A Case Series of Cystinuric Stone Formers in Western Cape, South Africa: SLC3A1 or SLC7A9 Mutations and Phenotype
Bibliographic record
Abstract
Objective To describe the genetic mutations and phenotype in the first African series of patients with cystinuria. Methods Patients with cystinuria were recruited from a specialist metabolic renal stone clinic in Cape Town, South Africa, for DNA sequencing to detect mutations in SLC3A1 and SLC7A9. Chart reviews and patient interviews were conducted to record demographics, previous medical history, family history, stone-specific history, age at first presentation, cystinuria complications, urine cystine:creatinine ratio, stone analysis, and serum creatinine. Results Nine patients were included: 3 male patients and 6 female patients. The mean age (± SD) of patients was 33.43 ± 19.96 years. The median age (± IQR) at initial diagnosis of cystinuria was 16 ± 18 years, but the age ranged from 2 to 66 years. Three of 9 patients included (33.3%) had chronic kidney disease (CKD); however, none were receiving dialysis. Most patients initially presented with a staghorn calculus (4/9; 44.4%). The mean serum creatinine (± SD) was 84 ± 38 μmol/L. The mean urine cystine (± SD) was 2083 ± 1249 nmoL/mg creatinine. Eight patients had mutations in the SLC3A1 gene; 1 had mutations in both SLC3A1 and SLC7A9. Of the patients with only SLC3A1 mutations, 1 patient was homozygous and the rest were compound heterozygotes (two different mutations identified in the same gene). Four patients had a pathogenic variant in addition to an “uncertain significance” variant in SLC3A1. There were 9 mutations (5 pathogenic and 4 “unknown significance”) in SLC3A1 and 1 mutation in SLC7A9. Two of these were novel mutations. Conclusion This “first in Africa” series of cystinuria patients showed marked heterogeneity in both phenotype and genotype, with a predominance of SLC3A1 mutations. This heterogeneity is similar to that reported in international cohorts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 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".