Severe Injury Is Associated With Insulin Resistance, Endoplasmic Reticulum Stress Response, and Unfolded Protein Response
Bibliographic record
Abstract
In Brief Objective: We determined whether postburn hyperglycemia and insulin resistance are associated with endoplasmic reticulum (ER) stress/unfolded protein response (UPR) activation leading to impaired insulin receptor signaling. Background: Inflammation and cellular stress, hallmarks of severely burned and critically ill patients, have been causally linked to insulin resistance in type 2 diabetes via induction of ER stress and the UPR. Methods: Twenty severely burned pediatric patients were compared with 36 nonburned children. Clinical markers, protein, and GeneChip analysis were used to identify transcriptional changes in ER stress and UPR and insulin resistance–related signaling cascades in peripheral blood leukocytes, fat, and muscle at admission and up to 466 days postburn. Results: Burn-induced inflammatory and stress responses are accompanied by profound insulin resistance and hyperglycemia. Genomic and protein analysis revealed that burn injury was associated with alterations in the signaling pathways that affect insulin resistance, ER/sarcoplasmic reticulum stress, inflammation, and cell growth/apoptosis up to 466 days postburn. Conclusion: Burn-induced insulin resistance is associated with persistent ER/sarcoplasmic reticulum stress/UPR and subsequent suppressed insulin receptor signaling over a prolonged period of time. Burn induced insulin resistance and hyperglycemia worsen post-burn morbidity and mortality. In this study we found a novel association that will broaden therapeutic possibilities by identifying that burn-induced insulin resistance is associated with persistent ER/SR stress/UPR and subsequent suppressed insulin receptor signaling over a prolonged period of time.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".