Loading [MathJax]/jax/output/SVG/jax.js
Research article

The composition of microbial communities in inflammatory periodontal diseases in young adults Tatars

  • Received: 15 October 2020 Accepted: 19 January 2021 Published: 27 January 2021
  • Host susceptibility and environmental factors are important for the development of gingivitis and periodontitis, but bacterial biofilms attached to the teeth and gingival tissues play a crucial role. We have analyzed and compared the subgingival microbial communities between subjects with dental plaque biofilm-induced generalized chronic gingivitis (CG), localized initial (Stage I) periodontitis (IP) and healthy controls (HC) of young people aged 18–19 years permanently residing in the city of Kazan (Tatarstan, Russia). The results showed that the α-diversity in groups with CG and IP was higher than in the healthy group. In a course of periodontal disease, a decrease in the relative abundance of dominates genera Rothia and Streptococcus was observed along with increase of class TM7-3 (Candidatus Saccharibacteria phylum) representatives. Also, the increase of red complex representatives Porphyromonadeceae, Treponema and Tannerella was detected together with statistically significant increase of Filifactor, Parvimonas, Peptostreptococcaceae, Veillonellaceae, Tissierelaceae and Mogibacteriaceae. Analysis of our data suggests that transition from HC to IP may be accompanied by a decrease in microbial diversity and a reduction in the abundance of family Rs-045 (Candidatus Saccharibacteria phylum), Desulfovibrionaceae Corynebacterium, Campylobacter and Selenomonas in young adults Kazan Tatars.

    Citation: Maya Kharitonova, Peter Vankov, Airat Abdrakhmanov, Elena Mamaeva, Galina Yakovleva, Olga Ilinskaya. The composition of microbial communities in inflammatory periodontal diseases in young adults Tatars[J]. AIMS Microbiology, 2021, 7(1): 59-74. doi: 10.3934/microbiol.2021005

    Related Papers:

    [1] Mingzhou Xu, Xuhang Kong . Note on complete convergence and complete moment convergence for negatively dependent random variables under sub-linear expectations. AIMS Mathematics, 2023, 8(4): 8504-8521. doi: 10.3934/math.2023428
    [2] Mingzhou Xu . Complete convergence and complete moment convergence for maximal weighted sums of extended negatively dependent random variables under sub-linear expectations. AIMS Mathematics, 2023, 8(8): 19442-19460. doi: 10.3934/math.2023992
    [3] Qingfeng Wu, Xili Tan, Shuang Guo, Peiyu Sun . Strong law of large numbers for weighted sums of $ m $-widely acceptable random variables under sub-linear expectation space. AIMS Mathematics, 2024, 9(11): 29773-29805. doi: 10.3934/math.20241442
    [4] Mingzhou Xu . Complete convergence of moving average processes produced by negatively dependent random variables under sub-linear expectations. AIMS Mathematics, 2023, 8(7): 17067-17080. doi: 10.3934/math.2023871
    [5] Haiye Liang, Feng Sun . Exponential inequalities and a strong law of large numbers for END random variables under sub-linear expectations. AIMS Mathematics, 2023, 8(7): 15585-15599. doi: 10.3934/math.2023795
    [6] He Dong, Xili Tan, Yong Zhang . Complete convergence and complete integration convergence for weighted sums of arrays of rowwise $ m $-END under sub-linear expectations space. AIMS Mathematics, 2023, 8(3): 6705-6724. doi: 10.3934/math.2023340
    [7] Yongfeng Wu . Limit theorems for negatively superadditive-dependent random variables with infinite or finite means. AIMS Mathematics, 2023, 8(11): 25311-25324. doi: 10.3934/math.20231291
    [8] Chao Wei . Parameter estimation for partially observed stochastic differential equations driven by fractional Brownian motion. AIMS Mathematics, 2022, 7(7): 12952-12961. doi: 10.3934/math.2022717
    [9] Min Woong Ahn . An elementary proof that the set of exceptions to the law of large numbers in Pierce expansions has full Hausdorff dimension. AIMS Mathematics, 2025, 10(3): 6025-6039. doi: 10.3934/math.2025275
    [10] Mingzhou Xu . On the complete moment convergence of moving average processes generated by negatively dependent random variables under sub-linear expectations. AIMS Mathematics, 2024, 9(2): 3369-3385. doi: 10.3934/math.2024165
  • Host susceptibility and environmental factors are important for the development of gingivitis and periodontitis, but bacterial biofilms attached to the teeth and gingival tissues play a crucial role. We have analyzed and compared the subgingival microbial communities between subjects with dental plaque biofilm-induced generalized chronic gingivitis (CG), localized initial (Stage I) periodontitis (IP) and healthy controls (HC) of young people aged 18–19 years permanently residing in the city of Kazan (Tatarstan, Russia). The results showed that the α-diversity in groups with CG and IP was higher than in the healthy group. In a course of periodontal disease, a decrease in the relative abundance of dominates genera Rothia and Streptococcus was observed along with increase of class TM7-3 (Candidatus Saccharibacteria phylum) representatives. Also, the increase of red complex representatives Porphyromonadeceae, Treponema and Tannerella was detected together with statistically significant increase of Filifactor, Parvimonas, Peptostreptococcaceae, Veillonellaceae, Tissierelaceae and Mogibacteriaceae. Analysis of our data suggests that transition from HC to IP may be accompanied by a decrease in microbial diversity and a reduction in the abundance of family Rs-045 (Candidatus Saccharibacteria phylum), Desulfovibrionaceae Corynebacterium, Campylobacter and Selenomonas in young adults Kazan Tatars.



    Since the 20th century, the probability theory has gained profound and extraordinary applications in the fields of mathematical statistics, information science, finance, and economics. The probability limit theory is an important branch of the probability theory. The probability limit theory has a broad range of applications. In the course of development, many important theorems and concepts have been proposed, such as the central limit theorem and the law of large numbers. These theorems are not only important in theory, but are also widely used in practical applications. Under the classical probability space, the mathematical expectation is additive, where one can solve many deterministic problems in real life. However, with the development of the society, many uncertainty phenomena have appeared in many new industries, such as insurance, finance, risk management, and other industries. In order to solve these uncertainty phenomena, Peng[1,2,3,4] broke away from the theoretical constraints of the classical probability space, constructed a sublinear expectation theoretical framework, and created a complete axiomatic system, which provides a new direction for solving these uncertainty problems.

