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3D bioprinting of the kidney—hype or hope?

  • Three-dimensional (3D) bioprinting is an evolving technique that is expected to revolutionize the field of regenerative medicine. Since the organ donation does not meet the demands for transplantable organs, it is important to think of another solution, which may and most likely will be provided by the technology of 3D bioprinting. However, even smaller parts of the printed renal tissue may be of help, e.g. in developing better drugs. Some simple tissues such as cartilage have been printed with success, but a lot of work is still required to successfully 3D bioprint complex organs such as the kidneys. However, few obstacles still persist such as the vascularization and the size of the printed organ. Nevertheless, many pieces of the puzzle are already available and it is just a matter of time to connect them together and 3D bioprint the kidneys. The 3D bioprinting technology provides the precision and fast speed required for generating organs. In this review, we describe the recent developments in the field of developmental biology concerning the kidneys; characterize the bioinks available for printing and suitable for kidney printing; present the existing printers and possible printing strategies. Moreover, we identify the most difficult challenges in printing of the kidneys and propose a solution, which may lead to successful bioprinting of the kidney.

    Citation: Sanna Turunen, Susanna Kaisto, Ilya Skovorodkin, Vladimir Mironov, Tomi Kalpio, Seppo Vainio, Aleksandra Rak-Raszewska. 3D bioprinting of the kidney—hype or hope?[J]. AIMS Cell and Tissue Engineering, 2018, 2(3): 119-162. doi: 10.3934/celltissue.2018.3.119

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  • Three-dimensional (3D) bioprinting is an evolving technique that is expected to revolutionize the field of regenerative medicine. Since the organ donation does not meet the demands for transplantable organs, it is important to think of another solution, which may and most likely will be provided by the technology of 3D bioprinting. However, even smaller parts of the printed renal tissue may be of help, e.g. in developing better drugs. Some simple tissues such as cartilage have been printed with success, but a lot of work is still required to successfully 3D bioprint complex organs such as the kidneys. However, few obstacles still persist such as the vascularization and the size of the printed organ. Nevertheless, many pieces of the puzzle are already available and it is just a matter of time to connect them together and 3D bioprint the kidneys. The 3D bioprinting technology provides the precision and fast speed required for generating organs. In this review, we describe the recent developments in the field of developmental biology concerning the kidneys; characterize the bioinks available for printing and suitable for kidney printing; present the existing printers and possible printing strategies. Moreover, we identify the most difficult challenges in printing of the kidneys and propose a solution, which may lead to successful bioprinting of the kidney.


    On January 25th, 2020, the first case of COVID-19 was confirmed in Toronto, Ontario [1]. As of November 2021, SARS-CoV-2, the virus that causes the disease COVID-19, has infected over 1.7 million Canadians [2]. Throughout the course of this pandemic, the general public has been burdened with the responsibility of reducing transmission through various public health efforts. Public health officials have successfully lobbied the general public to participate in transmission reduction by employing tools such as face masks, social distance, ventilation upgrades, and vaccinations. It is largely by employing these public health guidelines that further epidemic waves are avoided in local contexts.

    Mathematical models of the spread and evolution of COVID-19 have been utilized to help answer questions of public health policy, vaccine deployment, and allocation of treatment resources. Indeed, the successes of compartmental models in epidemiological contexts are hard to overstate. Such mathematical models are incredibly powerful at producing short-term forecasts of the evolution of epidemic waves. More importantly, however, these models create a test-bed by which various public health policy questions can be answered. As such, various situations can be simulated in order to gain insight into the possible effects that changes in the behavior of the populace can create. In either case, whether the model is utilized for prediction or for experimentation, any insights gained by the model are limited by the capacity for the underlying model to describe the spread of the disease within the local population.

    Fractional calculus has been employed in various contexts where classical (integer-order) derivatives have met success. Namely, since fractional derivatives are calculated by utilizing global, integral operators, the use of fractional derivatives in differential equations results in a dynamical system capable of capturing the effects of state memory and other non-local information that integer-order methods are incapable of utilizing [3,4]. As a result, fractional-order methods have met great success in biological systems in general such as in the modelling of cancers [5], modelling the spread of COVID-19 [6,7,8,9], and describing the dynamics of HIV [10,11]. More specifically, fractional-order methods have found great success in epidemiological compartmental systems in particular [12,13,14,15]. Fractional derivative operators are not the only derivative operators capable of producing memory effects, for instance, delay differential equations can introduce memory effects as well [16]. In principle, a delayed differential operator could be employed as well. However, given the success of fractional operators in the biological literature (and specifically the theoretical epidemiology literature), we focus on fractional derivative operators in this report. From a particular perspective, time series prediction can be accomplished via the use of iterative techniques, machine learning, and deep learning network approaches [17,18,19,20]. While these are popular methods for time series forecasting, we are focused on increasing the interpolative and predictive strength of phenomenological modeling of epidemics via modifications of classical compartmental models.

    From a practical standpoint, there are multiple choices of how to implement a fractional derivative. Of these choices, it is likely the Riemann–Liouville derivative and the Caputo derivative are the most popular. There are certain particulars to consider when choosing between implementing a Riemann-Liouville derivative or a Caputo derivative. For instance, the Riemann–Liouville has certain disadvantages for modeling physical or biological systems. The Riemann–Liouville derivative of a constant is not zero. In addition, if the Riemann-Liouville derivative is used in a dynamical system, then the initial conditions must contain the limit values of Riemann-Liouville derivatives at time t=0. This is concerning when modeling physical or biological systems as the initial conditions become decoupled from physical or biological interpretation. These disadvantages reduce the field of application of the Riemann–Liouville fractional derivative. On the other hand, some of the great advantages of the Caputo fractional derivative are that the Caputo fractional derivative of a constant vanishes, and those dynamical systems under the Caputo fractional operator allow for traditional initial and boundary conditions to be included in the formulation of the problem [21].

    It is often the case in mathematical biology contexts that the model of the phenomenon being considered contains certain tune-able, unknown parameters that must be calibrated to data in order for the model to produce meaningful descriptions of the physical behavior. The process of determining these unknown parameters is non-trivial, and it is often the case that some of these parameters can not be determined simultaneously, even in the presence of sufficiently large data sets [22,23,24,25]. These questions of the identifiability of unknown parameters, both structural and practical, are important theoretical considerations for any calibrated model. Here, by performing an identifiability analysis, our previous model of infectious disease in the Canadian province of Ontario can be investigated. Moreover, we compare this model with a modified fractional-order model and demonstrate to accurately describe the dynamics of multiple waves of epidemic infection.

    Our manuscript is laid out as follows. In Section 2, we first introduce the integer-order model of SARS-CoV-2 spread in the Canadian province of Ontario that we consider. Then, we extend this model to the context of the fractional Caputo derivative in Section 2.2. Next, we discuss the calibration methodology used to fit the model to the epidemiological data in Section 2.3. We begin the Results and Discussion by presenting and discussing the results of the model calibration process in Section 3.2. We then utilize the results of the fitting procedure in order to perform sensitivity and identifiability analysis post-hoc validating the results of the calibration process. Then, we compare the results of the integer and fractional models in Section 3.3, comparing not only the capacity by which either model can describe the data but also comparing the predicted public response function θ(t) and its effects on the time-dependent reproduction number Rt. Finally, we summarize our conclusions in Section 4.

    We consider the Distancing-SEIRD model from Eastman et al. [26]. This compartmental epidemic model stratifies the population into five distinct subgroups. Individuals in group S represent those susceptible individuals who have not yet been exposed to the virus, individuals in group E represent those who have been exposed to enough viral pathogen as to eventually become infectious themselves after some incubation period, and individuals in group I represent those who are infected with the disease and are infectious to others, while individuals in groups R and D represent those who have either recovered from the disease or have perished mode of COVID-19.

    Moreover, it is assumed that the population can further be stratified into two groups: those that actively follow public health guidelines to reduce transmission and those that do not. Rather than re-stratify the existing five compartments into ten compartments total, the authors decide to model the proportion of the population that is reducing transmission as a time-dependent proportion θ(t). When θ(t) is near 1, most of the population is attempting to reduce transmission. In contrast, when θ(t) is near 0, most of the population eschews public health guidelines for transmission reduction.

    dSdt=β(θ(t))SIContactwithInfection,dEdt=β(θ(t))SIContactwithInfection(γDθ(t)+γM(1θ(t)))EDiseaseProgression,dIdt=(γDθ(t)+γM(1θ(t)))EDiseaseProgression(αDθ(t)+αM(1θ(t))+δDθ(t)+δM(1θ(t)))IRecovery/Death,dRdt=(αDθ(t)+αM(1θ(t)))IRecovery,dDdt=(δDθ(t)+δM(1θ(t)))IDeath, (2.1)

    along with initial data

    S(0)=S0,E(0)=E0,I(0)=I0,R(0)=R0,D(0)=D0, (2.2)

    where

    β(θ(t))=βDDθ(t)2+(βDM+βMD)θ(t)(1θ(t))+βMM(1θ(t))2, (2.3)

    is a time-dependent transmission rate determined by θ(t). Furthermore, in order for the model to be biologically relevant, we consider Equations (2.1) only under non-negative initial data.

    Note that while, in principle, following transmission reduction guidelines may not mechanistically cause a change in disease recovery or disease mortality, there still may be a difference in these effects between groups due to the population effects of individuals who follow these behaviors. For instance, an individual who spends less time in public or interacting with friends may recover quicker solely because they spend more time resting as a result or to demographic effects of those more likely to follow transmission reduction guidelines. In any case, as in Eastman et al. [26], we make the simplifying assumption that disease-specific parameters are unaffected by the transmission reduction behavior of the host: in effect, we assume that αD=αM, δD=δM, γD=γM, and βDM=βMD in Equations (2.1) for simplicity.

    To model the fractional-order derivative of the epidemic model, we shall introduce a modified fractional differential operator as proposed by Caputo in his work on the theory of viscoelasticity [27]. While other fractional derivative operators could be considered [28,29,30], there is an abundance of literature in the physical sciences that use the Caputo operator. See for instance [31,32,33,34]. Indeed the Caputo operator has found great success in the modeling of infectious diseases [35,36,37,38].

