Impact of Plasma and Whole-Blood Anticoagulant Counter Ion Choice on Drug Stability and Matrix Effects During Bioanalysis
Bibliographic record
Abstract
BACKGROUND: Anticoagulants are used to prevent coagulation in blood samples. The plasma pH may change with a different counter ion and anticoagulant; thus, it is essential to determine effects on drug stability and the matrix effect during the bioanalytical method development. RESULTS: Cross-validation of multiple compounds between different counter ions was performed and no impact from the counter ion nature was demonstrated. Moreover, plasma stabilities and matrix effects for both fluconazole and granisetron were investigated thoroughly in numerous counter ions/anticoagulants (K(3)ethylenediaminetetraacetic acid [K(3)EDTA], K(2)EDTA, NaEDTA, NaHeparin and LiHeparin). Sirolimus, a large cyclic molecule, was also tested in different whole-blood EDTA counter ions. Results showed percentage deviation less than 8.5% and percentage cross-validation less than 8.4%. CONCLUSION: None of the compounds tested had an impact on the matrix stabilities and matrix effect.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".