",isbn:"978-1-80356-966-6",printIsbn:"978-1-80356-965-9",pdfIsbn:"978-1-80356-967-3",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"f86a9f720cc3ac0f1c385d0367ea89b9",bookSignature:"Dr. Fiaz Ahmad and Prof. Muhammad Sultan",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11624.jpg",keywords:"Agricultural Waste, Reuse, Reduction, Soil Health, Recycling, Agriculture and Environment, Modelling and Simulation, Agro-Industrial Waste, Bioresource Processing, Processing and Management, Crop Residue, Forest Waste",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 8th 2022",dateEndSecondStepPublish:"June 16th 2022",dateEndThirdStepPublish:"August 15th 2022",dateEndFourthStepPublish:"November 3rd 2022",dateEndFifthStepPublish:"January 2nd 2023",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Fiaz Ahmad is a researcher in the field of Agricultural Engineering with fifteen years of field and academic experience, currently in charge of the Agricultural Machinery Design Laboratory at Bahauddin Zakariya University. He applied for two patents at the national level.",coeditorOneBiosketch:"A renowned researcher in the field of Agricultural Engineering with 14 years of academic experience at Bahauddin Zakariya University. Winner of various prestigious fellowships, awards, and research grants. Published 250+ articles along with several books and chapters. Guest editor of seven ISI-SCI journals for publishers like SAGE, MDPI, and Frontiers.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"338219",title:"Dr.",name:"Fiaz",middleName:null,surname:"Ahmad",slug:"fiaz-ahmad",fullName:"Fiaz Ahmad",profilePictureURL:"https://mts.intechopen.com/storage/users/338219/images/system/338219.png",biography:"Dr. Fiaz Ahmad is an assistant professor and lecturer at the Department of Agricultural Engineering, Bahauddin Zakariya University, Multan, Pakistan. He obtained his Ph.D. in Agricultural Bioenvironmental and Energy Engineering from Nanjing Agriculture University, China, in 2015, and completed his postdoctorate in Agricultural Engineering from Jiangsu University, Zhenjiang, China, in 2020. He was awarded a fellowship from the Higher Education Commission of Pakistan for Ph.D. studies and from the Chinese Government for post-doctoral studies. He earned a BSc and MSc (Hons) in Agricultural Engineering from the University of Agriculture, Faisalabad, Pakistan, in 2004 and 2007, respectively. He is the author of more than fifty journal and conference articles. He has supervised six master’s students to date, and is currently supervising six master and two doctoral students. Dr. Ahmad has completed three research projects with his research interest focusing on the design of agricultural machinery, agricultural waste management, artificial intelligence (AI), and agricultural bioenvironment.",institutionString:"Bahauddin Zakariya University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Bahauddin Zakariya University",institutionURL:null,country:{name:"Pakistan"}}}],coeditorOne:{id:"199381",title:"Prof.",name:"Muhammad",middleName:null,surname:"Sultan",slug:"muhammad-sultan",fullName:"Muhammad Sultan",profilePictureURL:"https://mts.intechopen.com/storage/users/199381/images/system/199381.png",biography:"Muhammad Sultan is an Assistant Professor at the Department of Agricultural\r\nEngineering, Bahauddin Zakariya University, Multan (Pakistan). He completed his Ph.D.\r\nand Postdoc from Kyushu University (Japan) in the field of Energy & Environmental\r\nEngineering. He was an awardee of MEXT and JASSO fellowships (from the Japanese\r\nGovernment) during Ph.D. and Postdoc studies, respectively. He also did a Postdoc as\r\na Canadian Queen Elizabeth Advance Scholar at Simon Fraser University (Canada) in\r\nthe field of Mechatronic Systems Engineering. He worked for Kyushu University\r\nInternational Institute for Carbon-Neutral Energy Research (WPI-I2CNER) for two years.\r\nCurrently, he is working on 4 research projects funded by the Higher Education\r\nCommission (HEC) of Pakistan. He has completed six projects in past in the field of\r\nagricultural engineering. He has supervised 10+ M.Eng. and Ph.D. thesis and 10+\r\nstudents are currently working under his supervision. He has published 120+ journal\r\narticles, 100+ conference articles, 13 book chapters, and 6 books. He is serving as guest\r\neditor for the journals like Sustainability (MDPI), Agriculture (MDPI), Energies (MDPI),\r\nAdvances in Mechanical Engineering (SAGE), Frontiers in Mechanical Engineering, and\r\nEvergreen Journal of Kyushu University. His research is focused on developing energy-\r\nefficient temperature and humidity control systems for agricultural storage, greenhouse,\r\nlivestock, and poultry applications. His research keywords include desiccant air-\r\nconditioning, evaporative cooling, adsorption heat pump, Maisotsenko cycle (M-cycle),\r\nenergy recovery ventilators; adsorption desalination; wastewater treatment.",institutionString:"Bahauddin Zakariya University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"5",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Bahauddin Zakariya University",institutionURL:null,country:{name:"Pakistan"}}},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"5",title:"Agricultural and Biological Sciences",slug:"agricultural-and-biological-sciences"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"440212",firstName:"Elena",lastName:"Vracaric",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/440212/images/20007_n.jpg",email:"elena@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. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"10454",title:"Technology in Agriculture",subtitle:null,isOpenForSubmission:!1,hash:"dcfc52d92f694b0848977a3c11c13d00",slug:"technology-in-agriculture",bookSignature:"Fiaz Ahmad and Muhammad Sultan",coverURL:"https://cdn.intechopen.com/books/images_new/10454.jpg",editedByType:"Edited by",editors:[{id:"338219",title:"Dr.",name:"Fiaz",surname:"Ahmad",slug:"fiaz-ahmad",fullName:"Fiaz Ahmad"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6418",title:"Hyperspectral Imaging in Agriculture, Food and Environment",subtitle:null,isOpenForSubmission:!1,hash:"9005c36534a5dc065577a011aea13d4d",slug:"hyperspectral-imaging-in-agriculture-food-and-environment",bookSignature:"Alejandro Isabel Luna Maldonado, Humberto Rodríguez Fuentes and Juan Antonio Vidales Contreras",coverURL:"https://cdn.intechopen.com/books/images_new/6418.jpg",editedByType:"Edited by",editors:[{id:"105774",title:"Prof.",name:"Alejandro Isabel",surname:"Luna Maldonado",slug:"alejandro-isabel-luna-maldonado",fullName:"Alejandro Isabel Luna Maldonado"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10359",title:"Landraces",subtitle:"Traditional Variety and Natural Breed",isOpenForSubmission:!1,hash:"0600836fb2c422f7b624363d1e854f68",slug:"landraces-traditional-variety-and-natural-breed",bookSignature:"Amr Elkelish",coverURL:"https://cdn.intechopen.com/books/images_new/10359.jpg",editedByType:"Edited by",editors:[{id:"231337",title:"Dr.",name:"Amr",surname:"Elkelish",slug:"amr-elkelish",fullName:"Amr Elkelish"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophile",surname:"Theophanides",slug:"theophile-theophanides",fullName:"Theophile Theophanides"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. 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1. Introduction
Theoretical analysis on the bioheat transfer process has been an extremely important issue in a wide variety of bioengineering situations such as cancer hyperthermia, burn injury evaluation, brain hypothermia, disease diagnostics, thermal comfort analysis, cryosurgery and cryopreservation etc. In this chapter, the theoretical strategies towards exactly solving the three-dimensional (3-D) bioheat transfer problems for both cases with and without phase change were systematically illustrated based on the authors’ previous works. Typical closed form analytical solutions to the hyperthermia bioheat transfer problems with space or transient heating on skin surface or inside biological bodies were summarized. In addition, exact solutions to the 3-D temperature transients of tissues under various phase change processes such as cryopreservation of biomaterials or cryosurgery of living tissues subject to freezing by a single or multiple cryoprobes were also outlined. Such solution is comprehensive enough by taking full account of many different factors such as generalized initial and boundary conditions, blood perfusion heat transfer, volumetric heating of hyperthermia apparatus or heat sink of cryoprobes etc. For illustrating the applications of the present methods, part of the solutions were adopted to analyze the selected bioheat transfer problems. The versatility of these theoretical approaches to tackle more complex issues was also discussed. The obtained solutions are expected to serve as the basic foundation for theoretically analyzing bioheat transfer problems.
2. Motivations of analytical solutions to bioheat transfer problem
Analytical solutions to bioheat transfer problems are very important in a wide variety of biomedical applications [1]. Especially, understanding the heat transfer in biological tissues involving either raising or lowering of temperature is a necessity for many clinical practices such as tumor hyperthermia [2], burn injury evaluation [3, 4], brain hypothermia resuscitation [5], disease thermal diagnostics [6], thermal comfort analysis [7], cryosurgery planning [8, 9], and cryopreservation programming [10]. The bioheat transfer problems involved in the above applications can generally be divided into two categories: with and without phase change. In this chapter, the phase change especially denotes the solid-liquid phase transition of biological hydrated tissues. The cases without phase change usually include tumor hyperthermia, burn injury evaluation, brain hypothermia resuscitation, disease diagnostics, and thermal comfort analysis, while the cases with phase change include cryosurgery and cryopreservation.
To guarantee optimal clinical outputs for such applications, it is essential to predict in advance the transient temperature distribution of the target tissues. For example, in a tumor hyperthermia process, the primary objective is to raise the temperature of the diseased tissue to a therapeutic value, typically above 43ºC, and then thermally destroy it [11]. Temperature prediction would be used to find an optimum way either to induce or prevent such thermal damage to the target tissues. In contrast to the principle of hyperthermia, cryosurgery realizes its clinical purpose of controlled tissue destruction through deep freezing and thawing [12]. Applications of this treatment are quite wide in clinics owning to its outstanding virtues such as quick, clean, relatively painless, good homeostasis, and minimal scaring. An accurate understanding of the extent of the irregular shape of the frozen region, the direction of ice growth, and the temperature distribution within the ice balls during the freezing process is a basic requirement for the successful operation of a cryosurgery. Therefore, solving the bioheat transfer problems involved is very important for both hyperthermia and cryosurgery. Moreover, in thermal diagnostics, thermal comfort analysis, brain hypothermia resuscitation, and burn injury evaluation, similar bioheat transfer problems are also often encountered [13].
It is commonly accepted that mathematical model is the basis for solving many practical problems. Because modeling bioheat transfer is of the utmost importance in many biomedical applications such as proper device or heating/cooling protocol design, a number of bioheat transfer equations for living tissue have been proposed since the landmark work by Pennes published in 1948 [14], in which the perfusion heat source/sink was introduced. Until now, the classical Pennes equation is also commonly accepted as the best practical approach for modeling bioheat transfer in view of its simplicity and excellent validity [15]. This is because most of the other models either still lack sound experimental grounding or just appear too complex for mathematical solution. Although the real anatomical geometry of a biological body can be incorporated, the Pennes equation remains the most useful model for characterizing the heat transport process in most biomedical applications. For brevity, here only cases for space-dependent thermal properties will be mainly discussed. Then a generalized form of the Pennes equation for this purpose can be written as:
where, ρ, c are the density and the specific heat of tissue, respectively; ρband cb denote the density and the specific heat of blood, respectively; Xcontains the Cartesian coordinatesx, yandz; Ωdenotes the analyzed spatial domain; k(X)is the space dependent thermal conductivity; and ωb(X) is the space dependent blood perfusion. The value of blood perfusion represents the blood flow rate per unit tissue volume and is mainly from microcirculation including the capillary network plus small arterioles and venules. Tais the blood temperature in the arteries supplying the tissue and is often treated as a constant at 37°C; T(X,t)is the tissue temperature; Qm(X,t)is the metabolic heat generation; and Qr(X,t) the distributed volumetric heat source due to externally applied spatial heating.
From the historical viewpoint, we can find that the development of the bioheat transfer’s art and science can be termed as one to modify and improve the Pennes model [15]. Among the many efforts, the blood perfusion term in the Pennes equation has been substantially studied which led to several conceptually innovative bioheat transfer models such as Wulff’s continuum model [16], Chen-Holmes model addressing both the flow and perfusion properties of blood [17], and the Weinbaum-Jiji three-layer model to characterize the heat transfer in the peripheral tissues [18]. The bioheat transfer equation and its extended forms can be directly used to characterize the thermal process of the biological bodies subject to various external or interior factors such as convective interaction with a heated or cooled fluid, radiation by fire or laser, contact with a heating or freezing apparatus, electromagnetic effect, or a combination among them. Such issues can be treated using different boundary conditions as well as spatial heating or freezing patterns. Generally, the geometric shape, dimensions, thermal properties and physiological characteristics for tissues, as well as the arterial blood temperature, can be used as the input to the Pennes equation for a parametric study. According to a specific need in clinics, the bioheat transfer model can even be modified by taking more factors into concern [19]. Traditionally, for solving bioheat transfer problems, people relied too heavy on numerical approaches such as finite difference method, finite element method, and boundary element method etc. Numerical simulation is necessary when the analytical solutions are not available. But if both analytical and numerical solutions can be obtained for the same issue, the analytical one is often preferred. Except for its simplicity being used to compile computer codes, the analytical solution is very attractive since its efficiency depends weakly on the dimensions of the problem, in contrast to the numerical methods. For analytical method, solution at a desired point can be performed independently from that of the other points within the domain, which can be an asset when temperatures are needed at only some isolated sites or times. But for most of the conventional numerical methods (except Monte Carlo simulation), the temperatures at all mesh points must be simultaneously computed even when only the temperatures at a single point are needed [20]. In this sense, the analytical solution will save computational time greatly, which is valuable in clinical practices.
Based on the above considerations, we aimed in this chapter to present several typical closed form analytical solutions to bioheat transfer problems with or without phase change, in which relatively complex boundary or heating/cooling conditions, and existence of discrete large blood vessel were included. Derivation of the solutions was mainly based on the Green’s function method, which is beneficial for dealing with the non-homogeneous problems with spatial or transient heating source and initial temperature distribution, as well as complex cooling or boundary conditions. For generalized and practical purpose, complex bioheat transfer problems encountered in several typical clinical applications as well as basic studies such as tumor hyperthermia, cryosurgery, cryopreservation, and interpretation of physiological phenomena etc. will be especially addressed.
3. Bioheat transfer problems without phase change
3.1. Generalized analytical solutions to 3-D bioheat transfer problems
Derivation of the solutions was based on the Green’s function method, since the Green’s function obtained for the differential equation is independent of the source term. Therefore it can be flexibly used to calculate the temperature distribution for various spatial or temporal source profiles. Furthermore, the Green’s function method is capable of dealing with the transient or space-dependent boundary conditions. Up to now, quite a few studies have applied the Green’s function method to solve the bioheat transfer problems [21-25]. However, in most of the existing analytical studies, the available solutions to the bioheat transfer problem are for the cases with one dimensional geometry, steady state, infinite domain, constant heating, or heat conduction equations not considering blood perfusion, which may not be practical for some real bio-thermal situations. In this section, the generalized analytical solutions, which have incorporated relatively complex situations such as the 3-D tissue domain, the transient or space-dependent boundary conditions, and volumetric heating, were especially addressed. Such solutions are expected to be very useful in a variety of bio-thermal practices. The 3-D computational domain with widths s1 = s2 = 0.08m and height L = 0.03m was depicted as the shadowed region in Fig. 1, where s1 and s2 were widths of the tissue domain to be analyzed in y and z directions, respectively; the skin surface was defined at x=0 while the body core at x=L.
