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Capareli F, Costa F, Tuszynski JA, Sousa MC, Setogute YDC, Lima PD, Carvalho L, Santos E, Gumz BP, Sabbaga J, de Castria TB, Jardim DL, Freitas D, Horvat N, Bezerra ROF, Testagrossa L, Costa T, Zanesco T, Iemma AF, Abou‐Alfa GK. Low-energy amplitude-modulated electromagnetic field exposure: Feasibility study in patients with hepatocellular carcinoma. Cancer Med 2023; 12:12402-12412. [PMID: 37184216 PMCID: PMC10278519 DOI: 10.1002/cam4.5944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 03/23/2023] [Accepted: 03/31/2023] [Indexed: 05/16/2023] Open
Abstract
BACKGROUND Patients with advanced hepatocellular carcinoma (HCC) and poor liver function lack effective systemic therapies. Low-energy electromagnetic fields (EMFs) can influence cell biological processes via non-thermal effects and may represent a new treatment option. METHODS This single-site feasibility trial enrolled patients with advanced HCC, Child-Pugh A and B, Eastern Cooperative Oncology Group 0-2. Patients underwent 90-min amplitude-modulated EMF exposure procedures every 2-4 weeks, using the AutEMdev (Autem Therapeutics). Patients could also receive standard care. The primary endpoints were safety and the identification of hemodynamic variability patterns. Exploratory endpoints included health-related quality of life (HRQoL), overall survival (OS). and objective response rate (ORR) using RECIST v1.1. RESULTS Sixty-six patients with advanced HCC received 539 AutEMdev procedures (median follow-up, 30 months). No serious adverse events occurred during procedures. Self-limiting grade 1 somnolence occurred in 78.7% of patients. Hemodynamic variability during EMF exposure was associated with specific amplitude-modulation frequencies. HRQoL was maintained or improved among patients remaining on treatment. Median OS was 11.3 months (95% confidence interval [CI]: 6.0, 16.6) overall (16.0 months [95% CI: 4.4, 27.6] and 12.0 months [6.4, 17.6] for combination therapy and monotherapy, respectively). ORR was 24.3% (32% and 17% for combination therapy and monotherapy, respectively). CONCLUSION AutEMdev EMF exposure has an excellent safety profile in patients with advanced HCC. Hemodynamic alterations at personalized frequencies may represent a surrogate of anti-tumor efficacy. NCT01686412.
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Affiliation(s)
| | - Frederico Costa
- Oncology DepartmentHospital Sírio‐LibanêsSão PauloBrazil
- Autem Medical LLCHanoverNew HampshireUSA
| | - Jack A. Tuszynski
- Autem Medical LLCHanoverNew HampshireUSA
- Division of Experimental Oncology, Department of OncologyCross Cancer Institute, University of AlbertaEdmontonAlbertaCanada
| | | | | | - Pablo D. Lima
- Oncology DepartmentHospital Sírio‐LibanêsSão PauloBrazil
| | | | - Elizabeth Santos
- Oncology DepartmentHospital Sírio‐LibanêsSão PauloBrazil
- Oncology DepartmentA. C. Camargo Cancer CenterSão PauloBrazil
| | - Brenda P. Gumz
- Oncology DepartmentHospital Sírio‐LibanêsSão PauloBrazil
| | - Jorge Sabbaga
- Oncology DepartmentHospital Sírio‐LibanêsSão PauloBrazil
| | | | | | | | - Natally Horvat
- Memorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
| | | | | | - Tiago Costa
- Santa Casa de São Paulo School of Medical SciencesSão PauloBrazil
| | | | - Antonio F. Iemma
- Institute of Mathematics and Statistics, University of São PauloSão PauloBrazil
| | - Ghassan K. Abou‐Alfa
- Memorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
- Weill Medical College at Cornell UniversityNew YorkNew YorkUSA
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2
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The Analysis of Mammalian Hearing Systems Supports the Hypothesis That Criticality Favors Neuronal Information Representation but Not Computation. ENTROPY 2022; 24:e24040540. [PMID: 35455203 PMCID: PMC9029204 DOI: 10.3390/e24040540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 03/25/2022] [Accepted: 04/10/2022] [Indexed: 11/17/2022]
Abstract
In the neighborhood of critical states, distinct materials exhibit the same physical behavior, expressed by common simple laws among measurable observables, hence rendering a more detailed analysis of the individual systems obsolete. It is a widespread view that critical states are fundamental to neuroscience and directly favor computation. We argue here that from an evolutionary point of view, critical points seem indeed to be a natural phenomenon. Using mammalian hearing as our example, we show, however, explicitly that criticality does not describe the proper computational process and thus is only indirectly related to the computation in neural systems.