    Definition 1. The Caputo-fractional derivative of order a(k1,k) of f is defined as

    C0Datf(t)1Γ(ka)t0(tτ)ka1dkf(τ)dτkdτ,

    for t>0 (where Γ() refers to Gamma function). In the case a(0,1) we have

    C0Datf(t)1Γ(1a)t0(tτ)adf(τ)dτdτ. (2.4)

    From a computational point of view, we do not have any closed form for calculating the Caputo derivative Equation (2.4) a-priori. To overcome these issues, we consider a popular numerical discretization of the Caputo derivative operator. The most well-known approximation of Equation (2.4) is given by the so-called L1-formula as in the notation of Lin and Xu [39], the L1-discretization of Equation (2.5) over a uniform time mesh {0=t0,t1,,tn} with step size Δt

    C0Datf(tn)=Δt1aΓ(2a)n1i=0bif(tni)f(tn1i)Δt+ζt(f),0<a<1, (2.5)

    where bi=(i+1)1ai1a. We define ζt(f) as the truncation error, then ζt(f) satisfies the order-estimate

    ζt(f)=O(Δt2a).

    Hence, given a(0,1), the L1 discretization results in at-least linear order accuracy. In order to affect more accurate results, we can consider a higher-order approximation, such as the L1-2 formula presented due to Gao et al. [40]

    C0Datf(tn)=Δt1aΓ(2a)ni=1a(a)niδtfi12+Δt2aΓ(2a)ni=2b(a)niδ2tfi1+ζt(f), (2.6)

    where

    {a(a)i=(i+1)1ai1a,0in1,b(a)i=[(i+1)2ai2a  ](2a)[(i+1)1a+i1a  ]2,0i.

    Gao et al. [40] demonstrated that the truncation error satisfies the following order estimate ζt(f)=O(Δt3a). Hence, the L1-2 discretization represents an increase of a single order of magnitude over the L1 discretization alone (and hence for a(0,1), the L1-2 discretization is at least quadratic).

    For algebraic simplicity, we first transform the L1-2 discretization from (2.6) to the following form:

    C0Datf(tn)=ΔtaΓ(2a)[c(a)0f(tn)n1i=1(c(a)n1ic(a)ni)f(ti)c(a)n1f(t0)]+ζt(f), (2.7)

    in which c(a)0=a(a)0=1 for i=1, and for i2

    c(a)i={a(a)0+b(a)0,i=0,a(a)i+b(a)ib(a)i1,1in2,a(a)ib(a)i1,i=n1.

    For the numerical simulations of the fractional model in this work we consider the approximation of the Caputo fractional operator given by C0Datf(tn)ζt(f) as in Equation (2.7).

    Since the Caputo fractional derivative preserves the integer-order initial data, we can apply the Caputo fractional derivative in place of each derivative operator in Equations (2.1). Moreover, these 5 different applications of the Caputo fractional derivative can all be of unique order aS, aE, aI, aR, and aD.

    ΔtaS1Γ(aS+1)C0DaStS=β(θ(t))SI,ΔtaE1Γ(aE+1)C0DaEtE=β(θ(t))SIγE,ΔtaI1Γ(aI+1)C0DaItI=γE(α+δ)I,ΔtaR1Γ(aR+1)C0DaRtR=αI,ΔtaD1Γ(aD+1)C0DaDtD=δI, (2.8)

    along with initial data in Equation (2.2) and β(θ(t)) is defined as in Equation (2.3). The presence of the Δtai1/Γ(ai+1) factor in front of the derivative operator in Model 2.8 is a result of the fractional-order Taylor expansion, where Δt is a characteristic time. In numerical simulations, we took Δt to be the time step size of the model. For a full derivation, please see (for instance) [17]. We first note that initially in the epidemic model, the majority of the population mass is within the susceptible compartment. Moreover, according to the data from Berry et al. [2], the sum of the susceptible and exposed has only decreased in size by around 4% between January 25th, 2020 and, November 1st, 2021. Since reports that the latent period of the disease is on the order of days, we assume that the relative size of the susceptible compartment remains quite large over the course of the time frames being considered [41]. Since the relative size of S is the largest amongst the compartments, we suspect that replacing the derivative operator in the S equation with the Caputo fractional derivative operator will have the largest effect on the qualitative dynamics of the system. As a result, we consider 0<aS<1 with aE=aI=aR=aD=1. Having only one equation in the system be described as a fractional-order operator and the rest by an integer-order operator is computationally efficient in our approach. In [42,43] the authors consider a similar mixed fractional and integer-order scheme. Hence the modified Fractional-Distancing-SEIRD model that we consider is defined as

    ΔtaS1Γ(aS+1)C0DaStS=β(θ(t))SI,dEdt=β(θ(t))SIγE,dIdt=γE(α+δ)I,dRdt=αI,dDdt=δI, (2.9)

    along with initial data in Equation (2.2) and β(θ(t)) is defined as in Equation (2.3).

    To calibrate the models in Equations (2.9) and Equations (2.1), we utilize the data collected by the COVID-19 Canada Open Data Working Group for the Canadian province of Ontario [2]. This data-set contains data of the form {It,Rt,Dt} where It represents the cumulative known infections at time t, Rt represents the cumulative known recoveries at time t, and Dt represents the cumulative known fatalities at time t. As in Eastman et al. [26] we consider θ(t) as the simple linear spline. Hence θ(t) is the linear interpolant of the paired data {(0,θ0),(τ0,θ0),(τ1,θ1),,(τ20,θ20)}, where θ0, τ0, , τ20 are as in Table 2.

    To quantify the quality of the fit, we consider a simple weighted sum of squared errors of infection, recovered, and fatality data as in Equation (2.10)

    χ(ξ)=1n(WI+WR+WD)1(WIni=0(xvI(ti)Iti)2+WRni=0(R(ti)Rti)2+WDni=0(D(ti)Dti)2). (2.10)

    We then minimized this function in both the integer and fractional-order case by solving Equations (2.1) and Equations (2.9) for a given parameter set and calculating the value of Equation (2.10). This cost function value is then minimized via Matlab's implementation of the genetic algorithm [44]. We initialized the population used in the genetic algorithm with a stochastic scheme. For each of the parameters γ, α, βDD, βDM, and βMM in each of the initial population vectors, we initialized the value to be either a randomly selected value or the corresponding value from Eastman et al. [26]. Given that those values were selected by the authors for fitting the first wave of SARS-CoV-2 in Ontario, we would expect similar values to apply in this case. All other parameters were initially randomly selected. In both the fractional and integer-order case, we ran the genetic algorithm on a population of 1500 vectors for a maximum of 30,000 generations stopping early if the average relative fitness value has not changed by more than 106 for at least 100 generations. Finally, the fits are polished via Nelder-Mead's simplex algorithm to ensure convergence.

    For the integer-order case, Equations (2.1) were solved via Runge-Kutte of order 4/5. For the fractional-order case, Equations (2.9) were solved via the L1-2 operator in Equation (2.7) for the S compartment and the Runge-Kutte 4/5 operator in the remaining compartments. Both were solved over the time mesh (t0,t1,,tn)=(0,1,,376).

    The model calibration method, as outlined in Section 2.3, contains a large number of unknown parameters. In this section, we outline the process by which we verify that the fitting procedure can leverage sufficient information in order for the method to uniquely identify optimizing parameters. As such, we borrow the definition of practical identifiability from Miao et al. [45]

    Definition 2 ([45]). A dynamical system parameterized by ξ is practically identifiable if ξ can be uniquely determined from the measurable system output {y(ti)}.

    There are various methods by which one can determine the set of practically identifiable parameters for a given model. Moreover, these different methods are not guaranteed to produce the same set of identifiable parameters. For instance, in [22], López et al. consider such practical identifiability analysis methods such as the variance method, SVD method, QR method, and Monte-Carlo method. Moreover, the authors note that while the various methods rarely produce the same number of identifiable parameters, combinations of methods are often able to produce an identifiable subset of appropriate dimensions.

    For a given output signal y, the sensitivity matrix is defined as

    χ=yξ=[y(ti)ξj]ij, (2.11)

    from which the Fisher information matrix is defined F=χTχ.

    We follow the SVD and QR methods from [22] (though these methods are not unique to these authors, see for instance, [23,24,45,46,47]). Both the QR method and the SVD method depend upon the sensitivity matrix from Equation (2.11). In particular, both methods fail if the Fisher information matrix is singular. Given the output signal

    y=wIxvI+wRR+wDDwI+wR+wD. (2.12)

    There are certain choices of weights WI, WR, and WD that result in duplicate columns in χ (and hence, a singular Fisher information matrix).

    Lemma 3. Let (S,E,I,R,D) be a solution to Equations (2.1), then

    Iα=Iδ.

    Proof. First, let Sα=Sα, Eα=Eα, and Iα=Iα then

    dIαdt=α(dIdt)=α(γE(α+δ)I)=γEαI(α+δ)Iα.

    Similarly,

    dSαdt=β(θ(t))SαIβ(θ(t))SIα,anddEαdt=β(θ(t))SαI+β(θ(t))SIαγEα.

    If we define Sδ, Eδ, and Iδ analogously, then by a similar argument we find

    dSδdt=β(θ(t))SδIβ(θ(t))SIδ, (2.13)
    dEδdt=β(θ(t))SδI+β(θ(t))SIδγEδ, (2.14)
    dIδdt=γEδI(α+δ)Iδ. (2.15)

    Now the system

    dy1dt=β(θ(t))y1Iβ(θ(t))Sy3, (2.16)
    dy2dt=β(θ(t))y1I+β(θ(t))Sy3γy2, (2.17)
    dy3dt=γy2I(α+δ)y3, (2.18)

    has a unique solution and so Sα=Sδ, Iα=Iδ, and Eα=Eδ

    Lemma 4. Let (S,E,I,R,D) be a solution to Equations (2.1). For y=wIxvI+wRR+wDDwI+wR+wD then yα=yδ, if and only if, wR=wD.

    Proof. Let ˆwI=wIwI+wR+wD and let ˆwR and ˆwD be defined analogously. Then,

    yα=ˆwIxvIα+ˆwR(I+αIα)dt+ˆwDδIαdt.

    similarly,

    yδ=ˆwIxvIδ+ˆwRαIδdt+ˆwD(I+δIδ)dt.

    Hence, by Lemma 3,

    yαyδ=(ˆwRˆwD)Idt,

    from which the result follows.

    Hence, we consider the SVD and QR method on the model output in Equation (2.12) under the additional assumption WRWD. Moreover, we notice without loss of generality; we can take any weight in Equation (2.12) to be unitary. As a result, we set WR=1 and consider WD1 with WI arbitrary.

    The sensitivity matrix in Equation (2.11) can only be known exactly for systems that are simple enough to yield an analytic solution to the differential equations. For the purposes of our model, we approximate χ by performing a central difference derivative with step size h. When solving the differential systems under the perturbed parameters, we use a relative error tolerance of h2 in order to ensure the numerical stability of the scheme [23].

