Spitting for Science: Non-Invasive Drug Monitoring Through a Modified Liquid Micro Junction-Surface Sampling Probe
Bibliographic record
Abstract
The use of saliva as a convenient, non-invasive alternative for therapeutic drug monitoring has become increasingly favorable with the emergence of pharmacokinetic and bioequivalence research providing a link between blood plasma and saliva concentrations. Therapeutic drug monitoring is a practice through which specific drugs are monitored at given time intervals to maintain a constant concentration in the blood. It is only necessary for patients taking drugs with known pharmacokinetic variability, narrow therapeutic indices and potential adverse effects. Although the drug concentration in saliva is lower than in plasma, saliva has a complex matrix, consisting of salts, immunoglobins, proteins and more. The polar matrix in the saliva introduces potential interference with the sensitivity of the drug detection through salt suppression. In this project, 6 drugs used as anticonvulsants and antidepressants were analysed in artificial saliva using a modified version of the Liquid Micro Junction-Surface Sampling Probe (LMJ-SSP) to maximize the sensitivity of observed signals and minimize the detection limits. The main parameter which was targeted for optimizing the extraction and ionization of the drug in the saliva samples was the polarity of the carrier liquid, which continuously flows in the coaxial tube of the LMJ-SSP. Upon analyzing four different carrier liquids, the mixture containing methanol, water and chloroform was determined to provide the best sensitivity. Additionally, the enrichment factors of the peak areas using the chloroform mixture in the modified LMJ-SSP were enhanced by a factor ranging between 2.9 to 31.4, depending on the analyte. Overall, the data obtained from this project thus far displays that modifying the polarity by modifying the carrier liquid composition to balance the extraction and ionization during analysis using the modified LMJ-SSP optimizes the sensitivity of the detection.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".