Review Special Issues

Biofluid-based microRNA Biomarkers for Parkinsons Disease: an Overview and Update

  • Parkinson's disease (PD) is a highly debilitating motor disorder and is the second most common neurodegenerative disease after Alzheimer's disease. Its current method of diagnosis mainly relies on subjective clinical rating scales in the presence of clinical motor features. Early detection of PD is a known challenge as neuronal cell death may range from 50% to 80% when a patient is first diagnosed with PD. Therefore, there is an urgent need to identify and develop biomarkers for early detection of this progressive disease. This mini review focuses on the recent developments of biofluid-based microRNAs (miRNAs) as molecular biomarkers for PD. A comprehensive list of miRNA biomarkers found in blood, plasma, serum, and cerebral spinal fluid is presented. Challenges and future perspectives of using these PD-related molecular biomarkers in a “real-world” clinical setting are also discussed.

    Citation: Sapana Shinde, Sayantoni Mukhopadhyay, Ghada Mohsen, Sok Kean Khoo. Biofluid-based microRNA Biomarkers for Parkinsons Disease: an Overview and Update[J]. AIMS Medical Science, 2015, 2(1): 15-25. doi: 10.3934/medsci.2015.1.15

    Related Papers:

    [1] Pshtiwan Othman Mohammed, Hari Mohan Srivastava, Sarkhel Akbar Mahmood, Kamsing Nonlaopon, Khadijah M. Abualnaja, Y. S. Hamed . Positivity and monotonicity results for discrete fractional operators involving the exponential kernel. Mathematical Biosciences and Engineering, 2022, 19(5): 5120-5133. doi: 10.3934/mbe.2022239
    [2] Pshtiwan Othman Mohammed, Christopher S. Goodrich, Aram Bahroz Brzo, Dumitru Baleanu, Yasser S. Hamed . New classifications of monotonicity investigation for discrete operators with Mittag-Leffler kernel. Mathematical Biosciences and Engineering, 2022, 19(4): 4062-4074. doi: 10.3934/mbe.2022186
    [3] Maysaa Al Qurashi, Saima Rashid, Sobia Sultana, Hijaz Ahmad, Khaled A. Gepreel . New formulation for discrete dynamical type inequalities via $ h $-discrete fractional operator pertaining to nonsingular kernel. Mathematical Biosciences and Engineering, 2021, 18(2): 1794-1812. doi: 10.3934/mbe.2021093
    [4] M. Botros, E. A. A. Ziada, I. L. EL-Kalla . Semi-analytic solutions of nonlinear multidimensional fractional differential equations. Mathematical Biosciences and Engineering, 2022, 19(12): 13306-13320. doi: 10.3934/mbe.2022623
    [5] Jian Huang, Zhongdi Cen, Aimin Xu . An efficient numerical method for a time-fractional telegraph equation. Mathematical Biosciences and Engineering, 2022, 19(5): 4672-4689. doi: 10.3934/mbe.2022217
    [6] Tahir Khan, Roman Ullah, Gul Zaman, Jehad Alzabut . A mathematical model for the dynamics of SARS-CoV-2 virus using the Caputo-Fabrizio operator. Mathematical Biosciences and Engineering, 2021, 18(5): 6095-6116. doi: 10.3934/mbe.2021305
    [7] Ritu Agarwal, Pooja Airan, Mohammad Sajid . Numerical and graphical simulation of the non-linear fractional dynamical system of bone mineralization. Mathematical Biosciences and Engineering, 2024, 21(4): 5138-5163. doi: 10.3934/mbe.2024227
    [8] Noura Laksaci, Ahmed Boudaoui, Seham Mahyoub Al-Mekhlafi, Abdon Atangana . Mathematical analysis and numerical simulation for fractal-fractional cancer model. Mathematical Biosciences and Engineering, 2023, 20(10): 18083-18103. doi: 10.3934/mbe.2023803
    [9] H. M. Srivastava, Khaled M. Saad, J. F. Gómez-Aguilar, Abdulrhman A. Almadiy . Some new mathematical models of the fractional-order system of human immune against IAV infection. Mathematical Biosciences and Engineering, 2020, 17(5): 4942-4969. doi: 10.3934/mbe.2020268
    [10] Debao Yan . Existence results of fractional differential equations with nonlocal double-integral boundary conditions. Mathematical Biosciences and Engineering, 2023, 20(3): 4437-4454. doi: 10.3934/mbe.2023206
  • Parkinson's disease (PD) is a highly debilitating motor disorder and is the second most common neurodegenerative disease after Alzheimer's disease. Its current method of diagnosis mainly relies on subjective clinical rating scales in the presence of clinical motor features. Early detection of PD is a known challenge as neuronal cell death may range from 50% to 80% when a patient is first diagnosed with PD. Therefore, there is an urgent need to identify and develop biomarkers for early detection of this progressive disease. This mini review focuses on the recent developments of biofluid-based microRNAs (miRNAs) as molecular biomarkers for PD. A comprehensive list of miRNA biomarkers found in blood, plasma, serum, and cerebral spinal fluid is presented. Challenges and future perspectives of using these PD-related molecular biomarkers in a “real-world” clinical setting are also discussed.