    The identifiability analysis procedure outlined in Section 2.3.1 is dependent upon the choice of weights (WI,WR,WD) used in the weighting of the model output function in Equation (2.12). As observed at the end of Section 2.3.1: to avoid duplicate columns in the sensitivity matrix in Equation (2.11) and to avoid duplication of the model output function in Equation (2.12), we consider WR=1, WD1, with WI arbitrary. We performed the SVD and QR method across a lattice of weight choices where WI{1,2,,100} and WD{2,3,,100}. After this search, we selected a (non-unique) weight choice that gave the largest set of identifiable parameters in WI=100, WR=1, WD=10. We also note that this choice of weighting overemphasizes the importance of accurately fitting the active infections and ranks fitting the fatalities as more important than fitting the recoveries. While this is not the only choice of weights one could consider, we believe it represents a reasonable decision to fixate on the importance of tracking active cases and accurately reporting fatalities.

    We present the results of the identifiability analysis in Table 1. Notably, for the integer-order case, there are two commonly unidentifiable parameters (namely xv and θ20). Eastman et al. [26] had formulated xv to represent the "visible proportion of the infected population." Notably, this phenomenological factor was introduced to account for those individuals with active cases that have not been tested but nonetheless are seeding secondary cases. Given the difficulty of measuring this in another capacity, we simply set xv=1 for the purposes of performing the fitting procedure. Now, θ20 represents the final interpolating point in the public response function being considered. Given that τ20 corresponds with early February 2021, a time period wherein Ontario was in province-wide lockdown; we set θ20=1 to force a maximal reduction in transmission by the general populace. Similarly, for the fractional-order case, we note that xv, θ20, and aS are the common unidentifiable parameters. As such we consider xv=θ20=1 as in the integer-order case. For aS, we consider three possibilities: aS=0.7, aS=0.8, and aS=0.9 and cross-compare the results in Section 3.3. We chose these values of aS between 0.7 and 1 as has been observed elsewhere in the literature (for instance, a value of aS=0.725 was found optimal in Rajagopal et al. [48] and various values of fractional-order between 0.85 and 1 were considered in Khan et al. [49] for similar compartmental models of COVID-19).

    Table 1.  Results of Performing the SVD and QR Method with the model output function in Equation (2.12) (with WI=100, WR=1, and WD=10) on the integer-order and fractional-order systems from Equations (2.1) and 2.8. Note that in the integer case xv and θ20 are common unidentifiable parameters. Similarly, for the fractional-order case xv, θ20, and aS are unidentifiable parameters.
    Method Dimension of Identifiable Subset Unidentifiable Parameters
    Integer-Order Equations (2.1)
    QR 22 {θ3,θ10,θ11,θθ20,xv}
    SVD 21 {θ16,θ17,θ18,θ19,θθ20,xv}
    Fractional-Order Equations (2.9)
    QR 24 {θ6,θθ20,aS,xv}
    SVD 22 {βDD,βDM,βMM,θθ20,aS,xv}

     | Show Table
    DownLoad: CSV

    We present the result of the integer-order fitting procedure in Figure 1. In particular, this fit was obtained by setting xv=1=θ20 with θ0=0, as in Table 2. The values of the parameters discovered by the fitting process used to construct this figure can be found in Table 3.

    Figure 1.  A comparison of Ontario time-series data with the predictions of the Distancing-SEIRD model obtained using the fit parameters for the integer-order model in Table 3 and the common fixed parameters in Table 2.
    Table 2.  A Table of fixed parameter values common to both the integer-order model in Equations (2.1) as well as the three parametrizations of the fractional-order model in Equations (2.9).
    Chosen Parameter Value Unit
    N 14711827 person
    E0 20 person
    I0 1 person
    xv 1
    θ0 0
    θ20 1
    τ0 52 day
    τ1 67 day
    τ2 82 day
    τ3 97 day
    τ4 112 day
    τ5 127 day
    τ6 142 day
    τ7 157 day
    τ8 172 day
    τ9 187 day
    τ10 202 day
    τ11 217 day
    τ12 232 day
    τ13 250 day
    τ14 268 day
    τ15 286 day
    τ16 304 day
    τ17 322 day
    τ18 340 day
    τ19 358 day
    τ20 376 day

     | Show Table
    DownLoad: CSV
    Table 3.  Parameters determined by the fitting process for all four fits. The second column corresponds to parameters discovered to fit the integer-order model in Equations (2.1). The third, fourth, and fifth columns correspond to the parameters discovered to fit the fractional-order model in Equations (2.9) under the fractional-order aS=0.7, aS=0.8, aS=0.9.
    Parameter Integer Frac (aS=0.7) Frac (aS=0.8) Frac (aS=0.9) Unit
    γ 4.550e-01 1.429e-01 1.429e-01 1.429e-01 (day1)
    α 8.160e-02 7.585e-02 7.585e-02 7.585e-02 (day1)
    δ 2.768e-03 2.593e-03 2.593e-03 2.593e-03 (day1)
    βDD 2.734e-09 1.836e-09 1.841e-09 1.840e-09 (person1)(day1)
    βDM 7.200e-09 1.836e-09 1.841e-09 1.840e-09 (person1)(day1)
    βMM 1.227e-08 1.680e-08 1.680e-08 1.680e-08 (person1)(day1)
    θ1 5.241e-03 3.445e-10 7.804e-10 3.086e-10 (day1)
    θ2 1.519e-01 8.157e-02 8.157e-01 8.156e-01 (day1)
    θ3 9.163e-01 1.000e+00 1.000e+00 1.000e+00 (day1)
    θ4 3.947e-01 7.847e-01 7.850e-01 7.848e-01 (day1)
    θ5 6.251e-01 9.257e-01 9.256e-01 9.256e-01 (day1)
    θ6 6.903e-01 9.624e-01 9.626e-01 9.625e-01 (day1)
    θ7 7.882e-01 8.940e-01 8.940e-01 8.939e-01 (day1)
    θ8 4.813e-01 8.562e-01 8.563e-01 8.561e-01 (day1)
    θ9 4.956e-01 9.594e-01 9.595e-01 9.595e-01 (day1)
    θ10 9.054e-01 8.258e-01 8.260e-01 8.255e-01 (day1)
    θ11 1.278e-01 8.716e-01 8.715e-01 8.716e-01 (day1)
    θ12 2.715e-01 5.422e-01 5.420e-01 5.412e-01 (day1)
    θ13 3.243e-01 8.495e-01 8.495e-01 8.492e-01 (day1)
    θ14 4.657e-01 8.052e-01 8.052e-01 8.048e-01 (day1)
    θ15 3.190e-01 7.648e-01 7.645e-01 7.640e-01 (day1)
    θ16 4.058e-01 8.193e-01 8.191e-01 8.186e-01 (day1)
    θ17 5.145e-01 8.630e-01 8.629e-01 8.624e-01 (day1)
    θ18 2.511e-01 7.111e-01 7.104e-01 7.091e-01 (day1)
    θ19 6.623e-01 9.792e-01 9.792e-01 9.787e-01 (day1)

     | Show Table
    DownLoad: CSV

    Next, we present the fits of the fractional-order fitting procedure. These fits were likewise obtained by setting xv=1=θ20 with θ0=0. However, we also considered varying aS amongst aS=0.7, aS=0.8, and aS=0.9. The results of the fit with aS=0.7 are presented in Figure 2. As the plots are visually quite similar between the fractional-order cases with any deviations being difficult to see with the naked eye, we relegate plotting the results with aS=0.8 and the results with aS=0.9 to the supplement. The values of the parameters used to acquire these fits are reported in the appropriate columns of Table 3 for all three fractional-order fits.

    Figure 2.  The results of the fractional-order fitting method with aS=0.7 as compared with time series data in Ontario. The plots for the other fractional-orders considered are visually very similar. Note the tighter fit of the compartments to the data compared to Figure 1. Especially we note that while both the integer order and fractional-order method fit the second wave of infection quite closely, the fractional-order method provides a tighter fit to the first wave as well. This suggests that while the predictive power of both methodologies is similar, the fractional-order method does a better job of describing the entire history of the epidemic curves.

    In qualitatively assessing the fits, we note the integer-order model struggles to hug both waves considered as evidenced by how consistently the infections line falls below the data between April 2020 and Sept 2020 in Figure 1. Moreover, our fitting method is biased towards fitting infections more closely when the infections are largest, as was the case in the wave of the pandemic in the months of November 2020–February 2021. Despite this, the fractional-order fits visually fit the infection curves to the data in both waves much more closely while still maintaining the appropriate curvature in the recovered curves. All models have difficulty fully matching all three curves at once, as evidenced by the gap between the fatalities curves and the data. By tuning the weights, different qualities of fit can be achieved for different purposes. For the purposes of this manuscript, we considered fitting infections as the most important.

    As we can see from Table 3, the fractional-order fitting procedures all converged to the same form of the public response function θ(t). Moreover, Figure 3 shows a comparison of the exact values of the function θ(t) between the integer-order model and the fractional-order model. As we discussed in Section 3.3, the similarities between the fractional-order columns of Table 1 is an unsurprising result due to the practical unidentifiability of aS. In this case, some parameters remain constant when the unidentifiable parameter is varied. Indeed, the results of Table 3 suggest that the unidentifiability of aS is due to a correlation between aS and βDD (or, equivalently, βDD) in the fractional-order model.

    Figure 3.  Comparisons of the public response function θ(t) for the four fit procedures considered. Note that, for the fractional-order case, due to the practical unidentifiability of αS, any differences between the form of θ(t) are minor and all three plots, in this case, are stacked (almost exactly) atop one another.

    Moreover, the fractional-order fitting procedures all discovered values of βDD that were very close to those of βDM (with changes only appearing in the fifth significant figure at the 1014 level), suggesting that, at least in the fractional case, the model may be simplified by assuming βDD=βDM. We also note that the values of γ, α, and δ varied only trivially for different values of aS. For comparison with the integer-order method, we note that α and δ are very similar values between all four methods corresponding to a value of 1/(α+δ)12 days between all four methods suggesting that an individual is infectious for, on average, 12 days. Note that due to the way recovered tests are processed in Ontario (namely, cases without any medical follow-up are marked as "recovered" if 14 days have passed since symptom onset), this value is artificially biased towards a value of 14. One change between the integer-order fit and the fractional-order fits can be found in the value of γ. For the fractional-order fits, this value suggests a mean incubation period of approximately one week, while for the integer-order fits, this value suggests a mean incubation period of approximately 2-3 days.

