\r\n\tThis book aims to explore the issues around the rheology of polymers, with an emphasis on biopolymers as well as the modification of polymers using reactive extrusion.
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"5bc21841d2b87388ad498bc09910944b",bookSignature:"Dr. Casparus Johannes Verbeek and Dr. Velram Mohan",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/8880.jpg",keywords:"Extrusion, Injection Moulding, Thermoplastics, Natural Polymers, Biomass, Polymer Modification, Polymer Blends, Compatibilization, Processing Challenges, Reactive Compounding, Screw Design, Process Design",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 6th 2019",dateEndSecondStepPublish:"September 27th 2019",dateEndThirdStepPublish:"November 26th 2019",dateEndFourthStepPublish:"February 14th 2020",dateEndFifthStepPublish:"April 14th 2020",remainingDaysToSecondStep:"a year",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"102391",title:"Dr.",name:"Casparus",middleName:"Johannes",surname:"Verbeek",slug:"casparus-verbeek",fullName:"Casparus Verbeek",profilePictureURL:"https://mts.intechopen.com/storage/users/102391/images/system/102391.jpeg",biography:"Dr Verbeek is a Chemical Engineer, currently an associate professor at the School of Engineering at the University of Waikato and is also the R&D manager for Aduro Biopolymers. 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1. Introduction
This chapter highlights recent advances in wetting of solid surfaces by the self-spreading of phospholipid biomembranes upon deposition of lipid reservoirs [1, 2]. It should provide researchers with the necessary material to understand and evaluate spontaneous propagation of lipid monolayers [2, 3] and double bilayers [2, 4] on solid supports, which occurs when lipid reservoirs are brought in contact with low- and high-energy surfaces, respectively, in an aqueous environment. The first section provides a brief introduction of surfaces and interfaces, the second section is dedicated to the mechanism of interaction of lipid films with the supporting surface during wetting, and the third section introduces the formation of ruptures in double bilayers caused by that interaction.
Biological membranes organize cellular complexity, and thus establish and promote structure in the living world [5]. They compartmentalize the cell, form transport networks, organize proteins, serve as a smart barrier for molecules and ions, and establish the chemical identity of the cell. The fundamental structure of the cellular membrane is the phospholipid bilayer, consisting of a large number of individual phospholipid molecules, which organize themselves spontaneously in a self-assembly process. The membrane has peculiar characteristics, is highly dynamic, and features two-dimensional fluidity [6]. It can accommodate proteins and other functional molecules which fulfil important functions such as recognition, signal transduction, and transport of chemical entities through the membrane.
Biomembrane models, designed to capture some of the features of the cell membrane in a simplified setting, have become a popular research subject [7, 8]. They are naturally less complex than their biological counterpart, but can be relatively easily assembled, for example, on suitable flat solid surfaces (supported membranes), or as spherical membrane compartments, often referred to as vesicles or liposomes [9]. In the past few decades, a large number of model systems of increasing sophistication have been introduced, often with the purpose to identify and study the role and function of lipids and other membrane components in the cell [7, 10]. In particular, the two-dimensional fluidity of the membrane and their ability to harbour proteins have been in the centre of attention.
Although self-organization of lipid molecules to lipid membranes occurs spontaneously, which is frequently exploited to assemble membranes from lipid mixtures in solution in experimental settings, controlled assembly and preparation of stable, well-defined phospholipid films on supporting surfaces, such as glass, metals and metal oxides, are still challenging engineering tasks.
The deposition of lipid reservoirs onto various solid surfaces leads to formation of self-spreading surface-supported lipid films [1, 11]. Lipid reservoirs range from manually deposited single sources (multilamellar liposomes) to liposome suspensions of different particle sizes [10, 12], which can be directly transferred onto the substrate, either manually with glass microneedles [13] or by means of sophisticated microfluidic instrumentation [10, 14]. The following chapter is dedicated to the formation of lipid mono- and bilayer membranes by means of self-spreading from a lipid source. The result of lipid spreading is typically a solid-supported self-assembled membrane, i.e., a continuous supramolecular structure with two-dimensional fluidity. Each of the three forms of supported membranes mentioned above represents a real biological structure. Monolayers surround lipid droplets in cells, which are small stocks of lipid molecules regulating lipid metabolism of the cell [15]. Single bilayers are – in terms of composition – simplified versions of the plasma membrane of cells, which resides on top of the cytoskeleton. Spreading double bilayers are reminiscent of spreading cell membranes, which in addition to a lipid membrane contain actin filaments [16]. Accordingly, each of the supported membrane types can be utilized in experimental studies to address different questions. The supported bilayer is the most commonly used model system, as it directly resembles the membrane of the biological cell. Supported bilayer structures can be easily prepared in an aqueous environment on available surfaces that are compatible with microscopy experiments, such as glass, mica, or sapphire plates. Nowadays, with microfabrication and micromanipulation equipment being commonplace, a greater variety of surfaces is available, for example, silicon or aluminium oxide coated glass, which opened pathways for the generation of self-spreading double bilayers from lipid reservoirs. In particular, amorphous fluoropolymers and the epoxy photoresist SU-8, which can be utilized to coat and pattern a variety of surfaces with nanoscopic polymer films [17, 18], have enabled experiments where single reservoirs are manually deposited, allowing for the controlled generation of self-spreading lipid monolayers. Table 1 gives an overview over the most relevant publications [19 - 54] in the area of lipid film research from 1985 to present. It covers fabrication, biophysical characterization, and utilization in, for example, membrane protein studies.
First theoretical framework of spreading/wetting membranes and relation to friction (drag) coefficient; supports the current model explained throughout the chapter.
\n\t\t
\n\t\t
\n\t\t\t
\n\t\t\t\t3\n\t\t\t
\n\t\t\t
\n\t\t\t\tPhenomenology and kinetics of lipid bilayer spreading on hydrophilic surfaces.\n\t\t\t
Investigation of wetting and energetic properties of different lipid layers deposited on the glass surface, carried out by contact angles measurements and determination of the apparent surface free energy .
\n\t\t
\n\t\t
\n\t\t\t
\n\t\t\t\t21\n\t\t\t
\n\t\t\t
\n\t\t\t\tA Self-assembly route for double bilayer lipid membrane formation\n\t\t\t
\n\t\t\t\tUsing patterned supported lipid membranes to\n\t\t\t\t \n\t\t\t\tinvestigate the role of receptor organization in intercellular signaling\n\t\t\t
Involves surface-enhanced ellipsometric contrast microscopy to observe the spreading of egg phosphatidylcholine nanodroplets on a hydrophilic substrate
\n\t\t
\n\t\t
\n\t\t\t
\n\t\t\t\t28\n\t\t\t
\n\t\t\t
\n\t\t\t\tInstrumental methods to characterize molecular phospholipid films on solid supports\n\t\t\t
How self-spreading model membranes can be used to understand membrane-mediated transport processes. Findings indicate an intimate coupling between cellular lipidomes and glycomes.
\n\t\t
\n\t\t
\n\t\t\t
\n\t\t\t\t32\n\t\t\t
\n\t\t\t
\n\t\t\t\tMultiplexed biomimetic lipid membranes\n\t\t\t\t \n\t\t\t\ton graphene by dip-pen nanolithography\n\t\t\t
Solid support as micron-size silica beads (spherical) instead of flat surfaces
\n\t\t
\n\t
Table 1.
Selected publications in the field of lipid self-spreading/wetting
2. Surfaces and interfaces
Supported lipid membranes form on solid surfaces, where many of the membrane properties are depending on the properties of the underlying surface, including material and composition, surface charge, roughness, and surface tension. The wetting phenomena observed in double bilayers only occur on solid high-energy surfaces, such as silicon oxide, or aluminium oxide films. In contrast, the wetting phenomena leading to lipid monolayers occur exclusively on low-energy hydrophobic polymer surfaces. The surface tension is a central concept, that is, in principle, a measure of how much energy is associated with a surface per unit area. If in a thought experiment a cube with the side length a, consisting of a solid material, is split into two parts of identical size, work of cohesion W must be exerted, equal to the difference in free energy G2–G1, which characterize the system before and after the split. If A = a2 is the area of the separation, then F = W/A is the surface free energy of the newly created surfaces. The surface tension σ of the material can be defined, which is a measure of how much work is required to create a surface of area A [55]:
σ=F+A∂F∂AE1
High-energy surfaces (σ > 500 Nm/m) are composed of atoms which are attached to each other by covalent, ionic, or metallic bonds. Examples include metals, diamond, silica, glasses, and ceramics. Molecules of low-energy surfaces, such as various polymers, are attached to each other by the weaker van der Waals interactions or hydrogen bonds. It is evident that the intermolecular forces which determine the surface energy of a material also determine latent heat, melting, and boiling point. Materials with high boiling points (T > 2000°C) usually also have high-surface energies.
In water (and likewise in other monomolecular fluids), the molecules in the bulk are surrounded by other water molecules, where they are energetically indistinguishable. Only at solid and gas interfaces (e.g., the walls of a container, or the water vapour/water interface in an open container), there are molecules which experience different forces. The resulting energetic differences with respect to the bulk molecules are reflected by the interfacial tension of the solid–liquid interface σSL, and the surface tension of the liquid σLV at the liquid–vapour interface. Accordingly, the surface tension of a solid in equilibrium with a liquids vapour is σSV. A typical value of σLV for water is 73 mN/m [56, 57]. Temperature increase and added solutes lead to a noticeable decrease in surface tension. At a contact line between liquid, solid, and fluid at equilibrium (e.g., a fluid droplet in saturated air on a planar surface), the three interfacial tensions are force-balanced. The Young equation describes this force balance, and introduces a contact angle ϑ between the fluid/vapour and fluid/solid interface as a convenient measure of surface hydrophobicity/hydrophilicity:
σSV−σSL=σLVcosϑE2
Hydrophobic (or high-energy) surfaces feature small contact angles of ϑ < 90 °, since the system is minimizing its free energy by spreading the droplet across the surface. The dynamics of wetting and spreading of fluids on solid surfaces have been extensively reviewed in other publications [58] and will not be further discussed here. However, the Young equation allows only direct observation of σLV and ϑ. Direct comparison of solid surfaces by means of their surface tensions is not possible, since these values cannot be obtained in a straightforward manner. By introducing the concept of a critical surface tension σc, at which complete wetting occurs (cos ϑ = 1), solids can be well compared with respect to their wetting behaviour. Materials with strong cohesion, such as oxides and metals, can be categorized under high critical surface tension materials (σc >> 100 mN/m), whereas polymers and other organic films typically feature low critical surface tensions (σc < 50 mN/m). The nature of lipid films formed on such surfaces depends very much on the surface energy.
In addition, the roughness of a surface is of considerable importance to its wetting properties, which was discovered by Wenzel in 1936 [59]. In nature, topographical surfaces with regular or irregular features on the micro- or nanoscale have a water-repelling function. The most prominent examples are the leaves of the Sacred Lotus, which has a nanopatterned surface to protect it from water, and the skin of sharks, which give the animal the ability to move faster in water. These superhydrophobic surface features, which are characterized by very large contact angels of ϑ > 150°, cannot be described by the Young equation, but by the Cassie-Baxter surface model [59]. According to that model, which is applicable to extreme surface texture, water droplets cannot wet the surface, since they are forced to remain on top of the structures. The prominent topographical features prevent wettings, because the surface would have to bend down into the gaps, which would result in high local curvatures and high Laplace pressures, but also in an increased surface-to-volume ratio. Both surface energy and roughness have an influence on the wetting properties of lipid material when it comes in contact with the surface. For example, fluorocarbon films, generated by plasma decomposition and deposition of perfluorinated hydrocarbons, are more hydrophobic than epoxy polymer films, but their greater roughness makes them comparatively less suitable for large area spreading of lipid monolayers [60].
