Runtimes and the number of fluid particles for each model resolution of test case N. 1.
\r\n\tThe applications are those related to intelligent monitoring activities such as the quality assessment of the environmental matrices through the use of innovative approaches, case studies, best practices with bottom-up approaches, machine learning techniques, systems development (for example algorithms, sensors, etc.) to predict alterations of environmental matrices. The goal is also to be able to protect natural resources by making their use increasingly sustainable.
\r\n\r\n\tContributions related to the development of prototypes and software with an open-source component are very welcome.
\r\n\r\n\tThis book is intended to provide the reader with a comprehensive overview of the current state of the art in the field of Ambient Intelligence. A format rich in figures, tables, diagrams, and graphical abstracts is strongly encouraged.
",isbn:"978-1-83969-069-3",printIsbn:"978-1-83969-068-6",pdfIsbn:"978-1-83969-070-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"3fbf8f0bcc5cdff72aaf0949d7cbc12e",bookSignature:"Dr. Carmine Massarelli",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10391.jpg",keywords:"Embedded Systems, Technologies, Sensors, Remote Sensing, Smart Homes, Smart Cities, Integrated Monitoring Techniques, Agroecosystem, Smart Public Spaces, Computer Vision, Image Processing, Open-Source",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 12th 2020",dateEndSecondStepPublish:"November 9th 2020",dateEndThirdStepPublish:"January 8th 2021",dateEndFourthStepPublish:"March 29th 2021",dateEndFifthStepPublish:"May 28th 2021",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Environmental technologist expert in the development of Smart Technologies for water management and environmental monitoring, characterization, and monitoring of contaminated and degraded sites, integration of spatial data such as standard methodologies, interoperability, spectral data infrastructures.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"315689",title:"Dr.",name:"Carmine",middleName:null,surname:"Massarelli",slug:"carmine-massarelli",fullName:"Carmine Massarelli",profilePictureURL:"https://mts.intechopen.com/storage/users/315689/images/system/315689.jpg",biography:"Main activities:\n-development of Smart Technologies for water management and environmental monitoring;\n-characterization and monitoring of contaminated and degraded sites;\n-implementation of early warning systems and impact assessment systems also from multitemporal monitoring;\n-integration of spatial data: methodologies, standards, interoperability, spatial data infrastructures;\n-use of open source IT systems for the processing, analysis, and integration of remote sensing data with airborne and satellite sensors for thematic purposes such as characterization, control, and analysis of the territory in support of environmental policies relating to contaminated sites;\n-evaluation of the contamination of environmental matrices with specific tests and chemical analyses;\n-installation of airborne sensors and definition of flight parameters for Earth observation, CASI-1500 hyperspectral and TABI-320 thermal sensors;\n-acquisition of spectral signatures of objects through Fieldspec portable spectroradiometer and creation of databases in SQL language;\n-use of tools such as Ground Penetrating Radar for the advanced investigation of the subsoil with law enforcement agencies.",institutionString:"National Research Council",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"National Research Council",institutionURL:null,country:{name:"Italy"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"9",title:"Computer and Information Science",slug:"computer-and-information-science"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"297737",firstName:"Mateo",lastName:"Pulko",middleName:null,title:"Mr.",imageUrl:"https://mts.intechopen.com/storage/users/297737/images/8492_n.png",email:"mateo.p@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3621",title:"Silver Nanoparticles",subtitle:null,isOpenForSubmission:!1,hash:null,slug:"silver-nanoparticles",bookSignature:"David Pozo Perez",coverURL:"https://cdn.intechopen.com/books/images_new/3621.jpg",editedByType:"Edited by",editors:[{id:"6667",title:"Dr.",name:"David",surname:"Pozo",slug:"david-pozo",fullName:"David Pozo"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"57786",title:"Free-Surface Flow Simulations with Smoothed Particle Hydrodynamics Method using High-Performance Computing",doi:"10.5772/intechopen.71362",slug:"free-surface-flow-simulations-with-smoothed-particle-hydrodynamics-method-using-high-performance-com",body:'The non-stopping growing of computing power allowed increasing more and more spatial and temporal discretization when simulating engineering problems. The use of modern high-performance computing (HPC) systems, such as clusters equipped with graphics processing units (GPUs) or central processing units (CPUs) structured into a multi-node framework, let academics and professionals solve free-surface flow problems with resolutions unthinkable just a decade ago. Different spatial and temporal scales are often involved when simulating such kinds of phenomena, which may comprise wave generation, propagation, transformation and interaction with coastal or inland defences.
Among the others, smoothed particle hydrodynamics (SPH) method is a promising meshless technique for modelling fluid flows through the use of particles as it is capable to deal with large deformations, complex geometries and inlet wave shapes. Its original frame was developed in 1977 for astrophysical applications [1, 2]. Since then, it has been used in several research areas, e.g. coastal engineering [3, 4, 5, 6, 7], flooding forecast [8, 9, 10, 11], solid body transport [12, 13, 14, 15], soil mechanics [16, 17, 18, 19, 20], sediment erosion or entrainment processes [21, 22, 23, 24], fast-moving non-Newtonian flows [25, 26, 27, 28, 29, 30, 31, 32, 33], flows in porous media [34, 35, 36], solute transport [37, 38, 39], turbulent flows [40, 41, 42] and multiphase flows [43, 44, 45, 46, 47], not to mention manifold industrial applications (see, for instance [48, 49, 50, 51]). The main feature of SPH is that local quantities are evaluated by weighting information carried by neighbouring particles enclosed within a compact support, i.e. by performing short-range interactions among particles. Since the related neighbourhood definition takes most of the computing time, fast neighbour search algorithms have been developed so far [52, 53, 54, 55, 64, 79].