Figure 1.
Calculation geometry for 3-D case [13]
For brief, only 3-D case with constant thermal parameters will be particularly studied, which is a good approximation when no phase change occurred in tissue. The corresponding 3-D Pennes equation can be derived from Equation (1) as:
The generalized boundary conditions (BCs) often encountered in a practical clinical situation can be written as:
−k∂T∂x=f1(y,z;t),x=0E3
\n\t\t\t\t
or
−k∂T∂x=hf[f2(y,z;t)−T],x=0E4
\n\t\t\t\t
where, f1(y,z;t)is the time-dependent surface heat flux, f2(y,z;t)is the time-dependent temperature of the cooling medium, and hf is the heat convection coefficient between the medium and the skin surface. In this chapter, Equation (3) was named the second BC and Equation (4) the third BC.
The body core temperature was regarded as a constant (Tc) on considering that the biological body tends to keep its core temperature to be stable, i.e.
T=Tc,x=LE5
The BCs at y and z directions can be expressed as
−k∂T∂y=0,y=0E6
−k∂T∂y=0,y=s1E7
\n\t\t\t\t
−k∂T∂z=0,z=0E8
\n\t\t\t\t
−k∂T∂z=0,z=s2E9
The reason for adopting the adiabatic conditions in the two ends of the y and z directions is from the consideration that at the positions far from the beam center of the heat deposition apparatus, the temperatures there were almost not affected by the external heating, which generally has a strong decay in y and z directions. However, it should be mentioned that more generalized boundary conditions in y and z directions can also be dealt with by the present approach, but they were not listed here for brevity.
The initial temperature is
T(x,y,z;0)=T0(x,y,z),t=0E10
where, T0(x,y,z)can be approximated by the 1-D solution, representing the initial temperature field for the basal state of biological bodies, which can be obtained through solving the following equation sets:
where, h0is the apparent heat convection coefficient between the skin surface and the surrounding air under physiologically basal state and is an overall contribution from natural convection and radiation, and Tf the surrounding air temperature.
Using Green function method, W(x,y,z;t)can be solved from the combined Equations (14-24). The Green’s functions G1 and G2 to the second and third BCs can finally be obtained:
Clearly, the above method can also be extended to solve some other three-dimensional problems such as in spherical and cylindrical coordinates. But they will not be listed here for brevity. To illustrate the application of the above analytical solutions, a selective 3-D hyperthermia problem with point heating sources was particularly studied as an example. Accordingly, the temperature distribution of tissue subject to the point heating in volume was analytically solved. Practical examples for the point heating can be found in clinics where heat was deposited though inserting a conducting heating probe in the deep tumor site. Previously, such problems received relatively few attentions in compared with other heating patterns. Here, the point-heating source to be studied can be expressed as:
Qr(x,y,z,t)=P1(t)δ(x−x0)δ(y−y0)δ(z−z0)E37
\n\t\t\t\t
where, P1(t)is the point-heating power, δis the Dirac function, (x0,y0,z0)is the position of the point-heating source.
The results were given in Fig. 2, which represent the temperature distribution in biological bodies heated by one and two-point sources, respectively. In calculations, the typical tissue properties were applied as given in Table 1. In Fig. 2(a), the single heating source was fixed at position (0.021m, 0.04m, 0.04m); in Fig. 2(b), the two point-heating sources were at (0.021m, 0.032m, 0.04m) and (0.021m, 0.048m, 0.04m), respectively. It makes clear that the maximum temperatures of the tissues occur at the positions of the point-heating sources. Further, one can still observe that the temperature for the tissues surrounding the point-heating sources can fairly be kept at a lower temperature on the whole. This is very beneficial for the hyperthermia operation since one can then selectively control the temperature level at the diseased tissue sites while the healthy tissues at the surrounding area will just stay below the safe threshold. This may be one of the most attractive features why the invasive heating probes are frequently used to thermally kill the tumor in the deep tissue, although they may cause mechanical injury. The above solutions are expected to be valuable for such hyperthermia treatment planning.
Figure 2.
Temperature distribution at cross-section z=0.04m after 1200s’ heating
Unit
Value
Air temperature (Tf)
°C
25
Artery blood temperature (Ta)
°C
37
Blood perfusion of tissue (ωb)
ml/s/ml
0.0005
Body core temperature (Tc)
°C
37
Density of tissue (ρ)
Kg/m3
1000
Density of blood (ρb)
Kg/m3
1000
Heat convection coefficient (h0)
W/m2· °C
10
Heat convection coefficient (hf)
W/m2· °C
100
Metabolic heat generation of tissue (Qm)
W/m3
33800
Specific heat of tissue (c)
J/Kg· °C
4200
Specific heat of blood (cb)
J/Kg· °C
4200
Temperature of cooling medium (f2)
°C
15
Thermal conductivity of tissue (k)
W/m· °C
0.5
Table 1.
Typical thermophysical properties of soft biological tissues [13].
3.2. Analytical solutions to 3-D bioheat transfer involved in hyperthermia for prostate
Localized transurethral thermal therapy has been widely used as a non-surgical modality for treatment of benign prostatic hyperplasia [26]. One of the critical issues in clinical application is to effectively heat and cause coagulation necrosis in target tissue while simultaneously preserving the surrounding healthy tissue, especially the prostatic urethra and rectum. This requires administration of an optimal thermal dose which can induce the desired three dimensional tissue temperature distributions in the prostate during the therapy. In this section, the analytical approach to solving the transient 3-D temperature field was illustrated, which can be used to predict point-by-point tissue temperature mapping during the heating.
The transurethral microwave catheter (T3 catheter) was used as the heating apparatus in this section. Geometric presentation of the prostate with the inserted T3 catheter was shown in Fig. 3. It was modeled as a cylinder of 3.4cm in diameter and 3cm in length with constant temperature T∞ at the surface which is near the body core temperature. The catheter was represented by the inner cylinder. The induced volumetric heating in tissue is [26]:
where Q is the applied microwave power, Ct is a proportional constant, ε is the microwave attenuation constant in tissue, and z0 is the critical axial decay length along the catheter while L is the total length of the prostate.
Practically, the microwave antenna is located with an offsets from the geometrical center to produce an asymmetric microwave field, which can prevent overheating the rectum. The chilled water at a given temperature flows between the antenna and the inner catheter wall.
The Pennes equation for the 3-D temperature field in the prostate can then be applied as:
D configuration of the prostate under microwave heating [26]
To obtain an analytical solution, all these parameters were assumed to be uniform throughout the prostate and remained constant except for ωb which varied with respect to the heat power. The cooling effect from the chilled water running inside the catheter was modeled by an overall convection coefficient h. The external boundary at the capsule was prescribed as the body core temperatureT∞. Therefore, one has:
k∂T∂r=h(T−Tf),r=R0E40
\n\t\t\t\t
T=T∞,r=R2E41
where, R0 and R2 are the urethra and prostate radius, respectively; Tf is the coolant temperature. Considering the Gaussian distribution of microwave power deposition along the z direction, adiabatic conditions can be used at two ends of the prostate:
k∂T∂z=0,z=0,LE42
\n\t\t\t\t
The initial temperature is
T(r,θ,z,0)=T0(r,θ,z),t=0E43
\n\t\t\t\t
Using transformation:
T=Δ+T∞+(T∞−Tf)ln(r/R2)ln(R2/R0)+k/(hR0)E44
One can rewrite the above equations (Equations (39-44) as:
where, Q*(r,θ,z)and F(r,θ,z) were given in Equations (51-52), respectively.
This analytical solution has been applied to perform parametric studies on the bioheat transfer problems involved in prostate hyperthermia [26].
3.3. Analytical solutions to bioheat transfer with temperature fluctuation
Contributed from microcirculation including the capillary network plus small arterioles and venules of less than 100μm in diameter, blood perfusion plays an important role in the transport of oxygen, nutrients, pharmaceuticals, and heat through the body [27]. Although generally treated as a constant, blood perfusion is in fact a transient value even under physiological basal state. This is due to external perturbation and the self-regulation of biological body. The pulsative blood flow behavior also makes blood perfusion a fluctuating quantity. It is well accepted that blood perfusion is a fluctuating quantity around a mean value. Corresponding to the pulsative blood flow and very irregular distribution of blood vessels with various sizes, temperatures in intravital living tissues also appear fluctuating. To address this issue, we have obtained before an analytical model to characterize the temperature fluctuation in living tissues based on the Pennes equation [27]. It provides a theoretical foundation to better understanding the temperature fluctuation phenomena in living tissues. The Pennes equation used for the analysis can be rewritten as
ρC∂T∂t=K∇2T+WbCb(Ta−T)+QmE63
Considering that small perturbations of arterial blood temperature, blood perfusion, and metabolic heat generation will result in tissue temperature fluctuation, each of these parameters can be expressed as the sum of a mean and a fluctuation value, i.e.
T=T¯+T′E64
Ta=Ta¯+T′aE65
Wb=Wb¯+W′bE66
Qm=Qm¯+Q′mE67
\n\t\t\t\t
where, symbol “ — ” represents the mean value, and “ \' ” the fluctuation one. Here, temporal averaging is adopted and defined as:
A(x,y,z;t)¯=1Γ∫t−Γ/2t+Γ/2A(x,y,z;τ)dτE68
\n\t\t\t\t
where, A(x,y,z;t)denotes the transient physical quantity at the vicinity of point (x,y,z), and Γ the temporal averaging period.
Compared with the mean value, the fluctuation value is generally a small quantity. Then one has the following statistical relation:
Equations (73) and (74) consist of the theoretical models for characterizing the temperature fluctuation in living tissues. Derivation of the perturbation Equation (74) is similar to that of the well known Reynolds equation in fluid mechanics. Compared with the Pennes equation, there are two additional terms appearing in Equation (73) both of which have explicit physical meaning: CbW′bT′a¯and −CbW′bT′¯ represent the mean transferred energy due to perfusion perturbations and temperature fluctuations in tissue and arterial blood, respectively. It is from Equation (74) that the temperature fluctuation (T′) and the pulsative blood perfusion, arterial blood temperature and metabolic heat generation (W′, T′a, andQ′m) were correlated. Clearly, since the mean tissue temperature T¯ is a space dependent value, T′is expected to be different at various tissue positions. To solve for Equation (73) and (74), dimension analysis was performed to simplify the model. One can express the orders of magnitude of the mean and pulsative physical quantities as:
For the interpretation of temperature fluctuation in living tissues, it is reasonable to apply the 1-D degenerated forms of Equations (76) and (77). The boundary condition at the skin surface can be chosen as convective case which is often encountered in reality, i.e.
−K∂T∂x=hf(Tf−T),x=0E77
At the body core, a symmetrical or adiabatic condition can be used, namely
−K∂T∂x=0,x=LE78
\n\t\t\t\t
Then using the relations in Equations (65-68), the boundary and initial conditions of Equations (76) and (77) can be respectively obtained as:
−K∂T¯∂x=hf(Tf−T¯),x=0E79
\n\t\t\t\t
−K∂T¯∂x=0,x=LE80
\n\t\t\t\t
T¯=T0(x),t=0E81
\n\t\t\t\t
K∂T′∂x=hfT′,x=0E82
\n\t\t\t\t
−K∂T′∂x=0,x=LE83
\n\t\t\t\t
T′=0,t=0E84
where, x=0is defined as the skin surface while x=L the body core; hfis the apparent heat convection coefficient between the skin and the environment which is the contribution from the natural convection and radiation; Tfthe environment temperature; and T0(x) the initial temperature distribution. The following transformation was introduced [27]
T(x,t)¯=R(x,t)¯⋅exp(−Wb¯CbρCt)E85
Then Equations (76) and (80-82) were respectively rewritten as:
∂R¯∂t=α∂2R¯∂x2+Wb¯CbTa¯+Qm¯ρC⋅exp(Wb¯CbρCt)E86
\n\t\t\t\t
−K∂R¯∂x=hf[Tf⋅exp(Wb¯CbρCt)⋅H(t)−R¯],x=0E87
\n\t\t\t\t
−K∂R¯∂x=0,x=LE88
\n\t\t\t\t
R¯=T0(x),t=0E89
\n\t\t\t\t
where, α=K/ρCis the diffusivity of tissue, and H(t)={0,t<01,t>0 the Heaviside function.
If the Green’s function for the above equation system is obtained, its transient solution can thus be constructed [13]. Through introducing an auxiliary problem corresponding to Equations (87-90), the Green’s function G can finally be obtained as:
Substituting Equation (93) into Equation (86) leads to the mean temperatureT(x,t)¯. The above solution is applicable to any transient environmental temperature Tf(t) and space-dependent metabolic heat generationQm(x,t)¯. For the fluctuation temperature, the solving process is as follows. Using the similar transformation as Equation (86)
T′(x,t)=R′(x,t)⋅exp(−Wb¯CbρCt)E93
\n\t\t\t\t
Equations (77) and (83-85) can be respectively converted to
where, the mean temperature T(x,t)¯ was given by Equation (86). Substituting Equation (100) into Equation (94), the temperature fluctuation T′(x,t) can be obtained. As illustration, two calculation examples using the above analytical solutions were presented in this section to investigate the temperature fluctuation phenomena in living tissues. In the following illustration calculations, the typical values for tissue properties were taken from [27]. For clarity, only effect of the pulsative blood perfusion W′b alone will be considered while the pulsative arterial temperature and metabolic heat generation were not taken into account, namely, T′a=0,Q′m=0, and a constant mean blood perfusion Wb¯ was assumed. Here, the pulsative blood perfusion W′b was far less than the mean valueWb¯, and its simplest form can be expressed as a periodic quantity with constant frequency and oscillation amplitude such as Wbmcosωt (where ω is frequency, Wbmthe amplitude, andWbm<<Wb¯). However, to be more general, a stochastic pulsative perfusion form as W′b=0.1Wb¯(0.5−ran) (where, ranis random number generated by Fortran function) was adopted for calculations.
Figure 4.
Temperature fluctuation due to pulsative blood perfusion (Wb¯=0.5kg/m3°C)
Fig. 4 depicted both the skin surface temperature fluctuation (the mean perfusion Wb¯=0.5kg/m3°C) and the blood perfusion perturbation W′b causing this behavior. As a comparison, Fig. 4(a) and\n\t\t\t\t\tFig. 4(b) gave temperature fluctuations spanning two different time scope. It can be seen that small perturbation on blood perfusion resulted in an evident and observable temperature fluctuation in the living tissues. This result accords with the commonly observed phenomenon that the measured tissue temperature appears as fluctuating even when the measured animal is under physiologically basal state. It can also be found that the frequency of temperature fluctuation appears much smaller than that of the blood perfusion fluctuation, which implies that intravital biological tissue tends to keep its temperature stable. This result indicates that the stochastic fluctuation of blood perfusion in intravital biological tissue may also contribute to the tissue temperature oscillations, and the internal relations between blood perfusion fluctuation and the temperature oscillation need further clarification.
In this section, the perturbation model for characterizing the temperature fluctuation in living tissues was illustrated and its exact analytical solution was obtained which has wide applicability. One of the most important results in this section is perhaps that small perturbation in blood perfusion result in evidently observable temperature fluctuation in the living tissues. And the larger blood perfusion, the more liable for the living tissues to keep its temperature stable. This model provides a new theoretical foundation for better understanding the thermal fluctuation behavior in living tissues.