    While the exact values of the public response function θ(t) differ between the integer-order model and the fractional-order model, many of the qualitative features of the function are maintained, as is evidenced in Figure 3. In particular, considering the fractional-order θ(t) between April 2020 and August 2020, the value seems to oscillate around some baseline values. This suggests that the public response function could be replaced either with a constant value on this domain or a constant value with appropriately scaled sinusoidal oscillations. A similar phenomenon can be observed for the integer-order model in the same time frame, albeit with a different baseline through a similar oscillatory frequency. Both methods predict a drop in the public response function heading out of Summer 2020 into Autumn 2020, potentially due to the return of school. In the fractional-order case, the assumption that θ20=1 appears to be more realistic than in the integer-order case, as evidenced by θ19 being near 1 in the former but not the latter.

    For the purposes of ascertaining which of the models has the strongest predictive strength, we included additional historical data corresponding to an additional 42 days of time series data. This additional data was not used for fitting but only for evaluating the predictive power of the fit. To that end, the model was fit on the first 376 data points and evaluated on the remaining 42 data points. Now, the model requires a particular form of θ(t) for these additional data values. For the purposes of this comparison, we considered two such future θ(t) schedules: one where θ(t>376)=θ(376) (i.e., θ(t) was fixed at θ=1 for t376) and another where θ(t) was fixed at θ=1 for half of the new time points (376t397) and θ=0 for half of the new time points (397t). The motivation for these θ schedules is as follows: for the first schedule, we simply projected the last fit value of θ naively forward, and for the second schedule, we recognize that a stay-at-home order in Ontario was set to be lifted by mid-February 2021 as a result we would predict a reduced θ value corresponding to greater degrees of social mixing. For both of these schedules, we judged the quality of the prediction by considering the value under the cost function (2.10) for only the new time points. In both experiments, the fractional model corresponding to αs=0.8 performed the best, and so it is the only one we report. For the first θ, schedule χfrac0.26χint, and for the second χfrac1.02χint. This suggests that both the integer and fractional-order models are both similarly sensitive to the form of θ(t). As a result, the model presented is only appropriate for predicting over short time intervals (as sensitivities to the particular form of θ(t) prescribed for prediction begin to dominate shortly thereafter). However, in the first schedule considered where θ(t) is changing, the fractional-order model appears to do a better job at qualitatively describing the dynamics of the system. The results of these predictions are summarized in Figure 4. Note the similarities in the bottom plot suggesting that both methods perform similarly poorly when the prescribed form of θ(t) is a poor match to the data. Moreover, note that the presence of the so-called memory effect of fractional operators could be responsible for the increased predictive power for short-term predictions when the form of θ(t) is not perturbed. However, we also note that it is evident that the trajectories will soon begin to diverge from the data if the projection is carried forward in the top left figure in Figure 4.

    Figure 4.  Predictions for the fractional-order model (left) and the integer-order model (right). The two possible θ schedules considered were θ=1 for all future time (top) and θ=1 for the first half of the prediction time and θ=0 for the second half of the prediction time (bottom). The time interval for prediction is highlighted in red.

    Modeling the spread of an epidemic allows one to calculate metrics that are useful for targeting control of the disease spread. Other than standard epidemiological data like active infections, recoveries, and deaths, another important metric to track is that of the reproduction number. Famously, the basic reproduction number (denoted R0) represents the number of expected secondary cases that would be seeded by a single infectious individual in an otherwise wholly susceptible population. This number is a function not only of the disease being considered but also represents behavioral quirks of the citizenry under investigation. As the pandemic has progressed, behavior patterns of the general public have changed to embrace transmission reduction protocols through the implementation of remote work, maintenance of social distance, the wearing of face masks, etc. As a result, even if the virus itself does not change, the number of secondary cases can change due to changing public behavior. Hence, while R0 is an important number to calculate, the time-dependent effective reproduction number Rt is often of greater interest. This is especially true later in a pandemic as the decrease in transmission due to the boost in immunity from previous infection and vaccination is captured by Rt but not by R0. In fact, Rt is often argued to be the most important number to track in order to manage an epidemic outbreak. Broadly, Rt represents the expected number of secondary cases seeded by a single infectious individual at time t during the pandemic. If Rt<1 is maintained for a prolonged period of time, then one can expect that transmission of the virus is slowing. If the actions of the public keep Rt below 1 for long enough, then the virus will eventually die out in the population. In contrast, if Rt>1, then the transmission is increasing within the population.

    Here we used a Bayesian method that was developed by Bettencourt and Riberio [50] to estimate the effective reproduction number Rt from each of the trajectories of the four fit procedures. We then plot the mean value of these four procedures along with the union of the 95% confidence intervals of each procedure. This visualization is presented in Figure 5.

    Figure 5.  The time-dependent basic reproductive number Rt derived from the four fit procedures considered. The black line represents the mean value of Rt between all procedures and the grey band represents the union of the 95% confidence intervals of Rt across all fitting procedures.

    Following Bettencourt and Riberio, we assumed that new infection cases arise according to a Poisson distribution with a mean value equal to 0.25. This corresponds to a serial interval of 4 days for COVID-19 [51]. Further, it is assumed that the distribution of Rt is a Gaussian centered around Rt1 with a standard deviation σ=0.1. As the visualization in Figure 5 demonstrates, Rt remained above the bifurcation value of 1 for most of the Spring of 2020 and the Autumn of 2020 before falling below 1 briefly in the early months of 2021.

    We considered a modified model of SARS-CoV-2 spread in the Canadian province of Ontario. Under this expanded model, we demonstrated that a large number of the parameters were practically identifiable from the data. We then fit these parameter values using a combination of a genetic algorithm and a simplex method for the identifiable parameters and assigned reasonable choices to the unidentifiable parameters. Moreover, we expand our model to accurately describe the dynamics of multiple waves of epidemic infection with a modified fractional-order model. Thus, the fitting process was repeated for an integer-order model as well as three fractional-order models using the discretized Caputo operator. While the Caputo operator has been observed to be beneficial in mathematical biological systems, the results of the fitting procedures between these methods are qualitatively quite similar, though a modest decrease in cost function value of approximately 27% was observed by considering the fractional-order operator over the integer-order one. Future research should consider the effects of vaccination in our model. Moreover, our proposed model could be extended to account for hospitalized, quarantined, or home isolation individuals. With respect to the fractional-order model, comparing the effects of different definitions of the fractional derivative operator on the physical behavior of the model trajectories would be of interest.

    Financial support by the Natural Sciences and Engineering Research Council of Canada (NSERC)(MK) is gratefully acknowledged.

    The authors declare there is no conflict of interest.

