Development and validation of an ultrafast method of quantification of rivaroxaban in human serum using laser diode thermal desorption coupled to triple quadrupole mass spectrometry
Why this work is in the frame
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Bibliographic record
Abstract
RATIONALE: Rivaroxaban is an anticoagulant prescribed to patients who are at risk of medical conditions such as deep-vein thrombosis, pulmonary embolisms, and strokes caused by blood clots. The administration of this drug is monitored to adjust the dosage and evaluate patients' blood concentration. Rapid quantification of this drug in plasma could make it possible to ensure that the dose present in the blood of patients does not represent a danger for the medical intervention to be carried out. METHODS: Liquid chromatography-tandem mass spectrometry is usually employed to quantify rivaroxaban in blood, plasma, and serum. Here, an alternative method of analysis based on laser diode thermal desorption-triple quadrupole mass spectrometry (LDTD-QqQMS) was developed and comprehensively validated. This new method allows the quantification of rivaroxaban in less than 13 s from sample to sample. The extraction of rivaroxaban in human serum was done by a salting-out liquid-liquid extraction with acetonitrile and a saturated sodium chloride solution. RESULTS: The proposed method allows the quantification of rivaroxaban in less than 13 s from sample to sample. During validation, all criteria were respected. The accuracy was <15% of the nominal value, the precision was <15%CV, and the recovery was ≥89.9%. There were no observed carryover or matrix effects. Analysis of the extracted samples established the stability of dry (24 h) and wet samples (1 week) when samples cannot be analyzed immediately, a considerable advantage in a clinical setting. CONCLUSIONS: This method improves sample throughput by more than 1200% compared to liquid chromatography-tandem mass spectrometry methods of analysis of rivaroxaban and decreases analysis costs by reducing solvent consumption and instrument time.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it