Since a decade or so, SPH has been coded in the massive high-performance computing (HPC) context, making use of the Message Passing Interface (MPI) [56, 57] and the OpenMP library [58, 59], the standards for distributed and shared memory programming, respectively. Several applications involving multicore processors [60, 61] and graphics processing units (GPUs) [62, 63, 64, 65, 66, 67] have been proposed so far. Joselli and co-workers [68] showed in 2015 that performing neighbour search on GPUs yields up to 100 times speedup against CPU implementations, therefore proving the benefits on exploiting the high floating-point arithmetic performance of GPUs for general purpose calculations. The same conclusion was drawn earlier in Ref. [69]. The first versions of SPH running on GPUs were presented in Ref. [70] and then in Ref. [69]. Non-Newtonian fluid flow simulations have been carried as well. Bilotta and co-workers, for instance, applied their GPUSPH model to lava flows [71]. In 2013, Wu and co-workers run GPUSPH to model dam-break flood through complex city layouts [72, 73]. Rustico et al. [74] measured the overall efficiency of the GPUSPH parallelization by applying the Karp-Flatt metric [75]. In Ref. [76], massive simulations of free-surface flow phenomena were carried on single and multi-GPU clusters. They used the sorting radix algorithm for inter-GPU particle swapping and subdomain ‘halo’ building to allow SPH particles of different subdomains interacting. In 2015, Cercos-Pita proposed the software AQUAgpusph [77] based on the use of the freely available Open Computing Language (OpenCL) framework instead of using the Compute Unified Device Architecture (CUDA) platform. In Ref. [78], Gonnet proposed scalable algorithms based on hierarchical cell decompositions and sorted interactions executed on hybrid shared/distributed memory parallel architectures. In Ref. [79], a general rigid body dynamics and an absolute nodal coordinate formulation (ANCF) were implemented to model rigid and flexible objects interacting with a moving fluid. In 2012, Cherfils and co-workers released JOSEPHINE [80], a parallel weakly compressible SPH code written in Fortran 90, intended for free-surface flows. Incompressible SPH (ISPH) algorithms, running on GPUs, have been developed as well [81, 82, 83].
This chapter shows some numerical SPH results of typical coastal engineering problems obtained by means of two different supercomputers: the GPU-based machine maintained at the EPhysLab from Vigo University in Ourense (Spain), mounting 14 NVIDIA Kepler-based cards, with a total of 39168 CUDA cores and the Tier-1 Galileo cluster, introduced on January 2015 by the Italian computing centre CINECA, a non-profit consortium, made up of 70 Italian universities, 6 Italian research institutions and the Italian Ministry of Education, University and Research (MIUR). Galileo is equipped with 516 nodes, each mounting 2 8-cores Intel Haswell 2.40 GHz for a total of 8256 cores, up-to-date Intel Phi 7120p (2 per node on 384 nodes) and NVIDIA Tesla K80 accelerators (2 per node on 40 nodes). Comparison with theoretical and experimental results is also included.
Recent comprehensive reviews and related applications of the SPH method are given in [84, 85, 86, 87, 88, 89]. Governing equations describing the motion of fluids are usually given as a set of partial differential equations (PDEs). These are discretized by replacing the derivative operators with equivalent integral operators (the so-called integral representation or kernel approximation) that are in turn approximated on the particle location (particle approximation). Next, Section 2.1 gives further details about these two steps, with reference to a generic field f(
Following the concept of integral representation, any generic continuous function
where
Dirac delta function centred at the point x.
W is the so-called smoothing kernel function or simply kernel and h, acting as spatial scale, is the smoothing length defining the influence area where W is not zero. While Eq. (1) yields an exact formulation for the function
The kernel function W has to satisfy some properties (see, for instance, [90, 91]). The following condition
is known as partition of unity (or the zero-order consistency) as the integration of the smoothing function must yield the unity. Since W has to mimic the delta function, Eq. (3) can be rewritten as a limit condition in which the smoothing length tends to zero:
Still, W has to be defined even, positive and radial symmetric on the compact support:
where ϕ is a positive quantity defining the extent of the compact support. A large number of kernel functions are proposed in literature. Among the others, a computational-efficient and high accurate kernel is proposed by Wendland [92], defined as.
where A(nd), depending on the number of dimensions nd, denotes a scaling factor that ensures the consistency of Eq. (3), whereas q denotes the dimensionless distance
The integral representation given by Eq. (2) can be converted into a discretized summation over all particle N within the compact support (Figure 2), yielding the particle approximation:
where the index k refers to particles within the compact support (see bold ones in Figure 2), with mass mk and density ρk being carried. Note that in this case the particle approximation is marked by the ‘a’ pedix. The subscript will be avoided from now on. Eq. (7) can be rewritten with reference to particle ‘i’ as
A kernel function defined at the particle ‘i’ and its support of radius ϕh. Local neighbourhood corresponds to the bold particles.
Particle approximation of spatial derivatives of a field function, such as divergence and gradient, is expressed using the gradient of the kernel function rather than the derivatives of the function itself:
where the nabla operator
inside the integral in Eq. (2), yielding in this case
Likewise, the divergence, another particle approximation of the gradient, can be derived, taking into account the following equation:
yielding
Eqs. (12)–(14) are conveniently employed in fluid dynamics as they preserve the conservation of linear and angular momentum.
The mostly used governing laws ruling fluid motion are the Navier-Stokes equations, which specify that mass and linear momentum are preserved. Conservation laws in Lagrangian form are as follows:
in which ρ and
The material derivative of the velocity field can be deduced from Eq. (14) for the case of inviscid fluids, that is, ν = 0:
Numerical diffusion in terms of an artificial viscosity, e.g. proposed in Ref. [96], can be added in Eq. (17), allowing shock waves to be properly simulated:
The dissipative term
where
The notation
Problem closure is achieved by combining conservation equations in discrete form (16) and (18) with an equation of state, when the weakly compressible scheme is adopted. A relationship between pressure and density is given in Ref. [97]:
where c0 is the reference speed of sound, large enough to guarantee Mach numbers lower than 0.1–0.01,
DualSPHysics [98, 99, 100] is an open-source code developed by the University of Vigo (Spain) and the University of Manchester (UK) in collaboration with experts from all around the globe that can be freely downloaded from
Due to the Lagrangian nature of SPH, the particle interaction results to be the most time-consuming part of the whole algorithm. Each particle, as already stated, only interacts with its neighbour particles. Therefore, the construction of the neighbour list must be optimised. The cell-linked list described in Ref. [54] is implemented in DualSPHysics. This approach is preferred to the traditional approach, named Verlet list [101] that implies higher memory requirements than the cell-linked list. Besides, [54] proposed an innovative searching procedure based on a dynamic updating of the Verlet list and analyzed the efficiency of all the algorithms in terms of computational time and memory requirements.
DualSPHysics has proven its performance, reaching limits like being able to simulate more than 109 particles using 128 GPUs with an efficiency close to 100% [67].
