Peritubular Capillary Changes and C4d Deposits Are Associated with Transplant Glomerulopathy But Not IgA Nephropathy
Bibliographic record
Abstract
We examined our renal transplant population for glomerular diseases demonstrated on biopsy between January 1993 and April 2002, focusing on transplant glomerulopathy (TGP). Of 1156 patients followed in our clinics during this period, glomerular disease was diagnosed in 132 cases (11.4%). Glomerulonephritis was diagnosed in 86 transplants (7.4%), with IgA nephropathy (IgAN) being the commonest diagnosis [32 cases (2.8%)]. Thirty-one cases (2.7%) of biopsy-proven TGP were analyzed for associated factors compared with 27 cases (2.3%) of recurrent IgAN. Transplant glomerulopathy was less frequent with mycophenolate mofetil (MMF) and/or tacrolimus, whereas recurrent IgAN showed no such tendency (P= 0.02). Peritubular capillary (PTC) C4d deposition was observed in six of 24 cases (25%) with TGP but none with recurrent IgAN (P= 0.02). Peritubular capillary basement membrane (BM) multilayering was significantly greater in TGP (4.92 +/- 2.94) than in recurrent IgAN (1.86 +/- 1.04) (P < 0.001). The graft survival of TGP was worse than recurrent IgAN (P= 0.05). The association of TGP with BM multilayering and C4d deposits in PTC suggests a generalized disorder of the graft microcirculation and its BM, owing to antibody-mediated rejection in at least some cases. Transplant glomerulopathy has a serious prognosis but is less frequent in patients on newer immunosuppression, unlike recurrent IgAN.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".