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Stoop R. Why Hearing Aids Fail and How to Solve This. FRONTIERS IN NETWORK PHYSIOLOGY 2022; 2:868470. [PMID: 36926095 PMCID: PMC10013008 DOI: 10.3389/fnetp.2022.868470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 03/14/2022] [Indexed: 11/13/2022]
Abstract
Hearing is one of the human's foremost sensors; being able to hear again after suffering from a hearing loss is a great achievement, under all circumstances. However, in the long run, users of present-day hearing aids and cochlear implants are generally only halfway satisfied with what the commercial side offers. We demonstrate here that this is due to the failure of a full integration of these devices into the human physiological circuitry. Important parts of the hearing network that remain unestablished are the efferent connections to the cochlea, which strongly affects the faculty of listening. The latter provides the base for coping with the so-called cocktail party problem, or for a full enjoyment of multi-instrumental musical plays. While nature clearly points at how this could be remedied, to achieve this technologically will require the use of advanced high-precision electrodes and high-precision surgery, as we outline here. Corresponding efforts must be pushed forward by coordinated efforts from the side of science, as the commercial players in the field of hearing aids cannot be expected to have a substantial interest in advancements into this direction.
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Affiliation(s)
- Ruedi Stoop
- Institute of Neuroinformatics, University and ETH of Zürich, Zurich, Switzerland
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Klamser PP, Romanczuk P. Collective predator evasion: Putting the criticality hypothesis to the test. PLoS Comput Biol 2021; 17:e1008832. [PMID: 33720926 PMCID: PMC7993868 DOI: 10.1371/journal.pcbi.1008832] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 03/25/2021] [Accepted: 02/24/2021] [Indexed: 11/19/2022] Open
Abstract
According to the criticality hypothesis, collective biological systems should operate in a special parameter region, close to so-called critical points, where the collective behavior undergoes a qualitative change between different dynamical regimes. Critical systems exhibit unique properties, which may benefit collective information processing such as maximal responsiveness to external stimuli. Besides neuronal and gene-regulatory networks, recent empirical data suggests that also animal collectives may be examples of self-organized critical systems. However, open questions about self-organization mechanisms in animal groups remain: Evolutionary adaptation towards a group-level optimum (group-level selection), implicitly assumed in the "criticality hypothesis", appears in general not reasonable for fission-fusion groups composed of non-related individuals. Furthermore, previous theoretical work relies on non-spatial models, which ignore potentially important self-organization and spatial sorting effects. Using a generic, spatially-explicit model of schooling prey being attacked by a predator, we show first that schools operating at criticality perform best. However, this is not due to optimal response of the prey to the predator, as suggested by the "criticality hypothesis", but rather due to the spatial structure of the prey school at criticality. Secondly, by investigating individual-level evolution, we show that strong spatial self-sorting effects at the critical point lead to strong selection gradients, and make it an evolutionary unstable state. Our results demonstrate the decisive role of spatio-temporal phenomena in collective behavior, and that individual-level selection is in general not a viable mechanism for self-tuning of unrelated animal groups towards criticality.
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Affiliation(s)
- Pascal P. Klamser
- Department of Biology, Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Pawel Romanczuk
- Department of Biology, Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
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5
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Entropic forces drive clustering and spatial localization of influenza A M2 during viral budding. Proc Natl Acad Sci U S A 2018; 115:E8595-E8603. [PMID: 30150411 DOI: 10.1073/pnas.1805443115] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
The influenza A matrix 2 (M2) transmembrane protein facilitates virion release from the infected host cell. In particular, M2 plays a role in the induction of membrane curvature and/or in the scission process whereby the envelope is cut upon virion release. Here we show using coarse-grained computer simulations that various M2 assembly geometries emerge due to an entropic driving force, resulting in compact clusters or linearly extended aggregates as a direct consequence of the lateral membrane stresses. Conditions under which these protein assemblies will cause the lipid membrane to curve are explored, and we predict that a critical cluster size is required for this to happen. We go on to demonstrate that under the stress conditions taking place in the cellular membrane as it undergoes large-scale membrane remodeling, the M2 protein will, in principle, be able to both contribute to curvature induction and sense curvature to line up in manifolds where local membrane line tension is high. M2 is found to exhibit linactant behavior in liquid-disordered-liquid-ordered phase-separated lipid mixtures and to be excluded from the liquid-ordered phase, in near-quantitative agreement with experimental observations. Our findings support a role for M2 in membrane remodeling during influenza viral budding both as an inducer and a sensor of membrane curvature, and they suggest a mechanism by which localization of M2 can occur as the virion assembles and releases from the host cell, independent of how the membrane curvature is produced.
