The Role of the N-Terminal Domain of Thrombomodulin and the Potential of Recombinant Human Thrombomodulin as a Therapeutic Intervention for Shiga Toxin-Induced Hemolytic-Uremic Syndrome
Bibliographic record
Abstract
(STEC-HUS), characterized by severe acute kidney injury, thrombocytopenia and microangiopathic hemolytic anemia, and specific therapy is still lacking. Thrombomodulin (TM) is a multi-domain transmembrane endothelial cell protein and its N-terminal domain has been implicated in the pathophysiology of some cases of HUS. Indeed, the administration of recombinant human TM (rhTM) may have efficacy in HUS. We used a Stx-based murine model of HUS to characterize the role of the N-terminal domain of TM. We show that mice lacking that domain (TMLed (-/-)) are more sensitive to Stx, with enhanced HUS progression seen at 4 days and increased mortality at 7 days post-HUS induction. In spite of these changes, renal function was less affected in surviving Stx-challenged TMLed (-/-) mice compared to their wild-type counterparts TMLed (+/+) at 7 days. Contrary to few clinical case reports from Japan, the administration of rhTM (0.06 mg/kg) to wild-type mice (C57BL/6J) with HUS did not protect against disease progression. This overall promising, but also contradictory body of evidence, requires further systematic preclinical and clinical investigations to clarify the role of TM in HUS as a potential therapeutic strategy.
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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) | 0.000 | 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".