    The construction of discrete fractional sums and differences from the knowledge of samples of their corresponding continuous integrals and derivatives arises in the context of discrete fractional calculus; see [1,2,3,4,5,6] for more details. Recently, discrete fractional operators with more general forms of their kernels and properties have gathered attention in both areas of physics and mathematics; see [7,8,9,10].

    In discrete fractional calculus theory, we say that $ \mathtt{F} $ is monotonically increasing at a time step $ t $ if the nabla of $ \mathtt{F} $ is non-negative, i.e., $ \bigl(\nabla \mathtt{F}\bigr)(t): = \mathtt{F}(t)-\mathtt{F}(t-1)\geq 0 $ for each $ t $ in the time scale set $ \mathbb{N}_{c_{0}+1}: = \{c_{0}+1, c_{0}+2, \ldots\} $. Moreover, the function $ \mathtt{F} $ is $ \theta- $monotonically increasing (or decreasing) on $ \mathbb{N}_{c_{0}} $ if $ \mathtt{F}(t+1) > \theta\, \mathtt{F}(t) \quad \bigl(\rm{or}\; \mathtt{F}(t+1) < \theta\, \mathtt{F}(t)\bigr) $ for each $ t\in\mathbb{N}_{a}\bigr) $. In [11,12] the authors considered $ 1- $monotonicity analysis for standard discrete Riemann-Liouville fractional differences defined on $ \mathbb{N}_{0} $ and in [13] the authors generalized the above by introducing $ \theta- $monotonicity increasing and decreasing functions and then obtained some $ \theta- $monotonicity analysis results for discrete Riemann-Liouville fractional differences defined on $ \mathbb{N}_{0} $. In [14,15,16], the authors considered monotonicity and positivity analysis for discrete Caputo, Caputo-Fabrizio and Attangana-Baleanu fractional differences and in [17,18] the authors considered monotonicity and positivity results for abstract convolution equations that could be specialized to yield new insights into qualitative properties of fractional difference operators. In [19], the authors presented positivity and monotonicity results for discrete Caputo-Fabrizo fractional operators which cover both the sequential and non-sequential cases, and showed both similarities and dissimilarities between the exponential kernel case (that is included in Caputo-Fabrizo fractional operators) and fractional differences with other types of kernels. Also in [20] the authors extended the results in [19] to discrete Attangana-Baleanu fractional differences with Mittag-Leffler kernels. The main theoretical developments of monotonicity and positivity analysis in discrete fractional calculus can be found in [21,22,23,24] for nabla differences, and in [25,26,27,28] for delta differences.

    The main idea in this article is to analyse discrete Caputo-Fabrizo fractional differences with exponential kernels in the Riemann-Liouville sense. The results are based on a notable lemma combined with summation techniques. The purpose of this article is two-fold. First we show the positiveness of discrete fractional operators from a theoretical point of view. Second we shall complement the theoretical results numerically and graphically based on the standard plots and heat map plots.

    The plan of the article is as follows. In Section 2 we present discrete fractional operators and the main lemma. Section 3 analyses the discrete fractional operator in a theoretical sense. In Section 4 we discuss our theoretical strategy on standard plots (Subsection 4.1) and heat map plots (Subsection 4.2). Finally, in Section 5 we summarize our findings.

    First we recall the definitions in discrete fractional calculus; see [2,3,5] for more information.