Extensive research has been conducted over the last few years to develop accurate and efficient boundary conditions (BCs) in SPH method. Several approaches are proposed in the literature, such as boundary repulsive forces, fluid extensions to the solid boundary and boundary integral representing the term preservation. In DualSPHysics, boundaries (walls, bottom, coastal structures, wave generators, vessels, floating devices, etc.) are described using a discrete set of boundary particles that exert a repulsive force on the fluid particles when they approach. The so-called dynamic boundary condition [102] is used in DualSPHysics, where the boundary particles satisfy the same equations as the fluid particles; however, they do not move according to the forces exerted on them. Instead, they remain fixed (fixed boundary) or move according to some externally imposed movement (gates, flaps, etc.). Using this boundary condition, when a fluid particle approaches a boundary particle and the distance between them decreases beyond the kernel range, the density of the boundary particles increases giving rise to an increase of the pressure. This results in a repulsive force being exerted on the fluid particle due to the pressure term in the momentum equation. This dynamic boundary condition implemented in DualSPHysics does not include a specific value to define wall friction. However, this has been achieved in different validations by specifying a different viscosity value in the momentum equation when the fluid particles interact with the boundary ones.
The waves are generated in DualSPHysics by means of moving boundaries that aim to mimic the movement of a piston-type and flap-type wavemakers as in physical facilities. Only long-crested wave can be generated at this stage. The implementation of first-order and second-order wave generation theories is fully described in Ref. [3]. For monochromatic waves, this means to include super-harmonics. For random waves, subharmonic components are considered to suppress spurious long waves. Two standard wave spectra are implemented and used to generate random waves: JONSWAP and Pierson-Moskowitz spectra. The generation system allows having different random time series with the same significant wave height (Hm0) and the same peak period (Tp), just defining different phase seeds.
Wave reflection compensation is used in physical facilities to absorb the reflected waves at the wavemaker in order to avoid that they will be reflected back into the domain. In this way, the introduction into the system of extra spurious energy that will bias the results is prevented. The so-called active wave absorption system (AWAS) is implemented in DualSPHysics. The water surface elevation η at the wavemaker position is used and transformed by an appropriate time-domain filter to obtain a control signal that corrects the wave paddle displacement in order to absorb the reflected waves every time step. Hence, the target wavemaker position is corrected to avoid reflection at the wavemaker. The position in real time of the wavemaker is obtained through the velocity correction of its motion. Further details on AWAS in DualSPHysics are reported in Ref. [3].
The hybridization technique between DualSPHysics model and SWASH model (
Fast computations with large domains can be performed with SWASH, avoiding simulating large domains with DualSPHysics that requires huge computation times even using hardware acceleration.
SWASH is suitable for calculation where statistical analysis is necessary such as computing wave height and good accuracy is obtained for wave propagation.
SWASH is not suitable for calculation of wave impacts, while DualSPHysics can easily compute wave impacts, pressure load and exerted force onto coastal structures.
Complex geometries cannot be represented with SWASH, and computation stability problems may appear when applied to rapidly changing bathymetry. Using DualSPHysics, any complex geometry or varying bathymetry can be simulated.
The hybridization between DualSPHysics and SWASH has been obtained through a one-way hybridization at this stage. The basic idea is to run SWASH for the biggest part of the physical domain to impose some boundary conditions on a fictitious wall placed between both media. This fictitious wall acts as a nonconventional wave generator in DualSPHysics: each boundary particle that forms the wall (hereafter called moving boundary or MB) will experience a different movement to mimic the effect of the incoming waves. SWASH provides values of velocity in different levels of depth. These values are used to move the MB particles. The displacement of each particle can be calculated using a lineal interpolation of velocity in the vertical position of the particle. Therefore, the MB is a set of boundary particles whose displacement is imposed by the wave propagated by SWASH and only exists for DualSPHysics. A multilayer approach can be used in SWASH. The SWASH velocity measured at each layer is therefore interpolated and converted into displacement time series for DualSPHysics.
The GPU cluster maintained at the Environmental Physics Laboratory (EPhysLab) of Vigo University comprises four computing servers, whose details are as follows:
Supermicro 7047: 4× NVIDIA GeForce GTX Titan Black, 2880 × 4 = 11,520 CUDA cores, 2× Intel Xeon E5–2640 at 2 GHz (16 cores), RAM 64 GB, storage 16 TB, estimated performance 6800 GFLOPS
Supermicro 7047: 4× NVIDIA GeForce GTX Titan 2688 × 4 = 10,752 CUDA cores, 2× Intel Xeon E5-2640 at 2 GHz (16 cores), RAM 64 GB, storage 20 TB, estimated performance 6000 GFLOPS
Supermicro 7046: 4× NVIDIA GeForce GTX Titan Black, 2880 × 4 = 11,520 CUDA cores, 2× Intel Xeon E5620 at 2.4 GHz (8 cores), RAM 64 GB, storage 9 TB, estimated performance 6800 GFLOPS
Supermicro 6016GT-TF-TM2: 1× NVIDIA Tesla K40 2880 CUDA cores + 1× NVIDIA Tesla K20 2496 CUDA cores, 2× Intel X5550 at 2.66 GHz (8 cores), RAM 64 GB, storage 1.7 TB, estimated performance 2855 GFLOPS
Galileo is a Tier-1 supercomputer among the fastest available to Italian industrial and public researchers. Introduced in the Italian computing centre CINECA on January 2015, this IBM NeXtScale model is equipped with up-to-date Intel accelerators (Intel Phi 7120p), NVIDIA accelerators (NVIDIA Tesla K80), as well as a top-level programming environment and a number of application tools. It is characterised by:
516 computing nodes with Intel Haswell 2.40 GHz processors, 2 × 8 core each (8256 cores in total)
128 GB of RAM per computing node, 8 GB per core, 66 TB of total RAM
Internal network: InfiniBand with 4× QDR switches (≈40 Gb/s)
Two Intel accelerators Phi 7120p per node on 384 nodes (768 in total)
Two NVIDIA accelerators K80 per node on 40 nodes (80 in total, 20 available for scientific research)
Eight nodes devoted to login/visualisation
Theoretical peak performance 1.2 PFlops
≈480 GFLOPS single-node LINPACK (only CPU) sustained performance
Disc space: ≈2 PB of local scratch
Operating system: Linux CentOS 7.0
On June 2017, Galileo was ranked in 281st position on the top 500 supercomputer list (
Aiming to prove the capability of DualSPHysics model to reproduce accurately waves and wave-structure interaction phenomena, three different test cases have been selected and are here reported, namely:
Wave generation and propagation of random wave train in 2D.
Wave run-up on a cubic block breakwater in 3D.
Coupling of DualSPHysics with SWASH model and application to wave forces on coastal structures in shallow water conditions (2D).
