Hypertonic sodium lactate improves microcirculation, cardiac function, and inflammation in a rat model of sepsis
Bibliographic record
Abstract
Abstract Background Hypertonic sodium lactate (HSL) may be of interest during inflammation. We aimed to evaluate its effects during experimental sepsis in rats (cecal ligation and puncture (CLP)). Methods Three groups were analyzed (n = 10/group): sham, CLP-NaCl 0.9%, and CLP-HSL (2.5 mL/kg/h of fluids for 18 h after CLP). Mesenteric microcirculation, echocardiography, cytokines, and biochemical parameters were evaluated. Two additional experiments were performed for capillary leakage (Evans blue, n = 5/group) and cardiac hemodynamics (n = 7/group). Results HSL improved mesenteric microcirculation (CLP-HSL 736 [407–879] vs. CLP-NaCl 241 [209–391] UI/pixel, p = 0.0006), cardiac output (0.34 [0.28–0.43] vs. 0.14 [0.10–0.18] mL/min/g, p < 0.0001), and left ventricular fractional shortening (55 [46–73] vs. 39 [33–52] %, p = 0.009). HSL also raised dP/dtmax slope (6.3 [3.3–12.1] vs. 2.7 [2.0–3.9] 103 mmHg/s, p = 0.04), lowered left ventricular end-diastolic pressure-volume relation (1.9 [1.1–2.3] vs. 3.0 [2.2–3.7] RVU/mmHg, p = 0.005), and reduced Evans blue diffusion in the gut (37 [31–43] vs. 113 [63–142], p = 0.03), the lung (108 [82–174] vs. 273 [222–445], p = 0.006), and the liver (24 [14–37] vs. 70 [50–89] ng EB/mg, p = 0.04). Lactate and 3-hydroxybutyrate were higher in CLP-HSL (6.03 [3.08–10.30] vs. 3.19 [2.42–5.11] mmol/L, p = 0.04; 400 [174–626] vs. 189 [130–301] μmol/L, p = 0.03). Plasma cytokines were reduced in HSL (IL-1β, 172 [119–446] vs. 928 [245–1470] pg/mL, p = 0.004; TNFα, 17.9 [12.5–50.3] vs. 53.9 [30.8–85.6] pg/mL, p = 0.005; IL-10, 352 [267–912] vs. 905 [723–1243] pg/mL) as well as plasma VEGF-A (198 [185–250] vs. 261 [250–269] pg/mL, p = 0.009). Conclusions Hypertonic sodium lactate fluid protects against cardiac dysfunction, mesenteric microcirculation alteration, and capillary leakage during sepsis and simultaneously reduces inflammation and enhances ketone bodies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".