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      Boundary conditions investigation to improve computer simulation of cerebrospinal fluid dynamics in hydrocephalus patients

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          Abstract

          Three-D head geometrical models of eight healthy subjects and 11 hydrocephalus patients were built using their CINE phase-contrast MRI data and used for computer simulations under three different inlet/outlet boundary conditions (BCs). The maximum cerebrospinal fluid (CSF) pressure and the ventricular system volume were more effective and accurate than the other parameters in evaluating the patients’ conditions. In constant CSF pressure, the computational patient models were 18.5% more sensitive to CSF volume changes in the ventricular system under BC “C”. Pulsatile CSF flow rate diagrams were used for inlet and outlet BCs of BC “C”. BC “C” was suggested to evaluate the intracranial compliance of the hydrocephalus patients. The results suggested using the computational fluid dynamic (CFD) method and the fully coupled fluid-structure interaction (FSI) method for the CSF dynamic analysis in patients with external and internal hydrocephalus, respectively.

          Abstract

          Seifollah Gholampour et al. develop a computational model to examine the flow of cerebrospinal fluid (CSF) in hydrocephalus patients and healthy controls, and simulate how different biophysical parameters can influence CSF dynamics in the brain. Ultimately, their results could be used to better examine the CSF dynamics in a healthy or hydrocephalus brain, without the need for invasive procedures.

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          Structural and functional features of central nervous system lymphatics

          One of the characteristics of the CNS is the lack of a classical lymphatic drainage system. Although it is now accepted that the CNS undergoes constant immune surveillance that takes place within the meningeal compartment 1–3 , the mechanisms governing the entrance and exit of immune cells from the CNS remain poorly understood 4–6 . In searching for T cell gateways into and out of the meninges, we discovered functional lymphatic vessels lining the dural sinuses. These structures express all of the molecular hallmarks of lymphatic endothelial cells, are able to carry both fluid and immune cells from the CSF, and are connected to the deep cervical lymph nodes. The unique location of these vessels may have impeded their discovery to date, thereby contributing to the long-held concept of the absence of lymphatic vasculature in the CNS. The discovery of the CNS lymphatic system may call for a reassessment of basic assumptions in neuroimmunology and shed new light on the etiology of neuroinflammatory and neurodegenerative diseases associated with immune system dysfunction.
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            Statistics corner: A guide to appropriate use of correlation coefficient in medical research.

            M M Mukaka (2012)
            Correlation is a statistical method used to assess a possible linear association between two continuous variables. It is simple both to calculate and to interpret. However, misuse of correlation is so common among researchers that some statisticians have wished that the method had never been devised at all. The aim of this article is to provide a guide to appropriate use of correlation in medical research and to highlight some misuse. Examples of the applications of the correlation coefficient have been provided using data from statistical simulations as well as real data. Rule of thumb for interpreting size of a correlation coefficient has been provided.
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              Multiplicity of cerebrospinal fluid functions: New challenges in health and disease

