Heparin-Binding Protein (HBP)
Bibliographic record
Abstract
RATIONALE: Sepsis-induced acute kidney injury (AKI) is a common condition with high morbidity and mortality. Neutrophil-derived heparin-binding protein (HBP) induces vascular leakage and is a promising biomarker of sepsis-induced organ dysfunction. It remains unknown if HBP is prognostic of AKI in septic shock and if HBP could play a role in the pathophysiology of sepsis-induced AKI. OBJECTIVES: To determine the association of plasma HBP levels with development of AKI, investigate the role of HBP in the pathophysiology of sepsis-induced AKI, and test the effect of blocking HBP using heparin derivatives. METHODS: In 296 septic shock patients from the randomized multicenter Vasopressin and Septic Shock Trial (VASST) plasma HBP levels were associated with development of AKI and need for renal replacement therapy (RRT). Human renal tubular cells were exposed to recombinant HBP to evaluate inflammation and heparin derivatives were used to abrogate these effects. Finally, mice were exposed to HBP with and without heparin derivatives and the kidneys examined for signs of inflammation. FINDINGS: Plasma HBP levels were significantly higher in patients with AKI and those requiring RRT. HBP levels identified patients with moderate AKI with an area under curve (AUC) of 0.85. HBP increased IL-6 production in renal tubular epithelial cells. Different heparin derivatives abrogated the HBP-induced increased inflammatory response in vitro and in vivo. CONCLUSION: Elevated plasma HBP is associated with development of sepsis-induced AKI and HBP is involved in its pathophysiology. Our studies suggest that heparin(s) could be tested for efficacy and safety of prevention of sepsis-induced AKI.
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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 | 0.001 |
| 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) | 0.002 | 0.001 |
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".