Cerebrovascular Endothelial Cell Activation in Paediatric Diabetic Ketoacidosis
Bibliographic record
Abstract
Diabetic ketoacidosis (DKA) in children is associated with intracranial vascular complications. The mechanisms of DKA‐induced vascular dysfunction are unclear. We aimed to assess the effects/mechanisms of DKA plasma on activation of human cerebrovascular endothelial cells (hCMEC/D3; provided by Dr. P.O. Couraud, INSERM). Methods DKA‐blood plasma was obtained from paediatric patients with either DKA or well‐controlled type‐1 diabetes (control; CON). The levels of 21 inflammation‐relevant analytes in blood were assessed. In vitro hCMEC/D3 were stimulated with 20% (v/v) CON‐ or DKA‐plasma and assessed for activation markers. Results DKA in children resulted in increased circulating levels of IL‐2, IL‐6, IL‐8, GRO, INFα2, and G‐CSF. Stimulation of hCMEC/D3 with DKA‐plasma induced production of ROS and increased PMN‐leukocyte adhesion to hCMEC/D3 under “flow” conditions (shear stress 0.35 dyn/cm 2 ). PMN adhesion was replicated by stimulating hCMEC/D3 with IL‐8 and/or GROα (at the levels detected in DKA‐plasma), the phenomenon that was suppressed by neutralizing anti‐CXCR1 and/or CXCR2 antibodies. Conclusions DKA elicits systemic inflammation associated with increased oxidative stress and up‐regulation of the pro‐adhesive phenotype in cerebrovascular endothelium, potentially contributing to DKA‐associated intracranial vascular complications.
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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 | 0.001 |
| 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".