Nutrition Intervention: A Strategy Against Systemic Inflammatory Syndrome
Bibliographic record
Abstract
BACKGROUND: Sepsis and septic shock syndrome are the leading causes of death in critically ill patients. Lipopolysaccharide (LPS) released by the colonic microorganisms may translocate across a compromised lumen, leading to upregulated reactive oxidative stress, inflammation, and sepsis. The authors examined an enteral formula high in cysteine (antioxidant precursor), omega-3 fatty acids, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), and prebiotic fructooligosaccharides (FOS) against systemic inflammatory syndrome. METHODS: Rats were allocated to (1) standard soy-based diet high in cysteine and crude fiber and devoid of EPA-DHA (CHOW); (2) whey-peptide-based liquid diet high in cysteine, EPA-DHA, and FOS (CYSPUFA); or (3) casein-based liquid isonitrogenous diet, low in cysteine and devoid of EPA-DHA-FOS (CASN). Liquid diets provided 25% and CHOW, 23% of calories as protein. After 6 days on diets, rats received an intraperitoneal injection of LPS or saline. Animals gained weight on their respective diets and lost weight after LPS administration. The CYSPUFA group lost considerably less weight (vs CASN or CHOW, P < .05). Inflammatory cytokines significantly increased by 4 hours and subsided 18 hours after assault. The CASN group showed elevated liver enzyme alanine aminotransferase release from damaged hepatocytes and developed severe hepatic pathology with low hematocrit. The CHOW group developed more severe hepatic lesions compared with those on liquid diets. Concentration of liver enzyme and pathology were improved in rats receiving CYSPUFA. CONCLUSIONS: Data indicate that CYSPUFA, a diet rich in EPA-DHA-FOS, protects against LPS-induced systemic inflammatory responses and warrants clinical studies in critically ill patients.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".