Role of TNF-α in myocardial dysfunction after hemorrhagic shock and lower-torso ischemia
Bibliographic record
Abstract
Ruptured abdominal aortic aneurysm (RAAA) repair, a combination of hemorrhagic shock and lower-torso ischemia, is associated with a 50-70% mortality. Myocardial dysfunction may contribute to the high rate of mortality after aneurysm repair. We attempted to determine whether RAAA repair results in cardiac dysfunction mediated by tumor necrosis factor-alpha (TNF-alpha). We modeled aortic rupture and repair in the rat by inducing hemorrhagic shock to a mean blood pressure of 50 mmHg for 1 h, followed by supramesenteric clamping of the aorta for 45 min. After 90 min of reperfusion, cardiac contractile function was assessed with a Langendorff preparation. Myocardial TNF-alpha, ATP and creatine phosphate (CP) levels, and markers of oxidant stress (F(2)-isoprostanes) were measured. Cardiac function in the combined shock and clamp rats was significantly depressed compared with sham-operated control rats but was similar to that noted in animals subjected to shock alone. Myocardial TNF-alpha concentrations increased 10-fold in the combined shock and clamp rats compared with sham rats, although there was no difference in myocardial ATP, CP, or F(2)-isoprostanes. TNF-alpha neutralization improved cardiac function by 50% in the combined shock and clamp rats. Hemorrhagic shock is the primary insult inducing cardiac dysfunction in this model of RAAA repair. An improvement in cardiac contractile function after immunoneutralization of TNF-alpha indicates that TNF-alpha mediates a significant portion of the myocardial dysfunction in this model.
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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".