The first test case comprises the generation and absorption of random waves in DualSPHysics. The 150 s time series to be generated is calculated starting from a JONSWAP spectrum. The target wave conditions are Hm0 = 0.06 m and Tp = 1.3 s. The water depth is 0.36 m. The wavelength is 2.09 m. Second-order wave generation has been used (i.e. bound long waves). The wave conditions correspond to a second-order Stokes wave. The geometrical layout of the case is depicted in Figure 3: an 8.4-m long wave tank is modelled. A damping zone (passive absorption) is defined at the end of the tank. The water surface elevation and orbital velocities are measured using a 5-wave gauge array where the central wave gauge is at 2 L from the generator. The numerical results are compared with theoretical solutions.
Layout of test case N.1: (a) position of the wave gauges (dots on the free surface) and velocity measurements (inner dots) and (b) horizontal velocity field and indication of the damping zone in the fluid domain.
A sensitivity analysis on the initial interparticle distance, dp, has been carried out. Four different values of dp have been selected in a range of H/dp between 6 (coarsest resolution) and 20 (finest resolution). For each case, the number of fluid particles and the computational runtime are reported in Table 1.
H/dp | No. of fluid particles | Runtime [h] |
---|---|---|
6 | 29,365 | 0.9 |
10 | 82,541 | 2.9 |
12 | 119,209 | 4.5 |
20 | 333,081 | 16.6 |
Runtimes and the number of fluid particles for each model resolution of test case N. 1.
The case with H/dp = 10 has been simulated also on Galileo supercomputer, specifically using one node in order to compare the computing capabilities between Galileo and one GPU from the EPhysLab cluster. The comparison is expressed in terms of the number of calculation steps per second of computational time. For each node in Galileo, 23.9 step/s can be simulated, whereas with a Tesla K20, 156.3 step/s are achieved. These results refer to 2D simulations, and they are expected to be different for 3D modelling.
The numerical results for H/dp = 10 have been plotted against the theoretical ones for each sensor position. They are all depicted in Figures 4–6. The model accuracy has been estimated in terms of spectral values of wave height and period. The numerical error, together with the calculated values for Hm0 and Tm-1,0, is reported in Table 2 for WG3 (x = 4.18 m). For H/dp = 6, the wave height is underestimated about 4%; meanwhile, starting from H/dp = 10, the errors for both wave height and period are in the order of 1–2%. Similar results are attained for the other four wave gauges. The orbital velocities show same degree of accuracy.
Comparison between the numerical and theoretical free-surface elevation at the 5-wave gauge positions.
H/dp | Hm0-THE [m] | Tm-1,0-THE [s] | Hm0-SPH [m] | Tm-1,0-SPH [s] | εH [%] | εT [%] |
---|---|---|---|---|---|---|
6 | 0.061 | 1.214 | 0.058 | 1.250 | −4.52 | +3.00 |
10 | 0.060 | 1.233 | −1.00 | +1.54 | ||
12 | 0.061 | 1.231 | −0.12 | +1.43 | ||
20 | 0.062 | 1.234 | +1.70 | +1.67 |
Model accuracy at WG3 for different values of H/dp.
Comparison between the numerical and theoretical horizontal orbital velocity.
Comparison between the numerical and theoretical vertical orbital velocity.
The second test case consists of a 3D case where the wave run-up on an armour breakwater has been simulated. Cubic blocks are displaced forming two layers with regular pattern on the seaward face of the breakwater, which has an angle of 28.3° with the horizontal. The side of each block measures 0.058 m. The case resembles an experimental one carried out in the small-scale wave flume CIEMito at the Technical University of Barcelona, Spain. The flume width is 0.38 m. Monochromatic waves have been simulated. The simulated wave height is 0.10 m, with mean period equal to 0.97 s. In total, 15 s of physical time has been simulated. The initial interparticle distance was 0.012 m, about one-eighth of the target wave height, resulting in 1,039,775 fluid particles. The simulation took 8.7 h using the Tesla K20 from the EPhysLab cluster.
Four wave gauges are located to measure the water surface elevation along the flume. The first wave gauge is at 3.10 m from the wavemaker; the last one is at 4.23 m. The distance between the toe of the breakwater and the wavemaker is 5.95 m. Moving boundaries mimicking a piston-type wavemaker are used in DualSPHysics to generate waves. To measure the run-up, the water surface elevation has been measured at 4160 locations across the breakwater. The results, post-processed in Matlab, have given the time series of wave run-up. A three-dimensional view of the numerical model is depicted in Figure 7. Using post-processing tools of DualSPHysics, an isosurface of the fluid has been extracted and plotted in ParaView software (
3D view of the run-up simulation.
Figure 8 shows four different instants of time that make an entire run-up/run-down cycle over the breakwater. The colours indicate the fluid velocity field, i.e. horizontal orbital velocity. Red indicates high positive velocities (directed shorewards), whereas blue indicates negative velocities (directed seawards).
Snapshots of the wave run-up simulation during one run-up cycle.
The water surface elevation measured in the numerical tank is depicted in Figure 9 for each wave gauge location. The wave run-up has been calculated for 26 cross sections along the width of the flume: the averaged time series is shown in Figure 10.
Water surface elevation along the numerical wave tank.
Time series of wave run-up: average along the width of the numerical tank.
The third test case comprises the validation of the hybridization technique between DualSPHysics model and SWASH model to study the impact of overtopping flows on multifunctional sea dikes with shallow foreshore. The main aim is to prove that overtopping flow characteristics and wave forces are modelled correctly and that the hybridization can represent a reliable solution that can be used as complementary or alternative to physical modelling. The case of study is a typical case from the Belgian and Dutch coastline, where a building is constructed on the top of the dike. Physical model tests were carried out in a 4.0 m wide, 1.4 m deep and 70.0 m long wave flume at Flanders Hydraulics Research, Antwerp (Belgium) to measure forces on the vertical wall (i.e. building), the layer thickness and velocities of the overtopping flows [103]. The geometrical layout is depicted in Figure 11: the foreshore slope was 1:35 and dike height 0.1 m. The dike slope was 1:3. Here, we refer to only regular wave cases.
Layout of the flume at FHR and indication of the coupling point location for the SWASH-DualSPHysics model.
