Eosinophil Extracellular Traps and Inflammatory Pathologies—Untangling the Web!
Bibliographic record
Abstract
Eosinophils are an enigmatic white blood cell, whose immune functions are still under intense investigation. Classically, the eosinophil was considered to fulfill a protective role against parasitic infections, primarily large multicellular helminths. Although eosinophils are predominantly associated with parasite infections, evidence of a role for eosinophils in mediating immunity against bacterial, viral, and fungal infections has been recently reported. Among the mechanisms by which eosinophils are proposed to exert their protective effects is the production of DNA-based extracellular traps (ETs). Remarkably, DNA serves a role that extends beyond its biochemical function in encoding RNA and protein sequences; it is also a highly effective substance for entrapment of bacteria and other extracellular pathogens, and serves as valuable scaffolding for antimicrobial mediators such as granule proteins from immune cells. Extracellular trap formation from eosinophils appears to fulfill an important immune response against extracellular pathogens, although overproduction of traps is evident in pathologies. Here, we discuss the discovery and characterization of eosinophil extracellular traps (EETs) in response to a variety of stimuli, and suggest a role for these structures in the pathogenesis of disease as well as the establishment of autoimmunity in chronic, unresolved inflammation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".