Acute Tubulointerstitial Nephritis in a Patient on Anti-Programmed Death-Ligand 1 Triggered by COVID-19: A Case Report
Bibliographic record
Abstract
RATIONALE: Immune checkpoint inhibitors are monoclonal antibodies used in the treatment of various types of cancers. The downside of using such molecules is the potential risk of developing immune-related adverse events. Factors that trigger these autoimmune side effects are yet to be elucidated. Although any organ can potentially be affected, kidney involvement is usually rare. In this case report, we describe the first known instance of a patient being treated with an inhibitor of programmed death-ligand 1 (anti-PD-L1, a checkpoint inhibitor) who develops acute tubulointerstitial nephritis after contracting the severe acute respiratory syndrome coronavirus 2. PRESENTING CONCERNS OF THE PATIENT: A 62-year-old patient, on immunotherapy treatment for stage 4 squamous cell carcinoma, presents to the emergency department with symptoms of lower respiratory tract infection. Severe acute kidney injury is discovered with electrolyte imbalances requiring urgent dialysis initiation. Further testing reveals that the patient has contracted the severe acute respiratory syndrome coronavirus 2. DIAGNOSIS: A kidney biopsy was performed and was compatible with acute tubulointerstitial nephritis. INTERVENTIONS: The patient was treated with high dose corticosteroid therapy followed by progressive tapering. OUTCOMES: Rapid and sustained normalization of kidney function was achieved after completion of the steroid course. NOVEL FINDINGS: We hypothesize that the viral infection along with checkpoint inhibitor use has created a proinflammatory environment which led to a loss of self-tolerance to renal parenchyma. Viruses may play a more important role in the pathogenesis of autoimmunity in this patient population than was previously thought.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| 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".