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Record W4387159807 · doi:10.3791/65981

Establishment of an <em>Ex Vivo</em> Lung Perfusion Rat Model for Translational Insights in Lung Transplantation

2023· article· en· W4387159807 on OpenAlexaff
P. Oliveira, Keiji Yamanashi, Aizhou Wang, Marcelo Cypel

Bibliographic record

VenueJournal of Visualized Experiments · 2023
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsUniversity of TorontoToronto General HospitalUniversity Health Network
Fundersnot available
KeywordsLungLung transplantationTransplantationContext (archaeology)MedicineEx vivoRat modelIn vivoIntensive care medicineSurgeryBiologyInternal medicine

Abstract

fetched live from OpenAlex

Since the establishment of lung transplantation as a therapeutic strategy for advanced lung diseases, the scientific community is faced with the problem of a low number of lungs considered viable for the donation process. In recent decades, however, this scenario has been positively changed, given the development of ex vivo lung perfusion (EVLP) as a strategy for evaluating and reconditioning marginal lungs. The establishment of EVLP in large transplant centers has favored an increase in the number of lung transplants, both by increasing the diagnostic accuracy of lung function and by constituting an effective platform for the reconditioning of lung grafts. In this context, faced with ethical and logistical issues, as well as in the study of immunological factors associated with lung transplantation, the development of rodent EVLP models has become important, given their reliability, the possibility of genetic manipulation, and lower costs. This paper describes a protocol for establishing a rat EVLP model and shows the inflammatory profile associated with the perfused lungs. This will help propagate knowledge about the rat EVLP model, promoting our understanding of the biological responses associated with that revolutionary technique.

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 score0.431
Threshold uncertainty score0.911

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.044
GPT teacher head0.423
Teacher spread0.379 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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