Thermal Injury Activates the eEF2K‐Dependent eEF2 Pathway in Pediatric Patients
Bibliographic record
Abstract
BACKGROUND: Burn induces a hypermetabolic state characterized by alterations in protein metabolism, which is associated with increased morbidity and mortality. Eukaryotic elongation factor 2 (eEF2) plays a crucial role in regulating protein synthesis in many diseases, but whether it participates in burn-induced hypermetabolism is unclear. The aim of this study was to determine the expression of eEF2 and the upstream eEF2-inactivating kinase, eEF2K, in severely burned pediatric patients. METHODS: Eight pediatric patients (> 40% total body surface area) and 3 nonburned pediatric volunteers were enrolled. Muscle and skin biopsies were collected at early (0-10 days postburn [dpb]), middle (11-49 dpb), and late (50-365 dpb) time points. Resting energy expenditure (REE), body composition, and muscle protein fractional synthesis rate (FSR) were measured. Proteins were extracted and analyzed by Western blotting. To further investigate the protein synthesis pathway, microarray data from muscle and skin were examined from 22 nonburned and 20 burned children. RESULTS: Burn patients exhibited a profound hypermetabolic response, as seen by a significant increase in REE (P < .05) and loss of lean body mass without altered muscle FSR, indicating a shift to catabolism after thermal injury. In muscle, the phosphorylation of eEF2K-dependent eEF2 was down regulated early and middle postburn. Similar changes in eEF2K and eEF2 levels occurred in skin at the early time point. Total amounts of eEF2 and eEF2K were not altered. CONCLUSION: Burn induces prolonged activation of eEF2K and eEF2. Alterations in these mediators may contribute to profound hypermetabolism in severely burned patients.
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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 | 0.000 |
| 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".