NET balancing: a problem in inflammatory lung diseases
Bibliographic record
Abstract
Neutrophil extracellular traps (NETs) are beneficial antimicrobial defense structures that can help fight against invading pathogens in the host. However, recent studies reveal that NETs exert adverse effects in a number of diseases including those of the lung. Many inflammatory lung diseases are characterized with a massive influx of neutrophils into the airways. Neutrophils contribute to the pathology of these diseases. To date, NETs have been identified in the lungs of cystic fibrosis (CF), acute lung injury (ALI), allergic asthma, and lungs infected with bacteria, virus, or fungi. These microbes and several host factors can stimulate NET formation, or NETosis. Different forms of NETosis have been identified and are dependent on varying types of stimuli. All of these pathways however appear to result in the formation of NETs that contain DNA, modified extracellular histones, proteases, and cytotoxic enzymes. Some of the NET components are immunogenic and damaging to host tissue. Innate immune collectins, such as pulmonary surfactant protein D (SP-D), bind NETs, and enhance the clearance of dying cells and DNA by alveolar macrophages. In many inflammatory lung diseases, bronchoalveolar SP-D levels are altered and its deficiency results in the accumulation of DNA in the lungs. Some of the other therapeutic molecules under consideration for treating NET-related diseases include DNases, antiproteases, myeloperoxidase (MPO) inhibitors, peptidylarginine deiminase-4 inhibitors, and anti-histone antibodies. NETs could provide important biological advantage for the host to fight against certain microbial infections. However, too much of a good thing can be a bad thing. Maintaining the right balance of NET formation and reducing the amount of NETs that accumulate in tissues are essential for harnessing the power of NETs with minimal damage to the hosts.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".