Immunoaffinity Solid Phase Microextraction
Bibliographic record
Abstract
Biological fluids are commonly analyzed in clinical and forensic studies for drug concentration measurements. Conventional quantification procedures are always associated with lengthy sample pretreatment steps to eliminate the interfering compounds that potentially exist in complex biological matrices. The objective of this study was to address these problems employing solid-phase microextraction (SPME) technique. Antibodies (Abs) were employed to serve as an extremely specific extraction phase for direct extraction of analytes from untreated biological matrices based on their exquisite selectivity for antigens (Ags). \nMuch of the research was focused on selecting the most appropriate antibody (Ab) for a particular application based on evaluation of characteristics of various types of Abs obtained from four suppliers. Abs’ binding characteristics were evaluated before and after immobilization in terms of affinity, valence, homogeneity, capacity and cross-reactivity for three benzodiazepines. The performance of immunoaffinity probes of the same type provided by different suppliers was found to be comparable. Finally, the probes’ utility for extraction of benzodiazepines from plasma samples was evaluated. \nThe limit of detection of the method developed in this work was 0.01 ng/mL with upper limits of quantification of 0.5 ng/mL in buffer and 2 ng/mL in plasma. The method’s precision was 12% for extraction from buffer and less than 10% for extraction from plasma. With limits of detection similar to the current state-of-the-art methods available for quantification of drugs in biological matrices, the method presented in this thesis was found advantageous compared to other available methods due to its simplified sample preparation procedure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".