New insights into Shiga toxin-mediated endothelial dysfunction in hemolytic uremic syndrome
Bibliographic record
Abstract
Shiga toxin-producing E. coli represents a significant global health concern, especially as hypervirulent pathogens surface amidst outbreaks of hemolytic uremic syndrome (HUS). Shiga toxin (Stx) is key in the microangiopathic events underlying the disease and its central role is underscored by the unprecedented HUS outbreak in Germany in 2011. The mechanisms of Stx-mediated endothelial dysfunction have been a major focus of research that has contributed to the current understanding of the pathogenic changes in endothelial phenotype leading to HUS. Among the newer concepts are Stx-mediated gene regulation in the absence of protein synthesis inhibition, a potential role for complement activation, and accumulating evidence for detectable serum markers before the onset of the classic clinical features of HUS. Further investigation of newer therapeutic targets and potential prognostic markers is essential to assess their utility in mitigating disease and/or predicting outcomes and will provide an improved overall understanding of HUS pathogenesis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".