Alternative Mechanism for White Adipose Tissue Lipolysis after Thermal Injury
Bibliographic record
Abstract
Extensively burned patients often suffer from sepsis, a complication that enhances postburn hypermetabolism and contributes to increased incidence of multiple organ failure, morbidity and mortality. Despite the clinical importance of burn sepsis, the molecular and cellular mechanisms of such infection-related metabolic derangements and organ dysfunction are still largely unknown. We recently found that upon endoplasmic reticulum (ER) stress, the white adipose tissue (WAT) interacts with the liver via inflammatory and metabolic signals leading to profound hepatic alterations, including hepatocyte apoptosis and hepatic fatty infiltration. We therefore hypothesized that burn plus infection causes an increase in lipolysis of WAT after major burn, partially through induction of ER stress, contributing to hyperlipidemia and profound hepatic lipid infiltration. We used a two-hit rat model of 60% total body surface area scald burn, followed by intraperitoneal (IP) injection of Pseudomonas Aeruginosa -derived lipopolysaccharide (LPS) 3 d postburn. One day later, animals were euthanized and liver and epididymal WAT (EWAT) samples were collected for gene expression, protein analysis and histological study of inflammasome activation, ER stress, apoptosis and lipid metabolism. Our results showed that burn plus LPS profoundly increased lipolysis in WAT associated with significantly increased hepatic lipid infiltration. Burn plus LPS augmented ER stress by upregulating CHOP and activating ATF6, inducing NLRP3 inflammasome activation and leading to increased apoptosis and lipolysis in WAT with a distinct enzymatic mechanism related to inhibition of AMPK signaling. In conclusion, burn sepsis causes profound alterations in WAT and liver that are associated with changes in organ function and structure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".