Person: Gifford, David Kenneth
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Gifford
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David Kenneth
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Gifford, David Kenneth
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Publication Detection of Delirium in the Intensive Care Unit: Comparison of Confusion Assessment Method for the Intensive Care Unit with Confusion Assessment Method Ratings(Wiley-Blackwell, 2005) Gifford, David Kenneth; Inouye, Sharon; McNicoll, Lynn; Pisani, Margaret; Ely, E. WesleyObjectives: To compare the Confusion Assessment Method (CAM) and CAM for the Intensive Care Unit (CAM-ICU) methods for detecting delirium in alert, nonintubated older ICU patients. Design: Comparison study. Setting: Fourteen-bed medical ICU of an 800-bed university teaching hospital. Participants: Twenty-two patients aged 65 and older admitted to the ICU. Measurements: Two blinded, trained clinician-researchers who had undergone interrater reliability testing interviewed patients separately, usually within 10 minutes of each other (up to 120 minutes). Each researcher examined patients for the four key CAM criteria: acuteness, inattention, disorganized thinking, and altered level of consciousness. One researcher used the CAM method with the Mini-Mental State Examination and Digit Span; the other researcher used the CAM-ICU method with nonverbal cognitive and attention tasks. Results: Rates of delirium were 68% according to CAM and 50% according CAM-ICU. Comparing the two methods, agreement was 82%, with a kappa of 0.64. Using the CAM as the reference standard, the CAM-ICU had a sensitivity of 73% (95% confidence interval (CI)=60–86) and specificity of 100% (95% CI=56–100). There were four false-negative ratings using the CAM-ICU. Reasons for disparate results were that the CAM used more-detailed cognitive testing that detected more deficits (3 patients) and the time elapsed (90 minutes) between ratings in one patient with markedly fluctuating symptoms. Conclusion: CAM and CAM-ICU agreement was moderately high. Although the CAM-ICU is recommended for ICU patients because of its brevity and ease of use, the standard CAM method may detect more subtle cases of delirium in nonintubated, verbal ICU patients.Publication Long-term persistence and development of induced pancreatic beta cells generated by lineage conversion of acinar cells(Nature Publishing Group, 2014) Li, Weida; Cavelti-Weder, Claudia; Zhang, Yinying; Clement, Kendell; Donovan, Scott; Gonzalez, Gabriel; Zhu, Jiang; Stemann, Marianne; Xu, Ke; Hashimoto, Tatsu; Yamada, Takatsugu; Nakanishi, Mio; Zhang, Yuemei; Zeng, Samuel; Gifford, David Kenneth; Meissner, Alexander; Weir, Gordon; Zhou, QiaoDirect lineage conversion is a promising approach to generate therapeutically important cell types for disease modeling and tissue repair. However, the survival and function of lineage-reprogrammed cells in vivo over the long term has not been examined. Here, using an improved method for in vivo conversion of adult mouse pancreatic acinar cells toward beta cells, we show that induced beta cells persist for up to 13 months (the length of the experiment), form pancreatic islet–like structures and support normoglycemia in diabetic mice. Detailed molecular analyses of induced beta cells over 7 months reveal that global DNA methylation changes occur within 10 d, whereas the transcriptional network evolves over 2 months to resemble that of endogenous beta cells and remains stable thereafter. Progressive gain of beta-cell function occurs over 7 months, as measured by glucose-regulated insulin release and suppression of hyperglycemia. These studies demonstrate that lineage-reprogrammed cells persist for >1 year and undergo epigenetic, transcriptional, anatomical and functional development toward a beta-cell phenotype.Publication Lineage-Based Identification of Cellular States and Expression Programs(Oxford University Press, 2012) Hashimoto, Tatsunori; Jaakkola, Tommi; Sherwood, Richard; Mazzoni, Esteban O.; Wichterle, Hynek; Gifford, David KennethSummary: We present a method, LineageProgram, that uses the developmental lineage relationship of observed gene expression measurements to improve the learning of developmentally relevant cellular states and expression programs. We find that incorporating lineage information allows us to significantly improve both the predictive power and interpretability of expression programs that are derived from expression measurements from in vitro differentiation experiments. The lineage tree of a differentiation experiment is a tree graph whose nodes describe all of the unique expression states in the input expression measurements, and edges describe the experimental perturbations applied to cells. Our method, LineageProgram, is based on a log-linear model with parameters that reflect changes along the lineage tree. Regularization with L1 that based methods controls the parameters in three distinct ways: the number of genes change between two cellular states, the number of unique cellular states, and the number of underlying factors responsible for changes in cell state. The model is estimated with proximal operators to quickly discover a small number of key cell states and gene sets. Comparisons with existing factorization, techniques, such as singular value decomposition and non-negative matrix factorization show that our method provides higher predictive power in held, out tests while inducing sparse and biologically relevant gene sets.Publication Automated Discovery of Functional Generality of Human Gene Expression Programs(Public Library of Science, 2007) Gerber, Georg; Dowell, Robin D; Jaakkola, Tommi S; Gifford, David KennethAn important research problem in computational biology is the identification of expression programs, sets of co-expressed genes orchestrating normal or pathological processes, and the characterization of the functional breadth of these programs. The use of human expression data compendia for discovery of such programs presents several challenges including cellular inhomogeneity within samples, genetic and environmental variation across samples, uncertainty in the numbers of programs and sample populations, and temporal behavior. We developed GeneProgram, a new unsupervised computational framework based on Hierarchical Dirichlet Processes that addresses each of the above challenges. GeneProgram uses expression data to simultaneously organize tissues into groups and genes into overlapping programs with consistent temporal behavior, to produce maps of expression programs, which are sorted by generality scores that exploit the automatically learned groupings. Using synthetic and real gene expression data, we showed that GeneProgram outperformed several popular expression analysis methods. We applied GeneProgram to a compendium of 62 short time-series gene expression datasets exploring the responses of human cells to infectious agents and immune-modulating molecules. GeneProgram produced a map of 104 expression programs, a substantial number of which were significantly enriched for genes involved in key signaling pathways and/or bound by NF-κB transcription factors in genome-wide experiments. Further, GeneProgram discovered expression programs that appear to implicate surprising signaling pathways or receptor types in the response to infection, including Wnt signaling and neurotransmitter receptors. We believe the discovered map of expression programs involved in the response to infection will be useful for guiding future biological experiments; genes from programs with low generality scores might serve as new drug targets that exhibit minimal “cross-talk,” and genes from high generality programs may maintain common physiological responses that go awry in disease states. Further, our method is multipurpose, and can be applied readily to novel compendia of biological data.