High-throughput analysis using non-depletive SPME: challenges and applications to the determination of free and total concentrations in small sample volumes
Bibliographic record
Abstract
In vitro high-throughput non-depletive quantitation of chemicals in biofluids is of growing interest in many areas. Some of the challenges facing researchers include the limited volume of biofluids, rapid and high-throughput sampling requirements, and the lack of reliable methods. Coupled to the above, growing interest in the monitoring of kinetics and dynamics of miniaturized biosystems has spurred the demand for development of novel and revolutionary methodologies for analysis of biofluids. The applicability of solid-phase microextraction (SPME) is investigated as a potential technology to fulfill the aforementioned requirements. As analytes with sufficient diversity in their physicochemical features, nicotine, N,N-Diethyl-meta-toluamide, and diclofenac were selected as test compounds for the study. The objective was to develop methodologies that would allow repeated non-depletive sampling from 96-well plates, using 100 µL of sample. Initially, thin film-SPME was investigated. Results revealed substantial depletion and consequent disruption in the system. Therefore, new ultra-thin coated fibers were developed. The applicability of this device to the described sampling scenario was tested by determining the protein binding of the analytes. Results showed good agreement with rapid equilibrium dialysis. The presented method allows high-throughput analysis using small volumes, enabling fast reliable free and total concentration determinations without disruption of system equilibrium.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".