a three-dimensional (3D) in vitro model to elucidate the pathogenesis of Peyronie's disease.Methods: Peyronie's plaque tissues were placed in explant culture and expanded.Early passage cells were dissociated and placed either in six-well culture plates for 2D culture, Aggrewell800 plates to form 3D spheroids, or in a collagen-based hydrogel.To induce Peyronie's disease pathogenesis, transforming growth factor beta (TGFβ) was added to cultures at 3 ng/mL for 48 hours.Gene expression was analyzed by real-time polymerase chain reaction (RT-qPCR), and changes in morphology and cell phenotype were assessed by immunocytochemistry.Results: In 2D plaque cell cultures, exposure to TGFβ for 48 hours resulted in upregulation of genes associated with extracellular matrix protein production, such as collagen I (COL1A1), collagen III (COL3A1), elastin (ELN) and connective tissue growth factor (CTGF) (Fig. 1D).In contrast, 3D cultures displayed a more modest upregulation of COL3A1 and CTGF, but nevertheless were able to attach and spread after 48 hours, indicating increased deposition of matrix proteins (Figs 1A-C).The smooth muscle gene alpha smooth muscle actin 2 (aSMA2/ACTA2), a known marker of myofibroblasts and fibrosis, was upregulated in 2D cultures only, indicating a difference in the reactivity of the plaque cells in 3D vs. 2D cultures.Conclusions: This study describes two novel 3D culture models of Peyronie's disease and shows that the pro-fibrotic response differs in 3D environments compared to standard 2D cell culture, illustrating the importance of creating in vitro models that closely resemble the in vivo environment.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.553 | 0.280 |
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".