Browsing by Author "Tsalik, EL"

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    Human genetic and metabolite variation reveals that methylthioadenosine is a prognostic biomarker and an inflammatory regulator in sepsis
    (Science Advances, 2017-03-08) Wang, L; Ko, ER; Gilchrist, JJ; Pittman, KJ; Rautanen, A; Pirinen, M; Thompson, JW; Dubois, LG; Langley, RJ; Jaslow, SL; Salinas, RE; Rouse, DC; Moseley, MA; Mwarumba, S; Njuguna, P; Mturi, N; Williams, TN; Scott, JAG; Hill, AVS; Woods, CW; Ginsburg, GS; Tsalik, EL; Ko, DC
    Sepsis is a deleterious inflammatory response to infection with high mortality. Reliable sepsis biomarkers could improve diagnosis, prognosis, and treatment. Integration of human genetics, patient metabolite and cytokine measurements, and testing in a mouse model demonstrate that the methionine salvage pathway is a regulator of sepsis that can accurately predict prognosis in patients. Pathway-based genome-wide association analysis of nontyphoidal Salmonella bacteremia showed a strong enrichment for single-nucleotide polymorphisms near the components of the methionine salvage pathway. Measurement of the pathway’s substrate, methylthioadenosine (MTA), in two cohorts of sepsis patients demonstrated increased plasma MTA in nonsurvivors. Plasma MTA was correlated with levels of inflammatory cytokines, indicating that elevated MTA marks a subset of patients with excessive inflammation. A machine-learning model combining MTA and other variables yielded approximately 80% accuracy (area under the curve) in predicting death. Furthermore, mice infected with Salmonella had prolonged survival when MTA was administered before infection, suggesting that manipulating MTA levels could regulate the severity of the inflammatory response. Our results demonstrate how combining genetic data, biomolecule measurements, and animal models can shape our understanding of disease and lead to new biomarkers for patient stratification and potential therapeutic targeting.
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    Non-Gaussian discriminative factor models via the max-margin rank-likelihood
    (32nd International Conference on Machine Learning, ICML 2015, 2015-01-01) Yuan, X; Henao, R; Tsalik, EL; Langley, RJ; Carin, L
    Copyright © 2015 by the author(s).We consider the problem of discriminative factor analysis for data that are in general non-Gaussian. A Bayesian model based on the ranks of the data is proposed. We first introduce a new max-margin version of the rank-likelihood. A discriminative factor model is then developed, integrating the max-margin rank-likelihood and (linear) Bayesian support vector machines, which are also built on the max-margin principle. The discriminative factor model is further extended to the nonlinear case through mixtures of local linear classifiers, via Dirichlet processes. Fully local conjugacy of the model yields efficient inference with both Markov Chain Monte Carlo and variational Bayes approaches. Extensive experiments on benchmark and real data demonstrate superior performance of the proposed model and its potential for applications in computational biology.
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    The State of Infectious Diseases Clinical Trials: A Systematic Review of ClinicalTrials.gov
    (PLoS ONE, 2013-10-16) Goswami, ND; Pfeiffer, CD; Horton, JR; Chiswell, K; Tasneem, A; Tsalik, EL
    Background

    There is a paucity of clinical trials informing specific questions faced by infectious diseases (ID) specialists. The ClinicalTrials.gov registry offers an opportunity to evaluate the ID clinical trials portfolio.

    Methods

    We examined 40,970 interventional trials registered with ClinicalTrials.gov from 2007–2010, focusing on study conditions and interventions to identify ID-related trials. Relevance to ID was manually confirmed for each programmatically identified trial, yielding 3570 ID trials and 37,400 non-ID trials for analysis.

    Results

    The number of ID trials was similar to the number of trials identified as belonging to cardiovascular medicine (n = 3437) or mental health (n = 3695) specialties. Slightly over half of ID trials were treatment-oriented trials (53%, vs. 77% for non-ID trials) followed by prevention (38%, vs. 8% in non-ID trials). ID trials tended to be larger than those of other specialties, with a median enrollment of 125 subjects (interquartile range [IQR], 45–400) vs. 60 (IQR, 30–160) for non-ID trials. Most ID studies are randomized (73%) but nonblinded (56%). Industry was the funding source in 51% of ID trials vs. 10% that were primarily NIH-funded. HIV-AIDS trials constitute the largest subset of ID trials (n = 815 [23%]), followed by influenza vaccine (n = 375 [11%]), and hepatitis C (n = 339 [9%]) trials. Relative to U.S. and global mortality rates, HIV-AIDS and hepatitis C virus trials are over-represented, whereas lower respiratory tract infection trials are under-represented in this large sample of ID clinical trials.

    Conclusions

    This work is the first to characterize ID clinical trials registered in ClinicalTrials.gov, providing a framework to discuss prioritization, methodology, and policy.