SUMMARY A complex interplay of environmental factors impacts the metabolism of human cells, but neither traditional culture media nor mouse plasma mimic the metabolite composition of human plasma. Here, we developed a culture medium with polar metabolite concentrations comparable to those of human plasma (human plasma-like medium; HPLM). Culture in HPLM, relative to that in traditional media, had widespread effects on cellular metabolism, including on the metabolome, redox state, and glucose utilization. Among the most prominent is an inhibition of de novo pyrimidine synthesis – an effect traced to uric acid, which is 10-fold higher in the blood of humans than of mice and other non-primates. We find that uric acid directly inhibits UMP synthase and consequently reduces the sensitivity of cancer cells to the chemotherapeutic agent 5-fluorouracil. Thus, media that better recapitulates the composition of human plasma reveals unforeseen metabolic wiring and regulation, suggesting that HPLM should be of broad utility.

Background Diabetes Mellitus is an increasingly prevalent chronic disease characterized by the body’s inability to metabolize glucose. The objective of this study was to build an effective predictive model with high sensitivity and selectivity to better identify Canadian patients at risk of having Diabetes Mellitus based on patient demographic data and the laboratory results during their visits to medical facilities. Methods Using the most recent records of 13,309 Canadian patients aged between 18 and 90 years, along with their laboratory information (age, sex, fasting blood glucose, body mass index, high-density lipoprotein, triglycerides, blood pressure, and low-density lipoprotein), we built predictive models using Logistic Regression and Gradient Boosting Machine (GBM) techniques. The area under the receiver operating characteristic curve (AROC) was used to evaluate the discriminatory capability of these models. We used the adjusted threshold method and the class weight method to improve sensitivity – the proportion of Diabetes Mellitus patients correctly predicted by the model. We also compared these models to other learning machine techniques such as Decision Tree and Random Forest. Results The AROC for the proposed GBM model is 84.7% with a sensitivity of 71.6% and the AROC for the proposed Logistic Regression model is 84.0% with a sensitivity of 73.4%. The GBM and Logistic Regression models perform better than the Random Forest and Decision Tree models. Conclusions The ability of our model to predict patients with Diabetes using some commonly used lab results is high with satisfactory sensitivity. These models can be built into an online computer program to help physicians in predicting patients with future occurrence of diabetes and providing necessary preventive interventions. The model is developed and validated on the Canadian population which is more specific and powerful to apply on Canadian patients than existing models developed from US or other populations. Fasting blood glucose, body mass index, high-density lipoprotein, and triglycerides were the most important predictors in these models.

For high-dimensional data set with complicated dependency structures, the full likelihood approach often renders to intractable computational complexity. This imposes di±culty on model selection as most of the traditionally used information criteria require the evaluation of the full likelihood. We propose a composite likelihood version of the Bayesian information criterion (BIC) and establish its consistency property for the selection of the true underlying model. Under some mild regularity conditions, the proposed BIC is shown to be selection consistent, where the number of potential model parameters is allowed to increase to in¯nity at a certain rate of the sample size. Simulation studies demonstrate the empirical performance of this new BIC criterion, especially for the scenario that the number of parameters increases with the sample size.

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The effect of feeding Lactobacillus fermentum I5007 on the immune system of weaned pigs with or without E. coli challenge was determined. Twenty-four weaned barrows (6.07 +/- 0.63 kg BW) were randomly assigned to one of four treatments (N = 6) in a factorial design experiment. The first two treatments consisted of healthy piglets with half of the pigs receiving no treatment while the other half was orally administered with L. fermentum I5007 (10(8) CFU/ml) at a daily dose of 20 ml. Pigs in the second two treatments were challenged on the first day with 20 ml of E. coli K88ac (10(8) CFU/ml). Half of these pigs were not treated while the remaining pigs were treated with 20 ml of L. fermentum I5007 (10(8) CFU/ml). Peripheral blood lymphocytes subsets were determined using flow cytometry. The intestinal mucosal immunity of the pigs was monitored by real time polymerase chain reaction. The cytokine content of the pig's serum was also analyzed. Oral administration of L. fermentum I5007 increased blood CD4(+) lymphocyte subset percentage as well as tumor necrosis factor-alpha and interferon-gamma expression in the ileum. Pigs challenged with E. coli had elevated jejunal tumor necrosis factor-alpha while interferon-gamma expression was increased throughout the small intestine. There was no difference in the concentration of the cytokines interleukin-2, interleukin-6, tumor necrosis factor-alpha and interferon-gamma in the serum. CD8(+) and CD4(+)/CD8(+) in peripheral blood were not affected by treatment. In conclusion, L. fermentum I5007 can enhance T cell differentiation and induce ileum cytokine expression suggesting that this probiotic strain could modulate immune function in piglets.

SummaryWe consider situations where the data consist of a number of responses for each individual, which may include a mix of discrete and continuous variables. The data also include a class of predictors, where the same predictor may have different physical measurements across different experiments depending on how the predictor is measured. The goal is to select which predictors affect any of the responses, where the number of such informative predictors tends to infinity as the sample size increases. There are marginal likelihoods for each experiment; we specify a pseudolikelihood combining the marginal likelihoods, and propose a pseudolikelihood information criterion. Under regularity conditions, we establish selection consistency for this criterion with unbounded true model size. The proposed method includes a Bayesian information criterion with appropriate penalty term as a special case. Simulations indicate that data integration can dramatically improve upon using only one data source.