Simultaneous Determination of Lyso-Gb1 and Lyso-Gb3 in Plasma Using Salt-Assisted Liquid–Liquid Extraction Combined with LC–MS/MS
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Background: Lyso-Gb1 and Lyso-Gb3 are key biomarkers for Gaucher type 1 and Fabry diseases, offering high diagnostic accuracy and utility in disease monitoring. However, conventional sample preparation workflows for their LC–MS/MS quantification often require large volumes of organic solvents, generate hazardous waste, and involve time-consuming steps, limiting routine applicability. Objective: This study aimed to develop, validate, and apply a rapid, sensitive, and environmentally friendly LC–MS/MS method using salt-assisted liquid–liquid extraction (SALLE) for the simultaneous determination of Lyso-Gb1 and Lyso-Gb3 in human plasma. Methods: The SALLE procedure was optimized for extraction-solvent type and volume, salt type, and concentration, with heptane (450 μL) and ammonium acetate (5 M) selected as optimal conditions to achieve high recovery and minimal matrix effects. Method validation was performed according to ICH M10 guidelines, evaluating selectivity, linearity, accuracy, precision, recovery, matrix effect, carry-over, dilution integrity, stability, and reinjection reproducibility. Clinical applicability was assessed in two independent real-sample applications: (i) 31 healthy individuals and 20 Gaucher type 1 patients for Lyso-Gb1, and (ii) 29 healthy individuals and 13 Fabry disease patients for Lyso-Gb3. Result: The method demonstrated excellent linearity for Lyso-Gb1 (0.380–200 ng/mL, R 2 = 0.9890) and Lyso-Gb3 (0.756–200 ng/mL, R 2 = 0.9982), with low LLOQs (0.380 and 0.756 ng/mL, respectively), recoveries of 92.5–109.9%, and negligible matrix effects. In clinical samples, Lyso-Gb1 and Lyso-Gb3 concentrations were significantly higher in their respective patient groups compared to healthy controls ( p < 0.0001). ROC analysis showed perfect discrimination for both biomarkers (AUC = 1.000), with optimal cut-offs of 5.80 ng/mL for Lyso-Gb1 and 3.334 ng/mL for Lyso-Gb3, each yielding 100% sensitivity and specificity. Conclusion: The developed SALLE–LC–MS/MS workflow is robust, high-throughput, and fully compliant with green-chemistry principles, using minimal organic solvent, reducing hazardous waste, and shortening the preparation time. Its excellent analytical performance and perfect clinical discrimination support its integration into routine diagnostic laboratories for the efficient diagnosis and monitoring of Gaucher type 1 and Fabry diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".