4. Bioheat transfer problems with phase change
4.1. Analytical solutions to 3-D phase change problems during cryopreservation
Derivation such solutions was based on the moving heat source method, in which all the thermal properties were considered as constants, and phase transition was assumed to occur in a single temperature [28]. The density, specific heat and heat conductivity of solid phase were considered to be the same as those of the liquid phase, respectively. To simplify the problem, only computation in a regular geometry characterized by Cartesian coordinates was considered, as shown in Fig. 5. According to the geometrical symmetry, only 1/8 of the whole cubic tissue was chosen as the study object, whose center is set as the origin point, and l, s1, s2 represent the distances between the origin and the boundaries of the tissue along x, y, z directions, respectively.
Figure 5.
Schematic of 1/8 cuboidal tissue subject to cryopreservation [29]
The energy equations for different phase regions were then written. For the liquid phase:
wherek, ρ, c denote thermal conductivity, density and specific heat of tissue, respectively;Ts, Tlare the temperature distributions for solid and liquid phase, respectively; t is time;Rl, Rsdenote solid and liquid region; the subscript l, s represent the liquid and solid phase, respectively. To obtain an analytical solution, all the parameters were assumed as uniform and remain constant.
It should be pointed out that the physical properties for the biological tissues would change during the phase change process. Therefore it may cause certain errors when assuming both the frozen and unfreezing regions take the same physical parameters. According to existing measurements, the density changes little and thus can be used as a constant. However, the other parameters, especially the thermal conductivity and the specific heat, would change significantly. For such case, one can choose to adopt an equivalent physical property to represent the original parameter, i.e. the parameters could take into concern contributions from both frozen and unfreezing phase, such that k=k¯=(kl+ks)/2 andc=c¯=(cl+cs)/2. This will minimize the errors via a simple however intuitive way. The solid and liquid phases are separated by an obvious interface, which can be expressed as
S(x0,y0,z0,t)=z0−s(x0,y0,z0)=0E102
where, s denotes the moving solid-liquid interface, (x0,y0,z0)represents position of any point in this specific interface and each of them is time dependent.
In the solid-liquid interface, conservation of energy and continuum of temperature read as
k∂Ts∂n−k∂Tl∂n=ρL∂s∂t|n\tat\tS(x,y,z,t)=0,t>0E103
\n\t\t\t\t
Ts=Tl=TmatS(x,y,z,t)=0,t>0E104
\n\t\t\t\t
where n is the unit normal vector; L is the latent heat of tissue; Tmis the phase change temperature; vn(t)=∂s∂t|nrepresents the velocity of the interface. Based on the moving heat source method [28, 29], the above phase change problem can be equivalently combined to a heat conduction problem with an interior moving heat source term, i.e.
where, α=k/ρcis the thermal diffusivity; z0is z coordinate of the arbitrary point p in the interface; δ(z−z0)is the delta function. For brief, Equation (106) can be rewritten as
∇2T+qr(x,y,z,t)k=1α∂T∂tE106
\n\t\t\t\t
where, a generalized volumetric heat source has been expressed as
qr(x,y,z,t)=ρL∂s(x,y,t)∂tδ(z−z0)E107
\n\t\t\t\t
The typical cooling situations most encountered in a cryopreservation [8, 10] include the following cases: (a) convective cooling at all boundaries by liquid nitrogen; (b) fixed temperature cooling at all boundaries through contacting to copper plate with very low temperature; (c) fixed temperature cooling at upside and underside surface of tissues and convective cooling at side faces; (d) convective cooling at upside and underside surface of tissues and fixed temperature cooling at side faces. Usually, the boundary types as (c) and (d) were adopted to increase the cooling rate. In this section, the analytical solutions will be presented according to the above four cooling cases, respectively.
Case (a): Convective cooling at all boundaries
Clinically, one of the most commonly used cooling approaches is to immerse the processed tissue into liquid nitrogen and frequently shift it up and down so as to enhance the heat exchange between the tissue and the liquid. Such boundary conditions can be defined as
where, Tfis temperature of the cooling liquid; his the convective heat transfer coefficient. Here, the three planes, i.e.x=0,y=0,z=0 are defined as adiabatic boundaries, and the other three planes, i.e. x=l, y=s1, z=s2, are subjected to forced convective cooling conditions. Without losing any generality, the initial temperature is defined as
T(x,y,z,0)=T0(x,y,z)E109
\n\t\t\t\t
Using transformationθ=T−Tf, Equations (107), (109) and (110) can be rewritten as
where,N(βj)=l[βj2+(h/k)2]+h/k2[βj2+(h/k)2] ,βj are positive roots of the equationβjtan(βjl)=h/k;N(γn)=s1[γn2+(h/k)2]+h/k2[γn2+(h/k)2], γnare positive roots of equationγntan(γns1)=h/k; andN(μk)=s2[μk2+(h/k)2]+h/k2[μk2+(h/k)2], μkare positive roots ofμktan(μks2)=h/k.
Finally, according to the above results and expression for the heat source term in Equation (108), the analytical solution to the temperature field under totally convective cooling conditions can then be obtained as:
From the first term containing time in the above analytical solution, it can be seen that the thermal diffusivity α appears in the exponential term, i.e.exp[−α(βj2+γn2+μk2)(t−τ), which indicates that the time for the temperature to reach the thermal equilibrium state depends exponentially on the thermal diffusivity.
Case (b): Fixed temperature cooling at all boundaries
Clinically, direct cooling the tissues through contacting it to copper plate pre-cooled by liquid nitrogen has been proved to be more effective than cooling by convection [28]. Therefore, it is very essential to get the temperature field of the tissue under totally fixed temperature cooling boundary conditions. For this problem, the form of the control equations still remain the same, so did the solution procedures of the Green’s function method, since only the boundary conditions were slightly changed. Assuming that Tp is the temperature of the cooling plate, using transformationθ=T−Tp, and solving the auxiliary problem via the similar way as that used in the convective cooling case, one can obtain the Green’s function solution for the present case as:
In practice, demanded by certain specific cooling rate and mechanical factors, sometimes one has to apply different cooling strategies on each side of the tissue surfaces. Thus, it is essential to take into account the complex hybrid boundary conditions. For brief, we assume that the temperature of the cooling plate Tp is equal to that of the cooling fluidTf. Then the Green’s function solutions for the following two boundaries can be obtained accordingly.
Case (c): Fixed temperature cooling at upside and underside surface and convective cooling at side faces
Considering that expressions for the transient temperature field for the above two cases still remain similar to that of Equation (118), they have not been rewritten here for brief.
It should be pointed that there still exist many difficulties to calculate the exact temperature field from the above analytical solutions. However, the solution forms can still be flexibly applied to analyze certain special problems. As indicated in [28], in the freezing or warming process there must exist a maximum cooling or warming rate at some places of the tissue, which is varying with the time. Theoretically, this transient position can be predicted by using Equations (118) and (120) or other equations for corresponding processes. For example, one can obtain ∂∂xi[∂T(x,y,z,t)∂t] (where xi may represent x, y, z direction) easily by utilizing any of the above Green’s function solutions. After making it equal to zero, one can solve for xi which is just the position where the maximum cooling or rewarming rate occurs. Knowing such position is of importance for the operation of a successful cryopreservation procedure, since the maximum cooling or warming rate is the crucial factor to cause injury to biological materials. Here, computation of ∂T(x,y,z,t)/∂t can eliminate the time integral term in Equations (118) and (120), which would simplify the solution form. Overall, the present analytical method in virtue of its straightforward form is of great significance to evaluate the phase change problem in cryobiology.
4.2. Analytical solutions to 3-D phase change problems during cryosurgery
Cryosurgery is very different from cryopreservation, since living tissue has to be considered. Consequently, it must take into account the effects of blood perfusion and metabolic heat generation into bioheat equation. Here, the Pennes equation is applied to characterize the heat transfer process in the living tissue. To avoid the complex boundary conditions, the calculation tissue domain is chosen as a whole cuboid as shown in Fig. 6, where a cryoprobe with length l1 was settled in the center. Then the location of the cryoprobe is atx0=l/2,y0=s1/2 , and the range for z coordinate is0.5(s2−l1)≤z≤0.5(s2+l1).
Figure 6.
Schematic of the living tissue domain subject to cryosurgery
The energy equations for the tissue before and after it was frozen are respectively as:
where, ρb, cbare density, specific heat of blood; ωbis blood perfusion; Tais supplying arterial temperature; Qmis volumetric metabolic heat; Qcis heat sink, and the other parameters represent the same meanings as before.
The control equations in the solid-liquid interface are the same as before. The above phase change problem can then be equivalently transformed to a heat conduction problem, i.e.
ρc∂T∂t=k∇2T+QrE125
\n\t\t\t\t
where, Qris the moving heat source term, which consists of three parts in a cryosurgical process: the metabolic heat source termQm, the heat sink term Qcand the phase change heat source termqr, i.e.
where, qm\'=wbcbρb(Ta−T)+qm, reflecting contributions of the blood heat transfer and the metabolic heat generation in unfrozen region. Other parameters and functions have the same definitions with those of cryopreservation.
To simplify the problem, the cryoprobe inserted into the deep tissue is treated as a linear heat sink [29] and assumed to supply a constant cold amountQc, i.e.Qc=B, which can also include many different discrete terms representing cooling effects from multiple cryoprobes and expressed as Qc=∑Qiδ(x−xi,y−yi,z−zi) (which reflects the amount of cold at the location of(xi,yi,zi), where i is the sequence number of the cryoprobe; Qiis the amount of cold released by the ith probe; δis the Delta function). The solution expressed below indicated that the Green’s function method can deal with the multi-probe freezing problem. This is however not easy to be dealt with even by numerical computation. In this side, the analytical solution embodies its particular theoretical significance.
where,g(z,t)=[(ωbcbρbTa+qm)H(z−z0)+qr+Qc]/ρc. In a cryosurgical procedure, the cryoprobe is inserted into the target tissue, which will subject to a specific temperature drop due to cooling of heat sink effect of the probe. Since the skin surface is exposed to the ambient environment, the boundary conditions can thus be treated as:
As shown in Fig. 6, the origin (x=0, y=0, z=0) is defined differently from the cryopreservation. The adiabatic boundaries are applied on the side surfaces along the x, y axis. This is because the side surfaces are far from the heat sink and are not influenced by the heat sink in the deep tissue and the external convective conditions. Finally, the initial condition is defined asT(x,y,z,0)=T0(x,y,z), and every boundary is defined as adiabatic at this initial state. Although the control equations and boundary conditions in cryosurgery are very different from that in cryopreservation, the solution procedures still remain similar if certain transformations are introduced. In the following, only those steps different from the above will be addressed. To make solution of the problem feasible, we have adopted before the following specific transformation:
where, g1(z,t)={[ωbcbρb(Ta−T0)+qm]H(z−z0)+qr+Qc}/ρc. To simplify the equation, the volumetric moving heat source term in Equation (130) can be expressed as
where, f(t)=(Tf−T0)exp[ωbρbcbρcH(z−z0)t]H(t), and the initial condition is defined asW(x,y,z,t)=0. Applying the same theoretical strategies as used in solving cryopreservation into the above problem, one can obtain the Green’s function expression for a 3-D cryosurgical process as:
where,R1={1,j=02,j=1,2,3,⋅⋅⋅;R2={1,n=02,n=1,2,3,⋅⋅⋅;βj=jπl;γn=nπs1; μkis the positive root ofμktan(μks2)=h/k. Taking into account of the moving source term qr* and the boundary conditions, i.e. Equation (132), one can obtain the transient temperature field of the tissue corresponding to Equation (130) as:
The above procedures illustrate the basic strategy to exactly solve the three dimensional phase change problem of biological tissues in vivo, which involves the blood perfusion and metabolic heat etc. However, the integral equation is so complex due to moving phase change front inherited in the integral term, that calculating the equation based on the above analytical expressions is still a challenge. This requests certain development of the applied mathematics. However, a simplified form for the present solution can be utilized to analyze some specific one dimensional heat transfer problems.
4.3. Analytical solutions to 3-D temperature distribution in tissues embedded with large blood vessels during cryosurgery
From the viewpoint of heat transfer, a large blood vessel (also termed a thermally significant vessel) denotes a vessel larger than 0.5 mm in diameter [30]. Anatomically, tumors are often situated close to or embedded with large blood vessels, since a tumor’s quick growth ultimately depends on nutrients supplied by its blood vessel network. During cryosurgery, the blood flow inside a large vessel represents a source which heats the nearby frozen tissues and, thereby, limits freezing lesions during cryosurgery. Under this condition, a part of the vital tumor cells may remain in the cryolesion and lead to recurrence of tumors after cryosurgical treatment. More specifically, tumor cell survival in the vicinity of large blood vessels is often correlated with tumor recurrence after treatment [30]. Consequently, it is difficult to implement an effective cryosurgery when a tumor is contiguous to a large blood vessel. To better understand the effect of blood flow to the temperature distribution of living tissues subject to freezing, a conceptual model for characterizing the heat transfer in 3-D cylindrical tissues embedded with a single blood vessel was illustrated in this section. And a closed form analytical solution to this model was provided to explore different factors’ thermal influences to the freezing mechanism of living tissues.
The geometry used for the analysis is depicted as Fig. 7, which is consisted of three distinct concentric cylinders: the most interior region representing a large blood vessel, the intermediate for unfrozen liquid-phase tissue and the outer the frozen tissue. In Fig. 7, symmetrical condition in θ direction can be applied. Then the 3-D bioheat transfer will degenerate to a 2-D problem. The cylinder is long enough so that its end effects to the heat transfer can be neglected. For simplicity in analytical solution, only steady state temperature field was assumed in both the vessel and the surrounding tissue. And the same constant thermal properties for different tissue area were considered. Therefore heat conduction in the regions of both liquid and frozen tissue can be described by only a single equation.
Figure 7.
Schematic of cylindrical tissues embedded with a single blood vessel [30]
To carry out the theoretical analysis, additional assumptions for tissues were made as follows: there is no heat flow across the boundaries at z=0 and z=L. In reality, if the vessel length is long enough, these adiabatic boundary conditions can be closely satisfied. And the cylindrical tissue surface (r=b) was assumed to be immersed into a cooling medium or subjected to a circular cryoprobe with constant freezing temperature T0. This situation also reflects the cooling of an interior tissue inside the biological body, which is frozen on the surface. After introducing the dimensionless parameterTt*=(Tt−T0)/(Ta−T0), where Ta stands for the body core temperature usually fixed at 37°C, the temperature distribution for the cylindrical tissue area can be described by the following equations [30]
where, f(z) is temperature along the vessel wall, which is unknown here and needs to be calculated later using temperature continuity condition on the vessel wall.