3. Lipid self-spreading
A lipid reservoir brought into contact with a solid substrate leads to wetting of the surface by lipids in the form of a molecularly thin phospholipid film. Lipid reservoirs can be considered as stocks of phospholipid molecules and can exist in various forms. Rädler et al. have used “solid lipid sources” as reservoirs, which they have described as irregularly wrapped (entangled) layers of lipid bilayers [2]. The reservoirs have been referred to as “lipid lumps” in a few other studies and illustrated as liquid drops in which the individual lipid molecules are randomly oriented [22]. Multilamellar vesicles (MLVs) containing several hundreds of lipid bilayers packed in a compact sphere can also be employed as lipid reservoirs [4, 11]. Because of their dense, layered structure, such vesicles are also referred to as “onion shell vesicles” [61]. The internal molecular structure of such lipid reservoirs can be complex and the mechanisms of initiation of wetting from such reservoirs have not yet been fully understood. We will further comment on the potential impact of the lipid reservoir structure on wetting dynamics in the later paragraphs of this chapter.
Due to wetting, individual lipids originating from a lipid source self-assemble on a solid surface as a planar lipid film, extending the surface area over a distance of several tens to hundreds of microns. The wetting motion of lipids on solid substrates is therefore commonly referred to as “lipid spreading”. Since the spreading is generally not initiated due to an external stimulus, but begins rather spontaneously, ensuring energetically the most favourable conditions, the spreading is further defined as “lipid self-spreading”.
In context of this chapter, lipid wetting of a solid substrate occurs in biologically relevant conditions, e.g., in water-based physiological buffers. Let us consider the spreading of a lipid monolayer. When a lipid source is deposited on a hydrophobic substrate in the presence of an aqueous solution, surface tension at two interfaces may play a role in wetting. One is the tension at interface of lipids with the solid substrate (σL), the second is the tension at the interface of aqueous buffer (σA) with the solid substrate. In other words, the surface can be wetted by either phospholipids emerging from the lipid source or by the water molecules in the buffer. Lipid spreading will initiate spontaneously if the surface free energy of the system would be reduced during spreading. This is possible if σL is lower than σA. Accordingly, lipids would migrate from the lipid source to wet the substrate.
Structure of the wetting lipid film depends on the nature of the solid surface, e.g., if it is of high or low energy, a hydrophilic or hydrophobic substrate. Single bilayers tend to form on moderately hydrophilic substrates such as glass. Double bilayer wetting can be observed on silicon oxides and multiple metal oxide surfaces which are generally considered to be high-energy substrates. On hydrophobic surfaces like fluoropolymers or the epoxy SU-8, lipids spread as monolayers, i.e., as a single leaflet of a lipid bilayer. This can be expected since the hydrocarbon chains of lipid molecules would tend to face towards the hydrophobic surface.
The mechanism of interaction with the surface during wetting varies, depending on the nature of the substrate. The monolayer lipids form due to the hydrophobic interactions with the surface. Spreading monolayers screen along the buffer–surface interface, establishing a direct contact with the hydrophobic substrate (Figure 1a). During single bilayer spreading on glass or mica, there remains a thin (few nm) lubricating layer beneath the membrane (Figure 1b). The bilayers spread by “sliding” on top of the water layer. The interaction between the bilayer and the surface is governed by hydration and the van der Waals forces. Double bilayer spreading on highly oxidized silica or other metals is mostly under the influence of electrostatic interactions. Physiological buffers have high ionic strength and contain multivalent ions, for example, Ca2+ or Mg2+. These positively charged entities in the ambient solution act as “fusogenic agents” and establish bridging connections between the negatively charged phospholipid headgroups and the negatively charged surface (Figure 1c). This facilitates the spreading of lipid films on oxidized surfaces. Concentrations of only a few mM/L [62] of divalent cations and 10–5 M of trivalent cations have a dramatic effect on surface potentials. For instance, they can neutralize a negatively charged surface and even start to accumulate positive charges [62]. A few studies have reported on multi-bilayer spreading featuring up to seven bilayers on Si/SiO2 surfaces [22, 63], but very little is known about the spreading dynamics.
Self-spreading of multilamellar vesicles on solid substrates leads to circular lipid patches (Figure 1d). The main reason for the circular geometry lies in the spreading dynamics which is characterized by Darcy flow. Briefly, the spreading front can be described by the same equation that is used to describe a Saffman-Taylor instability (a specific form of Darcy flow), but with the opposite sign. Therefore, any perturbation from a circular shape during spreading will be rapidly damped. We will explain thoroughly why membrane flow is a form of Darcy flow in the next section.
The overall tension (σS) during lipid wetting is expected to be:
σS=σA−σLE3
Figure 1.
Lipid self-spreading. Illustrations showing in a side view the cross-sectional edge profiles of a spreading (a) monolayer, (b) single bilayer, and (c) double bilayer. The spreading monolayer is directly in contact with the substrate. The single bilayer exhibits sliding motion on a thin lubricating water layer. During double bilayer spreading, positively charged ions bridge the proximal (lower) bilayer to the surface, where the distal (upper) bilayer slides on a layer of water trapped in between the two layers. ζ indicates the region where the friction is effective during spreading. v is the velocity of the spreading membrane. (d) Laser scanning confocal micrograph of a self-spreading double bilayer (top view). The bright spot at the centre of the circular patch is the multilamellar lipid reservoir, from which lipid material is drawn.
Lipid self-spreading is therefore considered to be a form of Marangoni flow, which is the mass transfer between two fluids along an interface in a surface tension gradient. During spreading, there is mass transfer of lipids towards the buffer solution along the solid substrate.
The lipid reservoir has also an internal tension (σ0) which will resist the spreading. The spreading power (S) can thus be formulated as the difference between overall interfacial tension and the tension of the reservoir:
S=σS−σ0=σA−σL−σ0E4
Note that the terms in Eq. (3) and (4) may not fully represent the single and the double bilayers. As described above, during monolayer spreading the lipids replace the water molecules on the surface; therefore, terms σL and σA are appropriately describing the driving forces in lipid monolayer spreading. For the other two types of spreading, more complicated surface interactions may play a role. For instance, because of the existing lubricating water layer between the surface and the membrane during single and double bilayer spreading, the water molecules are not essentially replaced by lipids. Therefore, σS can simply be defined as the adhesion energy between the membrane and the surface, instead of the surface tension differences of σA and σL.
Different wetting modes of phospholipids can be distinguished by a kinetic spreading coefficient, β ; which is directly related to velocity (v) of the spreading membrane:
v(t)=βtE5
The spreading power (S), which quantifies the driving force for the spreading process, is composed of the spreading and the friction coefficients (ζ):
β=S2ζE6
Next, we will formulate the spreading coefficients for different modes of lipid self-spreading on solid supports. These modes include monolayers, single bilayers, and double bilayers. We will first adapt a one-dimensional model which had been originally proposed by Rädler et al. for single lipid bilayer spreading [2].
We make two main assumptions to establish our model, which would be valid for all three types of lipid spreading:
membrane flow can be described by a two-dimensional Stokes equation:
ζv⇀=∇σ−μ∇2v⇀E7
where μ is the membrane viscosity (10–3 dyn s cm–1) [64] and v is the membrane velocity. The first term describes the frictional forces and the second term the viscous shear forces. The viscous forces are only influential in membrane dynamics if the length of the membrane is below 30 nm. We will discuss in the next section in detail how the critical length (Lc), at which the impact of viscous forces is comparable to the frictional forces, has been determined. Since the lengths of the supported membranes are way above 30 nm, we now eliminate the second term in Eq. (7) and take into account only the first term:
ζv⇀=∇σ⇒ζv=dσdxE8
This means that the tension gradient (∇σ) which drives the spreading process is balanced by the frictional stress. For a monolayer or a single bilayer, ζ applies to the region between the surface and the lipid film. For a double bilayer membrane, ζ is effective between the proximal (lower) and the distal (upper) bilayers. This is due to the tank-thread-like motion of the double bilayer. In this type of motion, the proximal layer is immobilized as soon as it is fused to the surface via the multivalent ions and the distal bilayer slides over the proximal bilayer and lays onto the substrate. This is why the self-spreading double bilayer has been called by Rädler et al. “a rolling bilayer” [2].
To a first degree of approximation, lipid membranes are incompressible [20].
This means that the membrane flow caused by the tension difference over a radius R is dissipated by the friction. The velocity at the spreading edge of a double bilayer (vR) will be half the velocity of the membrane (v) since the spreading edge exhibits rolling motion, where only half of the membrane material is laid upon the substrate. The membrane velocity of a monolayer or a single bilayer will be equal to the velocity at the spreading edge since such rolling motion is not performed. Because of this distinction among the spreading dynamics of different types of membranes, we will explain the model from this point on under two groups: (1) monolayer and single bilayers, (2) double bilayers. The equations for the calculation of β for group 1 will be denoted as “a” in the left column, and for group 2 as “b” for the right column.
The membrane velocity with respect to the velocity at the spreading edge at a distance R from the center is:
During spreading, the radius of the circular lipid patch would grow from R0 (the reservoir radius) at time t0 to R(t) at time t. Integrating the left side of Eq. (13a) and (13b), from R(0) to R(t) and the right side of the equations from time t=0 to t gives:
As mentioned above, the spreading power S=σs−σ0, is the product of the spreading coefficient and the friction coefficient:
β=S2ζ\t(6)BB1
Inserting Eq. (6) into Eq. (16a) and (16b) will provide the spreading coefficient β for monolayer–single bilayer and double bilayer spreading, respectively.
After determining the spreading coefficient with regards to membrane velocity by adapting a one-dimensional model, we will now determine the relationships for a two-dimensional model. The two-dimensional model would provide more insights for lipid self-spreading in correlation with experiments, since the two-dimensional wetting of a lipid membrane can be monitored experimentally, for example, via confocal microscopy (Figure 1d). In such experiments, a self-spreading membrane doped with a membrane-attached fluorophore can be observed from top view as a circular patch with quite a distinct circumference (Figure 1d). On this circular patch, we imagine an arbitrary ring with an inner radius r1 and the outer radius r2 (Figure 1). During the spreading of an incompressible membrane, the number of lipid molecules within this ring should be constant. The law of mass conservation would require the flux – rate of lipid flow per cross-sectional area – at distance r1 (J1) and r2 (J2) to be equal.
