ψij | name |
i+j | First Zagreb index |
1√ij | Randić index |
2√iji+j | GA index |
√i+j−2ij | ABC index |
1√i+j | Sum–connectivity index |
(ij)3(i+j−2)3 | AZI index |
2i+j | Harmonic index |
|i−j| | Albertson index |
√i2+j2 | Sombor index |
iji+j | ISI index |
In this study we review the current state of the art for Dupuytren's disease (DD), while emphasising the need for a better integration of clinical, experimental and quantitative predictive approaches to understand the evolution of the disease and improve current treatments. We start with a brief review of the biology of this disease and current treatment approaches. Then, since certain aspects in the pathogenesis of this disorder have been compared to various biological aspects of wound healing and malignant processes, next we review some in silico (mathematical modelling and simulations) predictive approaches for complex multi-scale biological interactions occurring in wound healing and cancer. We also review the very few in silico approaches for DD, and emphasise the applicability of these approaches to address more biological questions related to this disease. We conclude by proposing new mathematical modelling and computational approaches for DD, which could be used in the absence of animal models to make qualitative and quantitative predictions about the evolution of this disease that could be further tested in vitro.
Citation: Georgiana Eftimie, Raluca Eftimie. Quantitative predictive approaches for Dupuytren disease: a brief review and future perspectives[J]. Mathematical Biosciences and Engineering, 2022, 19(3): 2876-2895. doi: 10.3934/mbe.2022132
[1] | Edil D. Molina, Paul Bosch, José M. Sigarreta, Eva Tourís . On the variable inverse sum deg index. Mathematical Biosciences and Engineering, 2023, 20(5): 8800-8813. doi: 10.3934/mbe.2023387 |
[2] | Mert Sinan Oz, Roberto Cruz, Juan Rada . Computation method of the Hosoya index of primitive coronoid systems. Mathematical Biosciences and Engineering, 2022, 19(10): 9842-9852. doi: 10.3934/mbe.2022458 |
[3] | William E. Fitzgibbon . The work of Glenn F. Webb. Mathematical Biosciences and Engineering, 2015, 12(4): v-xvi. doi: 10.3934/mbe.2015.12.4v |
[4] | V. R. Kulli, J. A. Méndez-Bermúdez, José M. Rodríguez, José M. Sigarreta . Revan Sombor indices: Analytical and statistical study. Mathematical Biosciences and Engineering, 2023, 20(2): 1801-1819. doi: 10.3934/mbe.2023082 |
[5] | Xinmei Liu, Xinfeng Liang, Xianya Geng . Expected Value of Multiplicative Degree-Kirchhoff Index in Random Polygonal Chains. Mathematical Biosciences and Engineering, 2023, 20(1): 707-719. doi: 10.3934/mbe.2023032 |
[6] | Saylé C. Sigarreta, Saylí M. Sigarreta, Hugo Cruz-Suárez . On degree–based topological indices of random polyomino chains. Mathematical Biosciences and Engineering, 2022, 19(9): 8760-8773. doi: 10.3934/mbe.2022406 |
[7] | Muhammad Akram, Adeel Farooq, Maria Shabir, Mohammed M. Ali Al-Shamiri, Mohammed M. Khalaf . Group decision-making analysis with complex spherical fuzzy N-soft sets. Mathematical Biosciences and Engineering, 2022, 19(5): 4991-5030. doi: 10.3934/mbe.2022234 |
[8] | Martin J. Blaser . Studying microbiology with Glenn F. Webb. Mathematical Biosciences and Engineering, 2015, 12(4): xvii-xxii. doi: 10.3934/mbe.2015.12.4xvii |
[9] | Wanlin Zhu, Minglei Fang, Xianya Geng . Enumeration of the Gutman and Schultz indices in the random polygonal chains. Mathematical Biosciences and Engineering, 2022, 19(11): 10826-10845. doi: 10.3934/mbe.2022506 |
[10] | Abdul Qadeer Khan, Azhar Zafar Kiyani, Imtiaz Ahmad . Bifurcations and hybrid control in a 3×3 discrete-time predator-prey model. Mathematical Biosciences and Engineering, 2020, 17(6): 6963-6992. doi: 10.3934/mbe.2020360 |
In this study we review the current state of the art for Dupuytren's disease (DD), while emphasising the need for a better integration of clinical, experimental and quantitative predictive approaches to understand the evolution of the disease and improve current treatments. We start with a brief review of the biology of this disease and current treatment approaches. Then, since certain aspects in the pathogenesis of this disorder have been compared to various biological aspects of wound healing and malignant processes, next we review some in silico (mathematical modelling and simulations) predictive approaches for complex multi-scale biological interactions occurring in wound healing and cancer. We also review the very few in silico approaches for DD, and emphasise the applicability of these approaches to address more biological questions related to this disease. We conclude by proposing new mathematical modelling and computational approaches for DD, which could be used in the absence of animal models to make qualitative and quantitative predictions about the evolution of this disease that could be further tested in vitro.
In mathematics chemistry and biology, a chemical compound can be represented by a molecular graph by converting atoms to vertices and bonds to edges. One of the primary mission of QSAR/QSPR research is to accurately convert molecular graphs into numerical values. Graph theoretic invariants of molecular graphs are called molecular descriptors which can be utilized to simulate the structural information of molecules, in order to make worthwhile physical and chemical properties of these molecules can be acquired by single numerical values. Such kinds of molecular descriptors are also referred to as topological indices.
In the chemical literature, various topological indices relying only on vertex degrees of the molecular graphs can be utilized in QSPR/QSAR investigation on account of them can be obtained directly from the molecular architecture, and can be rapidly calculated for generous molecules (see [1,2]), and we call them VDB (vertex–degree–based) topological indices. To be more precise, for designated nonnegative real numbers {ψij} (1≤i≤j≤n−1), a VDB topological index of a an n-order (molecular) graph G is expressed as
TI(G)=∑1≤i≤j≤n−1mijψij, | (1.1) |
where mij is the amount of edges connecting an i-vertex and a j-vertex of G. A great deal of well–known VDB topological indices can be obtained by different ψij in expression (1.1). We list some VDB topological indices in Table 1.
ψij | name |
i+j | First Zagreb index |
1√ij | Randić index |
2√iji+j | GA index |
√i+j−2ij | ABC index |
1√i+j | Sum–connectivity index |
(ij)3(i+j−2)3 | AZI index |
2i+j | Harmonic index |
|i−j| | Albertson index |
√i2+j2 | Sombor index |
iji+j | ISI index |
The first Zagreb index [3] is the very first VDB topological index, as powerful molecular structure-descriptors [2], Zagreb indices can describe the peculiarities of the degree of branching in molecular carbon-atom skeleton. Thereafter, many VDB topological indices have been put forward to simulate physical, chemical, biological, and other attributes of molecules [4,5,6,7]. In 2021, Gutman [8] introduced a new VDB topological index named as the Sombor index which has a linear correlation with the entropy and the enthalpy of vaporization of octanes [9]. Das et al., give sharp bounds for Sombor index of graphs by means of some useful graph parameters and they reveal the relationships between the Sombor index and Zagreb indices of graphs [10]. Recently, Steiner Gutman index was introduced by Mao and Das [11] which incorporate Steiner distance of a connected graph G. Nordhaus-Gaddum-type results for the Steiner Gutman index of graphs were given in [12]. In 2022, Shang study the Sombor index and degree-related properties of simplicial networks [13]. For more details of VDB topological indices, one can see [3,14,15,16,17,18,19,20,21,22,23,24,25,26] and the books [27,28,29].
Fluoranthene is a eminent conjugated hydrocarbon which abound in coal tar [30]. A fluoranthene–type benzenoid system (f-benzenoid for short) is formed from two benzenoid units joined by a pentagon [31,32]. The ordinary structure modality of a f-benzenoid F is shown in Figure 1, where segments X and Y are two benzenoid systems. Each f-benzenoid possesses exactly one pentagon [32]. More and more attention is paid to f-benzenoids after the flash vacuum pyrolysis experiments of these nonalternant polycyclic aromatic hydrocarbons [33].
In the whole article, the terminology and notation are chiefly derived from [34,35,36,37,38,39,40,41]. A vertex of degree k is called a k-vertex, and an edge linking a k-vertex and a j-vertex is designated as a (k,j)-edge. Let nk be the number of k-vertices and let mkj be the number of (k,j)-edges in the molecular graph G. A benzenoid system without internal vertices is said to be catacondensed. Analogously, a f-benzenoid F containing a unique internal vertex is referred to as catacatacondensed. We use h-hexagon benzenoid system (or h-hexagon f-benzenoid) to represent a benzenoid system (or f-benzenoid) containing h hexagons.
Let Lh represent the h-hexagon linear chain (as shown in Figure 2(a)). An f-benzenoid FLh (h≥3) obtaining from pieces X=L2 and Y=Lh−2 is named as f-linear chain (as shown in Figure 2(b)).
A fissure (resp. bay, cove, fjord and lagoon) of a f-benzenoid F is a path of degree sequences (2,3,2) (resp. (2,3,3,2), (2,3,3,3,2), (2,3,3,3,3,2) and (2,3,3,3,3,3,2)) on the perimeter of F (see Figure 3). Fissures, bays, coves, fjords and lagoons are said to be different kinds of inlets and their number are signified by f, B, C, Fj and L, respectively [32,37]. Inlets determine many electronic and topological properties of f-benzenoids. Then, it can be found that f+2B+3C+4FJ+5L is the number of 3-vertices on the perimeter of F. It is noted that lagoons cannot occur in the theory of benzenoid systems. For convenience, let r=f+B+C+Fj+L to represent the total number of inlets and b=B+2C+3Fj+4L is referred to as the quantity of bay regions, In addition, b is exactly the quantity of (3,3)-edges on the perimeter of F. It is obvious that b≥2 for any f-benzenoid F.
