Improvement in Spinal Cord Injury-Induced Bladder Fibrosis Using Mesenchymal Stem Cell Transplantation into the Bladder Wall
Bibliographic record
Abstract
Experiments on spinal cord injury (SCI) have largely focused on the transplantation of stem cells into injured spinal cords for motor recovery while neglecting to investigate bladder dysfunction. The present study was performed to investigate the effect of B10 human mesenchymal stem cells (hMSCs) directly transplanted into the bladder wall of SCI rats and to determine whether they are capable of inhibiting collagen deposition and improving cystometric parameters in SCI rats. Forty 6-week-old female Sprague-Dawley rats were divided into four groups (group 1: control, group 2: sham operated, group 3: SCI, group 4: SCI rats that received B10 cells). B10 cells were labeled with fluorescent magnetic nanoparticles (MNPs). Four weeks after the onset of SCI, MNP-labeled B10 cells were injected to the bladder wall. Serial magnetic resonance (MR) images were taken immediately after MNP-B10 injection and at 4 weeks posttransplantation. Voiding function was assessed at 4 weeks posttransplantation, and the bladder was harvested. Improvements in bladder fibrosis and bladder function were monitored by molecular MR imaging. Transplantation of B10 cells into the SCI rats markedly reduced their weights and collagen deposition. MR images showed a clear hypointense signal induced by the MNP-labeled B10 cells at 4 weeks posttransplantation. Transplanted B10 cells were found to differentiate into smooth muscle cells. The intercontraction interval decreased, and the maximal voiding pressure increased after SCI but recovered after B10 cell transplantation. Survival of B10 cells was found at 4 weeks posttransplantation using anti-human mitochondria antibody staining and MR imaging. The transplanted B10 cells inhibited bladder fibrosis and ameliorated bladder dysfunction in the rat SCI model. MSC-based cell transplantation may be a novel therapeutic strategy for bladder dysfunction in patients with SCI.
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) | 0.000 | 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 | 0.001 |
| 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".