Fluid Mechanics of Blood Cells and Vesicles Squeezing Through Narrow Constrictions

The popular book by Drs. Enderle, Blanchard and Bronzino was now published in a second edition. The first edition, published on 1999, became the major textbook in many introductory courses given in the first or second year of Biomedical Engineering (BME) undergraduate programs. The second edition is aimed at serving the same purpose as the first one, i.e. to provide in depth and in breadth overview of the continuously growing field of BME. The dynamics of the field since the first edition was released is reflected in major changes in the second edition. Specifically, the authors kept the division into two gross parts: (i) core biomedical engineering areas (chapters 4–10: biomechanics, rehabilitation, biomaterials, tissue engineering, bioinstrumentation, sensors and biosignal processing), and (ii) biomedical technology (chapters 12–17: modeling, genomics, computational biology, imaging, lasers and optics). The additions to part (ii), biomedical technology, are a chapter on genomics and bioinformatics (chapter 13, which replaces biotechnology in the first edition), and a chapter on computational biology and complexity (chapter 14). The new chapter on genomics (chapter 13) is motivated by the recent sequencing of the human genome (as well as numerous viruses, microbes, eukaryotes, yeast and rice). The second new chapter on computational biology and complexity (chapter 14) includes examples of cellular process models in individual cells, as well as in cell populations and systems. On the other hand, the texts on imaging were reduced and condensed, so that the technologies of ultrasound and MRI, each occupying a separate chapter in the first edition, are now surveyed under one chapter of imaging. The major parts (i, ii) follow background of basic anatomy, physiology and cell theory (chapter 1, which, as in the previous edition, serves a limited purpose of providing the terminology used in later chapters), and of moral and ethical issues (chapter 2). The chapter on moral and ethical issues had been extended and now includes practical sections on marketing medical devices in the US, and on the role of biomedical engineers in the process for FDA approval. Real and hypothetical case studies were also added here, to illustrate ethical issues, patient privacy concerns, and medical liability questions. Overall, this remains an excellent textbook for BME students, and the progress in the field over the last 6 years is well reflected. Each chapter includes example problems with solutions and some 10–30 exercises. The list of suggested additional reading material, which concludes each chapter, was updated to cover literature published since the first edition was released. Figures are of good quality and are informative. Particularly useful is the new appendix on Matlab and Simulink software tools, which are required for solving some of the problems and exercises in this book. This not only contributes to the completeness, but also focuses the students on the computational abilities of these powerful software tools which are commonly used in BME work (e.g. solving polynomial and differential equations, plotting data, and simulating dynamic systems). My only reservation relates to the level of mathematics and basic engineering sciences (e.g. solid and fluid mechanics, electrical circuit analysis etc.) which is expected from students in the tasks provided. Given that an introduction to BME course is offered in many undergraduate programs during the first year of studies, students may be frustrated by not having the necessary background. In my introduction to BME course at Tel Aviv University, Israel, which was based on the first edition, I had the impression that students do not take full advantage of what this book has to offer, simply because they did not yet study differential equations, numerical methods, statistics, solid and fluid mechanics, and electrical circuits. In BME programs where an introduction to BME course is offered in the second year of studies, this issue may be resolved, but often the motivation in teaching an introduction to BME course during the first year is to provide students with the taste and flavor of BME while they are dedicating most of their time to mathematics, physics, biology, and basic engineering science courses. The second edition does not solve this conflict. In closure, despite the above reservation, this is certainly the most comprehensive textbook of its kind, and is recommended not only for undergraduate BME students but also for BME engineers in the industry or at the graduate level in the academia, as a reference book for a quick dive into new topics, or for an up-to-date survey of recent developments in this field.

The advent of microfluidics has provided a tremendous boost to the field of health care for the development of practical in-situ medical diagnoses and Point-of-Care (POC) testing methods. Optical microfluidics offers a lot of scope for carrying out successful biodetections through different target detection techniques such as optical absorption, fluorescence, etc. Two main issues in carrying out successful biodetection on microfluidic platform are the problem of biomolecule immobilization onto the surface of the microfluidic channel and efficient mixing of the bio-fluids necessary to achieve proper bio-interaction. In most cases, the biodetection involves two or more biological specimens, such as enzyme-substrate, antigen-antibody, protein-protein etc., and therefore, it is necessary to discover a solution which addresses to the needs of both immobilization and multi-molecular interactions. In this work, a novel technique of flow controlled molecular sorting is presented, wherein, by appropriate design of the microfluidic channel and by careful control of fluid flow in the system, optimal interaction of the specimens can be achieved through biomolecular sorting, thereby overcoming the problem of bio-immobilization onto the surface of the microfluidic channel. Herein, Finite Element Modeling (FEM) of flow behavior within the microfluidic channel has been carried out for different channel geometries, which is essential for the appropriate choice of microfluidic system for the present application. The technique of implementing the immobilization-free multi molecular bio-interactions in the proposed microfluidic system is explained and the feasibility of carrying out optical microfluidics based biodetection is demonstrated.

