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Record W1976979675 · doi:10.1159/000230675

Segmental Ischemia of the Liver – Microdialysis in a Novel Porcine Model

2009· article· en· W1976979675 on OpenAlexaff
Anders Winbladh, Per Sandström, Hans Olsson, Joar Svanvik, P. Gullstrand

Bibliographic record

VenueEuropean Surgical Research · 2009
Typearticle
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsUniversity Hospital
FundersForskningsrådet i Sydöstra Sverige
KeywordsMicrodialysisIschemiaMedicineIn vivoIschemic preconditioningInternal medicineAnesthesiaEndocrinologyBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Segmental liver ischemia is often used in rodents to study ischemia and reperfusion injuries (IRI). There are no reports of protocols using segmental ischemia in porcine models. Microdialysis (MD) provides the opportunity to study local effects of IRI in vivo. METHODS: Eight pigs received an MD catheter placed in liver segments IV and V, respectively. All circulation to segment IV was stopped for 80 min, and reperfusion was followed for 240 min. RESULTS: During ischemia the levels of lactate, glycerol and glucose increased 3-fold (p < 0.001), 40-fold (p < 0.001) and 4-fold (p < 0.01), respectively, in the ischemic segment compared to the perfused segment, whereas the levels of pyruvate fell to a tenth of the preischemic level (p < 0.001). All values returned to baseline after reperfusion. Serum levels of aspartate aminotransferase increased (p < 0.05). Polymorphonuclear cells increased in both segments, although the density was significantly higher in segment IV. CONCLUSION: Clamping of one liver segment in pigs is a simple, stable and reproducible model to study IRI with minimal systemic effects. MD revealed no signs of anaerobic metabolism in the perfused segment but still there was an increase in the number of polymorphonuclear neutrophils in this segment, although it was lower than that in the ischemic segment.

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 score0.339
Threshold uncertainty score0.191

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.082
GPT teacher head0.380
Teacher spread0.298 · 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

Citations11
Published2009
Admission routes1
Has abstractyes

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