Ultrahigh-throughput absolute quantitative analysis of linezolid in human plasma by direct analysis in real time mass spectrometry without chromatographic separation and its application to a pharmacokinetic study.

Therapeutic drug monitoring (TDM) is necessary in the clinical management of linezolid to improve its efficacy and reduce the risk of time- and dose-dependent toxicity. A novel and ultrahigh-throughput analytical method for the determination of linezolid in human plasma was developed based on direct analysis in real-time tandem mass spectrometry (DART-MS/MS) without chromatographic separation. After solid-phase extraction with Waters Oasis HLB, the linezolid and internal standard linezolid-d3 were detected by positive ion electrospray ionization followed by multiple reaction monitoring (MRM) of the transition at m/z 338.1 → 296.2 and 341.2 → 297.3, respectively. The use of DART-MS obviates the need for chromatographic separation and allowed determination of linezolid in a total run time of only 24 s per sample. The method was linear in the concentration range 0.20-25 µg mL-1 with intraday and interday precision <14.5% and accuracy ranging from -3.85% to 12.7%. The method was successfully applied to a pharmacokinetic study of linezolid in healthy male volunteers after oral administration of a 600 mg tablet. DART-MS/MS provides a rapid and sensitive method for the determination of linezolid that does not require chromatographic separation. It is eminently suitable to meet the high-throughput challenge of clinical TDM. Graphical abstract.

Understanding Users' Continuance Usage Behavior Towards Digital Health Information System Driven by the Digital Revolution Under COVID-19 Context: An Extended UTAUT Model.

Background: Improving the health status of users through the use of digital health information systems has drawn the attention of practitioners and academics under the tide of the digital revolution, specifically in the time of global pandemic of COVID-19, and as a result, online medical consultation has developed rapidly. Purpose: Empirical studies, however, are lacking in terms of gaining insight into use digital health information system driven by the digital revolution under COVID-19 and identifying the factors for retaining users and encouraging their continuing use. To solve this problem, this study seeks to explore the factors that influence users' intention to use digital health information system. Methods: This study extended the Unified Theory of Acceptance and Use of Technology (UTAUT) model by introducing components of perceived risks into the model. Structural equation modeling was adopted to evaluate the research model based on an empirical survey of 241 users in China. Results: As indicated by the results, users' continuance usage behavior to digital health information system is shaped by intention to use and facilitating conditions, with effort expectancy, social influence, perceived ease of use and perceived enjoyment exerting indirect positive effects on continuance usage behavior via intention to use. In contrast, perceived risk and perceived cost have indirect negative impact on continuance usage behavior. Conclusion: The findings of this study can not only help the practitioners better understand the users' continuance usage behavior towards digital health information system driven by digital revolution in the time of COVID-19 pandemic and further tap into the potential market but also make up the short of traditional technology acceptance model explanatory by the extended UTAUT model.