Neutrophil Extracellular Traps in Pediatric Infections: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Neutrophil extracellular traps (NETs) are granule- and nucleus-derived structures that support innate immunity. While the contribution of NETs to adult infections and autoimmune diseases is well studied, evidence in children is still inconsistent. This review aimed to summarize current findings on NETs in pediatric infections. METHODS: This study followed the Cochrane Handbook for Systematic Reviews of Interventions and adhered to the PRISMA guidelines. A search was conducted in major databases (MEDLINE/PubMed and Scopus) from inception until 5 September 2025. The study quality was evaluated using the modified Newcastle-Ottawa Scale. RESULTS: Eleven studies were included in the systematic review. In respiratory disease, the role of NETs was well described and their formation correlated with severity. Patients with febrile urinary tract infections showed elevated urinary NET-associated markers. In COVID-19 infection, NET levels were unchanged in uncomplicated cases but elevated in multisystem inflammatory syndrome in children. Findings in sepsis were inconsistent. CONCLUSIONS: This systematic review presents the published evidence on NET formation in the pediatric population, assessing the current knowledge and identifying the gaps to guide research. Future studies should aim to standardize NET detection methods, evaluate their prognostic value in large prospective cohorts, and explore the various NET-associated mechanisms in children.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".