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Record W2418663591

Impact of fibrin glue and urinary bladder cell spraying on the in-vivo acellular matrix cellularization: a porcine pilot study.

2006· article· en· W2418663591 on OpenAlexaff
Walid A. Farhat, Jun Chen, Christopher Sherman, Lisa Cartwright, André Bahoric, Herman Yeger

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsFibrin glueIn vivoFibrinMedicineUrinary bladderExtracellular matrixPathologyFibrosisTissue engineeringIn vitroCellBiomedical engineeringUrologyCell biologySurgeryChemistryImmunologyBiology
DOInot available

Abstract

fetched live from OpenAlex

PURPOSE: Urinary bladder tissue engineering utilizing autologous cell-seeded scaffolds requires enough bladder cells to populate a large surface area which may be difficult to obtain from abnormal bladders. We evaluated whether a fibrin glue spray technique enhances cell seeded acellular matrix (ACM) repopulation in a porcine bladder model. MATERIALS AND METHODS: Porcine urothelial and smooth muscle cells cultured from open bladder biopsy were sprayed with or without fibrin glue onto porcine bladder ACM. After 10 days in vitro, constructs were implanted onto porcine bladders (4/group) and harvested after 1 or 6 weeks for H&E and immunohistochemical staining. RESULTS: In vitro, fibrin glue was associated with more continuous cell growth and enhanced cellular organization, maintained particularly in the periphery in vivo, where both groups demonstrated central fibrosis. CONCLUSIONS: While fibrin glue enhanced cellular organization on ACM in vitro, central fibrosis in vivo suggests that factors supporting seeded cell survival are lacking.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.020
GPT teacher head0.243
Teacher spread0.223 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations11
Published2006
Admission routes1
Has abstractyes

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