Primary Human Derived Blood Outgrowth Endothelial Cells: An Appropriate In Vitro Model to Study Shiga Toxin Mediated Damage of Endothelial Cells
Bibliographic record
Abstract
Hemolytic uremic syndrome (HUS) is a rare disease primarily characterized by hemolytic anemia, thrombocytopenia, and acute renal failure. Endothelial damage is the hallmark of the pathogenesis of HUS with an infection with the Shiga toxin (Stx) producing Escherichia coli (STEC-HUS) as the main underlying cause in childhood. In this study, blood outgrowth endothelial cells (BOECs) were isolated from healthy donors serving as controls and patients recovered from STEC-HUS. We hypothesized that Stx is more cytotoxic for STEC-HUS BOECs compared to healthy donor control BOECs explained via a higher amount of Stx bound to the cell surface. Binding of Shiga toxin-2a (Stx2a) was investigated and the effect on cytotoxicity, protein synthesis, wound healing, and cell proliferation was studied in static conditions. Results show that BOECs are highly susceptible for Stx2a. Stx2a is able to bind to the cell surface of BOECs with cytotoxicity in a dose-dependent manner as a result. Pre-treatment with tumor necrosis factor alpha (TNF-α) results in enhanced Stx binding with 20–30% increased lactate dehydrogenase (LDH) release. Endothelial wound healing is delayed in a Stx2a-rich environment; however, this is not caused by an effect on the proliferation rate of BOECs. No significant differences were found between control BOECs and BOECs from recovered STEC-HUS patients in terms of Stx2a binding and inhibition of protein synthesis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".