    Definition 2.1. (see [2,Definition 2.24]). Let $ c_0\in\mathbb{R} $, $ 0 < \theta\leq 1 $, $ \mathtt{F} $ be defined on $ \mathbb{N}_{c_{0}} $ and $ \Lambda(\theta) > 0 $ be a normalization constant. Then the following operator

    $ (CFRc0θF)(t):=Λ(θ)ttr=c0+1F(r)(1θ)tr{tNc0+1},
    $

    is called the discrete Caputo-Fabrizio fractional operator with exponential kernels in the Riemann-Liouville sense $ \mathrm{CF_{R}} $, and the following operator

    $ (CFCc0θF)(t):=Λ(θ)tr=c0+1(rF)(r)(1θ)tr{tNc0+1},
    $

    is called the discrete Caputo-Fabrizo fractional operator with exponential kernels in the Caputo sense $ \mathrm{CF_{C}} $.

    Definition 2.2 (see [3]). For $ \mathtt{F}:\mathbb{N}_{c_{0}-\kappa}\to\mathbb{R} $ with $ \kappa < \theta\leq \kappa+1 $ and $ \kappa\in\mathbb{N}_{0} $, the discrete nabla $ \mathrm{CF_{C}} $ and $ \mathrm{CF_{R}} $ fractional differences can be expressed as follows:

    $ (CFCc0θF)(t)=(CFCc0θκκF)(t),
    $

    and

    $ (CFRc0θF)(t)=(CFRc0θκκF)(t),
    $

    respectively, for each $ t\in\mathbb{N}_{c_{0}+1} $.

    The following lemma is essential later.

    Lemma 2.1. Assume that $ \mathtt{F} $ is defined on $ \mathbb{N}_{c_{0}} $ and $ 1 < \theta < 2 $. Then the $ \mathrm{CF_{R}} $ fractional difference is

    $ (CFRc0θF)(t)=Λ(θ1){(F)(t)+(1θ)(2θ)tc02(F)(c0+1)+(1θ)t1r=c0+2(rF)(r)(2θ)tr1},
    $

    for each $ t\in\mathbb{N}_{c_{0}+2} $.

    Proof. From Definitions 2.1 and 2.2, the following can be deduced for $ 1 < \theta < 2: $

    $ (CFRc0θF)(t)=Λ(θ1){tr=c0+1(rF)(r)(2θ)trt1r=c0+1(rF)(r)(2θ)tr1}=Λ(θ1){(F)(t)+t1r=c0+1(rF)(r)[(2θ)tr(2θ)tr1]}=Λ(θ1){(F)(t)+(1θ)(2θ)tc02(F)(c0+1)+(1θ)t1r=c0+2(rF)(r)(2θ)tr1},
    $

    for each $ t\in\mathbb{N}_{c_{0}+2} $.

    In the following theorem, we will show that $ \mathtt{F} $ is monotonically increasing at two time steps even if $ \left({}_{{c_0}}^{C{F_R}}\nabla^{\theta}\mathtt{F}\right)(t) $ is negative at the two time steps.

    Theorem 3.1. Let the function $ \mathtt{F} $ be defined on $ \mathbb{N}_{c_{0}+1} $, and let $ 1 < \theta < 2 $ and $ \epsilon > 0 $. Assume that

    $ (CFRc0θF)(t)>ϵΛ(θ1)(F)(c0+1)fort{c0+2,c0+3}s.t.(F)(c0+1)0.
    $
    (3.1)

    If $ (1-\theta)(2-\theta) < -\epsilon $, then $ \bigl(\nabla \mathtt{F}\bigr)(c_{0}+2) $ and $ \bigl(\nabla \mathtt{F}\bigr)(c_{0}+3) $ are both nonnegative.