    Many important results and theorems in classical probability spaces can be proven and applied to the sublinear expectation spaces. Therefore, some important research directions in the classical probability space can also be extrapolated to the sublinear expectation space. More and more scholars have begun to study the related theoretical achievements under sublinear expectations. For example, Xu and Kong [5] proved the complete integral convergence and complete convergence of negatively dependent (ND) random variables under sublinear expectations. Hu and Wu [6] proved the complete convergence theorems for an array of row-wise extended negatively dependent (END) random variables utilizing truncated methods under sublinear expectations. Wang and Wu [7] used truncated methods to derive the complete convergence and complete integral convergence of the weighted sums of END random variables under sublinear expectations. In addition, many scholars have received numerous theoretical results about the law of large numbers and the law of iterated logarithms from their investigations, and have obtained many theoretical achievements under sublinear expectations. Chen [8], Hu[9,10], Zhang[11], and Song[12] studied the strong law of large numbers for independent identically distributed (IID) random variables under different conditions. Wu et al.[13] established inequalities such as the exponential inequality, the Rosenthal inequality, and obtained the Marcinkiewicz-Zygmund type strong law of large numbers for weighted sums of m-widely acceptable random variables under sublinear expectations. Chen and Wu[14] established the weak and strong law of large numbers for Pareto-type random variables, so that the relevant conclusions in the traditional probability space were extended to the sublinear expectation space. Chen et al.[15] studied the properties associated with weakly negatively dependent (WND) random variables and established the strong law of large numbers for WND random variables under sublinear expectations. Zhang[16] studied the limit behavior of linear processes under sublinear expectations and obtained a strong law of large numbers for linear processes generated by independent random variables. Zhang[17] provided the sufficient and necessary conditions of the strong law of large numbers for IID random variables under the sub-linear expectation. Guo[18] introduced the concept of pseudo-independence under sublinear expectations and derived the weak and strong laws of large numbers. Zhang [19] established some general forms of the law of the iterated logarithms for independent random variables in a sublinear expectation space. Wu and Liu [20] studied the Chover-type law of iterated logarithms for IID random variables. Zhang [21] studied the law of iterated logarithms for sequences of END random variables with different conditions. Guo et al.[22] studied two types of Hartman-Wintner iterated logarithmic laws for pseudo-independent random variables with a finite quadratic Choquet expectation and extended the existed achievements.

    The goal of this article is to prove the Marcinkiewicz-Zygmund type weak law of large numbers for an array of row-wise WND random variables, and the strong law of large numbers for linear processes generated by WND random variables under sublinear expectations. The rest of the paper is as follows: in Section 2, we recall some basic definitions, notations, and lemmas needed to prove the main theorems under sublinear expectations; in Section 3, we state our main results; in Section 4, the proofs of these theorems are given; in Section 5, we conclude the paper.

    We use the framework and notation of Peng [1,2,3,4]. Considering the following sublinear expectation space(Ω,H,ˆE), if X1,X2,,XnH, then ψ(X1,X2,,Xn)H for each ψCb,Lip(Rn), where Cb,Lip(Rn) denotes the linear space of functions ψ satisfying the following bounded Lipschitz condition:

    |ψ(x)|C,|ψ(x)ψ(y)|C|xy|,x,yRn,

    where the constant C>0 depending on ψ.

    Definition 2.1. [4] A sublinear expectation ˆE is a functional ˆE: HR satisfying the following:

    (a) Monotonicity: ˆE(X)ˆE(Y) if XY;

    (b) Constant preserving: ˆE(c)=c for cR;

    (c) Sub-additivity: For each X,YH, ˆE(X+Y)ˆE(X)+ˆE(Y);

    (d) Positive homogeneity: ˆE(λX)=λˆE(X), for λ0.

    The triple (Ω,H,ˆE) is called a sublinear expectation space.

    Through a sublinear expectation ˆE, we can use ˆεX=ˆE(X),XH to define the conjugate expectation of ˆE.

    From the above definition, for any X,YH we obtain the following:

    ˆε(X)ˆE(X),ˆE(X+c)=ˆE(X)+c,|ˆE(XY)|ˆE|XY|,ˆE(X)ˆE(Y)ˆE(XY).

    Definition 2.2. [23] A function V : F[0,1] is said a capacity satisfying the following:

    (a) V()=0, V(Ω)=1;

    (b) V(A)V(B), AB, A, BF.

    It is called to be sub-additive if V(AB)V(A)+V(B) for any A, BF with ABF. Let (Ω,H,ˆE) be a sub-linear expectation space; we define capacities of a pair (V,V) by the following:

    V(A):=inf{ˆE(ξ):IAξ,ξH},V(A)=1V(Ac),AF.

    From the above definition, we have the following:

    ˆE(f1)V(A)ˆE(f2),iff1I(A)f2,f1,f2H. (2.1)

    Because V may be not countably sub-additive in general, we define another capacity V.

    Definition 2.3.[19] A countably sub-additive extension V of V is defined by the following:

    V(A)=inf{n=1V(An):An=1An},V(A)=1V(Ac),AF.

    Then, V is a countably sub-additive capacity with V(A)V(A) and the following properties:

    (a) If V is countably sub-additive, then VV;

    (b) If I(A)g,gH, then V(A)ˆE(g). Furthermore, if ˆE is countably sub-additive, then

    ˆE(f)V(A)V(A)ˆE(g),fI(A)g,f,gH;

    (c) V is the largest countably sub-additive capacity satisfying the property that V(A)ˆE(g)whenever I(A)gH (i.e., if V is also a countably sub-additive capacity satisfying V(A)ˆE(g) whenever I(A)gH, then V(A)V(A)).

    Definition 2.4. [24] In a sublinear expectation space (Ω,H,ˆE), let φ be a monotonically bounded function if for any X,YH that satisfies

    ˆE[φ(X+Y)]ˆE[ˆE[φ(x+Y)]x=X], (2.2)

    then the random variable Y is said to be WND on X under sublinear expectations. {Xi,iZ} is said to be a sequence of WND random variables if Xm is WND on (Xmn,Xmn+1,,Xm1) for any mZ,nN+.

    Remark 2.1. By Chen [15], if {Xn,n1} is a sequence of WND random variables under sublinear expectations, then for any XkH,1kn, we have the following:

    ˆE[exp(nk=1cXk)]nk=1ˆE[exp(cXk)],cR. (2.3)

    Definition 2.5.[3] The Choquet integral of X with respect to V is defined as following:

    CV(X)=0V(Xt)dt+0[V(Xt)1]dt.

    Usually, we denote the Choquet integral of V and V by CV and CV, respectively.

    Definition 2.6.[25] If a sublinear expectation ˆE satisfies ˆE[X]n=1ˆE[Xn]<, then ˆE is said to be countably sub-additive, where Xn=1Xn<, X,XnH, and X,Xn0,n1.

