Exploring the Link Between Hepatic Perfusion and Endotoxemia in Hemodialysis
Bibliographic record
Abstract
IntroductionThe liver receives gut-derived endotoxin via the portal vein, clearing it before it enters systemic circulation. Hemodialysis negatively impacts the perfusion and function of multiple organs systems. Dialysate cooling reduces hemodialysis-induced circulatory stress and protects organs from ischemic injury. This study examined how hemodialysis disrupts liver hemodynamics and function, its effect on endotoxemia, and the potential protective effect of dialysate cooling.MethodsFifteen patients were randomized to receive either standard (36.5°C dialysate temperature) or cooled (35.0°C) hemodialysis first in a two-visit crossover trial. We applied computed tomography (CT) liver perfusion imaging to patients before, 3 hours into and after each hemodialysis session. We measured hepatic perfusion and perfusion heterogeneity. Hepatic function was measured by indocyanine green (ICG) clearance. Endotoxin levels in blood throughout dialysis were also measured.ResultsDuring hemodialysis, overall liver perfusion did not significantly change, but portal vein perfusion trended towards increasing (P = 0.14) and perfusion heterogeneity significantly increased (P = 0.038). In addition, ICG clearance decreased significantly during hemodialysis (P = 0.016), and endotoxin levels trended towards increasing during hemodialysis (P = 0.15) and increased significantly after hemodialysis (P = 0.037). Applying dialysate cooling trended towards abrogating these changes but did not reach statistical significance compared to standard hemodialysis.ConclusionHemodialysis redistributes liver perfusion, attenuates hepatic function, and results in endotoxemia. Higher endotoxin levels in end-stage renal disease (ESRD) patients may result from the combination of decreased hepatic clearance function and increasing fraction of liver perfusion coming from toxin-laden portal vein during hemodialysis. The protective potential of dialysate cooling should be explored further in future research studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".