    Proof. From Lemma 2.1 and condition (3.1) we have

    $ (F)(t)(F)(c0+1)[(1θ)(2θ)tc02+ϵ](1θ)t1r=c0+2(rF)(r)(2θ)tr1,
    $
    (3.2)

    for each $ t\in \mathbb{N}_{c_{0}+2} $. At $ t = c_{0}+2 $, we have

    $ (F)(c0+2)(F)(c0+1)[(1θ)+ϵ](1θ)c0+1r=c0+2(rF)(r)(2θ)c0+1r=00,
    $

    where we have used $ (1-\theta) < (1-\theta)(2-\theta) < -\epsilon $ and $ \bigl(\nabla \mathtt{F}\bigr)(c_{0}+1)\geq 0 $ by assumption. At $ t = c_{0}+3 $, it follows from (3.2) that

    $ (F)(c0+3)=(F)(c0+1)[(1θ)(2θ)+ϵ](1θ)c0+2r=c0+2(rF)(r)(2θ)c0+2r=(F)(c0+1)0[(1θ)(2θ)+ϵ]<0(1θ)<0(F)(c0+2)00,
    $
    (3.3)

    as required. Hence the proof is completed.

    Remark 3.1. It worth mentioning that Figure 1 shows the graph of $ \theta\mapsto (1-\theta)(2-\theta) $ for $ \theta\in (1, 2) $.

    Figure 1.  Graph of $ \theta\mapsto (1-\theta)(2-\theta) $ for $ \theta\in (1, 2) $.

    In order for Theorem 3.1 to be applicable, the allowable range of $ \epsilon $ is $ \epsilon \in \bigl(0, -(2-\theta)(1-\theta) \bigr) $ for a fixed $ \theta \in (1, 2) $

    Now, we can define the set $ \mathscr{H}_{\kappa, \epsilon} $ as follows

    $ Hκ,ϵ:={θ(1,2):(1θ)(2θ)κc02<ϵ}(1,2),κNc0+3.
    $

    The following lemma shows that the collection $ \left\lbrace \mathscr{H}_{\kappa, \epsilon} \right\rbrace _{\kappa = c_{0}+1}^{\infty} $ forms a nested collection of decreasing sets for each $ \epsilon > 0 $.

    Lemma 3.1. Let $ 1 < \theta < 2 $. Then, for each $ \epsilon > 0 $ and $ \kappa\in\mathbb{N}_{c_{0}+3} $ we have that $ \mathscr{H}_{\kappa+1, \epsilon} \subseteq \mathscr{H}_{\kappa, \epsilon} $.

    Proof. Let $ \theta \in \mathscr{H}_{\kappa+1, \epsilon} $ for some fixed but arbitrary $ \kappa\in\mathbb{N}_{c_{0}+3} $ and $ \epsilon > 0 $. Then we have

    $ (1θ)(2θ)κc01=(1θ)(2θ)(2θ)κc02<ϵ.
    $

    Considering $ 1 < \theta < 2 $ and $ \kappa\in\mathbb{N}_{c_{0}+3} $, we have $ 0 < 2-\theta < 1 $. Consequently, we have

    $ (1θ)(2θ)κc02<ϵ 12θ>1<ϵ.
    $

    This implies that $ \theta \in \mathscr{H}_{\kappa, \epsilon} $, and thus $ \mathscr{H}_{\kappa+1, \epsilon} \subseteq \mathscr{H}_{\kappa, \epsilon} $.

    Now, Theorem 3.1 and Lemma 3.1 lead to the following corollary.

    Corollary 3.1. Let $ \mathtt{F} $ be a function defined on $ \mathbb{N}_{c_{0}+1} $, $ \theta\in(1, 2) $ and

    $ (CFRc0θF)(t)>ϵΛ(θ1)(F)(c0+1)suchthat(F)(c0+1)0,
    $
    (3.4)

    for each $ t\in\mathbb{N}_{c_{0}+3}^{s}: = \{c_{0}+3, c_{0}+4, \ldots, s\} $ and some $ s\in\mathbb{N}_{c_{0}+3} $. If $ \theta \in \mathscr{H}_{s, \epsilon} $, then we have $ \bigl(\nabla \mathtt{F}\bigr)(t)\geq 0 $ for each $ t\in\mathbb{N}_{c_{0}+1}^{s} $.

    Proof. From the assumption $ \theta \in \mathscr{H}_{s, \epsilon} $ and Lemma 3.1, we have

    $ θHs,ϵ=Hs,ϵs1κ=c0+3Hκ,ϵ.
    $

    This leads to

    $ (1θ)(2θ)tc02<ϵ,
    $
    (3.5)

    for each $ t\in\mathbb{N}_{c_{0}+3}^{s} $.