This book discusses geometric and mathematical models that can be used to study fluid and structural mechanics in the cardiovascular system. Where traditional research methodologies in the human cardiovascular system are challenging due to its invasive nature, several recent advances in medical imaging and computational fluid and solid mechanics modelling now provide new and exciting research opportunities. This emerging field of study is multi-disciplinary, involving numerical methods, computational science, fluid and structural mechanics, and biomedical engineering. Certainly any new student or researcher in this field may feel overwhelmed by the wide range of disciplines that need to be understood.This unique book is one of the first to bring together knowledge from multiple disciplines, providing a starting point to each of the individual disciplines involved, attempting to ease the steep learning curve. This book presents elementary knowledge on the physiology of the cardiovascular system; basic knowledge and techniques on reconstructing geometric models from medical imaging; mathematics that describe fluid and structural mechanics, and corresponding numerical/computational methods to solve its equations and problems.Many practical examples and case studies are presented to reinforce best practice guidelines for setting high quality computational models and simulations. These examples contain a large number of images for visualization, to explain cardiovascular physiological functions and disease. The reader is then exposed to some of the latest research activities through a summary of breakthrough research models, findings, and techniques.The books approach is aimed at students and researchers entering this field from engineering, applied mathematics, biotechnology or medicine, wishing to engage in this emerging and exciting field of computational hemodynamics modelling.

First Page

B-cells are promising candidate autologous antigen-presenting cells (APCs) to prime antigen-specific T-cells both in vitro and in vivo. However to date, a significant barrier to utilizing B-cells as APCs is their low capacity for non-specific antigen uptake compared to “professional” APCs such as dendritic cells. Here we utilize a microfluidic device that employs many parallel channels to pass single cells through narrow constrictions in high throughput. This microscale “cell squeezing” process creates transient pores in the plasma membrane, enabling intracellular delivery of whole proteins from the surrounding medium into B-cells via mechano-poration. We demonstrate that both resting and activated B-cells process and present antigens delivered via mechano-poration exclusively to antigen-specific CD8+T-cells, and not CD4+T-cells. Squeezed B-cells primed and expanded large numbers of effector CD8+T-cells in vitro that produced effector cytokines critical to cytolytic function, including granzyme B and interferon-γ. Finally, antigen-loaded B-cells were also able to prime antigen-specific CD8+T-cells in vivo when adoptively transferred into mice. Altogether, these data demonstrate crucial proof-of-concept for mechano-poration as an enabling technology for B-cell antigen loading, priming of antigen-specific CD8+T-cells, and decoupling of antigen uptake from B-cell activation.

Urinary flow is governed by the principles of fluid mechanics. Urodynamic studies have revealed the fundamental kinematics and dynamics of urinary flow in various physiological and pathological conditions, which are cornerstones for future development of diagnostic knowledge and innovative devices. There are three primary approaches to study the fluid mechanical characteristics of urinary flow: reduced order, computational, and experimental methods. Reduced-order methods exploit the disparate length scales inherent in the system to reveal the key dominant physics. Computational models can simulate fully three-dimensional, time-dependent flows in physiologically-inspired anatomical domains. Finally, experimental models provide an excellent counterpart to reduced and computational models by providing physical tests under various physiological and pathological conditions. While the interdisciplinary approaches to date have provided a wealth of insight into the fluid mechanical properties of the stented ureter, the next challenge is to develop new theoretical, computational and experimental models to capture the complex interplay between the fluid dynamics in stented ureters and biofilm/encrustation growth. Such studies will (1) enable identification of clinically relevant scenarios to improve patients’ treatment, and (2) provide physical guidelines for next-generation stent design.

Heat transfer enhancement in a trapezoidal cavity due to the cavity orientation and marangoni convection

