1,588 research outputs found
Differential and Joint Effects of Metformin and Statins on Overall Survival of Elderly Patients with Pancreatic Adenocarcinoma: A Large Population-Based Study.
Background: Published evidence indicates that individual use of metformin and statin is associated with reduced cancer mortality. However, their differential and joint effects on pancreatic cancer survival are inconclusive.Methods: We identified a large population-based cohort of 12,572 patients ages 65 years or older with primary pancreatic ductal adenocarcinoma (PDAC) diagnosed between 2008 and 2011 from the Surveillance, Epidemiology, and End Results (SEER)-Medicare-linked database. Exposure to metformin and statins was ascertained from Medicare Prescription Drug Event files. Cox proportional hazards models with time-varying covariates adjusted for propensity scores were used to assess the association while controlling for potential confounders.Results: Of 12,572 PDAC patients, 950 (7.56%) had used metformin alone, 4,506 (35.84%) had used statin alone, and 2,445 (19.45%) were dual users. Statin use was significantly associated with improved overall survival [HR, 0.94; 95% confidence interval (CI), 0.90-0.98], and survival was more pronounced in postdiagnosis statin users (HR, 0.69; 95% CI, 0.56-0.86). Metformin use was not significantly associated with overall survival (HR, 1.01; 95% CI, 0.94-1.09). No beneficial effect was observed for dual users (HR, 1.00; 95% CI, 0.95-1.05).Conclusions: Our findings suggest potential benefits of statins on improving survival among elderly PDAC patients; further prospective studies are warranted to corroborate the putative benefit of statin therapy in pancreatic cancer.Impact: Although more studies are needed to confirm our findings, our data add to the body of evidence on potential anticancer effects of statins. Cancer Epidemiol Biomarkers Prev; 26(8); 1225-32. ©2017 AACR
Cellular trafficking of Sn-2 phosphatidylcholine prodrugs studied with fluorescence lifetime imaging and super-resolution microscopy
While th
Explainable Machine Learning for Hydrogen Diffusion in Metals and Random Binary Alloys
Hydrogen diffusion in metals and alloys plays an important role in the
discovery of new materials for fuel cell and energy storage technology. While
analytic models use hand-selected features that have clear physical ties to
hydrogen diffusion, they often lack accuracy when making quantitative
predictions. Machine learning models are capable of making accurate
predictions, but their inner workings are obscured, rendering it unclear which
physical features are truly important. To develop interpretable machine
learning models to predict the activation energies of hydrogen diffusion in
metals and random binary alloys, we create a database for physical and chemical
properties of the species and use it to fit six machine learning models. Our
models achieve root-mean-squared-errors between 98-119 meV on the testing data
and accurately predict that elemental Ru has a large activation energy, while
elemental Cr and Fe have small activation energies.By analyzing the feature
importances of these fitted models, we identify relevant physical properties
for predicting hydrogen diffusivity. While metrics for measuring the individual
feature importances for machine learning models exist, correlations between the
features lead to disagreement between models and limit the conclusions that can
be drawn. Instead grouped feature importances, formed by combining the features
via their correlations, agree across the six models and reveal that the two
groups containing the packing factor and electronic specific heat are
particularly significant for predicting hydrogen diffusion in metals and random
binary alloys. This framework allows us to interpret machine learning models
and enables rapid screening of new materials with the desired rates of hydrogen
diffusion.Comment: 36 pages, 8 figures, supplemental materia
Large Spatial Database Indexing with aX-tree
Spatial databases are optimized for the management of data stored based on their geometric space. Researchers through high degree scalability have proposed several spatial indexing structures towards this effect. Among these indexing structures is the X-tree. The existing X-trees and its variants are designed for dynamic environment, with the capability for handling insertions and deletions. Notwithstanding, the X-tree degrades on retrieval performance as dimensionality increases and brings about poor worst-case performance than sequential scan. We propose a new X-tree packing techniques for static spatial databases which performs better in space utilization through cautious packing. This new improved structure yields two basic advantage: It reduces the space overhead of the index and produces a better response time, because the aX-tree has a higher fan-out and so the tree always ends up shorter. New model for super-node construction and effective method for optimal packing using an improved str bulk-loading technique is proposed. The study reveals that proposed system performs better than many existing spatial indexing structure
Antibody Fc Glycosylation Discriminates Between Latent and Active Tuberculosis
Background. Mycobacterium tuberculosis remains a global health problem and clinical management is complicated by difficulty in discriminating between latent infection and active disease. While M. tuberculosis-reactive antibody levels are heterogeneous, studies suggest that levels of IgG glycosylation differ between disease states. Here we extend this observation across antibody domains and M. tuberculosis specificities to define changes with the greatest resolving power.
