Experimental Glomerular Endothelial Injury In Vivo
Bibliographic record
Abstract
The microvascular endothelium of the kidney glomerulus is injured in Shiga-like toxigenic bacterial infection, genetic or acquired loss of complement regulatory protein function, and allo-immune responses of solid-organ or bone marrow transplantation. Existing models of diseases with glomerular endothelial cell (EC) injury, collectively grouped as thrombotic microangiopathies, are problematic, impeding investigation of the mechanisms of microvascular defense and repair. To develop a model of glomerular endothelial injury in the mouse, we conjugated the M. oreades lectin to the cytotoxin, saporin, (LS) to selectively injure the glomerular endothelium. Injury of the microvasculature was evaluated by light, immunofluorescence, and electron microscopy, and by quantitative RT-PCR of cell-type specific transcripts. Renal function was evaluated by quantitation of serum creatinine. The toxin conjugate induced apoptosis of microvascular ECs in vitro, and subtle histologic features of thrombotic microangiopathy in vivo that were enhanced by co-injection of 50 μg/kg LPS. Among LS/LPS-treated animals, loss of glomerular EC staining correlated with decreased expression of EC-specific transcripts, and impaired kidney function. Selective injury of the glomerular microvasculature with LS toxin conjugate and LPS elicits histologic features of thrombotic microangiopathy and acute kidney failure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".