SWASH has been previously validated against the physical model results: wave propagation, transformation and breaking have been accurately modelled, and the conditions at the toe of the dike are reproduced as in the physical model test. Then, SWASH has been implemented together with DualSPHysics to model the wave impact. Eight layers have been used in SWASH simulation. A hybridization point along the physical domain has been defined, and it is located at x = 30.24 m from the physical wavemaker in its neutral position (Figure 11), far enough from the location where the waves start to break (≈35.5 m). SWASH provides the boundary conditions for DualSPHysics at that location. DualSPHysics is used to model the part of domain between the coupling point and the dike. The quantities that have been measured and compared with the experimental results are (a) free-surface elevation after the coupling point, (b) overtopping flow thickness in three different locations along the dike crest and (c) wave forces on the vertical wall (measured in the physical model by means of two-load cells of model series Tedea-Huntleigh 614). Both free-surface elevation and layer thickness were measured in the physical model by means of resistive wave gauges.
An initial interparticle distance, dp, of 0.003 m has been used leading to 494,388 fluid particles in DualSPHysics model. Fifty seconds of physical time has been simulated in the TITAN X graphic card, taking 10.8 h. A case with the whole physical domain modelled in DualSPHysics has been also modelled: in such case, the moving boundary is represented by the physical wave generator, and its location is then at x = 0.00 m. This stand-alone DualSPHysics model took 95.6 h using the same TITAN X to simulate 3,389,266 fluid particles, about 10 times slower than the hybridised model.
The numerical and experimental free-surface elevation and layer thickness are plotted in Figure 12, showing that the numerical solution resembles the experimental accurately. The forces on the wall are represented in Figure 13. The differences between numerical and experimental results might be explained because of the highly turbulent and stochastic nature of the overtopping wave impact in this case, which makes the experimental test not repeatable (see [3] for further discussion on model inaccuracy for wave impacts).
Results of free-surface elevation (left image) and overtopping layer thickness: numerical (red dash-dot line) vs. experimental (black solid line).
Results of overtopping wave forces on the wall: numerical (red dash-dot line) vs. experimental (black solid line).
The chapter offers a panoramic on the application of the SPH-based DualSPHysics code on supercomputers maintained at the EPhysLab from Vigo University in Ourense (Spain) and the Italian computing centre CINECA. Three test cases were selected in the general context of the coastal engineering: (1) wave generation and propagation of random wave train in 2D, (2) wave run-up on a cubic block breakwater in 3D and (3) coupling of DualSPHysics with SWASH model and application to wave forces on coastal structures in shallow water conditions (2D). Scalability is discussed by varying the spatial resolution, and efficiency is proved in the case of the hybridization. Comparison with theoretical free-surface elevation and orbital velocities for test case N. 1 and measured overtopping layer thickness and forces on vertical walls for test case N. 3 was satisfactory.
We acknowledge the CINECA award under the ISCRA initiative, for the availability of high-performance computing resources and support. Part of the computations was carried within the high-performance computing for Environmental Fluid Mechanics (HPCEFM17) project.
The bile duct carcinoma or known as cholangiocarcinoma (CCA) by the definition is a malignancy that originate from cholangiocytes lining the biliary tree. It is included in liver malignancy and become the second most common primary liver malignancy after hepatocellular carcinoma. [1, 2] Incidence of this malignancy is 10–20% cases of all hepatic cancer. [2, 3] Although cholangiocarcinoma is a rare cancer, it has an aggressive feature with very poor prognosis. The data showed that the incidence of cholangiocarcinoma among gastrointestinal cancer approximately reaches 3% but has nearly 20% of death from all hepatobiliary cancer. [3, 4] In addition, cholangiocarcinoma is a clinically silent disease at early stage. Therefore, the diseases are usually diagnosed at advanced stage with poor prognosis.
CCA may occur anywhere in the biliary tract, however, based on where the tumor arises in the biliary tree, it is classified into intrahepatic (iCCA) and extrahepatic bile duct cholangiocarcinoma (eCCA). Extrahepatic bile duct cholangiocarcinoma is divided into two types, perihilar (pCCA) and distal (dCCA) cholangiocarcinoma. iCCAs arises above the second - order of the bile ducts. In contrary, the point anatomical which is distinction pCCA and dCCA is the insertion of the cystic duct. The majority of cholangiocarcinoma are in the perihilar (50–60% cases) and distal region (20–30% cases), and only 10% of CCA are located in intrahepatic. [5]
The tumor is considered rare in most countries with incidence rate from 2001 to 2015 was 1.26 cases per 100,000 persons and has a mortality rate 1–6 per 100,000. [1, 6] Nevertheless, this malignancy is still an endemic disease with high prevalence and incidence in some countries or regions such as Thailand and South Korea. The epidemiological profile of cholangiocarcinoma varies widely across the world, which is reflecting the exposure of different risk factor, such as chronic inflammatory disease of the biliary tract, specific infectious disease, and congenital malformation. In western countries, primary sclerosing cholangitis (PSC) causing biliary obliterative fibrosis, is the major etiology of CCA. [7] Specific in endemic area, Northeast Thailand, with incidence rate 118.5 per 100.000, which is 100 times higher than the global rate. [8] Number of mortality cases from liver and bile duct cancer is the leading cause of death in Thai males and places the third place in female with total number 28.000 deaths per year. [9] Northeast region of Thailand showed the highest number of liver mortality, comprising 70% of cases. [9] In this area, incidence of CCA is strongly related to liver fluke infestation that is endemic in Mekong River. Liver fluke infection is caused by water-borne parasites known as Opisthorchis viverrini, Clonorchis sinensis, and Opisthorchis feluneus. These parasites are transmitted to human by the consumption of raw, pickled, or undercooked infected fish associated with local tradition and poor income. [10, 11, 12]
