Nitric oxide (NO) is endogenously produced free radical that plays important biological roles, such as, the promotion of vasodilation, angiogenesis, tissue repair, wound healing process, antioxidant, antitumoral and antimicrobial actions. Although the regenerative effects of NO in soft tissues have been extensively reported, its role in bone tissue repair has not been completely addressed. Both constitutive and inducible forms of NO synthase (NOS) are expressed in bone-derived cells, and some important cytokines, such as IL-1 and TNF, are potent stimulators of NO production. The effects of NO on bone tissue are dependent on its concentration. NO has dichotomous biological effects, at low concentrations (pico-nano molar range), NO may promote proliferation, differentiation and survival of osteoblasts, whereas at high concentrations (micromolar range) NO may inhibit bone resorption and formation. Therefore, at a certain concentration range, NO can avoid osteoclast-mediated bone resorption and promote osteoblast growth. Due to the potential beneficial effects of NO in bone tissue regeneration, the exogenous administration of NO might find important biomedical applications. As NO is a free radical and a gas, the administration of NO donors/generators has been explored in tissue repair. The delivery of NO to the bone using macromolecular NO releasing scaffolds has been shown to increase osteogenesis with a relevant impact in dental and orthopedist areas. In this sense, this review presents and discusses the recent and important progresses in the effects of NO/NO donors in bone tissue, and highlights the promising approach in the design and use of NO donors allied to biomaterials in the sustained and localized NO release for bone tissue regeneration.
1.
Introduction
Malaria is one of the world's most significant infectious diseases [1]. Malaria is a life-threatening disease caused by parasites that is usually transmitted to persons through the bites of female Anopheles mosquitoes [2]. Malaria gives rise to great pressure for the global prevention and control of infectious diseases [3]. World Health Organization reported [2] that there were an estimated 247 million malaria cases, including 619,000 deaths worldwide in 2021, and the majority of cases and deaths occurred in sub-Saharan Africa. The African region accounted for a disproportionate share of the global malaria burdens [4,5]. In 2021, the African region was home to 95% of global malaria cases and 96% of global malaria deaths, and children under 5 years old accounted for about 80% of all malaria deaths there [2]. There are 5 kinds of parasite species that cause malaria in humans, and two of these species P. falciparum and P. vivax pose the greatest threat [6]. The first malaria symptoms such as headache, fever and chills usually appear 10–15 days after the bite of a malaria mosquito and may be mild and difficult to be recognized as malaria, which implies that malaria exists the incubation period [2]. It was reported that asymptomatic infections were more prevalent in sub-Saharan Africa, where an estimated 24 million people had asymptomatic malaria infections [7]. Thus asymptomatic infections can occur during malaria transmission.
Asymptomatic infected people have no clinical symptoms, but they are contagious and the impact of asymptomatic infections on malaria transmission is enormous [8,9]. Bousema et al. [10] pointed out that asymptomatic carriers contributed to sustained transmission of malaria in local populations, and there was substantial evidence that an increase in the number of asymptomatic carriers at specific time intervals affected the dynamics of malaria transmission. Laishram et al. [4] concluded that asymptomatic malaria infections was a challenge for malaria control programs.
Since the emergence of malaria, scholars at home and abroad have been studying the pathogenesis and transmission dynamics of malaria. In all research methods, mathematical modeling is undoubtedly one of the most intuitive and effective methods. Many researchers have studied the dynamic evolution of malaria transmission by applying some mathematical models of malaria. In 1911, Ross [11] put forward a basic ordinary differential equations (ODEs) malaria model. Afterwards, MacDonald [12] extended Ross's model, and gave first the definition of the basic reproduction number. The extended Ross's model was said to be the Ross-Macdonald model. Subsequently, the Ross-Macdonald model has been extended to higher dimensions and more factors affecting malaria transmission have been taken into account (see, e.g., [1,6,13]). For example, Kingsolver [14] extended the Ross-Macdonald model and explained the greater attraction of infectious humans to mosquitoes in 1987. Safan and Ghazi [1] developed a 4D ODEs malaria transmission model with standard incidence rates, and analyzed the dynamic properties of equilibria of the malaria model. In 2020, Aguilar and Gutierrez [6] established a high-dimensional ODEs malaria model with asymptomatic carriers and standard incidence rate, and dealt with local dynamics of the disease-free equilibrium of the malaria model.