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Kanders K, Lorimer T, Stoop R. Avalanche and edge-of-chaos criticality do not necessarily co-occur in neural networks. CHAOS (WOODBURY, N.Y.) 2017; 27:047408. [PMID: 28456175 DOI: 10.1063/1.4978998] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
There are indications that for optimizing neural computation, neural networks may operate at criticality. Previous approaches have used distinct fingerprints of criticality, leaving open the question whether the different notions would necessarily reflect different aspects of one and the same instance of criticality, or whether they could potentially refer to distinct instances of criticality. In this work, we choose avalanche criticality and edge-of-chaos criticality and demonstrate for a recurrent spiking neural network that avalanche criticality does not necessarily entrain dynamical edge-of-chaos criticality. This suggests that the different fingerprints may pertain to distinct phenomena.
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Affiliation(s)
- Karlis Kanders
- Institute of Neuroinformatics and Institute for Computational Science, University of Zurich and ETH Zurich, Winterthurerstr. 190, 8057 Zurich, Switzerland
| | - Tom Lorimer
- Institute of Neuroinformatics and Institute for Computational Science, University of Zurich and ETH Zurich, Winterthurerstr. 190, 8057 Zurich, Switzerland
| | - Ruedi Stoop
- Institute of Neuroinformatics and Institute for Computational Science, University of Zurich and ETH Zurich, Winterthurerstr. 190, 8057 Zurich, Switzerland
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7
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Rai A, Pradhan P, Nagraj J, Lohitesh K, Chowdhury R, Jalan S. Understanding cancer complexome using networks, spectral graph theory and multilayer framework. Sci Rep 2017; 7:41676. [PMID: 28155908 PMCID: PMC5290734 DOI: 10.1038/srep41676] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2016] [Accepted: 12/15/2016] [Indexed: 02/06/2023] Open
Abstract
Cancer complexome comprises a heterogeneous and multifactorial milieu that varies in cytology, physiology, signaling mechanisms and response to therapy. The combined framework of network theory and spectral graph theory along with the multilayer analysis provides a comprehensive approach to analyze the proteomic data of seven different cancers, namely, breast, oral, ovarian, cervical, lung, colon and prostate. Our analysis demonstrates that the protein-protein interaction networks of the normal and the cancerous tissues associated with the seven cancers have overall similar structural and spectral properties. However, few of these properties implicate unsystematic changes from the normal to the disease networks depicting difference in the interactions and highlighting changes in the complexity of different cancers. Importantly, analysis of common proteins of all the cancer networks reveals few proteins namely the sensors, which not only occupy significant position in all the layers but also have direct involvement in causing cancer. The prediction and analysis of miRNAs targeting these sensor proteins hint towards the possible role of these proteins in tumorigenesis. This novel approach helps in understanding cancer at the fundamental level and provides a clue to develop promising and nascent concept of single drug therapy for multiple diseases as well as personalized medicine.
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Affiliation(s)
- Aparna Rai
- Centre for Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Simrol, Indore, Madhya Pradesh 453552, India
| | - Priodyuti Pradhan
- Complex Systems Lab, Discipline of Physics, Indian Institute of Technology Indore, Simrol, Indore, Madhya Pradesh 453552, India
| | - Jyothi Nagraj
- Department of Biological Sciences, Birla Institute of Technology and Science, Vidya Vihar, Pilani, Rajasthan 333031, India
| | - K. Lohitesh
- Department of Biological Sciences, Birla Institute of Technology and Science, Vidya Vihar, Pilani, Rajasthan 333031, India
| | - Rajdeep Chowdhury
- Department of Biological Sciences, Birla Institute of Technology and Science, Vidya Vihar, Pilani, Rajasthan 333031, India
| | - Sarika Jalan
- Centre for Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Simrol, Indore, Madhya Pradesh 453552, India
- Complex Systems Lab, Discipline of Physics, Indian Institute of Technology Indore, Simrol, Indore, Madhya Pradesh 453552, India
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8
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Stoop R, Gomez F. Auditory Power-Law Activation Avalanches Exhibit a Fundamental Computational Ground State. PHYSICAL REVIEW LETTERS 2016; 117:038102. [PMID: 27472144 DOI: 10.1103/physrevlett.117.038102] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2015] [Indexed: 06/06/2023]
Abstract
The cochlea provides a biological information-processing paradigm that we are only beginning to understand in its full complexity. Our work reveals an interacting network of strongly nonlinear dynamical nodes, on which even a simple sound input triggers subnetworks of activated elements that follow power-law size statistics ("avalanches"). From dynamical systems theory, power-law size distributions relate to a fundamental ground state of biological information processing. Learning destroys these power laws. These results strongly modify the models of mammalian sound processing and provide a novel methodological perspective for understanding how the brain processes information.
