Protein Biomarker Analysis of Primary Peyronie’s Disease Cells
Bibliographic record
Abstract
INTRODUCTION: The molecular pathogenesis of Peyronie's Disease (PD) remains unclear more than 250 years after its initial description. Because of this, no test is currently available to accurately predict PD progression among those affected. AIM: To investigate the expression of wound healing and fibrosis-associated proteins in primary cell cultures of PD fibroblasts to determine whether altered protein expression patterns can be used as predictors of clinical course and natural history. METHODS: Primary cell cultures derived from normal Tunica albuginea tissue and PD plaque tissue were examined by immuno-cytochemistry. Protein expression profiles were analyzed by Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS) and Western immunoblotting. MAIN OUTCOME MEASURES: Expression of wound healing and fibrosis-associated proteins and protein expression patterns were assessed. RESULTS: Statistically significant increases in smooth muscle alpha-actin, beta-catenin, and Heat shock proteins (Hsp47) were identified in cells derived from PD relative to cells derived from normal Tunica albuginea tissue. Changes in TGFbeta-1 receptor and Fibronectin were also observed. In addition, altered expression of additional as yet unidentified proteins at 4.7, 8.9, 10.8, 16.8, and 76.8 kDa were detected by complementary SELDI-TOF-MS approaches. CONCLUSIONS: Primary cells derived from PD plaques display up-regulated expression of several proteins that are established components of fibrosis and wound healing. In addition, changes in other, as yet unidentified proteins were measured. It will be of interest to conduct further studies to see whether these dysregulated protein peaks represent potential biological markers of disease progression.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".