Differentiation of patient-specific void urine-derived human induced pluripotent stem cells to fibroblasts and skeletal muscle myocytes
Bibliographic record
Abstract
Cell-based therapy is a major focus for treatment of stress urinary incontinence (SUI). However, derivation of primary cells requires tissue biopsies, which often have adverse effects on patients. A recent study used human induced pluripotent stem cells (iPSC)-derived smooth muscle myocytes for urethral sphincter regeneration in rats. Here, we establish a workflow using iPSC-derived fibroblasts and skeletal myocytes for urethral tissue regeneration: (1) Cells from voided urine of women were reprogrammed into iPSC. (2) The iPSC line U1 and hESC line H9 (control) were differentiated into fibroblasts expressing FSP1, TE7, vinculin, vimentin, αSMA, fibronectin and paxillin. (3) Myogenic differentiation of U1 and H9 was induced by small molecule CHIR99021 and confirmed by protein expression of myogenic factors PAX7, MYOD, MYOG, and MF20. Striated muscle cells enriched by FACS expressed NCAM1, TITIN, DESMIN, TNNT3. (4) Human iPSC-derived fibroblasts and myocytes were engrafted into the periurethral region of RNU rats. Injected cells were labelled with ferric nanoparticles and traced by Prussian Blue stain, human-specific nuclear protein KU80, and human anti-mitochondria antibody. This workflow allows the scalable derivation, culture, and in vivo tracing of patient-specific fibroblasts and myocytes, which can be assessed in rat SUI models to regenerate urethral damages and restore continence.
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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) | 0.002 | 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".