    Next, we need the following notations and lemmas. Let C be a positive constant that takes on different values in different places as needed. I(A) stands for the indicator function of A. Given a capacity V, a set A is said to be a polar set if V(A)=0. Additionally, we say a property holds "quasi-surely" (q.s.) if it holds outside a polar set. In this paper, the capacity V is countably sub-additive and lower continuous. Similar to Hu [10], we let Φc denote the set of nonnegative functions ϕ(x) defined on [0,), and ϕ(x) satisfies the following:

    (1) Function ϕ(x) is positive and nondecreasing on (0,), and the series n=11nϕ(n)<;

    (2) For any x>0 and fixed a>0, there exists C>0 such that ϕ(x+a)Cϕ(x).

    For example, functions (ln(1+x))1+α and xα(α>0) belong to the Φc.

    Lemma 2.1. [8] (Borel-Canteli's Lemma) Let {An,n1} be a sequence of events in F. Suppose that V is a countably sub-additive capacity. If n=1V(An)<, then

    V(An,i.o.)=0,

    where {An,i.o.}=n=1i=nAi.

    Lemma 2.2. Let {X,Xm,m1} be a sequence of random variables under the sublinear expectations space.

    (1) Chebyshev inequality[8]: Function f(x) is positive and nondecreasing on R; then

    V(Xx)ˆE[f(X)]f(x),V(Xx)ˆε[f(X)]f(x).

    (2) Cr inequality [3]: Let X1, X2, , XmH for m1; then

    ˆE|X1+X2++Xm|rCr[ˆE|X1|r+ˆE|X2|r++ˆE|Xm|r],

    where

    Cr={1,0<r1,mr-1,r>1.

    (3) Markov inequality [8]: For any XH, we have

    V(|X|x)ˆE(|X|p)xp,x>0,p>0.

    Lemma 2.3. [26] Let {xm,m1} and {bm,m1} be sequences of real numbers with 0<bm. If the series m=1xmbm<, then limm1bmmi=1xi=0.

    Lemma 2.4. [21] Suppose that ˆE is countably sub-additive; then, for any XH, we have ˆE(|X|)CV(|X|).

    Lemma 2.5. Let {Xni,1ikn,n1} be an array of row-wise random variables under sublinear expectation (Ω,H,ˆE) and supi1CV((|Xni|pc)+)0,c,p(0,2); if ˆE is countably sub-additive for any XniH, then we have supi1ˆE[(|Xni|pc)+]0,c.

    Proof. From Lemma 2.4, we have ˆE(|X|)CV(|X|). Let X=(|Xni|pc)+; then, we have

    supi1ˆE[(|Xni|pc)+]supi1CV((|Xni|pc)+).

    Thus, we get supi1ˆE[(|Xni|pc)+]0,c.

    Lemma 2.6. If {Xni,1ikn,n1} is an array of row-wise random variables under sublinear expectations, and supi1CV((|Xni|pc)+)0,c,p(0,2), then we have the following:

    limnkni=1V(|Xni|pakn)=0,a>0.

    Proof. From the condition supi1CV((|Xni|pc)+)0,c and the definition of a Choquet integral, it follows that for any a>0, we have the following:

    kni=1V(|Xni|pakn)2knkni=1knkn2V(|Xni|pat)dt2supi1knkn2V(|Xni|pat)dt2supi1kn2V(|Xni|pat)dt=2supi10V(1a|Xni|pkn2t)dt=2asupi1CV[(|Xni|pakn2)+].

    When kn, we obtain the following:

    kni=1V(|Xni|pakn)2asupi1CV[(|Xni|pakn2)+]0.

    Thus, the proof of limnkni=1V(|Xni|p>akn)=0 is finished.

    Lemma 2.7. [10] If ˆE|X|<, then |X|<,q.s.V.

    Lemma 2.8. [10] Suppose ϕ(x)Φc; then, n=11nϕ(nln(1+n))<.

    Proof. Since ϕ(x)Φc, we have ϕ(nln(1+n))ϕ(n); it is only necessary to show that n=11nϕ(n)<. From n=11nϕ(n)<, we obtian the following:

    n=11nϕ(n)=i=1i2n<(i+1)21nϕ(n)i=12iϕ(i)+i=11i2ϕ(i)<.

    Then, the Lemma 2.8 is proven.

    Lemma 2.9. [10] If {εi,iZ} is a sequence of random variables, and there exists a constant c>0 such that |εn|2cnln(1+n),n1, supiZˆE[|εi|ϕ(|εi|)]<, ϕ(x)ΦC, and {αi,i0} is a sequence of real numbers, ani=nir=0αr, T=supk0|ak|<, then for any t>1,

    sup1intln(1+n)|ani|ˆE[|εi|ln(1+tln(1+n)n|ani||εi|)]0,n. (2.4)

    Proof. Becase |εn|2cnln(1+n),n1, then

    |εi|ln(1+tln(1+n)n|ani||εi|)=|εi|ln(1+tln(1+n)n|ani||εi|)I(|εi|n13)+|εi|ln(1+tln(1+n)n|ani||εi|)I(n13<|εi|2cnln(1+n)).

    Let I1=|εi|ln(1+tln(1+n)n|ani||εi|)I(|εi|n13), since T=supk0|ak|<, when n, we have

    I1n13ln(1+tTln(1+n)n23)tTln(1+n)n13. (2.5)

    Let I2=|εi|ln(1+tln(1+n)n|ani||εi|)I(n13<|εi|2cnln(1+n)), and l(x)=ϕ(x)ln(1+x); thus, we obtian the following:

    I2|εi|ϕ(|εi|)ln(1+tTln(1+n)n2cnln(1+n))ϕ(n13)|εi|ϕ(|εi|)ln(1+2ctT)ϕ(n13)|εi|ϕ(|εi|)ln(1+2ctT)ln(1+n13)l(n13). (2.6)

    Since ϕ(x)Φc, the function l(x)=ϕ(x)ln(1+x),x; then, combining (2.5) and (2.6), when n, we have the following:

    sup1intln(1+n)|ani|ˆE[|εi|ln(1+tln(1+n)n|ani||εi|)](tT)2(ln(1+n))2n13+sup1inˆE[|εi|ϕ(|εi|)]tTln(1+n)ln(1+2ctT)ln(1+n13)l(n13)(tT)2(ln(1+n))2n13+supiZˆE[|εi|ϕ(|εi|)]tTln(1+n)ln(1+2ctT)ln(1+n13)l(n13)0.

    Thus, the proof is finished.