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Affiliation(s)
- Ruedi Stoop
- Institute of Neuroinformatics and Institute of Computational Science, University of Zurich and ETH Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
| | - Florian Gomez
- Institute of Neuroinformatics and Institute of Computational Science, University of Zurich and ETH Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
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9
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Marshall N, Timme NM, Bennett N, Ripp M, Lautzenhiser E, Beggs JM. Analysis of Power Laws, Shape Collapses, and Neural Complexity: New Techniques and MATLAB Support via the NCC Toolbox. Front Physiol 2016; 7:250. [PMID: 27445842 PMCID: PMC4921690 DOI: 10.3389/fphys.2016.00250] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2016] [Accepted: 06/08/2016] [Indexed: 11/13/2022] Open
Abstract
Neural systems include interactions that occur across many scales. Two divergent methods for characterizing such interactions have drawn on the physical analysis of critical phenomena and the mathematical study of information. Inferring criticality in neural systems has traditionally rested on fitting power laws to the property distributions of "neural avalanches" (contiguous bursts of activity), but the fractal nature of avalanche shapes has recently emerged as another signature of criticality. On the other hand, neural complexity, an information theoretic measure, has been used to capture the interplay between the functional localization of brain regions and their integration for higher cognitive functions. Unfortunately, treatments of all three methods-power-law fitting, avalanche shape collapse, and neural complexity-have suffered from shortcomings. Empirical data often contain biases that introduce deviations from true power law in the tail and head of the distribution, but deviations in the tail have often been unconsidered; avalanche shape collapse has required manual parameter tuning; and the estimation of neural complexity has relied on small data sets or statistical assumptions for the sake of computational efficiency. In this paper we present technical advancements in the analysis of criticality and complexity in neural systems. We use maximum-likelihood estimation to automatically fit power laws with left and right cutoffs, present the first automated shape collapse algorithm, and describe new techniques to account for large numbers of neural variables and small data sets in the calculation of neural complexity. In order to facilitate future research in criticality and complexity, we have made the software utilized in this analysis freely available online in the MATLAB NCC (Neural Complexity and Criticality) Toolbox.
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Affiliation(s)
- Najja Marshall
- Department of Neuroscience, Columbia University New York, NY, USA
| | - Nicholas M Timme
- Department of Psychology, Indiana University - Purdue University Indianapolis Indianapolis, IN, USA
| | | | - Monica Ripp
- Department of Physics, Syracuse University Syracuse, NY, USA
| | | | - John M Beggs
- Department of Physics, Indiana UniversityBloomington, IN, USA; Biocomplexity Institute, Indiana UniversityBloomington, IN, USA
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10
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Sendiña-Nadal I, Danziger MM, Wang Z, Havlin S, Boccaletti S. Assortativity and leadership emerge from anti-preferential attachment in heterogeneous networks. Sci Rep 2016; 6:21297. [PMID: 26887684 PMCID: PMC4758035 DOI: 10.1038/srep21297] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2015] [Accepted: 01/18/2016] [Indexed: 01/08/2023] Open
Abstract
Real-world networks have distinct topologies, with marked deviations from purely random networks. Many of them exhibit degree-assortativity, with nodes of similar degree more likely to link to one another. Though microscopic mechanisms have been suggested for the emergence of other topological features, assortativity has proven elusive. Assortativity can be artificially implanted in a network via degree-preserving link permutations, however this destroys the graph's hierarchical clustering and does not correspond to any microscopic mechanism. Here, we propose the first generative model which creates heterogeneous networks with scale-free-like properties in degree and clustering distributions and tunable realistic assortativity. Two distinct populations of nodes are incrementally added to an initial network by selecting a subgraph to connect to at random. One population (the followers) follows preferential attachment, while the other population (the potential leaders) connects via anti-preferential attachment: they link to lower degree nodes when added to the network. By selecting the lower degree nodes, the potential leader nodes maintain high visibility during the growth process, eventually growing into hubs. The evolution of links in Facebook empirically validates the connection between the initial anti-preferential attachment and long term high degree. In this way, our work sheds new light on the structure and evolution of social networks.
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Affiliation(s)
- I. Sendiña-Nadal
- Complex Systems Group & GISC, Universidad Rey Juan Carlos, 28933 Móstoles, Madrid, Spain
- Center for Biomedical Technology, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Madrid, Spain
| | - M. M. Danziger
- Department of Physics, Bar Ilan University, Ramat Gan 52900, Israel
| | - Z. Wang
- School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
- Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka, 816-8580, Japan
| | - S. Havlin
- Department of Physics, Bar Ilan University, Ramat Gan 52900, Israel
| | - S. Boccaletti
- CNR- Institute of Complex Systems, Via Madonna del Piano, 10, 50019 Sesto Fiorentino, Florence, Italy
- The Italian Embassy in Israel, 25 Hamered st., 68125 Tel Aviv, Israel
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