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Record W3010139538 · doi:10.1093/jbcr/iraa024.049

45 Unravelling the Unique Burn-induced Temporal Alterations in Adipose Tissue Metabolism

2020· article· en· W3010139538 on OpenAlexaff
Carly M. Knuth, Chris Auger, Abdikarim Abdullahi, Marc G. Jeschke

Bibliographic record

VenueJournal of Burn Care & Research · 2020
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsAdipose tissueLipolysisHypermetabolismInternal medicineWhite adipose tissueEndocrinologyThermogeninAdipocyteMedicineBurn injuryScaldingBrown adipose tissueTotal body surface areaInflammationBiologySurgeryFood science

Abstract

fetched live from OpenAlex

Abstract Introduction A severe burn elicits a systemic hypermetabolic response that substantially alters the function of multiple organs and contributes to increased morbidity and mortality. A consequence of hypermetabolism is the activation of UCP1-mediated browning of white adipose tissue (WAT), which may further facilitate the hypermetabolic response. In this study, we aimed to provide comprehensive characterization of the acute and long term pathophysiological responses to burns to determine the persistence of adipose tissue browning and its potential contribution to the hypermetabolic response. Methods Mice were subjected to either a 30% total body surface area (TBSA) scald burn or were denoted sham. Body weight and food intake were monitored throughout the duration of the study. Cohorts were sacrificed at 6hrs, 1, 3, 5, 7, 14, 30 and 60d post-burn and adipose tissue depots were harvested. Mitochondrial respiration, protein expression, and morphology in adipose tissues were assessed. Results Despite consuming considerably more food, the burn group lost significantly more weight throughout the duration of the study. We also detected increases in free fatty acids and interleukin-6, markers of whole-body lipolysis and inflammation, respectively. At the tissue level, eWAT mass significantly decreased over time, suggesting that this depot provides substrate to fuel the hypermetabolic response. This was further supported by a decrease in adipocyte area and an increase in lipolytic markers which remains significant up until 60d post-burn relative to sham. There were no significant difference in iWAT mass, however we detected significant increases in the protein content of UCP1, the master regulator of adipose tissue browning, as early as day 3 which persisted until day 60. This was corroborated by the presence of UCP1+ adipocytes. Conclusions Consistent with previous human studies, a burn injury elicits a dynamic response that cannot be simply characterized by a single timepoint. The alterations that occur in adipose tissue are depot-specific, time-dependent, and this notion likely extends to other metabolic tissues. Further, we demonstrate that in our 30% TBSA burn murine model, the effects of the hypermetabolic response persist for up to 60 days following initial injury. Applicability of Research to Practice Our data indicate the hypermetabolic response persists for up to 60 days, the equivalent of approximately 7 years in humans. This underscores the severity of adipose tissue browning and potentially provides an explanation as to how the hypermetabolic response persists even after the wound has healed. Moreover, providing a comprehensive map of the time-dependent changes in a murine model gives clinicians a better indication of the metabolic effects in a burn patient and will contribute to the development of effective, targeted treatments.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.099
GPT teacher head0.399
Teacher spread0.300 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2020
Admission routes1
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