Proteomic profile of an acute partial bladder outlet obstruction
Bibliographic record
Abstract
INTRODUCTION: Partial bladder outlet obstruction (pBOO) is a ubiquitous problem in urology. From posterior urethral valves to prostatic hypertrophy, pBOO results in significant morbidity and mortality. However, the pathophysiology is not completely understood. Proteomics uses mass spectrometry to accurately quantify change in tissue protein concentration. Therefore, we have applied proteomic analysis to a rodent model to assess for protein changes after a surgically induced pBOO. We hypothesize that proteomic analysis after an acute obstruction will determine the most prevalent initial protein response and, potentially, novel molecular pathways. METHODS: Sprague Dawley rats underwent a surgically induced pBOO (n = 3 per group) for 3, 7, or 14 days. Bladders were assessed for weight and urodynamic parameters. Proteomics used liquid-chromatography based mass spectrometry. Polymerase chain reaction (PCR) was performed on tissue samples to confirm increased mRNA transcription. RESULTS: Bladder weight and capacity increased over the experimental period, but no changes were seen in bladder pressure. Statistically significant increases in protein quantities were seen in 3 proteins related to endoplasmic reticulum stress: GRP-78 (3.66-fold), RhoA (1.90-fold), and RhoA-GDP (1.95-fold), and 2 cytoskeleton molecules: actin (1.7-fold) and tubulin a/b (3.01-fold). Decorin and lumican, members of the small leucine rich proteoglycan (SLRP) family, were also elevated (0.35- and 0.34-fold, respectively). Real-time PCR data confirmed protein elevation. CONCLUSION: Our experiment confirms that molecular changes occur very soon after the initiation of pBOO, and implicates several molecular pathways. We believe these insights may provide insight into novel prevention and treatment strategies targeted at the pathophysiology of pBOO.
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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 | 0.001 | 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 | 0.000 |
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".