Jesus: myth or history?

Thesis (M.A.)--Boston University, 1936. This item was digitized by the Internet Archive

Modeling Disease Management Decisions for Patients with Pneumonia-related Sepsis

Sepsis, the tenth-leading cause of death in the United States, accounts for more than $16.7 billion in annual health care spending. A significant factor in these costs are unnecessarily long hospital lengths of stay, which stem from the lack of optimal hospital discharge policies and the inability to assess a patient's true underlying health state effectively. Researchers have explored ways of standardizing hospital discharge policies by comparing various strategies, but have not been able to determine optimal policies due to the large number of treatment options. Furthering the state of research into decisions made in the management of patients with sepsis, this dissertation presents clinically based optimization models of pneumonia-related sepsis that use patient data to model disease progression over time. Formulated using Markov Decision Process (MDP) and Partially Observable Markov Decision Process (POMDP) techniques, these models consider the clinician's decisions of when to test for additional information about the patient's underlying health state and when to discharge the patient from the hospital. This work utilizes data from the Genetic and Inflammatory Markers for Sepsis (GenIMS) study, a large multi-center clinical trial led by the University of Pittsburgh School of Medicine. A key aim of the GenIMS trial is to demonstrate that the levels of certain cytokines are predictors of patient survival. Utilizing these results, the models presented in this dissertation consider the question of when to test for cytokine levels using testing procedures that may be costly and inaccurate. A significant result of this dissertation demonstrates that testing should be performed when a clinician is considering the decision to discharge the patient from the hospital. This study characterizes optimal testing and hospital discharge policies for multiple problem instances. In particular, multi-region control-limit policies are demonstrated for specific patient cohorts defined by age and race. It is shown that these control-limit policies depend on the patient's length of stay in the hospital. The effects of testing cost and accuracy on the optimal testing and discharge policies are also explored. Finally, clinical interpretations of the optimal policies are provided to demonstrate how these models can be used to inform clinical practice

Prediction of survival probabilities with Bayesian Decision Trees

Practitioners use Trauma and Injury Severity Score (TRISS) models for predicting the survival probability of an injured patient. The accuracy of TRISS predictions is acceptable for patients with up to three typical injuries, but unacceptable for patients with a larger number of injuries or with atypical injuries. Based on a regression model, the TRISS methodology does not provide the predictive density required for accurate assessment of risk. Moreover, the regression model is difficult to interpret. We therefore consider Bayesian inference for estimating the predictive distribution of survival. The inference is based on decision tree models which recursively split data along explanatory variables, and so practitioners can understand these models. We propose the Bayesian method for estimating the predictive density and show that it outperforms the TRISS method in terms of both goodness-of-fit and classification accuracy. The developed method has been made available for evaluation purposes as a stand-alone application

Uptake, accumulation and metabolization of the antidepressant fluoxetine by Mytilus galloprovincialis

Fluoxetine, a selective serotonin re-uptake inhibitor (SSRI) antidepressant, is among the most prescribed pharmaceutical active substances worldwide. This study aimed to assess its accumulation and metabolization in the mussel Mytillus galloprovincialis, considered an excellent sentinel species for traditional and emerging pollutants. Mussels were collected from Ria Formosa Lagoon, Portugal, and exposed to a nominal concentration of fluoxetine (75 ng L-1) for 15 days. Approximately 1 g of whole mussel soft tissues was extracted with acetonitrile:formic acid, loaded into an Oasis MCX cartridge, and fluoxetine analysed by liquid chromatography with tandem mass spectrometry (LC-MSn). After 3 days of exposure, fluoxetine was accumulated in 70% of the samples, with a mean of 2.53 ng g(-1) dry weight (d.w.) and norfluoxetine was only detected in one sample (10%), at 3.06 ng g(-1) d.w. After 7 days of exposure, the accumulation of fluoxetine and norfluoxetine increased up to 80 and 50% respectively, and their mean accumulated levels in mussel tissues were up to 4.43 and 2.85 ng g(-1) d.w., respectively. By the end of the exposure period (15 days), both compounds were detected in 100% of the samples (mean of 9.31 and 11.65 ng g(-1) d.w., respectively). Statistical analysis revealed significant accumulation differences between the 3rd and 15th day of exposure for fluoxetine, and between the 3rd and 7th against the 15th day of exposure for norfluoxetine. These results suggest that the fluoxetine accumulated in mussel tissues is likely to be metabolised into norfluoxetine with the increase of the time of exposure, giving evidence that at these realistic environmental concentrations, toxic effects of fluoxetine in mussel tissues may occur. (C) 2016 Elsevier Ltd. All rights reserved

``Over the horizon`` SANS: Measurements on near-surface Poiseuille shear-induced ordering of dilute solutions of threadlike micelles

