Anti‐High‐Mobility Group Box Chromosomal Protein 1 Antibodies Improve Survival of Rats with Sepsis
Bibliographic record
Abstract
BACKGROUND: High-mobility group box chromosomal protein 1 (HMGB1) has recently been shown to be an important late mediator of endotoxin shock, intraabdominal sepsis, and acute lung injury, and a promising therapeutic target of severe sepsis. We sought to investigate the effect of antibodies to HMGB1 on severe sepsis in a rat cecal ligation and puncture (CLP) model. METHODS: Adult male Sprague-Dawley rats underwent CLP and then were randomly divided into two groups: treatment with anti-HMGB1 polyclonal antibodies, and non-immune IgG-treated controls. The serum HMGB1 concentrations were measured at ten time points (preoperatively, and postoperatively at 4, 8, 20, 32, and 48 h and at 3, 4, 5, and 6 days). Hematoxylin-eosin staining, elastica-Masson staining, and immunohistochemical staining for HMGB1 were performed on the cecum and the lung to assess pathological changes 24 h after the CLP procedure. RESULTS: Treatment with anti-HMGB1 antibodies significantly increased survival [55% (anti-HMGB1) vs. 9% (controls); P< 0.01]. The serum HMGB1 concentrations at postoperative hours 20 and 32 of the anti-HMGB1 antibody-treated animals were significantly lower than those of the controls (P < 0.05). Treatment with anti-HMGB1 antibodies markedly diminished the pathological changes and the number of HMGB1-positive cells in the cecum and the lung. CONCLUSIONS: The present study demonstrates that anti-HMGB1 antibodies are effective in the treatment of severe sepsis in a rat model, thereby supporting the relevance of HMGB1 eradication therapy for severe sepsis.
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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 | 0.001 | 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) | 0.002 | 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".