Methods. Capillary electrophoretic glycan analysis was performed on bulk non-antigen–specific IgG, bulk Fc domain, bulk Fab domain, and purified protein derivative (PPD)- and Ag85A-specific IgG from subjects with latent (n = 10) and active (n = 20) tuberculosis. PPD-specific isotype/subclass, PPD-specific antibody-dependent phagocytosis, cellular cytotoxicity, and natural killer cell activation were assessed. Discriminatory potentials of antibody features were evaluated individually and by multivariate analysis.
Results. Parallel profiling of whole, Fc, and Fab domain-specific IgG glycosylation pointed to enhanced differential glycosylation on the Fc domain. Differential glycosylation was observed across antigen-specific antibody populations. Multivariate modeling highlighted Fc domain glycan species as the top discriminatory features, with combined PPD IgG titers and Fc domain glycans providing the highest classification accuracy.
Conclusions. Differential glycosylation occurs preferentially on the Fc domain, providing significant discriminatory power between different states of M. tuberculosis infection and disease
Genetic inhibition of neurotransmission reveals role of glutamatergic input to dopamine neurons in high-effort behavior
Midbrain dopamine neurons are crucial for many behavioral and cognitive functions. As the major excitatory input, glutamatergic afferents are important for control of the activity and plasticity of dopamine neurons. However, the role of glutamatergic input as a whole onto dopamine neurons remains unclear. Here we developed a mouse line in which glutamatergic inputs onto dopamine neurons are specifically impaired, and utilized this genetic model to directly test the role of glutamatergic inputs in dopamine-related functions. We found that while motor coordination and reward learning were largely unchanged, these animals showed prominent deficits in effort-related behavioral tasks. These results provide genetic evidence that glutamatergic transmission onto dopaminergic neurons underlies incentive motivation, a willingness to exert high levels of effort to obtain reinforcers, and have important implications for understanding the normal function of the midbrain dopamine system.Fil: Hutchison, M. A.. National Institutes of Health; Estados UnidosFil: Gu, X.. National Institutes of Health; Estados UnidosFil: Adrover, MartĂn Federico. National Institutes of Health; Estados Unidos. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Instituto de Investigaciones en IngenierĂa GenĂ©tica y BiologĂa Molecular "Dr. HĂ©ctor N. Torres"; ArgentinaFil: Lee, M. R.. National Institutes of Health; Estados UnidosFil: Hnasko, T. S.. University of California at San Diego; Estados UnidosFil: Alvarez, V. A.. National Institutes of Health; Estados UnidosFil: Lu, W.. National Institutes of Health; Estados Unido
Assessing the Health of Richibucto Estuary with the Latent Health Factor Index
The ability to quantitatively assess the health of an ecosystem is often of
great interest to those tasked with monitoring and conserving ecosystems. For
decades, research in this area has relied upon multimetric indices of various
forms. Although indices may be numbers, many are constructed based on
procedures that are highly qualitative in nature, thus limiting the
quantitative rigour of the practical interpretations made from these indices.
The statistical modelling approach to construct the latent health factor index
(LHFI) was recently developed to express ecological data, collected to
construct conventional multimetric health indices, in a rigorous quantitative
model that integrates qualitative features of ecosystem health and preconceived
ecological relationships among such features. This hierarchical modelling
approach allows (a) statistical inference of health for observed sites and (b)
prediction of health for unobserved sites, all accompanied by formal
uncertainty statements. Thus far, the LHFI approach has been demonstrated and
validated on freshwater ecosystems. The goal of this paper is to adapt this
approach to modelling estuarine ecosystem health, particularly that of the
previously unassessed system in Richibucto in New Brunswick, Canada. Field data
correspond to biotic health metrics that constitute the AZTI marine biotic
index (AMBI) and abiotic predictors preconceived to influence biota. We also
briefly discuss related LHFI research involving additional metrics that form
the infaunal trophic index (ITI). Our paper is the first to construct a
scientifically sensible model to rigorously identify the collective explanatory
capacity of salinity, distance downstream, channel depth, and silt-clay content
--- all regarded a priori as qualitatively important abiotic drivers ---
towards site health in the Richibucto ecosystem.Comment: On 2013-05-01, a revised version of this article was accepted for
publication in PLoS One. See Journal reference and DOI belo
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