Blood flow velocity profile in the vessel can be obtained as u=u0(1−r2a2) by solving the Navier-Stokes equation for an incompressible steady-state fluid, where a stands for the radius of blood vessel, and u0 the average blood flow velocity over the vessel cross-section. Both temperature and heat flux on the vessel wall (r=a) satisfy continuity conditions. Defining the following dimensionless parameters:Tb*=Tb−T0Ta−T0,Tc*=Tc−T0Ta−T0 , the governing equations for the vessel read as
where, Tc*is the vessel bulk temperature within cylindrical symmetry, and defined as
Tc*=4a2∫0aTb*(1−r2a2)rdrE137
\n\t\t\t\t
In the above equations, subscript t, b designate tissue and blood respectively, * means the dimensionless parameters. The variable separation method was applied to solve the Equation (135), whose solution can be rewritten as product of an axial term Z(z) and a radial one R0(r), i.e.Tt*(r,z)=R0(r)Z(z), with Z(z) satisfying
Exact calculation on C6(z) from Equation (155) appears extremely difficult. However, if treating C6 as a constant, and substituting Equation (156) and Equation (157) into Equation (155), then integrating it from z = 0 to L, one has
C¯6=a2[KbKtln(ab)−34]E158
\n\t\t\t\t
This constant C¯6 does not exactly satisfy Equation (155) indeed. However it is a simple and intuitive approximation. According to former calculation [30], it can be found that heat fluxes calculated using temperature from both sides of the blood vessel wall were almost identical. Therefore, an approximate estimation on C6 can also be made as follows. In Equation (155), if the axial position z is fixed, C6can be obtained through numerical iteration. And the relative error [C6(z)−C¯6]/C¯6was very small along the axial direction. For the case of aorta, the largest error is less than 1%, while for the case of terminal branches, it is 6%. Overall, the smaller vessel diameter, the larger error in the estimated valueC¯6. However, since the present discussion was mainly focused on the case of large blood vessel (for tissues with extremely small vessel, a collective model such as Pennes equation is often applicable), therefore treating C6(z) as a constant was an acceptable approximation. But for more complex situations where the above approximation cannot hold, exactly solving the Equation (158) for C6(z) is still very necessary in the future.
When analyzing the thermal effect of large blood vessel in cryosurgery, an important issue is when the blood vessel begins to freeze and how to control cryoprobe’s temperature to completely freeze the target tumor. Substituting Equation (158) into Equation (153), one can set up the relation for the blood vessel temperature Tb(a,L) to reach the phase change point Tm, i.e.
Up to now, the above analysis was based on a steady state assumption. And only a single blood vessel was considered for the sake of analytical solution, although it does provide certain important information for understanding the phase change heat transfer in living tissues with blood vessel. Clinically, knowledge on the transient temperature response is still very necessary for the successful operation of a cryosurgery. However, such non-steady state problem cannot be dealt with by the present method. This needs further efforts in the near future using numerical approach.
5. Conclusion
This chapter has presented an overview on several typical closed form analytical solutions to 3-D bioheat transfer problems with or without phase change as developed before in the authors’ laboratory. In these solutions, relatively complex boundary conditions and heating/cooling on skin surface or inside biological bodies were addressed. In addition, the theoretical strategies towards analytically solving the complex 3-D bioheat transfer problems were outlined by the mathematical transformation, the Green’s function method, and the moving heat source model etc.
The analytical solutions introduced in this chapter can be used to predicate the evolution of temperature distribution inside the target tissues during tumor hyperthermia, cryosurgery, cryopreservation, thermal diagnostics, thermal comfort analysis, brain hypothermia resuscitation, and burn injury evaluation. Through fitting the predicted with the experimentally measured temperatures at the skin surface, some thermal parameters of biological tissues such as blood perfusion, thermal conductivity, and heat capacity, can be estimated non-invasively. Moreover, based on the requirements for freezing/heating necrosis temperature of tissue, an approach to optimize the parameters of cryosurgical/hyperthermic treatment can be obtained using the presented analytical solutions. Therefore, the presented analytical solutions are very useful for a variety of thermal-oriented biomedical studies. However, it should be pointed out that although such analytical solutions have some versatility in dealing many bioheat transfer problems, numerical approaches are still needed for more complex situations. In fact, the relation between analytical and numerical solutions should be complementary. On one hand, numerical approach can deal with more complex problem than analytical way. On the other hand, the analytical results can serve as benchmark solutions for numerical analyses on complex situations. In summary, it is believed that even the applications with some simplified conditions do not affect the applicability of the present analytical solutions.
Nomenclature
Acknowledgement
Part of the researches as presented in this chapter has been supported by the National Natural Science Foundation of China under grants Grant Nos. 51076161 and 81071255, the Specialized Research Fund for the Doctoral Program of Higher Education, and Research Fund from Tsinghua University under Grant No. 523003001.
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Introduction",level:"1"},{id:"sec_2",title:"2. Motivations of analytical solutions to bioheat transfer problem",level:"1"},{id:"sec_3",title:"3. Bioheat transfer problems without phase change",level:"1"},{id:"sec_3_2",title:"3.1. Generalized analytical solutions to 3-D bioheat transfer problems",level:"2"},{id:"sec_4_2",title:"3.2. Analytical solutions to 3-D bioheat transfer involved in hyperthermia for prostate",level:"2"},{id:"sec_5_2",title:"3.3. Analytical solutions to bioheat transfer with temperature fluctuation",level:"2"},{id:"sec_7",title:"4. Bioheat transfer problems with phase change",level:"1"},{id:"sec_7_2",title:"4.1. Analytical solutions to 3-D phase change problems during cryopreservation",level:"2"},{id:"sec_8_2",title:"4.2. Analytical solutions to 3-D phase change problems during cryosurgery",level:"2"},{id:"sec_9_2",title:"4.3. Analytical solutions to 3-D temperature distribution in tissues embedded with large blood vessels during cryosurgery",level:"2"},{id:"sec_11",title:"5. Conclusion",level:"1"},{id:"sec_11_2",title:"Nomenclature",level:"2"},{id:"sec_12_2",title:"Acknowledgement",level:"2"}],chapterReferences:[{id:"B1",body:'LiuJ.WangC. C.1997Bioheat Transfer (in Chinese). Beijing: Science Press. 435 p.'},{id:"B2",body:'LiuJ.DengZ. S.2008Physics of Tumor Hyperthermia (in Chinese). Beijing: Science Press. 381 p.'},{id:"B3",body:'LiuJ.ChenX.XuL. X.1999New Thermal Wave Aspects on Burn Evaluation of Skin Subjected to Instantaneous Heating. IEEE Transactions on Biomedical Engineering 46420428'},{id:"B4",body:'LvY. G.LiuJ.ZhangJ.2006Theoretical Evaluation on Burn Injury of Human Respiratory Tract due to Inhalation of Hot Gas at the Early Stage of Fires. Burns 32436446'},{id:"B5",body:'LiuJ.2007Cooling Strategies and Transport Theories for Brain Hypothermia Resuscitation. 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S.LiuJ.2002Analytical Study on Bioheat Transfer Problems with Spatial or Transient Heating on Skin Surface or Inside Biological Bodies. ASME Journal of Biomechanical Engineering. 124638649'},{id:"B14",body:'Pennes HH1948Analysis of Tissue and Arterial Blood Temperatures in the Resting Human Forearm. Journal of Applied Physiology. 193122'},{id:"B15",body:'LiuJ.2006Bioheat Transfer Model. In: Akay M, editor. Wiley Encyclopedia of Biomedical Engineering. John Wiley & Sons. 429438'},{id:"B16",body:'WulffW.1974The Energy Conservation Equation for Living Tissues. IEEE Transactions on Biomedical Engineering. 21494497'},{id:"B17",body:'Chen MM, Holmes KR1980Microvascular Contributions in Tissue Heat Transfer. Annals of the New York Academy of Sciences. 335137150'},{id:"B18",body:'WeinbaumS.JijiL. M.1985A New Simplified Bioheat Equation for the Effect of Blood Flow on Local Average Tissue Temperature. ASME Journal of Biomechanical Engineering. 107131139'},{id:"B19",body:'LiuJ.DengZ. S.2009Numerical Methods for Solving Bioheat Transfer Equations in Complex Situations. In: Minkowycz WJ, Sparrow EM, Abraham JP, editors. Advances in Numerical Heat Transfer (3Taylor & Francis. 75120'},{id:"B20",body:'DengZ. S.LiuJ.2002Monte Carlo Method to Solve Multi-Dimensional Bioheat Transfer Problem. Numerical Heat Transfer, Part B: Fundamentals. 42543567'},{id:"B21",body:'VyasR.RustgiM. L.1992Green’s Function Solution to the Tissue Bioheat Equation. Medical Physics. 1913191324'},{id:"B22",body:'GaoB.LangerS.CorryP. M.1995Application of the Time-Dependent Green’s Function and Fourier Transforms to the Solution of the Bioheat Equation. International Journal of Hyperthermia. 11267285'},{id:"B23",body:'Durkee JW, Antich PP, Lee CE1990Exact-Solutions to the Multiregion Time-Dependent Bioheat Equation- Solution Development. Physics in Medicine and Biology. 35847867'},{id:"B24",body:'Durkee JW, Antich PP1991Exact-Solution to the Multiregion Time-Dependent Bioheat Equation with Transient Heat-Sources and Boundary-Conditions. Physics in Medicine and Biology. 36345368'},{id:"B25",body:'Kou HS, Shih TC, Lin WL2003Effect of the Directional Blood Flow on Thermal Dose Distribution during Thermal Therapy: An Application of a Green’s Function Based on the Porous Model. Physics in Medicine and Biology. 4815771589'},{id:"B26",body:'LiuJ.ZhuL.XuL. X.2000Studies on the Three-Dimensional Temperature Transients in the Canine Prostate during Transurethral Microwave Thermal Therapy. ASME Journal of Biomechanical Engineering. 122372379'},{id:"B27",body:'DengZ. S.LiuJ.2001Blood Perfusion Based Model for Characterizing the Temperature Fluctuation in Living Tissues. Physica A: Statistical Mechanics and Its Applications. 300521530'},{id:"B28",body:'LiuJ.ZhouY. X.2002Analytical Study on the Freezing and Thawing Processes of Biological Skin with Finite Thickness. Heat and Mass Transfer. 38319326'},{id:"B29",body:'LiF. F.LiuJ.YueK.2009Exact Analytical Solution to Three-Dimensional Phase Change Heat Transfer Problems in Biological Tissues Subject to Freezing. Applied Mathematics and Mechanics. 306372'},{id:"B30",body:'ZhangY. T.LiuJ.ZhouY. X.2002Pilot Study on Cryogenic Heat Transfer in Biological Tissues Embedded with Large Blood Vessels. Forschung im Ingenieurwesen- Engineering Research. 67188197\n\t\t\t'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Zhong-Shan Deng",address:null,affiliation:'
Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing, China
Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing, China
Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
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1. Introduction
The recent pandemic of the highly contagious coronavirus disease 2019 (COVID-19) caused by a novel severe acute respiratory syndrome coronavirus (SARS-CoV-2) has developed devastating consequences on human health, economy, and ecosystem services as an important public health concern [1]. Until 13 January 2022, over 307,373,791 cases have been reported, including over 5,492,154 deaths [2]. SARS-CoV-2 is a beta coronavirus that belongs to the family Coronaviridae and the order Nidovirales [3]. Coronaviruses (CoVs), and the newly discovered SARS-CoV-2, are spherical or pleomorphic enveloped viruses with a diameter of 100–160 nm [4] characterized by spike proteins projecting to the virion surface [5]. The primary structure comprises four structural proteins: spike (S), envelope (E), membrane (M), and nucleocapsid (N) encoded at the 3′ end of the viral genome [5, 6]. SARS-CoV-2 has a single-stranded genomic RNA (gRNA) positive-sense, approximately 30 kb [7]. This gRNA is among the largest RNA genomes known with a 5′-cap structure and a 3′-poly (A) tail acting like an mRNA for the immediate translation of viral polyproteins [8]. The 5′ and 3′ ends of the gRNA contain highly structured untranslated regions (UTR) that regulate RNA replication and transcription. There is one stem-loop and one pseudo-knot present in the 3′-UTR region mutually exclusive, since their sequences overlap, while seven stem-loop structures are included in the 5′-UTR region. The SARS-CoV-2 genome contains 14 open reading frames (ORFs), preceded by transcriptional regulatory sequences (TRS). The two main transcriptional units, ORF1a and ORF1ab, encode polyprotein replicase 1a (PP1a) and polyprotein 1ab (PP1ab), respectively [9]. These polyproteins are co- and post-translationally processed into 16 nonstructural proteins (NSPS), most of which drive viral genome replication and sub-genomic mRNA (sgmRNA) synthesis [8]. Usually, the transmission of SARS-CoV-2 was accounted occurs through inhalation of respiratory droplets diffused by coughing or sneezing from an infected patient, and through direct contact with contaminated surfaces or objects [10]. This respiratory syndrome leads to developing several chronic or acute disorders in patients such as fever, cough, fatigue, anosmia and ageusia, dyspnea, chest pain, muscle pain, chills, sore throat, rhinitis, headache [11], and gastrointestinal symptoms (nausea, vomiting, diarrhea) [11]. SARS-CoV-2 genomic RNA has been detected in patient stool and urine samples [12], suggesting the possibility that the virus may be transmitted via the fecal-oral route, besides droplet and fomite transmission. Therefore, the mode of transmission of SARS-CoV-2 becomes crucial paramount for human and environmental health. Recently, the world faces a large wave of COVID-19 infections caused by the highly contagious variant of SARS-CoV-2 Omicron can infect people even if they are vaccinated. The record number of people catching the COVID-19 from the beginning of the pandemic has left health systems under severe strain especially in developed countries. However, It has proven the particular importance of WBE (wastewater-based epidemiology) in monitoring the circulation and the transmission of the epidemic in the community, it could provide an early warning sign that reflects possible disease outbreaks in a community [13, 14] and an effective tool for epidemiological surveillance of SARS-CoV-2 viral diversity in samples and to anticipate the detection of certain mutations before they are detected in clinical samples. This communication will provide a basis for understanding SARS-CoV-2 and other viruses from the environmental perspective to design alternative strategies to counteract enteric virus transmission and to reduce the severity of the pandemic [15]. In this review, we present the different human and zoonotic coronaviruses, the sources of contamination of wastewater by coronaviruses, and their potential procedures of disinfection and eradication from wastewater.
2. Human coronaviruses
Coronaviruses (CoV) are enveloped viruses with a single positive-strand RNA genome (∼26–32 kb). They belong to the subfamily Ortho-Coronaviridae of the family Coronaviridae and are classified into four genera: Alpha coronavirus (α), Beta coronavirus (β), Gamma-coronavirus (γ) and Delta-coronavirus (δ) [16].
Until the present time, seven human coronaviruses (HCoVs) can be transmitted between humans. Human alpha coronaviruses, 229E and NL63, and beta-coronaviruses, OC43, and HKU1 are common respiratory viruses usually causing mild upper respiratory illness. Unlike these, the three other human beta-coronaviruses, severe acute respiratory syndrome coronavirus (SARS), Middle East respiratory syndrome coronavirus (MERS), and SARS-CoV-2, are highly pathogenic in humans [17]. All seven HCoVs are the product of a Spillover, they have a zoonotic origin from bats, mice, or domestic animals. Multiple justifications support an evolutionary origin of all HCoVs from bats, where viruses are non-pathogenic and well adapted, so they show great genetic diversity. Genome analysis of the virus identified a high sequence similarity with Chinese bat coronaviruses (highest homology to bat coronavirus RaTG13). Beta coronavirus phylogenetic tree showing that SARS-CoV-2 is related to bat coronaviruses ZC45 and ZXC21. SARS-CoV-2 showed 99% sequence homology with pangolin CoV according to the findings of a research team from the South China University of Agriculture [18].
