Alzheimer’s Disease: A Thermodynamic Perspective

Alzheimer’s disease is investigated using a thermodynamic approach based on ion fluxes across the neuronal membrane. Our study indicates that the onset of Alzheimer’s may be aided by a hyperpolarization of this membrane, because hyperpolarization-activated cyclic nucleotide gated HCN channels 1–4 conduct inward, with the consequence of depolarising Na+/K+ currents which in turn impacts synaptic transmission and reduces plasticity

THERMODYNAMICS AND SARS-COV-2: NEUROLOGICAL EFFECTS IN POST-COVID 19 SYNDROME

There is increasing evidence that infection with SARS-CoV-2 can cause a spectrum of neurological symptoms. In this paper, we develop a theoretical concept underlying such neurological COVID-19 consequences by employing a non-equilibrium thermodynamic approach that allows linking the neuronal electric potential with a virus induced pH variation. Our theoretical findings support further experimental work on therapeutically correcting electrolyte imbalances, such as Na+ and K+, to attenuate the neurological effects of SARS-CoV-2

A Thermodynamic Approach to the Metaboloepigenetics of Cancer

We present a novel thermodynamic approach to the epigenomics of cancer metabolism. Here, any change in a cancer cell’s membrane electric potential is completely irreversible, and as such, cells must consume metabolites to reverse the potential whenever required to maintain cell activity, a process driven by ion fluxes. Moreover, the link between cell proliferation and the membrane’s electric potential is for the first time analytically proven using a thermodynamic approach, highlighting how its control is related to inflow and outflow of ions; consequently, a close interaction between environment and cell activity emerges. Lastly, we illustrate the concept by evaluating the Fe2+-flux in the presence of carcinogenesis-promoting mutations of the TET1/2/3 gene family

Glioma Expansion in Collagen I Matrices: Analyzing Collagen Concentration-Dependent Growth and Motility Patterns

Kaufman, L. J., C. P. Brangwynne, K. E. Kasza, E. Filippidi, V. D. Gordon, T. S. Deisboeck, and D. A. Weitz. “Glioma Expansion in Collagen I Matrices: Analyzing Collagen Concentration-Dependent Growth and Motility Patterns.” Biophysical Journal 89, no. 1 (July 2005): 635–50. doi:10.1529/biophysj.105.061994. -- C. P. Brangwynne, K. E. Kasza, and E. Filippidi, are with the Division of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts -- L. J. Kaufman, V. D. Gordon (currently with UT Austin), and D. A.Weitz are with the Department of Physics, Harvard University, Cambridge, Massachusetts -- T. S. Deisboeck is with the Molecular Neuro-Oncology Laboratory, Massachusetts General Hospital, Charlestown, Massachusetts and {Complex Biosystems Modeling Laboratory, Harvard-MIT (HST) Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts -- L. J. Kaufman is with the Center for Imaging and Mesoscale Structures, Harvard University, Cambridge, Massachusetts; andWe study the growth and invasion of glioblastoma multiforme (GBM) in three-dimensional collagen I matrices of varying collagen concentration. Phase-contrast microscopy studies of the entire GBM system show that invasiveness at early times is limited by available collagen fibers. At early times, high collagen concentration correlates with more effective invasion. Conversely, high collagen concentration correlates with inhibition in the growth of the central portion of GBM, the multicellular tumor spheroid. Analysis of confocal reflectance images of the collagen matrices quantifies how the collagen matrices differ as a function of concentration. Studying invasion on the length scale of individual invading cells with a combination of confocal and coherent anti-Stokes Raman scattering microscopy reveals that the invasive GBM cells rely heavily on cell-matrix interactions during invasion and remodeling.Chemistr

The k-statistics approach to epidemiology

A great variety of complex physical, natural and artificial systems are governed by statistical distributions, which often follow a standard exponential function in the bulk, while their tail obeys the Pareto power law. The recently introduced $\kappa$-statistics framework predicts distribution functions with this feature. A growing number of applications in different fields of investigation are beginning to prove the relevance and effectiveness of $\kappa$-statistics in fitting empirical data. In this paper, we use $\kappa$-statistics to formulate a statistical approach for epidemiological analysis. We validate the theoretical results by fitting the derived $\kappa$-Weibull distributions with data from the plague pandemic of 1417 in Florence as well as data from the COVID-19 pandemic in China over the entire cycle that concludes in April 16, 2020. As further validation of the proposed approach we present a more systematic analysis of COVID-19 data from countries such as Germany, Italy, Spain and United Kingdom, obtaining very good agreement between theoretical predictions and empirical observations. For these countries we also study the entire first cycle of the pandemic which extends until the end of July 2020. The fact that both the data of the Florence plague and those of the Covid-19 pandemic are successfully described by the same theoretical model, even though the two events are caused by different diseases and they are separated by more than 600 years, is evidence that the $\kappa$-Weibull model has universal features.Comment: 15 pages, 1 table, 5 figure

Surgical impact on brain tumor invasion: A physical perspective

It is conventional strategy to treat highly malignant brain tumors initially with cytoreductive surgery followed by adjuvant radio- and chemotherapy. However, in spite of all such efforts, the patients' prognosis remains dismal since residual glioma cells continue to infiltrate adjacent parenchyma and the tumors almost always recur. On the basis of a simple biomechanical conjecture that we have introduced previously, we argue here that by affecting the 'volume-pressure' relationship and minimizing surface tension of the remaining tumor cells, gross total resection may have an inductive effect on the invasiveness of the tumor cells left behind. Potential implications for treatment strategies are discussed

Emergent Properties of Tumor Microenvironment in a Real-life Model of Multicell Tumor Spheroids

Multicellular tumor spheroids are an important {\it in vitro} model of the pre-vascular phase of solid tumors, for sizes well below the diagnostic limit: therefore a biophysical model of spheroids has the ability to shed light on the internal workings and organization of tumors at a critical phase of their development. To this end, we have developed a computer program that integrates the behavior of individual cells and their interactions with other cells and the surrounding environment. It is based on a quantitative description of metabolism, growth, proliferation and death of single tumor cells, and on equations that model biochemical and mechanical cell-cell and cell-environment interactions. The program reproduces existing experimental data on spheroids, and yields unique views of their microenvironment. Simulations show complex internal flows and motions of nutrients, metabolites and cells, that are otherwise unobservable with current experimental techniques, and give novel clues on tumor development and strong hints for future therapies.Comment: 20 pages, 10 figures. Accepted for publication in PLOS One. The published version contains links to a supplementary text and three video file