Electronic medical records (EMRs) were primarily introduced as a digital health tool in hospitals to improve patient care, but over the past decade, research works have implemented EMR data in clinical trials and omics studies to increase translational potential in drug development. EMRs could help discover phenotypegenotype associations, enhance clinical trial protocols, automate adverse drug event detection and prevention, and accelerate precision medicine research. Although feasible, data mining in EMRs still faces challenges. Existing machine learning tools may help overcome these bottlenecks in EMR mining to unlock new approaches in drug development. This chapter will explore the role of EMRs in drug development while evaluating the viability and bottlenecks of their uses in data mining. This will include discussions on EMR usage in drug development while highlighting successful outcomes in oncology and exploring ML tools to complement and enhance EMR as a widely accepted drug-research source, a section on current clinical applications of EMRs, and a conclusion to summarize and imagine what a future drug research pipeline from EMR to patient treatment may look like.
Purpose
To determine the effect of altering anesthetic oxygen protocols on measurements of cerebral perfusion and metabolism in the rodent brain.
Methods
Seven rats were anesthetized and underwent serial MRI scans with hyperpolarized [1–13C]pyruvate and perfusion weighted imaging. The anesthetic carrier gas protocol used varied from 100:0% to 90:10% to 60:40% O2:N2O. Spectra were quantified with AMARES and perfusion imaging was processed using model‐free deconvolution. A 1‐way ANOVA was used to compare results across groups, with pairwise t tests performed with correction for multiple comparisons. Spearman's correlation analysis was performed between O2% and MR measurements.
Results
There was a significant increase in bicarbonate:total 13C carbon and bicarbonate:13C pyruvate when moving between 100:0 to 90:10 and 100:0 to 60:40 O2:N2O % (0.02 ± 0.01 vs. 0.019 ± 0.005 and 0.02 ± 0.01 vs. 0.05 ± 0.02, respectively) and (0.04 ± 0.01 vs. 0.03 ± 0.01 and 0.04 ± 0.01 vs. 0.08 ± 0.02, respectively). There was a significant difference in 13C pyruvate time to peak when moving between 100:0 to 90:10 and 100:0 to 60:40 O2:N2O % (13 ± 2 vs. 10 ± 1 and 13 ± 2 vs. 7.5 ± 0.5 s, respectively) as well as significant differences in cerebral blood flow (CBF) between gas protocols. Significant correlations between bicarbonate:13C pyruvate and gas protocol (ρ = −0.47), mean transit time and gas protocol (ρ = 0.41) and 13C pyruvate time‐to‐peak and cerebral blood flow (ρ = −0.54) were also observed.
Conclusions
These results demonstrate that the detection and quantification of cerebral metabolism and perfusion is dependent on the oxygen protocol used in the anesthetized rodent brain.
Skeletal muscles produce secretory factors termed as myokines, which alter physiological functions of target tissues. We recently identified C-X-C chemokine ligand 10 (CXCL10) as a novel myokine, which is downregulated in response to exercise. In the present study, we investigated whether the nutritional changes affect CXCL10 expression in mouse skeletal muscle. Expression of CXCL10 was evaluated in mice fed a normal diet or a high fat diet for 10 weeks. In animals fed on HFD, Cxcl10 expression was significantly induced in fast-twitched muscles, and was accompanied by increased blood glucose and free fatty acid levels. In vitro experiments using C2C12 myotubes suggested that the increased levels of glucose and palmitic acids directly enhanced CXCL10 expression. Interestingly, the effect of palmitic acids was attenuated by palmitoleic acids. Considering its potent angiostatic activity, induction of CXCL10 by nutritional changes may contribute to the impairment of microvascular networks in skeletal muscles.
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