Differential Expression of Class I Small Leucine-Rich Proteoglycans in an Animal Model of Partial Bladder Outlet Obstruction
Bibliographic record
Abstract
PURPOSE: Partial bladder outlet obstruction has been shown in a rat model to progress from inflammation to hypertrophy to fibrosis. Small leucine-rich proteoglycans are extracellular matrix components associated with collagen fibrillogenesis and resultant scar formation. Two such critical small leucine-rich proteoglycans are decorin and biglycan. We hypothesized that in keeping with other scar models, decorin would be down-regulated and biglycan would be up-regulated with the onset of fibrosis compared to sham. MATERIALS AND METHODS: We challenged our hypothesis with female Fisher rats that underwent ligation of the bladder neck or sham surgery. Animals were sacrificed at 4, 8 and 12 weeks, and bladders were harvested. Frozen sections were stained for immunofluorescence for decorin and biglycan. mRNA expression for decorin and biglycan was analyzed using quantitative reverse transcriptase polymerase chain reaction. RESULTS: All rats survived to specified experimental end points in good health. Immunofluorescent stains showed progressive down-regulation of decorin and up-regulation of biglycan during the 12-week course by 0.36 and 1.82-fold, respectively (p = 0.02 and p = 0.02), compared to shams. Quantitative real-time reverse transcriptase polymerase chain reaction confirmed these findings in 12-week specimens, showing a down-regulation of decorin by a factor of 0.45 (p = 0.02) and up-regulation of biglycan by a factor of 2.04-fold (p = 0.08). CONCLUSIONS: We present the first identification to our knowledge of small leucine-rich proteoglycans in normal and abnormal bladder tissue, and their differential expression in the process of bladder fibrosis, consistent with experimental findings in other anatomical sites. Further investigation into small leucine-rich proteoglycan expression and regulation may allow for the development of new antifibrotic therapeutics.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".