Inhibition of Collagen Deposit in Obstructed Rat Bladder Outlet by Transplantation of Superparamagnetic Iron Oxide-Labeled Human Mesenchymal Stem Cells as Monitored by Molecular Magnetic Resonance Imaging (MRI)
Bibliographic record
Abstract
Bladder outlet obstruction (BOO) caused by collagen deposit is one of the most common problems in elderly males. The present study is to investigate if human mesenchymal stem cells (MSCs) are capable of inhibiting collagen deposition and improve cystometric parameters in bladder outlet obstruction in rats. Human MSCs were labeled with nanoparticles containing superparamagnetic iron oxide (SPION), and transplanted in rat BOO lesion site. Forty 6-week-old female Sprague-Dawley rats were divided into four groups (group 1: control, group 2: sham operation, group 3: BOO, and group 4: BOO rats receiving SPION-hMSCs). Two weeks after the onset of BOO, 1 × 10(6) SPION-hMSCs were injected into the bladder wall. Serial T2-weighted MR images were taken immediately after transplantation of SPION-labeled human MSCs and at 4 weeks posttransplantation. T2-weighted MR images showed a clear hypointense signal induced by the SPION-labeled MSCs. While the expression of collagen and TGF-β protein increased after BOO, the expression of both returned to the original levels after MSC transplantation. Expression of HGF and c-met protein also increased in the group with MSC transplantation. Maximal voiding pressure and residual urine volume increased after BOO but they recovered after MSC transplantation. Human MSCs transplanted in rat BOO models inhibited the bladder fibrosis and mediated recovery of bladder dysfunction. Transplantation of MSC-based cell therapy could be a novel therapeutic strategy against bladder fibrosis in patients with bladder outlet obstruction.
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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) | 0.000 | 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".