    Lemma 2.10.[16] Suppose that {αi,i0} is a sequence of real numbers, ani=nir=0αr, T=supk0|ak|<. {εi,iZ} is a sequence of WND random variables under the sublinear expectation space (Ω,H,ˆE), ˆE[εi]=ˉμ, supiZˆE[|εi|ϕ(|εi|)]<, ϕ(x)ΦC, and there exists a constant c>0 such that |εiˉμ|2ciln(1+i), i1; then, for any t1,

    supn1ˆE[exp(tln(1+n)nni=1ani(εiˉμ))]<. (2.7)

    Proof. For any xR, we have the inequality ex1+x+|x|ln(1+|x|)e2|x|. Let x=tln(1+n)nani(εiˉμ); then,

    exp(tln(1+n)nani(εiˉμ))1+tln(1+n)nani(εiˉμ)+|tln(1+n)nani(εiˉμ)|ln(1+|tln(1+n)nani(εiˉμ)|)exp(2tln(1+n)nani(εiˉμ)). (2.8)

    Since T=supk0|ak|<, for any in, we have the following:

    |tln(1+n)nani(εiˉμ)|tln(1+n)nT2ciln(1+i)2ctT. (2.9)

    By supiZˆE[|εi|ϕ(|εi|)]< and ϕ(x+a)Cϕ(x), we have the following:

    supiZˆE[|εiˉμ|ϕ(|εiˉμ|)]supiZˆE[(|εi|+|ˉμ|)ϕ(|εi|+|ˉμ|)]CsupiZˆE[(|εi|+|ˉμ|)ϕ(|εi|)]<.

    Thus, {εiˉμ,iZ}satisfies the conditions of Lemma 2.9; furthermore, we have

    sup1intln(1+n)n|ani|ˆE[|εiˉμ|ln(1+tln(1+n)n|ani||εiˉμ|)]Cn. (2.10)

    Taking ˆE for both sides of (2.8) and combining (2.9) and (2.10), we have the following:

    ˆE[exp(tln(1+n)nani(εiˉμ))]1+Cne4ctTeCne4ctT.

    From (2.3), we obtain the following:

    ˆE[exp(tln(1+n)nni=1ani(εiˉμ))]ni=1ˆE[exp(tln(1+n)nani(εiˉμ))](eCne4ctT)neCe4ctT<.

    Theorem 3.1. Let {kn,n1} be a sequence of positive numbers, and limnkn=. Assume that ˆE is countably sub-additive. For any i,n1, ˆE[Xni]=ˉμni, ˆE[Xni]=μ_ni.

    (1) Let {Xni,1ikn,n1} be an array of row-wise random variables under the sublinear expectation (Ω,H,ˆE). Suppose that supi1CV((|Xni|pc)+)0,c for any p(0,1); then,

    limnV(1(kn)1p|kni=1Xni|ε)=0. (3.1)

    (2) Let {Xni,1ikn,n1} be an array of row-wise WND random variables under sublinear expectation (Ω,H,ˆE). Suppose that supi1CV((|Xni|pc)+)0,c for any p[1,2); then,

    limnV({1(kn)1pkni=1Xni1(kn)1pkni=1ˉμni+ε}{1(kn)1pkni=1Xni1(kn)1pkni=1μ_niε})=0. (3.2)

    For a fixed n1 in Theorem 3.1, we obtain the Corollary 3.1.

    Corollary 3.1. Assume that ˆE is countably sub-additive.

    (1) Let {Xi,i1} be a sequence of random variables under the sublinear expectation space (Ω,H,ˆE). Suppose that supi1CV((|Xi|pc)+)0,c for any p(0,1); then,

    limnV(1n1p|ni=1Xi|ε)=0. (3.3)

    (2) Let {Xi,i1} be a sequence of WND random variables under the sublinear expectation space (Ω,H,ˆE) and for any i1,ˆE[Xi]=ˉμi,ˆE[Xi]=μ_i. Suppose that supi1CV((|Xi|pc)+)0,c for any p[1,2); then,

    limnV({1n1pni=1Xi1n1pni=1ˉμi+ε}{1n1pni=1Xi1n1pni=1μ_iε})=0. (3.4)

    Theorem 3.2. Suppose that ˆE is countably sub-additive. Let {αi,i0} be a sequence of real numbers satisfying i=0i|αi|<,i=0αi=A>0, and {εi,iZ} be a sequence of WND random variables under sublinear expectations satisfying ˆE[εi]=ˉμ,ˆE[εi]=μ_, supiZˆE[|εi|ϕ(|εi|)]<,ϕΦC. {Xt,t1} is a sequence of linear processes satisfying Xt=i=0αiεti. Note that Tn=nt=1Xt; then,

    V({lim infnTnn<Aμ_}{lim supnTnn>Aˉμ})=0. (3.5)

    Remark 3.1. Under the sub-linear expectations, the main purpose of Theorem 3.1 is to extend the range of p and improve the result of Fu [24] from the Kolmogorov type weak law of large numbers to the Marcinkiewicz-Zygmund type weak law of large numbers.

    Remark 3.2. Under the sub-linear expectations, the main purpose of Theorem 3.2 is to improve the result of Zhang [16] from IID random variables to WND random variables under a more general moment condition.

    The proof of Theorem 3.1. (1) For a fixed constant c, let Yni=((c)Xni)c and Zni=XniYni. Using the Cr inequality and the Markov inequality in Lemma 2.2, we obtain the following:

    V(1(kn)1p|kni=1Xni|>ε)V(kni=1|Yni|(kn)1pε2)+V(kni=1|Zni|(kn)1pε2)V(c(kn)1p1ε2)+2pknεpˆE[(kni=1|Zni|)p]V(c(kn)1p1ε2)+2pknεpkni=1ˆE[|Zni|p]V(c(kn)1p1ε2)+2pεpsupi1ˆE[|Zni|p].

    Thus,

    limnV(1(kn)1p|kni=1Xni|>ε)2pεpsupi1ˆE[|Zni|p]. (4.1)

    Therefore,

    |Zni|p=|Zni|pI(|Xni|c)+|Zni|pI(|Xni|c)=|Zni|pI(Xni>c)+|Zni|pI(Xni<c)=|Xnic|pI(Xni>c)+|Xni+c|pI(Xni<c)(|Xni|c)pI(|Xni|>c)C(|Xni|pc)+.

    Taking ˆE for both sides of the above inequality, when c, we have the following:

    supi1ˆE[|Zni|p]Csupi1ˆE((|Xni|pc)+)Csupi1CV((|Xni|pc)+)0. (4.2)

    Substituting (4.2) into (4.1), we get that (3.1) holds.