    [1] European Commision (2014) Journalist Workshop on organ donation and transplantation, Recent facts and figures.
    [2] Weiner DE (2009) Public health consequences of chronic kidney disease. Clin Pharmacol Ther 86: 566–569. doi: 10.1038/clpt.2009.137
    [3] Hesuani Y, Pereira FDAS, Parfenov V, et al. (2016) Design and Implementation of Novel Multifunctional 3D Bioprinter. 3D Print Addit Manuf 3: 64. doi: 10.1089/3dp.2015.0040
    [4] Colaco M, Igel DA, Atala A (2018) The potential of 3D printing in urological research and patient care. Nat Rev Urol 15: 213–221. doi: 10.1038/nrurol.2018.6
    [5] Cui X, Breitenkamp K, Lotz M, et al. (2012) Synergistic action of fibroblast growth factor-2 and transforming growth factor-beta1 enhances bioprinted human neocartilage formation. Biotechnol Bioeng 109: 2357–2368. doi: 10.1002/bit.24488
    [6] Murphy SV, Atala A (2014) 3D bioprinting of tissues and organs. Nat Biotechnol 32: 773–785. doi: 10.1038/nbt.2958
    [7] Skardal A, Atala A (2015) Biomaterials for integration with 3-D bioprinting. Ann Biomed Eng 43: 730–746. doi: 10.1007/s10439-014-1207-1
    [8] Bulanova EA, Koudan EV, Degosserie J, et al. (2017) Bioprinting of functional vascularized mouse thyroid gland construct. Biofabrication 9: 034105. doi: 10.1088/1758-5090/aa7fdd
    [9] Rak-Raszewska A, Hauser PV, Vainio S (2015) Organ In Vitro Culture: What Have We Learned about Early Kidney Development? Stem Cells Int 2015: 959807.
    [10] Grobstein C (1953) Inductive epitheliomesenchymal interaction in cultured organ rudiments of the mouse. Sci 118: 52–55. doi: 10.1126/science.118.3054.52
    [11] Grobstein C (1956) Inductive tissue interaction in development. Adv Cancer Res 4: 187–236. doi: 10.1016/S0065-230X(08)60725-3
    [12] Saxen L, Wartiovaara J (1966) Cell contact and cell adhesion during tissue organization. Int J Cancer 1: 271–290. doi: 10.1002/ijc.2910010307
    [13] Herrera M, Mirotsou M (2014) Stem cells: potential and challenges for kidney repair. Am J Physiol Renal Physiol 306: F12–23. doi: 10.1152/ajprenal.00238.2013
    [14] Auerbach R, Grobstein C (1958) Inductive interaction of embryonic tissues after dissociation and reaggregation. Exp Cell Res 15: 384–397. doi: 10.1016/0014-4827(58)90039-9
    [15] Unbekandt M, Davies JA (2010) Dissociation of embryonic kidneys followed by reaggregation allows the formation of renal tissues. Kidney Int 77: 407–416. doi: 10.1038/ki.2009.482
    [16] Junttila S, Saarela U, Halt K, et al. (2015) Functional genetic targeting of embryonic kidney progenitor cells ex vivo. J Am Soc Nephrol 26: 1126–1137. doi: 10.1681/ASN.2013060584
    [17] Lancaster MA, Knoblich JA (2014) Organogenesis in a dish: modeling development and disease using organoid technologies. Sci 345: 1247125. doi: 10.1126/science.1247125
    [18] Schutgens F, Verhaar MC, Rookmaaker MB (2016) Pluripotent stem cell-derived kidney organoids: An in vivo-like in vitro technology. Eur J Pharmacol 790: 12–20. doi: 10.1016/j.ejphar.2016.06.059
    [19] Xinaris C, Benedetti V, Rizzo P, et al. (2012) In vivo maturation of functional renal organoids formed from embryonic cell suspensions. J Am Soc Nephrol 23: 1857–1868. doi: 10.1681/ASN.2012050505
    [20] Xinaris C, Benedetti V, Novelli R, et al. (2015) Functional Human Podocytes Generated in Organoids from Amniotic Fluid Stem Cells. J Am Soc Nephrol 27: 1400–1411.
    [21] Bantounas I, Ranjzad P, Tengku F, et al. (2018) Generation of Functioning Nephrons by Implanting Human Pluripotent Stem Cell-Derived Kidney Progenitors. Stem Cell Reports 10: 766–779. doi: 10.1016/j.stemcr.2018.01.008
    [22] Bertram JF, Douglas-Denton RN, Diouf B, et al. (2011) Human nephron number: implications for health and disease. Pediatr Nephrol 26: 1529–1533. doi: 10.1007/s00467-011-1843-8
    [23] Ogunlade O, Connell JJ, Huang JL, et al. (2017) In vivo 3-dimensional photoacoustic imaging of the renal vasculature in preclinical rodent models. Am J Physiol Renal Physiol 314: F1145–1153.
    [24] Wilmer MJ, Saleem MA, Masereeuw R, et al. (2010) Novel conditionally immortalized human proximal tubule cell line expressing functional influx and efflux transporters. Cell Tissue Res 339: 449–457. doi: 10.1007/s00441-009-0882-y
    [25] Saleem MA, O'Hare MJ, Reiser J, et al. (2002) A conditionally immortalized human podocyte cell line demonstrating nephrin and podocin expression. J Am Soc Nephrol 13: 630–638.
    [26] Rak-Raszewska A, Wilm B, Edgar D, et al. (2012) Development of embryonic stem cells in recombinant kidneys. Organog 8: 125–136. doi: 10.4161/org.22597
    [27] Takasato M, Er PX, Becroft M, et al. (2014) Directing human embryonic stem cell differentiation towards a renal lineage generates a self-organizing kidney. Nat Cell Biol 16: 118–126. doi: 10.1038/ncb2894
    [28] Taguchi A, Kaku Y, Ohmori T, et al. (2014) Redefining the in vivo origin of metanephric nephron progenitors enables generation of complex kidney structures from pluripotent stem cells. Cell Stem Cell 14: 53–67. doi: 10.1016/j.stem.2013.11.010
    [29] Kang M, Han YM (2014) Differentiation of human pluripotent stem cells into nephron progenitor cells in a serum and feeder free system. PLoS One 9: e94888. doi: 10.1371/journal.pone.0094888
    [30] Toyohara T, Mae S, Sueta S, et al. (2015) Cell Therapy Using Human Induced Pluripotent Stem Cell-Derived Renal Progenitors Ameliorates Acute Kidney Injury in Mice. Stem Cells Transl Med 4: 980–992. doi: 10.5966/sctm.2014-0219
    [31] Takasato M, Er PX, Chiu HS, et al. (2015) Kidney organoids from human iPS cells contain multiple lineages and model human nephrogenesis. Nat 526: 564–568. doi: 10.1038/nature15695
    [32] Freedman BS, Brooks CR, Lam AQ, et al. (2015) Modelling kidney disease with CRISPR-mutant kidney organoids derived from human pluripotent epiblast spheroids. Nat Commun 6: 8715. doi: 10.1038/ncomms9715
    [33] Morizane R, Lam AQ, Freedman BS, et al. (2015) Nephron organoids derived from human pluripotent stem cells model kidney development and injury. Nat Biotechnol 33: 1193–1200. doi: 10.1038/nbt.3392
    [34] Morizane R, Bonventre JV (2017) Generation of nephron progenitor cells and kidney organoids from human pluripotent stem cells. Nat Protoc 12: 195–207.
    [35] Xia Y, Sancho-Martinez I, Nivet E, et al. (2014) The generation of kidney organoids by differentiation of human pluripotent cells to ureteric bud progenitor-like cells. Nat Protoc 9: 2693–2704. doi: 10.1038/nprot.2014.182
    [36] He Y, Yang F, Zhao H, et al. (2016) Research on the printability of hydrogels in 3D bioprinting. Sci Rep 6: 29977. doi: 10.1038/srep29977
    [37] Aljohani W, Ullah MW, Zhang X, et al. (2018) Bioprinting and its applications in tissue engineering and regenerative medicine. Int J Biol Macromol 107: 261–275. doi: 10.1016/j.ijbiomac.2017.08.171
    [38] Jose RR, Rodriguez MJ, Dixon TA, et al. (2016) Evolution of Bioinks and Additive Manufacturing Technologies for 3D Bioprinting. ACS Biomater Sci Eng 2: 1662–1678. doi: 10.1021/acsbiomaterials.6b00088
    [39] Garreta E, Oria R, Tarantino C, et al. (2017) Tissue engineering by decellularization and 3D bioprinting. Mater Today 20: 166–178. doi: 10.1016/j.mattod.2016.12.005
    [40] Murphy SV, Skardal A, Atala A (2013) Evaluation of hydrogels for bio-printing applications. J Biomed Mater Res A 101: 272–284.
    [41] Cui H, Nowicki M, Fisher JP, et al. (2017) 3D Bioprinting for Organ Regeneration. Adv Healthc Mater 6: 1601118. doi: 10.1002/adhm.201601118
    [42] Peppas NA, Bures P, Leobandung W, et al. (2000) Hydrogels in pharmaceutical formulations. Eur J Pharm Biopharm 50: 27–46. doi: 10.1016/S0939-6411(00)00090-4
    [43] Drury JL, Mooney DJ (2003) Hydrogels for tissue engineering: scaffold design variables and applications. Biomater 24: 4337–4351. doi: 10.1016/S0142-9612(03)00340-5
    [44] Peloso A, Tamburrini R, Edgar L, et al. (2016) Extracellular matrix scaffolds as a platform for kidney regeneration. Eur J Pharmacol 790: 21–27. doi: 10.1016/j.ejphar.2016.07.038
    [45] Pati F, Jang J, Ha DH, et al. (2014) Printing three-dimensional tissue analogues with decellularized extracellular matrix bioink. Nat Commun 5: 3935. doi: 10.1038/ncomms4935
    [46] Skardal A, Devarasetty M, Kang HW, et al. (2015) A hydrogel bioink toolkit for mimicking native tissue biochemical and mechanical properties in bioprinted tissue constructs. Acta Biomater 25: 24–34. doi: 10.1016/j.actbio.2015.07.030
    [47] Jang J, Kim TG, Kim BS, et al. (2016) Tailoring mechanical properties of decellularized extracellular matrix bioink by vitamin B2-induced photo-crosslinking. Acta Biomater 33: 88–95. doi: 10.1016/j.actbio.2016.01.013
    [48] Beamish JA, Chen E, Putnam AJ (2017) Engineered extracellular matrices with controlled mechanics modulate renal proximal tubular cell epithelialization. PLoS One 12: e0181085. doi: 10.1371/journal.pone.0181085
    [49] Chen WC, Lin HH, Tang MJ (2014) Regulation of proximal tubular cell differentiation and proliferation in primary culture by matrix stiffness and ECM components. Am J Physiol Renal Physiol 307: F695–707. doi: 10.1152/ajprenal.00684.2013
    [50] Homan KA, Kolesky DB, Skylar-Scott MA, et al. (2016) Bioprinting of 3D Convoluted Renal Proximal Tubules on Perfusable Chips. Sci Rep 6: 34845. doi: 10.1038/srep34845
    [51] Kleinman HK, Martin GR (2005) Matrigel: basement membrane matrix with biological activity. Semin Cancer Biol 15: 378–386. doi: 10.1016/j.semcancer.2005.05.004
    [52] Moll S, Ebeling M, Weibel F, et al. (2013) Epithelial cells as active player in fibrosis: findings from an in vitro model. PLoS One 8: e56575. doi: 10.1371/journal.pone.0056575