It is noted that any f-benzenoid F contains merely either 2-vertex or 3-vertex. The vertices not on the perimeter are said to be internal, and we use ni to represent their number.
Lemma 1.1. [32] Let F be an n-order, h-hexagon f-benzenoid with m edges and ni internal vertices. Then
(i) n=4h+5−ni;
(ii) m=5h+5−ni.
Lemma 1.2. [32] Let F be an n-order and h-hexagon f-benzenoid with r inlets, Then
(i) m22=n−2h−r;
(ii) m23=2r;
(iii) m33=3h−r.
From the perspective of mathematics and chemistry, finding the extremal values of some useful TI for significant classes of graphs is very interesting [14,19,23,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56].
As a matter of convenience, we use Γm to represent the collection of f-benzenoids containing exactly m edges. In [45], we derived extremal values for TI among all f-benzenoids with given order. It is noted that structure of f-benzenoids with given order is different from that of f-benzenoids with given number of edges. And we found that the technique for studying TI among all f-benzenoids with given order can not be used directly to investigate TI for all f-benzenoids with fixed number of edges. For this reason, we concentrate on the research of extremal values for TI among all f-benzenoids with given size.
The main idea of this work is to construct f-benzenoids owning maximal r and minimal h at the same time in Γm depending on the number m is congruent to 0,1,2,3 or 4 modulo 5. By making use of this technique, we obtain the extremum of TI over Γm and characterize their corresponding graphs on the basis of m is congruent to 0,1,2,3 or 4 modulo 5. Afterwards the extremums of some well-known TI over Γm can be got by use of the previous results.
The structure of this paper is as below. We first determine the maximal r in the set Γm in Section 2. By utilizing these results, we find the extremum of several famed TI over Γm in Section 3.
We will find the f-benzenoids with maximal r in Γm in this section. Figure 4 illustrates three f-benzenoids pertaining to Γ42.
At first, we try to obtain the maximum and minimum number of hexagons in any F∈Γm.
The spiral benzenoid system [57] Th is a benzenoid system whose structure is in a "spiral" manner as illustrated in Figure 5. Th has maximal ni in all h-hexagon benzenoid systems.
As a matter of convenience, let SHh (h≥3) represent the collection of f-benzenoids formed by two spiral benzenoids X and Y. Particularly, a f-spiral benzenoid is a f-benzenoid F∗∈SHh in which X=Th−1 and Y=T1 (as shown in Figure 6). It is easy to see that that
ni(F∗)=2h−⌈√12(h−1)−3⌉. |
In [40], we proved that for every F′∈SHh (h≥3), the inequality
ni(F′)≤ni(F∗) | (2.1) |
holds, and the following graph operations were introduced.
Operation 1. For any h-hexagon f-benzenoid F having two segments X and Y, let h1=h(X) and h2=h(Y). By substituting spiral benzenoid systems Th1 and Th2 for X and Y, severally, another f-benzenoid F′∈SHh can be obtained (as shown in Figure 7).
For any h-hexagon f-benzenoid F, when h=3, it is easily checked that
ni(F)=1=2×3−⌈√12(3−1)−3⌉. | (2.2) |
When h≥4, let h1=h(X) and h2=h(Y). Another F′∈SHh (as shown in Figure 7) in which X=Th1 and Y=Th2 can be acquired by applying Operation 1 to F. It is apparently that ni(X)≤ni(Th1), ni(Y)≤ni(Th2), therefore
ni(F)=ni(X)+ni(Y)+1≤ni(Th1)+ni(Th2)+1=ni(F′). | (2.3) |
So, the following Lemma can be deduced by Eqs (2.1) and (2.3).
Lemma 2.1. [41] Let F be an h(h≥3)-hexagon f-benzenoid. Then
ni(F)≤2h−⌈√12(h−1)−3⌉, | (2.4) |
and the equality is established when F is F∗.
For any F∈Γm, h(F) over Γm is variable. Sharp bounds for h(F) in Γm is given below.
Theorem 2.1. For any f-benzenoid F∈Γm,
⌈15(m−4)⌉≤h(F)≤m−1−⌈13(2m+√4m−31)⌉, | (2.5) |
where ⌈x⌉ is the smallest integer larger or equal to x.
Proof. On one hand, from Lemma 1.1 (ii) we know that m=5h(F)+5−ni(F). Combining the fact that ni(F)≥1 for any F∈Γm, we get
h(F)≥⌈15(m−4)⌉. |
On the other hand, by Lemma 2.1 we know that ni(F)≤ni(F∗). Consequently, from m=5h(F)+5−ni(F) we have
m−3h(F)−5≥⌈√12(h(F)−1)−3⌉≥√12(h(F)−1)−3. |
Hence,
(3h(F)+(3−m))2≥4m−31. |
Due to the fact that 3h(F)+(3−m)<0, we deduce
3h(F)+(3−m)≤−√4m−31, |
i.e., h(F)≤m−1−⌈13(2m+√4m−31)⌉.
Remark 1. Theorem 2.1 implies that f-spiral benzenoid F∗ has the maximal number of hexagons over Γm.
For the sake of obtaining the extremum TI among all f-benzenoids in Γm, we need to find the f-benzenoids F∈Γm possessing maximal r.
Recall that convex benzenoid systems (CBS for brevity) are a particular sort of benzenoid systems lack of bay regions [14]. Let HSh be the collection of benzenoid systems containing h hexagons.
Lemma 2.2. [42] Let H∈HSh. Under the below cases, H is definitely not a CBS:
(i) If h≥4 and ni=1;
(ii) If h≥5 and ni=2;
(iii) If h≥6 and ni=3.
Lemma 2.3. [52] Let H∈HSh such that ni(H)=4. Then H is bound to embody a subbenzenoid system given in Figure 8, there does not exist hexagons which are adjacent to fissures.
Lemma 2.4. Let S∈HSh. If h≥7 and ni(S)=4, then S is not a CBS.
Proof. Let S be an h (h≥7)-hexagon benzenoid system, ni(S)=4, then by Lemma 2.3 S must contain one of the benzenoid systems of the form given in Figure 7. The proof is carried out in two cases.
Case 1. If these four internal vertices form a path P4 or a K1,3, then S contains one of benzenoid systems (d)–(f) in Figure 7 as its subbenzenoid systems. It is noted that h≥7, by Lemma 2.2, it must not exist hexagons contiguous to the fissures, so, S has at least one hexagon contiguous to a (2,2)-edge, by means of such hexagons, it is succeeded in converting one of the fissures into a cove, bay or fjord. Hence, b(S)≥1.
Case 2. If these four internal vertices are not adjacent then S has possibility subbenzenoid systems as follows.
1) There exist one type (a) and one type (c) benzenoid systems in S;
2) There exist two type (b) benzenoid systems in S;
3) There exist two type (a) and one type (b) benzenoid systems in S.
4) There exist four type (a) benzenoid systems in S
By Lemma 2.2, neither hexagons may be adjacent to the fissures in any of the cases indicated above. Since h≥7, S has at least one hexagon contiguous to a (2,2)-edge, by means of such hexagons, it is succeeded in making one of the fissures become a cove, bay or fjord. Therefore, b(S)≥1.
The proof is completed.
Lemma 2.5. [45] Let F be an h-hexagon f-benzenoid. Then
1) If ni=1, then r(F)≤r(FLh)=2h−3 (h≥3);
2) If ni=2, then r(F)≤r(Gh)=2h−4 (h≥4);
3) If ni=3, then r(F)≤r(Rh)=2h−5 (h≥5);
4) If ni=4, then r(F)≤r(Zh)=2h−6 (h≥6).
Next we find the f-benzenoids with maximal r in Γm with a fixed ni. Recall that Mh, Nh and Qh (see Figure 9) are benzenoid systems, and Gh (see Figure 10), Rh (see Figure 11), Zh (see Figure 12) are f-benzenoids.
Lemma 2.6. [41] Let F be an h-hexagon f-benzenoid. Then
r(F)≤r(FLh)=2h−3. |
Lemma 2.7. [32] For any h-hexagon f-benzenoid including ni internal vertices and b bay regions, the number of (2,2)-edge and (2,3)-edge are m22=b+5,m23=4h−2ni−2b, respectively.
From Lemmas 1.2 (ii) and 8, we get
r=2h−ni−b | (2.6) |
Furthermore, by Lemma 1.1 (ii) and Eq (2.6), we deduce
r=m−3h−5−b | (2.7) |
Theorem 2.2. Let F be an h-hexagon f-benzenoid. If ni=5, then r(F)≤r(Uh)=2h−7 (h≥7).
Proof. Let h1=h(X) and h2=h(Y), X and Y are two segments of F. If ni=5, by the structure of f-benzenoid, equality ni(X)+ni(Y)=4 holds, so, we have the following five cases.
Case 1. ni(X)=1, ni(Y)=3, i.e., there exist one internal vertex and three internal vertices in X and Y, respectively.
Subcase 1.1. If h1=3, then X=M3.
Subcase 1.1.1. If h2=5, i.e., Y=Q5, then F is the f-benzenoid D1, D2 or D3 (see Figure 14). It is clear that r(F)=r(D1)=8≤2h−7, r(F)=r(D2)=7≤2h−7 or r(F)=r(D3)=8≤2h−7.
Subcase 1.1.2. If h2≥6, by Lemma 2.2 and the hypothesis that ni(Y)=3, Y is not a CBS, so b(Y)≥1. Furthermore, b(F)≥3, combining Eq (2.6) we obtain r=2h−ni−b≤2h−8<2h−7.
