N-acetylcysteine (NAC) is an antidote to prevent acetaminophen (paracetamol-APAP)-induced acute liver injury (ALI). The 3-bag licensed 20.25 h standard regimen, and a 12 h modified regimen, are used to treat APAP overdose. This study evaluated the redox thiol response and APAP metabolites, in patients with a single APAP overdose treated with either the 20.25 h standard or 12 h modified regimen. We used liquid chromatography tandem mass spectrometry to quantify clinically important oxidative stress biomarkers and APAP metabolites in plasma samples from 45 patients who participated in a randomized controlled trial (SNAP trial). We investigated the time course response of plasma metabolites at predose, 12 h, and 20.25 h post-start of NAC infusion. The results showed that the 12 h modified regimen resulted in a significant elevation of plasma NAC and cysteine concentrations at 12 h post-infusion. We found no significant alteration in the metabolism of APAP, mitochondrial, amino acids, and other thiol biomarkers with the two regimens. We examined APAP and purine metabolism in overdose patients who developed ALI. We showed the major APAP-metabolites and xanthine were significantly higher in patients with ALI. These biomarkers correlated well with alanine aminotransferase activity at admission. Receiver operating characteristic analysis showed that at admission, plasma APAP-metabolites and xanthine concentrations were predictive for ALI. In conclusion, a significantly higher redox thiol response with the modified NAC regimen at 12 h postdose suggests this regimen may produce greater antioxidant efficacy. At baseline, plasma APAP and purine metabolites may be useful biomarkers for early prediction of APAP-induced ALI.
Moving away from traditional "one-size-fits-all" treatment to precision-based medicine has tremendously improved disease prognosis, accuracy of diagnosis, disease progression prediction, and targeted-treatment. The current cutting-edge of 5G network technology is enabling a growing trend in precision medicine to extend its utility and value to the smart healthcare system. The 5G network technology will bring together big data, artificial intelligence, and machine learning to provide essential levels of connectivity to enable a new health ecosystem toward precision medicine. In the 5G-enabled health ecosystem, its applications involve predictive and preventative measurements which enable advances in patient personalization. This review aims to discuss the opportunities, challenges, and prospects posed to 5G network technology in moving forward to deliver personalized treatments and patient-centric care via a precision medicine approach.