J1=J2BB2
The lipid molecules coming from the reservoir will pass through r1 with velocity v1, and subsequently r2 with velocity v2 :
2πr1ρvr1=2πr2ρvr2BB3
where ρ is the particle density. Note that we had earlier assumed the membrane to be incompressible, thus ρ is constant. Therefore, rvr is constant. This means that the velocity vr of the lipid molecules at any arbitrary circle with radius r can be related to the velocity vR of the lipids at the spreading edge as following:
rvr=RvRE19
We will initiate the formulation of our two-dimensional model by rearranging Eq. (19): vr=RrvR. As explained above, during the determination of the one-dimensional model, the dynamics of spreading monolayers-single bilayers and the double bilayers will be formulated separately due to the variations in the spreading edge velocity. We will use notation “a” for the equations defining the monolayers/single bilayers and “b” for the equations defining the double bilayers.
Two-dimensional monolayer and single bilayervr=RrdRdt\t(a)Two-dimensional double bilayervr=2RrdRdt\t(b)E20
The velocity of the spreading edge of the double bilayer membrane is again half the velocity of the membrane (vr). The radial tension gradient drives the spreading process and it is balanced by the frictional stress (Eq. 7):
Two-dimensional monolayer and single bilayerζvr=RrdRdt=dσdr\t\t(a)Two-dimensional double bilayerζvr=RrdRdt=2dσdr\t\t(b)E21
Integrating Equations (20a) and (20b) from R0 (the reservoir radius at time t0) to R (the radius of the growing circular lipid film at time t) gives:
Two-dimensional monolayer and single bilayerζRdRdtln(RR0)=σs−σ0\t(a)Two-dimensional double bilayer2ζRdRdtln(RR0)=σs−σ0\t(b)E22
The spreading power (S=σs−σ0) is the product of spreading coefficient and friction coefficient. Eq. (21a) and (21b) are therefore rearranged as following to obtain the two-dimensional spreading coefficients:
Two-dimensional monolayer and single bilayerRdRdtln(RR0)=σs−σ0ζ=2β\t\t(a)Two-dimensional double bilayerRdRdtln(RR0)=σs−σ02ζ=2β\t\t(b)E23
By monitoring the self-spreading of a fluorescently labelled monolayer on a SU-8 surface over time under a microscope, β has been calculated to be 1−3μm2/s. [3] Note that SU-8 is a hydrophobic epoxy polymer that is commonly used as substrate, due to its favourable processing properties [3, 60]. In content of the same study, Eq. (22 a/b) have been solved numerically and shown to yield a good fit with the experimental data. The tension of the reservoir is considered to be on the order of the lysis tension of a single bilayer membrane (6mN/m). “Lysis” can be thought of as the process of breaking down a single bilayer. Since a multilamellar reservoir consists of several bilayers, monolayer spreading can only be initiated by the lysis of a single bilayer within the reservoir. The spreading power S is the difference in the adhesion energy at the membrane-SU-8 interface (σS) and the reservoir (σ0): σS−σ0. For simplicity, S has been assumed to be in the order of the reservoir tension. Taking β≈3μm2/s and S as 6mN/m, ζ for monolayers spreading on a SU-8 has been estimated to be ∼109Pas/m. This value is comparable to the friction coefficients estimated between the monolayer leaflets within a bilayer (∼108−109Pas/m) [3, 65].
Rädler et al. have reported the experimental values of β for a sliding bilayer to be up to 43μm2/s. [2] The high β values indicate a much faster spreading motion compared to the monolayers. This is predictable since the presence of the lubricating water layer (25Å) enhances the sliding by reducing friction.
The friction coefficient, which characterizes the lower monolayer leaflet of the distal bilayer and the upper monolayer leaflet of the proximal bilayer during bilayer rolling (double bilayer spreading), has been experimentally determined to be 5x107Pa.s/m. [4] However, a spreading coefficient for double bilayers has not yet been revealed. This may be due to the relatively complicated spreading dynamics of double bilayer membranes. The few assumptions we have made above to develop a theoretical model for the calculation of spreading coefficients may not necessarily be fully satisfied for double bilayers. For example, we assumed that the tension of the reservoir was constant at all times during spreading. However, there is evidence confirming that the inter-bilayer defects naturally exist or spontaneously form in such reservoirs [42, 66]. In this case, the reservoir tension would be expected to vary, as it cannot be maintained. Similarly, Rädler et al. have reported ceasing of double bilayer self-spreading due to the exhaustion of the lipid source [2]. Another variable parameter during double bilayer spreading would be the thickness of the water layer, on which the distal bilayer slides. During the spreading of single bilayers, the inter-bilayer distance is regulated by the hydration forces and van der Waals interactions, as mentioned above. For a distal bilayer sliding on a proximal bilayer, no such forces apply. Additionally, surface roughness, irregularities or defects may cause the thickness of the water layer to alter, which would in turn influence the magnitude of friction.
4. Lipid membrane rupturing
In the previous sub-chapter, we described the details of the spreading motion of lipids on solid supports in various forms, without reporting on the eventual outcome of such spreading. Spreading continues until the reservoir is depleted, or exhausted due to defects. This will cause termination of lipid supply from the source to the spreading membrane, so that the spreading motion will slow down and eventually almost stop. In the meantime, the adhesion energy remains constant. This means that regardless of the insufficient lipid supply, the spreading edge of the membrane will favour lipid wetting and tend towards adhering. Note that a lipid membrane cannot stretch more than 5% of its surface area [67]. The fate of the spreading from this point on will differ depending on the type of the surface and corresponding spreading mode.
When the reservoir is completely consumed and the spreading velocity reaches zero, the circumference of a spreading monolayer starts to “evaporate” [68]. Evaporation occurs when the hydrophobic tails of individual lipids lay open and completely adhere to the substrate. If the membrane is tagged with fluorophores, the evaporating rim of the membrane can be observed as a fuzzy edge rather than a distinct one. The driving force for the evaporation of monolayers is the increase in entropy which in turn minimizes the Gibbs free energy.
Similarly, single bilayer spreading simply comes to an end when the reservoir is depleted. It has recently been shown that an additional stock of lipids can be provided to a supported bilayer lacking a reservoir, by using a microfluidic pipette device for continuous supply [10]. Briefly, at the tip of the microfluidic pipette, a virtual flow cell provides a steady hydrodynamically confined flow, featuring a low Reynolds (Re) and a high Péclet (Pe) number. This flow cell can therefore deliver liquid cargo locally to a surface under highly controlled laminar flow conditions. By using the microfluidic pipette, it is possible to supply and fuse small unilamellar lipid reservoirs (vesicles ~100nm) into an existing lipid bilayer patch. This can lead to interesting wetting behaviour. If the new supply of lipids are zwitterionic with a net charge of zero (e.g., POPC), no further effect in wetting is observed and the size of the bilayer lipid patch remains constant over time. If the lipid stock provided to the single bilayer patch consists of cationic transfection lipids (e.g., DOTAP), the single bilayer starts to further wet the substrate. This is possible due to the intercalation of DOTAP lipids into the membrane, which are exposed via the microfluidic pipette. By means of the microfluidic device, spreading of membranes can be precisely controlled, and patches of desired size and composition can be easily generated. This provides a means of prototyping supported membranes in a rapid and reproducible fashion.
For a double bilayer membrane, the free energy can further be minimized, if the distal bilayer ruptures and adheres on the substrate. Rupturing of the distal membrane is possible if the tension of the membrane increases due to the continuous adhesion of the membrane edge to the substrate, and exceeds the lysis tension. In 2010, two forms of ruptures occurring in the distal bilayer of spreading double bilayer membranes have been reported. The “floral” ruptures, named after pore morphologies resembling flowers, can be observed mostly at the centre of a circular lipid spread propagating towards its periphery (Figure 2a). Such pores continuously grow until the double bilayer membrane entirely transforms into a single bilayer membrane on the solid support. The second rupture type appears in “fractal” patterns, most frequently at the circumference of the lipid patch, and develops inwards in the form of avalanches (Figure 2b). Within the fractal ruptures there remain “islands”, the entrapped regions of the distal membrane which are strongly pinned to the proximal bilayer. Except for the islands, the lipid material of the upper bilayer migrates towards the edges (cf. Figure 1c) and is deposited on the surface in the same manner as during floral rupturing. Since the fractal ruptures propagate in form of avalanches, the wetted area on the surface increases step-wise over time. Both types of rupture formation are spontaneous, and occur when the lipid reservoir is exhausted. Discrimination among the two rupture types has been attributed to the amount of pinning between the two bilayers. The pinning can be established by means of Ca2+ or other multivalent cations present in the ambient buffer. A high number of pinning sites is assumed to favour fractal morphology. Precise control of the number and location of pinning sites during spreading/rupture experiments has not yet been achieved.
We want to dedicate this sub-chapter to membrane ruptures, as the displacement of the membrane on the surface during rupturing is a form of wetting of the solid substrate, and also a simultaneous form of de-wetting of the proximal bilayer membrane (Figure 2c). Next, we will describe a mathematical analogy between the dynamics of floral ruptures and the dynamics of flow in conventional “porous media”. A porous medium can be depicted as a fluidic compartment packed regularly with particles, for example, micron-size beads. Such fluidic media can consist of, for example, glycerol, oil, or water. If now a secondary fluid of lower viscosity is pushed through the porous medium to displace the existing fluid, a morphologically instable interface between the two immiscible fluids is formed. Common injection fluids in flow experiments in porous media are water and air. Water can be injected only into liquids with higher viscosity, e.g., glycerol, but is often used as the main medium itself, if the injected fluid is air. The instability at the boundary of two immiscible fluids formed during the flow in a porous medium exhibits complex, finger-like patterns, therefore, is referred to as “viscous fingering” [69]. Membrane ruptures highly resemble these fingering instabilities. A membrane flow causing edge instabilities is comprehensible since the lipid membranes are considered as two-dimensional fluids. One interesting aspect regarding the similarities between the viscous fingering and the membrane pore edge instabilities is the difference in length scales: membrane ruptures are of micrometer size where fingering patterns is in the order of centimetres.
Viscous fingering instabilities can be observed in a “Hele-Shaw cell”, which is an experimental set up explicitly designed to simulate the flow in a three-dimensional porous media in a two-dimensional environment. The cell consists of two flat glass plates, positioned in parallel and separated with an infinitesimally small distance h. If h is very small, the flow inside the cell becomes incompressible (∇v→=0). Eq. (7), which describes the force balance for spreading solid-supported membranes, is in fact mathematically identical to the Brinkman equation used to describe flow in porous media. Correspondingly, a characteristic length scale Lc, which can be determined from Eq. (7), is equivalent to the Brinkman length scale.
Eq. (7), which describes the membrane flow (ζv→=∇σ), is the same as the Hele-Shaw equation, a form of the Darcy equation specific to the Hele-Shaw flow. This flow can be expressed as:
v→=−h212η∇PE24
where v→ is the velocity field. This vector mathematically describes the motion of the fluid where the length of the vector field is the flow speed, h is the distance between the two plates, η is the viscosity of the fluid, and ∇P is the pressure gradient. η may depict the viscosity of the existing in the Hele-Shaw cell (η1) or the subsequently introduced fluid (η2).
