Unraveling the diagnostic enigma: laboratory diagnosis of sphingolipid activator protein deficiencies
Bibliographic record
Abstract
Abstract: Sphingolipids are degraded by specific lysosomal acid hydrolases. Efficient degradation of glycosphingolipids with short carbohydrate chains by those enzymes relies on the assistance of sphingolipid activator proteins (SAPs), including GM2 activator and four saposins (Saposin A, B, C, and D). Deficiencies in these enzymes or SAPs lead to the accumulation of specific sphingolipids. While enzyme activity assays are diagnostic for sphingolipidoses caused by enzyme deficiencies, the laboratory diagnosis of SAP deficiencies presents challenges due to the complexity of directly assessing these proteins’ functions. Given the exceptional rarity of SAP deficiencies and the clinical similarities to the corresponding enzyme deficiencies, there is an increased risk of oversight and misdiagnosis. Biomarkers such as GM2 ganglioside (34:1), sulfatide, glucosylsphingosine (lyso-Gb1), globotriaosylsphingosine (lyso-Gb3), and psychosine are important in addressing this diagnostic complexity, with molecular genetic analysis serving as the essential confirmatory test. In GM2 activator, saposin B, and saposin C deficiencies, enzyme activity assays using regular synthetic substrates typically demonstrate normal enzyme activities. The discrepancy between an abnormal biomarker level and a normal enzyme activity may strongly suggest an activator defect. In cases of saposin A and prosaposin deficiencies, abnormal enzyme activities may be observed even when using regular synthetic substrates, potentially leading to the misdiagnosis of corresponding enzyme deficiencies. In such scenarios, if molecular genetic analyses of the genes encoding those enzymes return negative results, it may be necessary to conduct follow-up molecular testing targeting the PSAP gene for diagnostic purposes. This review paper delves into biochemical laboratory techniques to illuminate the intricacies involved in diagnosing SAP deficiencies. The aim is to streamline the laboratory diagnostic process and reduce diagnostic uncertainties.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.009 |
| 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".