Relative Role of Genetic Complement Abnormalities in Sporadic and Familial aHUS and Their Impact on Clinical Phenotype
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Hemolytic uremic syndrome (HUS) is characterized by microangiopathic hemolytic anemia, thrombocytopenia, and renal impairment. Most childhood cases are caused by Shiga toxin-producing bacteria. The other form, atypical HUS (aHUS), accounts for 10% of cases and has a poor prognosis. Genetic complement abnormalities have been found in aHUS. DESIGN, SETTING, PARTICIPANTS, AND MEASUREMENTS: We screened 273 consecutive patients with aHUS for complement abnormalities and studied their role in predicting clinical phenotype and response to treatment. We compared mutation frequencies and localization and clinical outcome in familial (82) and sporadic (191) cases. RESULTS: In >70% of sporadic and familial cases, gene mutations, disease-associated factor H (CFH) polymorphisms, or anti-CFH autoantibodies were found. Either mutations or CFH polymorphisms were also found in the majority of patients with secondary aHUS, suggesting a genetic predisposition. Familial cases showed a higher prevalence of mutations in SCR20 of CFH and more severe disease than sporadic cases. Patients with CFH or THBD (thrombomodulin) mutations had the earliest onset and highest mortality. Membrane-cofactor protein (MCP) mutations were associated with the best prognosis. Plasma therapy induced remission in 55 to 80% of episodes in patients with CFH, C3, or THBD mutations or autoantibodies, whereas patients with CFI (factor I) mutations were poor responders. aHUS recurred frequently after kidney transplantation except for patients with MCP mutations. CONCLUSIONS: Results underline the need of genetic screening for all susceptibility factors as part of clinical management of aHUS and for identification of patients who could safely benefit from kidney transplant.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".