Subcase 1.2. If h1≥4, according to Lemma 2.2, X is definitely not a CBS, i.e., b(X)≥1.
Subcase 1.2.1. If h2=5, i.e., Y=Q5. It is clear that b(F)≥4, then Eq (2.6) deduces r≤2h−9<2h−7.
Subcase 1.2.2. If h2≥6, Y is definitely not not a CBS according to Lemma 2.2, so, b(Y)≥1. It is clear that b(F)≥5, consequently from Eq (2.6) we obtain r≤2h−10<2h−7.
Case 2. ni(X)=3 and ni(Y)=1.
Subcase 2.1. If h1=5, then X=Q5.
Subcase 2.1.1. If h2=3, i.e., Y=M3, then F is the f-benzenoid D4, D5, D6 (see Figure 14), or D7 (as shown in Figure 15). r(F)=r(D4)=8≤2h−7, r(F)=r(D5)=7≤2h−7, r(F)=r(D6)=8≤2h−7, r(F)=r(D7)=7≤2h−7.
Subcase 2.1.2. If h2≥4, Y is surely not a CBS in light of Lemma 2.2, i.e., b(X)≥1. Hence, we have b(F)≥4, it follows from Eq (2.6) that r≤2h−9<2h−7.
Subcase 2.2. If h1≥6, by Lemma 2.2, X is definitely not a CBS, hence b(X)≥1.
Subcase 2.2.1. If h2=3, i.e., Y=M3. We have b(F)≥4, and Eq (2.6) infers that r≤2h−9<2h−7.
Subcase 2.2.2. f h2≥4, by Lemma 2.2, Y is certainly not a CBS, i.e., b(X)≥1. Hence we have b(F)≥5, by Eq (2.6), r≤2h−10<2h−7.
Case 3. ni(X)=2, ni(Y)=2, i.e., X and Y both have two internal vertices.
Subcase 3.1. If h1=4, we note that ni(X)=2, so X must be the benzenoid system (b) in Figure 9.
Subcase 3.1.1. If h2=4, Y is surely the benzenoid system (b) in Figure 9 according to the hypothesis ni(Y)=2, therefore, F is D8 or D9 (as shown in Figure 15). We get r(F)=r(D8)=8<2h−7 or r(F)=r(D9)=7<2h−7.
Subcase 3.1.2. If h2≥5, by Lemma 2.2 and that ni(Y)=2, Y is not a CBS, so we know that b(X)≥1. Then b(F)≥4, by Eq (2.6) and the fact that ni=5, r≤2h−9<2h−7.
Subcase 3.2. If h2=4, we note that ni(Y)=2, so Y must be the benzenoid system (b) in Figure 8.
Subcase 3.2.1. If h1=4, X must also be the benzenoid system (b) in Figure 9. Hence, F is D8 or D9 (as shown in Figure 15). r(F)=r(D8)=8≤2h−7 or r(F)=r(D9)=7≤2h−7.
Subcase 3.2.2. If h1≥5, by Lemma 2.2 and ni(X)=2, X is definitely not a CBS, i.e., b(X)≥1. Hence, b(F)≥4, by Eq (2.6) and the fact that ni=5, we have r≤2h−9<2h−7.
Subcase 3.3. If h1≥5, h2≥5, it is noted that ni(X)=ni(Y)=2, neither X nor Y are definitely CBS according to Lemma 2.2. So, both b(X) and b(Y) are greater than 1. Hence, b(F)≥5, on the basis of Eq (2.6) we get r≤2h−10<2h−7.
Case 4. ni(X)=4 and ni(Y)=0, i.e., X contains four internal vertices, Y is a catacondensed benzenoid system.
Subcase 4.1. If h1=6, then X is the benzenoid system (d), (e) or (f) in Figure 9.
Subcase 4.1.1. If h2=1, F is the f-benzenoid D10, D11, D12 (see Figure 16), D13 (see Figure 17) or U7 (see Figure 12). r(F)=r(D10)=6≤2h−7, r(F)=r(D11)=6≤2h−7, r(F)=r(D12)=6≤2h−7, r(F)=r(D13)=6≤2h−7 or r(F)=r(U7)=7=2h−7.
Subcase 4.1.2. If h2≥2, we have b(F)≥2, by Eq (2.6), r≤2h−7.
Subcase 4.2. If h1≥7, in the light of Lemma 2.4, X is definitely not a CBS, hence b(Y)≥1. In this situation b(F)≥3, we get the inequality r≤2h−8<2h−7 according to Eq (2.6).
Case 5. ni(X)=0 and ni(Y)=4, i.e., X is a catacondensed benzenoid system, Y has four internal vertices.
Subcase 5.1. If h2=6, then Y is the benzenoid system (d), (e) or (f) in Figure 8.
Subcase 5.1.1. If h1=2, X must be the linear chain L2. In this event, F is D14, D15, D16, D17, D18, D19, D20 or D21 (see Figure 17). By further checking, we gain that r(F)=r(D14)=7≤2h−7, r(F)=r(D15)=8≤2h−7, r(F)=r(D16)=8≤2h−7, r(F)=r(D17)=7≤2h−7, r(F)=r(D18)=7≤2h−7, r(F)=r(D19)=8≤2h−7, r(F)=r(D20)=6≤2h−7 or r(F)=r(D21)=6≤2h−7.
Subcase 5.1.2. If h1≥3, bearing in mind that X is a catacondensed benzenoid system and Y is the benzenoid system (d), (e) or (f) in Figure 8, then F must have f-benzenoid D14, D15, D16, D17, D18, D19, D20 or D21 (see Figure 17) as its subgraph.
Subcase 5.1.2.1. If D14 is a subgraph in F, it is obvious that D14 has two coves. Since X is a catacondensed benzenoid system and h1≥3, F has at least one hexagon contiguous to a (2,2)-edge of X, and such hexagons can convert one fissure into a bay, or convert one cove into a fjord, or convert one fjord into a lagoon. In this instance b(F)≥4. Consequently, r≤2h−9<2h−7 can be got according to Eq (2.6).
Subcase 5.1.2.2. If D15, D16 or D19 is a subpart f-benzenoid in F, it is obvious each one of D15, D16 and D19 has a bay and a cove. Since X is a catacondensed benzenoid system and h1≥3, F contains at least one hexagon adjoining a (2,2)-edge of X, and such hexagons will make one fissure become a bay, or make one cove become a fjord, or make one fjord become a lagoon. Consequently, b(F)≥4, by Eq (2.6) it follows that r≤2h−9<2h−7.
Subcase 5.1.2.3. If D17 is a subpart f-benzenoid in F, it is obvious that D17 has a fjord and a bay. Since X is a catacondensed benzenoid system and h1≥3, F has at least one hexagon adjoining a (2,2)-edge of X, and such hexagons will convert one fissure into a bay, or convert one cove into a fjord, or convert one fjord into a lagoon. Consequently, b(F)≥4, by Eq (2.6) it follows that r≤2h−9<2h−7.
Subcase 5.1.2.4. If D18 is a subpart f-benzenoid in F, it is obvious that D18 has a fjord and two bays. Since X is a catacondensed benzenoid system and h1≥3, there exists has at least one hexagon adjoining a (2,2)-edge of X in F, and these hexagons will convert one of the fissures into a bay, or convert one cove into a fjord, or convert one fjord into a lagoon. Consequently, b(F)≥4, in light of Eq (2.6), r≤2h−9<2h−7.
Subcase 5.1.2.5. If D20 or D21 is a subpart f-benzenoid in F, it is obvious that both D20 and D21 have a bay and two fjords. Since X is a catacondensed benzenoid system and h1≥3, F contains at least one hexagon adjoining a (2,2)-edge of X, and such hexagons will make one fissure become a bay, or make one cove become a fjord, or make one fjord become a lagoon. Consequently, b(F)≥4, according to Eq (2.6), r≤2h−9<2h−7.
Subcase 5.2. If h2≥7, by Lemma 2.4 and the fact that ni(Y)=4, Y is certainly not a CBS, i.e., b(Y)≥1.
Subcase 5.2.1. If h1=2, i.e., X=L2. From the structure of f-benzenoid, F is formed from X and Y joined by a pentagon, it is easily seen that there are at least one bay or one cove arisen in the process of construction of F. It is clear that b(F)≥2, by Eq (2.6) we have r≤2h−7.
Subcase 5.2.2. If h1≥3, we know that F is formed by joining from X and Y through a pentagon, in this construction process of F, it is easily seen that there are at least one bay or one cove arisen. Then b(F)≥2, by Eq (2.6), r≤2h−7.
The proof is completed.
We recall that FLh is the f-linear chain with h hexagons [40]. Extremal f-benzenoids with maximal r in Γm were determined in the following theorem.
Theorem 2.3. Let F∈Γm. Then
1) If m≡0(mod5), then r(F)≤2m−355=r(Um5);
2) If m≡1(mod5), then r(F)≤2m−325=r(Zm−15);
3) If m≡2(mod5), then r(F)≤2m−295=r(Rm−25);
4) If m≡3(mod5), then r(F)≤2m−265=r(Gm−35);
5) If m≡4(mod5), then r(F)≤2m−235=r(FLm−45).
