Diffuse C4d staining of peritubular capillaries in renal allograft following bamlanivimab therapy
Bibliographic record
Abstract
Neutralizing monoclonal antibodies such as bamlanivimab emerged as promising agents in treating kidney transplant recipients with COVID-19. However, the impact of bamlanivimab on kidney allograft histology remains unknown. We report a case of a kidney transplant recipient who received bamlanivimab for COVID-19 with subsequent histologic findings of diffuse peritubular capillary C4d staining. A 33-year-old man with end-stage kidney disease secondary to hypertension who received an ABO compatible kidney from a living donor, presented for his 4-month protocol visit. He was diagnosed with COVID-19 44 days prior to his visit and had received bamlanivimab with an uneventful recovery. His 4-month surveillance biopsy showed diffuse C4d staining of the peritubular capillaries without other features of antibody-mediated rejection (ABMR). Donor-specific antibodies were negative on repeat evaluations. ABMR gene expression panel was negative. His creatinine was stable at 1.3 mg/dl, without albuminuria. Given the temporal relationship between bamlanivimab and our observations of diffuse C4d staining of the peritubular capillaries, we hypothesize that bamlanivimab might bind to angiotensin-converting enzyme 2, resulting in classical complement pathway and C4d deposition. We elected to closely monitor kidney function which has been stable at 6 months after the biopsy. In conclusion, diffuse C4d may present following bamlanivimab administration without any evidence of ABMR.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".