The inviscid edge at the moving boundary of the fluids is balanced by the pressure of the invading liquid:
P=−γLcE25
where P is the pressure, γL is the surface tension of the boundary, and c is the edge curvature. If the viscosity of the injected fluid, such as air, is significantly lower than the viscosity of the accommodating liquid, e.g., water or oil. In practice, the fluid can be considered to be inviscid (zero viscosity). When a pore opens in the membrane, the water in the ambient buffer penetrates into the ruptured areas of the membrane. However, mainly the friction influences the flow dynamics of a supported membrane and the effect of water viscosity is considered to be insignificant. Hence, the opening of a pore in the membrane is equivalent to air injection to a Hele-Shaw cell. At the boundary of a rupture, the membrane tension σ is balanced by the line tension γ :
σ=γcE26
where c is the edge curvature of the rupture. The line tension emerges when lipids curve at any membrane edge to avoid the exposure of the hydrophobic fraction of the lipids to the aqueous environment.
The instability of a membrane pore edge, where the pore void represents an inviscid fluid, is mathematically analogous to a Saffman-Taylor instability [69]. A periodic membrane edge modulation can be expressed as u=ε(t)sin(qx), where for a wavelength of L, ε is the peak amplitude of the modulation. If Eq. (7) and (26) are solved with this boundary condition, where ∇v→=0, the resulting wavelength gives the dynamics picture equivalent to the Saffman-Taylor instability observed in a Hele-Shaw cell. The growth rate of an amplitude ε(q) with wave number q = 2πL therefore is:
∂ε∂t=q(V−γq2ζ)εE27
Here, γ is the edge tension of the pore which corresponds to γL. V is the mean velocity of the interface at the pore edge.
In a basic Hele-Shaw cell the porosity is regular. An inhomogeneous porosity can be introduced into the cell by placing grains, e.g., glass beads at random locations. This leads to irregular permeability where the capillary forces become significantly effective and eventually cause fractal displacements termed invasion percolation clusters. The percolation clusters form when invading fluid chooses the “path of least resistance”, entrapping islands of the displaced fluid. This can be achieved, for example, by injecting air into water. The displacements appear in characteristic bursts with a broad size distribution, known as Haines jumps, which are similar to the avalanches observed during fracturing of the lipid membranes. The islands within the clusters are comparable to the islands surrounded by the fractal ruptures. Another similarity is the fractal dimension (D) of the ruptures: 1.71. This is lower than the theoretically estimated dimension of a percolation cluster D = 1.83, but equal to typical experimental values (1.70–1.71) [70].
A Hele-Shaw cell contains particles or beads which provide the effect of porosity. In between the proximal and the distal membranes, there are no particles, but a corresponding effect is established by “pinning”. The pinning can be due to the Ca2+ ions which bridge bits of the two bilayers together [71]. The pinned regions become visible during formation of floral ruptures in forms of thin threads at the pore edges [4]. The pinning points, where the fluidity is reduced, act like solid particles and play the role of grains in a Hele-Shaw cell. One other reason for pinning can be the surface structure. Nanometer-sized grains of silicon dioxide (SiO2) are known to create incisions in solid-supported lipid membranes. The granules of surface therefore can punch through the proximal bilayer and act like particles placed in between the two bilayers. The flow of lipids during rupturing is therefore considered to be through a porous medium.
A clearly defined analogy between the fractal ruptures and the invasion percolation instabilities, as we have previously shown for the floral ruptures and the Saffman-Taylor instabilities, has not yet been established. However, it is possible to estimate a characteristic length scale for membrane pores, within which they are not expected to exhibit instabilities.
Free standing membranes produce circular pores with straight edges [72]. An instable pore edge in such a membrane can be pictured as a wave or a modulation. The excess energy of an instability compared to a straight edge (u = 0) over one wavelength is E=γ∫0Ldx12(dudx)2=π2γε2L. The instable membrane pore edge will over time relax to a straight edge due to dissipation which emerges from the two-dimensional Stokes flow of surfactants in the membrane. This means we have to take into account the membrane viscosity (μ) regarding dissipation of the excess energy at a pore edge. The dissipation scales as TS˙~μL2(VL)2~μV2~με˙2. This has been obtained by integrating over one wavelength L, the second term of the dissipation function of spreading membranes (Eq. (32)), on which we will elaborate in the paragraphs further down.
Balancing the excess edge energy with dissipation (TS˙+E˙=0) leads (on a scaling level) to ε˙~1τε. Therefore, the relaxation time (τ) is:
τ~μγLE28
In a supported membrane, τ is much longer and increases rapidly with the wavelength of the modulation: the dissipation due to sliding friction, over one wavelength L, scales as TS˙ = ∫dA12ζv2~ζL2ε˙2, where ζ is the friction coefficient between the proximal and the distal bilayers.
Balancing the dissipation and the excess energy of the instability gives:
τ~ζγL3E29
A rupture propagating at velocity v exposes a membrane edge length L in time τ=Lv. By inserting τ into Eq. (29) gives a characteristic length (Lc):
Lc=γζvE30
For modulations larger than Lc, the ruptures will propagate before the edge shape modulation can possibly relax. v has been determined from experimental observations of fractal rupture [4] to be 20−30μms. Taking γ≈ 10 pN and ζ~5x107Pa.sm, [4] Lc is calculated as 0.1μm. Above this length, pore edge dynamics is too slow to secure a stable pore edge. The size of fractal ruptures is in the order of several tens of micrometers and is therefore in agreement with the prediction.
Biological cells can also spread their membrane material on solid supports, often in order to be able to migrate. In some instances, a 200-nm thick lamellipodial sheet protrudes from the cell body onto the substrate. [73] The sheet includes a double layer of plasma membrane in addition to actin filaments sandwiched in between the layers. The detailed mechanism of cellular wetting is a subject still under debate. Whether the proceeding edge is rolling or sliding driven by actin polymerization is not yet known. The lamellipodia-based cellular wetting, however, have been found to follow a similar power law as discussed in Section 1.3. [73] Spreading of cells can also be promoted by introducing trivalent ions to the substrate, for example, Eu3+ ions onto SiO2. [4] In such conditions, Chinese Hamster Ovary (CHO) cells continuously adhere onto the substrate. Interestingly, the adhesion leads to the formation of fractal ruptures [4] as well as the islands, in distal plasma membrane of spreading CHO cells. Upon rupturing, the wetted area on the substrate suddenly increases in a step-wise manner, similar to the fractal ruptures of the self-spreading double bilayers. The plasma membranes are connected to the underlying scaffolding layer (cytoskeleton) via linking molecules. The membrane flows around the linkers; the plasma membrane flow on cytoskeleton, therefore, can be considered as a two-dimensional porous media flow, i.e., Darcy flow. The tension causing the ruptures is still moderate and in the range of plasma membrane adhesion to the cytoskeleton, or membrane–membrane adhesion, suggesting that such ruptures can in fact occur in vivo.
Another form of lipid wetting on solid substrates involving ruptures is accommodated by inter-membrane “defects” or “fusion pores” (Figure 2d). The fusion pores are nanometer-sized circular conduits connecting two membranes in the shape of an hour-glass. The dimension of pores (~ nm) makes direct observations with the current microscopy technologies tremendously challenging. An alternative way is to study the flow phenomena, indirectly, by the fluorescent intensity-based wetting area analysis of membranes. [42] In the following paragraphs, we will talk about double bilayer membranes, 1% of which consist of fluorescently tagged lipids. From the top view, such fluorescent distal membranes emit twice as intensely as the proximal membranes; therefore, the two bilayers can be easily distinguished.
The double bilayer membranes mentioned above exhibiting floral or fractal ruptures consists of two bilayers which are intact, performing a rolling motion. In some occasions, the proximal and distal bilayers split along the circumference. After splitting, the proximal bilayer continues to wet the surface, which can be observed by increasing the wetted area on the substrate. The area of the distal bilayer membrane remains unchanged over the time period of a few minutes, followed by sudden decreases caused by instant avalanche ruptures. The decrease in the area of the distal bilayer simultaneously causes an increase in the wetted area by the proximal bilayer. This supports the notion of a physical connection among the two bilayers, through which the lipids are transferred. The outer border of the distal membrane does not expand along the peripheries upon rupturing, as occurs for the floral and fractal ruptures, indicating that the distal membrane is not dragged by the proximal membrane along the circumference. The stretching and eventual rupturing of the distal membrane can therefore be caused by the downwards lipid flow towards the proximal bilayer, most likely through narrow vertical channels. [42]
Defects among lipid membranes may have already formed during swelling of MLVs or form dynamically during spreading as a response to physical or chemical cues. We had briefly mentioned in Section 1.3 the defects existing in onion vesicles or MLVs. Since the lipid layers packed in the reservoir later spreads on the surface, these defects can be transferred to the supported membranes. Additionally, changes in membrane tension may cause instantaneous (~ nanoseconds) formation of defects in membranes. Fusion is a thermally agitated process and alterations in line tension can promote the formation of fusion pores. Formation of defects can also be induced by the presence of multivalent ions, e.g., Ca2+, in the ambient buffer. 2 mM of Ca2+ has been reported to advance the formation of defects among myelin sheaths of neurons, 80% of which consists of lipid material. The lipid transfer among the bilayers can as well be enhanced by hemi-fusion pores through which the dynamics change from bilayer sliding to monolayer sliding. As we had mentioned in Section 1.3, the sliding friction coefficient for monolayers is much higher than bilayers. Furthermore, the transformation of hemi-fusion to fusion pores is very energy-intensive and complex. For the simplicity of calculations, we will only assume defects to be in the shape of fusion pores in this section. We will discuss the monolayer sliding among the bilayers further in this section in another context.
The dynamics of lipid transfer via a fusion pore can be characterized semi-quantitatively via a “dissipation” function. Our model consists of a circular proximal bilayer, a circular distal bilayer and as an initial assumption: a single fusion pore with a diameter of 10 nm connecting these two membranes. Lipids flow from distal to proximal bilayer through the 10 nm defect, driven by the continuous adhesion of proximal bilayer to the substrate (Figure 2d). In this flow, there would be two separate forms of energy loss (dissipation). The first one is friction. The friction applies to the region (1) in between the proximal bilayer and the surface, (2) in between two bilayers since distal bilayer is de-wetting the proximal bilayer. The second form of dissipation is due to the viscous flow around the fusion pore. The lipid flow is expected to be different in remote areas of the membrane compared to the proximity of the pore. This is because the pore is small and the surfactants flowing through the pore collide with each other more intensely than they would in distant areas. Next, we will quantify and compare these two types of dissipations. If the magnitudes are compatible in relevant time scales, we will conclude that a single pore of 10 nm is sufficient to accommodate such a flow in between the membranes. If viscous flow (Stokes flow) cause dissipation that is much stronger than the frictional, this will indicate that several pores are required.
This brings us back to Eq. (7), where the difference of frictional and viscous forces determines the tension gradient across the membrane. A characteristic length scale where the frictional forces are of the same order as the viscous forces can be obtained based on Eq. (7) as: ζV∼μVL2.V is the characteristic velocity to determine the characteristic length scale. The cross-over length scale Lc=μζ can help us to compare the viscous forces to the frictional forces. One can think of Lc as the “Reynolds number” of fluid mechanics, which is the ratio of inertial forces to viscous forces within a fluid. Low Reynolds numbers indicates laminar, high Reynolds numbers the turbulent flow. Similarly, above Lc the frictional forces are dominating membrane flow dynamics, whereas below Lc, the viscous forces are effective. In the following we will estimate Lc in order to be able to confirm that it is rational to take into account the viscosity, considering a membrane flow around a pore of 10 nm.