Proof. We know by Eq (2.5) that
⌈15(m−4)⌉≤h(F)≤m−1−⌈13(2m+√4m−31)⌉. |
1) If m≡0(mod5), then ⌈15(m−4)⌉=m5. If h=m5, then by Lemma 1.1 (ii)
m=5h(F)+5−ni(F)=m+5−ni(F), |
it means that ni(F)=5. Furthermore, Theorem 2.2 infers that r(F)≤r(Um5) and we are done. So assume now that h(F)≥m5+1, then by equality (2.7) and the fact that b(F)≥2
r(F)=m−5−3h(F)−b(F)≤m−5−3(m5+1)−b(F) |
≤2m5−10=2m−505≤2m−355=r(Um5). |
2) If m≡1(mod5), then ⌈15(m−4)⌉=m−15. If h(F)=m−15, then by Lemma 1.1 (ii)
m=5h(F)+5−ni(F)=m+4−ni(F), |
thus ni(F)=4. Then r(F)≤r(Zm−15) by part 4 of Lemma 2.5. Otherwise h(F)≥m−15+1, then by equality (2.7) and the obvious fact that b(F)≥2
r(F)=m−5−3h(F)−b(F)≤m−5−3(m−15+1)−b(F) |
≤2m+35−10=2m−475≤2m−325=r(Zm−15). |
3) If m≡2(mod5), then ⌈15(m−4)⌉=m−25. If h(F)=m−25, then by Lemma 1.1 (ii)
m=5h(F)+5−ni(F)=m+3−ni(F), |
and so ni(F)=3. Then r(F)≤r(Rm−25) by part 3 of Lemma 2.5. So assume now that h(F)≥m−25+1, then by Eq (2.7) and the fact that b(F)≥2
r(F)=m−5−3h(F)−b(F)≤m−5−3(m−25+1)−b(F) |
≤2m+65−10=2m−445≤2m−295=r(Rm−25). |
4) If m≡3(mod5), then ⌈15(m−4)⌉=m−35. If h(F)=m−35, then by Lemma 1.1 (ii)
m=5h(F)+5−ni(F)=m+2−ni(F), |
thus ni(F)=2. By Lemma 2.5, r(F)≤r(Gm−35) and we are done. If h(F)≥m−35+1, then by equality (2.7) and the fact that b(F)≥2
r(F)=m−5−3h(F)−b(F)≤m−5−3(m−35+1)−b(F) |
≤2m+95−10=2m−415≤2m−265=r(Gm−35). |
5) If m≡4(mod5), then ⌈15(m−4)⌉=m−45. Since h≥m−45 and b(F)≥2, then by Eq (2.7), we have
r(F)=m−5−3h(F)−b(F)≤m−5−3m−125−b(F) |
≤2m+125−7=2m−235=r(FLm−45). |
In this part, we attempt to find the extremal values of TI over Γm.
It is noted that a f-benzenoid F contains only 2-vertex and 3-vertex. Hence, equation (1.1) reduces to
TI(F)=m22ψ22+m23ψ23+m33ψ33, | (3.1) |
In the light of Lemmas 1.1 and 1.2,
TI(F)=ψ22m+3(ψ33−ψ22)h+(2ψ23−ψ22−ψ33)r, | (3.2) |
If U,V∈Γm then clearly
TI(U)−TI(V)=3(ψ33−ψ22)(h(U)−h(V)) +(2ψ23−ψ22−ψ33)(r(U)−r(V)). | (3.3) |
For convenience, we set s=ψ33−ψ22, q=2ψ23−ψ22−ψ33.
Theorem 3.1. For any F∈Γm, we have the following results.
a. If s≤0 and q≥0,
TI(F)≤{TI(Um5),if m≡0(mod 5)TI(Zm−15),if m≡1(mod 5)TI(Rm−25),if m≡2(mod 5)TI(Gm−35),if m≡3(mod 5)TI(FLm−45),if m≡4(mod 5) |
b. If s≥0 and q≤0,
TI(F)≥{TI(Um5),if m≡0(mod 5)TI(Zm−15),if m≡1(mod 5)TI(Rm−25),if m≡2(mod 5)TI(Gm−35),if m≡3(mod 5)TI(FLm−45),if m≡4(mod 5) |
Proof. Let F∈Γm. By Eq (2.5)
h(F)≥⌈15(m−4)⌉={h(Um5),if m≡0(mod 5)h(Zm−15),if m≡1(mod 5)h(Rm−25),if m≡2(mod 5)h(Gm−35),if m≡3(mod 5)h(FLm−45),if m≡4(mod 5) |
i.e., f-benzenoids Um5, Zm−15, Rm−25, Gm−35 and FLm−45 have minimal h over the set Γm. Meanwhile, by Theorem 2.3, we have
r(F)≤{r(Um5),if m≡0(mod 5)r(Zm−15),if m≡1(mod 5)r(Rm−25),if m≡2(mod 5)r(Gm−35),if m≡3(mod 5)r(FLm−45),if m≡4(mod 5) |
i.e., these five f-benzenoids have maximal number of inlets over Γm. Hence, for any f-benzenoids F∈Γm and V∈{Um5,Zm−15,Rm−25,Gm−35,FLm−45}, h(F)−h(V)≥0 and r(F)−r(V)≤0 hold simultaneously, from Eq (2.7), we have
TI(F)−TI(V)=3s(h(F)−h(V))+q(r(F)−r(V)). |
If s≤0 and q≥0, then TI(F)−TI(V)≤0, i.e., V reaches the maximum value of TI over Γm. If s≥0 and q≤0, then TI(F)−TI(V)≥0, i.e., V reaches the minimum value of TI over Γm. Furthermore, which V∈{Um5,Zm−15,Rm−25,Gm−35,FLm−45} is the extremal graph depending on m is congruent to 0,1,2,3 or 4 modulo 5.
Example 1. Values of s and q for several famous TI are listed in Table 2:
ij | 1√ij | 2√iji+j | 1√i+j | (ij)3(i+j−2)3 | √i+j−2ij | |
q | -1 | -0.0168 | -0.0404 | -0.0138 | -3.390 | 0.040 |
s | 5 | -0.1667 | 0 | -0.091 | 3.390 | -0.040 |
Therefore, the minimum extreme value of TI for the second Zagreb index, GA index and the AZI index can be determined in the light of Theorems 2.3 and 3.1, and we can obtain the maximum extreme value of TI for the ABC index.
If f-benzenoid F∈Γm, then from the Eqs (2.3) and (2.6) and Lemma 1.1(ii) we have
TI(F)=(2ψ23−ψ33)m+6(ψ33−ψ23)h−(2ψ23−ψ22−ψ33)b −5(2ψ23−ψ22−ψ33). | (3.4) |
Consequently, for f-benzenoids U,V∈Γm
TI(U)−TI(V)=6(ψ33−ψ23)(h(U)−h(V)) +(−2ψ23+ψ22+ψ33)(b(U)−b(V)). | (3.5) |
Set u=6(ψ33−ψ23) and keep in mind that q=2ψ23−ψ22−ψ33. Then
TI(U)−TI(V)=u(h(U)−h(V))−q(b(U)−b(V)). | (3.6) |
It is noted that Eq (3.6) can be decided only by h, b and the signs of u and q. For any F∈Γm, We know that
h(F)≤m−1−⌈13(2m+√4m−31)⌉, |
and the equality can be achieved precisely when F is the f-spiral benzenoid F∗ [41].
In [41], we proved that ni(F∗)=2h−⌈√12(h−1)−3⌉. But, b(F∗)≠2 may occur. It is noticeable if X in F∗ is a CBS, F∗ is a f-benzenoid satisfying that b(F∗)=2 or 3. For the sake of simplicity, Let N be the set of positive integers.
The CBS, W=H(l1,l2,l3,l4,l5,l6) (as shown in Figure 18), can be completely determined by the positive integers l1,l2,l3,l4 [14].
The following lemma gave requirements that there exists CBS with maximal ni [53].
Lemma 3.1. [53] Let h∈N. The conditions below are isovalent:
(a) There is a CBS W containing h hexagons and 2h+1−⌈√12h−3 ⌉ number of internal vertices.
(b) There exist l1,l2,l3,l4∈N satisfying the following equation
h=l1l3+l1l4+l2l3+l2l4−l2−l3−12l1(l1+1)−12l4(l4+1)+1⌈√12h−3 ⌉=l1+2l2+2l3+l4−3} | (3.7) |
If for h∈N, Eq (3.7) has a solution l1,l2,l3,l4∈N, then there is a CBS W meeting the conditions that ni(W)=ni(Th).
Now, we concentrate on the research for TI of f-benzenoids. For a h−1∈N, supposing that the system below
h−1=l1l3+l1l4+l2l3+l2l4−l2−l3−12l1(l1+1)−12l4(l4+1)+1⌈√12(h−1)−3 ⌉=l1+2l2+2l3+l4−3∃ li∈{l1,l2,l3,l4,l5,l6}, li=2} | (3.8) |
has a solution {l1,l2,l3,l4}, then a CBS Wh−1 containing ni(Wh−1)=2(h−1)+1−⌈√12(h−1)−3⌉ number of internal vertices exists. Note that li=2 in system (3.8), i.e., there exists one fissure on the side of li of Wh−1, let u,w,v in Figure 1 represent the three vertices of this fissure. Now, we obtain an f-spiral benzenoid F∗1 in which X=Wh−1 and Y=L1. It is obvious that
ni(F∗1)=2h−⌈√12(h−1)−3⌉ | (3.9) |
and b(F∗1)=2. (as shown in Figure 19)
Theorem 3.2. Let h−1∈N such that the Eq (3.8) has a solution, and m=3h+5+⌈√12(h−1)−3⌉. Then for any F∈Γm
1) TI(F∗1)≥TI(F), when u≥0 and q≥0;
2) TI(F∗1)≤TI(F), when u≤0 and q≤0.