Although the behavior of a fluid under shear near a surface can be expected to be critically important to its drag and lubrication properties, most shear measurements to date have been of the bulk. This paper outlines the use of a specially developed Poiseuille shear cell at grazing incidence to measure the small-angle neutron scattering (SANS) signal from the first few tens of microns in the interfacial region. The authors illustrate the technique with measurements made on the near-surface ordering in flow past a quartz surface of dilute surfactant solutions comprising highly extended self-assembling ``threadlike`` micelles

Shear stress induces osteogenic differentiation of human mesenchymal stem cells

Aim: To determine whether fluid flow-induced shear stress affects the differentiation of bone marrow-derived human mesenchymal stem cells (hMSCs) into osteogenic cells. Materials & methods: hMSCs cultured with or without osteogenic differentiation medium were exposed to fluid flow-induced shear stress and analyzed for alkaline phosphatase activity and expression of osteogenic genes. Results: Immediately following shear stress, alkaline phosphatase activity in osteogenic medium was significantly increased. At days 4 and 8 of culture the mRNA expression of bone morphogenetic protein-2 and osteopontin was significantly higher in hMSCs subjected to shear stress than those cultured in static conditions. However, hMSCs cultured in osteogenic differentiation medium were less responsive in gene expression of alkaline phosphatase and bone morphogenetic protein-2. Conclusion: These data demonstrate that shear stress stimulates hMSCs towards an osteoblastic phenotype in the absence of chemical induction, suggesting that certain mechanical stresses may serve as an alternative to chemical stimulation of stem cell differentiation

Thermosensitive Nanocables Prepared by Surface-Initiated Atom Transfer Radical Polymerization

Thermosensitive nanocables consisting of Au nanowire cores and poly(N-isopropylacrylamide) sheaths (denoted as Au/PNIPAAm) were synthesized by surface-initiated atom transfer radical polymerization (SI-ATRP). The formation of PNIPAAm sheath was verified by Fourier transform infrared (FTIR) and hydrogen nuclear magnetic resonance (1H NMR) spectroscopy. Transmission electron microscope (TEM) results confirmed the core/shell structure of nanohybrids. The thickness and density of PNIPAAm sheaths can be adjusted by controlling the amount of cross-linker during the polymerization. Signature temperature response was observed from Au/cross-linked-PNIPAAm nanocables. Such smart nanocables show immense potentials as building blocks for novel thermosensitive nanodevices in future

Computational methodology to determine fluid related parameters on non regular three-dimensional scaffolds

The application of three-dimensional (3D) biomaterials to facilitate the adhesion, proliferation, and differentiation of cells has been widely studied for tissue engineering purposes. The fabrication methods used to improve the mechanical response of the scaffold produce complex and non regular structures. Apart from the mechanical aspect, the fluid behavior in the inner part of the scaffold should also be considered. Parameters such as permeability (k) or wall shear stress (WSS) are important aspects in the provision of nutrients, the removal of metabolic waste products or the mechanically-induced differentiation of cells attached in the trabecular network of the scaffolds. Experimental measurements of these parameters are not available in all labs. However, fluid parameters should be known prior to other types of experiments. The present work compares an experimental study with a computational fluid dynamics (CFD) methodology to determine the related fluid parameters (k and WSS) of complex non regular poly(L-lactic acid) scaffolds based only on the treatment of microphotographic images obtained with a microCT (lCT). The CFD analysis shows similar tendencies and results with low relative difference compared to those of the experimental study, for high flow rates. For low flow rates the accuracy of this prediction reduces. The correlation between the computational and experimental results validates the robustness of the proposed methodology.The authors gratefully acknowledge research support from the Spanish Ministry of Science and Innovation through research project DPI2010-20399-C04-01. The Instituto de Salud Carlos III (ISCIII) through the CIBER initiative and the Platform for Biological Tissue Characterization of the Centro de Investigacion Biomedica en Red en Bioingenieria, Biomateriales y Nanomedicina (CIBER-BBN) are also gratefully acknowledged.Acosta Santamaría, VA.; Malvé, M.; Duizabo, A.; Mena Tobar, A.; Gallego Ferrer, G.; García Aznar, J.; Doblare Castellano, M.... (2013). Computational methodology to determine fluid related parameters on non regular three-dimensional scaffolds. Annals of Biomedical Engineering. 41(11):2367-2380. https://doi.org/10.1007/s10439-013-0849-8S236723804111Acosta Santamaría, V., H. Deplaine, D. Mariggió, A. R. Villanueva-Molines, J. M. García-Aznar, J. L. Gómez Ribelles, M. Doblaré, G. Gallego Ferrer, and I. Ochoa. Influence of the macro and micro-porous structure on the mechanical behavior of poly(l-lactic acid) scaffolds. J. Non-Cryst. Solids 358(23):3141–3149, 2012.Adachi, T., Y. Osako, M. Tanaka, M. Hojo, and S. J. Hollister. 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