    Now we use the induction process. First for $ t = c_{0}+3 $ we obtain $ \left(\nabla \mathtt{F}\right)(c_{0}+3) \geq 0 $ directly as in Theorem 3.1 by considering inequalities (Eq 3.4) and (Eq 3.5) together with the given assumption $ (\nabla \mathtt{F})(c_{0}+1) \geq 0 $. As a result, we can inductively iterate inequality (Eq 3.2) to get

    $ (F)(t)0,
    $

    for each $ t\in\mathbb{N}_{c_{0}+2}^{s} $. Moreover, $ \bigl(\nabla \mathtt{F}\bigr)(c_{0}+1)\geq 0 $ by assumption. Thus, $ \left(\nabla \mathtt{F}\right)(t)\geq 0 $ for each $ t\in\mathbb{N}_{c_{0}+1}^{s} $ as desired.

    In this section, we consider the methodology for the positivity of $ \nabla \mathtt{F} $ based on previous observations in Theorem 3.1 and Corollary 3.1 in such a way that the initial conditions are known. Later, we will illustrate other parts of our article via standard plots and heat maps for different values of $ \theta $ and $ \epsilon $. The computations in this section were performed with MATLAB software.

    Example 4.1. Considering Lemma 1 with $ t: = c_{0}+3 $:

    $ (CFRc0θF)(c0+3)=Λ(θ1){(F)(c0+3)+(1θ)(2θ)(F)(c0+1)+(1θ)c0+2r=c0+2(rF)(r)(2θ)c0+2r}.
    $

    For $ c_{0} = 0 $, it follows that

    $ (CFR0θF)(3)=Λ(θ1){(F)(3)+(1θ)(2θ)(F)(1)+(1θ)2r=2(rF)(r)(2θ)2r}=Λ(θ1){(F)(3)+(1θ)(2θ)(F)(1)+(1θ)(F)(2)}=Λ(θ1){F(3)F(2)+(1θ)(2θ)[F(1)F(0)]+(1θ)[F(2)F(1)]}.
    $

    If we take $ \theta = 1.99, \mathtt{F}(0) = 0, \mathtt{F}(1) = 1, \mathtt{F}(2) = 1.001, \mathtt{F}(3) = 1.005 $, and $ \epsilon = 0.007 $, we have

    $ (CFR01.99F)(3)=Λ(0.99){0.004+(0.99)(0.01)(0)+(0.99)(0.001)}=0.0069Λ(0.99)>0.007Λ(0.99)=ϵΛ(0.99)(F)(1).
    $

    In addition, we see that $ (1-\theta)(2-\theta) = -0.0099 < -0.007 = -\epsilon $. Since the required conditions are satisfied, Theorem 3.1 ensures that $ (\nabla \mathtt{F})(3) > 0 $.

    In Figure 2, the sets $ \mathscr{H}_{\kappa, 0.008} $ and $ \mathscr{H}_{\kappa, 0.004} $ are shown for different values of $ \kappa $, respectively in Figure 2a, b. It is noted that $ \mathscr{H}_{\kappa, 0.008} $ and $ \mathscr{H}_{\kappa, 0.004} $ decrease by increasing the values of $ \kappa $. Moreover, in Figure 2a, the set $ \mathscr{H}_{\kappa, 0.008} $ becomes empty for $ \kappa\geq 45 $; however, in Figure 2b, we observe the non-emptiness of the set $ \mathscr{H}_{\kappa, 0.004} $ for many larger values of $ \kappa $ up to $ 90 $. We think that the measures of $ \mathscr{H}_{\kappa, 0.008} $ and $ \mathscr{H}_{\kappa, 0.004} $ are not symmetrically distributed when $ \kappa $ increases (see Figure 2a, b). We do not have a good conceptual explanation for why this symmetric behavior is observed. In fact, it is not clear why the discrete nabla fractional difference $ {}_{{c_0}}^{C{F_R}}\nabla^{\theta} $ seems to give monotonically when $ \theta\to 1 $ rather than for $ \theta\to 2 $, specifically, it gives a maximal information when $ \theta $ is very close to $ 1 $ as $ \epsilon\to 0^+ $.

    Figure 2.  Graph of $ \mathscr{H}_{\kappa, \epsilon} $ for different values of $ \kappa $ and $ \epsilon $.

