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Relevant bibliographies by topics / Disease propagation

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Journal articles

Academic literature on the topic 'Disease propagation'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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Journal articles on the topic "Disease propagation"

1

Nagaprasad, S., T. Pushpalatha, and S. Naga Lakshmi. "Heart Disease Prediction Propagation approach." International Journal of Machine Learning and Networked Collaborative Engineering 4, no. 2 (2020): 72–77. http://dx.doi.org/10.30991/ijmlnce.2020v04i02.003.

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2

Brightman, B. K., Q. X. Li, D. J. Trepp, and H. Fan. "Differential disease restriction of Moloney and Friend murine leukemia viruses by the mouse Rmcf gene is governed by the viral long terminal repeat." Journal of Experimental Medicine 174, no. 2 (1991): 389–96. http://dx.doi.org/10.1084/jem.174.2.389.

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Neonatal CxD2 (Rmcfr) and Balb/c (Rmcfs) mice inoculated with Moloney murine leukemia virus (M-MuLV) exhibited approximately equivalent time course and pathology for disease. CxD2 mice showed only slightly reduced presence of Moloney mink cell focus-forming virus (M-MCF) provirus as seen by Southern blot analysis compared to Balb/c mice. This lack of restriction for disease and spread of MCF was in sharp contrast to that seen for CxD2 mice inoculated with Friend murine leukemia virus (F-MuLV), where incidence of disease and propagation of MCFs were severely restricted, as previously reported.

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3

Rajna, Zalán, Heli Mattila, Niko Huotari, et al. "Cardiovascular brain impulses in Alzheimer’s disease." Brain 144, no. 7 (2021): 2214–26. http://dx.doi.org/10.1093/brain/awab144.

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Abstract Accumulation of amyloid-β is a key neuropathological feature in brain of Alzheimer’s disease patients. Alterations in cerebral haemodynamics, such as arterial impulse propagation driving the (peri)vascular CSF flux, predict future Alzheimer’s disease progression. We now present a non-invasive method to quantify the three-dimensional propagation of cardiovascular impulses in human brain using ultrafast 10 Hz magnetic resonance encephalography. This technique revealed spatio-temporal abnormalities in impulse propagation in Alzheimer’s disease. The arrival latency and propagation speed b

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4

Wilson, Spencer, Abdullah Alabdulkarim, and David Goldsman. "Green Simulation of Pandemic Disease Propagation." Symmetry 11, no. 4 (2019): 580. http://dx.doi.org/10.3390/sym11040580.

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This paper is concerned with the efficient stochastic simulation of multiple scenarios of an infectious disease as it propagates through a population. In particular, we propose a simple “green” method to speed up the simulation of disease transmission as we vary the probability of infection of the disease from scenario to scenario. After running a baseline scenario, we incrementally increase the probability of infection, and use the common random numbers variance reduction technique to avoid re-simulating certain events in the new scenario that would not otherwise have changed from the previou

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5

Yates, Darran. "Propagation of disease pathology in PD." Nature Reviews Neurology 5, no. 10 (2009): 522. http://dx.doi.org/10.1038/nrneurol.2009.142.

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6

MAGDOŃ-MAKSYMOWICZ, M. S., A. Z. MAKSYMOWICZ, and J. GOŁDASZ. "SIMULATION OF MAD COW DISEASE PROPAGATION." International Journal of Modern Physics C 17, no. 02 (2006): 213–22. http://dx.doi.org/10.1142/s0129183106008935.

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Computer simulation of dynamic of BSE disease is presented. Both vertical (to baby) and horizontal (to neighbor) mechanisms of the disease spread are considered. The game takes place on a two-dimensional square lattice Nx×Ny = 1000×1000 with initial population randomly distributed on the net. The disease may be introduced either with the initial population or by a spontaneous development of BSE in an item, at a small frequency. Main results show a critical probability of the BSE transmission above which the disease is present in the population. This value is vulnerable to possible spatial clus

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7

Soto, Claudio, and Gabriela P. Saborı́o. "Prions: disease propagation and disease therapy by conformational transmission." Trends in Molecular Medicine 7, no. 3 (2001): 109–14. http://dx.doi.org/10.1016/s1471-4914(01)01931-1.

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8

Luk, Kelvin C., and Virginia M. Y. Lee. "Modeling Lewy pathology propagation in Parkinson's disease." Parkinsonism & Related Disorders 20 (January 2014): S85—S87. http://dx.doi.org/10.1016/s1353-8020(13)70022-1.

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9

Potterat, J. J., R. B. Rothenberg, and S. Q. Muth. "Network structural dynamics and infectious disease propagation." International Journal of STD & AIDS 10, no. 3 (1999): 182–85. http://dx.doi.org/10.1258/0956462991913853.

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10

Qian, Yu, Søren Besenbacher, Thomas Mailund, and Mikkel Heide Schierup. "Identifying disease associated genes by network propagation." BMC Systems Biology 8, Suppl 1 (2014): S6. http://dx.doi.org/10.1186/1752-0509-8-s1-s6.

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