Proof. From Lemma 1.1 (ii) and Eq (3.9), we have
m(F∗1)=5h+5−(2h−⌈√12(h−1)−3⌉)=3h+5+⌈√12(h−1)−3⌉ |
and so
h=m−1−⌈13(2m+√4m−31)⌉. |
It is obvious that b(F∗1)=2 and b(F)≥2 for any F∈Γm. Hence by Eq (3.6), we have
TI(F)−TI(F∗1)=u(h(F)−h(F∗1))−q(b(F)−b(F∗1)) |
=u[h(F)−(m−1−⌈13(2m+√4m−31)⌉)]−q[b(F)−2]. |
And by Eq (2.5)
h(F)≤m−1−⌈13(2m+√4m−31)⌉. |
If u≥0 and q≥0 then TI(F)−TI(F∗1)≤0, i.e., F∗1 achieves maximal TI in Γm. Similarly, if u≤0 and q≤0 then TI(F)−TI(F∗1)≥0, i.e., F∗1 obtains minimal TI in Γm.
Example 2. The values of u and q for some famous TI are listed in the following Table 3:
ij | 1√ij | 2√iji+j | 1√i+j | (ij)3(i+j−2)3 | √i+j−2ij | |
q | -1 | -0.0168 | -0.0404 | -0.0138 | -3.390 | 0.040 |
u | 18 | -0.449 | 0.121 | -0.233 | 20.344 | -0.242 |
Hence, by Theorem 3.1 we can deduce the minimal values of the Randć index and the the sum–connectivity index in f-spiral benzenoid F∗1 for those h such that Eq (3.8) holds.
Example 3. Take consideration of the generalized Randć index
Rα(G)=∑1≤i≤j≤n−1mij(ij)α, |
where α∈R. Note that
q=2(6α)−4α−9α=−4α((32)α−1)2≤0 |
for all α∈R. Moreover, s=9α−4α≥0 if and only if α≥0 if and only if u=6(9α−6α)≥0. Hence, by Theorem 3.1, the minimal value of Rα(G) is obtained for all α≥0, and for any α≤0, the minimal value of Rα(G) can be attained by the f-spiral benzenoid F∗1 for those h such that Eq (3.8) holds.
This work investigates extremum TI over the collection of f-benzenoids having same number of edges. In practical terms, there are many other types of very useful topological indices for instance graph energy [58,59,60,61,62], Wiener index [63], Randić energy [64], Wiener polarity index [65], incidence energy [66], Harary index [67], entropy measures [68,69] and HOMO-LUMO index [70]. So, determining these topological indices for f-benzenoids is going to be extraordinary fascinating.
It is noted that the current framework is for studying topological indices of deterministic networks. But random networks would be a very promising direction. In [71,72], the distance Estrada index of random graphs was discussed, and the author went deeply into (Laplacian) Estrada index for random interdependent graphs. So, studying VDB topological indices of random and random interdependent graphs is another interesting problem.
This work was supported by Ningbo Natural Science Foundation (No. 2021J234). The authors are very grateful to anonymous referees and editor for their constructive suggestions and insightful comments, which have considerably improved the presentation of this paper.
The authors declare there is no conflict of interest.
[1] |
C. Ball, D. Izadi, L. Verjee, J. Chan, J. Nanchahal, Systematic review of non-surgical treatments for early Dupuytren's disease, BMC Musculoskeletal Disord., 17 (2016), 345. https://doi.org/10.1186/s12891-016-1200-y doi: 10.1186/s12891-016-1200-y
![]() |
[2] |
R. Grazina, S. Teixeira, R. Ramos, H. Sousa, A. Ferreira, R. Lemos, Dupuytren's disease: where do we stand?, EFORT Open Rev., 4 (2019), 63–69. https://doi.org/10.1302/2058-5241.4.180021 doi: 10.1302/2058-5241.4.180021
![]() |
[3] |
G. Rayan, Dupuytren disease: anatomy, pathology, presentation, and treatment, J. Bone Joint Surg., 89 (2007), 190–198. https://doi.org/10.2106/00004623-200701000-00026 doi: 10.2106/00004623-200701000-00026
![]() |
[4] | C. Eaton, Dupuytren disease and related diseases - The cutting edge, Springer International Publishing Switzerland, 2017. https: //doi.org/10.1007/978-3-319-32199-8 |
[5] |
R.B. Shaw. Jr., A.K.S. Chong, A. Zhang, V. R. Hentz, J. Chang, Dupuytren's disease: history, diagnosis, and treatment, Plast. Reconstr. Surg., 120 (2007), 44e. https://doi.org/10.1016/s0363-5023(10)80134-0 doi: 10.1016/s0363-5023(10)80134-0
![]() |
[6] |
R. McFarlane, Dupuytren's disease: Relation to work and injury, J. Hand Surg. Am., 16 (1991), 775–779. https://doi.org/10.1016/s0363-5023(10)80134-0 doi: 10.1016/s0363-5023(10)80134-0
![]() |
[7] |
E. Critchley, S. Vakil, H. Hayward, V. Owen, Dupuytren's disease in epilepsy: result of prolonged administration of anticonvulsants, J. Neurol. Neurosurg. Psychiatry, 39 (1976), 498–503. https://doi.org/10.1136/jnnp.39.5.498 doi: 10.1136/jnnp.39.5.498
![]() |
[8] |
P. Burge, G. Hoy, P. Regan, R. Milne, Smoking, alcohol and the risk of Dupuytren's contracture, J. Bone Joint Surg. Br., 79 (1997), 206–210. https://doi.org/10.1302/0301-620x.79b2.6990 doi: 10.1302/0301-620x.79b2.6990
![]() |
[9] | K. Denkler, Surgical complications associated with fascietomy for Dupuytren's disease: A 20-year review of the English literature, Eplasty, 10 (2010), e15. |
[10] |
H. Khan, F. Verrijp, S. Hovius, C. van Nieuwenhoven, D. D. Group, R. Selles, Recurrence of Dupuytren's contracture: a consensus-based definition, PLoS One, 12 (2017), e0164849. https://doi.org/10.1371/journal.pone.0164849 doi: 10.1371/journal.pone.0164849
![]() |
[11] |
R. Meek, S. McLellan, J. Crossan, Dupuytren's disease: A model for the mechanisms of fibrosis and its modulation by steroids, J. Bone Joint Surg. Br., 81 (1999), 732–738. https://doi.org/10.1302/0301-620x.81b4.9163 doi: 10.1302/0301-620x.81b4.9163
![]() |
[12] |
L. Verjee, J. Verhoekx, J. Chan, T. Krausgruber, V. Nicolaidou, D. Izadi, et al., Unraveling the signaling pathways promoting fibrosis in Dupuytren's disease reveals TNF as a therapeutic target, Proc. Natl. Acad. Sci. USA, 110 (2013), E928–E937. https://doi.org/10.1073/pnas.1301100110 doi: 10.1073/pnas.1301100110
![]() |
[13] |
S. Iqbal, M. Hayton, J. Watson, P. Szczypa, A. Bayat, First identification of resident and circulating fibrocytes in Dupuytren's disease shown to be inhibited by Serum Amyloid P and Xiapex, PLoS One, 9 (2014), e99967. https://doi.org/10.1371/journal.pone.0099967 doi: 10.1371/journal.pone.0099967
![]() |
[14] |
J. Neumüller, J. Menzel, H. Millesi, Prevalence of HLA-DR3 and autoantibodies to connective tissue components in Dupuytren's contracture, Clin. Immunol. Immunopathol., 71 (1994), 142–148. https://doi.org/10.1006/clin.1994.1064 doi: 10.1006/clin.1994.1064
![]() |
[15] |
R. Pereira, C. Black, S. Turner, J. Spencer, Antibodies to collagen types I-VI in Dupuytren's contracture, J. Hand Surg. Br., 11 (1986), 58–60. https://doi.org/10.1016/0266-7681(86)90014-8 doi: 10.1016/0266-7681(86)90014-8
![]() |
[16] |
J. Wilkinson, R. Davidson, T. Swingler, E. Jones, A. Corps and P. Johnston, et al., MMP-14 and MMP-2 are key metalloproteases in Dupuytren's disease fibroblast-mediated contraction, Biochim. Biophys. Acta, 1822 (2012), 897–905. https://doi.org/10.1016/j.bbadis.2012.02.001 doi: 10.1016/j.bbadis.2012.02.001
![]() |
[17] |
B. Costas, S. Coleman, G. Kaufman, R. James, B. Cohen, R. Gaston, Efficacy and safety of collagenase clostridium histolyticum for Dupuytren disease nodules: a randomized controlled trial, BMC Musculoskeletal Disord., 18 (2017), 374. https://doi.org/10.1186/s12891-017-1713-z doi: 10.1186/s12891-017-1713-z
![]() |
[18] |
G. Caravalhana, I. Auquit-Auckbur, P. Y. Miliez, Maladie de Dupuytren : état des connaissances et de la recherche en physiopathologie, Chirurgie de la Main, 30 (2011), 239–245. https://doi.org/10.1016/j.main.2011.03.002 doi: 10.1016/j.main.2011.03.002
![]() |
[19] |
K. Gudmunsson, R. Amgrimsson, N. Sigfússon, T. Jónsson, Increased total mortality and cancer mortality in men with Dupuytren's disease: a 15-year follow-up study, J. Clin. Epidem., 55 (2002), 5–10. https://doi.org/10.1016/s0895-4356(01)00413-9 doi: 10.1016/s0895-4356(01)00413-9
![]() |
[20] |
