A simple method for the determination of acyclovir concentrations in human plasma using high-performance liquid chromatography
Bibliographic record
Abstract
Abstract Background Acyclovir is an anti-viral medication given to treat herpes simplex and herpes zoster infection. In some severe conditions such as herpes encephalitis, acyclovir is administered intravenously. However, high acyclovir doses may cause acute kidney injury and low acyclovir dose may predispose the patient to inadequate exposure to acyclovir which could be fatal in some conditions. In such cases, the acyclovir plasma concentrations will potentially guide the diagnosis and management of the kidney injury. In this study, we provide a simple and time-efficient method for analyzing acyclovir in human plasma using high-performance liquid chromatography (HPLC). Results The process starts with a single protein precipitation step by adding acetonitrile to deproteinize 300 µL of plasma. The chromatographic separation conditions consist of a mobile phase of water: methanol (97:3, v/v), a flow rate of 1 mL/min, a run time of 17 min, and a detection wavelength of 254 nm. The calibration curve was linear over the range of (0.70–60 mg/L) (r2 ˃ 0.99). The retention times of acyclovir and the internal standard were around 15 and 12 min, respectively. The intra-day and inter-day analysis of acyclovir in plasma using this method exhibited accuracy and precision of less than 7%, which lies within the acceptable range. Different greenness assessment tools confirmed that the proposed method is eco-friendly. Conclusion The proposed method of analysis of acyclovir in the plasma using HPLC is simple, green and accurate method. This method could be applied in clinical settings where monitoring acyclovir concentrations is essential as it has wide range of the concentrations that could be detected.
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 | 0.002 | 0.002 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".