IgA nephropathy: the lectin pathway and implications for targeted therapy
Bibliographic record
Abstract
Many patients with immunoglobulin A nephropathy (IgAN) progress to kidney failure even with optimal supportive care. An improved understanding of the pathophysiology of IgAN in recent years has led to the investigation of targeted therapies with acceptable tolerability that may address the underlying causes of IgAN or the pathogenesis of kidney injury. The complement system-particularly the lectin and alternative pathways of complement-has emerged as a key mediator of kidney injury in IgAN and a possible target for investigational therapy. This review will focus on the lectin pathway. The examination of kidney biopsies has consistently shown glomerular deposition of mannan-binding lectin (1 of 6 pattern-recognition molecules that activate the lectin pathway) together with IgA1 in up to 50% of patients with IgAN. Glomerular deposition of pattern-recognition molecules for the lectin pathway is associated with more severe glomerular damage and more severe proteinuria and hematuria. Emerging research suggests that the lectin pathway may also contribute to tubulointerstitial fibrosis in IgAN and that collectin-11 is a key mediator of this association. This review summarizes the growing scientific and clinical evidence supporting the role of the lectin pathway in IgAN and examines the possible therapeutic role of lectin pathway inhibition for these patients.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".