The life cycle of this parasite is quite complex, involving two intermediate host (snail to fish) and including several changes of morphological feature. Fish contaminated with metacercaria is ingested by the human. [2] Infected human excretes the egg produced by the mature adult worms in their feces. [2, 13] Feces then contaminated the fresh water and then ingested by snail and the larva develop and hatch in the digestive tract of the snail. [2, 13] After that, thousands of cercariae were excreted into the water and penetrate the skin fish, encyst, and forming metacercaria. In the body of human, this parasite excyst in the duodenum and ascend to the bile duct via the ampulla of Vater then migrate further into the smaller and proximal bile duct, then become mature worm and able to sexually produce. [2, 13] Adult worm could survive up to 25 years in the biliary tree and causing mild symptoms such as malaise, abdominal discomfort, and diarrhea. Long term complication of this infection associated with hepatomegaly, chronic infection, cholecystitis, gallstone, and periportal fibrosis. [2, 13] Long term of chronic inflammation found to be a major etiological precursor of hepatobiliary malignancy, predominantly of CCA. Once a person is infected and suffered from chronic infection and inflammation, the risk for having CCA is increasing and could present within 30–40 years after infection. [11] Until now, the prognosis of CCA is remain poor and death tend to occur within 3–6 months after diagnosis. [11] There are several hypothesizes on the mechanism or pathway how the chronic infection could develop become malignancy: 1) mechanical damage caused by the fluke sucker, 2) fluke toxic secretary product, and 3) immunopathological host response. [11] These pathways then caused proliferative response and formation of precursor lesion such as epithelial and adenomatous hyperplasia, and goblet cell metaplasia. [11]
Beside parasite infection, primary sclerosing cholangitis (PSC) is another common etiology of cholangiocarcinoma, especially in the western population. PSC is a progressive cholestatic biliary characterized by the chronic inflammation that leads to destruction of the intra and extrahepatic bile duct. [14] The incidence rate of PSC ranges from 0 to 1.3 per 100.000 people. [15] At early stage, PSC is asymptomatic and is usually already diagnosed at advanced stage whereas jaundice and pruritus are the major complaint due to cholestasis. It has been also strongly associated with inflammatory bowel disease (IBD). On the other hand, PSC is often found with portal hypertension, cirrhosis, and in hepatobiliary and colorectal malignancies. [16, 17]
The other risk factor for developing CCA is biliary stones which is formed in the biliary tree, substantially in intrahepatic bile duct or known as hepatolithiasis. Biliary stones are typically concomitant with biliary stasis, cholangitis, strictures, and bacterial infection, leading to long term inflammation and biliary injury, and at the end, increasing the risk of malignant cholangiocytes growth. [18] Abnormal morphological also increase the risk for malignant transformation. Choledochal cysts is a rare congenital malformation characterized by dilatation of the biliary tree, can be single or multiple, and can be developed in the intra or extra hepatic bile ducts. [17, 19] Moreover, the coincidence of abnormal pancreatobiliary duct junctions increases the possibility of cholangiocarcinogenesis. This due to pancreatic enzyme reflux, cholestasis, and elevated bile acid concentrations. [19]
Exposure to chemical carcinogens such as Thorotrast, halogenated hydrocarbon solvent, and 1,2-dichloropropane were found to be associated with CCA incidence. [20, 21] Carcinogens-induced liver insult has been showed to promote hepatocyte remodeling, genomic instability, DNA methylation, and disrupt the liver architecture. Moreover, some studies reported few genetic mutations related to hepatobiliary malignancy. [22] Hepatic disease associated with CCA include alcoholic liver disease, cirrhosis, and cholangitis are included become risk factor. [17]
Diagnosis of CCA is quite challenging. CCA is generally asymptomatic in the early stage. Therefore, management of this malignancy is often delayed due to late diagnosed, where it already metastasis or compress the bile duct. The clinical features of CCA are heterogenous, with general malaise, cachexia, abdominal pain, night sweats, fatigue, weight loss, asthenia, and/or jaundice which is more frequent symptom in pCCA and dCCA due to biliary tract obstruction. [23, 24] Diagnosis of CCA is usually confirmed by combining nonspecific biomarkers in serum, biopsy specimens, and imaging technique. To date, there is no specific serum marker available for diagnosing CCA. Liver function parameters such as serum bilirubin, alkaline phosphatase, and aminotransferase enzyme usually elevate when biliary obstruction is presence. [24, 25] However, it is not specific signs for biliary malignancy. Serum tumor marker such as carbohydrate antigen (CA) 19–9, CA-125, and carcinoembryonic antigen (CEA) are the most widely used markers for suspected CCA. [25] But this diagnostic tool should not be used alone due to their poor diagnostic performance and inherent limitations.
Imaging techniques which are required to help diagnosis CCA are trans-abdominal ultrasonography (US), contrast-enhanced ultrasonography (CEUS), CT scan, and MRI. Becoming diagnostic tools, imaging techniques play a key role in the management of CCA in term of diagnosis, staging, follow-up, and assessment of favorable treatment response. The accuracy of diagnosis is depending on the anatomical location and growth pattern of CCA. Magnetic resonance cholangiopancreatography (MRCP) has the higher diagnostic accuracy for sizing strictures and localizing. [24, 25]
But unfortunately, there are no specific CCA radiology pattern exists. Therefore, histopathology or cytological analysis is also necessary for confirming the diagnosis. Definitive diagnosis is usually made by undergoing endoscopic retrograde cholangiopancreatography (ERCP) procedure for fluid cytology, brush cytology, fluorescence in situ hybridization (FISH), and cholangioscope or chromoendoscopy-guided biopsy. [26, 27, 28] Those multiple diagnostic modalities are required to 1) establish strictures anatomical location; 2) distinguish between benign and malignant strictures; 3) differentiate CCA from gallbladder cancer; 4) stage and grade the tumor; and 5) plan treatment approach. Based on WHO classification of biliary tract cancer it is showing an adenocarcinoma or mucinous carcinoma, with tubular and/or papillary structures and a variable fibrous stroma. [24, 25]
Determine staging of CCA is important for choosing the treatment, its resectability, and the outcome of the treatment. TNM classification system of American Joint Committee on Cancer (AJCC) and Union for International Cancer Control (UICC) has been used at present to determine the staging of CCA. TNM staging system is based on imaging tests which is evaluating the number of primary nodules, vascular invasion, direct extension in neighboring tissue, and bile duct involvement. [29] pCCA can be further divided according to the Bismuth-Corlette classification, depending on the size of the tumor, disease extension in the main bile duct, hepatic artery and/or portal involvement, lymph node involvement, distant metastasis, and remnant liver volume after resection. [30] iCCA could be classified based on 3 growth pattern which has different prognosis of each pattern: mass-forming (MF-iCCA), periductal infiltration (PI-iCCA), and intraductal growth (IG-iCCA). [31]
Treatment for managing cholangiocarcinoma is quite difficult too and should be managed in the tertiary hospital with a multidisciplinary team experienced in endoscopic, percutaneous, and surgical approaches. Management of this malignancy also depends on the staging of the tumor. Surgical treatment with complete resection could give benefit only for patient with early stage of the disease [32].