Over the years, considering the incubation period of malaria, lots of researchers established some time-delayed malaria models (see, e.g., [3,5,15,16,17,18]). For instance, in 2008, Ruan et al. [5] first established a class of Ross-Macdonald model with two time delays, and investigated the stability of equilibria of the model and the impact of time delays on the basic reproduction number. In 2019, Ding et al. [3] proposed a malaria model with time delay, and investigated the global stability of the uninfected equilibrium of the model as well as its uniform persistence. For the moment, there are few theoretical analysis of the model of malaria with standard incidence rate. Recently, Guo et al. [13] established a malaria transmission model with time delay and standard incidence rate, and they studied the global dynamic properties of equilibria of the model. Based on this, we extend and improve the model in [13], namely, we establish a malaria transmission model with asymptomatic infections, standard incidence rate and time delay, and then study the global dynamic properties of equilibria of the malaria model.
The remainder of this paper is organized as follows. In Section 2, we put forward a time-delayed dynamic model of malaria with asymptomatic infections and standard incidence rate, and prove the well-posedness as well as dissipativeness of the system. In Section 3, we obtain the existence conditions of malaria-free and malaria-infected equilibria of the system, and verify the local dynamic properties of equilibria in terms of the basic reproduction number R0. In Section 4, to obtain the global dynamic property of the malaria-infected equilibrium for R0>1, we acquire the weak persistence of the system through some analysis techniques. In Section 5, by utilizing the Lyapunov functional method and the limiting system of the model combining stability of partial variables, we obtain the global stability results of malaria-free and malaria-infected equilibria in terms of R0, respectively.
2.
Model formulation
In order to delve into the details of malaria transmission, we develop a time-delayed model with asymptomatic infections and standard incidence rate. The population is classified into four compartments, which are denoted by Sh: susceptible individuals, Ah: asymptomatic infected individuals, Ih: symptomatic infected individuals, Rh: recovered individuals, respectively. The mosquitoes are classified into two compartments, which are denoted by Sm: susceptible mosquitoes and Im: infected mosquitoes, respectively. Then the model of malaria transmission is proposed as follows:
where Nm(t)=Sm(t)+Im(t). Here, time delay τ≥0, and all other parameters of system (2.1) are assumed to be positive and p∈(0,1). The description of parameters are listed in Table 1.
The phase space of system (2.1) is
where C is the Banach space of continuous functions mapping from [−τ,0] to R6+ with R+=[0,∞) and the supremum norm. In the following, the well-posedness as well as dissipativeness of system (2.1) will be investigated in C+.
Theorem 2.1. The solution u(t)=(Sm(t),Im(t),Sh(t),Ah(t),Ih(t),Rh(t))T of system (2.1) with any ϕ∈C+ exists uniquely, and is non-negative and ultimately bounded on R+. In particular, (Sm(t),Sh(t))T≫0 on (0,∞), and C+ is positively invariant for system (2.1).
Proof. In view of the basic theory of delay differential equations (DDEs) [19,20], the solution u(t) of system (2.1) with any ϕ∈C+ is unique on its maximum interval [0,Tϕ) of existence. Firstly, we will prove that the solution u(t) is non-negative on [0,Tϕ). According to the continuous dependence of solutions of DDEs on parameters [19,20], then for any b∈(0,Tϕ) and a sufficiently small ε>0, the solution u(t,ε)=(u1(t,ε),u2(t,ε),u3(t,ε),u4(t,ε),u5(t,ε),u6(t,ε))T through ϕ of the following model:
uniformly exists on [0,b]. Consequently, we claim u(t,ε)≫0 on [0,b). It is clear that ˙ui(0,ε)>0,i∈I6={1,2,3,4,5,6} whenever ui(0,ε)=0. Next, we prove the claim by contradiction. Suppose that there exists ˉt∈(0,b) such that ui(ˉt,ε)=0 for some i∈I6 and u(t,ε)≫0 for t∈(0,ˉt), where
As a result, it holds
Since
it follows from (2.2) that ˙ui(ˉt,ε)>0, which yields a contradiction to (2.3). Thus, we have u(t,ε)≫0 for t∈(0,b).