One of the fundamental problems in simulating the motion of sharp interfaces between immiscible fluids is a description of the transition that occurs when the interfaces merge and reconnect. It is well known that classical methods involving sharp interfaces fail to describe this type of phenomena. Following some previous work in this area, we suggest a physically motivated regularization of the Euler equations which allows topological transitions to occur smoothly. In this model, the sharp interface is replaced by a narrow transition layer across which the fluids may mix. The model describes a flow of a binary mixture, and the internal structure of the interface is determined by both diffusion and motion. An advantage of our regularization is that it automatically yields a continuous description of surface tension, which can play an important role in topological transitions. An additional scalar field is introduced to describe the concentration of one of the fluid components and the resulting system of equations couples the Euler (or Navier–Stokes) and the Cahn–Hilliard equations. The model takes into account weak non–locality (dispersion) associated with an internal length scale and localized dissipation due to mixing. The non–locality introduces a dimensional surface energy; dissipation is added to handle the loss of regularity of solutions to the sharp interface equations and to provide a mechanism for topological changes. In particular, we study a non–trivial limit when both components are incompressible, the pressure is kinematic but the velocity field is non–solenoidal (quasi–incompressibility). To demonstrate the effects of quasi–incompressibility, we analyse the linear stage of spinodal decomposition in one dimension. We show that when the densities of the fluids are not perfectly matched, the evolution of the concentration field causes fluid motion even if the fluids are inviscid. In the limit of infinitely thin and well–separated interfacial layers, an appropriately scaled quasi–incompressible Euler–Cahn–Hilliard system converges to the classical sharp interface model. In order to investigate the behaviour of the model outside the range of parameters where the sharp interface approximation is sufficient, we consider a simple example of a change of topology and show that the model permits the transition to occur without an associated singularity.

Time-lapse microscopy is an effective research tool to monitor cell behavior and cell divisions. Recent advances in microfluidics have accelerated the adoption of time-lapse microscopy in research. However, it is challenging to visualize and interpret the time-series data gathered through time-lapse microscopy. We have developed a circular plotting software tool, μPolar, to visualize the trends and patterns of the cell movements and cell division events in a time-series. μPolar is interactive and easy to use. We demonstrate the utility of μPolar by visualizing the events of dividing yeast cells where cell divisions lead to oscillating plotting patterns, and in migrating mouse fibroblasts where cell shapes change during the migration. μPolar potentially could be applied to other types of time-series of microscopic images.

Time-lapse microscopy is an effective research tool to monitor cell behavior and cell divisions. Recent advances in microfluidics have accelerated the adoption of time-lapse microscopy in research. However, it is challenging to visualize and interpret the time-series data gathered through time-lapse microscopy. We have developed a circular plotting software tool, μPolar, to visualize the trends and patterns of the cell movements and cell division events in a time-series. μPolar is interactive and easy to use. We demonstrate the utility of μPolar by visualizing the events of dividing yeast cells where cell divisions lead to oscillating plotting patterns, and in migrating mouse fibroblasts where cell shapes change during the migration. μPolar potentially could be applied to other types of time-series of microscopic images. This R package μPolar is available through GitHub.

As an enabling technique, microfluidic systems have developed rapidly in recent years to emerge as a powerful tool in the field of biomedical engineering. Microfluidics enables miniaturized, integratable, high‐throughput and automated biochemical analysis, fabrication of biomaterials with precisely controlled structures and compositions, and construction of organ‐on‐a‐chip systems with specific organ features and functions. Recent advances in microfluidics for biomedical engineering applications are outlined in this review, emphasizing the basic concepts and research trends in this field. The review covers recent research in microfluidics for bioassays, biofabrication, and tissue engineering. The technologies involved in these applications are highlighted, such as those for fabricating sensitive and portable bioassays, for fabricating biomaterials in a regulated manner, and for fabricating 3D cell culture scaffolds and microenvironments.

For 40 years we have relied upon morphological assessment of the human embryo for its selection for transfer. With the advent of time-lapse microscopy (TLM), we have been able examine the timings of key developmental events and create algorithms to further advance embryo selection. However, such algorithms do not utilise all available data. Artificial intelligence (AI) can utilise the hundreds of successive images of each embryo as it develops to produce highly predictive rankings. This approach increases the speed of assessment, while decreasing variation typically associated with the subjective analysis of individual embryologists. Therefore, AI represents a means to increase both accuracy and standardisation of embryo selection. In the IVF laboratory, beyond its current function in embryo selection, AI will be used for gamete selection prior to fertilisation, and to increase laboratory function by its integration into Quality Control and Management systems. Although TLM combined with AI holds great potential, it does not quantitate the physiological status of the embryo. Analysis of spent culture medium has revealed that metabolism (glucose uptake and the utilisation of amino acids) of the human blastocyst is related to pregnancy. Currently, analysis of metabolic function by individual embryos requires highly specialised technologies, but with the advent of microfluidics and microfabrication we are entering an era of novel benchtop technologies capable of rapid single embryo analysis. Subsequently, it is envisaged that AI will be used in combination with TLM and the analysis of spent culture medium (to quantitate metabolism and plausibly cell-free DNA), to identify non-invasively the healthiest embryo for transfer.