    (2) When 1p<2, we construct a function Ψ(y)C2b(R); for any ε>0, we have Ψ(y)=0 when y0, 0<Ψ(y)<1 when 0<y<ε, and Ψ(y)=1 when yε. It is obvious that I(yε)Ψ(y). Let Yni=Xniˉμni; then, we have the following:

    V(1(kn)1pkni=1Yniε)ˆE[Ψ(1(kn)1pkni=1Yni)]=knm=1{ˆE[Ψ(1(kn)1pmi=1Yni)]ˆE[Ψ(1(kn)1pm1i=1Yni)]}. (4.3)

    Let h(y)=ˆE[Ψ(y+Ynm(kn)1p)]; by Definition 2.4 and the sub-additivity of ˆE, then we obtain the following:

    ˆE[Ψ(1(kn)1pmi=1Yni)]ˆE[Ψ(1(kn)1pm1i=1Yni)]ˆE[ˆE[Ψ(y+Ynm(kn)1p)]y=1(kn)1pm1i=1Yni]ˆE[Ψ(1(kn)1pm1i=1Yni)]=ˆE[h(1(kn)1pm1i=1Yni)]ˆE[Ψ(1(kn)1pm1i=1Yni)]ˆE[h(1(kn)1pm1i=1Yni)Ψ(1(kn)1pm1i=1Yni)]supyR{h(y)Ψ(y)}=supyR{ˆE[Ψ(y+Ynm(kn)1p)]Ψ(y)}=supyRˆE[Ψ(y+Ynm(kn)1p)Ψ(y)]. (4.4)

    Let g(x)Cl,Lip(R); for any x, we have 0g(x)1, g(x)=1 when |x|μ, and g(x)=0 when |x|>1. Then, we have the following:

    I(|x|μ)g(x)I(|x|1),I(|x|>1)1g(x)I(|x|>μ). (4.5)

    For any 1mkn, there exist λnm,ˉλnm[0,1] such that

    Ψ(y+Ynm(kn)1p)Ψ(y)=Ψ(y)Ynm(kn)1p+(Ψ(y+λnmYnm(kn)1p)Ψ(y))Ynm(kn)1p,Ψ(y+λnmYnm(kn)1p)Ψ(y)=Ψ(y+λnmˉλnmYnm(kn)1p)λnmYnm(kn)1p. (4.6)

    Since Ψ(y)C2b(R), then we have |Ψ(y)|supyR|Ψ(y)|C |Ψ(y)|supyR|Ψ(y)|C and |Ψ(y)|supyR|Ψ(y)|C. Combining (4.5), (4.6), and the Cr-inequality in Lemma 2.2, then for any δ>0, we have the following:

    Ψ(y+Ynm(kn)1p)Ψ(y)Ψ(y)Ynm(kn)1p+|Ψ(y+λnmYnm(kn)1p)Ψ(y)||Ynm|(kn)1pCYnm(kn)1p+|Ψ(y+λnmYnm(kn)1p)Ψ(y)||Ynm|(kn)1pI(|Xnm|>δ(kn)1p)+|Ψ(y+λnmˉλnmYnm(kn)1p)||λnm||Ynm|2(kn)2pI(|Xnm|δ(kn)1p)CYnm(kn)1p+2C(kn)1p|Xnm|I(|Xnm|>δ(kn)1p)+2C(kn)1p|ˉμnm|I(|Xnm|>δ(kn)1p)+2C(kn)2p|Xnm|2I(|Xnm|δ(kn)1p)+2C(kn)2p|ˉμnm|2I(|Xnm|δ(kn)1p)CYnm(kn)1p+2Cknδp1|Xnm|pI(|Xnm|>δ(kn)1p)+2C|ˉμnm|(kn)1p+1δp|Xnm|p+2Cδ2pkn|Xnm|p+2C(kn)2p|ˉμnm|2CYnm(kn)1p+2Cknδp1[(|Xnm|pkn)++knI(|Xnm|>δ(kn)1p)]+2C|ˉμnm|(kn)1p+1δp|Xnm|p+2Cδ2pkn|Xnm|p+2C(kn)2p|ˉμnm|2CYnm(kn)1p+2Cknδp1(|Xnm|pkn)++2Cδp1I(|Xnm|>δ(kn)1p)+2C|ˉμnm|(kn)1p+1δp|Xnm|p+2Cδ2pkn|Xnm|p+2C(kn)2p|ˉμnm|2CYnm(kn)1p+2Cknδp1(|Xnm|pkn)++2Cδp1(1g(Xnmδ(kn)1p))+2C|ˉμnm|(kn)1p+1δp|Xn,m|p+2Cδ2pkn|Xnm|p+2C(kn)2p|ˉμnm|2. (4.7)

    Substituting (4.4), (4.7), into (4.3), then combining (2.1) and (4.5), we obtain the following:

    V(1(kn)1pkni=1Yniε)2Cδp1supm1ˆE(|Xnm|pkn)++2Cδp1knm=1V(|Xnm|p>μpδpkn)+2C|ˉμnm|(kn)1pδpsupm1CV(|Xnm|p)+2Cδ2psupm1CV(|Xnm|p)+2C(kn)2p1|ˉμnm|2.

    Taking the limit of the above inequality at both sides, then by Lemma 2.6, we obtain

    limnV(1(kn)1pkni=1Yniε)=2Cδ2psupm1CV(|Xnm|p).

    Because supm1CV((|Xnm|c)+)0,c means supm1CV(|Xnm|p)<, and from the arbitrariness of δ, we obtain the following:

    limnV(1(kn)1pkni=1Xni1(kn)1pkni=1ˉμni+ε)=0. (4.8)

    Similarly, for {Xni,1ikn,n1}, we obtain the following:

    limnV(1(kn)1pkni=1Xni1(kn)1pkni=1μ_niε)=0. (4.9)

    Using the sub-additivity of V and combining (4.8) and (4.9), we obtain the following:

    limnV({1(kn)1pkni=1Xni1(kn)1pkni=1ˉμni+ε}{1(kn)1pkni=1Xni1(kn)1pkni=1μ_niε})=0.

    The proof of Theorem 3.1 is completed.

    The proof of Theorem 3.2. To prove Theorem 3.2, we only need to show that

    V(lim supnTnn>Aˉμ)=0, (4.10)

    and

    V(lim infnTnn<Aμ_)=0. (4.11)

    First, we prove Eq (4.10); then, we need to show that

    V(lim supnTnn>Aˉμ+ϵ)=0,ϵ>0.

    It is obvious that

    Tn=nt=1Xt=nt=1i=0αiεti=nt=1i=tαiεti+ni=1εinit=0αt:=Nn+Mn.

    It is only necessary to show that

    limnNnn=0,q.s.V, (4.12)

    and

    V(lim supnMnn>Aˉμ+ϵ)=0,ϵ>0. (4.13)

    To prove(4.12), we need to prove limti=tαiεti=0,q.s.V.