The friction coefficient is:
ζ=ηwdwE31
where ηw is viscosity and dw is the thickness of the water layer on which the bilayer is sliding. There may be two bilayers sliding on a water layer under the spreading conditions described above: the distal bilayer slides on the proximal and the proximal slides on the surface. If we take ηw as 8.9 × 10–3 dyn s cm–1 and dw as 1nm, we can determine Lc to be ~30nm. The thickness of the water layer between the bilayers can be slightly higher than the one between the surface and the proximal bilayer. With increasing dw, the order of magnitude for Lc would not change. Above Lc = 30 nm, the sliding friction dominates over viscosity. Since we assume the pore size to be ∼10nm, it can be concluded that the pore vicinity will high likely be under the influence of the viscous forces.
For two-dimensional incompressible flow of the membrane, Eq. (7) leads to the following dissipation function [20]:
TS˙=∫dA[ζv→2+2μ(∂vi∂xk+∂vk∂xi)2]E32
vi, vk are the components of velocity and xk, xi are the components of position, where i and k change from 1 to 2 in a two-dimensional membrane. We assume that the membrane flow is radially symmetric, where v=rprvp around a pore with a membrane velocity of vp in the pore channel. Integrating Eq. (32) from the pore radius rp to the radius of the distal membrane island patch R (Figure 2d) leads to:
The integral Eq. (33) converges rapidly when R increases. This means that the viscous dissipation is intense in the pore area and is not as significantly effective in remote areas of the membrane. Taking into account the dissipation on both the proximal and the distal bilayer side of the pore, we obtain TS˙=8πμvp2, which is the total dissipation due to the viscous flow around a circular pore. The membrane viscosity μ is in the order of water viscosity (μ=η(waterviscosity)x1μm).
The dissipation caused by the sliding friction is composed of two parts: The sliding between the two bilayers (ζ) is expressed by the first term and the sliding of the proximal bilayer on the surface (ζ\n\t\t\t\ts) by the second term of Eq. (34):
TS˙=2πζ(2vp)2rp2ln(Rrp)+2πζsvp2rp2ln(Rsrp)E34
Rs is the radius of the proximal bilayer (Figure 2d). Unlike viscous flow, the dissipation due to sliding friction is not local and depends logarithmically on the size of the membrane patch. ζ (5 × 105 Pa.s.m–1) and ζs (5 × 107 Pa.s.m–1) are not material constants and their values depend on the amount of water trapped between the bilayers, as well as on the degree of adhesion. Assuming ζ=5x105Pa.sm, ζs=5x107Pa.sm, Rs=100μm, R=10μm and rp=10nm the viscous dissipation around a 10 nm defect turns out to be spatially localized and high, but it is not expected to exceed the dissipation due to the sliding friction of the membrane. As a result, only a single pore can be sufficient to accommodate the rapid lipid transfer.
Figure 2.
Biomembrane ruptures as a cause of wetting. Confocal fluorescence micrographs of (a) floral (b) fractal membrane ruptures (top view). The double bilayer membrane areas can be visualized as twice as intense as the single bilayer areas. Darker regions are the proximal membrane which is visible through the ruptures in the distal membrane. (c) Cross-sectional schematic view of a rupturing distal membrane, representing (a) or (b). (d) Cross-sectional schematic view of a fusion pore connecting proximal and distal bilayers. ζ and ζs are the friction coefficients which apply to the region between the bilayers and between the proximal bilayer and the surface, respectively. Pore/rupture repair via (e) bilayer sliding (f) monolayer sliding. In (e), there is a direct connection of the distal membrane to the lipid reservoir. In (f), the pore is around the reservoir which does not have any direct connection to the distal bilayer. The lipid transfer to the pore region can be via inter-monolayer sliding through the inverted micelle like defects (inset to the left) or simply by reverse sliding of the inner monolayer leaflet. The latter is represented by the inset to the right, in which the sliding monolayer is depicted in blue.
One can think of opening of large area pores in distal membranes as the process of de-wetting of the proximal bilayer. It is possible to reverse de-wetting by treating the pores with chemical ‘repair’ agents. When pores open in the membrane, the multivalent ions in the ambient buffer such as Ca2+ can penetrate through the pore edges and eventually pin fractions of the upper and lower bilayers together. At this instant, chelators such as 1,2-bis (o-aminophenoxy)ethane-N,N,N\',N\'-tetraacetic acid (BAPTA) can be introduced to the ambient buffer to target and deplete the Ca2+ ions. The chelators can bind to Ca2+ with high affinity and remove them from the pore edges and from the surface. This in turn frees the pore edges and reduces the overall membrane tension. Eventually, the membrane ruptures heal due to the pore edge tension (γ) and the distal membrane completely re-wets the proximal membrane. The lipid material sealing the pores originates from the reservoir and re-locates around the pore area by means of two different mechanisms. In the following, we will explain these mechanisms in detail.
The large area pores can form at different locations in the distal membrane. A fraction of the ruptures appear towards the edges of the distal bilayer as we had described above (Figure 2 b,e). There, one side of the pore edge maintains a physical connection to the MLV through the distal bilayer. The sealing of pores becomes possible through “bilayer-on-bilayer sliding” from the reservoir towards the pore region. Alternatively, rupturing may occur around the MLV (Figure 2a,f). In this case, there remains no direct contact of the distal bilayer to the lipid reservoir. The re-location of lipids towards the pore area can only be through the proximal bilayer. The free positive surface charges in the areas which are not wetted by the lipid membrane are mostly removed by BAPTA. This means depletion of fusogenic agents and termination of spreading. On the other hand, the access into the confined region between the proximal bilayer and the surface is impeded and chelation of the ions in the region above surface and underneath the membrane is expected to take significantly longer time. The total wetted area of the membrane on the solid substrate therefore can remain constant over several hours, confirmed by the experimental analysis [42]. While the edges of the spreading patch is pinned, reverse bilayer rolling becomes unlikely and one can presume the monolayer sliding to be the dominant flow mechanism for repair of such pores (Figure 2f).
To calculate the dissipation (TS˙) during bilayer-on-bilayer sliding, we can neglect the second term in Eq. (32), and integrate the friction term from rp to Rc. It is complicated to calculate dissipation regarding a pore at an arbitrary position in a circular spread since in that case the flow field would not be radial. Thus, we assume that the pore is centred, and consider Rc as the cut-off characteristic length scale. Rc is comparable to the size of the spread RS. The integral will give πζrp2r˙p2 ln (Rcrp), where ζ is the sliding friction coefficient between the distal and the proximal bilayers, v is the velocity of the sliding distal bilayer.
The dissipation (TS˙) caused by the bilayer-on-bilayer sliding friction will be balanced by the edge tension energy E˙ of the pore: (2πγr˙p) [74]:
ζrp2rp˙ln(Rcrp)=−2γE35
where rp˙=drpdt. Integrating the left side of Eq. (35) from Rp (pore radius at t = 0, i.e., initial pore radius) to rp = 0 (when the pore is closed), and the right side of the equation from 0 to τ (the time required to relax the pore) leads to:
τ=ζRp3γ.16[ln(RsRp)+13]∼ζRp3γE36
The radius of the proximal membrane Rs in Eq. (36) is only a logarithmic factor. This means that the size of the membrane spread on the surface does not determine the pore relaxation time and τ is rather highly depending on the initial pore size Rp. Note that Eq. (36) is a different form of Eq. (29) and can therefore be used to describe pores in cell membranes.
Now we will calculate the dissipation for the second scnerio, where the membrane flow towards the pore area is through the monolayer sliding. The monolayer sliding occurs between the two leaflets within a bilayer and will be opposed by friction. The friction coefficient (ζm) can be calculated by rearranging the first term of Eq. (32) as below:
TS˙=∫IdA12ζmvI2+∫IIdA12ζmvII2E37
Here, index I refers to the surface area of the proximal bilayer; index II of the distal bilayer. v is the velocity of the sliding monolayer. Integrating the first term of Eq. (37) from RL (effective radius of the lipid source, RL~R0) to RS (radius of the spread) and the second term from rp (pore radius) to RS gives:
TS˙=4πζmrp2r˙p2ln(Rs2RLrp)E38
where rp˙=drpdt. Balancing Eq. (38) with the line tension energy of the pore (TS˙ = E˙) gives
2ζmrp2γr˙pln(Rs2RLrp)=1E39
Based on experimental values inserted into Eq. (39), ζm has been calculated to be 105 to 106Nsm. This friction coefficient is at least two orders of magnitude lower than typical values reported in the literature: 108 to 109Nsm [3, 65]. This means that a possible inter-leaflet monolayer sliding in the experimental conditions described above is occurring at much faster speed. Even though unlikely circumstances where a monolayer can slide under such rates have been previously simulated [75], alternatively, the “hemi-fusion pores” or stalks can accommodate the relocation of surfactants via monolayer sliding. One can think of hemi-fusion pores as intermediate forms of fusion pores where the two merged bilayers are continuous but have not yet evolved into an aqueous connection. A portion of pore closure mechanisms based on monolayer sliding can be assisted by hemi-fusion pores or defects resembling inverted-micelles (Figure 2f). Monolayer sliding in some areas may also be complemented by bilayer sliding which can explain the relatively low sliding frictions observed.
5. Summary
Research on model membranes has been conducted for decades, and the understanding of the dynamics of lipid films has reached advanced levels. However, enabled by the rapid advances in micro- and nano-technologies and analytical capabilities, new phenomena are frequently discovered, such as the occurrence of the fractal membrane ruptures in double bilayer membranes, which created a new, exciting link between solid materials and the biological soft matter world. The discovery of this rupture phenomenon was closely related to the spontaneous wetting of high-energy surfaces, which was experimentally established in a microenvironment under the microscope. This and the other wetting phenomena described in the previous sub-chapters are feature-rich and have possible implications not only for future technological advancements, such as membrane protein studies, cell migration, but also for very advanced applications such as chemistry confined to two dimension. The double bilayer, which was at the heart of these investigations, can be easily classified as a new membrane model, which adds to the mono-, bi-, and cushioned bilayers. One can perhaps also view it, on one hand, as a self-cushioning bilayer, but on the other hand, it is essentially a flat giant unilamellar vesicle, with an approximately 10 nm thin water layer encapsulated between the two bilayer sheets. The thus encapsulated volume is on the order of a few hundred femtoliters. It bears a richness in possibilities for application in nanofluidics and artificial cell models, and potentially allows through its spreading an rupturing dynamics greater insights into, for example, the membrane-related mechanisms of cell migration and chemotaxis. We have provided in this chapter an overview over the wetting and rupturing properties and features of phospholipid monolayers and double bilayers on solid support, which should provide the foundation for the design of new experiments, and in many cases the prediction of their outcome. The dynamics of pores in membranes and associated materials transport phenomena, which are also accompanied with wetting phenomena, are also discussed. There are some particular points where further research is required. For example, attempts to establish a relationship for the spreading coefficient to quantitatively describe the spreading dynamics of double bilayers have so far been unsuccessful, which leaves the spreading approach to lipid film formation in this case still not entirely predictable.
