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Record W4319080394 · doi:10.3390/antibiotics12020321

Simple HPLC-UV Method for Piperacillin/Tazobactam Assay in Human Plasma

2023· article· en· W4319080394 on OpenAlexafffund
Khaled Abdelkawy, Tyson Le, Sherif Hanafy Mahmoud

Bibliographic record

VenueAntibiotics · 2023
Typearticle
Languageen
FieldMedicine
TopicAntibiotics Pharmacokinetics and Efficacy
Canadian institutionsUniversity of Alberta
FundersFaculty of Pharmacy and Pharmaceutical Sciences, University of AlbertaUniversity Hospital FoundationUniversity of Alberta
KeywordsChromatographyHigh-performance liquid chromatographyTazobactamAcetonitrileElutionChemistryPiperacillin/tazobactamTherapeutic drug monitoringPiperacillinDrugPharmacologyMedicineAntibioticsBacteriaBiochemistry

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.390
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2023
Admission routes2
Has abstractyes

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