Letting ε→0+ gives that u(t,0)=u(t)≥0 for any t∈[0,b). Note that b∈(0,Tϕ) is chosen arbitrarily, so that u(t)≥0 on [0,Tϕ). It is obvious that Tϕ>τ. Therefore, from system (2.1), we have that for any t≥τ,
As a consequence, by the comparison principle, we can obtain that u(t) is bounded. Accordingly, from the continuation theorem of solutions of DDEs [19], it follows Tϕ=∞. Consequently, we have
Therefore, the solution u(t) with any ϕ∈C+ uniquely exists, and is non-negative and ultimately bounded on R+. Moreover, it is not difficult to get that (Sm(t),Sh(t))T≫0 on R+∖{0}, and C+ is a positive invariant set for system (2.1).
3.
Local stability
To begin with, it follows easily the malaria-free equilibrium E0=(S0m,0,S0h,0,0,0)T, where S0m=λm/μm and S0h=λh/μh. By using the similar method in [21,22], we can calculate the basic reproduction number
To get a malaria-infected equilibrium (i.e., positive equilibrium) E∗=(S∗m,I∗m,S∗h,A∗h,I∗h,R∗h)T, we have the following lemma.
Lemma 3.1. System (2.1) exists a unique E∗≫0 when and only when R0>1.
Proof. First of all, the malaria-infected equilibrium equations can be obtained as follows:
Note that S∗m+I∗m=λm/μm, it follows from (3.1) that
Substituting the third and the fourth equations in (3.2) and S∗m=λm/μm−I∗m into the first equation in (3.2), there holds
In consequence, (3.3) possesses a unique positive root
if and only if R0>1. Thus, we can conclude that E∗ is a unique malaria-infected equilibrium of system (2.1) if and only if R0>1, where E∗ satisfies
Next, by adopting similar techniques in [23,24,25], we will discuss the local dynamic properties of the malaria-free equilibrium E0 and the the malaria-infected equilibrium E∗ with respect to R0. First of all, for the local stability of the equilibrium E0, we have the theorem as follows.
Theorem 3.1. For any τ≥0, the malaria-free equilibrium E0 is locally asymptotically stable (LAS) when R0<1, and unstable when R0>1.
Proof. With some calculations, the characteristic equation of the linear system of system (2.1) at E0 can be obtained as follows:
where
Clearly, Eq (3.5) possesses three negative real roots: −μh (double) and −μm. The other roots of Eq (3.5) satisty H(λ)=0. Next, we will prove that any root λ of H(λ)=0 has negative real part. Suppose, by contradiction, λ has the nonegative real part. Then it follows from H(λ)=0 that
Taking the modulus of both sides in (3.7), we have
and
for R0<1 and τ≥0, which leads to a contradiction. Therefore, the real part of each root of the Eq (3.5) is negative. Accordingly, E0 is LAS for R0<1 and τ≥0.
Now, we prove that the E0 is unstable for R0>1 and τ≥0 by the zero theorem. Clearly, for R0>1 and τ≥0, we can get
According to the zero theorem, there must exsit a positive real root in Eq (3.6). Thus, E0 is unstable for R0>1 and τ≥0.
For the local stability of the equilibrium E∗, we can obtain the theorem as follows.
Theorem 3.2. For any τ≥0, the malaria-infected equilibrium E∗ is LAS if and only if R0>1.
Proof. By Lemma 3.1, we just require to demonstrate the sufficiency. Let
With direct calculation, the characteristic equation of the linear system of system (2.1) at E∗ can be got as follows:
where
Clearly, Eq (3.8) has two negative roots: −μh and −μm. The other roots of Eq (3.8) satisfy g(λ)=0. Then, we will prove that any root λ of g(λ)=0 has negative real part by contradiction. Assume that λ has the non-negative real part. By g(λ)=0, we can get
Taking the modulus of both sides in (3.9), for R0>1 any τ≥0, it follows
and
Obviously, this is a contradiction. Hence, the real part of each root of the Eq (3.8) is negative for R0>1 and τ≥0, which ensures the local stability of the equilibrium E∗.
