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Record W2064702594 · doi:10.1155/2014/719853

Development of a Murine Model of Early Sepsis in Diet-Induced Obesity

2014· article· en· W2064702594 on OpenAlexafffund
Momina Khan, Amanda L. Patrick, Alison Fox‐Robichaud, The Canadian Critical Care Translational Biology Group

Bibliographic record

VenueBioMed Research International · 2014
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsUniversity of TorontoMcMaster UniversityThrombosis and Atherosclerosis Research Institute
FundersCanadian Institutes of Health ResearchCanadian Blood ServicesMcMaster University
KeywordsSepsisSteatosisInflammationMedicineLungInternal medicineOxidative stressObesityAdipose tissueEndocrinologyPhysiologyGastroenterologyPathology

Abstract

fetched live from OpenAlex

Sepsis, a global health issue, is the most common cause of mortality in the intensive care unit. The aim of this study was to develop a new model of sepsis that investigates the impact of prolonged western diet (WD) induced obesity on the response to early sepsis. Male C57BL/6 mice were fed either a high fat WD or normal chow diet (NCD) for 6, 15, or 27 weeks. Septic obese mice at 15 and 27 weeks had significantly lower levels of lung myeloperoxidase (26.3 ± 3.80 U/mg tissue) compared to age matched ad lib (44.1 ± 2.86 U/mg tissue) and diet restricted (63.2 ± 5.60 U/mg tissue) controls. Low levels of lung inflammation were not associated with changes in hepatic cytokines and oxidative stress levels. Obese mice had significantly (P < 0.0001) larger livers compared to controls. Histological examination of the livers demonstrated that WD fed mice had increased inflammation with pronounced fat infiltration, steatosis, and hepatocyte ballooning. Using this model of prolonged exposure to high fat diet we have data that agree with recent clinical observations suggesting obese individuals are protected from sepsis-induced lung injury. This model will allow us to investigate the links between damage to the hepatic microcirculation, immune response, and lung injury.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.317
GPT teacher head0.459
Teacher spread0.142 · 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

Citations17
Published2014
Admission routes2
Has abstractyes

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