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Record W4301509800 · doi:10.3791/60628-v

Reduced Complications after Arterial Reconnection in a Rat Model of Orthotopic Liver Transplantation

2020· article· en· W4301509800 on OpenAlexaff
Xuchun Chen, Manmeet Sekhon, Xue‐Zhong Ma, Justin Manuel, Sai Chung, Eddie He, Agata Bartczak, Sandra E. Fischer, Cornelia Thoeni, Graziano Oldani, Cátia T. Perciani, Sonya A. MacParland, Ian D. McGilvray

Bibliographic record

VenueJournal of Visualized Experiments · 2020
Typearticle
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsLiver transplantationTransplantationInferior vena cavaMedicineOrthotopic liver transplantationPortal veinArteryPerfusionCardiologyVena cavaSurgeryInternal medicine

Abstract

fetched live from OpenAlex

The rat orthotopic liver transplantation (OLT) model is a powerful tool to study acute and chronic rejection. However, it is not a complete representation of human liver transplantation due to the absence of arterial reconnection. Described here is a modified transplantation procedure that includes the incorporation of hepatic artery (HA) reconnection, leading to a marked improvement in transplant outcomes. With a mean anhepatic time of 12 min and 14 s, HA reconnection results in improved perfusion of the transplanted liver and an increase in long-term recipient survival from 37.5% to 88.2%. This protocol includes the use of 3D-printed cuffs and holders to connect the portal vein and infrahepatic inferior vena cava. It can be implemented for studying multiple aspects of liver transplantation, from immune response and infection to technical aspects of the procedure. By incorporating a simple and practical method for arterial reconnection using a microvascular technique, this modified rat OLT protocol closely mimics aspects of human liver transplantation and will serve as a valuable and clinically relevant research model.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.300

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 head0.057
GPT teacher head0.407
Teacher spread0.350 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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