4.
Weak persistence
Generally, to obtain the global stability of the equilibrium E∗, we need to prove the strong persistence or uniform persistence of system (2.1). However, we study the weak persistence of system (2.1), which can ensure the global stability of the equilibrium E∗. Of course, the weak persistence of system (2.1) is more accessible than its strong or uniform persistence. Now, we define
and let
be the solution of system (2.1) with any ϕ∈ϝ. It follows easily that ϝ is a positive invariant set of system (2.1), and u(t)≫0 for t>0. Hence, we discuss the weak persistence of system (2.1) in ϝ.
According to [26], system (2.1) is said to be weakly persistent if
We define ut=(Smt,Imt,Sht,Aht,Iht,Rht)T∈C+ to be ut(θ)=u(t+θ), θ∈[−τ,0] for t≥0, and ut is the solution of system (2.1) with ϕ. Inspired by the work in [13], we study the weak persistence of system (2.1). First, we have the following lemma.
Lemma 4.1. Assume that R0>1, θ∈(0,1) and lim supt→∞Im(t)≤θI∗m. Then
Proof. It follows from (2.4) that
For any ϵ>1, there can be found ϱ=ϱ(ϕ,ϵ)≥0 such that
and then
Consequently,
Letting ϵ→1+, it holds
The malaria-infected equilibrium equations imply that
Theorem 4.1. Let R0>1. Then lim supt→∞Im(t)≥I∗m.
Proof. We will use the proof by contradiction to verify this result. Provided that lim supt→∞Im(t)<I∗m. Whereupon, one can find a θ∈(0,1) such that lim supt→∞Im(t)≤θI∗m. Using Lemma 4.1, we can get that there is an ϵ0>0 such that for any ϵ∈(0,ϵ0),
Thanks to Lemma 4.1, it follows that for any ϵ∈(0,ϵ0), there can be found T≡T(ϵ,ϕ)>0 such that
Now, we define the functional on ϝ as follows,
Obviously, L(ut) is bounded since L is continous on ϝ. Then for t≥T, the derivative of L along the solution ut is given by
Denote
Next, we will prove that Im(t)≥c for t≥T. If not, there exists a T0≥0 such that Im(t)≥c for t∈[T,T+τ+T0], Im(T+τ+T0)=c and ˙Im(T+τ+T0)≤0. Then it follows that for t∈[T,T+τ+T0],
It easily follows from Eq (4.2) that for t∈[T,T+τ+T0],
Analogously, one can get
for t∈[T,T+τ+T0]. By R20=S0hS0m/S∗hS∗m and (4.1), we have
Accordingly, we conclude that
Clearly, this contradicts ˙Im(T+τ+T0)≤0. As a result, Im(T)≥c for t≥T. Hence, for t≥τ,
which hints L(ut)→∞ as t→∞. Accordingly, this contradicts the boundedness of L(ut).
According to Theorem 4.1, we have the following result.
Corollary 4.1. If R0>1, then for any τ≥0, system (2.1) is weakly persistent.
5.
Global stability
We will study the global asymptotic stability of the equilibria E0 and E∗ with respect to R0. For this purpose, we get from (2.4) the following limiting system of system (2.1):
Adopting a similar argument as in the proof of Theorem 2.1, it follows that the solution
of system (5.1) through any φ=(φ1,φ2,φ3,φ4,φ5,φ6)T∈C+ uniquely exists, and is non-negative and ultimately bounded on [0,∞). Setting
gives that zt=(Smt,Imt,Sht,Aht,Iht,Rht)T∈C+ is also the solution of system (5.1) through φ for t≥0. We can find easily that E∗ and E0 are also the equilibria of system (5.1), and C+ is a positive invariant set of system (5.1). By the way, (Sm(t),Sh(t))T≫0 for t>0. Define H(v)=v−1−lnv,v>0. Thereupon, for the global dynamic property of the equilibrium E0 of system (2.1), we have the theorem as follows.