    In the next figure (Figure 3), we have chosen a smaller $ \epsilon $ ($ \epsilon = 0.001 $), we see that the set $ \mathscr{H}_{\kappa, 0.001} $ is non-empty for $ \kappa > 320 $. This tells us that small choices in $ \epsilon $ give us a more widely applicable result.

    Figure 3.  Graph of $ \mathscr{H}_{\kappa, \epsilon} $ for $ \kappa\in\mathrm{N}_{3}^{350} $ and $ \epsilon = 0.001 $.

    In this part, we introduce the set $ \mathcal{H}_{\kappa}: = \{\kappa:\; \theta \in \mathscr{H}_{\kappa, \epsilon}\} $ to simulate our main theoretical findings for the cardinality of the set $ \mathcal{H}_{\kappa} $ via heat maps in Figure 4ad. In these figures: we mean the warm colors such as red ones and the cool colors such as blue ones. Moreover, the $ \theta $ values are on the $ x- $axis and $ \epsilon $ values are on the $ y- $axis. We choose $ \epsilon $ in the interval $ [0.00001 $, $ 0.0001] $. Then, the conclusion of these figures are as follows:

    Figure 4.  The cardinality of $ \mathcal{H}_{\kappa} $ for different values of $ \theta $ with $ 0.00001\leq\epsilon\leq 0.0001 $ in heat maps.

    ● In Figure 4a when $ \theta\in(1, 2) $ and Figure 4b when $ \theta\in(1, 1.5) $, we observe that the warmer colors are somewhat skewed toward $ \theta $ very close to $ 1 $, and the cooler colors cover the rest of the figures for $ \theta $ above $ 1.05 $.

    ● In Figure 4c, d, the warmest colors move strongly towards the lower values of $ \theta $, especially, when $ \theta\in(1, 1.05) $. Furthermore, when as $ \theta $ increases to up to $ 1.0368 $, it drops sharply from magenta to cyan, which implies a sharp decrease in the cardinality of $ \mathcal{H}_{\kappa} $ for a small values of $ \epsilon $ as in the interval $ [0.00001 $, $ 0.0001] $.

    On the other hand, for larger values of $ \epsilon $, the set $ \mathcal{H}_{\kappa} $ will tend to be empty even if we select a smaller $ \theta $ in such an interval $ (1, 1.05) $. See the following Figure 5a, b for more.

    Figure 5.  The cardinality of $ \mathcal{H}_{\kappa} $ for different values of $ \epsilon $ with $ 1 < \theta < 1.05 $ in heat maps.

    In conclusion, from Figures 4 and 5, we see that: For a smaller value of $ \epsilon $, the set $ \mathscr{H}_{\kappa, \epsilon} $ tends to remain non-empty (see Figure 4), unlike for a larger value of $ \epsilon $ (see Figure 5). Furthermore, these verify that Corollary 3.1 will be more applicable for $ 1 < \theta < 1.05 $ and $ 0.01 < \epsilon < 0.1 $ as shown in Figure 4d.

    Although, our numerical data strongly note the sensitivity of the set $ \mathcal{H}_{\kappa} $ when slight increasing in $ \epsilon $ is observed for $ \theta $ close to $ 2 $ compares with $ \theta $ close to $ 1 $.

    In this paper we developed a positivity method for analysing discrete fractional operators of Riemann-Liouville type based on exponential kernels. In our work we have found that $ (\nabla \mathtt{F})(3)\geq 0 $ when $ \left({}_{{c_0}}^{C{F_R}}\nabla^{\theta} \mathtt{F}\right)(t) > -\epsilon\, \Lambda(\theta-1)\, \bigl(\nabla \mathtt{F}\bigr)(c_{0}+1) $ such that $ \bigl(\nabla \mathtt{F}\bigr)(c_{0}+1)\geq 0 $ and $ \epsilon > 0 $. We continue to extend this result for each value of $ t $ in $ \mathbb{N}_{c_{0}+1}^{s} $ as we have done in Corollary 3.1.