\n',keywords:"Phospholipid bilayer, phospholipid double bilayer, phospholipid monolayer, supported bilayer, lipid self-spreading",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/49569.pdf",chapterXML:"https://mts.intechopen.com/source/xml/49569.xml",downloadPdfUrl:"/chapter/pdf-download/49569",previewPdfUrl:"/chapter/pdf-preview/49569",totalDownloads:1581,totalViews:422,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,dateSubmitted:"November 3rd 2014",dateReviewed:"September 22nd 2015",datePrePublished:null,datePublished:"December 16th 2015",dateFinished:null,readingETA:"0",abstract:"This chapter is dedicated to wetting and fracturing processes involving molecular phospholipid films and high-energy solid surfaces. In these systems, wetting of planar surfaces occurs in an aqueous environment by means of self-spreading of phospholipid membranes from artificially generated lipid sources, which range from manually deposited single sources (multilamellar liposomes) to liposome suspensions of different particle sizes, which are directly pipetted onto the substrate. The most prominent of the molecular lipid films is the phospholipid bilayer, which constitutes the fundamental structure of the biological cell membrane. Lipid membranes have peculiar characteristics, are highly dynamic, feature two-dimensional fluidity, and can accommodate functional molecules. Understanding the interactions of lipid films with solid interfaces is of high importance in areas like cell biology, biomedical engineering, and drug delivery.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/49569",risUrl:"/chapter/ris/49569",book:{slug:"surface-energy"},signatures:"Irep Gözen, Paul Dommersnes and Aldo Jesorka",authors:[{id:"114596",title:"Dr",name:null,middleName:null,surname:"Jesorka",fullName:"Jesorka",slug:"jesorka",email:"aldo@chalmers.se",position:null,institution:null},{id:"174347",title:"Dr.",name:"Irep",middleName:null,surname:"Gözen",fullName:"Irep Gözen",slug:"irep-gozen",email:"irep@seas.harvard.edu",position:null,institution:null},{id:"174348",title:"Prof.",name:"Paul",middleName:null,surname:"Dommersnes",fullName:"Paul Dommersnes",slug:"paul-dommersnes",email:"paul.dommersnes@gmail.com",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Surfaces and interfaces",level:"1"},{id:"sec_3",title:"3. Lipid self-spreading",level:"1"},{id:"sec_4",title:"4. Lipid membrane rupturing",level:"1"},{id:"sec_5",title:"5. Summary",level:"1"}],chapterReferences:[{id:"B1",body:'Czolkos I, Jesorka A, Orwar O. Molecular phospholipid films on solid supports. Soft Matter 2011;7 (11):4562.'},{id:"B2",body:'Radler J, Strey H, Sackmann E. Phenomenology and kinetics of lipid bilayer spreading on hydrophilic surfaces. Langmuir 1995;11 (12):4539.'},{id:"B3",body:'Czolkos I, Erkan Y, Dommersnes P, et al. Controlled formation and mixing of two-dimensional fluids. Nano Letters 2007;7 (7):1980.'},{id:"B4",body:'Gozen I, Dommersnes P, Czolkos I, et al. Fractal avalanche ruptures in biological membranes. Nature Materials 2010;9 (12):908.'},{id:"B5",body:'van Meer G, Voelker DR, Feigenson GW. Membrane lipids: where they are and how they behave. Nature Reviews Molecular Cell Biology 2008;9 (2):112.'},{id:"B6",body:'Bloom M, Evans E, Mouritsen OG. 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Biophysical Journal 2007;93 (2):423.'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Irep Gözen",address:null,affiliation:'
Department of Engineering, Harvard University, Harvard School of Engineering and Applied Sciences, Pierce Hall, Cambridge, MA, USA
Biophysical Technology Laboratory, Department of Chemistry and Chemical Engineering, Chalmers University of Technology, Göteborg, Sweden
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1. Introduction
Antibiotics have been one of the most significant discoveries in medicine, to the point that some researches talk about pre-antibiotic and post-antibiotic eras in clinical practice due to their repercussion in the treatment of infectious diseases [1]. However, bacteria are able to develop resistance against antibiotics when they are exposed to them for a long period of time. Indeed, in a short time after the widespread usage of a novel antibiotic, new strains of bacteria resistant to it have appeared [2]. The problem is so severe that international health organizations claim that “… a post-antibiotic era -in which common infections and minor injures can kill- is a very real possibility for the 21st century” [3].
It has been recognized by global health organizations that one of the main causes of this problem, also known as antimicrobial resistance, is the overuse of antibiotics. For this reason, they are encouraging the adoption of antimicrobial stewardship programmes (ASPs) [4, 5, 6, 7], in order to develop specific actuations and protocols to improve the antimicrobial usage and limit the rise of antimicrobial resistance. These programs include recommendations about all the actions related to the use of antimicrobials in clinical practice, such as the appropriateness of a particular antibiotic for an infection, its dosage, the route of administration or the duration of the therapy [4].
Due to the complexity of this problem, one of the recommendations for the hospital setting is to constitute a multidisciplinary team, called the antimicrobial stewardship programme team (ASP team). This team should be composed of specialists from different clinical areas such as physicians, epidemiologists, pharmacists, microbiologists and managers. The coordination of efforts obtained thanks to the diversity of the ASP team members is seen as fundamental in order to achieve an improvement in the rational use of antimicrobials within the institution.
First experiences in the implementation of ASP teams have obtained limited but satisfactory results regarding the usage of antimicrobials [8, 9]. A well-known disadvantage of the ASP methodology is the amount of time required by ASP members to review alerts and documentation [10]. Therefore, these studies also state the need for computerized tools to help with these complex tasks. The use of clinical decision support systems (CDSSs) can be a key factor in improving the results of ASP teams, taking into account the multi-user perspective of the problem, the need for data and knowledge integration from different sources, and the requirement to provide support both for a particular patient and for the whole institution in a coordinated manner. In this chapter, we perform an overview of different studies related with decision support in antimicrobial resistance and perform a detailed description of the Wise Antimicrobial Stewardship Program Support System (WASPSS), a CDSS focused on ASP teams.
2. Decision support in the context of antimicrobial resistance
Infectious diseases were one of first clinical topics related with decision support systems. One of these first systems was MYCIN [11], which relied on production rules derived from discussions with clinical experts to provide suggestions about the diagnosis and appropriate management of infected patients. Although the system was never used clinically, it was a relevant reference for the research and development of CDSSs during the 1980s [12].
Another relevant work was the HELP system [13], which provided alerts, reports and suggestions obtained from the data available in clinical records. With additional epidemiologic data and expert knowledge, it was used to provide recommendations about antibiotic treatments and therapies [14].
The infections acquired within clinical institutions (i.e., nosocomial infections) are commonly caused by bacteria resistant to antibiotics and, therefore, harder to treat. The relevance of these infections led researchers to focus on this problem. The GermWatcher expert system [15, 16] is capable of evaluating the results of cultures with the aim of detecting nosocomial infections. GermWatcher uses a rule engine whose knowledge base is based on the guidelines of the Nosocomial Infection Surveillance System from the Centers for Disease Control. Thanks to them, it is capable of classifying the alert level of a microorganism and deciding whether or not to report it as a nosocomial infection.
The Mercurio system [17] is also focused on the detection and monitoring of nosocomial infections. It was intended for different kinds of users, such as laboratory physicians, clinicians and epidemiologists, and included knowledge from several sources, such as antibiotic hierarchical definitions and international guidelines for microbiological laboratories. It is capable of checking the susceptibility tests in order to ensure that they have been performed according to the guidelines, differentiating among possible strains of bacteria, and warning about possible outbreaks of a particular strain. Furthermore, association rules are used to identify local resistance patterns that are not covered by the standard guidelines.
Another example focused on this problem is the HASIS information system [18] that implements the guidelines for hospital-acquired infections published by the Centers for Disease Control and Prevention and includes algorithms for the detection of suspicious cases. It employs a service-oriented architecture as a basis on which to integrate the surveillance data required for this task.
However, the rise and spread of multidrug-resistant bacteria has forced the research community to tackle the problem of nosocomial infections from the broader perspective of antimicrobial stewardship.
The DebugIT (Detecting and Eliminating Bacteria UsinG Information Technology) European project [19] was developed to gather information about antimicrobial resistance from heterogeneous sources [20]. It uses semantic web technologies to structure data and is capable of performing data analysis on it in order to provide decision support [21].
From yet another point of view, the TREAT system [22] uses causal probabilistic networks to propose an antibiotic treatment when the infectious agent is still unknown, based on the local epidemiological data and clinical observations such as the site of infection, symptoms and background disorders. This signifies that the spectrum of the treatments can be narrower, and less antimicrobial resistance is, therefore, developed.
Other proposals are focused on intensive care units (ICUs), since they are the hospital wards with the highest number of nosocomial infections and multi-resistant bacteria.
For example, the objective of the MoniICU system [23] is to identify and monitor nosocomial infections in ICUs. It uses production rules defined by using ARDEN syntax, an HL7 standard oriented toward sharing medical knowledge. Furthermore, it includes a fuzzy abstraction module for the definition of nosocomial infections.
Another example is the COSARA platform [24], which is focused on infection surveillance in ICUs. It provides functionalities for the automatic integration of clinical data and clinical decision support and alerts to alarming trends, among others.
Recently, some relevant works have been finally focused on supporting ASP teams in order to assess in the use of antibiotics by hospitals. The Antimicrobial Prescription Surveillance System [25] is focused on identifying potentially inappropriate antimicrobial prescriptions and reporting them to pharmacists. It has a knowledge base with contraindications regarding drug–drug interactions, drug-bug mismatches and maximum daily dose, among others. Furthermore, the system includes a learning algorithm with which to evaluate the feedback regarding the launched alerts and reduce the number of those that are erroneous or clinically irrelevant.
In Evans et al. [26], a framework of medical informatics tools is proposed in order to provide ASP teams with support. It automatically generates spreadsheets containing the antibiotics that are used daily and the parameters available to decide the antimicrobial therapy for specific patients. It additionally sends e-mail and SMS alerts to ASP members when timely-critical laboratory results are available, such as positive blood or cerebrospinal fluid cultures. Finally, it generates different reports based on the National Healthcare Safety Network specifications.
The HAITooL system [27] is also focused on ASP teams. It monitors antibiotic usage, rates antibiotic-resistant bacteria and allows early identification of outbreaks. It integrates evidence-based algorithms with which to support proper antibiotic prescription.
And a final example is the Wise Antimicrobial Stewardship Program Support System (WASPSS) [10]. WASPSS is focused on ASP team support and gathers information for multiple hospital information systems in order to provide a unified decision support for typical ASP tasks. It is the CDSS explained in detail in this chapter.
3. The WASPSS project
The University Hospital of Getafe (UHG) is a medium-size hospital (approximately 450 beds), which is located in Madrid, Spain. It has units covering most medical specialities, including a burns unit, although there are no cardiac surgery or transplant units. In 2014, the UHG set up an antibiotic stewardship programme denominated as “program for the multidisciplinary care in the assessment and control of the antimicrobial therapy” (PAMACTA) [10]. The PAMACTA team is composed of members from different specialities, including pharmacists, microbiologists, surgeons, internists, intensivists and infection preventionists. This team has been formed to address two organizational proposals: (a) to include a representative of each hospital service, or at least those services with a higher use of antibiotics, and (b) to involve all the physicians as part of the ASP. The intention of these two proposals is to reduce the time spent on distributing the task among departments and the integration of individual clinical expertise with the best available external clinical evidence, a basic principle of evidence-based medicine [28].
