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Record W4281630441 · doi:10.3791/64069

Procurement and Decellularization of Rat Hindlimbs Using an <em>Ex Vivo</em> Perfusion-Based Bioreactor for Vascularized Composite Allotransplantation

2022· article· en· W4281630441 on OpenAlexaff
Aisha Adil, Golnaz Karoubi, Siba Haykal

Bibliographic record

VenueJournal of Visualized Experiments · 2022
Typearticle
Languageen
FieldMedicine
TopicOrgan and Tissue Transplantation Research
Canadian institutionsToronto General HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsDecellularizationEx vivoHindlimbTissue engineeringPerfusionBiomedical engineeringIn vivoMedicineAnatomyBiologyInternal medicine

Abstract

fetched live from OpenAlex

Patients with severe traumatic injuries and tissue loss require complex surgical reconstruction. Vascularized composite allotransplantation (VCA) is an evolving reconstructive avenue for transferring multiple tissues as a composite subunit. Despite the promising nature of VCA, the long-term immunosuppressive requirements are a significant limitation due to the increased risk of malignancies, end-organ toxicity, and opportunistic infections. Tissue engineering of acellular composite scaffolds is a potential alternative in reducing the need for immunosuppression. Herein, the procurement of a rat hindlimb and its subsequent decellularization using sodium dodecyl sulfate (SDS) is described. The procurement strategy presented is based upon the common femoral artery. A machine perfusion-based bioreactor system was constructed and used for ex vivo decellularization of the hindlimb. Successful perfusion decellularization was performed, resulting in a white translucent-like appearance of the hindlimb. An intact, perfusable, vascular network throughout the hindlimb was observed. Histological analyses showed the removal of nuclear contents and the preservation of tissue architecture across all tissue compartments.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.794

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.048
GPT teacher head0.397
Teacher spread0.349 · 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 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

Citations4
Published2022
Admission routes1
Has abstractyes

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