The
application of metabolomics in translational research suffers
from several technological bottlenecks, such as data reproducibility
issues and the lack of standardization of sample profiling procedures.
Here, we report an automated high-throughput metabolite array technology
that can rapidly and quantitatively determine 324 metabolites including
fatty acids, amino acids, organic acids, carbohydrates, and bile acids.
Metabolite identification and quantification is achieved using the
Targeted Metabolome Batch Quantification (TMBQ) software, the first
cross-vendor data processing pipeline. A test of this metabolite array
was performed by analyzing serum samples from patients with chronic
liver disease (N = 1234). With high detection efficiency
and sensitivity in serum, urine, feces, cell lysates, and liver tissue
samples and suitable for different mass spectrometry systems, this
metabolite array technology holds great potential for biomarker discovery
and high throughput clinical testing. Additionally, data generated
from such standardized procedures can be used to generate a clinical
metabolomics database suitable for precision medicine in next-generation
healthcare.
Nonlinear random effects models with finite mixture structures are used to identify polymorphism in pharmacokinetic/pharmacodynamic phenotypes. An EM algorithm for maximum likelihood estimation approach is developed and uses sampling-based methods to implement the expectation step, that results in an analytically tractable maximization step. A benefit of the approach is that no model linearization is performed and the estimation precision can be arbitrarily controlled by the sampling process. A detailed simulation study illustrates the feasibility of the estimation approach and evaluates its performance. Applications of the proposed nonlinear random effects mixture model approach to other population pharmacokinetic/pharmacodynamic problems will be of interest for future investigation.
Background
Anesthesia may induce neurotoxicity and neurocognitive impairment in young mice. However, the underlying mechanism remains largely to be determined. Meanwhile, autophagy is involved in brain development and contributes to neurodegenerative diseases. We, therefore, set out to determine the effects of sevoflurane on autophagy in the hippocampus of young mice and on cognitive function in the mice.
Methods
Six day-old mice received 3% sevoflurane, for two hours daily, on postnatal days (P) 6, 7 and 8. We then decapitated the mice and harvested the hippocampus of the young mice at P8. The level of LC3, the ratio of LC3-II to LC3-I, and SQSTM1/p62 level associated with the autophagy in the hippocampus of the mice were assessed by using Western blotting. We used different groups of mice for behavioral testing via the Morris Water Maze from P31 to P37.
Results
The anesthetic sevoflurane increased the level of LC3-II and ratio of LC3-II/LC3-I, decreased the p62 level in the hippocampus of the young mice, and induced cognitive impairment in the mice. 3-Methyladenine, the inhibitor of autophagy, attenuated the activation of autophagy and ameliorated the cognitive impairment induced by sevoflurane in the young mice.
Conclusion
These data showed that sevoflurane anesthesia might induce cognitive impairment in the young mice via activation of autophagy in the hippocampus of the young mice. These findings from the proof of concept studies have established a system and suggest the role of autophagy in anesthesia neurotoxicity and cognitive impairment in the young mice, pending further investigation.
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