    For any ϵ>0, using the Chebyshev inequality in Lemma 2.2, and the countable sub-additivity of ˆE, we obtain the following:

    t=1V(|i=tαiεti|>ϵ)=t=1ˆE[|i=tαiεti|]ϵ1ϵt=1i=t|αi|ˆE|εti|1ϵsupiZˆE|εi|t=1i=t|αi|=1ϵsupiZˆE|εi|i=1i|αi|<.

    By Lemma 2.1, it follows that

    V(lim supt|i=tαiεti|>ϵ)=0.

    Therefore, by the arbitrariness of ϵ, it follows that

    limti=tαiεti=0,q.s.V.

    Thus, (4.12) holds. Let ani=nir=0αr and T=supk0|ak|<; we prove Eq (4.13) in two steps.

    Step 1: If for any i1 we have |εiˉμ|2ciln(1+i),c>0, then we can directly utilize the conclusion of Lemma 2.10; for any t1, we have the following:

    supn1ˆE[exp(tln(1+n)nni=1ani(εiˉμ))]<.

    Since limnnk=1ankn=A, then V(lim supnni=1ani(εiˉμ)n>ϵ)=0 is equivalent to (4.13). Choosing a suitable t, such that t>1ϵ, using the Chebyshev inequality in Lemma 2.2, we have the following:

    V(ni=1ani(εiˉμ)nϵ)=V(tln(1+n)ni=1ani(εiˉμ)nϵtln(1+n))1(1+n)ϵtsupn1ˆE[exp(tln(1+n)nni=1ani(εiˉμ))].

    By Lemma 2.10 and the convergence of infinite series n=11(1+n)ϵt, we obtain the following:

    n=1V(ni=1ani(εiˉμ)nϵ)n=11(1+n)ϵtsupn1ˆE[exp(tln(1+n)nni=1ani(εiˉμ))]<.

    By Lemma 2.1, it follows that

    V(lim supnni=1ani(εiˉμ)n>ϵ)=0.

    Therefore, (4.13) is proven.

    Step 2: Assume that {εi,iZ} only satisfies the conditions of Theorem 3.2. Let g(x)Cl,Lip(R); for any x, we have 0g(x)1, g(x)=1 when |x|μ, and g(x)=0 when |x|>1. Then we have the following:

    I(|x|μ)g(x)I(|x|1),I(|x|>1)1g(x)I(|x|>μ). (4.14)

    Let ˜εi=ˆE[(εiˉμ)g(μ(εiˉμ)ln(1+i)i)]+(εiˉμ)g(μ(εiˉμ)ln(1+i)i)+ˉμ; for any i1, we have ˆE(˜εi)=ˉμ and |˜εiˉμ|2ciln(1+i). Then, {˜εi,i1} satisfies the conditions of Lemma 2.10. Let ˜Mn=ni=1ani˜εi; similar to the proof of step 1, we obtain the following:

    V(lim supn˜Mnn>Aˉμ+ϵ)=0,ϵ>0. (4.15)

    By the definition of ˜εi, we have the following:

    εi=˜εi+ˆE[(εiˉμ)g(μ(εiˉμ)ln(1+i)i)]+(εiˉμ)[1g(μ(εiˉμ)ln(1+i)i)].

    Since T=supk0|ak|<, then we have the following:

    Mnn˜Mnn+Tnni=1ˆE[(εiˉμ)g(μ(εiˉμ)ln(1+i)i)]+Tnni=1(εiˉμ)[1g(μ(εiˉμ)ln(1+i)i)]. (4.16)

    Note that

    ˆE[(εiˉμ)g(μ(εiˉμ)ln(1+i)i)]ˆE[|εiˉμ|(1g(μ(εiˉμ)ln(1+i)i))]. (4.17)

    Substituting (4.17) into (4.16), we only need to prove

    limn1nni=1ˆE[|εiˉμ|(1g(μ(εiˉμ)ln(1+i)i))]=0, (4.18)

    and

    limn1nni=1|εiˉμ|[1g(μ(εiˉμ)ln(1+i)i)]=0,q.s.V. (4.19)

    By (4.14), we have the following:

    |εiˉμ|[1g(μ(εiˉμ)ln(1+i)i)]|εiˉμ|I(|εiˉμ|>iln(1+i))|εiˉμ|ϕ(|εiˉμ|)ϕ(iln(1+i)).

    Then, combining supiZˆE[|εiˉμ|ϕ(|εiˉμ|)]< and Lemma 2.8, we obtain the following:

    i=11iˆE[|εiˉμ|(1g(μ(εiˉμ)ln(1+i)i))]supiZˆE[|εiˉμ|ϕ(|εiˉμ|)]i=11iϕ(iln(1+i))<.

    By Lemma 2.3, (4.18) holds.

    Since ˆE is countably sub-additive, we have the following:

    ˆE[i=11i|εiˉμ|(1g(μ(εiˉμ)ln(1+i)i))]i=11iˆE[|εiˉμ|(1g(μ(εiˉμ)ln(1+i)i))]<.

    From Lemma 2.7, we obtain the following:

    i=11i|εiˉμ|(1g(μ(εiˉμ)ln(1+i)i))<,q.s.V.

    By Lemma 2.3, (4.19) holds. Combining (4.14), (4.18), and (4.19), it follows that (4.13) holds.

    Similarly, for {εi,iZ}, and ˆE(εi)=ˉμ, we obtain the following:

    V(lim infnTnn<Aμ_)=0.

    Using the sub-additivity of V, the proof of Theorem 3.2 is completed.

    In the framework of sublinear expectations, we established the Marcinkiewicz-Zygmund type weak law of large numbers, and the strong law of large numbers for WND random variables using the Chebyshev inequality, the Cr inequality, and so on. Theorem 3.1 extends the result of Fu[24] from the Kolmogorov type weak law of large numbers to the Marcinkiewicz-Zygmund type weak law of large numbers. Theorem 3.2 extends the result of Zhang[16] from IID random variables to WND random variables under a more general moment condition. In the future, we will try to develop broader results for other sequences of dependent random variables under sublinear expectations.

    Yuyan Wei: conceptualization, formal analysis, investigation, methodology, writing-original draft, writing-review and editing; Xili Tan: funding acquisition, project administration, supervision; Peiyu Sun: formal analysis, writing-review and editing; Shuang Guo: writing-review and editing. All authors have read and approved the final version of the manuscript for publication.

    The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.

    This paper was supported by the Department of Science and Technology of Jilin Province (Grant No.YDZJ202101ZYTS156), and Graduate Innovation Project of Beihua University (2023004).

    All authors declare no conflicts of interest in this paper.


    Acknowledgments



    The study was performed within the Russian Government Program of Competitive Growth of Kazan Federal University. Equipment of the Interdisciplinary Center for Collective Use [ID RFMEFI59414X0003] sponsored by Ministry of Education and Science of the Russian Federation was used.