Resection could be performed in approximately 30% of patient with CCA. This is the only option that provides a real possibility for long-term survival in patient diagnosed with CCA. The indication and extension of surgery are determined based on clinical features of the patient, functional liver reserve, and the location and extension of the tumor, which include the association with vascular structure and negative metastatic disease. [33, 34]
Criteria for patients who are considered as absolute unresectability are the presence of nonresectable extrahepatic, hepatic metastases, bilateral extension of the tumor with involvement of the secondary biliary tract, complete occlusion of the main portal vein, thrombosis in portal vein contralateral to the tumor. [23] The most common postoperative complications are hemorrhage, infection, liver failure, cardiorespiratory failure, and adrenal failure. Mortality and morbidity for postoperative patient are still remaining high, 8,2% and 50%, respectively. [35] In several condition, drainage should be applied. But in the recent years, increasing number of patients with unresectable intrahepatic and extrahepatic CCA are being included to be candidate for liver transplant. Other treatment modalities as adjuvant therapy are also developed to improve the survival of the patient, such as chemotherapy, radiotherapy, molecular targeted therapy, targeting angiogenesis and EGFR, and immunotherapy.
Cancer immunotherapy is significantly progressing and rapidly advancing. In the recent years, immunotherapy is considered to be the fifth pillar of cancer therapy and management modality besides surgery, cytotoxic chemotherapy, radiation, and targeted therapy. The mechanism of immunotherapy in cancer management is to determine a manipulation of the immune system by using immune agents such as vaccine, cytokine, cell therapies and humoral, transfection agent. Cancer immunotherapy has to stimulate the host anti-tumor response by increasing the effector cell number and production of soluble mediators, decrease the host’s suppressor mechanism by inducing tumor killing environment, and could modulate immune checkpoint. [36, 37]
In 1891, William Coley, who is known today as the Father of Immunotherapy, injected heat inactivated bacteria or known as Coley toxins to the sarcoma patient who was inoperable. [38] This first experiment resulted in long term regression of the sarcoma after an erysipelas infection after injecting the toxin. [38] By late 1970s, immunotherapy for managing cancer was discovered. The first experiment was done in bladder cancer case which is managed by using BCG (Bacillus Calmette-Guerin). Then, it is continued with IFN therapy in malignant melanoma. [39] Brief background review of immune system is classically considered to be comprised of the innate and adaptive arms. Immune system which are included in innate immune system are dendritic cells, natural killer cells (NK), macrophages, neutrophils, eosinophils, basophils, and mast cells. As we known, this group of immune system does not need prior stimulation by antigen, and it plays role as first line of defense against foreign antigens. In the contrary, adaptive immune system consists of B lymphocytes, CD4 helper T lymphocytes, and CD 8 cytotoxic T lymphocytes (CTLs). This group of immune system requires formal presentation by antigen presenting cells (APCs) for its activation. [40, 41]
Several kinds of malignant cells are able to evade the tumor immunosurveillance system by manipulating their own characteristic as well as the cells in their microenvironment to become successful tumors. The concept that the immune system is capable for detecting and killing nascent non-self-malignant cells was developed. Elimination, equilibrium, and escape are three main phases of immunoediting process. [42] The elimination phase is the initial damage process and destruction of the tumor cell by innate immune system, then tumor antigens are presented to the dendritic cells, followed by presentation to the T cell and then create tumor-specific CD4 and CD8 T-cells. Second phase occurs when tumor cells survive after the initial destruction but are not able to progress and being maintained in an equilibrium state. The last phase is escape phase. [42] In this phase, tumor cells are growing rapidly, followed by metastasize of tumor cell due to loss control of the immune system and the tumor cells do not presented antigens on its surface or even losing their MHC class1 expression. Tumor cell could protect their self from T cell by expressing immune checkpoint (IC) molecules on their surface. [42]
The ability of this malignant cells to evade immune destruction by modulating its own cellular characteristic and creating its own “tumor microenvironment” by recruiting apparently normal immune cells to help shield it from attack of immune system. In addition, tumor cell can influence the systemic environment by altering hematopoiesis and tissue parenchyma of organs at distant sites. Cancer immunotherapies play role in manipulating these tumor microenvironments. But the loss of MHC class 1 expression manipulating is remaining challenge. [43, 44, 45]
First, older, and non-specific immunotherapies are the kind of immune stimulator cytokines such as interleukin-2 IL-2) and interferon (IFN). [46] Beside that, synthetic analogue of bacterial cell wall called L-MTP could activate monocytes and macrophages is one of the immunostimulatory cytokines. Vaccine trials using multiple neoantigens specific to and individual patient’s tumor have shown promising results in two small early trials with the aim to expose patients to those tumor antigens which can provoke an antitumor immune response via the generation of tumor specific antibodies and T cells. [46] BCG was the first vaccine used as cancer immunotherapy for treating bladder carcinoma. [47]
Oncolytic viruses are the combination of biologic therapy and immunotherapy. Viruses which are used for this method has genetically modified to lack virulence against normal cell but has a selective feature to invade and lyse cancer cells. Viral-induced tumor cell destruction undergoing further attack by an immune system. [48]
Adoptive cell therapy (ACT) is one type of immunotherapy which involves in the isolation and in-vitro expansion of tumor-specific T-cells, which is given through infusion in the cancer patient. ACT using NK cells could be used to treat solid tumor metastasis and hematological cancers. [49] Several forms of ACT using different techniques are culturing tumor infiltrating lymphocytes directly from the tumor, isolating and expanding one particular T-cell or the clone, using T cell which have been engineered in vitro so that it could recognize and attack the tumor cells or known as chimeric antigen receptor T-cell (CAR T-cell) therapy. ACT has produced remarkable result in clinical trials with melanoma and hematologic malignancies. But some studies reported death have occurred in the trial phase due to cytokine release syndrome or cytokine storm. [50]
Another immunotherapy, Immune checkpoint, work by targeting molecules that serve as checks in the regulation of immune responses and block inhibitory molecules or activate stimulatory molecules and enhance pre-existing anti-cancer immune response. [51]
Cancer immunotherapy works to stimulate the host’s anti-tumor response. The mechanism included are increasing the effector cell number and production of soluble mediators, decreasing the host’s suppressor mechanism by inducing tumor killing environment, and modulating immune checkpoints. The usefulness of cancer immunotherapy was introduced in the beginning to manage bladder cancer. The overall 5-year survival after transmitting immunotherapy is 77%. [39] Patients with moderate and high-grade bladder cancer who received intravesical immunotherapy with BCG have shown good result. Immune checkpoint inhibitors showed a promising clinical research in managing anti-cancer immune responses. Several studies using Nivolumab, Ipilimumab, and Pertuzumab are still on progress in metastatic bladder cancer. Some cytokines which are messenger molecules, play a role to control the growth and activity of immune system cells. [52] Treatment using cytokines as immunotherapy can enhance the activity of the immune system against tumors. The link of IL-2 to the antibody, ALT-801, and cytokines can target IL-2 to cancer cells. [53] Oncolytic virus therapy could also be used to treat bladder cancer using adenovirus which expresses the immune stimulating cytokine GM-CSF. [54]
