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In Vivo
Published in Margarida M. Barroso, Xavier Intes, In Vivo, 2020
David Entenberg, Maja H. Oktay, John Condeelis
Cellular motility is a crucial biological process in both health and disease. In particular, different cellular motility phenotypes, such as single-cell and streaming, have been implicated in metastatic spread. While the connection between streaming migration and hematogenous dissemination of tumor cells has been well established, more work still needs to be done to definitively establish whether collective migration is involved in metastatic spread. In particular, it is still to be determined whether the speculated process of collective vascular invasion occurs in vivo. The current evidence obtained by intravital imaging points to a model of metastasis, summarized in Figure 11.4a, in which a minority of tumor cells within the bulk tumor are able to dissociate from their neighboring cells, migrate in close association with macrophages toward blood vessels, and then enter the blood vasculature at TMEM doorways. Upon arrival to the secondary site, these tumor cells extravasate and subsequently grow into macrometastases, whereupon the process of dissemination may begin again (Figure 11.4b)
with
Published in Ole B. Jensen, Claus Lassen, Ida Sofie Gøtzsche Lange, Material Mobilities, 2019
The conceptual concerns with regard to levels of corporality, of bodiliness, in understanding movement and mobility, and the links to questions of temporality, reverberate in the concept of motility. Motility is defined as the capacity or potential to move or be mobile. Rather than just studying observable mobilities, such as walking, motility analyses the before of movement by considering the choices and limitations which precede movement (Kaufmann, 2002, pp. 37, my emphasis). It thereby addresses the lack of a temporal dimension in many mobility models and their critical limitation to questions of space and territoriality, ignoring, for example, the importance of motivation, imagination or desires in moving, which has been noted in archaeological, anthropological, sociological and geographical discussions (Kaufmann, 2002; Oetelaar, 2006; Salazar, 2010; Merriman, 2012; Weig, 2017).5Motility is also termed ‘mobility capital’ and understood as a form of capital that can be actively or passively exchanged with others (Kaufmann et al., 2004; Kellerman, 2012).
Biomolecules and Complex Biological Entities
Published in Simona Badilescu, Muthukumaran Packirisamy, BioMEMS, 2016
Simona Badilescu, Muthukumaran Packirisamy
Cell movement or motility is a highly dynamic phenomenon that is essential to a variety of biological processes.22 Although cell movement was observed by van Leeuwenhoek as early as 1675, the molecular mechanisms behind cell movement have been studied only in the past few decades. Experimental techniques such as fluorescence microscopy and advances in molecular biology have enabled the discovery of the processes underlying motility. Biophysical studies helped identify regions where different force-generating proteins are located and measured the forces associated with movement.
Entropy generation phenomenon for ciliated pumping flow of aluminum oxide and silver nanoparticles with Hall device significances
Published in Waves in Random and Complex Media, 2023
Khurram Javid, Momen Khan, Sami Ullah Khan
Due to applications of cilia pumping in biological processes, cilia have been stated as a dominant field of research activities to control the transport mechanisms not only in the medical domain but also in the chemical industry. Cilia are tiny hair assemblies, which swells from cell surfaces and play vital roles in motility and development in the bulk of eukaryotes including human. Their capabilities produce a wave structure that generates a periodic motion of surrounding liquids and flagella [1]. These artificially generated waves are known as metachronal waves. In the human body, cilia are present in the brain, lungs, efferent ducts, eyes, and fallopian tubes. The motility of ciliated cells has a remarkable role in the clearance of mucosa in the airways, oocytes motion in the fallopian tubes, and cerebrospinal fluid circulation in the brain. While non-motile cilia exist in human sensory organs such as the nose and eye [2,3]. Some early studies in this domain were comprehensively reviewed by a few researchers and their detail is given in the following references [4–7].
A computational framework for investigating bacteria transport in microvasculature
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2023
Peter Windes, Danesh K. Tafti, Bahareh Behkam
E. coli and S. Typhimurium utilize their propulsive organelles known as flagella to swim in aqueous media (Wadhwa and Berg 2022).The swimming motility of these multi-flagellated bacteria comprises two distinct states of ‘run’ and ‘tumble.’ During a run, the flagellar motors rotate counterclockwise, causing the flagella to form a bundle and produce a propulsion force (∼0.48 pN) that moves the bacterium forward at a speed of 15–35 (Berg and Brown 1972; Broadway et al. 2017). Periods of run (∼0.9 s) are interspaced by periods of tumble (∼0.1 s) during which one or multiple flagella rotate clockwise, disrupting the flagellar bundle and causing a random change in the bacterium’s orientation. A stochastic motility model was used to recreate the motile behavior of a bacterium (Figure S1.1), and the bacterial cell was modeled as a spherocylinder. Using the previously reported experimental data (Berg and Brown 1972), at the beginning of a bacterium run phase, the run duration was randomly chosen from a log-normal distribution with a mean of and a standard deviation of The run velocity is given by, where is the bacterium’s forward direction unit vector. The magnitude of the run velocity, is a constant in the model based on the bacteria species and strain. A speed of was used in the present work. Upon completion of the run phase, a tumble phase is initiated and implemented, as depicted in Figure 2. According to the previously obtained experimental data (Berg and Brown 1972), the tumble angle and rotational speed follow a log normal distribution with mean of 68° and a standard deviation of 36° for (Figure S1.2a), and with a mean of 706°s−1 and standard deviation 365°s−1 for (Figure S1.2b). At the conclusion of a tumble, a new run begins using an updated forward direction vector and the process repeats. The model was implemented and tested in a large open domain without fluid interaction. Two characteristic bacterium paths are shown in Figure S1.3.