    In addition we presented standard plots and heat map plots for the discrete problem that is solved numerically. Two of the graphs are standard plots for $ \mathscr{H}_{\kappa, \epsilon} $ for different values of $ \kappa $ and $ \epsilon $ (see Figure 2), and the other six graphs consider the cardinality of $ \mathcal{H}_{\kappa} $ for different values of $ \epsilon $ and $ \theta $ (see Figures 4 and 5). These graphs ensure the validity of our theoretical results.

    In the future we hope to apply our method to other types of discrete fractional operators which include Mittag-Leffler and their extensions in kernels; see for example [5,6].

    This work was supported by the Taif University Researchers Supporting Project Number (TURSP-2020/86), Taif University, Taif, Saudi Arabia.

    The authors declare there is no conflict of interest.

    [1] Braak H, Del Tredici K, Rub U, et al. (2003) Staging of brain pathology related to sporadic Parkinson's disease. Neurobiol Aging 24: 197-211. doi: 10.1016/S0197-4580(02)00065-9
    [2] Savitt JM, Dawson VL, Dawson TM (2006) Diagnosis and treatment of Parkinson disease: molecules to medicine. J Clin Invest 116: 1744-1754. doi: 10.1172/JCI29178
    [3] Dickson DW, Braak H, Duda JE, et al. (2009) Neuropathological assessment of Parkinson's disease: refining the diagnostic criteria. Lancet Neurol 8: 1150-1157. doi: 10.1016/S1474-4422(09)70238-8
    [4] Schapira AH (2013) Recent developments in biomarkers in Parkinson disease. Curr Opin Neurol26: 395-400.
    [5] Liotta LA, Ferrari M, Petricoin E (2003) Clinical proteomics: written in blood. Nature 425: 905. doi: 10.1038/425905a
    [6] Keller A, Leidinger P, Bauer A, et al. (2011) Toward the blood-borne miRNome of human diseases. Nat Methods 8: 841-843. doi: 10.1038/nmeth.1682
    [7] Ambros V (2004) The functions of animal microRNAs. Nature 431: 350-355. doi: 10.1038/nature02871
    [8] Bartel DP (2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116:281-297. doi: 10.1016/S0092-8674(04)00045-5
    [9] Lim LP, Lau NC, Garrett-Engele P, et al. (2005) Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature 433: 769-773. doi: 10.1038/nature03315
    [10] Hobert O (2008) Gene regulation by transcription factors and microRNAs. Science 319:1785-1786. doi: 10.1126/science.1151651
    [11] Goodall EF, Heath PR, Bandmann O, et al. (2013) Neuronal dark matter: the emerging role of microRNAs in neurodegeneration. Front Cell Neurosci 7: 178.
    [12] Khoo SK, Neuman LA, Forsgren L, et al. (2013) Could miRNA expression changes be a reliable clinical biomarker for Parkinson's disease? Neurodegener Dis Manag 3: 455-465. doi: 10.2217/nmt.13.53
    [13] Lawrie CH, Gal S, Dunlop HM, et al. (2008) Detection of elevated levels of tumour-associated microRNAs in serum of patients with diffuse large B-cell lymphoma. Br J Haematol 141:672-675. doi: 10.1111/j.1365-2141.2008.07077.x
    [14] Zen K, Zhang CY (2012) Circulating microRNAs: a novel class of biomarkers to diagnose and monitor human cancers. Med Res Rev 32: 326-348. doi: 10.1002/med.20215
    [15] Valadi H, Ekstrom K, Bossios A, et al. (2007) Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nat Cell Biol 9: 654-659. doi: 10.1038/ncb1596
    [16] Hunter MP, Ismail N, Zhang X, et al. (2008) Detection of microRNA expression in human peripheral blood microvesicles. PloS One 3: e3694. doi: 10.1371/journal.pone.0003694
    [17] Zernecke A, Bidzhekov K, Noels H, et al. (2009) Delivery of microRNA-126 by apoptotic bodies induces CXCL12-dependent vascular protection. Sci Signal 2: ra81.
    [18] Wang K, Zhang S, Weber J, et al. (2010) Export of microRNAs and microRNA-protective protein by mammalian cells. Nucleic Acids Res 38: 7248-7259. doi: 10.1093/nar/gkq601