S. Karkampouna, P. Kloen, M. Obdejin, S. Riester, A. van Wijnen, M. K. de Julio, Human Dupuytren's ex vivo culture for the study of myofibroblasts and extracellular matrix interactions, J. Vis. Exp., 98 (2015), 52534. https://doi.org/10.3791/52534 doi: 10.3791/52534
![]() |
[21] |
S. Ud-Din, A. Bayat, Non-animal models of wound healing in cutaneous repair: in silico, in vitro, ex vivo, and in vivo models of wounds and scars in human skin, Wound Repair Regen., 25 (2017), 164–176. https://doi.org/10.1111/wrr.12513 doi: 10.1111/wrr.12513
![]() |
[22] |
C. Jean-Quartier, F. Jeanquartier, I. Jurisica, A. Holzinger, In silico cancer research towards 3R, BMC Cancer, 18 (2018), 408. https://doi.org/10.1186/s12885-018-4302-0 doi: 10.1186/s12885-018-4302-0
![]() |
[23] |
N. Menke, J. Cain, A. Reynolds, D. Chan, R. Segal, T. W. Witten, et al., An in silico approach to the analysis of acute wound healing, Wound Repair Regen., 18 (2010), 105–113. https://doi.org/10.1111/j.1524-475X.2009.00549.x doi: 10.1111/j.1524-475X.2009.00549.x
![]() |
[24] |
C. A. Alfonso-Rodríguez, I. Garzón, J. Garrido-Gómez, A. C. X. Oliveira, M. A. Martín-Piedra, G. Scionti, et al., Identification of histological patterns in clinically affected and unaffected palm regions in Dupuytren's disease, PLoS One, 9 (2014), e112457. https://doi.org/10.1371/journal.pone.0112457 doi: 10.1371/journal.pone.0112457
![]() |
[25] |
S. Rehman, Y. Xu, W. Dunn, P. Day, H. Westerhoff, R. Goodacre, et al., Dupuytren's disease metabolite analyses reveals alterations following initial short-term fibroblast culturing, Mol. BioSyst., 8 (2012), 2274–2288. https://doi.org/10.1039/c2mb25173f doi: 10.1039/c2mb25173f
![]() |
[26] | J. Luck, Dupuytren's contracture: a new concept of the pathogenesis correlated with surgical management, J. Bone Joint Surg., 41 (1959), 635–664. |
[27] |
T. Layton, J. Hanchahal, Recent advanced in the understanding of Dupuytren's disease, F1000Res, 8 (2019), 231. https://doi.org/10.12688/f1000research.17779.1 doi: 10.12688/f1000research.17779.1
![]() |
[28] |
K. Moyer, D. Banducci, W. Graham, H. Ehrlich, Dupuytren's disease: physiologic changes in nodule and cord fibroblasts through aging in vitro, Plast. Reconstr. Surg., 110 (2002), 187–196. https://doi.org/10.1097/00006534-200207000-00031 doi: 10.1097/00006534-200207000-00031
![]() |
[29] |
L. Vi, L. Feng, R. Zhu, Y. Wu, L. Satish, B. Gan, et al., Periostin differentially induces proliferation, contraction and apoptosis of primary Dupuytren's disease and adjacent palmar fascia cells, Exp. Cell Res., 315 (2009), 3574–3586. https://doi.org/10.1016/j.yexcr.2009.07.015 doi: 10.1016/j.yexcr.2009.07.015
![]() |
[30] |
M. van Beuge, E. J. ten Dam, P. Werker, R. Bank, Matrix and cell phenotype differences in Dupuytren's disease, Fibrogenesis Tissue Repair, 9 (2016), 9. https://doi.org/10.1186/s13069-016-0046-0 doi: 10.1186/s13069-016-0046-0
![]() |
[31] |
E. Bianchi, S. Taurone, L. Bardella, A. Signore, E. Pompili and V. Sessa, et al., Involvement of pro-inflammatory cytokines and growth factors in the pathogenesis of the Dupuytren's contracture: a novel target for a possible future therapeutic strategy?, Clin. Sci., 129 (2015), 711–720. https://doi.org/10.1042/CS20150088 doi: 10.1042/CS20150088
![]() |
[32] |
J. Verhoekx, K. Beckett, M. Bisson, D. McGrouther, A. Grobbelaar, V. Mudera, The mechanical environment in Dupuytren's contracture determines cell contractility and associated MMP-mediated matrix remodeling, J. Orthop. Res., 31 (2013), 328–334. https://doi.org/10.1002/jor.22220 doi: 10.1002/jor.22220
![]() |
[33] |
J. Andrew, S. Andrew, A. Ash, B. Turner, An investigation into the role of inflammatory cells in Dupuytren's disease, J. Hand Surg. Br., 16 (1991), 267–271. https://doi.org/10.1016/0266-7681(91)90051-o doi: 10.1016/0266-7681(91)90051-o
![]() |
[34] |
C. Mayerl, B. D. Frari, W. Parson, G. Boeck, H. Piza-Katzer, G. Wick, et al., Characterisation of the inflammatory response in Dupuytren's disease, J. Plast. Surg. Hand Surg., 50 (2016), 171–179. https://doi.org/10.3109/2000656X.2016.1140054 doi: 10.3109/2000656X.2016.1140054
![]() |
[35] |
M. Tripoli, A. Cordova, F. Moschella, Updates on the role of molecular factors and fibroblasts in the pathogenesis of Dupuytren's disease, J. Cell Commun Signal., 10 (2016), 315–330. https://doi.org/10.1007/s12079-016-0331-0 doi: 10.1007/s12079-016-0331-0
![]() |
[36] | T. Wynn, T. Ramalingam, Mechanisms of fibrosis: therapeutic translation for fibrotic disease, Nat. Med., 18 (2012), 1028–1040. https://doi.org/https://doi.org/10.1038/nm.2807 |
[37] |
L. Verjee, K. Midwood, D. Davidson, D. Essex, A. Sandison, J. Nanchahal, Myofibroblast distribution in Dupuytren's cords: correlation with digital contracture, J. Hand Surg. Am., 34 (2009), 1785–1794. https://doi.org/10.1016/j.jhsa.2009.08.005 doi: 10.1016/j.jhsa.2009.08.005
![]() |
[38] |
B. Behm, P. Babilas, M. Landthaler, S. Schreml, Cytokines, chemokines and growth factors in wound healing, J. Eur. Acad. Dermatol Venerol, 26 (2012), 812–820. https://doi.org/10.1111/j.1468-3083.2011.04415.x doi: 10.1111/j.1468-3083.2011.04415.x
![]() |
[39] |
G. Landskron, M. D. L. Fuente, P. Thuwajit, C. Tuwajit, M. Hermoso, Chronic inflammation and cytokines in the tumour microenvironment, J. Immunol. Res., 2014 (2014), 149185. https://doi.org/10.1155/2014/149185 doi: 10.1155/2014/149185
![]() |
[40] |
K. Baird, J. Crossan, S. Ralston, Abnormal growth factor and cytokine expression in Dupuytren's contracture, J. Clin. Pathol., 46 (1993), 425–428. https://doi.org/10.1136/jcp.46.5.425 doi: 10.1136/jcp.46.5.425
![]() |
[41] |
K. Augoff, J. Kula, J. Gosk, R. Rutowski, Epidermal growth factor in Dupuytren's disease, Plast Reconstr. Surg., 115 (2005), 128–133. https://doi.org/10.1097/01.PRS.0000146038.61595.4A doi: 10.1097/01.PRS.0000146038.61595.4A
![]() |
[42] |
K. Augoff, R. Tabola, J. Kula, J. Gosk, R. Rutowski, Epidermal growth factor receptor (EGF-R) in Dupuytren's disease, J. Hand Surg. Br., 30 (2005), 570–573. https://doi.org/10.1016/j.jhsb.2005.06.008 doi: 10.1016/j.jhsb.2005.06.008
![]() |
[43] |
S. Sigismund, D. Avanzato, L. Lanzetti, Emerging functions of the EGFR in cancer, Mol. Oncol., 12 (2018), 3–20. https://doi.org/10.1002/1878-0261.12155 doi: 10.1002/1878-0261.12155
![]() |
[44] |
N. Jain, J. Moeller, V. Vogel, Mechanobiology of macrophages: how physical factors coregulate macrophage plasticity and phagocytosis, Ann. Rev. Biomed. Eng., 21 (2019), 267–297. https://doi.org/10.1146/annurev-bioeng-062117-121224 doi: 10.1146/annurev-bioeng-062117-121224
![]() |
[45] |
J. Lim, B. Choi, S. Lee, Y. Jang, J. Choi, Y. Kim, Regulation of wound healing by granulocyte-macrophage colony-stimulating factor after vocal fold injury, PLOS ONE, 8 (2013), e54256. https://doi.org/10.1371/journal.pone.0054256 doi: 10.1371/journal.pone.0054256
![]() |
[46] | R. McFarlane, Patterns of the diseased fascia in the fingers in Dupuytren's contracture, Plast. Reconstr. Surg., 54 (1974), 31–44. https://doi.org/ |
[47] |
D. Schwartz, Dupuytren's diathesis revisited: evaluation of prognostic indicators for risk of disease recurrence, J. Hand Ther., 20 (2007), 280–281. https://doi.org/https://doi.org/10.1197/j.jht.2007.04.014 doi: 10.1197/j.jht.2007.04.014
![]() |
[48] |
C. Eaton, Evidence-based medicine: Dupuytren contracture, Plast Reconstr. Surg., 133 (2014), 1239. https://doi.org/10.1097/PRS.0000000000000089 doi: 10.1097/PRS.0000000000000089
![]() |
[49] |
