High-Throughput Screening and Quantitation of Target Compounds in Biofluids by Coated Blade Spray-Mass Spectrometry
Why this work is in the frame
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Bibliographic record
Abstract
Most contemporary methods of screening and quantitating controlled substances and therapeutic drugs in biofluids typically require laborious, time-consuming, and expensive analytical workflows. In recent years, our group has worked toward developing microextraction (μe)-mass spectrometry (MS) technologies that merge all of the tedious steps of the classical methods into a simple, efficient, and low-cost methodology. Unquestionably, the automation of these technologies allows for faster sample throughput, greater reproducibility, and radically reduced analysis times. Coated blade spray (CBS) is a μe technology engineered for extracting/enriching analytes of interest in complex matrices, and it can be directly coupled with MS instruments to achieve efficient screening and quantitative analysis. In this study, we introduced CBS as a technology that can be arranged to perform either rapid diagnostics (single vial) or the high-throughput (96-well plate) analysis of biofluids. Furthermore, we demonstrate that performing 96-CBS extractions at the same time allows the total analysis time to be reduced to less than 55 s per sample. Aiming to validate the versatility of CBS, substances comprising a broad range of molecular weights, moieties, protein binding, and polarities were selected. Thus, the high-throughput (HT)-CBS technology was used for the concomitant quantitation of 18 compounds (mixture of anabolics, β-2 agonists, diuretics, stimulants, narcotics, and β-blockers) spiked in human urine and plasma samples. Excellent precision (∼2.5%), accuracy (≥90%), and linearity ( R 2 ≥ 0.99) were attained for all the studied compounds, and the limits of quantitation (LOQs) were within the range of 0.1 to 10 ng·mL –1 for plasma and 0.25 to 10 ng·mL –1 for urine. The results reported in this paper confirm CBS’s great potential for achieving subsixty-second analyses of target compounds in a broad range of fields such as those related to clinical diagnosis, food, the environment, and forensics.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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