2.1 HCoV-229E and HCoV-OC43
HCoV-229E is the first strain isolated from patients with upper respiratory tract contamination in the year 1966 [19]. Patients infected with HCoV-229E showed cold symptoms, including headache, sneezing, malaise, and sore throat, with fever and cough in 10–20% of cases [20]. Later, in 1967, HCoV-OC43 was disengaged from organ culture, resulting in a sequential entry in the cerebrum of nursing mice. The clinical features of HCoV-OC43 infection give off an impression of resembling those caused by HCoV-229E, which are indistinguishable from diseases with other respiratory tract pathogens such as influenza A viruses and rhinoviruses [21]. Both HCoV-229E and HCoV-OC43 circulate globally, and they are prevalently diffused during the cold period in a moderate climate [22]. Developing these two viruses is less than one week, straggled by around a 2-week disease [21]. According to a human volunteer study, healthy individuals infested with HCoV-229E developed a slight common cold [23].
2.2 SARS-CoV
The first case of SARS-CoV-1 was discovered in late 2002 in Guangdong Province of China. SARS is an infectious disease caused by a virus belonging to the coronavirus family, SARS-CoV-1. The SARS epidemic has expanded across many countries and continents and caused about 8096 reported cases with 774 deaths. The incubation period of SARS-CoV-1 was 4 to 7 days and the peak of viral load was estimated on the 10th day of illness.
Patients infected with SARS-CoV-1 showed initial symptoms of myalgia, headache, fever, malaise, and chills, followed by dyspnea, cough, and respiratory distress as late symptoms of lymphopenia. However, deranged liver function tests and elevated creatine kinase are common laboratory abnormalities of SARS [24, 25]. The insectivorous bat has been identified as an animal reservoir of the SARS coronavirus. The intermediate host that allowed the virus transmission to humans is the masked palm civet, a wild animal sold in markets and eaten in southern China [26].
2.3 HCoV-NL63 et HCoV-HKU1
In late 2004, HCoV-NL63 was isolated from a 7-month-old child in the Netherlands. It was initially prevalent in young children, the elderly, and immunocompromised patients with respiratory illnesses [27]. The common symptoms of the disease caused by HCoV-NL63 are coryza, conjunctivitis, fever, and bronchiolitis [28]. It is distributed globally and it has been estimated that HCoV-NL63 accounts for nearly 4.7% of common respiratory diseases, and its peak incidence occurs during early summer, spring, and winter [22].
In the same year, HCoV-HKU1 was isolated in Hong Kong from a 71-year-old man hospitalized with pneumonia and bronchiolitis [29]. HCoV-HKU1 was reported to be associated with acute asthmatic exacerbation besides community-acquired pneumonia and bronchiolitis [30]. Alike to HCoV-NL63, HCoV-229E, and HCoV-OC43, HCoV-HKU1 was found worldwide, producing mild respiratory diseases [30].
These four community-acquired HCoVs have been well accustomed to humans and are less probable to mutate to produce exceptionally pathogenic diseases, however, accidents can occur for unclear details as in the uncommon case of subtype HCoV-NL63, which is more virulent and has recently been reported to cause severe lower respiratory tract infection in China [31].
As it has been shown for HCoV-NL63, HCoV-229E, and HCoV-OC43, HCoV-HKU1 has a worldwide distribution and causes mild respiratory diseases [30]. However, the subtype HCoV-NL63, which was found to be more virulent, caused recently severe respiratory tract infection in China [31].
2.4 MERS-CoV
In 2012, a new respiratory virus called MERS-CoV for Middle East Respiratory Syndrome coronavirus was detected in the lung of a 60-year-old patient who developed acute pneumonia and renal failure in Saudi Arabia [32, 33].
The virus was then reported in several countries in the Middle East. Since then, 1219 cases have been diagnosed, resulting in 449 deaths. Few cases have been detected in Europe, including 2 cases in France and 3 cases in Tunisia in 2013 [34]. Later in 2015, 186 confirmed cases were reported in South Korea. Compared to SARS, MERS is a similar disease with a progressive acute pneumonia. However, unlike SARS, many patients with MERS also developed acute renal failure [32, 33]. Over 30% of patients also showed gastrointestinal symptoms, such as diarrhea and vomiting [32, 33].
As of February 2020, over 2500 confirmed cases were accounted for with an intense case fatality of 34.4%, making MERS-CoV one of the most pathogenic viruses known to humans [35].
2.5 SARS-CoV-2
SARS-CoV-2 was first reported in a group of pneumonia patients of unknown etiology who witnessed their visit to Huanan Seafood Wholesale Market in December 2019 in Wuhan, Hubei Province, China [36, 37]. At the beginning of the 2020s, the 2019 coronavirus disease emerged around the world and become a pandemic, which disrupts human activity through general confinements and strict sanitary measures. The incubation of SARS-CoV-2 lasts from 2 to 14 days [38]. The most frequent signs in patients were fever, cough, fatigue, anosmia, ageusia, muscle pain, chills, sore throat, rhinitis, and headache head [11]. Additionally, gastrointestinal symptoms have been reported, diarrhea, stomach pain, vomiting, nausea, and poor appetite [11]. The nucleotide sequence of SARS-CoV-2 revealed about 51.8 and 79.0% of similarity with MERS-CoV and SARS-CoV-1, respectively, and is closely related to SARS-like coronavirus of bald origin—mouse (bat-SL-CoVZC45) with 87.6–89% identity [16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39]. Recent studies strongly support the hypothesis that SARS-CoV-2 may have originated in bats and may have undergone host jumping to another intermediate mammal, including pangolins (Manis javanica) [10, 40]. Phylogenetic analyzes of the SARS-CoV-2 genome revealed this virus bind with the same human cellular receptor (ACE2: Angiotensin-Converting Enzyme 2) as same as SARS-CoV-1 to get into host cells with 10 times affinity higher than SARS-CoV-1, while MERS-CoV uses another (DPP4) [41, 42] … Genetic variability of SARS-CoV-2 can be the consequence of nucleotide incorporation errors by viral RNA polymerase, genomic editing by cellular restriction factors, or even homologous recombination. The expansion of SARS-CoV-2 variants was observed in fall 2020 [43]. The evolutionary mutation rate of SARS-CoV-2 is estimated at 1.103 nucleotide substitutions per site per year [44] equivalent to approximately one substitution every two weeks in the genome [45]. Currently, the WHO considered five variants as “worrying,” which were first detected in England, South Africa, and then later in Brazil (two variants were observed there, including P1 classified as worrying). In October 2020, a fourth variant (Delta) appeared in India received particular attention. This country of 1.3 billion people has seen an explosion of cases is resisted by other nations. At the end of November 2021, it was the Omicron variant, detected in South Africa, which caused the recent wave and concern all over the world.
Besides other variants called VOI (“variant under investigation” or “variant of interest” in English) has been detected in multiple countries and identified with mutations that lead to amino acid changes associated with phenotypic changes (confirmed or suspected) responsible for community transmission or multiple confirmed cases or clusters (Table 1) [46].
And diverse variants called under evaluation, or VUM (“variant under monitoring”) exhibit genetic changes suspected of affecting the characteristics of the virus, indicating that it may pose a future risk without evidence of phenotypic or epidemiological repercussions being clear at this time, and which should be investigated repeated evaluation and enhanced surveillance pending confirmation of new evidence (Table 2) [46].
Lines PANGO*
Clade GISAID
Clade next strain
First samples listed
Date of designation
B.1.427 B.1.429
GH/452R. V1
21C
U.S.A., Mar 2020
VOI: 5 Mar, 2 July 2021 VUM: 6 Apr 2021
R.1
GR
—
Many countries, Jan 2021
07 Apr 2021
B.1.466.2
GH
—
Indonesia, Nov 2020
28 Apr 2021
B.1.1.318
GR
20B
Many countries, Jan 2021
02 June 2021
B.1.1.519
GR
20. B/S.732 A
Many countries, Jan 2020
02 June 2021
C.36.3
GR
—
Many countries, Jan 2021
16 June 2021
B.1.214.2
G
—
Many countries, Nov 2020
30 June 2021
B.1.1.523
GR
—
Many countries, May 2020
14 July 2021
B.1.619
G
20 A/S.126 A
Many countries, May 2020
14 July 2021
B.1.620
G
—
Many countries, Nov 2020
14 July 2021
C.1.2
GR
—
South Africa, May 2021
01 Sept 2021
B.1.617.1§
G/452R. V3
21B
India, Oct 2020
VOI: 4 Apr 2021 VUM: 20 Sept 2021
B.1.526
GH/253G. V1
21F
U.S.A., Nov 2020
VOI: 24 Mar 2021 VUM: 20 Sept 2021
B.1.525
G/484 K. V3
21D
Many countries, Dec 2020
VOI: 17 Mar 2021 VUM: 20 Sept 2021
Table 2.
Variants under intensive care or VUM of SARS-CoV-2.
3. Animal coronavirus diseases
3.1 Pet coronavirus (dogs and cats)
Canine coronaviruses: Canine enteric coronavirus (CCoV) typically infects dogs, especially those housed in large groups such as kennels, shelters, and breeding facilities. CCoV belongs to the family Coronaviridae, order Nidovirales, and was first isolated in 1971 during an outbreak of gastroenteritis in military dogs and then perceived as a pathogen of dogs [47]. Dogs attracted with CCoV developed self-limiting enteritis with mild diarrheal disease. Two types of canine coronaviruses are known: CCoV is a member of the Alpha-coronavirus genus [48], and canine respiratory coronavirus (CRCoV) belonging to the beta coronavirus genus [49]. CCoV is closely associated with infectious gastroenteritis virus (TGEV) of pigs, ferret coronavirus, and feline coronavirus (FCoV) [48], while CRCoV is more related to bovine coronavirus [50]. All enteric CCoVs (along with the related viruses of cats, pigs, and ferrets) are given the similar strain designation (Alpha-coronavirus-1) from a taxonomic perspective. However, there are two distinct serotypes of CCoV: type I and type II [51, 52].
Cat coronaviruses: Two alpha coronaviruses are known for cats: feline enteric coronavirus or feline enteric peritonitis (FECV) associated with mild or asymptomatic diarrhea and can mutate to cause more serious feline infectious peritonitis (FIP) or feline infectious peritonitis (FIPV). Subclinical carriers of FECV handle the shedding and transmission of the virus to different felines through the fecal-oral route. Experiences realized on young, ancient, or immunocompromised subjects have revealed two clinical forms of FIP, a wet form, and a dry form. The wet form developed ascites which can be clinically apparent (abdominal distention) and confirmed by ultrasound. The dry form is associated with granulomatous lesions of various locations, basically affecting the eye, the central nervous system, the liver, and kidneys. In Addition, uveitis with keratin deposits on the level of the cornea was reported. The FIP is lethal and there is no particular treatment or vaccine marketed in France for this disease. Cats can also be infected with other coronaviruses such as SARS-CoV-2, transmissible porcine gastroenteritis, canine coronavirus, or human coronavirus 229E [53].
3.2 Coronaviruses of production animals
The bovine, porcine, and avian coronaviruses are mainly affecting production animals. These coronaviruses belong to alpha, beta, gamma, and/or delta-coronaviruses.
Bovine Coronavirus: Bovine coronaviruses (BCoVs) are pneumo-enteric viruses that infect the upper and lower respiratory tract of cattle and wild ruminants and it is rejected in feces and nasal secretions. In cattle, BCoV causes 3 different clinical syndromes in cattle: calf diarrhea, winter dysentery with hemorrhagic diarrhea in adults and respiratory infections in cattle of different ages and the bovine respiratory disease complex or shipping fever of feedlot cattle. Distinction between these syndromes is not yet possible as antigenic or genetic specific markers have not been recognized. To our knowledge, no BCoV vaccines to prevent respiratory BCoV diseases in cattle [54].
Swine Coronavirus: Porcine coronaviruses are members of three genera: alpha-, beta- and delta- coronavirus. Since 1946, transmissible gastroenteritis virus or TGEV in alpha coronaviruses has been discovered. It developed severe, often fatal enteritis in piglets. Symptoms of vomiting and profuse diarrhea are registered for piglets less than a week old and the mortality rate was about 100%.
Later, in 1971, porcine epidemic diarrhea or DEP (Porcine enteritis disease virus or PEDV) was first described in England and caused watery diarrhea occasionally accompanied by vomiting [55]. Since 2013, a severe pathogenic variant of DEP has affected North America and then spread throughout the world, causing serious economic losses. This disease has been included in France to the list of first-category health hazards for emerging animal species. Several variants of the TGE virus are the source of several strains of porcine respiratory coronavirus (Porcine respiratory coronavirus or PRCV), responsible for discreet respiratory disorders [53]. Among these viruses, one of them infected almost most European pig herds in 1984. Two other coronaviruses were identified in 2016 also with digestive tropism [55]: the porcine enteritis coronavirus (swine enteritis coronavirus or SeCoV), a recombinant virus containing a TGEV genome in which the gene S is replaced by that of PEDV, and the porcine acute diarrhea virus (swine acute diarrhea syndrome or SADS) [56].
3.3 Avian coronaviruses
CoVs in the chicken: Among avian coronaviruses classified as gamma and delta-coronaviruses, the first avian coronavirus (infectious bronchitis virus or IBV virus) was described in 1931 and caused many economic losses in poultry farms (laying eggs and broilers). The disease is characterized by various lesions and damages in the genital tract with a drop in the rate of laying, malformation of the eggs, and low mortality. Besides other systems may be affected such as the respiratory tract, kidneys, etc. [53].
CoV of turkeys: Aside from IBV in chickens, the main avian species in which CoV has been definitively associated with the disease are the turkey, pheasant, and guinea fowl. Turkey coronavirus (TCoV) has been known, since the 1940s, to cause of enteric disease in turkeys in the USA. This disease is reported is found worldwide [57, 58]. Turkeys of all ages can be infected with high mortality in young poults. Most frequently reported clinical signs include decreased feed and water intake, wet droppings, diarrhea, and loss of body weight. TCoV is likewise associated with poultry enteritis and mortality syndrome (PEMS) which means high mortality, growth retardation, and immune dysfunction. Inbreeding turkeys, aberrant egg-laying performance is related to TCoV disease, similar to that seen in IBV infections of chickens [57, 58].
CoVs of pheasants (PhCoV): In pheasants, respiratory and renal problems have been associated with infections with CoV. PhCoV is closely related closely related to IBV and TCoV [59].
CoVs of guinea fowl (GfCoV): GfCoV causes acute enteritis, showing a high death rate, possibly pancreatic degeneration, and fulminating disease in guinea fowl [60]. Genetically, GfCoV shows similarity to both IBV and TCoV, however, differences were observed in the spike gene, and a common ancestor has been suggested for the three viruses [60].
3.4 SARS-CoV-2 animal infections
Under natural conditions, SARS-CoV-2 infection has been observed in owner-infected animals. This case is called Spillback when infections are gained by animals through contact with humans. Few confirmed cases of SARS-CoV-2 in pets were reported in diverse countries: France (2 cats), Spain (2 cats), Germany (1 cat), Russia (1 cat), China (2 dogs and a cat in Hong Kong), Belgium (4 cats), the United States (31 cats and 24 dogs), United Kingdom (one cat), Japan (4 dogs), Chile (one cat), Canada (one dog), Brazil (one cat), Denmark (a dog), Italy (a dog) [61]. A recent French study has shown for the first time a significant circulation of SARS-CoV-2 in a population of pets (34 cats and 13 dogs) whose owners were infected with COVID-19 [62].
Experimental conditions have revealed that pigs and poultry are resistant to every inoculation with SARS-CoV-2 [63] while rabbits (which are also pets or laboratory animals) [64], and other laboratory animals include the golden hamster (Mesocricetus auratus) and rhesus macaque (Macaca mulatta) have been susceptible to SARS-CoV-2 [65]. Contrarily, laboratory mice and rats were resistant to SARS-CoV-2 [66]. The SARS-CoV-2 could also threaten many species beyond the great apes. In January 2021, gorillas at the San Diego Zoo were tested positive for COVID-19. None of the animals died, luckily, but were suffered from high fevers, lethargy, and cough like humans [67].
On November 29, 2021, recent reports have proven that SARS-CoV-2 has been transmitted from humans to wild white-tailed deer in the United States, but conversely, no cases of transmission from deer to humans have been reported. All were “apparently in good health,” and “showed no clinical signs of the disease” [67].
In the United States, 4 tigers and 3 lions were probably infected by humans in a zoo in the Bronx. They presented mild respiratory symptoms. Since then, a tiger and a puma have been also reported infected [68]. Recent studies from the Friedrich-Loeffler Institute in Germany reported raccoon dogs (canids bred in China for their fur) previously susceptible to SARS-CoV-1 were also susceptible to SARS-CoV-2 and could contaminate other raccoon dogs by direct contact with no clinical signs. These animals can be intermediate hosts potentially involved in the emergence of COVID-19 [69]. On the other side, infected mink farms by SARS-CoV-2 were detected (2 on April 26, 33 to August 14, then 52 to September 14). Two million mink were then culled by the Dutch authorities. As of September 1, the first human cases contaminated by mink were reported [70] 66 of the 97 employees of these farms tested positive for SARS-CoV-2, with whole-genome sequencing revealing mink-like variants in 47 cases [71].
4. Detection of SARS-CoV-2 in wastewater
Wastewater-Based epidemiology (WBE) has been successfully used to investigate polio circulation within the community. This novel biomonitoring tool has been successfully used to evaluate international poliovirus vaccine campaigns and to investigate the use of some illicit drugs. Additionally, this tool has been successfully used to detect the occurrence of hepatitis and norovirus outbreaks [72, 73].
The environmental circulation of viruses as human pathogens has been given more attention since the first occurrence an spread of Severe Acute Respiratory Syndrome Coronavirus 1 appeared (SARS-CoV-1) in 2003 and Middle East Respiratory Syndrome (MERS) in 2012. Even more focus on the development of surveillance systems of viruses in the environment has been reported since the first occurrence of COVID-19 in December 2019 in Wuhan, China [73, 74].
Since most patients infected with SARS-Cov-2 might be asymptomatic, rapid and accurate detection of potential virus carriers is a critical step to suppress the risk of disease transmission at an early stage of the disease [75]. SARS-CoV-2 has been shown to replicate actively in enterocytes of the human intestine, where there is the highest expression of ACE2 in the human body and the virus is excreted in the feces [76]. SARS-CoV-2 RNA has been detected worldwide in raw wastewater and sometimes in treated wastewater, which could imply potential environmental transmission via the water cycle [77, 78, 79]. SARS-CoV-2 RNA has been reported in wastewater treatment plants in various nations around the world such as Australia, Italy, Spain, the Netherlands, the United States, Japan, Germany, the Arab Emirates States, Istanbul, and Brazil [12]. The duration of the shedding through feces can be as long as 33 days, with a decreased shedding rate, ranging from 106 to 1012 gc/L, which is lower than some other infectious viruses, like MERS-CoV, and SARS-CoV-1 [80, 81].
Detection of SARS-CoV-2 RNA in wastewater was performed by PCR-based methods such as reverse transcription-polymerase chain reaction (RT-PCR) and digital PCR using the amplification of parts of the viral genome, such as the genes coding for the nucleocapsid [82] and the viral envelope [83]. To gain insights into the fate and transport of SARS- CoV-2 in WWTFs, the general workflow for SARS-CoV-2 testing in wastewater is conducted in the following order sample collection, sample concentration, RNA extraction and analysis, and data reporting [84, 85]. Molecular detection of viral RNA involves three major steps:
Viral concentration/enrichment: A viral enrichment step is recommended before RNA extraction because of the potential low concentration of viral titer in the wastewater. Viral particles are concentrated and recovered by polyethylene glycol (PEG) precipitation, or by filtration using 0.2 μm filters [7] ultrafilters [78], and ultracentrifugation [86]. Direct RNA extraction from electronegative membranes (0.45 μm) is another method that can be used [87]. For virus concentration, a variety of techniques have been explored, including polyethylene glycol (PEG)-NaCl precipitation, ultrafiltration, AlCl3, flocculation, and others [88]. Because of its better selectivity and tolerance to PCR inhibitors in wastewater, PEG-NaCl precipitation is the most widely used technique [89].
RNA extraction: RNA extraction has typically been performed using commercial kits from a variety of supplies. The most commonly used RNA extraction kits are the RNeasy Power Microbiome kit [78, 87, 90], the BioMérieux Nuclisens kit [78], Power Fecal Pro-Kit [78], and RNeasy Power water Kit [87].
Amplification of viral RNA: Amplification of viral RNA extracted from wastewater was performed with a set of five primers/probes. These primers and probes target different regions of the viral particle in Table 3.
Primers/probes used for amplification of SARS-CoV-2 RNA in wastewater [91].
Varying results have been reported using these primer/probe sets targeting different parts of the viral genome. For example, [79] found that primer N1 resulted in positive amplification of all study sites (6), but primers N3 and E resulted in positive amplification of 5 and 4 study sites, respectively. However, Rimoldi et al. [97] found a high frequency of positive amplification targeting the ORF1ab gene, compared to only three positive wastewater samples for the N and E genes. As a result, our findings are equivocal in terms of the optimum primer/probe combination for viral RNA amplification in wastewater. This could be attributed to the sensitivity of primers/probes, PCR inhibitors in wastewater samples from different regions/sites, and the potential stability of the virus and viral genome in these different areas [98]. Droplet digital PCR is another molecular technique used for the detection of coronaviruses in clinical and sewage samples. This was found to have an improved, more sensitive, and more accurate lower limit of detection than RT-PCR for environmental samples [99, 100].
Khan et al. [101] discovered that smaller sample volumes (50–100 ml), 30% (w/v) PEG-NaCl, a 12-hour incubation interval, and a 24-hour storage period resulted in improved RNA recoveries in terms of N1 and N2. RNA concentrations were always at least one order of magnitude greater in RT-qPCR than in RT-ddPCR. However, under all test conditions, both RT-qPCR and RT-ddPCR revealed that RNA is generally absent in the sludge samples, resulting in a false-negative result.
5. Risks of environmental transmission of SARS-CoV-2 in wastewater
Fecal-oral transmission of SARS-CoV-2 is yet to be approved, but additional research is essential to clarify the potential risks of the novel coronavirus in sanitation systems. The SARS-CoV-2 virus has been detected in fecal samples and effluents. Contaminated drinking water, contaminated raw, undercooked aquatic aquaculture, sewage-irrigated food, and vector-mediated transmission are all possible sub-pathways of the fecal-oral mode of transmission. Seepage from sanitation systems (pit latrines and septic tanks), landfill leachates without geomembrane protection toward shallow groundwater systems can pollute drinking water sources. In other types of coronaviruses, one study found 99.9% percent fatality after 10 days in tap water at 23°C and over 100 days at 4°C. This data also suggests that coronaviruses have a longer survival duration in tap water than in wastewater [102].
The exposure of humans to viruses, including SARS-CoV-2 through bioaerosol and wastewater aerosols has been highlighted. For example, a laboratory study investigating the persistence of SARS-CoV-2 in aerosols showed that the virus keeps its viability and infectivity in aerosols for up to 16 h [103].
Therefore, human and animal exposure to SARS-CoV-2 via wastewater aerosols could be significant in shared sanitation systems, especially in crowded informal settlements in developing countries [104]. Various studies have registered the prevalence of SARS-CoV-2 in urban and rural sewer systems. This wastewater might contaminate fresh water; it can pass through untreated effluent discharged to surface waters or leak and affect the supply of traditionally treated graywater. These recycled urban waters also represent possible modes of transmission [104].
In some regions with a high prevalence of COVID-19 disease, SARS-CoV-2 was prevalent in surface water, including both saltwater and freshwater. Coronaviruses from anthropogenic activities were confirmed in different water bodies [102, 105]. Marine and fresh aquatic foods such as fish and crustaceans may be contaminated by raw wastewater. Marine foods from coastal areas receiving untreated wastewater, aquatic food acquired from surface aquatic systems receiving raw or partially treated wastewater, and raw wastewater-irrigated salad crops are all possible sources of food transmission.
Raw wastewater-aquacultural systems and raw wastewater irrigation of crops consumed raw, such as salads, are two more techniques that promote food contamination. However, more research is needed to determine the prevalence and durability of SARS-CoV-2 in marine and surface aquatic systems, as well as food derived from these sources. Such research should also look into the effects of various food pre-treatments and culinary processes on SARV-CoV-2 persistence. Studies based on genomic and phylogenetic analyses are needed to evaluate whether SARS-CoV-2 may leap from aquatic environments to humans. This is important given the interactions between humans and wildlife, including the widespread consumption of aquatic and terrestrial animals [106].
6. Disinfection and eradication procedures of SARS-CoV-2 in wastewater
Information from the general suppression of viruses and surrogates of coronaviruses could be used, with caution, to give additional information on the possible suppression of these viruses. For example, [107] observed that activated sludge treatment (ASP) processes in subtropical conditions removed over 3 logs 10 of enteric viruses. ASP is a commonly used wastewater treatment process around the world [108, 109, 110]. This treatment process includes primary settling, biological degradation, and secondary clarification [107, 111]. Ye et al. [112] demonstrated that during ASP processes, the highest removal of coronaviruses can occur at the primary settling stage.
For example, a sewage pond system [113] reported an average reduction of 1 log10 of viruses for 14.5 to 20.9 days of retention. Besides adsorption on particles, a longer HRT (hydraulic retention time) may be required for coronavirus inactivation in wastewater. Because coronaviruses adsorb to solid surfaces, a large concentration can be expected in the sludge. Anaerobic digestion of sludge, which is a typical sludge treatment method, reduces pathogenic bacteria. The most commonly used membrane technologies in wastewater treatment are microfiltration (0.1–0.2 μm) and ultrafiltration (0.005 ≈ 10 μm). There are reports of microfiltration membranes with larger pore sizes (0.2 to 0.4) being used [114]. The best membrane technology for coronavirus removal is ultrafiltration with an average viral particle diameter of 120 nm (0.12 μm) and an envelope diameter of 80 nm (0.08 μm) [115]. Adsorption of coronaviruses on wastewater solids can enhance their removal. Tertiary wastewater treatment processes such as chlorination and UV treatment can also result in further removal of remaining coronaviruses in wastewater [98]. Chlorine has been reported to inactivate viruses through the cleavage of the virus capsid protein backbone, inhibiting the injection of the viral genome into host cells [116, 117].
The inactivation of coronaviruses by UV irradiation has also been reported in several studies [118, 119, 120]. Enveloped viruses, like coronaviruses, are more sensitive to UV than non-enveloped viruses. The mechanism by which UV inactivates coronaviruses is the generation of pyrimidine dimers which damage nucleic acid [94]. Methods of disinfection used in the drinking water treatment inactivate efficiently SARS-CoV-2 in water [121]. However, there is a need to investigate and ameliorate the performance of disinfection technologies to be adopted for the inactivation of SARS-CoV-2 in municipal and hospital wastewater to reduce the related risk of possible infections [121].
7. Conclusion
There has been a significant expansion that proved pathogenic viruses in the wastewater and/or treatment plants, including the novel coronavirus. Understanding the destiny of SARS-CoV-2 in wastewater treatment plants has arisen as an issue of extreme importance. The epidemiological surveillance of these viruses in wastewater would help to prevent the spread of the viral disease while producing safe treated water for reuse. Thus, the performance of various treatment procedures is now being explored to reduce viral disease outbreaks.
Potential dangers of SARS-CoV-2 transmission through water infrastructure are a major source of concern in the environmental setting, and detection and eradication will play a key role in limiting the virus’s spread in the population. To comprehend the early warning of outbreaks and to effectively inactivate before emerging, the virus must be a regular criterion for routine monitoring with other quality metrics with environmental samples. A regulatory framework that incorporates environmental systems will help to protect the global community from future outbreaks and transmissions.
\n',keywords:"SARS-CoV-2, human coronaviruses, zoonotic coronaviruses, disinfection procedures, wastewater",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/81861.pdf",chapterXML:"https://mts.intechopen.com/source/xml/81861.xml",downloadPdfUrl:"/chapter/pdf-download/81861",previewPdfUrl:"/chapter/pdf-preview/81861",totalDownloads:25,totalViews:0,totalCrossrefCites:0,dateSubmitted:"January 29th 2022",dateReviewed:"February 22nd 2022",datePrePublished:"May 19th 2022",datePublished:null,dateFinished:"May 18th 2022",readingETA:"0",abstract:"In December 2019, there was a cluster of pneumonia cases in Wuhan, a city of about 11 million people in Hubei Province. The World Health Organization (WHO), qualified CoVid-19 as an emerging infectious disease on March 11, 2020, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which spreads around the world. Coronaviruses are also included in the list of viruses likely to be found in raw sewage, as are other viruses belonging to the Picornaviridae family. SRAS-CoV-2 has been detected in wastewater worldwide such as the USA, France, Netherlands, Australia, and Italy according to the National Research Institute for Public Health and the Environment. In addition, the SARS-CoV-2 could infect many animals since it has been noticed in pigs, domestic and wild birds, bats, rodents, dogs, cats, tigers, cattle. Therefore, the SARS-CoV-2 molecular characterization in the environment, particularly in wastewater and animals, appeared to be a novel approach to monitor the outbreaks of viral pandemics. This review will be focused on the description of some virological characteristics of these emerging viruses, the different human and zoonotic coronaviruses, the sources of contamination of wastewater by coronaviruses and their potential procedures of disinfection from wastewater.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/81861",risUrl:"/chapter/ris/81861",signatures:"Chourouk Ibrahim, Salah Hammami, Eya Ghanmi and Abdennaceur Hassen",book:{id:"11173",type:"book",title:"Wastewater Treatment",subtitle:null,fullTitle:"Wastewater Treatment",slug:null,publishedDate:null,bookSignature:"Prof. Muharrem Ince and Dr. Olcay Kaplan Ince",coverURL:"https://cdn.intechopen.com/books/images_new/11173.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-80355-847-9",printIsbn:"978-1-80355-846-2",pdfIsbn:"978-1-80355-848-6",isAvailableForWebshopOrdering:!0,editors:[{id:"258431",title:"Prof.",name:"Muharrem",middleName:null,surname:"Ince",slug:"muharrem-ince",fullName:"Muharrem Ince"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Human coronaviruses",level:"1"},{id:"sec_2_2",title:"2.1 HCoV-229E and HCoV-OC43",level:"2"},{id:"sec_3_2",title:"2.2 SARS-CoV",level:"2"},{id:"sec_4_2",title:"2.3 HCoV-NL63 et HCoV-HKU1",level:"2"},{id:"sec_5_2",title:"2.4 MERS-CoV",level:"2"},{id:"sec_6_2",title:"2.5 SARS-CoV-2",level:"2"},{id:"sec_8",title:"3. Animal coronavirus diseases",level:"1"},{id:"sec_8_2",title:"3.1 Pet coronavirus (dogs and cats)",level:"2"},{id:"sec_9_2",title:"3.2 Coronaviruses of production animals",level:"2"},{id:"sec_10_2",title:"3.3 Avian coronaviruses",level:"2"},{id:"sec_11_2",title:"3.4 SARS-CoV-2 animal infections",level:"2"},{id:"sec_13",title:"4. Detection of SARS-CoV-2 in wastewater",level:"1"},{id:"sec_14",title:"5. Risks of environmental transmission of SARS-CoV-2 in wastewater",level:"1"},{id:"sec_15",title:"6. Disinfection and eradication procedures of SARS-CoV-2 in wastewater",level:"1"},{id:"sec_16",title:"7. 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First proof of the capability of wastewater surveillance for COVID-19 in India through detection of the genetic material of SARS-CoV-2. Science of the Total Environment. 2020;746:141326. DOI: 10.1016/j.scitotenv.2020.141326'},{id:"B97",body:'Rimoldi SG, Stefani F, Gigantiello A, Polesello S, Comandatore F, Mileto D, et al. Presence and infectivity of SARS-CoV-2 virus in wastewater and rivers. Science of the Total Environment. 2020;744:140911. DOI: 10.1016/j.scitotenv.2020.140911'},{id:"B98",body:'Amoah ID, Kumari S, Bux F. Coronaviruses in wastewater processes: Source, fate and potential risks Environment International.May 2020;143:105962. 10.1016/j.envint.2020.105962.'},{id:"B99",body:'Lu R, Wang J, Li M, Wang Y, Dong J, Cai W. SARS-CoV-2 detection using digital PCR for COVID-19 diagnosis, treatment monitoring and criteria for discharge. MedRxiv. 2020. DOI: 10.1101/2020.03.24.20042689'},{id:"B100",body:'Dong L, Wang X, Wang S, Du M, Niu C, Yang J, et al. Interlaboratory assessment of droplet digital PCR for quantification of BRAF V600E mutation using a novel DNA reference material. Talanta. 2020;207. DOI: 10.1016/j.talanta.2019.120293'},{id:"B101",body:'Khan K, Tighe SW, Badireddy AR. Facteurs influençant la récupération de l’ARN du SRAS-CoV-2 dans les eaux usées brutes et les boues d’épuration à l’aide de la méthode de concentration à base de polyéthylène glycol. Journal of Biomolecular Techniques. 2021;32(3):172-179. DOI: 10.7171/jbt.21-3203-012'},{id:"B102",body:'La Rosa G, Bonadonna L, Lucentini L, Kenmoe S, Suffredini E. Coronavirus in water environments: Occurrence, persistence and concentration methods—A scoping review. Water Research. 2020;179:115899. DOI: 10.1016/j.watres.2020.115899'},{id:"B103",body:'Fears AC, Klimstra WB, Duprex P, Hartman A, Weaver SC, Plante KS, et al. Persistence of severe acute respiratory syndrome coronavirus 2 in aerosol suspensions. Emerging Infectious Diseases. 2020;26(9):2168-2171. 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DOI: 10.1016/j.scitotenv.2017.10.265'},{id:"B108",body:'Kitajima M, Iker BC, Pepper IL, Gerba CP. Relative abundance and treatment reduction of viruses during wastewater treatment processes—Identification of potential viral indicators. Science of the Total Environment. 2014;488-489(1):290-296. DOI: 10.1016/j.scitotenv.2014.04.087'},{id:"B109",body:'Simmons FJ, Xagoraraki I. Release of infectious human enteric viruses by full-scale wastewater utilities. Water Research. 2011;45(12):3590-3598. DOI: 10.1016/j.watres.2011.04.001'},{id:"B110",body:'Nordgren J, Matussek A, Mattsson A, Svensson L, Lindgren PE. Prevalence of norovirus and factors influencing virus concentrations during one year in a full-scale wastewater treatment plant. Water Research. 2009;43(4):1117-1125. DOI: 10.1016/j.watres.2008.11.053'},{id:"B111",body:'Keegan AR, Robinson B, Monis P, Biebrick M, Liston C. Validation of activated sludge plant performance for virus and protozoan reduction. Journal of Water Reuse and Desalination. 2013;3(2):140-147. DOI: 10.2166/wrd.2013.032'},{id:"B112",body:'Ye Y, Ellenberg RM, Graham KE, Wigginton KR. Survivability, partitioning, and recovery of enveloped viruses in untreated municipal wastewater. Environmental Science & Technology. 2016;50(10):5077-5085. DOI: 10.1021/acs.est.6b00876'},{id:"B113",body:'Verbyla ME, Mihelcic JR. A review of virus removal in wastewater treatment pond systems. Water Research. 2015;71:107-124. DOI: 10.1016/j.watres.2014.12.031'},{id:"B114",body:'Nqombolo A, Mpupa A, Moutloali RM, Nomngongo PN. Wastewater treatment using membrane technology. In: Wastewater and Water Quality. Rijeka: InTech; 2018. DOI: 10.5772/intechopen.76624'},{id:"B115",body:'Neuman BW, Buchmeier MJ. Supramolecular architecture of the coronavirus particle. Advances in Virus Research. 2016;96:1-27. DOI: 10.1016/bs.aivir.2016.08.005'},{id:"B116",body:'Wigginton KR, Pecson BM, Sigstam T, Bosshard F, Kohn T. Virus inactivation mechanisms: Impact of disinfectants on virus function and structural integrity. Environmental Science and Technology. 2012;46(21):12069-12078. DOI: 10.1021/es3029473'},{id:"B117",body:'Page MA, Shisler JL, Mariñas BJ. Mechanistic aspects of adenovirus serotype 2 inactivation with free chlorine. Applied and Environmental Microbiology. 2010;76(9):2946-2954. DOI: 10.1128/AEM.02267-09'},{id:"B118",body:'Shirbandi K, Barghandan S, Mobinfar O, Rahim F. Inactivation of coronavirus with ultraviolet irradiation: What? How? Why? SSRN Electronic Journal. 2020. DOI: 10.2139/ssrn.3571418'},{id:"B119",body:'Kim J, Jang J. Inactivation of airborne viruses using vacuum ultraviolet photocatalysis for a flow-through indoor air purifier with short irradiation time. Aerosol Science and Technology. 2018;52(5):557-566. DOI: 10.1080/02786826.2018.14313'},{id:"B120",body:'Casanova L, Rutala WA, Weber DJ, Sobsey MD. Survival of surrogate coronaviruses in water. Water Research. 2009;43(7):1893-1898. DOI: 10.1016/j.watres.2009.02.002'},{id:"B121",body:'Smith EC, Denison MR. Coronaviruses as DNA wannabes: A new model for the regulation of RNA virus replication fidelity. PLoS Pathogens. 2013;9(12):e1003760. DOI: 10.1371/journal.ppat.1003760'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Chourouk Ibrahim",address:"ibrahimchourouk@yahoo.fr",affiliation:'
Faculty of Mathematical, Physical and Natural Sciences of Tunis, University of Tunis El Manar, Tunisia
Centre of Research and Water Technologies (CERTE), Laboratory of Treatment and Wastewater Valorization, Techno Park of Borj-Cédria, Tunisia
Centre of Research and Water Technologies (CERTE), Laboratory of Treatment and Wastewater Valorization, Techno Park of Borj-Cédria, Tunisia
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He completed postdoctoral degrees in Engineering and Mechanics, Virginia Tech, Blacksburg, Virginia, USA (1994-1995) and (Visiting Professor) at Virginia Tech, Blacksburg, Virginia, USA (2007,2012) and at the University of Illinois at Champaign- Urbana, IL, USA- (2010). He recently visited: La Sapienza, Rome, Italy, (2011), Univ. (University of Akron, OH, and MIT (BOSTON), USA, (2015) and Cornell University, Ithaca, NY, USA, (2017). Prof. Balthazar also delivered Courses abroad: Warsaw (Technical University) – (2011, 2013) and Turkey (Yildiz Univ), (2012). Prof. Balthazar has given guest lectures at renowned centers such as: MIT (Boston), Moscow Academy of Sciences (Russia) and Belgrade Academy of Sciences (Servia), Kharkov National University, Ukraine, etc. Prof. Balthazar is Currently Associate Editor for South America, Journal of Vibration Engineering & Technologies (JVET) and Member of the Main Editorial Board of the Journal of Vibration and Control (JVC), both indexed in (Web of Sciences). He is a member of the editorial board of the International Journal of Nonlinear Dynamics and Control (IJNDC) and is a reviewer of the main International Circulation Journals on the theme: Nonlinear Dynamics, Chaos and Control with Engineering Applications.",institutionString:"São Paulo State University, Faculty of Mechanical Engineering of Bauru, Federal University of Technology - Paraná",institution:null},{id:"171167",title:"M.Sc.",name:"Lauren",surname:"Ramsay",slug:"lauren-ramsay",fullName:"Lauren Ramsay",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Guelph",institutionURL:null,country:{name:"Canada"}}},{id:"172971",title:"Dr.",name:"Christopher",surname:"Charles",slug:"christopher-charles",fullName:"Christopher Charles",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null}]},generic:{page:{slug:"our-story",title:"Our story",intro:"
The company was founded in Vienna in 2004 by Alex Lazinica and Vedran Kordic, two PhD students researching robotics. While completing our PhDs, we found it difficult to access the research we needed. So, we decided to create a new Open Access publisher. A better one, where researchers like us could find the information they needed easily. The result is IntechOpen, an Open Access publisher that puts the academic needs of the researchers before the business interests of publishers.
",metaTitle:"Our story",metaDescription:"The company was founded in Vienna in 2004 by Alex Lazinica and Vedran Kordic, two PhD students researching robotics. While completing our PhDs, we found it difficult to access the research we needed. So, we decided to create a new Open Access publisher. A better one, where researchers like us could find the information they needed easily. The result is IntechOpen, an Open Access publisher that puts the academic needs of the researchers before the business interests of publishers.",metaKeywords:null,canonicalURL:"/page/our-story",contentRaw:'[{"type":"htmlEditorComponent","content":"
We started by publishing journals and books from the fields of science we were most familiar with - AI, robotics, manufacturing and operations research. Through our growing network of institutions and authors, we soon expanded into related fields like environmental engineering, nanotechnology, computer science, renewable energy and electrical engineering, Today, we are the world’s largest Open Access publisher of scientific research, with over 4,200 books and 54,000 scientific works including peer-reviewed content from more than 116,000 scientists spanning 161 countries. Our authors range from globally-renowned Nobel Prize winners to up-and-coming researchers at the cutting edge of scientific discovery.
\\n\\n
In the same year that IntechOpen was founded, we launched what was at the time the first ever Open Access, peer-reviewed journal in its field: the International Journal of Advanced Robotic Systems (IJARS).
\\n\\n
The IntechOpen timeline
\\n\\n
2004
\\n\\n
\\n\\t
Intech Open is founded in Vienna, Austria, by Alex Lazinica and Vedran Kordic, two PhD students, and their first Open Access journals and books are published.
\\n\\t
Alex and Vedran launch the first Open Access, peer-reviewed robotics journal and IntechOpen’s flagship publication, the International Journal of Advanced Robotic Systems (IJARS).
\\n
\\n\\n
2005
\\n\\n
\\n\\t
IntechOpen publishes its first Open Access book: Cutting Edge Robotics.
\\n
\\n\\n
2006
\\n\\n
\\n\\t
IntechOpen publishes a special issue of IJARS, featuring contributions from NASA scientists regarding the Mars Exploration Rover missions.
\\n
\\n\\n
2008
\\n\\n
\\n\\t
Downloads milestone: 200,000 downloads reached
\\n
\\n\\n
2009
\\n\\n
\\n\\t
Publishing milestone: the first 100 Open Access STM books are published
\\n
\\n\\n
2010
\\n\\n
\\n\\t
Downloads milestone: one million downloads reached
\\n\\t
IntechOpen expands its book publishing into a new field: medicine.
\\n
\\n\\n
2011
\\n\\n
\\n\\t
Publishing milestone: More than five million downloads reached
\\n\\t
IntechOpen publishes 1996 Nobel Prize in Chemistry winner Harold W. Kroto’s “Strategies to Successfully Cross-Link Carbon Nanotubes”. Find it here.
\\n\\t
IntechOpen and TBI collaborate on a project to explore the changing needs of researchers and the evolving ways that they discover, publish and exchange information. The result is the survey “Author Attitudes Towards Open Access Publishing: A Market Research Program”.
\\n\\t
IntechOpen hosts SHOW - Share Open Access Worldwide; a series of lectures, debates, round-tables and events to bring people together in discussion of open source principles, intellectual property, content licensing innovations, remixed and shared culture and free knowledge.
\\n
\\n\\n
2012
\\n\\n
\\n\\t
Publishing milestone: 10 million downloads reached
\\n\\t
IntechOpen holds Interact2012, a free series of workshops held by figureheads of the scientific community including Professor Hiroshi Ishiguro, director of the Intelligent Robotics Laboratory, who took the audience through some of the most impressive human-robot interactions observed in his lab.
\\n
\\n\\n
2013
\\n\\n
\\n\\t
IntechOpen joins the Committee on Publication Ethics (COPE) as part of a commitment to guaranteeing the highest standards of publishing.
\\n
\\n\\n
2014
\\n\\n
\\n\\t
IntechOpen turns 10, with more than 30 million downloads to date.
\\n\\t
IntechOpen appoints its first Regional Representatives - members of the team situated around the world dedicated to increasing the visibility of our authors’ published work within their local scientific communities.
\\n
\\n\\n
2015
\\n\\n
\\n\\t
Downloads milestone: More than 70 million downloads reached, more than doubling since the previous year.
\\n\\t
Publishing milestone: IntechOpen publishes its 2,500th book and 40,000th Open Access chapter, reaching 20,000 citations in Thomson Reuters ISI Web of Science.
\\n\\t
40 IntechOpen authors are included in the top one per cent of the world’s most-cited researchers.
\\n\\t
Thomson Reuters’ ISI Web of Science Book Citation Index begins indexing IntechOpen’s books in its database.
\\n
\\n\\n
2016
\\n\\n
\\n\\t
IntechOpen is identified as a world leader in Simba Information’s Open Access Book Publishing 2016-2020 report and forecast. IntechOpen came in as the world’s largest Open Access book publisher by title count.
\\n
\\n\\n
2017
\\n\\n
\\n\\t
Downloads milestone: IntechOpen reaches more than 100 million downloads
\\n\\t
Publishing milestone: IntechOpen publishes its 3,000th Open Access book, making it the largest Open Access book collection in the world
We started by publishing journals and books from the fields of science we were most familiar with - AI, robotics, manufacturing and operations research. Through our growing network of institutions and authors, we soon expanded into related fields like environmental engineering, nanotechnology, computer science, renewable energy and electrical engineering, Today, we are the world’s largest Open Access publisher of scientific research, with over 4,200 books and 54,000 scientific works including peer-reviewed content from more than 116,000 scientists spanning 161 countries. Our authors range from globally-renowned Nobel Prize winners to up-and-coming researchers at the cutting edge of scientific discovery.
