Role of the mTOR Signalling Pathway in Human Sepsis-Induced Myocardial Dysfunction
Bibliographic record
Abstract
BACKGROUND: Sepsis-induced myocardial dysfunction (SIMD) is a life-threatening complication of sepsis. Mammalian target of rapamycin (mTOR) signalling pathway is significantly associated with SIMD in an animal model; however, there have been no clinical studies of the association in humans. METHODS: We enrolled 88 patients with sepsis who were admitted to the intensive care unit (ICU) between April 2017, and April 2018. Biochemical indexes, hemodynamic parameters, and bedside echocardiographic parameters were recorded. Serum levels of mTOR, phosphorylated ribosome S6 protein kinase (PS6K), microtubule-associated protein light chain 3 type II (LC3B), Bcl-2-interacting mediator of cell death (BIM), interleukin 6, interleukin 10, and interferon-γ were examined. RESULTS: Compared with non-SIMD patients, patients with SIMD had higher ICU and 28-day mortality, PS6K and BIM levels, but lower LC3B levels. Serum PS6K levels in patients with SIMD were significantly negatively and positively correlated with LC3B and BIM, respectively. Multivariate regression analysis revealed that PS6K concentration at admission was an independent predictor of 28-day mortality. Receiver operating characteristic curve analysis indicated that a PS6K concentration cutoff of 42.43 pg/mL at ICU admission could predict the incidence of SIMD with a sensitivity and specificity of 91.7% and 96.2%, whereas a cutoff concentration of 41.17 pg/mL PS6K could predict 28-day mortality with a sensitivity and specificity of 83.3% and 54.3%, respectively. CONCLUSIONS: Patients with sepsis and SIMD had higher ICU and 28-day mortality. Higher serum PS6K concentrations were significantly associated with SIMD incidence and 28-day mortality, suggesting that activation of the mTOR pathway may play a major role in SIMD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".