    [53] Mu X, Zheng W, Xiao L, et al. (2013) Engineering a 3D vascular network in hydrogel for mimicking a nephron. Lab Chip 13: 1612–1618. doi: 10.1039/c3lc41342j
    [54] King SM, Higgins JW, Nino CR, et al. (2017) 3D Proximal Tubule Tissues Recapitulate Key Aspects of Renal Physiology to Enable Nephrotoxicity Testing. Front Physiol 8: 123.
    [55] Nguyen DG, King SM, Presnell SC (2016) Engineered Renal Tissues, Arrays Thereof, and Methods of Making the Same.
    [56] Matak D, Brodaczewska KK, Lipiec M, et al. (2017) Colony, hanging drop, and methylcellulose three dimensional hypoxic growth optimization of renal cell carcinoma cell lines. Cytotechnology 69: 565–578. doi: 10.1007/s10616-016-0063-2
    [57] Astashkina AI, Mann BK, Prestwich GD, et al. (2012) A 3-D organoid kidney culture model engineered for high-throughput nephrotoxicity assays. Biomaterials 33: 4700–4711. doi: 10.1016/j.biomaterials.2012.02.063
    [58] Astashkina AI, Mann BK, Prestwich GD, et al. (2012) Comparing predictive drug nephrotoxicity biomarkers in kidney 3-D primary organoid culture and immortalized cell lines. Biomaterials 33: 4712–4721. doi: 10.1016/j.biomaterials.2012.03.001
    [59] Ozbolat IT, Hospodiuk M (2016) Current advances and future perspectives in extrusion-based bioprinting. Biomaterials 76: 321–343. doi: 10.1016/j.biomaterials.2015.10.076
    [60] Alconcel SNS, Baas AS, Maynard HD (2017) FDA-approved poly(ethylene glycol)–protein conjugate drugs. Polymer Chemistry 2: 1442–1448.
    [61] Holzl K, Lin S, Tytgat L, et al. (2016) Bioink properties before, during and after 3D bioprinting. Biofabrication 8: 032002. doi: 10.1088/1758-5090/8/3/032002
    [62] Hospodiuk M, Dey M, Sosnoski D, et al. (2017) The bioink: A comprehensive review on bioprintable materials. Biotechnol Adv 35: 217–239. doi: 10.1016/j.biotechadv.2016.12.006
    [63] Nguyen KT, West JL (2002) Photopolymerizable hydrogels for tissue engineering applications. Biomaterials 23: 4307–4314. doi: 10.1016/S0142-9612(02)00175-8
    [64] Arcaute K, Mann BK, Wicker RB (2006) Stereolithography of three-dimensional bioactive poly(ethylene glycol) constructs with encapsulated cells. Ann Biomed Eng 34: 1429–1441. doi: 10.1007/s10439-006-9156-y
    [65] Hockaday LA, Kang KH, Colangelo NW, et al. (2012) Rapid 3D printing of anatomically accurate and mechanically heterogeneous aortic valve hydrogel scaffolds. Biofabrication 4: 035005. doi: 10.1088/1758-5082/4/3/035005
    [66] He Y, Tuck CJ, Prina E, et al. (2017) A new photocrosslinkable polycaprolactone-based ink for three-dimensional inkjet printing. J Biomed Mater Res B Appl Biomater 105: 1645–1657. doi: 10.1002/jbm.b.33699
    [67] Hribar KC, Soman P, Warner J, et al. (2014) Light-assisted direct-write of 3D functional biomaterials. Lab Chip 14: 268–275. doi: 10.1039/C3LC50634G
    [68] Li WW, Li H, Liu ZF, et al. (2009) Determination of residual acrylamide in medical polyacrylamide hydrogel by high performance liquid chromatography tandem mass spectroscopy. Biomed Environ Sci 22: 28–31. doi: 10.1016/S0895-3988(09)60018-0
    [69] Rana D, Ramalingam M (2017) Enhanced proliferation of human bone marrow derived mesenchymal stem cells on tough hydrogel substrates. Mater Sci Eng C Mater Biol Appl 76: 1057–1065. doi: 10.1016/j.msec.2017.03.202
    [70] Hron P, Slechtova J, Smetana K, et al. (1997) Silicone rubber-hydrogel composites as polymeric biomaterials. IX. Composites containing powdery polyacrylamide hydrogel. Biomaterials 18: 1069–1073.
    [71] Xi TF, Fan CX, Feng XM, et al. (2006) Cytotoxicity and altered c-myc gene expression by medical polyacrylamide hydrogel. J Biomed Mater Res A 78: 283–290.
    [72] Xi L, Wang T, Zhao F, et al. (2014) Evaluation of an injectable thermosensitive hydrogel as drug delivery implant for ocular glaucoma surgery. PLoS One 9: e100632. doi: 10.1371/journal.pone.0100632
    [73] Dumortier G, Grossiord JL, Agnely F, et al. (2006) A review of poloxamer 407 pharmaceutical and pharmacological characteristics. Pharm Res 23: 2709–2728. doi: 10.1007/s11095-006-9104-4
    [74] Wu W, DeConinck A, Lewis JA (2011) Omnidirectional printing of 3D microvascular networks. Adv Mater 23: H178–83. doi: 10.1002/adma.201004625
    [75] Ozbolat IT (2017) 3D Bioprinting: Fundamentals, principles and Applications, London: Academy Press.
    [76] Gómez-Guillén MC, Giménez B, López-Caballero ME, et al. (2011) Functional and bioactive properties of collagen and gelatin from alternative sources: A review. Food Hydrocolloids 25: 1813–1827. doi: 10.1016/j.foodhyd.2011.02.007
    [77] Kuijpers AJ, van Wachem PB, van Luyn MJ, et al. (2000) In vivo compatibility and degradation of crosslinked gelatin gels incorporated in knitted Dacron. J Biomed Mater Res 51: 136–145. doi: 10.1002/(SICI)1097-4636(200007)51:1<136::AID-JBM18>3.0.CO;2-W
    [78] Kolesky DB, Homan KA, Skylar-Scott MA, et al. (2016) Three-dimensional bioprinting of thick vascularized tissues. Proc Natl Acad Sci U S A 113: 3179–3184. doi: 10.1073/pnas.1521342113
    [79] McManus MC, Boland ED, Koo HP, et al. (2006) Mechanical properties of electrospun fibrinogen structures. Acta Biomater 2: 19–28. doi: 10.1016/j.actbio.2005.09.008
    [80] Cui X, Boland T (2009) Human microvasculature fabrication using thermal inkjet printing technology. Biomater 30: 6221–6227. doi: 10.1016/j.biomaterials.2009.07.056
    [81] Fussenegger M, Meinhart J, Hobling W, et al. (2003) Stabilized autologous fibrin-chondrocyte constructs for cartilage repair in vivo. Ann Plast Surg 51: 493–498. doi: 10.1097/01.sap.0000067726.32731.E1
    [82] Achilli M, Mantovani D (2010) Tailoring Mechanical Properties of Collagen-Based Scaffolds for Vascular Tissue Engineering: The Effects of pH, Temperature and Ionic Strength on Gelation. Polymers 2: 664–680. doi: 10.3390/polym2040664
    [83] Park JY, Choi JC, Shim JH, et al. (2014) A comparative study on collagen type I and hyaluronic acid dependent cell behavior for osteochondral tissue bioprinting. Biofabrication 6: 035004. doi: 10.1088/1758-5082/6/3/035004
    [84] Lee KY, Moone DJ (2012) Alginate: Properties and biomedical applications. Progress in Polymer Science 37: 106–126. doi: 10.1016/j.progpolymsci.2011.06.003
    [85] Cohen DL, Lo W, Tsavaris A, et al. (2011) Increased mixing improves hydrogel homogeneity and quality of three-dimensional printed constructs. Tissue Eng Part C Methods 17: 239–248. doi: 10.1089/ten.tec.2010.0093
    [86] Gudapati H, Dey M, Ozbolat I (2016) A comprehensive review on droplet-based bioprinting: Past, present and future. Biomaterials 102: 20–42. doi: 10.1016/j.biomaterials.2016.06.012
    [87] Yan J, Huang Y, Chrisey DB (2013) Laser-assisted printing of alginate long tubes and annular constructs. Biofabrication 5: 015002.
    [88] Nguyen DG, Funk J, Robbins JB, et al. (2016) Bioprinted 3D Primary Liver Tissues Allow Assessment of Organ-Level Response to Clinical Drug Induced Toxicity In Vitro. PLoS One 11: e0158674. doi: 10.1371/journal.pone.0158674
    [89] Thirumala S, Gimble JM, Devireddy RV (2013) Methylcellulose based thermally reversible hydrogel system for tissue engineering applications. Cells 2: 460–475. doi: 10.3390/cells2030460
    [90] Lott JR, McAllister JW, Arvidson SA, et al. (2013) Fibrillar structure of methylcellulose hydrogels. Biomacromolecules 14: 2484–2488. doi: 10.1021/bm400694r
    [91] Oxlund H, Andreassen TT (1980) The roles of hyaluronic acid, collagen and elastin in the mechanical properties of connective tissues. J Anat 131: 611–620.
    [92] Luo Y, Kirker KR, Prestwich GD (2000) Cross-linked hyaluronic acid hydrogel films: new biomaterials for drug delivery. J Control Release 69: 169–184. doi: 10.1016/S0168-3659(00)00300-X
    [93] Jeon O, Song SJ, Lee K, et al. (2007) Mechanical properties and degradation behaviors of hyaluronic acid hydrogels cross-linked at various cross-linking densities. Carbohydrate Polymers 70: 251–257. doi: 10.1016/j.carbpol.2007.04.002
    [94] Gruene M, Pflaum M, Hess C, et al. (2011) Laser printing of three-dimensional multicellular arrays for studies of cell-cell and cell-environment interactions. Tissue Eng Part C Methods 17: 973–982. doi: 10.1089/ten.tec.2011.0185
    [95] Chuah JKC, Zink D (2017) Stem cell-derived kidney cells and organoids: Recent breakthroughs and emerging applications. Biotechnol Adv 35: 150–167. doi: 10.1016/j.biotechadv.2016.12.001
    [96] Reint G, Rak-Raszewska A, Vainio SJ (2017) Kidney development and perspectives for organ engineering. Cell Tissue Res 369: 171–183. doi: 10.1007/s00441-017-2616-x
    [97] Ozbolat I, Khoda A (2014) Design of a New Parametric Path Plan for Additive Manufacturing of Hollow Porous Structures With Functionally Graded Materials. ASME J Comput Inf Sci Eng 14: 041005. doi: 10.1115/1.4028418
    [98] Kamisuki S, Hagata T, Tezuka C, et al. (1998) A low power, small, electrostatically-driven commercial inkjet head: Proceedings MEMS 98. IEEE. Eleventh Annual International Workshop on Micro Electro Mechanical Systems. An Investigation of Micro Structures, Sensors, Actuators, Machines and Systems 1998 Jan 2529, Heidelberg, Germany, pp. 63–68.
    [99] Gasperini L, Maniglio D, Motta A, et al. (2015) An electrohydrodynamic bioprinter for alginate hydrogels containing living cells. Tissue Eng Part C Methods 21: 123–132. doi: 10.1089/ten.tec.2014.0149
    [100] Moghadam H, Samimi M, Samimi A, et al. (2010) Electrospray modeling of highly viscous and non-Newtonian liquids. Journal of Applied Polymer Science 118: 1288–1296.
