RESUMO
5F-MDMB-PICA, an indole-type synthetic cannabinoid (SC), was classified illicit globally in 2020. Although the extensive metabolism of 5F-MDMB-PICA in the human body warrants the development of robust analytical methods for metabolite detection and quantification, a current lack of reference standards for characteristic metabolites hinders such method creation. This work described the synthesis of 18 reference standards for 5F-MDMB-PICA and its possible Phase I metabolites, including three hydroxylated positional isomers R14 to R16. All the compounds were systematic characterized via nuclear magnetic resonance, Fourier transform infrared spectroscopy, and high-resolution mass spectrometry. Furthermore, two methods were developed for the simultaneous detection of all standards using liquid chromatography-tandem mass spectrometry and ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. By comparison with authentic samples, R17 was identified as a suitable urine biomarker for 5F-MDMB-PICA uptake.
RESUMO
BACKGROUND: Chronic kidney disease (CKD) is a global health burden. However, the efficacy of different modes of eHealth care in facilitating self-management for patients with CKD is unclear. OBJECTIVE: The aim of this study was to evaluate the effectiveness of a mobile app-based intelligent care system in improving the kidney outcomes of patients with CKD. METHODS: Our study was a retrospective analysis based on the KidneyOnline intelligent system developed in China. Patients with CKD but not dependent on dialysis who registered on the KidneyOnline app between January 2017 and January 2021 were screened. Patients in the the KidneyOnline intelligent system group and those in the conventional care group were 1:1 matched according to their baseline characteristics. The intervention group received center-based follow-up combined with the KidneyOnline intelligent patient care system, which was a nurse-led, patient-oriented collaborative management system. Health-related data uploaded by the patients were integrated using deep learning optical character recognition (OCR). Artificial intelligence (AI)-generated personalized recipes, lifestyle intervention suggestions, early warnings, real-time questions and answers, and personalized follow-up plans were also provided. Patients in the conventional group could get professional suggestions from the nephrologists through regular clinical visits, but they did not have access to the service provided by AI and the health coach team. Patients were followed for at least 3 months after recruitment or until death or start of renal replacement therapy. RESULTS: A total of 2060 eligible patients who registered on the KidneyOnline app from 2017 to 2021 were enrolled for the analysis. Of those, 902 (43.8%) patients were assessed for survival analysis after propensity score matching, with 451(50%) patients in the KidneyOnline intelligent patient care system group and 451(50%) patients in the conventional care group. After a mean follow-up period of 15.8 (SD 9.5) months, the primary composite kidney outcome occurred in 28 (6%) participants in the KidneyOnline intelligent patient care system group and 32 (7%) in the conventional care group, with a hazard ratio of 0.391 (95% CI 0.231-0.660; P<.001). Subgroup survival analysis demonstrated that the KidneyOnline care system significantly reduced the risk of composite kidney outcome, irrespective of age, sex, baseline estimated glomerular filtration rate (eGFR), and proteinuria. In addition, the mean arterial pressure (MAP) significantly decreased from 88.9 (SD 10.5) mmHg at baseline to 85.6 (SD 7.9) mmHg at 6 months (P<.001) in the KidneyOnline intelligent patient care system group and from 89.3 (SD 11.1) mmHg to 87.5 (SD 8.2) mmHg (P=.002) in the conventional CKD care group. CONCLUSIONS: The utilization of the KidneyOnline intelligent care system was associated with reduced risk of unfavorable kidney outcomes in nondiabetic patients with CKD.
