Hypo-uricémie rénale héréditaire chez un sujet d’origine caucasienne : présentation d’un cas clinique et revue de la littérature
Bibliographic record
Abstract
L’hypo-uricémie rénale héréditaire se caractérise par un niveau sérique d’acide urique abaissé, une fraction d’excrétion de l’acide urique supérieure à la normale et l’absence d’autre cause d’hypo-uricémie hyperuricosurique. Cette pathologie, le plus souvent causée par une mutation du transporteur URAT1, est relativement fréquente dans les populations d’origine asiatique, mais très rare chez les Caucasiens. Son association avec l’insuffisance rénale aiguë induite par l’exercice est bien connue. Cet article présente le cas d’un homme d’origine italienne âgé de 47 ans chez qui un diagnostic d’hypo-uricémie rénale héréditaire a été posé après un épisode d’insuffisance rénale aiguë induite par l’exercice. Une analyse moléculaire du gène SLC22A12 codant pour URAT1 a été réalisée sur l’ADN génomique du patient. Le dépistage de polymorphisme par analyse simple brin (SSCP) et le séquençage direct n’ont révélé aucune mutation d’URAT1. Ce résultat suggère qu’un autre gène puisse être impliqué dans l’hypo-uricémie familiale. Hereditary renal hypouricemia is characterized by a decreased serum uric acid, a uric acid fractional excretion above normal and the absence of another cause of hyperuricosuric hypouricemia. This pathology, generally caused by a mutation of urate renal transporter URAT1, is relatively common in Asia, but occurs very infrequently in Caucasian populations. The disease's association with exercise-induced acute renal failure is well known. This article reports the case of a 47-year-old man of Italian origin who was diagnosed with hereditary renal hypouricemia after an episode of exercise-induced acute renal failure. Molecular analysis of SLC22A12 encoding URAT1 for renal hypouricemia using peripheral blood genomic DNA of the patient was performed. Single-strand conformation polymorphism screening, amplification, and direct sequencing of SLC22A12 revealed no mutation in this patient. This suggests that another gene can be involved in this disease.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".