    Conflict of interest



    The authors declare no conflict of interest.

    Author contributions



    Conceptualization, O.I. and E.M.; acquisition and analysis of data, A.A., G.Y., M.K.; visualization and interpretation of data, P.V. and M.K.; writing-original draft preparation, O.I. and M.K.; writing-review and editing, P.V., A.A., M.K., E.M., G.Y. and O.I. All authors have read and agreed to the published version of the manuscript.

    [1] Research, Science and Therapy Committee Guidelines of the American Academy of Periodontology (2001) Treatment of plaque-induced gingivitis, chronic periodontitis, and other clinical conditions. J Periodontol 72: 1790-1800.
    [2] Wade WG (2013) The oral microbiome in health and disease. Pharmacol Res 69: 137-143. doi: 10.1016/j.phrs.2012.11.006
    [3] Aruni AW, Mishra A, Dou Y, et al. (2015) Filifactor alocis–a new emerging periodontal pathogen. Microbes Infect 17: 517-530. doi: 10.1016/j.micinf.2015.03.011
    [4] Kinane DF, Stathopoulou PG, Papapanou PN (2017) Periodontal diseases. Nat Rev Dis Primers 3: 17038. doi: 10.1038/nrdp.2017.38
    [5] Dewhirst FE, Chen T, Izard J, et al. (2010) The human oral microbiome. J Bacteriol 192: 5002-5017. doi: 10.1128/JB.00542-10
    [6] Do T, Devine D, Marsh PD (2013) Oral biofilms: molecular analysis, challenges, and future prospects in dental diagnostics. Clin Cosmet Invest Dent 5: 11-19.
    [7] Griffen AL, Beall CJ, Campbell JH, et al. (2012) Distinct and complex bacterial profiles in human periodontitis and health revealed by 16S pyrosequencing. ISME J 6: 1176-1185. doi: 10.1038/ismej.2011.191
    [8] Belstrøm D, Constancias F, Liu Y, et al. (2017) Metagenomic and metatranscriptomic analysis of saliva reveals disease-associated microbiota in patients with periodontitis and dental caries. NPJ Biofilms Microbiomes 3: 23. doi: 10.1038/s41522-017-0031-4
    [9] Hiranmayi KV, Sirisha K, Ramoji Rao MV, et al. (2017) Novel Pathogens in Periodontal Microbiology. J Pharm Bioallied Sci 9: 155-163. doi: 10.4103/jpbs.JPBS_288_16
    [10] Guthmiller JM, Novak KF (2002) Periodontal Diseases. Polymicrobial Diseases Washington: ASM Press.
    [11] Kriebel K, Hieke C, Müller-Hilke B, et al. (2018) Oral biofilms from symbiotic to pathogenic interactions and associated disease–connection of periodontitis and rheumatic arthritis by peptidylarginine deiminase. Front Microbiol 9: 53. doi: 10.3389/fmicb.2018.00053
    [12] Corrêa JD, Fernandes GR, Calderaro DC, et al. (2019) Oral microbial dysbiosis linked to worsened periodontal condition in rheumatoid arthritis patients. Sci Rep 9: 8379. doi: 10.1038/s41598-019-44674-6
    [13] Isola G, Matarese G, Ramaglia L, et al. (2020) Association between periodontitis and glycosylated haemoglobin before diabetes onset: a cross-sectional study. Clin Oral Invest 24: 2799-2808. doi: 10.1007/s00784-019-03143-0
    [14] Caton J, Armitage G, Berglundh T, et al. (2018) A new classification scheme for periodontal and peri-implant diseases and conditions–Introduction and key changes from the 1999 classification. J Periodontol 89: S1-S8. doi: 10.1002/JPER.18-0157
    [15] Jervøe-Storm PM, Alahdab H, Koltzscher M, et al. (2007) Comparison of curet and paper point sampling of subgingival bacteria as analyzed by real–time polymerase chain reaction. J Periodontol 78: 909-917. doi: 10.1902/jop.2007.060218
    [16] Mark Welch JL, Rossetti BJ, Rieken CW, et al. (2016) Biogeography of a human oral microbiome at the micron scale. Proc Natl Acad Sci USA 113: e791-e800. doi: 10.1073/pnas.1522149113
    [17] Suzuki N, Yoneda M, Hirofuji T (2013) Mixed Red-Complex Bacterial Infection in Periodontitis. Int J Dent 2013: 587279. doi: 10.1155/2013/587279
    [18] Settem RP, El-Hassan AT, Honma K, et al. (2012) Fusobacterium nucleatum and Tannerella forsythia induce synergistic alveolar bone less in a mouse periodontitis model. Infect Immun 80: 2436-2443. doi: 10.1128/IAI.06276-11
    [19] Kistler JO, Booth V, Bradshaw DJ, et al. (2013) Bacterial community development in experimental gingivitis. PLoS One 8: e71227. doi: 10.1371/journal.pone.0071227
    [20] Balan P, Brandt BW, Chong, YS, et al. (2020) Subgingival microbiota during healthy pregnancy and pregnancy gingivitis. JDR Clin Transl Res .
    [21] Kirst ME, Li EC, Alfant B, et al. (2015) Dysbiosis and alterations in predicted functions of the subgingival microbiome in chronic periodontitis. Appl Environ Microbiol 81: 783-793. doi: 10.1128/AEM.02712-14
    [22] Socransky SS, Haffajee AD, Smith C, et al. (1991) Relation of counts of microbial species to clinical status at the sampled site. J Clin Periodontol 18: 766-775. doi: 10.1111/j.1600-051X.1991.tb00070.x
    [23] Zambon JJ, Christersson LA, Slots J (1983) Actinobacillus actinomycetemcomitans in human periodontal disease. Prevalence in patient groups and distribution of biotypes and serotypes within families. J Periodontol 54: 707-711. doi: 10.1902/jop.1983.54.12.707
    [24] Dani S, Prabhu A, Chaitra KR, et al. (2016) Assessment of Streptococcus mutans in healthy versus gingivitis and chronic periodontitis: A clinic-microbiological study. Contem Clin Dent 7: 529-534. doi: 10.4103/0976-237X.194114
    [25] Larsen T, Fiehn NE (2017) Dental biofilm infections–an update. APMIS 125: 376-384. doi: 10.1111/apm.12688
    [26] Carrouel F, Viennot S, Santamaria J, et al. (2016) Quantitative molecular detection of 19 major pathogens in the interdental biofilm of periodontally healthy young adults. Front Microbiol 7: 840. doi: 10.3389/fmicb.2016.00840
    [27] Bourgeois D, David A, Inquimbert C, et al. (2017) Quantification of carious pathogens in the interdental microbiota of young caries-free adults. PLoS One 12: e0185804. doi: 10.1371/journal.pone.0185804