Immunotherapy is developed to manage some immunogenic cancer cases besides bladder cancer. The using of immunotherapy for managing breast cancer have been improved and approved in the recent years. Although the best treatment of breast cancer is surgery, but combination therapy followed by chemotherapy, radiation therapy, or immunotherapy could increase clinical outcome for patient. A promising immunotherapy using immune checkpoint inhibitors that work by targeting molecules that serve as checks in the regulation of immune response and block inhibitory molecules or activate stimulatory molecules. [39] The other form of immunotherapy which can be used for breast cancer is monoclonal antibodies and adoptive T cell transfer. By definition, adoptive T cell transfer is a process of removing T cell from the patient, then it would be modified genetically or treated with chemical to enhance its activity and re-introduced into the patient. Specifically, in breast cancer, T cell genetically is modified to target the carcinoembryonic antigen (CEA). [55]
Another immunogenic cancer is cervical cancer caused by infection of human papillomavirus (HPV). Cervical cancer is the third most frequent cancer among women in the world. [56] The prevalence of this cancer is decreasing due to development widespread of screening tools Pap test and vaccine to prevent HPV infection. In the recent years, monoclonal antibodies, checkpoint inhibitor, and adoptive T cell transfer have become additional therapy for managing progressivity of cancer cell. [39]
Immunotherapies are also developed as a new modality treatment to treat brain cancer, colorectal cancer, esophageal cancer, and biliary tract cancer. Probably, in time, immunotherapy could lead to personalized medicine that will increase overall survival and progression free survival for many treatments. [39]
Biliary tract malignancy is an invasive carcinoma which can be originated from gallbladder or bile duct. It has been known that the immune system in human body has a significant role in the surveillance and eradication of cancer cells. Tumor that lack the mismatch repair system harbor more mutation than tumor without this deficiency. Thus, the neoantigen generated and be recognize as immunogenic antigen. The characteristic of mismatch repair deficient tumors is microsatellite instability (MSI). There are approximately 3% of CCA are mismatch repair-deficient/MSI-high. [56] This feature makes the tumor cells are susceptible to programmed cell death protein 1 (PD-1) inhibitors. Zhu et al. studied about efficacy and safety of gemcitabine, oxaliplatin, and bevacizumab in advanced biliary-tract cancers and the correlation of changes in 18-fluorodeoxyglucose PET with clinical outcome in a phase 2 study showed that combination of chemotherapy and immunotherapy have anti-tumor effect with tolerable safety and promising efficacy for managing advanced biliary tract malignancy. This combination treatment was generally well tolerated with less adverse event and manageable toxicity. [57]
Another clinical data about immune-directed therapy in CCA is still scanty. Vaccine for preventing CCA has been developed and tested but no data has showed successful result. [58] CAR T cell immunotherapy in recent years has been developed. Guo et al. in their study about expanded and parallel clinical trial of EGFR-specific chimeric antigen receptor-engineered autologous T (CART) cell immunotherapy. The aim of this study is to assess the safety and activity of CART-EGFR cell therapy in EGFR-positive advanced unresectable, relapsed/metastatic biliary tract cancer. Total sample of this study is 19 patients and showed that CART-EGFR cell infusion was tolerated, 1 achieved complete response and 10 achieved stable disease. We can conclude that CART-EGFR cell immunotherapy was a safe and active strategy for EGFR-positive advanced biliary tract cancer. [59] Wei et al. showed that in some patients, immune checkpoint blockade using monoclonal antibodies has shown remarkable and durable response rate in a many kind of malignancy cell. [60] Le et al. in their study concluded that mismatch-repair status predicted clinical benefit of immune checkpoint blockade with pembrolizumab and achieving objective responses in up to 40% of patients. [61] Study by Ott et al. in KEYNOTE-028 basket trial of pembrolizumab included patients with advanced biliary tract cancer resulted the objective response rate was 17% with median progression-free survival of 1.8 months. [62] However, further studies are required either combination immunotherapeutic approaches targeting both the innate and adaptive immune system or combined strategies involving chemotherapy or radiation.
Bile duct cancer is still one of the challenging malignancies in the gastroenterology field due to the difficulty in early detection and most of patients come in the late stage of the disease. Chemotherapy is still the main option of management despite surgery and biliary drainage. Immunotherapy is a promising treatment option in the future; however, further studies would be needed to give strong evidence before it can be used in common clinical practice.
Supporting women in scientific research and encouraging more women to pursue careers in STEM fields has been an issue on the global agenda for many years. But there is still much to be done. And IntechOpen wants to help.
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\\n\\nWe aim to publish 100 books in our Women in Science program over the next three years. We are looking for books written, edited, or co-edited by women. Contributing chapters by men are welcome. As always, the quality of the research we publish is paramount.
\\n\\nAll project proposals go through a two-stage peer review process and are selected based on the following criteria:
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\\n\\nAdvantages of Publishing with IntechOpen
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\n\nWe aim to publish 100 books in our Women in Science program over the next three years. We are looking for books written, edited, or co-edited by women. Contributing chapters by men are welcome. As always, the quality of the research we publish is paramount.
\n\nAll project proposals go through a two-stage peer review process and are selected based on the following criteria:
\n\nPlus, we want this project to have an impact beyond scientific circles. We will publicize the research in the Women in Science program for a wider general audience through:
\n\nInterested? If you have an idea for an edited volume or a monograph, we’d love to hear from you! Contact Ana Pantar at book.idea@intechopen.com.
\n\n“My scientific path has given me the opportunity to work with colleagues all over Europe, including Germany, France, and Norway. Editing the book Graph Theory: Advanced Algorithms and Applications with IntechOpen emphasized for me the importance of providing valuable, Open Access literature to our scientific colleagues around the world. So I am highly enthusiastic about the Women in Science book collection, which will highlight the outstanding accomplishments of women scientists and encourage others to walk the challenging path to becoming a recognized scientist." Beril Sirmacek, TU Delft, The Netherlands
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