The Wise Antibiotic Stewardship Program Support System (WASPSS) platform [10, 29] is a CDSS focused on ASPs and infectious disease management. It was developed by the University of Murcia in collaboration with UHG at the same time as the PAMACTA team was created, which provided the opportunity to focus on the current needs of an ASP team starting from scratch. WASPSS is currently in the production stage and it is used daily by the PAMACTA team. Furthermore, it is being piloted in other seven public hospitals in Spain.
4. System architecture overview
An overview of the WASPSS software architecture is shown in Figure 1. The system is capable of obtaining information from multiple hospital information systems, which is a key factor to provide a proper support to the ASP team. The hospital systems and some examples of data collected by WASPSS are the following:
Pharmacy systems: the dispensations or administrations of antimicrobials
Microbiology laboratory: the results of cultures, the microorganisms found, and the resistances or susceptibilities found in the laboratory
Clinical tests laboratory: white-cell count, creatinine levels and other infection-related tests
Electronic health records (EHR): data related to patient, such as previous stays, current ward, and stays at ICU.
Figure 1.
Overview of the WASPSS platform and its different modules.
To perform data integration, a Health Level 7 (HL7) interface engine has been included. HL7 is a widely accepted set of standards for interoperability between health care organizations. HL7 provides messaging for exchanging, sharing and retrieving electronic health information between medical applications in near real time. Most HIS vendors adhere to these standards when developing application interfaces to exchange patient data. Additionally, WASPSS also supports the batch-loading of data files for those cases in which the HL7 interface is not available. Furthermore, a Java Message Service server is also used for the communication between WASPSS modules.
The data collected from hospital systems are filtered, structured and stored into an operational database by using extract-transform-load processes. This database acts as a centralized data hub for all the WASPSS modules.
WASPSS uses artificial intelligence techniques to provide proper support for ASP teams. In particular, WASPSS includes a knowledge module to incorporate clinical literature and daily practice knowledge [30, 31, 32]. To this end, the module implements a rule-based reasoning architecture composed of a knowledge base and a rule-based engine. The knowledge base contains production rules, ontologies and other business process models related with antimicrobial stewardship. The rule-based engine can make use of both the knowledge base and the data collected from the hospital to perform abstractions, raise alerts or evaluate processes, and its results can be stored back into the database or communicated through JMS to the other WASPSS modules.
Finally, the system includes a web-based interface that offers a different front-end for each user role of the ASP team, facilitating its usage for each clinician involved with antimicrobial stewardship within the institution. In the next sections, we describe in detail the capabilities of WASPSS to provide decision support for tasks related with both particular patients and the whole clinical organization.
5. Patient-centered decision support
The capability of WASPSS to aggregate information from different hospital systems allows to provide comprehensive decision support for a particular patient. This is especially useful in antibiotic prescription and patient-related alerts, as explained next.
5.1 Antibiotic prescription
The selection of the most appropriate antibiotic treatment is a key decision both to improve the patient’s health and to reduce the risk of antimicrobial resistance for the community. However, it usually occurs that the microorganism responsible for the infection has not yet been determined when the clinician must decide the antibiotic therapy to use. In these situations, clinicians must use all the available information about the patient and the infection (possible focus, previous infections, etc.) along with the information regarding the common pathogens found in the hospital and their resistance patterns, which is called the local cumulative antibiogram. After deciding this initial therapy, known as empiric therapy, clinicians can adjust, suspend or incorporate antibiotics when the infecting microorganism is found and analyzed, starting then a targeted therapy. However, the development of a local cumulative antibiogram is a time-consuming task that requires gathering information about the microorganisms found within the institution, and therefore, it is usually generated yearly.
The antibiotic prescription module of WASPSS assists physicians in the prescription of appropriate empiric or targeted antibiotic treatments (Figure 2). Thanks to the data collected from the microbiology system, WASPSS can calculate the cumulative antibiograms instantly. Furthermore, it can use data from any period and stratify them attending to different criteria in order to obtain, for example, the most effective antibiotic for blood infections in the ICU ward during the last year.
Figure 2.
Screenshot of the antibiotic prescription module. Given a search criteria, a rank of most effective antimicrobials is provided based on the local cumulative antibiogram (a). A graphical bipartite model is also available in order to show relationships between antibiotics and microorganisms (b). The antimicrobial efficacy data shown in this picture are fictitious.
The efficacy of each antibiotic is computed under those criteria, from 1 (less effective) to 100 (fully effective), based on the number needed to fail measure [33]. Finally, a list with the available antibiotics ranked by efficacy and confidence interval is proposed to the physician. The tool shows additional notes about each option, such as whether a restricted use of the antibiotic is recommended or if it can be administered orally. Furthermore, the tool can present the list of most common microorganisms found under the search criteria, along with their susceptibilities to antimicrobials. A graphical view of the results is also available through a bipartite model, which represents the relationships between microorganisms and antibiotics by means of channels of different width [34]. With these tools, physicians can take a more informed decision related with both empiric and targeted antimicrobial therapies.
5.2 Alerts, recommendations and assessment of infections
One of the common decision support tasks from CDSSs is to alert clinicians when certain events occur. In case of the antimicrobial stewardship scenario, typical alerts are related with the detection of bacteria resistant to multiple antibiotics, the results of tests that indicate severe infections, the prescription of restricted antibiotics or the opportunities to change the route of administration of an antibiotic to a less aggressive route for a patient.
WASPSS includes a pre-defined set of these alerts in the form of production rules within its knowledge base. Furthermore, as shown in Figure 3, clinicians may use the web-based interface to perform detailed queries that can also be stored as new alerts. If that is the case, the query is translated into production rules, stored in the knowledge base and executed daily. Each new case found will be notified then as any other alert automatically.
Figure 3.
Screenshot of the alerts module. Clinicians can build detailed queries with a user-friendly interface. Those queries considered as relevant can be automatically translated into production rules and stored as new alerts in the knowledge base.
One recurrent problem is CDSSs is the alarm fatigue, that is, users tend to ignore all alerts when too many of them are launched [35]. In order to cope with this problem, WASPSS allows to define alerts with three different levels of severity. Furthermore, alerts can be restricted to specific user profiles. By this way, each user receives only those alerts relevant to their role in the ASP team, and they can decide whether to revise only those with higher severity or all of them.
To manage each particular alert, WASPSS displays a patient-centered timeline view (Figure 4). This view provides a temporal perspective of all the data recovered from hospital systems that are related with antimicrobial stewardship, such as treatments, cultures, clinical tests and stays in wards. When an alert is fired, it is linked with the clinical events that caused it and they are highlighted in this view. By this way, the ASP team can evaluate all the relevant data regarding a patient when deciding which clinical action must be made in response to each alert.
Figure 4.
Screenshot of the timeline view with the details of the alert and all the patient’s clinical events. The patient’s data shown in this picture are fictitious.
Most of the clinical actions undertaken by the ASP team consist of performing recommendations to the physician staff related to changes in the antibiotic therapy of a particular patient, or to suggest cultures or other clinical tests whose results can improve the treatment of an infection. WASPSS allows the ASP team to create those recommendations for a specific patient, attaching their reasons and the suggested actions. Physicians can then access these recommendations by using the WASPSS platform. Optionally, these recommendations can be sent through the HL7 interface to other systems in order to facilitate the daily hospital workflow.
Alerts can also generate automatically informed recommendations that only need to be validated by the ASP team in order to be communicated. For example, WASPSS can detect that an antibiotic that is being administered by the intravenous route for many days can also be administered orally, which is less aggressive for the patient. Then, an alert is raised with a recommendation completed by the system, which includes the antibiotic, the suggestion of changing its administration route, and also the conditions that must be checked before undertaking this action, such as that the patient has no digestive problems that contraindicate the ingest of antibiotics.
WASPSS also facilitates the assessment of the infection for a particular patient. The patient-centered view includes the relevant data obtained from the EHR, along with the results from the microbiology tests and the rest of the clinical tests performed to the patient. The visualization of all these data in a clear and organized way facilitates the evaluation of the patient’s infection by clinicians.
6. Institutional decision support
Along with the patient-centered perspective, a broader vision is required in order to perform a proper antimicrobial stewardship within the institution. The monitoring of both clinical and process outcomes is important for proposing new strategic actions, and also for removing measures or policies that are not having any real impact on patient safety, economy or antibiotic resistance.
Global surveillance and monitoring in WASPSS is achieved by four modules. First, the epidemiology module (Figure 5a) visualizes the evolution of statistical measures such as prevalence and incidence of microorganisms, antibiotics, microbiological sample types and alerts.
Figure 5.
Global surveillance support in WASPSS: (a) epidemiology and (b) dashboarding modules. The data shown in this picture are fictitious.
Second, the dashboarding module (Figure 5b) provides interactive charts that summarize global measures related to antibiograms, alerts and ASP recommendations over a specified time interval.
Third, actionable reports are generated using the reporting module (Figure 6a). Currently, two kinds of parameterized reports are available: the first one lists all the antibiotics and microbiological tests of hospitalized patients during a specified period of time, grouped by department and sorted by bed number; the second one includes statistical information (prevalence, incidence, etc.) about all the microorganisms, antibiotics, sample types and alerts occurred over a specified time interval.
Figure 6.
Global surveillance support in WASPSS: (a) reporting and (b) ASP indicator modules. The data shown in this picture are fictitious.
Last, the ASP indicator module computes several process indicators (Figure 6b) whose results are presented in two forms: (i) as a run chart showing the evolution of the indicator over time and (ii) as a control table showing results of the current month and year, labeled with color codes indicating the goodness of the result.
7. Incorporation of clinical knowledge
WASPSS is also designed to incorporate different kinds of knowledge related with antimicrobial resistance [30].
The main source for the knowledge included is the expert rules from the EUropean Committee on Antimicrobial Susceptibility Testing (EUCAST) [36, 37]. These rules include the antibiotics to which each bacterial species is intrinsically resistant, those resistant patterns that are considered as exceptional and that should be re-checked to discard any problem on the laboratory tests, and resistance patterns that can be inferred from others found in laboratory.
WASPSS combines production rules with ontologies in order to incorporate the EUCAST rules in its knowledge base. Production rules are used to model most of the EUCAST knowledge, while ontologies are needed to model the hierarchical relationships of antibiotics and bacteria [31, 38].
This knowledge can be useful for many tasks such as the detection of incoherencies in laboratory results (e.g., bacteria that are found susceptible to an antibiotic to which they should be intrinsically resistant according to the EUCAST rules) and the detection of possible therapy failure because the infecting agent is found as resistant to all the antibiotics currently administered to the patient. In this last example, this knowledge has proved to be useful for increasing the number of cases detected and their clinical relevance [39].
8. Experimental features
The knowledge base and the inference engine provide opportunities for further knowledge-based expansions, incorporating knowledge from different sources. One of these sources can be the Clinical Practice Guidelines (CPGs), that is, widely accepted recommendations, processes and decision steps based on clinical evidence focused on improving the assistance of patients [40, 41]. Despite the fact that the use of CPGs has proved to improve clinical outcomes, they must be adapted to the particularities of each individual patient, which sometimes present a cumbersome problem for they use in daily practice [42].
WASPSS includes an experimental module to facilitate the visualization, adoption and evaluation of guidelines related with antimicrobial stewardship, focusing on the processes and decisions included in the guideline and those tasks that can be monitored thanks to the information gathered by the system [32]. An example of the graphical interface of this module is shown in Figure 7. The processes included in the guideline must be modeled by using the Business Process Model Notation (BPMN) and the decision-related recommendations by using the Decision Model Notation (DMN). These models are stored within the knowledge base and can be used to graphically visualize the tasks and decisions contained in the guideline. Production rules are then used to evaluate the status of each relevant task of the guideline and provide an estimation of the tasks already finished, those on-going and also those that may have been omitted. Finally, in combination with the patient-centered timeline view, the next guideline tasks can be scheduled in a flexible way, allowing the adaptation of the guideline to the particularities of each patient.
Figure 7.
Graphical interface for the CPG experimental module of WASPSS. The patient’s data shown in this picture are fictitious.
Another experimental extension is the incorporation of prediction models within the knowledge base. WASPSS includes an experimental clinical prediction rules module that is capable of incorporating prediction models based on logistic regression and evaluating them for a particular patient. Logistic regression is one of the most used techniques for developing models in clinical practice and can be combined with other techniques to deal with common problems related with data mining in antimicrobial resistance, such as unbalanced datasets or concept drift [43].
9. Future work
When dealing with clinical data, it is usual to find uncertainties in test results, clinical guidelines or prediction models, especially due to missing patient data. As a future research line, we plan to detect these uncertainties as well as the data required to solve them when possible. Then, these data will be asked to the user and incorporated into the knowledge base to improve the results of the different WASPSS modules.
We consider that a grounded explanation of a recommendation is one of the key factors for the success of a CDSS. The methods and models used in WASPSS (production rules, ontologies, etc.) allow the traceability of any alert, decision or abstraction. Following this idea, we are studying the incorporation of explainable machine learning methods to improve the justifications of the results of the prediction models.
The use of WASPSS in several hospitals offers us the opportunity to combine efforts among different ASP teams to improve the global outcomes in antimicrobial stewardship. On the other hand, it raises the problems of the interchange of knowledge among hospitals [31] and the need for dealing with a huge amount of data, which may require the adaptation of WASPSS to a big data environment [44].
Other future changes may be taken into consideration as new technologies are adopted by the hospitals using WASPSS. For example, the Fast Healthcare Interoperability Resources (FHIR) protocol has become very popular in last years and its incorporation into WASPSS may be considered in the medium term.
10. Conclusions
CDSSs have been widely used to assist physicians when dealing with infectious diseases. However, the need for coordinated efforts against the rise of antimicrobial resistance urges them to provide new functionalities.
WASPSS is our proposal for a CDSS focused on the assistance of ASP teams in the hospital environment. It combines techniques from business intelligence and artificial intelligence to provide an integrated perspective for antimicrobial stewardship.
On the one hand, WASPSS collects data from multiple hospital information systems and incorporates knowledge in the form of alerts and expert EUCAST rules. It is also capable of incorporating knowledge from CPGs and prediction models related with antimicrobial resistance.
On the other hand, WASPSS provides alerts and functionalities for antibiotic prescription, ASP recommendations and infection assessment through a patient-centered view in order to assist physicians when taking decisions concerning a particular patient, and global surveillance modules to provide support for strategic decisions related with epidemiology, global clinical outcomes and reporting.
In conclusion and according to our experience with WASPSS, a multi-user perspective, capabilities to integrate data and knowledge from different sources, and both patient-centered and institutional perspectives are key requisites for any CDSS focused on antimicrobial stewardship.
Acknowledgments
This work was partially funded by the SITSUS project (Ref: RTI2018-094832-B-I00), given by the Spanish Ministry of Science, Innovation and Universities (MCIU), the Spanish Agency for Research (AEI) and the European Fund for Regional Development (FEDER).
Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"antimicrobial stewardship, antimicrobial resistance, infection management, rule-based systems, data and knowledge integration, clinical practice guidelines",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/71316.pdf",chapterXML:"https://mts.intechopen.com/source/xml/71316.xml",downloadPdfUrl:"/chapter/pdf-download/71316",previewPdfUrl:"/chapter/pdf-preview/71316",totalDownloads:166,totalViews:0,totalCrossrefCites:0,dateSubmitted:"May 8th 2019",dateReviewed:"February 6th 2020",datePrePublished:"April 1st 2020",datePublished:"February 3rd 2021",dateFinished:"March 4th 2020",readingETA:"0",abstract:"The increase of infections caused by resistant bacteria has become one of the major health-care problems worldwide. The creation of multidisciplinary teams dedicated to the implementation of antimicrobial stewardship programmes (ASPs) is encouraged by all clinical institutions to cope with this problem. In this chapter, we describe the Wise Antimicrobial Stewardship Program Support System (WASPSS), a CDSS focused on providing support for ASP teams. WASPSS gathers the required information from other hospital systems in order to provide decision support in antimicrobial stewardship from both patient-centered and global perspectives. To achieve this, it combines business intelligence techniques with a rule-based inference engine to integrate the data and knowledge required in this scenario. The system provides functions such as alerts, recommendations, antimicrobial prescription support and global surveillance. Furthermore, it includes experimental modules for improving the adoption of clinical guidelines and applying prediction models related with antimicrobial resistance. All these functionalities are provided through a multi-user web interface, personalized for each role of the ASP team.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/71316",risUrl:"/chapter/ris/71316",signatures:"Bernardo Cánovas Segura, Antonio Morales, Jose M. Juarez, Manuel Campos and Francisco Palacios",book:{id:"9134",title:"Recent Advances in Digital System Diagnosis and Management of Healthcare",subtitle:null,fullTitle:"Recent Advances in Digital System Diagnosis and Management of Healthcare",slug:"recent-advances-in-digital-system-diagnosis-and-management-of-healthcare",publishedDate:"February 3rd 2021",bookSignature:"Kamran Sartipi and Thierry Edoh",coverURL:"https://cdn.intechopen.com/books/images_new/9134.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"29601",title:"Dr.",name:"Kamran",middleName:null,surname:"Sartipi",slug:"kamran-sartipi",fullName:"Kamran Sartipi"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"304477",title:"Ph.D.",name:"Bernardo",middleName:null,surname:"Canovas Segura",fullName:"Bernardo Canovas Segura",slug:"bernardo-canovas-segura",email:"bernardocs@um.es",position:null,institution:null},{id:"309732",title:"Dr.",name:"Antonio",middleName:null,surname:"Morales",fullName:"Antonio Morales",slug:"antonio-morales",email:"morales@um.es",position:null,institution:null},{id:"309733",title:"Prof.",name:"Manuel",middleName:null,surname:"Campos",fullName:"Manuel Campos",slug:"manuel-campos",email:"manuelcampos@um.es",position:null,institution:null},{id:"309734",title:"Prof.",name:"Jose M.",middleName:null,surname:"Juarez",fullName:"Jose M. Juarez",slug:"jose-m.-juarez",email:"jmjuarez@um.es",position:null,institution:null},{id:"309736",title:"Dr.",name:"Francisco",middleName:null,surname:"Palacios",fullName:"Francisco Palacios",slug:"francisco-palacios",email:"franciscodepaula@gmail.com",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Decision support in the context of antimicrobial resistance",level:"1"},{id:"sec_3",title:"3. The WASPSS project",level:"1"},{id:"sec_4",title:"4. System architecture overview",level:"1"},{id:"sec_5",title:"5. Patient-centered decision support",level:"1"},{id:"sec_5_2",title:"5.1 Antibiotic prescription",level:"2"},{id:"sec_6_2",title:"5.2 Alerts, recommendations and assessment of infections",level:"2"},{id:"sec_8",title:"6. Institutional decision support",level:"1"},{id:"sec_9",title:"7. Incorporation of clinical knowledge",level:"1"},{id:"sec_10",title:"8. Experimental features",level:"1"},{id:"sec_11",title:"9. Future work",level:"1"},{id:"sec_12",title:"10. Conclusions",level:"1"},{id:"sec_13",title:"Acknowledgments",level:"1"},{id:"sec_16",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Aminov RI. A brief history of the antibiotic era: Lessons learned and challenges for the future. Frontiers in Microbiology. 2010;1(134):1-7'},{id:"B2",body:'Clatworthy AE, Pierson E, Hung DT. Targeting virulence: A new paradigm for antimicrobial therapy. Nature Chemical Biology. 2007;3(9):541-548'},{id:"B3",body:'Antimicrobial Resistance: Global Report on Surveillance—2014 Summary. Geneva: World Health Organization; 2014. Available from: http://www.who.int/drugresistance/documents/surveillancereport/en/. [Accessed: 28 August 2018]'},{id:"B4",body:'Dellit TH, Owens RC, McGowan JE, Gerding DN, Weinstein RA, Burke JP, et al. 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Faculty of Computer Science, University of Murcia, Spain
Faculty of Computer Science, University of Murcia, Spain
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ALPSP
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The Association of Learned and Professional Society Publishers (ALPSP) is the largest association of scholarly and professional publishers in the world. Its mission is to connect, inform, develop and represent the international scholarly and professional publishing community. IntechOpen has been a member of ALPSP since 2016 and has consequently stayed informed about industry trends through connecting with peers and developing jointly.
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OASPA
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The Open Access Scholarly Publishers Association (OASPA) was established in 2008 to represent the interests of Open Access (OA) publishers globally in all scientific, technical and scholarly disciplines. Its mission is carried out through exchange of information, the setting of standards, advancing models, advocacy, education, and the promotion of innovation.
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STM
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COPE
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Creative Commons
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Creative Commons (CC) is a nonprofit organization that enables the sharing and use of creativity and knowledge through free legal tools. IntechOpen uses the CC BY 3.0 license for chapters, meaning Authors retain copyright and their work can be reused and adapted as long as the source is properly cited and Authors are acknowledged.
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Crossref
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Altmetric and Dimensions from Digital Science
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Digital Science is a technology company serving the needs of scientific and research communities at key points along the full cycle of research. They support innovative businesses and technologies that make all parts of the research process more open, efficient and effective. IntechOpen integrates tools such as Altmetric to enable our researchers to track and measure the activity around their academic research and Dimensions, to ease access to the most relevant information and better understand and analyze the global research landscape.
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CLOCKSS
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DORA
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iThenticate
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Enago
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Amazon
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DHL
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OASPA
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The Open Access Scholarly Publishers Association (OASPA) was established in 2008 to represent the interests of Open Access (OA) publishers globally in all scientific, technical and scholarly disciplines. Its mission is carried out through exchange of information, the setting of standards, advancing models, advocacy, education, and the promotion of innovation.
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STM
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The International Association of Scientific, Technical and Medical Publishers (STM) is the leading global trade association for academic and professional publishers. As a member, IntechOpen has not only made a commitment to STM's Ethical Principles.
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COPE
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Creative Commons
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Crossref
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Altmetric and Dimensions from Digital Science
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CLOCKSS
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CLOCKSS preserves scholarly publications in original formats, ensuring that they always remain available and openly accessible to everyone.
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Counter
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iThenticate
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Enago
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SPi Global
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Amazon
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DHL
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