Brownian dynamics simulations are used to characterize the time scales involved in polymer electrophoresis through narrow constrictions. The polymer is modeled as a freely jointed bead−rod chain with a total charge distributed uniformly among the beads. The narrow constriction is a thin channel with height hs < Rg which separates two thicker channels, both of height hl ∼ Rg where Rg is the polymer radius of gyration. The polymer is initially placed in a thick channel, and an applied electric field drives it into the next thick channel through the intervening narrow constriction. We find that the electrophoresis of the polymer is characterized by three time scales, each of which depends on the polymer chain length, N. An approach time, τapp, describes the motion of the polymer to the entrance of the thin channel. Upon reaching the entrance of the thin channel, the polymer is entropically trapped, and its escape from the trap is associated with an activation time, τact. After the activation event, the motion of the polymer through the thin channel and into the next thick channel is characterized by a crossing time, τcross. We find that whereas τapp and τact decrease with N, τcross increases with N. As a consequence, it is found that the transit velocity of the polymer, vtransit, first increases with N and then decreases beyond a certain value of N. The position of the maximum in vtransit is shown to depend on the applied electric field strength, the relative values of hs and hl, and whether the channel is two-dimensional or three-dimensional. We discuss the relevance of this behavior to polymer electrophoresis in microfluidic channels exhibiting entropic trapping effects and polymer translocation through nanopores.

Cancer therapy mediated by nanoparticles is gaining recognition for shifting the paradigm of targeted and/or personalized cancer therapy. Despite the great promise, only a few nanoformulations have been clinically approved due to the complexities that limit effective and efficient nanodrug development. Moreover, the preparation of cancer nanodrug has not yet been optimized for clinical approval in patient treatment. Computational fluid dynamics (CFD) is a new technique that simulates and analyzes fluid flows and their interactions with surfaces using computer algorithms and numerical analysis. This simulation and modeling tool provides a distinct advantage in understanding tumor-host mechano-biology and mechanisms that help identify the main factors affecting the transport of tumor-targeting nanoagents. Taking these factors into consideration, the advent of computational fluid dynamics simulation and modeling represents a shift in the optimization of cancer nanoagents’ fluidics. This review briefly introduces the fluid mechanism along with its principles and foundations relating to cancer drug delivery. Key components of tumor microenvironments relating to temperature, flow velocity, fluid pressure, and tumor rheology, as well as physicochemical properties of nanoparticles modulating fluid mechanics, were discussed. It also includes a thorough examination of the advantages and challenges of using nanoformulations such as liposomes, polymers, and extracellular matrix in exploring the progress made in computational fluid dynamics simulation to study the mechanism of nanoparticle delivery and interactions with cancerous tumors. The convergence of Machine Learning algorithms and CFD simulation in tumor-nanodrug interactions. The application of ML algorithms provides high predictive accuracy of nanodrug delivery that can benefit cancer biomedicine research by predicting how flow affects drug efficacy. The future of the ML-CFD is detailed to include imaging and 3D-CFD simulations to increase the credibility of these models and advancement to translational clinical research. This review concluded by urging collaborative efforts for a multiscale approach by biomedical engineers and scientists, as well as oncologists, to develop a modeling framework that advances precision medical care for effective cancer treatment. Standardization of the model and approaches, together with nanoparticle synthesis, is recommended to advance this research to the translational and clinical stage.

The combined effect of Coriolis force due to rotation and magnetic field dependent (MFD) viscosity on the onset of Benard–Marangoni convection in a horizontal layer of ferrofluid is investigated theoretically. The lower boundary is taken to be rigid–isothermal, while the upper free boundary open to the atmosphere is flat and subject to a convective surface boundary condition. The Galerkin technique is employed to extract the critical stability parameters numerically. It is shown that convection sets in as oscillatory motions provided that the Prandtl number ( ) is less than unity. A mechanism for suppressing or augmenting Benard–Marangoni ferroconvection by Coriolis force ( ), MFD viscosity parameter ( ), Biot number ( ), magnetic number ( ) and nonlinearity of fluid magnetization parameter ( ) is discussed in detail. It is found that the onset of Benard–Marangoni ferroconvection is delayed with an increase in , , but opposite is the case with an increase in , . Further, increase in and decrease in and is to decrease the size of the convection cells. Comparisons of results between the present and the existing ones are made under the limiting conditions and good agreement is found. Keywords: Benard–Marangoni convection, ferrofluids, Coriolis force, MFD viscosity, heat transfer coefficient. Cite this Article Savitha B, C E Nanjundappa C E. Effect MFD Viscosity on Benard–Marangoni Ferroconvection in a Rotating Ferrofluid Layer with Convective Surface Boundary Condition. Recent Trends in Fluid Mechanics . 2020; 7(2): 12–32p.