Effects of fluids on the liver microcirculation and cell free DNA in sepsis
Bibliographic record
Abstract
Fluid resuscitation is a crucial therapy for sepsis. The release of DNA into the circulation in sepsis may predict mortality. Our objective was to determine if our fluids alter hepatic leukocyte recruitment or the levels of plasma cell free DNA (cfDNA). Cecal ligation and perforation (CLP) was used to induce sepsis in C57BL/6 mice, and compared to sham‐operated controls. Briefly, the cecum was ligated distally, punctured and returned post fecal extrusion. The right jugular vein was catheterized to deliver Seplyte L (SepL) and Seplyte H (SepH), and with or without albumin. Hepatic leukocyte recruitment was assessed by intravital microscopy and plasma cfDNA measured. CLP mice had higher leukocyte adhesion in sinusoidal and post‐sinusoidal venules compared to shams in all groups. Post‐sinusoidal adhesion was reduced in CLP mice given colloidal SepL and SepH vs. base solutions (13.6± 0.8 vs. 8.1±1.4 and 13.4±1.1 vs. 7.6 ± 0.6; p < 0.05). Six hrs post‐CLP, cfDNA was higher in septic mice when given SepL (9.2±0.3 vs. 4.9±0.5 μg/mL), SepH (8.6±1.4 vs. 4.9±0.4), SepL albumin (9.4±1.1 vs. 5.3±0.2) and SepH albumin (7.2±0.7 vs. 5.3±0.3) (p < 0.03). There was no difference in cfDNA between solutions. The addition of a colloid to fluids results in less systemic inflammation in early sepsis as measured by hepatic leukocyte recruitment. The production of cfDNA in early sepsis however is not altered by the presence of colloid.
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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 | 0.001 |
| 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".