    [101] Demirci U, Montesano G (2007) Single cell epitaxy by acoustic picolitre droplets. Lab Chip 7: 1139–1145. doi: 10.1039/b704965j
    [102] Khalil S, Nam J, Sun W (2005) Multi-nozzle deposition for construction of 3D biopolymer tissue scaffolds. Rapid Prototyp J 11: 9–17. doi: 10.1108/13552540510573347
    [103] Bajaj P, Schweller RM, Khademhosseini A, et al. (2014) 3D biofabrication strategies for tissue engineering and regenerative medicine. Annu Rev Biomed Eng 16: 247–276. doi: 10.1146/annurev-bioeng-071813-105155
    [104] Bhattacharjee N, Urrios A, Kang S, et al. (2016) The upcoming 3D-printing revolution in microfluidics. Lab Chip 16: 1720–1742. doi: 10.1039/C6LC00163G
    [105] Ovsianikov A, Muhleder S, Torgersen J, et al. (2014) Laser photofabrication of cell-containing hydrogel constructs. Langmuir 30: 3787–3794. doi: 10.1021/la402346z
    [106] Turunen S, Joki T, Hiltunen ML, et al. (2017) Direct Laser Writing of Tubular Microtowers for 3D Culture of Human Pluripotent Stem Cell-Derived Neuronal Cells. ACS Appl Mater Interfaces 9: 25717–25730. doi: 10.1021/acsami.7b05536
    [107] Odde DJ, Renn MJ (2000) Laser-guided direct writing of living cells. Biotechnol Bioeng 67: 312–318. doi: 10.1002/(SICI)1097-0290(20000205)67:3<312::AID-BIT7>3.0.CO;2-F
    [108] Delaporte P, Alloncle A (2016) Laser-induced forward transfer: A high resolution additive manufacturing technology. Optics & Laser Technology 78: 33–41.
    [109] Yan Y, Li S, Zhang R, et al. (2009) Rapid prototyping and manufacturing technology: principles, representative techniques, applications and development trends. Tsinghua Science & Technology 14: 1–12.
    [110] Zein I, Hutmacher DW, Tan KC, et al. (2002) Fused deposition modeling of novel scaffold architectures for tissue engineering applications. Biomaterials 23: 1169–1185. doi: 10.1016/S0142-9612(01)00232-0
    [111] Boland T, Mironov V, Gutowska A, et al. (2003) Cell and organ printing 2: fusion of cell aggregates in three-dimensional gels. Anat Rec A Discov Mol Cell Evol Biol 272: 497–502.
    [112] Bakhshinejad A, D'Souza R (2015) A brief comparison between available bio-printing methods. IEEE Great Lakes Biomedical Conference (GLBC), 2015 May 14–17, Milwaukee, WI, pp. 1–3.
    [113] Wilson WC, Boland T (2003) Cell and organ printing 1: protein and cell printers. Anat Rec A Discov Mol Cell Evol Biol 272: 491–496.
    [114] Klebe RJ (1988) Cytoscribing: a method for micropositioning cells and the construction of two- and three-dimensional synthetic tissues. Exp Cell Res 179: 362–373. doi: 10.1016/0014-4827(88)90275-3
    [115] Boland T, Tao X, Damon B, et al. (2007) Drop-on-demand printing of cells and materials for designer tissue constructs. Mater Sci Eng C 27: 372–376. doi: 10.1016/j.msec.2006.05.047
    [116] Derby B (2008) Bioprinting: Inkjet printing protein and hybrid cell-containing materials and structures. J Mater Chem 18: 5717–5721. doi: 10.1039/b807560c
    [117] Choi YJ, Yi HG, Kim SW, et al. (2017) 3D Cell Printed Tissue Analogues: A New Platform for Theranostics. Theranostics 7: 3118–3137. doi: 10.7150/thno.19396
    [118] Dhariwala B, Hunt E, Boland T (2004) Rapid prototyping of tissue-engineering constructs, using photopolymerizable hydrogels and stereolithography. Tissue Eng 10: 1316–1322. doi: 10.1089/ten.2004.10.1316
    [119] Odde DJ, Renn MJ (1999) Laser-guided direct writing for applications in biotechnology. Trends Biotechnol 17: 385–389. doi: 10.1016/S0167-7799(99)01355-4
    [120] Ringeisen BR, Kim H, Barron JA, et al. (2004) Laser printing of pluripotent embryonal carcinoma cells. Tissue Eng 10: 483–491. doi: 10.1089/107632704323061843
    [121] Ovsianikov A, Schlie S, Ngezahayo A, et al. (2007) Two-photon polymerization technique for microfabrication of CAD-designed 3D scaffolds from commercially available photosensitive materials. J Tissue Eng Regen Med 1: 443–449. doi: 10.1002/term.57
    [122] Guillemot F, Souquet A, Catros S, et al. (2010) High-throughput laser printing of cells and biomaterials for tissue engineering. Acta Biomater 6: 2494–2500. doi: 10.1016/j.actbio.2009.09.029
    [123] Gittard SD, Narayan RJ (2010) Laser direct writing of micro- and nano-scale medical devices. Expert Rev Med Devices 7: 343–356. doi: 10.1586/erd.10.14
    [124] Jang J, Yi H, Cho D (2016) 3D Printed Tissue Models: Present and Future. ACS Biomater Sci Eng 2: 1722–1731. doi: 10.1021/acsbiomaterials.6b00129
    [125] Ozbolat IT, Yu Y (2013) Bioprinting toward organ fabrication: challenges and future trends. IEEE Trans Biomed Eng 60: 691–699. doi: 10.1109/TBME.2013.2243912
    [126] Mironov V, Visconti RP, Kasyanov V, et al. (2009) Organ printing: tissue spheroids as building blocks. Biomaterials 30: 2164–2174. doi: 10.1016/j.biomaterials.2008.12.084
    [127] Jakab K, Norotte C, Marga F, et al. (2010) Tissue engineering by self-assembly and bio-printing of living cells. Biofabrication 2: 022001. doi: 10.1088/1758-5082/2/2/022001
    [128] Chung JHY, Naficy S, Yue Z, et al. (2013) Bio-ink properties and printability for extrusion printing living cells. Biomater Sci 1: 763–773. doi: 10.1039/c3bm00012e
    [129] Levato R, Visser J, Planell JA, et al. (2014) Biofabrication of tissue constructs by 3D bioprinting of cell-laden microcarriers. Biofabrication 6: 035020. doi: 10.1088/1758-5082/6/3/035020
    [130] Pati F, Ha DH, Jang J, et al. (2015) Biomimetic 3D tissue printing for soft tissue regeneration. Biomaterials 62: 164–175. doi: 10.1016/j.biomaterials.2015.05.043
    [131] Kang HW, Lee SJ, Ko IK, et al. (2016) A 3D bioprinting system to produce human-scale tissue constructs with structural integrity. Nat Biotechnol 34: 312–319. doi: 10.1038/nbt.3413
    [132] Rouwkema J, Rivron NC, van Blitterswijk CA (2008) Vascularization in tissue engineering. Trends Biotechnol 26: 434–441. doi: 10.1016/j.tibtech.2008.04.009
    [133] Rouwkema J, Khademhosseini A (2016) Vascularization and Angiogenesis in Tissue Engineering: Beyond Creating Static Networks. Trends Biotechnol 34: 733–745. doi: 10.1016/j.tibtech.2016.03.002
    [134] Lovett M, Lee K, Edwards A, et al. (2009) Vascularization strategies for tissue engineering. Tissue Eng Part B Rev 15: 353–370. doi: 10.1089/ten.teb.2009.0085
    [135] Costa-Almeida R, Granja PL, Soares R, et al. (2014) Cellular strategies to promote vascularisation in tissue engineering applications. Eur Cell Mater 28: 51–66; discussion 66–67.
    [136] Novosel EC, Kleinhans C, Kluger PJ (2011) Vascularization is the key challenge in tissue engineering. Adv Drug Deliv Rev 63: 300–311. doi: 10.1016/j.addr.2011.03.004
    [137] Zhang C, Hou J, Zheng S, et al. (2011) Vascularized atrial tissue patch cardiomyoplasty with omentopexy improves cardiac performance after myocardial infarction. Ann Thorac Surg 92: 1435–1442. doi: 10.1016/j.athoracsur.2011.05.054
    [138] Hammerman MR (2002) Xenotransplantation of developing kidneys. Am J Physiol Renal Physiol 283: F601–606. doi: 10.1152/ajprenal.00126.2002
    [139] Halt KJ, Parssinen HE, Junttila SM, et al. (2016) CD146(+) cells are essential for kidney vasculature development. Kidney Int 90: 311–324. doi: 10.1016/j.kint.2016.02.021
    [140] Robert B, St John PL, Abrahamson DR (1998) Direct visualization of renal vascular morphogenesis in Flk1 heterozygous mutant mice. Am J Physiol 275: F164–172.
    [141] Gao X, Chen X, Taglienti M, et al. (2005) Angioblast-mesenchyme induction of early kidney development is mediated by Wt1 and Vegfa. Development 132: 5437–5449. doi: 10.1242/dev.02095
    [142] Robert B, St John PL, Hyink DP, et al. (1996) Evidence that embryonic kidney cells expressing flk-1 are intrinsic, vasculogenic angioblasts. Am J Physiol 271: F744–753.
    [143] Hyink DP, Tucker DC, St John PL, et al. (1996) Endogenous origin of glomerular endothelial and mesangial cells in grafts of embryonic kidneys. Am J Physiol 270: F886–899.
    [144] Loughna S, Hardman P, Landels E, et al. (1997) A molecular and genetic analysis of renalglomerular capillary development. Angiogenesis 1: 84–101. doi: 10.1023/A:1018357116559
    [145] Rymer CC, Sims-Lucas S (2015) In utero intra-cardiac tomato-lectin injections on mouse embryos to gauge renal blood flow. J Vis Exp 2015: 52398 .
    [146] Serluca FC, Drummond IA, Fishman MC (2002) Endothelial signaling in kidney morphogenesis: a role for hemodynamic forces. Curr Biol 12: 492–497. doi: 10.1016/S0960-9822(02)00694-2
    [147] Bersini S, Moretti M (2015) 3D functional and perfusable microvascular networks for organotypic microfluidic models. J Mater Sci Mater Med 26: 180. doi: 10.1007/s10856-015-5520-5
    [148] Song JW, Bazou D, Munn LL (2015) Microfluidic model of angiogenic sprouting. Methods Mol Biol 1214: 243–254. doi: 10.1007/978-1-4939-1462-3_15
    [149] Jeon JS, Bersini S, Gilardi M, et al. (2015) Human 3D vascularized organotypic microfluidic assays to study breast cancer cell extravasation. Proc Natl Acad Sci U S A 112: 214–219. doi: 10.1073/pnas.1417115112
    [150] Miller JS, Stevens KR, Yang MT, et al. (2012) Rapid casting of patterned vascular networks for perfusable engineered three-dimensional tissues. Nat Mater 11: 768–774. doi: 10.1038/nmat3357
    [151] Kolesky DB, Truby RL, Gladman AS, et al. (2014) 3D bioprinting of vascularized, heterogeneous cell-laden tissue constructs. Adv Mater 26: 3124–3130. doi: 10.1002/adma.201305506
    [152] Huling J, Ko IK, Atala A, et al. (2016) Fabrication of biomimetic vascular scaffolds for 3D tissue constructs using vascular corrosion casts. Acta Biomater 32: 190–197. doi: 10.1016/j.actbio.2016.01.005