    [28] Camelo-Castillo AJ, Mira A, Pico A, et al. (2015) Subgingival microbiota in health compared to periodontitis and the influence of smoking. Front Microbiol 6: 119. doi: 10.3389/fmicb.2015.00119
    [29] Signat B, Roques C, Poulet P, et al. (2011) Fusobacterium nucleatum in periodontal health and disease. Curr Issues Mol Biol 13: 25-36.
    [30] Oscarsson J, DiRienzo J, Johansson A (2020) Editorial comments to the special issue: ‘Aggregaterbacter actinomycetemcomitans–gram-negative bacterial pathogen’. Pathogens 9: 441. doi: 10.3390/pathogens9060441
    [31] Lee WH, Chen HM, Yang SF, et al. (2017) Bacterial alterations in salivary microbiota and their association in oral cancer. Sci Rep 7: 16540. doi: 10.1038/s41598-017-16418-x
    [32] Coretti L, Cuomo M, Florio E, et al. (2017) Subgingival dysbiosis in smoker and non-smoker patients with chronic periodontitis. Mol Med Rep 15: 2007-2014. doi: 10.3892/mmr.2017.6269
    [33] Yang I, Knight AK, Dunlop AL, et al. (2019) Characterizing the subgingival microbiome of pregnant African American women. J Obstet Gynecol Neonatal Nurs 48: 140-152. doi: 10.1016/j.jogn.2018.12.003
    [34] Faveri M, Mayer MP, Feres M, et al. (2008) Microbiological diversity of generalized aggressive periodontitis by 16S rRNA clonal analysis. Oral Microbiol Immun 23: 112-118. doi: 10.1111/j.1399-302X.2007.00397.x
    [35] Brinig MM, Lepp PW, Ouverney CC, et al. (2003) Prevalence of bacteria of division TM7 in human subgingival plaque and their association with disease. Appl Environ Microbiol 69: 1687-1694. doi: 10.1128/AEM.69.3.1687-1694.2003
    [36] Islam MM, Ekuni D, Toyama N, et al. (2020) Relationship of salivary microbiome with the worsening of the periodontal health status in young adults: A 3-Year cohort study. Int J Environ Res Public Health 17: 1764. doi: 10.3390/ijerph17051764
    [37] Rylev M, Bek-Thomsen M, Reinholdt J, et al. (2011) Microbiological and immunological characteristics of young Moroccan patients with aggressive periodontitis with and without detectable Aggregatibacter actinomycetemcomitans JP2 infection. Mol Oral Microbiology 26: 35-51. doi: 10.1111/j.2041-1014.2010.00593.x
    [38] He X, McLean JS, Edlund A, et al. (2015) Cultivation of a human-associated TM7 phylotype reveals a reduced genome and epibiotic parasitic lifestyle. Proc Natl Acad Sci USA 112: 244-249. doi: 10.1073/pnas.1419038112
    [39] Bor B, Bedree JK, Shi W, et al. (2019) Saccharibacteria (TM7) in the Human Oral Microbiome. J Dent Res 98: 500-509. doi: 10.1177/0022034519831671
    [40] Soro V, Dutton LC, Sprague SV, et al. (2014) Axenic culture of a Candidate division TM7 bacterium from the human oral cavity and biofilm interactions with other oral bacteria. Appl Environ Microbiol 80: 6480-6489. doi: 10.1128/AEM.01827-14
    [41] Bedree JK, Bor B, Cen L, et al. (2018) Quorum sensing modulates the epibiotic-parasitic relationship between Actinomyces odontolyticus and its saccharibacteria epibiont, a Nanosynbacter lyticus Strain, TM7x. Front Microbiol 9: 2049. doi: 10.3389/fmicb.2018.02049
    [42] Albertsen M, Hugenholtz P, Skarshewski A, et al. (2013) Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes. Nat Biotechnol 31: 533-538. doi: 10.1038/nbt.2579
    [43] Podar M, Abulencia CB, Walcher M, et al. (2007) Targeted access to the genomes of low-abundance organisms in complex microbial communities. Appl Environ Microbiol 73: 3205-3214. doi: 10.1128/AEM.02985-06
    [44] Tanner A, Maiden MF, Macuch PJ, et al. (1998) Microbiota of health, gingivitis, and initial periodontitis. J Clin Periodontol 25: 85-98. doi: 10.1111/j.1600-051X.1998.tb02414.x
    [45] Premaraj TS, Vella R, Chung J, et al. (2020) Ethnic variation of oral microbiota in children. Sci Rep 10: 14788. doi: 10.1038/s41598-020-71422-y
    [46] Mason MR, Nagaraja HN, Camerlengo T, et al. (2013) Deep sequencing identifies ethnicity-specific bacterial signatures in the oral microbiome. PLoS One 8: e77287. doi: 10.1371/journal.pone.0077287
    [47] Li J, Quinque D, Horz HP, et al. (2014) Comparative analysis of the human saliva microbiome from different climate zones: Alaska, Germany, and Africa. BMC Microbiol 14: 316. doi: 10.1186/s12866-014-0316-1
    [48] Gao L, Xu T, Huang G, et al. (2018) Oral microbiomes: more and more importance in oral cavity and whole body. Protein Cell 9: 488-500. doi: 10.1007/s13238-018-0548-1
    [49] Bourgeois D, Inquimbert C, Ottolenghi L, et al. (2019) Periodontal pathogens as risk factors of cardiovascular diseases, diabetes, rheumatoid arthritis, cancer, and chronic obstructive pulmonary disease-is there cause for consideration? Microorganisms 7: 424. doi: 10.3390/microorganisms7100424
    [50] Fan X, Alekseyenko AV, Wu J, et al. (2018) Human oral microbiome and prospective risk for pancreatic cancer: a population-based nested case-control study. Gut 67: 120-127. doi: 10.1136/gutjnl-2016-312580
    [51] Willis JR, Gabaldón T (2020) The Human Oral Microbiome in Health and Disease: From Sequences to Ecosystems. Microorganisms 8: 308. doi: 10.3390/microorganisms8020308
    [52] McHugh J (2017) Rheumatoid arthritis: New model linking periodontitis and RA. Nat Rev Rheumatol 13: 66. doi: 10.1038/nrrheum.2016.221
    [53] Stein SP, Steffen MJ, Smith C, et al. (2012) Serum antibodies to periodontal pathogens are a risk factor for Alzheimer's disease. Alzheimers Dement 8: 196-203. doi: 10.1016/j.jalz.2011.04.006
  • Reader Comments
  • © 2021 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(3490) PDF downloads(141) Cited by(18)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog