Shiga Toxin 2a Induces NETosis via NOX-Dependent Pathway
Bibliographic record
Abstract
(STEC) infection is the most common cause of hemolytic uremic syndrome (HUS), one of the main causes of acute kidney injury in children. Stx plays an important role in endothelium damage and pathogenesis of STEC-HUS. However, the effects of Stx on neutrophils and neutrophil extracellular trap (NET) formation are not well understood. In this study, we investigated how Stx2a affects NET formation and NETotic pathways (NADPH or NOX-dependent and -independent) using neutrophils isolated from healthy donors and patients with STEC-HUS, during the acute and recovery phase of the disease. Stx2a dose-dependently induced NETosis in neutrophils isolated from both healthy controls and STEC-HUS patients. NETosis kinetics and mechanistic data with pathway-specific inhibitors including diphenyleneiodonium (DPI)-, ERK-, and P38-inhibitors showed that Stx2a-induced NETosis via the NOX-dependent pathway. Neutrophils from STEC-HUS patients in the acute phase showed less ROS and NETs formation compared to neutrophils of the recovery phase of the disease and in healthy controls. NETs induced by Stx2a may lead to the activation of endothelial cells, which might contribute to the manifestation of thrombotic microangiopathy in STEC-HUS.
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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 | 0.000 |
| 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) | 0.001 | 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".