    [153] Atala A (2004) Tissue engineering for the replacement of organ function in the genitourinary system. Am J Transplant 4 Suppl 6: 58–73.
    [154] Cui T, Terlecki R, Atala A (2014) Tissue engineering in urethral reconstruction. Arch Esp Urol 67: 29–34.
    [155] Atala A (2011) Tissue engineering of human bladder. Br Med Bull 97: 81–104. doi: 10.1093/bmb/ldr003
    [156] de Kemp V, de Graaf P, Fledderus JO, et al. (2015) Tissue engineering for human urethral reconstruction: systematic review of recent literature. PLoS One 10: e0118653. doi: 10.1371/journal.pone.0118653
    [157] Hirashima T, Hoshuyama M, Adachi T (2017) In vitro tubulogenesis of Madin-Darby canine kidney (MDCK) spheroids occurs depending on constituent cell number and scaffold gel concentration. J Theor Biol 435: 110–115.
    [158] Zhang K, Fu Q, Yoo J, et al. (2017) 3D bioprinting of urethra with PCL/PLCL blend and dual autologous cells in fibrin hydrogel: An in vitro evaluation of biomimetic mechanical property and cell growth environment. Acta Biomater 50: 154–164. doi: 10.1016/j.actbio.2016.12.008
    [159] Mironov V, Kasyanov V, Drake C, et al. (2008) Organ printing: promises and challenges. Regen Med 3: 93–103. doi: 10.2217/17460751.3.1.93
    [160] Visconti RP, Kasyanov V, Gentile C, et al. (2010) Towards organ printing: engineering an intra-organ branched vascular tree. Expert Opin Biol Ther 10: 409–420. doi: 10.1517/14712590903563352
    [161] Kelm JM, Lorber V, Snedeker JG, et al. (2010) A novel concept for scaffold-free vessel tissue engineering: self-assembly of microtissue building blocks. J Biotechnol 148: 46–55. doi: 10.1016/j.jbiotec.2010.03.002
    [162] Itoh M, Nakayama K, Noguchi R, et al. (2015) Scaffold-Free Tubular Tissues Created by a Bio-3D Printer Undergo Remodeling and Endothelialization when Implanted in Rat Aortae. PLoS One 10: e0136681. doi: 10.1371/journal.pone.0136681
    [163] Norotte C, Marga FS, Niklason LE, et al. (2009) Scaffold-free vascular tissue engineering using bioprinting. Biomaterials 30: 5910–5917. doi: 10.1016/j.biomaterials.2009.06.034
    [164] Gentile C, Fleming PA, Mironov V, et al. (2008) VEGF-mediated fusion in the generation of uniluminal vascular spheroids. Dev Dyn 237: 2918–2925. doi: 10.1002/dvdy.21720
    [165] Fleming PA, Argraves WS, Gentile C, et al. (2010) Fusion of uniluminal vascular spheroids: a model for assembly of blood vessels. Dev Dyn 239: 398–406. doi: 10.1002/dvdy.22161
    [166] Davis GE, Bayless KJ, Mavila A (2002) Molecular basis of endothelial cell morphogenesis in three-dimensional extracellular matrices. Anat Rec 268: 252–275. doi: 10.1002/ar.10159
    [167] Kamei M, Saunders WB, Bayless KJ, et al. (2006) Endothelial tubes assemble from intracellular vacuoles in vivo. Nature 442: 453–456. doi: 10.1038/nature04923
    [168] Nunes SS, Krishnan L, Gerard CS, et al. (2010) Angiogenic potential of microvessel fragments is independent of the tissue of origin and can be influenced by the cellular composition of the implants. Microcirculation 17: 557–567.
    [169] Laschke MW, Menger MD (2015) Adipose tissue-derived microvascular fragments: natural vascularization units for regenerative medicine. Trends Biotechnol 33: 442–448. doi: 10.1016/j.tibtech.2015.06.001
    [170] Alajati A, Laib AM, Weber H, et al. (2008) Spheroid-based engineering of a human vasculature in mice. Nat Methods 5: 439–445. doi: 10.1038/nmeth.1198
    [171] Klein D (2018) iPSCs-based generation of vascular cells: reprogramming approaches and applications. Cell Mol Life Sci 75: 1411–1433. doi: 10.1007/s00018-017-2730-7
    [172] Pazhayattil GS, Shirali AC (2014) Drug-induced impairment of renal function. Int J Nephrol Renovasc Dis 7: 457–468.
    [173] Perneger TV, Whelton PK, Klag MJ (1994) Risk of kidney failure associated with the use of acetaminophen, aspirin, and nonsteroidal antiinflammatory drugs. N Engl J Med 331: 1675–1679. doi: 10.1056/NEJM199412223312502
    [174] Kataria A, Trasande L, Trachtman H (2015) The effects of environmental chemicals on renal function. Nat Rev Nephrol 11: 610–625.
    [175] Gutierrez OM (2013) Sodium- and phosphorus-based food additives: persistent but surmountable hurdles in the management of nutrition in chronic kidney disease. Adv Chronic Kidney Dis 20: 150–156. doi: 10.1053/j.ackd.2012.10.008
    [176] Jha V, Rathi M (2008) Natural medicines causing acute kidney injury. Semin Nephrol 28: 416–428. doi: 10.1016/j.semnephrol.2008.04.010
    [177] Isnard Bagnis C, Deray G, Baumelou A, et al. (2004) Herbs and the kidney. Am J Kidney Dis 44: 1–11. doi: 10.1016/S0272-6386(04)01098-4
    [178] Gintant G, Sager PT, Stockbridge N (2016) Evolution of strategies to improve preclinical cardiac safety testing. Nat Rev Drug Discov 15: 457–471. doi: 10.1038/nrd.2015.34
    [179] Mehta RL, Pascual MT, Soroko S, et al. (2004) Spectrum of acute renal failure in the intensive care unit: the PICARD experience. Kidney Int 66: 1613–1621. doi: 10.1111/j.1523-1755.2004.00927.x
    [180] Uchino S, Kellum JA, Bellomo R, et al. (2005) Acute renal failure in critically ill patients: a multinational, multicenter study. JAMA 294: 813–818. doi: 10.1001/jama.294.7.813
    [181] Chu X, Bleasby K, Evers R (2013) Species differences in drug transporters and implications for translating preclinical findings to humans. Expert Opin Drug Metab Toxicol 9: 237–252. doi: 10.1517/17425255.2013.741589
    [182] Greek R, Menache A (2013) Systematic reviews of animal models: methodology versus epistemology. Int J Med Sci 10: 206–221. doi: 10.7150/ijms.5529
    [183] Olson H, Betton G, Robinson D, et al. (2000) Concordance of the toxicity of pharmaceuticals in humans and in animals. Regul Toxicol Pharmacol 32: 56–67. doi: 10.1006/rtph.2000.1399
    [184] Huang HC, Chang YJ, Chen WC, et al. (2013) Enhancement of renal epithelial cell functions through microfluidic-based coculture with adipose-derived stem cells. Tissue Eng Part A 19: 2024–2034. doi: 10.1089/ten.tea.2012.0605
    [185] Jang KJ, Cho HS, Kang DH, et al. (2011) Fluid-shear-stress-induced translocation of aquaporin-2 and reorganization of actin cytoskeleton in renal tubular epithelial cells. Integr Biol (Camb) 3: 134–141. doi: 10.1039/C0IB00018C
    [186] Sciancalepore AG, Sallustio F, Girardo S, et al. (2014) A bioartificial renal tubule device embedding human renal stem/progenitor cells. PLoS One 9: e87496. doi: 10.1371/journal.pone.0087496
    [187] Sochol RD, Gupta NR, Bonventre JV (2016) A Role for 3D Printing in Kidney-on-a-Chip Platforms. Curr Transplant Rep 3: 82–92. doi: 10.1007/s40472-016-0085-x
    [188] Lee J, Kim S (2018) Kidney-on-a-Chip: a New Technology for Predicting Drug Efficacy, Interactions, and Drug-induced Nephrotoxicity. Curr Drug Metab 19: 577–583. doi: 10.2174/1389200219666180309101844
    [189] Wilmer MJ, Ng CP, Lanz HL, et al. (2016) Kidney-on-a-Chip Technology for Drug-Induced Nephrotoxicity Screening. Trends Biotechnol 34: 156–170. doi: 10.1016/j.tibtech.2015.11.001
    [190] Xie HG, Wang SK, Cao CC, et al. (2013) Qualified kidney biomarkers and their potential significance in drug safety evaluation and prediction. Pharmacol Ther 137: 100–107. doi: 10.1016/j.pharmthera.2012.09.004
    [191] Cruz NM, Freedman BS (2018) CRISPR Gene Editing in the Kidney. Am J Kidney Dis 71: 874–883. doi: 10.1053/j.ajkd.2018.02.347
    [192] Mundel P (2017) Podocytes and the quest for precision medicines for kidney diseases. Pflugers Arch 469: 1029–1037. doi: 10.1007/s00424-017-2015-x
    [193] Jansson K, Nguyen AN, Magenheimer BS, et al. (2012) Endogenous concentrations of ouabain act as a cofactor to stimulate fluid secretion and cyst growth of in vitro ADPKD models via cAMP and EGFR-Src-MEK pathways. Am J Physiol Renal Physiol 303: F982–90. doi: 10.1152/ajprenal.00677.2011
    [194] Freedman BS, Lam AQ, Sundsbak JL, et al. (2013) Reduced ciliary polycystin-2 in induced pluripotent stem cells from polycystic kidney disease patients with PKD1 mutations. J Am Soc Nephrol 24: 1571–1586. doi: 10.1681/ASN.2012111089
    [195] Cruz NM, Song X, Czerniecki SM, et al. (2017) Organoid cystogenesis reveals a critical role of microenvironment in human polycystic kidney disease. Nat Mater 16: 1112–1119. doi: 10.1038/nmat4994
    [196] Wang L, Tao T, Su W, et al. (2017) A disease model of diabetic nephropathy in a glomerulus-on-a-chip microdevice. Lab Chip 17: 1749–1760. doi: 10.1039/C7LC00134G
    [197] Waters JP, Richards YC, Skepper JN, et al. (2017) A 3D tri-culture system reveals that activin receptor-like kinase 5 and connective tissue growth factor drive human glomerulosclerosis. J Pathol 243: 390–400. doi: 10.1002/path.4960
    [198] Freedman BS (2015) Modeling Kidney Disease with iPS Cells. Biomark Insights 10: 153–169.
    [199] Clevers H (2016) Modeling Development and Disease with Organoids. Cell 165: 1586–1597. doi: 10.1016/j.cell.2016.05.082
    [200] Garreta E, Montserrat N, Belmonte JCI (2018) Kidney organoids for disease modeling. Oncotarget 9: 12552–12553.
    [201] Kim YK, Refaeli I, Brooks CR, et al. (2017) Gene-Edited Human Kidney Organoids Reveal Mechanisms of Disease in Podocyte Development. Stem Cells 35: 2366–2378. doi: 10.1002/stem.2707
    [202] Little MH, McMahon AP (2012) Mammalian kidney development: principles, progress, and projections. Cold Spring Harb Perspect Biol 4: a008300.
    [203] Yokote S, Matsunari H, Iwai S, et al. (2015) Urine excretion strategy for stem cell-generated embryonic kidneys. Proc Natl Acad Sci U S A 112: 12980–12985. doi: 10.1073/pnas.1507803112
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