Theorem 5.1. For any τ≥0, the malaria-free equilibrium E0 is GAS when R0<1 and GA when R0=1 in C+.
Proof. By Theorem 3.1, it follows that for R0<1, E0 is LAS. Thus, we only need to prove that for R0≤1, E0 is GA. Let ut be the solution of system (2.1) with any ϕ∈C+ and zt be the solution of system (5.1) though any φ∈C+. Let ω(ϕ) be the ω-limit set of ϕ with respect to system (2.1). In order to prove the global attractivity of E0, we just need to show that ω(ϕ)={E0}. By Theorem 2.1, we know that ut is bounded on C+. Hence, it follows from (2.4) that ω(ϕ) is a compact set, and is also a subset of C+.
Let us define the following functional V on L1={φ∈C+:φ1(0)>0,φ3(0)>0}⊆C+
where
Obviously, V1 is continuous on L1. Since zt∈L1 for t≥1, the derivative of V along zt (t≥1) is given by
where h=βhμm/λm. Considering (5.2) and (5.3), we can conclude that both Sh(t) and Sm(t) are persistent. In other words, there exists a σ=σ(φ)>0 such that lim inft→∞Sh(t)>σ and lim inft→∞Sm(t)>σ. As a result, ω(φ)⊆L1, where ω(φ) is the ω-limit set of φ with respect to system (5.1). It is evident that V is a Lyapunov functional on {zt:t≥1}⊆L1. Then it follows from [27,Corollry 2.1] that ˙V(ψ)=0, ∀ψ∈ω(φ).
Assume that zt is the solution of system (5.1) through any ψ∈ω(φ). Then the invariance of ω(φ) gives that zt∈ω(φ) for t∈R. According to (5.3), we have Sm(t)=S0m and Sh(t)=S0h for t∈R. From system (5.1) and the invariance of ω(φ), it follows that Im(t)=Ah(t)=Ih(t)=Rh(t)=0 for t∈R. Thus for R0≤1, it holds that ω(φ)={E0}, which implies that Ws(E0)=C+, where Ws(E0) is the stable set of E0 with respect to system (5.1).
Now, we prove that the equilibrium E0 of system (5.1) is uniformly stable for R0≤1 by using the similar approach in [28,29]. Observe that the first five equations of system (5.1) can constitute an independent subsystem
It is not difficult to find that
is a positive invariant set with respect to system (5.4). Clearly, system (5.4) has a malaria-free equilibrium X0=(S0m,0,S0h,0,0)T. According to (5.2), (5.3), [27,Corollary 3.3] hints that X0 is uniformly stable. Define
By the definition of uniform stability of X0, it follows that for any ϵ>0, there is δ≤2ϵ/3 such that for any ξ∈C+ and ‖ξ−X0‖<δ, there holds
where Xt is the solution of system (5.4) with ξ. Then considering the sixth equation of system (5.1), we can get
Consequently, for any ϕ∈C+ and ‖ϕ−E0‖<δ, it follows that for any t≥0,
and then
Thus, the equilibrium E0 is uniformly stable for system (5.1).
Next, we claim that ω(ϕ)={E0} for R0≤1. We first have E0∈ω(ϕ) since ω(ϕ)⊆C+=Ws(E0). Assume that there exists ψ∈ω(ϕ) such that ψ≠E0. Let α(ψ) be the α-limit set of ψ for system (5.1). Then it follows from the invariance and the compactness of ω(ϕ) that α(ψ)⊆ω(ϕ). The invariance of α(ψ) and the stable set C+ of E0 yield that E0∈α(ψ). Obviously, this contradicts to the stability of E0 for system (5.1). Therefore, ω(ϕ)={E0}.
Remark 5.1. In fact, the stability of the malaria-free equilibrium E0 of system (5.1) can be acquired for R0=1 in the proof of Theorem 5.1. But using the proof of Theorem 3.1, we can not obtain the stability of the equilibrium E0 for R0=1.
For the global dynamic property of the equilibrium E∗ of system (2.1), we can draw the following theorem.
