Is There a Role for Proteomics in Peyronie's Disease?
Bibliographic record
Abstract
INTRODUCTION: Peyronie's disease (PD) continues to be a major source of sexual dysfunction among the 3-9% of affected men. The challenge in treating PD is determining the natural history and clinical course for the individual patient. Currently, there exists no reliable means to predict whether a penile plaque of PD will progress, regress, or remain stable. This represents a significant deficiency in contemporary management, one that may be addressed with newer technologies such as proteomic profiling. AIM: This review assesses the potential use of protein alterations measured by various novel technologies, to predict progression, regression, or stabilization of PD in an affected individual. METHODS: A comprehensive literature review of the past decade in the field of gene profiling and protein expression of PD was performed. MAIN OUTCOME MEASURES: A critical analysis of the existing worldwide literature evaluating surface-enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF-MS or SELDI) and other proteonomic techniques. RESULTS: SELDI and other technologies can provide the clinician with innovative data indicating the presence of unique individual factors that act to suppress or promote the fibrotic process in PD. Determining the clinical implications of altered protein expression in an individual is not yet defined. CONCLUSIONS: The area of proteomics has begun to revolutionize the study of medicine in the postgenomic era, by allowing researchers to study the role that proteins play in health and disease. Applying this knowledge clinically has already led to innovative discoveries in early cancer detection in a number of malignancies, including prostate, ovarian, and bladder. Prior to the widespread use and acceptance of proteomic technology in PD, a critical assessment of its therapeutic and diagnostic value will be required.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".