Netting Liver Disease: Neutrophil Extracellular Traps in the Initiation and Exacerbation of Liver Pathology
Bibliographic record
Abstract
The liver plays a vital role in the immune system. Its unique position in the portal circulation and the architecture of the hepatic sinusoids, in combination with the wide-ranged population of immunocompetent cells, make the liver function as an immune filter. To aid in pathogen clearance, once challenged, the liver initiates the rapid recruitment of a wide variety of inflammatory cells, including neutrophils. These neutrophils, in conjunction with platelets, facilitate the release of neutrophil extracellular traps (NETs), which are web-like structures of decondensed nuclear DNA, histones, and neutrophil proteins. NETs function as both a physical and a chemical barrier, binding and killing pathogens circulating in the blood stream. In addition to their antimicrobial role, NETs also bind platelets, activate coagulation, and exacerbate host inflammatory response. This interplay between inflammation and coagulation drives microvascular occlusion, ischemia, and (sterile) damage in liver disease. Although direct clinical evidence of this interplay is scarce, preliminary studies indicate that NETs contribute to progression of liver disease and (thrombotic) complications. Here, we provide an overview of the pathological mechanisms of NETs in liver disease. In addition, we summarize clinical evidence for NETs in different disease etiologies and complications of liver disease and discuss the possible implications for the use of NETs as a diagnostic marker and a therapeutic target in liver disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".