Application of Solid-Phase Microextraction in the Determination of Diazepam Binding to Human Serum Albumin
Bibliographic record
Abstract
In this paper, protein-drug interactions were studied by solid-phase microextraction (SPME) using diazepam binding to human serum albumin as a model system. Since drug compounds are normally polar and nonvolatile by nature, direct SPME is used in this work. The SPME extraction is an equilibrium process among the concentrations of the analyte partitioned onto the SPME fiber, free and bound drug in the solution. A calibration curve was first constructed by employing the amount of the analytes partitioned on the fiber versus the free analyte concentration in the solution in the absence of protein. In method I, the extraction was performed in the protein solution with known diazepam concentration. In method II, diazepam was first loaded onto the fiber by extracting in solution with known diazepam concentration. This fiber was subsequently transferred into the protein solution for desorption. The amount of the analyte left on the fiber was analyzed after the system reached equilibrium. The free drug concentration was then obtained from the calibration curve for both methods. The Scatchard plot was finally employed to obtain the number of binding sites and the equilibrium binding constants. Since only a very small amount of the protein solution is required (150 microL for each extraction), method II is very useful for circumstances where the protein amount is very limited. The direct measurement method proposed in this paper does not need a GC response factor, which significantly decreases the experimental error. The only measurement needed is the area count change (ratio) of the fiber injections before and after the protein was introduced into the solution. The difference between the direct measurement method for method I and method II is discussed. The result illustrated that the SPME direct measurement method provided both theoretical accuracy and simplicity in such applications.
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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 | 0.000 |
| 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".