    [19] Vickers KC, Palmisano BT, Shoucri BM, et al. (2011) MicroRNAs are transported in plasma and delivered to recipient cells by high-density lipoproteins. Nat Cell Biol 13: 423-433. doi: 10.1038/ncb2210
    [20] Mitchell PS, Parkin RK, Kroh EM, et al. (2008) Circulating microRNAs as stable blood-based markers for cancer detection. Proc Natl Acad Sci U S A 105: 10513-10518. doi: 10.1073/pnas.0804549105
    [21] Chen X, Ba Y, Ma L, et al. (2008) Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases. Cell Res 18: 997-1006. doi: 10.1038/cr.2008.282
    [22] Khoo SK, Petillo D, Kang UJ, et al. (2012) Plasma-based circulating microRNA biomarkers for Parkinson's disease. J Parkinson Dis 2: 321-331.
    [23] Sheinerman KS, Umansky SR (2013) Circulating cell-free microRNA as biomarkers for screening, diagnosis and monitoring of neurodegenerative diseases and other neurologic pathologies. Front Cell Neurosci 7: 150.
    [24] Cogswell JP, Ward J, Taylor IA, et al. (2008) Identification of miRNA changes in Alzheimer's disease brain and CSF yields putative biomarkers and insights into disease pathways. J Alzheimers Dis 14: 27-41.
    [25] Margis R, Rieder CR (2011) Identification of blood microRNAs associated to Parkinsonis disease. J Biotechnol 152: 96-101. doi: 10.1016/j.jbiotec.2011.01.023
    [26] Martins M, Rosa A, Guedes LC, et al. (2011) Convergence of miRNA expression profiling, alpha-synuclein interacton and GWAS in Parkinson's disease. PloS one 6: e25443. doi: 10.1371/journal.pone.0025443
    [27] Soreq L, Salomonis N, Bronstein M, et al. (2013) Small RNA sequencing-microarray analyses in Parkinson leukocytes reveal deep brain stimulation-induced splicing changes that classify brain region transcriptomes. Front Mol Neurosci 6: 10.
    [28] Cardo LF, Coto E, de Mena L, et al. (2013) Profile of microRNAs in the plasma of Parkinson's disease patients and healthy controls. J Neurol 260: 1420-1422. doi: 10.1007/s00415-013-6900-8
    [29] Burgos K, Malenica I, Metpally R, et al. (2014) Profiles of extracellular miRNA in cerebrospinal fluid and serum from patients with Alzheimer's and Parkinson's diseases correlate with disease status and features of pathology. PLoS One 9: e94839. doi: 10.1371/journal.pone.0094839
    [30] Botta-Orfila T, Morato X, Compta Y, et al. (2014) Identification of blood serum micro-RNAs associated with idiopathic and LRRK2 Parkinson's disease. J Neurosci Res 92: 1071-1077. doi: 10.1002/jnr.23377
    [31] Wang K, Zhang S, Weber J, et al. (2010) Export of microRNAs and microRNA-protective protein by mammalian cells. Nucleic acids res 38: 7248-7259. doi: 10.1093/nar/gkq601
    [32] Faure J, Lachenal G, Court M, et al. (2006) Exosomes are released by cultured cortical neurones. Mol Cell Neurosci 31: 642-648. doi: 10.1016/j.mcn.2005.12.003
    [33] Potolicchio I, Carven GJ, Xu X, et al. (2005) Proteomic analysis of microglia-derived exosomes: metabolic role of the aminopeptidase CD13 in neuropeptide catabolism. J Immunol 175:2237-2243. doi: 10.4049/jimmunol.175.4.2237
    [34] Kramer-Albers EM, Bretz N, Tenzer S, et al. (2007) Oligodendrocytes secrete exosomes containing major myelin and stress-protective proteins: Trophic support for axons? Proteom Clin appl 1: 1446-1461. doi: 10.1002/prca.200700522
    [35] Lachenal G, Pernet-Gallay K, Chivet M, et al. (2011) Release of exosomes from differentiated neurons and its regulation by synaptic glutamatergic activity. Mol Cell Neurosci 46: 409-418. doi: 10.1016/j.mcn.2010.11.004
    [36] Russo I, Bubacco L, Greggio E (2012) Exosomes-associated neurodegeneration and progression of Parkinson's disease. Am J Neurodegener Dis 1: 217-225.
  • Reader Comments
  • © 2015 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(8316) PDF downloads(1259) Cited by(9)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog