Skin Fairness Cream–Associated NELL-1 Membranous Nephropathy Treatment With Mercury Chelation and Immunosuppressive Therapy: An Educational Case Report
Bibliographic record
Abstract
Case Description: A 38-year-old man presented with NELL-1 positive membranous nephropathy (MN). Initial treatment with rituximab and later cyclosporine failed to result in sustained clinical improvement. Upon further review, the patient had been applying a skin fairness cream nightly for 2 years preceding diagnosis. The cream was discontinued, and follow-up testing confirmed markedly elevated serum mercury levels of 66.8 µg/L (normal less than 20 µg/L), and 24-hour urine mercury of 103.9 nmol/d. Two rounds of chelation therapy were arranged, the first with dimercaptosuccinic acid (DMSA) and the second with dimercaptopropane-1-sulfonic acid (DMPS) given cost and availability. Repeat mercury levels normalized but follow up kidney biopsy confirmed persistent immune complex glomerulonephritis. The patient was subsequently treated with prednisone followed by additional rituximab resulting in improvement of proteinuria and stabilization of kidney function. Rationale/Teaching Points: This case reinforces the risk of mercury containing topicals and is the first to report systemic absorption of this magnitude. While chelation therapy is effective at improving systemic mercury levels, patients may require additional immunosuppression to treat immune complex-mediated MN following exposure.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| 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".