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Record W4220728364 · doi:10.1093/jbcr/irac012.046

43 Mapping the Hypermetabolic Response in Burn Patients

2022· article· en· W4220728364 on OpenAlexaff
Carly M. Knuth, Zachary Ricciuti, Dalia Barayan, Sarah Rehou, Marc G. Jeschke

Bibliographic record

VenueJournal of Burn Care & Research · 2022
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsTD Bank GroupHealth Sciences CentreSunnybrook HospitalUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsHypermetabolismTotal body surface areaMedicineResting energy expenditureBurn injuryAdipose tissueInternal medicineCatabolismEndocrinologyWhite adipose tissueBrown adipose tissueSurgeryMetabolismEnergy expenditure

Abstract

fetched live from OpenAlex

Abstract Introduction Hypermetabolism, characterized by drastic increases in whole-body catabolism and resting energy expenditure (REE), is a hallmark response to a severe burn injury. This is believed to be driven in part by alterations in adipose tissue metabolism. We proposed to define the hypermetabolic response in adipose tissue from burn patients and create a roadmap of markers indicative of hypermetabolism to improve prognosis. We hypothesized that catabolic markers, such as uncoupling protein-1 (Ucp1) and growth differentiation factor-15 (Gdf15), would positively correlate with increasing days post-burn and REE. Methods Adult burn patients (n=65) admitted to our burn center between 2011—2019 were included in this study. Subcutaneous white adipose tissues (sWAT) from the site of injury (n=85) and plasma were collected from severely burned patients ( ³20% total body surface area). Gene expression and circulating cytokine levels were measured by RT-qPCR and multiplex assays, respectively. Results We found a significant correlation between increasing Ucp1 gene expression and days post-burn (p< 0.0001). Moreover, when samples were stratified into acute (1-3 days post-burn), moderate (4-9 days post-burn), and long-term ( >10 days post-burn) timepoints, a significant increase in Ucp1 gene expression was detected only in adipose tissues from long-term time points in comparison to non-burned control tissues (p< 0.01). However, we found that REE remained stagnant throughout hospital stay after a burn injury in our patient cohort. Thus, we did not detect a significant correlation between Ucp1 gene expression and REE. Further, while Gdf15 expression was most pronounced, albeit statistically insignificant, during the moderate timepoints, we did not detect any significant differences when correlated with days post-burn. Additionally, we determined that circulating levels of IL-6, IL-10, and monocyte chemoattractant protein-1 (MCP-1) were greatly elevated within the first seven days post-burn and gradually decreased over time, while vascular endothelial growth factor (VEGF) concentrations followed a similar pattern to Ucp1 gene expression. Conclusions While Gdf15 expression may not accurately reflect catabolism in the adipose tissues of burn patients, Ucp1 gene expression may be used as a marker indicating a peak hypermetabolic period after ten days post-burn. This may also be reflected by circulating concentrations of VEGF. Moreover, IL-6, IL-10 and MCP-1 may be used as early determinants before the onset of hypermetabolism.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.087
GPT teacher head0.378
Teacher spread0.291 · 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
Published2022
Admission routes1
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