\n\n
In the same year that IntechOpen was founded, we launched what was at the time the first ever Open Access, peer-reviewed journal in its field: the International Journal of Advanced Robotic Systems (IJARS).
\n\n
The IntechOpen timeline
\n\n
2004
\n\n
\n\t
Intech Open is founded in Vienna, Austria, by Alex Lazinica and Vedran Kordic, two PhD students, and their first Open Access journals and books are published.
\n\t
Alex and Vedran launch the first Open Access, peer-reviewed robotics journal and IntechOpen’s flagship publication, the International Journal of Advanced Robotic Systems (IJARS).
\n
\n\n
2005
\n\n
\n\t
IntechOpen publishes its first Open Access book: Cutting Edge Robotics.
\n
\n\n
2006
\n\n
\n\t
IntechOpen publishes a special issue of IJARS, featuring contributions from NASA scientists regarding the Mars Exploration Rover missions.
\n
\n\n
2008
\n\n
\n\t
Downloads milestone: 200,000 downloads reached
\n
\n\n
2009
\n\n
\n\t
Publishing milestone: the first 100 Open Access STM books are published
\n
\n\n
2010
\n\n
\n\t
Downloads milestone: one million downloads reached
\n\t
IntechOpen expands its book publishing into a new field: medicine.
\n
\n\n
2011
\n\n
\n\t
Publishing milestone: More than five million downloads reached
\n\t
IntechOpen publishes 1996 Nobel Prize in Chemistry winner Harold W. Kroto’s “Strategies to Successfully Cross-Link Carbon Nanotubes”. Find it here.
\n\t
IntechOpen and TBI collaborate on a project to explore the changing needs of researchers and the evolving ways that they discover, publish and exchange information. The result is the survey “Author Attitudes Towards Open Access Publishing: A Market Research Program”.
\n\t
IntechOpen hosts SHOW - Share Open Access Worldwide; a series of lectures, debates, round-tables and events to bring people together in discussion of open source principles, intellectual property, content licensing innovations, remixed and shared culture and free knowledge.
\n
\n\n
2012
\n\n
\n\t
Publishing milestone: 10 million downloads reached
\n\t
IntechOpen holds Interact2012, a free series of workshops held by figureheads of the scientific community including Professor Hiroshi Ishiguro, director of the Intelligent Robotics Laboratory, who took the audience through some of the most impressive human-robot interactions observed in his lab.
\n
\n\n
2013
\n\n
\n\t
IntechOpen joins the Committee on Publication Ethics (COPE) as part of a commitment to guaranteeing the highest standards of publishing.
\n
\n\n
2014
\n\n
\n\t
IntechOpen turns 10, with more than 30 million downloads to date.
\n\t
IntechOpen appoints its first Regional Representatives - members of the team situated around the world dedicated to increasing the visibility of our authors’ published work within their local scientific communities.
\n
\n\n
2015
\n\n
\n\t
Downloads milestone: More than 70 million downloads reached, more than doubling since the previous year.
\n\t
Publishing milestone: IntechOpen publishes its 2,500th book and 40,000th Open Access chapter, reaching 20,000 citations in Thomson Reuters ISI Web of Science.
\n\t
40 IntechOpen authors are included in the top one per cent of the world’s most-cited researchers.
\n\t
Thomson Reuters’ ISI Web of Science Book Citation Index begins indexing IntechOpen’s books in its database.
\n
\n\n
2016
\n\n
\n\t
IntechOpen is identified as a world leader in Simba Information’s Open Access Book Publishing 2016-2020 report and forecast. IntechOpen came in as the world’s largest Open Access book publisher by title count.
\n
\n\n
2017
\n\n
\n\t
Downloads milestone: IntechOpen reaches more than 100 million downloads
\n\t
Publishing milestone: IntechOpen publishes its 3,000th Open Access book, making it the largest Open Access book collection in the world
\n
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On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. 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From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. His current research interests are in the fields of intelligent control and robotics.",institutionString:null,institution:{name:"Technical University of Sofia",country:{name:"Bulgaria"}}},{id:"585",title:"Prof.",name:"Munir",middleName:null,surname:"Merdan",slug:"munir-merdan",fullName:"Munir Merdan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/585/images/system/585.jpg",biography:"Munir Merdan received the M.Sc. degree in mechanical engineering from the Technical University of Sarajevo, Bosnia and Herzegovina, in 2001, and the Ph.D. degree in electrical engineering from the Vienna University of Technology, Vienna, Austria, in 2009.Since 2005, he has been at the Automation and Control Institute, Vienna University of Technology, where he is currently a Senior Researcher. 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After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. 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Badria",profilePictureURL:"https://mts.intechopen.com/storage/users/41865/images/system/41865.jpg",institutionString:"Mansoura University",institution:{name:"Mansoura University",institutionURL:null,country:{name:"Egypt"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null},{type:"book",id:"9659",title:"Fibroblasts",subtitle:"Advances in Inflammation, Autoimmunity and Cancer",coverURL:"https://cdn.intechopen.com/books/images_new/9659.jpg",slug:"fibroblasts-advances-in-inflammation-autoimmunity-and-cancer",publishedDate:"December 22nd 2021",editedByType:"Edited by",bookSignature:"Mojca Frank Bertoncelj and Katja Lakota",hash:"926fa6446f6befbd363fc74971a56de2",volumeInSeries:25,fullTitle:"Fibroblasts - Advances in Inflammation, Autoimmunity and Cancer",editors:[{id:"328755",title:"Ph.D.",name:"Mojca",middleName:null,surname:"Frank Bertoncelj",slug:"mojca-frank-bertoncelj",fullName:"Mojca Frank Bertoncelj",profilePictureURL:"https://mts.intechopen.com/storage/users/328755/images/system/328755.jpg",institutionString:"BioMed X Institute",institution:{name:"University Hospital of Zurich",institutionURL:null,country:{name:"Switzerland"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null},{type:"book",id:"8977",title:"Protein Kinases",subtitle:"Promising Targets for Anticancer Drug Research",coverURL:"https://cdn.intechopen.com/books/images_new/8977.jpg",slug:"protein-kinases-promising-targets-for-anticancer-drug-research",publishedDate:"December 8th 2021",editedByType:"Edited by",bookSignature:"Rajesh Kumar Singh",hash:"6d200cc031706a565b554fdb1c478901",volumeInSeries:24,fullTitle:"Protein Kinases - Promising Targets for Anticancer Drug Research",editors:[{id:"329385",title:"Dr.",name:"Rajesh K.",middleName:"Kumar",surname:"Singh",slug:"rajesh-k.-singh",fullName:"Rajesh K. Singh",profilePictureURL:"https://mts.intechopen.com/storage/users/329385/images/system/329385.png",institutionString:"Punjab Technical University",institution:{name:"Punjab Technical University",institutionURL:null,country:{name:"India"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null},{type:"book",id:"8018",title:"Extracellular Matrix",subtitle:"Developments and Therapeutics",coverURL:"https://cdn.intechopen.com/books/images_new/8018.jpg",slug:"extracellular-matrix-developments-and-therapeutics",publishedDate:"October 27th 2021",editedByType:"Edited by",bookSignature:"Rama Sashank Madhurapantula, Joseph Orgel P.R.O. and Zvi Loewy",hash:"c85e82851e80b40282ff9be99ddf2046",volumeInSeries:23,fullTitle:"Extracellular Matrix - Developments and Therapeutics",editors:[{id:"212416",title:"Dr.",name:"Rama Sashank",middleName:null,surname:"Madhurapantula",slug:"rama-sashank-madhurapantula",fullName:"Rama Sashank Madhurapantula",profilePictureURL:"https://mts.intechopen.com/storage/users/212416/images/system/212416.jpg",institutionString:"Illinois Institute of Technology",institution:{name:"Illinois Institute of Technology",institutionURL:null,country:{name:"United States of America"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null}]},subseriesFiltersForPublishedBooks:[{group:"subseries",caption:"Proteomics",value:18,count:4},{group:"subseries",caption:"Metabolism",value:17,count:6},{group:"subseries",caption:"Cell and Molecular Biology",value:14,count:9},{group:"subseries",caption:"Chemical Biology",value:15,count:14}],publicationYearFilters:[{group:"publicationYear",caption:"2022",value:2022,count:9},{group:"publicationYear",caption:"2021",value:2021,count:7},{group:"publicationYear",caption:"2020",value:2020,count:12},{group:"publicationYear",caption:"2019",value:2019,count:3},{group:"publicationYear",caption:"2018",value:2018,count:2}],authors:{paginationCount:148,paginationItems:[{id:"165328",title:"Dr.",name:"Vahid",middleName:null,surname:"Asadpour",slug:"vahid-asadpour",fullName:"Vahid Asadpour",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/165328/images/system/165328.jpg",biography:"Vahid Asadpour, MS, Ph.D., is currently with the Department of Research and Evaluation, Kaiser Permanente Southern California. He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:{name:"Association for Computing Machinery",country:{name:"United States of America"}}},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. 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\r\n\tThe era of antibiotics led us to the illusion that the problem of bacterial infection is over. However, bacterial flexibility and adaptation mechanisms allow them to survive and grow in extreme conditions. The best example is the formation of a sophisticated society of bacteria defined as a biofilm. Understanding the mechanism of bacterial biofilm formation has changed our perception of the development of bacterial infection but successfully eradicating biofilm remains a challenge. Considering the above, it is not surprising that bacteria remain a major public health threat despite the development of many groups of antibiotics. Additionally, increasing prevalence of acquired antibiotic resistance forces us to realize that we are far from controlling the development of bacterial infections. On the other hand, many infections are endogenous and result from an unbalanced relationship between the host and the microorganism. The increasing use of immunosuppressants, such as chemotherapy or organ transplantation, increases the incidence of patients highly susceptible to bacterial infections in the population.
\r\n
\r\n\tThis topic will focus on the current challenges and advantages in the diagnosis and treatment of bacterial infections. We will discuss the host-microbiota relationship, the treatment of chronic infections due to biofilm formation, and the development of new diagnostic tools to rapidly distinguish between colonization and probable infection.
",coverUrl:"https://cdn.intechopen.com/series_topics/covers/3.jpg",keywords:"Antibiotics, Biofilm, Antibiotic Resistance, Host-microbiota Relationship, Treatment, Diagnostic Tools"},{id:"4",title:"Fungal Infectious Diseases",scope:"Fungi are ubiquitous and there are almost no non-pathogenic fungi. Fungal infectious illness prevalence and prognosis are determined by the exposure between fungi and host, host immunological state, fungal virulence, and early and accurate diagnosis and treatment. \r\nPatients with both congenital and acquired immunodeficiency are more likely to be infected with opportunistic mycosis. Fungal infectious disease outbreaks are common during the post- disaster rebuilding era, which is characterised by high population density, migration, and poor health and medical conditions.\r\nSystemic or local fungal infection is mainly associated with the fungi directly inhaled or inoculated in the environment during the disaster. The most common fungal infection pathways are human to human (anthropophilic), animal to human (zoophilic), and environment to human (soilophile). Diseases are common as a result of widespread exposure to pathogenic fungus dispersed into the environment. \r\nFungi that are both common and emerging are intertwined. In Southeast Asia, for example, Talaromyces marneffei is an important pathogenic thermally dimorphic fungus that causes systemic mycosis. Widespread fungal infections with complicated and variable clinical manifestations, such as Candida auris infection resistant to several antifungal medicines, Covid-19 associated with Trichoderma, and terbinafine resistant dermatophytosis in India, are among the most serious disorders. \r\nInappropriate local or systemic use of glucocorticoids, as well as their immunosuppressive effects, may lead to changes in fungal infection spectrum and clinical characteristics. Hematogenous candidiasis is a worrisome issue that affects people all over the world, particularly ICU patients. CARD9 deficiency and fungal infection have been major issues in recent years. Invasive aspergillosis is associated with a significant death rate. Special attention should be given to endemic fungal infections, identification of important clinical fungal infections advanced in yeasts, filamentous fungal infections, skin mycobiome and fungal genomes, and immunity to fungal infections.\r\nIn addition, endemic fungal diseases or uncommon fungal infections caused by Mucor irregularis, dermatophytosis, Malassezia, cryptococcosis, chromoblastomycosis, coccidiosis, blastomycosis, histoplasmosis, sporotrichosis, and other fungi, should be monitored. \r\nThis topic includes the research progress on the etiology and pathogenesis of fungal infections, new methods of isolation and identification, rapid detection, drug sensitivity testing, new antifungal drugs, schemes and case series reports. It will provide significant opportunities and support for scientists, clinical doctors, mycologists, antifungal drug researchers, public health practitioners, and epidemiologists from all over the world to share new research, ideas and solutions to promote the development and progress of medical mycology.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/4.jpg",keywords:"Emerging Fungal Pathogens, Invasive Infections, Epidemiology, Cell Membrane, Fungal Virulence, Diagnosis, Treatment"},{id:"5",title:"Parasitic Infectious Diseases",scope:"Parasitic diseases have evolved alongside their human hosts. In many cases, these diseases have adapted so well that they have developed efficient resilience methods in the human host and can live in the host for years. Others, particularly some blood parasites, can cause very acute diseases and are responsible for millions of deaths yearly. Many parasitic diseases are classified as neglected tropical diseases because they have received minimal funding over recent years and, in many cases, are under-reported despite the critical role they play in morbidity and mortality among human and animal hosts. The current topic, Parasitic Infectious Diseases, in the Infectious Diseases Series aims to publish studies on the systematics, epidemiology, molecular biology, genomics, pathogenesis, genetics, and clinical significance of parasitic diseases from blood borne to intestinal parasites as well as zoonotic parasites. We hope to cover all aspects of parasitic diseases to provide current and relevant research data on these very important diseases. In the current atmosphere of the Coronavirus pandemic, communities around the world, particularly those in different underdeveloped areas, are faced with the growing challenges of the high burden of parasitic diseases. At the same time, they are faced with the Covid-19 pandemic leading to what some authors have called potential syndemics that might worsen the outcome of such infections. Therefore, it is important to conduct studies that examine parasitic infections in the context of the coronavirus pandemic for the benefit of all communities to help foster more informed decisions for the betterment of human and animal health.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/5.jpg",keywords:"Blood Borne Parasites, Intestinal Parasites, Protozoa, Helminths, Arthropods, Water Born Parasites, Epidemiology, Molecular Biology, Systematics, Genomics, Proteomics, Ecology"},{id:"6",title:"Viral Infectious Diseases",scope:"The Viral Infectious Diseases Book Series aims to provide a comprehensive overview of recent research trends and discoveries in various viral infectious diseases emerging around the globe. The emergence of any viral disease is hard to anticipate, which often contributes to death. A viral disease can be defined as an infectious disease that has recently appeared within a population or exists in nature with the rapid expansion of incident or geographic range. This series will focus on various crucial factors related to emerging viral infectious diseases, including epidemiology, pathogenesis, host immune response, clinical manifestations, diagnosis, treatment, and clinical recommendations for managing viral infectious diseases, highlighting the recent issues with future directions for effective therapeutic strategies.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/6.jpg",keywords:"Novel Viruses, Virus Transmission, Virus Evolution, Molecular Virology, Control and Prevention, Virus-host Interaction"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:null,selectedSubseries:null},seriesLanding:{item:null},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/171220",hash:"",query:{},params:{id:"171220"},fullPath:"/profiles/171220",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var m;(m=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(m)}()