F. Syed, A. Thomas, S. S. ad V. Kolluru, S. Hart, A. Bayat, In vitro study of novel collagenase (XIAFLEX) on Dupuytren's disease fibroblasts displays unique drug related properties, PLOS ONE, 7 (2012), e31430. https://doi.org/10.1371/journal.pone.0031430 doi: 10.1371/journal.pone.0031430
![]() |
[50] |
A. Mantovani, M. Locati, Tumor-associated macrophages as a paradigm of macrophage plasticity, diversity, and polarization: lessons and open questions., Arterioscler. Thromb. Vasc. Biol., 33 (2013), 1478–1483. https://doi.org/10.1161/ATVBAHA.113.300168 doi: 10.1161/ATVBAHA.113.300168
![]() |
[51] |
C. Ferrante, S. Leibovich, Regulation of macrophage polarisation and wound healing, Adv. Wound Care, 1 (2012), 10–16. https://doi.org/10.1089/wound.2011.0307 doi: 10.1089/wound.2011.0307
![]() |
[52] |
S. Yotsukura, H. Mamitsuka, Evaluation of serum-based cancer biomarkers: a brief review from a clinical and computational viewpoint, Critical Reviews in Oncology/Hematology, 93 (2015), 103–115. https://doi.org/10.1016/j.critrevonc.2014.10.002 doi: 10.1016/j.critrevonc.2014.10.002
![]() |
[53] |
A. Bayat, D. McGrouther, Management of Dupuytren's disease - clear advice for an elusive condition, Ann. R. Coll. Surg. Engl., 88 (2006), 3–8. https://doi.org/10.1308/003588406X83104 doi: 10.1308/003588406X83104
![]() |
[54] |
G. Dolmans, P. Werker, H. Hennies, D. Furniss, E. Festen, L. Franke, et al., Wnt signaling and Dupuytren's disease, N. Engl. J. Med, 365 (2011), 307–317. https://doi.org/10.1056/NEJMoa1101029 doi: 10.1056/NEJMoa1101029
![]() |
[55] |
Y. Kim, J. Wallace, F. Li, M. Ostrowski, A. Friedman, Transformed epithelial cells and fibroblasts/myofibroblasts interaction in breast tumour: a mathematical model and experiments, J. Math. Biol., 61 (2010), 401–421. https://doi.org/10.1007/s00285-009-0307-2 doi: 10.1007/s00285-009-0307-2
![]() |
[56] |
Y. Kim, A. Friedman, Interaction of tumour with its micro-environment: a mathematical model, Bull. Math. Biol., 72 (2010), 1029–1068. https://doi.org/10.1007/s11538-009-9481-z doi: 10.1007/s11538-009-9481-z
![]() |
[57] |
A. Friedman, Y. Kim, Tumour cell proliferation and migration under the influence of their microenvironment, Math. Biosci. Eng., 8 (2011), 371–383. https://doi.org/10.3934/mbe.2011.8.371 doi: 10.3934/mbe.2011.8.371
![]() |
[58] |
K. Norton, K. Jin, A. Popel, Modeling triple-negative breast cancer heterogeneity: effects of stromal macrophages, fibroblasts and tumour vasculature, J. Theor. Biol., 452 (2018), 56–68. https://doi.org/10.1016/j.jtbi.2018.05.003 doi: 10.1016/j.jtbi.2018.05.003
![]() |
[59] |
N. Picco, E. Sahai, P. Maini, A. Anderson, Integrating models to quantify environment-mediated drug resistance, Cancer Res., 77 (2017), 5409–5418. https://doi.org/10.1158/0008-5472.CAN-17-0835 doi: 10.1158/0008-5472.CAN-17-0835
![]() |
[60] |
R. Wadlow, B. Wittner, S. Finley, H. Bergquist, R. Upadhyay and S. Finn, et al., Systems-level modelling of cancer-fibroblast interaction, PLoS One, 4 (2009), e6888. https://doi.org/10.1371/journal.pone.0006888 doi: 10.1371/journal.pone.0006888
![]() |
[61] |
A. B. Tepole, E. Kuhl, Computational modelling of chemo-bio-mechanical coupling: a systems-biology approach toward wound healing, Comput. Methods Biomech. Biomed. Engin., 19 (2016), 13–30. https://doi.org/10.1080/10255842.2014.980821 doi: 10.1080/10255842.2014.980821
![]() |
[62] |
R. Cooper, R. Segal, R. Diegelmann, A. Reynolds, Modelling the effects of systemic mediators on the inflammatory phase of wound healing, J. Theor. Biol., 367 (2015), 86–99. https://doi.org/10.1016/j.jtbi.2014.11.008 doi: 10.1016/j.jtbi.2014.11.008
![]() |
[63] |
J. Dallon, J. Sherratt, P. Maini, Mathematical modelling of extracellular matrix dynamics using discrete cells: fiber orientation and tissue regeneration, J. Theor. Biol., 199 (1999), 449–471. https://doi.org/10.1006/jtbi.1999.0971 doi: 10.1006/jtbi.1999.0971
![]() |
[64] | J. Flegg, S. Menon, P. Maini, D. McElwain, On the mathematical modelling of wound healing angiogenesis in skin as a reaction-transport process, Front. Physiol., 6 (2015), 262. https://doi.org/https://doi.org/10.3389/fphys.2015.00262 |
[65] |
S. Jorgensen, J. Sanders, Mathematical models of wound healing and closure: a comprehensive review, Med. Biol. Eng. Comput., 54 (2016), 1297–1316. https://doi.org/10.1007/s11517-015-1435-z doi: 10.1007/s11517-015-1435-z
![]() |
[66] |
D. Koppenol, F. Vermolen, F. Niessen, P. van Zuijlen, K. Vuik, A biomechanical mathematical model for the collagen bundle distribution-dependent contraction and subsequent retraction of healing dermal wounds, Biomech. Model. Mechanobiol., 16 (2016), 345–361. https://doi.org/10.1007/s10237-016-0821-2 doi: 10.1007/s10237-016-0821-2
![]() |
[67] |
S. McDougall, J. Dallon, J. Sherratt, P. Maini, Fibroblast migration and collagen deposition during dermal wound healing: mathematical modelling and clinical implications, Phil. Trans. R. Soc. A, 364 (2006), 1385–1405. https://doi.org/10.1098/rsta.2006.1773 doi: 10.1098/rsta.2006.1773
![]() |
[68] |
L. Olsen, J. Sherratt, P. Maini, A mechanochemical model for adult dermal wound contraction and the permanence of the contracted tissue displacement profile, J. Theor. Biol., 177 (1995), 113–128. https://doi.org/10.1006/jtbi.1995.0230 doi: 10.1006/jtbi.1995.0230
![]() |
[69] |
L. Olsen, J. Sherratt, P. Maini, A mathematical model for fibro-proliferative wound healing disorders, Bull. Math. Biol., 58 (1996), 787–808. https://doi.org/10.1007/BF02459482 doi: 10.1007/BF02459482
![]() |
[70] |
R. Tranquillo, J. Murray, Continuum model for fibroblast-driven wound contraction: inflammation mediation, J. Theor. Biol., 158 (1992), 135–172. https://doi.org/10.1016/s0022-5193(05)80715-5 doi: 10.1016/s0022-5193(05)80715-5
![]() |
[71] |
E. Valero, J. Garcia-Aznar, A. Menzel, M. Gomez-Benito, Challenges in the modelling of wound healing mechanisms in soft biological tissues, Ann. Biomed. Eng., 43 (2014), 1654–1665. https://doi.org/10.1007/s10439-014-1200-8 doi: 10.1007/s10439-014-1200-8
![]() |
[72] |
Y. Wang, C. Guerrero‐Juarez, Y. Qiu, H. Du, W. Chen, S. Figueroa, et al., A multiscale hybrid mathematical model of epidermal-dermal interactions during skin wound healing, Exp. Dermatol., 28 (2019), 493–502. https://doi.org/10.1111/exd.13909 doi: 10.1111/exd.13909
![]() |
[73] |
H. Waugh, J. Sherratt, Modelling the effects of treating diabetic wounds with engineered skin substitutes, Wound Rep. Reg., 15 (2007), 556–565. https://doi.org/10.1111/j.1524-475X.2007.00270.x doi: 10.1111/j.1524-475X.2007.00270.x
![]() |
[74] |
C. Xue, A. Friedman, C. Sen, A mathematical mode of ischemic cutaneous wounds, Proc. Natl. Acad. Sci. USA, 106 (2009), 16782–16787. https://doi.org/10.1073/pnas.0909115106 doi: 10.1073/pnas.0909115106
![]() |
[75] |
C. Ziraldo, Q. Mi, G. An, Y. Vodovotz, Computational modeling of inflammation and wound healing, Adv. wound care, 2 (2013), 527–537. https://doi.org/10.1089/wound.2012.0416 doi: 10.1089/wound.2012.0416
![]() |
[76] |
F. Balkwill, M. Capasso, T. Hagemann, The tumour microenvironment at a glance, J. Cell Sci., 125 (2012), 5591–5596. https://doi.org/10.1242/jcs.116392 doi: 10.1242/jcs.116392
![]() |
[77] |
J. Junker, E. Caterson, E. Eriksson, The microenvironment of wound healing, J. Craniofac. Surg., 24 (2013), 12–16. https://doi.org/10.1097/SCS.0b013e31827104fb doi: 10.1097/SCS.0b013e31827104fb
![]() |
[78] | K. Chen, X. Hu, S. Blemker, J. Holmes, Multiscale computational model of Achilles tendon wound healing: Untangling the effects of repair and loading, PLoS Comput. Biol., 14 (2018), e1006652. https://doi.org/https://doi.org/10.1371/journal.pcbi.1006652 |