Theorem 5.2. For any τ≥0, the malaria-infected equilibrium E∗ is globally asymptotically stable if and only if R0>1 in ϝ.
Proof. From Lemma 3.1 and Theorem 3.2, we just require to prove that E∗ is GA for R0>1. Let ut be the solution of system (2.1) with any ϕ∈ϝ and zt be the solution of system (5.1) through any φ∈ϝ. We can obtain that ϝ is positively invariant for system (5.1), and z(t)≫0 for t≥0. In order to show that E∗ is GA, we only need to show that ω(ϕ)={E∗}. It follows from Theorem 2.1 that ut is bounded on ϝ. Thus, it holds that ω(ϕ) is compact.
Let us define a functional V on L2={φ∈C+:φi(0)>0,i=1,2,3,4,5}⊆ϝ as follows
where
Clearly, V2 is continuous on L2. In as much as zt∈L2 for t≥τ+1, the derivative of the functional V along zt in t≥τ+1 is given by
where h=βhμm/λm. Further, it follows from the equilibrium equations that
By (5.6) and (5.7), it follows that ω(φ)⊆L2. It is clear that V is a Lyapunov functional on {zt:t≥τ+1}⊆L2. As a consequence, [27,Corollary2.1] implies that ˙V(ψ)=0 for any ψ∈ω(φ).
Let zt be the solution of system (5.1) for any ψ∈ω(φ). Then the invariance of ω(φ) indicates that zt∈ω(φ) for any t∈R. Thus, from (5.7), it follows that for any t∈R,
By (5.8) and the third equation of system (5.1), we get that for any t∈R,Im(t)=I∗m, Ah(t)=A∗h and Ih(t)=I∗h. Consequently, by the invariance of ω(φ) and system (5.1), it holds that Rh(t)=R∗h for any t∈R. Therefore, it follows that z0=ψ=E∗, and then ω(φ)={E∗}, which implies Ws(E∗)=ϝ, where Ws(E∗) is the stable set of E∗ with respect to system (5.1).
Now, we prove that the equilibrium E∗ of system (5.1) is uniformly stable by using the similar argument in [28,29]. Note that system (5.4) has a unique malaria-infected equilibrium X∗=(S∗m,I∗m,S∗h,A∗h,I∗h). It follows from (5.6), (5.7) and [27,Corollary 3.3] that X∗ is uniformly stable. By the definition of uniform stability of X∗, it follows that for any ϵ>0, there is δ≤2ϵ/3 such that for any ξ∈C+ and ‖ξ−X∗‖<δ, there holds
where Xt is the solution of system (5.4) through ξ. Hence, for any ϕ∈L2 and ‖ϕ−E∗‖<δ, it follow from (5.5) that
for any t≥0, where
is used. Thus, we have
which gives that the equilibrium E∗ is uniformly stable with respect to system (5.1).
Next, we claim that ω(ϕ)={E∗}. From Theorem 4.1, it follows that ω(ϕ)∩ϝ≠∅, which gives that E∗∈ω(ϕ). Assume that there is ψ∈ω(ϕ) such that ψ≠E∗. Then the invariance and the compactness of ω(ϕ) implies that α(ψ)⊆ω(ϕ). By Theorem 3.2, we have that E∗∈α(ψ). Obviously, this contradicts to the stability of E∗ with respect to system (5.1). Therefore, ω(ϕ)={E∗}.
Remark 5.2. Indeed, the proof of Theorem 5.2 can be simplified, i.e., the stability of system (5.1) is not a must, because E∗∈ω(ϕ) and Theorem 3.2 can imply that ω(ϕ)={E∗}. But if we use [30,Theorem 4.1] to prove that ω(ϕ)={E∗}, then the stability of system (5.1) is required.
Acknowledgements
This work is partially supported by the National NSF of China (No. 11901027), the Major Program of the National NSF of China (No. 12090014), the State Key Program of the National NSF of China (No. 12031020), the China Postdoctoral Science Foundation (No. 2021M703426), the Pyramid Talent Training Project of BUCEA (No. JDYC20200327), and the BUCEA Post Graduate Innovation Project (No. PG2022143).
Conflict of interest
The authors declare there is no conflict of interest.