Application of thrombelastography in liver injury induced by endotoxin in rat
Bibliographic record
Abstract
Liver injury developing in patients with sepsis may lead to an increased risk of mortality. Thrombelastography (TEG) is generally applied to evaluate hemostatic disturbance in patients undergoing liver transplantation or cardiopulmonary bypass. The aim of this study was to investigate the development of liver injury and coagulopathy in a lipopolysaccharide (LPS)-induced animal model and to assess the relationship between TEG variables and liver injury. Male Wistar rats received LPS (30 mg/kg over a 4-h intravenous infusion) to induce experimental liver injury or isotonic saline as a control. Variables of hemodynamics and liver biochemistry were measured during the subsequent 6 h after the start of infusion. TEG variables (R-time, K-time, α-angle and maximal amplitude), thrombin-antithrombin complex and plasminogen activator inhibitor-1 were also measured. After LPS infusion, liver injury [examined by biochemical variables (e.g. alanine aminotransferase, ALT) and histological studies] was developed and inflammatory cytokines (tumor necrosis factor-α and interleukin-6) were raised. At the initial period of LPS infusion, R-time was shortened and α-angle was increased. Thereafter, α-angle and maximal amplitude were decreased progressively, demonstrating that endotoxin induced coagulation disturbances. Furthermore, there were strong positive correlation between K-time and natural log (Ln)(ALT) (r = 0.823, P = 0.001); also, there were strong negative correlations between α-angle and Ln(ALT) (r = -0.762, P = 0.002) as well as maximal amplitude and Ln(ALT) (r = -0.732, P = 0.004) at 6 h after LPS infusion. These results demonstrated that TEG could be a potential tool to evaluate the development of liver injury in endotoxemia.
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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 | 0.001 | 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 | 0.001 |
| 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".