Simple HPLC-UV Method for Piperacillin/Tazobactam Assay in Human Plasma
Bibliographic record
Abstract
BACKGROUND: Piperacillin (Pip)/tazobactam (Taz) is a broad-spectrum antimicrobial agent that has been commonly used in the intensive care unit for severe and life-threatening infections. Recent evidence suggests that therapeutic drug monitoring (TDM) for Pip could be beneficial in clinical practice to facilitate dose optimization and increase the odds of treatment success. The aim was to develop and validate a sensitive and simple high-performance liquid chromatography (HPLC) method for the simultaneous quantification of Pip and Taz in human plasma. METHODS: Samples (0.3 mL) were deproteinized with acetonitrile. The supernatant was evaporated and then reconstituted and injected into the HPLC. The chromatographic analysis was carried out by using the C18 column and gradient elution with the acetonitrile:water mobile phase mixture with 0.1% trifluoracetic acid at a flow rate of 0.8 mL/min using a UV detector at 218 nm. RESULTS: > 0.99) over the concentration ranges of 0.5-400 μg/mL and 1-100 μg/mL for Pip and Taz, respectively. The method demonstrated acceptable inter- and intra-day precision and accuracy within ±20% with adequate stability results. CONCLUSION: The developed method is sensitive and simple and utilizes simple sample preparation and elution steps, making it suitable and practical for Pip/Taz TDM.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".