              This review integrates eight aspects of cerebrospinal fluid (CSF) circulatory dynamics: formation rate, pressure, flow, volume, turnover rate, composition, recycling and reabsorption. Novel ways to modulate CSF formation emanate from recent analyses of choroid plexus transcription factors (E2F5), ion transporters (NaHCO3 cotransport), transport enzymes (isoforms of carbonic anhydrase), aquaporin 1 regulation, and plasticity of receptors for fluid-regulating neuropeptides. A greater appreciation of CSF pressure (CSFP) is being generated by fresh insights on peptidergic regulatory servomechanisms, the role of dysfunctional ependyma and circumventricular organs in causing congenital hydrocephalus, and the clinical use of algorithms to delineate CSFP waveforms for diagnostic and prognostic utility. Increasing attention focuses on CSF flow: how it impacts cerebral metabolism and hemodynamics, neural stem cell progression in the subventricular zone, and catabolite/peptide clearance from the CNS. The pathophysiological significance of changes in CSF volume is assessed from the respective viewpoints of hemodynamics (choroid plexus blood flow and pulsatility), hydrodynamics (choroidal hypo- and hypersecretion) and neuroendocrine factors (i.e., coordinated regulation by atrial natriuretic peptide, arginine vasopressin and basic fibroblast growth factor). In aging, normal pressure hydrocephalus and Alzheimer's disease, the expanding CSF space reduces the CSF turnover rate, thus compromising the CSF sink action to clear harmful metabolites (e.g., amyloid) from the CNS. Dwindling CSF dynamics greatly harms the interstitial environment of neurons. Accordingly the altered CSF composition in neurodegenerative diseases and senescence, because of adverse effects on neural processes and cognition, needs more effective clinical management. CSF recycling between subarachnoid space, brain and ventricles promotes interstitial fluid (ISF) convection with both trophic and excretory benefits. Finally, CSF reabsorption via multiple pathways (olfactory and spinal arachnoidal bulk flow) is likely complemented by fluid clearance across capillary walls (aquaporin 4) and arachnoid villi when CSFP and fluid retention are markedly elevated. A model is presented that links CSF and ISF homeostasis to coordinated fluxes of water and solutes at both the blood-CSF and blood-brain transport interfaces. Outline 1 Overview 2 CSF formation 2.1 Transcription factors 2.2 Ion transporters 2.3 Enzymes that modulate transport 2.4 Aquaporins or water channels 2.5 Receptors for neuropeptides 3 CSF pressure 3.1 Servomechanism regulatory hypothesis 3.2 Ontogeny of CSF pressure generation 3.3 Congenital hydrocephalus and periventricular regions 3.4 Brain response to elevated CSF pressure 3.5 Advances in measuring CSF waveforms 4 CSF flow 4.1 CSF flow and brain metabolism 4.2 Flow effects on fetal germinal matrix 4.3 Decreasing CSF flow in aging CNS 4.4 Refinement of non-invasive flow measurements 5 CSF volume 5.1 Hemodynamic factors 5.2 Hydrodynamic factors 5.3 Neuroendocrine factors 6 CSF turnover rate 6.1 Adverse effect of ventriculomegaly 6.2 Attenuated CSF sink action 7 CSF composition 7.1 Kidney-like action of CP-CSF system 7.2 Altered CSF biochemistry in aging and disease 7.3 Importance of clearance transport 7.4 Therapeutic manipulation of composition 8 CSF recycling in relation to ISF dynamics 8.1 CSF exchange with brain interstitium 8.2 Components of ISF movement in brain 8.3 Compromised ISF/CSF dynamics and amyloid retention 9 CSF reabsorption 9.1 Arachnoidal outflow resistance 9.2 Arachnoid villi vs. olfactory drainage routes 9.3 Fluid reabsorption along spinal nerves 9.4 Reabsorption across capillary aquaporin channels 10 Developing translationally effective models for restoring CSF balance 11 Conclusion
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                Author and article information

                Contributors
                s.gholampour@iau-tnb.ac.ir
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                23 March 2021
                23 March 2021
                2021
                : 4
                : 394
                Affiliations
                [1 ]GRID grid.411463.5, ISNI 0000 0001 0706 2472, Department of Biomedical Engineering, North Tehran Branch, , Islamic Azad University, ; Tehran, Iran
                [2 ]GRID grid.411368.9, ISNI 0000 0004 0611 6995, Biological Fluid Mechanics Research Laboratory, Biomechanics Department, Biomedical Engineering Faculty, , Amirkabir University of Technology, ; Tehran, Iran
                Author information
                http://orcid.org/0000-0002-4924-4239
                Article
                1920
                10.1038/s42003-021-01920-w
                7988041
                33758352
                80101649-2724-4268-86a1-7dc95fdef2eb
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 13 October 2020
                : 1 March 2021
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                Article
                Custom metadata
                © The Author(s) 2021

                biophysical models,computational biophysics
                biophysical models, computational biophysics

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