Endoplasmic reticulum stress and insulin resistance post‐trauma: similarities to type 2 diabetes
Bibliographic record
Abstract
Abstract Introduction Insulin resistance and hyperglycaemia in burn patients Euglycaemic control in critically ill and burn patients Molecular mechanisms associated with post‐burn insulin resistance: endoplasmic reticulum stress and unfolded protein response Induction of the ER stress response and intracellular calcium stores Type 2 diabetes in animal models and links to the ER stress response Clinical relevance of insulin resistance and ER stress in the post‐burn response Targeting the ER stress response as a therapeutic for burn injury Conclusions Type 2 diabetes, a rapidly growing disease of modern aetiology, has a profound impact on morbidity and mortality. Explosions in the understanding of the underlying cellular mechanisms which lead to type 2 diabetes have recently been elucidated. In particular, the central role of endoplasmic reticulum stress (ER stress) and the unfolding protein response (UPR) in insulin resistance in type 2 diabetes has recently been discovered. We hypothesize that ER stress and UPR are not only central for type 2 diabetes but also for stress‐induced diabetes. We review here the evidence that post‐burn insulin resistance and hyperglycaemia have pathophysiologic mechanisms in common with type 2 diabetes. These recent discoveries not only highlight the importance of ER stress in the post‐burn patient recovery, but furthermore enable new models to study fundamental and interventional aspects of type 2 diabetes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".