Background
Although the post-mortem increase of ammonium in biological fluids is well known, ammonium analysis in vitreous humour has never been used in recent times for the determination of the post-mortem interval. The present work represents a new application of capillary electrophoresis with indirect UV detection in the field of forensic analysis.
Methods
The electrophoretic separation was carried out in a running buffer made of 5 mM imidazole, 5 mM 18-crown-6 ether and 6 mM d,l-α-hydroxybutyric acid (HIBA). To overcome the lack of optical absorption of ammonium, indirect UV detection was applied. The used wavelength was 214 nm.
Results
The method showed good linearity in the concentration range from 0.16 to 5.0 mM. The limit of detection, 0.039 mmol/L, was established on the basis of the linearity curve. Precision and bias studies carried out on the pure ammonium solutions and in real biological samples, revealed %RSDs well below 20%. A preliminary application to real cases where the death time was precisely known (14 bodies) was carried out plotting vitreous humour ammonium vs. post-mortem interval with a resulting good linear correlation until 100 h post-mortem.
Conclusions
After validation in real cases, the present method can become a powerful tool to unravel one of the most challenging issues of forensic investigation: determination of the time of death.
Metabolomics is a promising technology for the application of translational medicine to cardiovascular risk. Here, we applied a liquid chromatography/tandem mass spectrometry approach to explore the associations between plasma concentrations of amino acids, methylarginines, acylcarnitines, and tryptophan catabolism metabolites and cardiometabolic risk factors in patients diagnosed with arterial hypertension (HTA) (n = 61), coronary artery disease (CAD) (n = 48), and non-cardiovascular disease (CVD) individuals (n = 27). In total, almost all significantly different acylcarnitines, amino acids, methylarginines, and intermediates of the kynurenic and indolic tryptophan conversion pathways presented increased (p < 0.05) in concentration levels during the progression of CVD, indicating an association of inflammation, mitochondrial imbalance, and oxidative stress with early stages of CVD. Additionally, the random forest algorithm was found to have the highest prediction power in multiclass and binary classification patients with CAD, HTA, and non-CVD individuals and globally between CVD and non-CVD individuals (accuracy equal to 0.80 and 0.91, respectively). Thus, the present study provided a complex approach for the risk stratification of patients with CAD, patients with HTA, and non-CVD individuals using targeted metabolomics profiling.
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