[79] |
R. Shuttleworth, D. Trucu, Multiscale modelling of fibres dynamics and cell adhesion within boundary cancer invasion, Bull. Math. Biol., 81 (2019), 2176–2219. https://doi.org/10.1007/s11538-019-00598-w doi: 10.1007/s11538-019-00598-w
![]() |
[80] |
J. Jansen, E. Gaffney, J. Wagg, M. Coles, Combining mathematical models with experimentation to drive novel mechanistic insights into macrophage function, Front. Immunol., 10 (2019), 1283. https://doi.org/10.3389/fimmu.2019.01283 doi: 10.3389/fimmu.2019.01283
![]() |
[81] |
H. Warsinske, A. Wheaton, K. Kim, J. Linderman, B. Moore, D. Kirschner, Computational modeling predicts simultaneous targeting of fibroblasts and epithelial cells is necessary for treatment of pulmonary fibrosis, Front. Pharmacol., 7 (2016), 183. https://doi.org/10.3389/fphar.2016.00183 doi: 10.3389/fphar.2016.00183
![]() |
[82] |
A. Oppelt, D. Kaschek, S. Huppelschoten, R. Sison-Young, F. Zhang and M. Buck-Wiese, et al., Model-based identification of TNFα-induced IKKβ-mediated and IκBα-mediated regulation of NFκB signal transduction as a tool to quantify the impact of drug-induced liver injury compounds, NPJ Syst. Biol. Appl., 4 (2018), 23. https://doi.org/10.1038/s41540-018-0058-z doi: 10.1038/s41540-018-0058-z
![]() |
[83] |
J. Moermans, Recurrences after surgery for Dupuytren's disease, Eur. J. Plast. Surg., 20 (1997), 240–245. https://doi.org/10.1007/BF01159481 doi: 10.1007/BF01159481
![]() |
[84] |
S. Rehman, R. Goodacre, P. Day, A. Bayat, H. Westerhoff, Dupuytren's: a systems biology disease, Arthritis Res. Ther., 13 (2011), 238. https://doi.org/10.1186/ar3438 doi: 10.1186/ar3438
![]() |
[85] |
S. Rehman, P. Day, A. Bayat, H. Westerhoff, Understanding Dupuytren's disease using systems biology: a move away from reductionism, Front. Physiol., 3 (2012), 316. https://doi.org/10.3389/fphys.2012.00316 doi: 10.3389/fphys.2012.00316
![]() |
[86] |
M. van Beuge, E. ten Dam, P. Werker, R. Bank, Wnt pathway in Dupuytren disease: connecting profibrotic signals, Transl. Res., 166 (2015), 762–771. https://doi.org/10.1016/j.trsl.2015.09.006 doi: 10.1016/j.trsl.2015.09.006
![]() |
[87] |
W. Du, O. Elemento, Cancer systems biology: embracing complexity to develop better anticancer therapeutic strategies, Oncogene, 34 (2015), 3215–3225. https://doi.org/10.1038/onc.2014.291 doi: 10.1038/onc.2014.291
![]() |
[88] | I. Korsunsky, K. McGovern, T. LaGatta, L. Loohuis, T. Grosso-Applewhite and N. Griffeth, et al., Systems biology of cancer: a challenging expedition for clinical and quantitative biologists, Front. Bioeng. Biotechnol., 2 (2014), 27. https://doi.org/https://doi.org/10.3389/fbioe.2014.00027 |
[89] |
M. Martins-Green, Y. Vodovotz, P. Liu, Systems biology applied to wound healing, Wound Repair Regen., 18 (2010), 1–2. https://doi.org/10.1111/j.1524-475X.2009.00567.x doi: 10.1111/j.1524-475X.2009.00567.x
![]() |
[90] |
Y. Vodovotz, Translational systems biology of inflammation and healing, Wound Repair Regen., 18 (2010), 3–7. https://doi.org/10.1111/j.1524-475X.2009.00566.x doi: 10.1111/j.1524-475X.2009.00566.x
![]() |
[91] |
H. Baltzer, P. Binhammer, Cost-effectiveness in the management of Dupuytren's contracture, Bone Joint J., 95-B (2013), 1094–1100. https://doi.org/10.1302/0301-620X.95B8.31822 doi: 10.1302/0301-620X.95B8.31822
![]() |
[92] |
M. Brazzelii, M. Cruickshank, E. Tassie, P. McNamee, C. Robertson, A. Elders, et al., Collagenase clostridium histolycum for the treatment of Dupuytren's contracture: a systematic review and economic evaluation, Health Technol. Assess., 19 (2015), 90. https://doi.org/10.3310/hta19900 doi: 10.3310/hta19900
![]() |
[93] |
N. Chen, M. Shauver, K. Chung, Cost-effectiveness of open partial fasciectomy, needle aponeurotomy, and collagenase injection for Dupuytren contracture, J. Hand Surg. Am., 36 (2011), 1826–1834. https://doi.org/10.1016/j.jhsa.2011.08.004 doi: 10.1016/j.jhsa.2011.08.004
![]() |
[94] |
M. Dritsaki, O. Rivero-Arias, A. Gray, C. Ball, J. Nanchahal, What do we know about managing Dupuytren's disease cost-effectively?, BMC Musculoskeletal Disord., 19 (2018), 34. https://doi.org/10.1186/s12891-018-1949-2 doi: 10.1186/s12891-018-1949-2
![]() |
[95] |
C. Sau, M. Bounthavong, J. Tran, R. Wilson, Cost-utility analysis of collagenase clostridium histolycum, limited fasciectomy, and percutaneous needle fasciotomy in Dupuytren's contracture, Value Health, 14 (2011), A128. https://doi.org/10.1016/j.jval.2011.02.714 doi: 10.1016/j.jval.2011.02.714
![]() |
[96] |
M. Khashan, P. Smitham, W. Khan, N. Goddard, Dupuytren's disease: review of the current literature, Open Orthop. J., 5 (2011), 283–288. https://doi.org/10.2174/1874325001105010283 doi: 10.2174/1874325001105010283
![]() |
[97] |
I. Stura, D. Gabriele, C. Guiot, A simple PSA-based computational approach predicts the timing of cancer relapse in prostatectomised patients, Cancer Res., 76 (2016), 4941–4947. https://doi.org/10.1158/0008-5472.CAN-16-0460 doi: 10.1158/0008-5472.CAN-16-0460
![]() |
[98] |
S. Hori, S. Gambhir, Mathematical model identified blood biomarker-based early cancer detection strategies and limitations, Sci. Transl. Med., 3 (2011), 109ra116. https://doi.org/10.1126/scitranslmed.3003110 doi: 10.1126/scitranslmed.3003110
![]() |
[99] |
R. Eftimie, E. Hassanein, Improving cancer detection through combinations of cancer and immune biomarkers: a modelling approach, J. Transl. Med., 16 (2018), 73. https://doi.org/10.1186/s12967-018-1432-8 doi: 10.1186/s12967-018-1432-8
![]() |
[100] |
M. McKenna, J. Weis, V. Quaranta, T. Yankeelov, Leveraging mathematical modeling to quantify pharmacokinetic and pharmacodynamic pathways: equivalent dose metric, Front. Physiol., 10 (2019), 616. https://doi.org/10.3389/fphys.2019.00616 doi: 10.3389/fphys.2019.00616
![]() |
1. | Fengwei Li, Qingfang Ye, Extremal graphs with given parameters in respect of general ABS index, 2024, 482, 00963003, 128974, 10.1016/j.amc.2024.128974 | |
2. | Shabana Anwar, Muhammad Kamran Jamil, Amal S. Alali, Mehwish Zegham, Aisha Javed, Extremal values of the first reformulated Zagreb index for molecular trees with application to octane isomers, 2023, 9, 2473-6988, 289, 10.3934/math.2024017 |
ψij | name |
i+j | First Zagreb index |
1√ij | Randić index |
2√iji+j | GA index |
√i+j−2ij | ABC index |
1√i+j | Sum–connectivity index |
(ij)3(i+j−2)3 | AZI index |
2i+j | Harmonic index |
|i−j| | Albertson index |
√i2+j2 | Sombor index |
iji+j | ISI index |
ij | 1√ij | 2√iji+j | 1√i+j | (ij)3(i+j−2)3 | √i+j−2ij | |
q | -1 | -0.0168 | -0.0404 | -0.0138 | -3.390 | 0.040 |
s | 5 | -0.1667 | 0 | -0.091 | 3.390 | -0.040 |
ij | 1√ij | 2√iji+j | 1√i+j | (ij)3(i+j−2)3 | √i+j−2ij | |
q | -1 | -0.0168 | -0.0404 | -0.0138 | -3.390 | 0.040 |
u | 18 | -0.449 | 0.121 | -0.233 | 20.344 | -0.242 |
ψij | name |
i+j | First Zagreb index |
1√ij | Randić index |
2√iji+j | GA index |
√i+j−2ij | ABC index |
1√i+j | Sum–connectivity index |
(ij)3(i+j−2)3 | AZI index |
2i+j | Harmonic index |
|i−j| | Albertson index |
√i2+j2 | Sombor index |
iji+j | ISI index |
ij | 1√ij | 2√iji+j | 1√i+j | (ij)3(i+j−2)3 | √i+j−2ij | |
q | -1 | -0.0168 | -0.0404 | -0.0138 | -3.390 | 0.040 |
s | 5 | -0.1667 | 0 | -0.091 | 3.390 | -0.040 |
ij | 1√ij | 2√iji+j | 1√i+j | (ij)3(i+j−2)3 | √i+j−2ij | |
q | -1 | -0.0168 | -0.0404 | -0.0138 | -3.390 | 0.040 |
u | 18 | -0.449 | 0.121 | -0.233 | 20.344 | -0.242 |