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Record W4412110992 · doi:10.1093/stcltm/szaf033

Use of adipose-derived stem cells on decellularized bladder scaffolds for functional bladder mucosa regeneration

2025· article· en· W4412110992 on OpenAlexaff
César U. Monjarás-Ávila, Ana Cecilia Luque-Badillo, Nicholas Carr, Anthony Papp, Alan So, Claudia Chávez‐Muñoz

Bibliographic record

VenueStem Cells Translational Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDecellularizationRegeneration (biology)Adipose tissueStem cellTissue engineeringCell biologyMedicinePathologyBiologyBiomedical engineeringInternal medicine

Abstract

fetched live from OpenAlex

This study explores the potential for adipocyte-derived stem cells (ASCs) to be used in bladder reconstruction. Current methods, such as enterocystoplasty, have significant limitations, making new approaches necessary. Tissue engineering, specifically using acellular scaffolds such as the bladder acellular matrix, offers a promising basis for this development. For this study, ASCs were isolated from adipose tissue derived from liposuction and co-cultured with urothelial cells (UC; SV-HUC) to induce transdifferentiation. Results indicate successful isolation and characterization of ASCs, displaying positive markers for stem cells. The co-culture of ASCs with SV-HUC cells resulted in changes resembling epithelial cells, indicating a potential transdifferentiation process, and is corroborated by the mRNA and protein levels. For the functional assay, urothelial-like cells were seeded onto decellularized bladder tissues. These findings demonstrate the successful transdifferentiation of ASCs into functional UC, presenting a promising strategy for bladder reconstruction and a potential alternative to current approaches.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.600
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.047
GPT teacher head0.265
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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