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An update on lateral flow immunoassay for the rapid detection of SARS-CoV-2 antibodies

  • Received: 01 November 2022 Revised: 31 March 2023 Accepted: 04 April 2023 Published: 13 April 2023
  • Over the last three years, after the outbreak of the COVID-19 pandemic, an unprecedented number of novel diagnostic tests have been developed. Assays to evaluate the immune response to SARS-CoV-2 have been widely considered as part of the control strategy. The lateral flow immunoassay (LFIA), to detect both IgM and IgG against SARS-CoV-2, has been widely studied as a point-of-care (POC) test. Compared to laboratory tests, LFIAs are faster, cheaper and user-friendly, thus available also in areas with low economic resources. Soon after the onset of the pandemic, numerous kits for rapid antibody detection were put on the market with an emergency use authorization. However, since then, scientists have tried to better define the accuracy of these tests and their usefulness in different contexts. In fact, while during the first phase of the pandemic LFIAs for antibody detection were auxiliary to molecular tests for the diagnosis of COVID-19, successively these tests became a tool of seroprevalence surveillance to address infection control policies. When in 2021 a massive vaccination campaign was implemented worldwide, the interest in LFIA reemerged due to the need to establish the extent and the longevity of immunization in the vaccinated population and to establish priorities to guide health policies in low-income countries with limited access to vaccines. Here, we summarize the accuracy, the advantages and limits of LFIAs as POC tests for antibody detection, highlighting the efforts that have been made to improve this technology over the last few years.

    Citation: Lucia Spicuzza, Davide Campagna, Chiara Di Maria, Enrico Sciacca, Salvatore Mancuso, Carlo Vancheri, Gianluca Sambataro. An update on lateral flow immunoassay for the rapid detection of SARS-CoV-2 antibodies[J]. AIMS Microbiology, 2023, 9(2): 375-401. doi: 10.3934/microbiol.2023020

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  • Over the last three years, after the outbreak of the COVID-19 pandemic, an unprecedented number of novel diagnostic tests have been developed. Assays to evaluate the immune response to SARS-CoV-2 have been widely considered as part of the control strategy. The lateral flow immunoassay (LFIA), to detect both IgM and IgG against SARS-CoV-2, has been widely studied as a point-of-care (POC) test. Compared to laboratory tests, LFIAs are faster, cheaper and user-friendly, thus available also in areas with low economic resources. Soon after the onset of the pandemic, numerous kits for rapid antibody detection were put on the market with an emergency use authorization. However, since then, scientists have tried to better define the accuracy of these tests and their usefulness in different contexts. In fact, while during the first phase of the pandemic LFIAs for antibody detection were auxiliary to molecular tests for the diagnosis of COVID-19, successively these tests became a tool of seroprevalence surveillance to address infection control policies. When in 2021 a massive vaccination campaign was implemented worldwide, the interest in LFIA reemerged due to the need to establish the extent and the longevity of immunization in the vaccinated population and to establish priorities to guide health policies in low-income countries with limited access to vaccines. Here, we summarize the accuracy, the advantages and limits of LFIAs as POC tests for antibody detection, highlighting the efforts that have been made to improve this technology over the last few years.



    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} (1ijn1), a VDB topological index of a an n-order (molecular) graph G is expressed as

    TI(G)=1ijn1mijψij, (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.

    Table 1.  Some well-known VDB topological indices.
    ψij name
    i+j First Zagreb index
    1ij Randić index
    2iji+j GA index
    i+j2ij ABC index
    1i+j Sum–connectivity index
    (ij)3(i+j2)3 AZI index
    2i+j Harmonic index
    |ij| Albertson index
    i2+j2 Sombor index
    iji+j ISI index

     | Show Table
    DownLoad: CSV

    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].

    Figure 1.  The ordinary structure modality of a f-benzenoid (F) and its construction from two benzenoid systems X and Y.

    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 (h3) obtaining from pieces X=L2 and Y=Lh2 is named as f-linear chain (as shown in Figure 2(b)).

    Figure 2.  Linear chain and f-linear chain.

    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 b2 for any f-benzenoid F.

    Figure 3.  Structural features occurring on the perimeter of f-benzenoids.

    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+5ni;

    (ii) m=5h+5ni.

    Lemma 1.2. [32] Let F be an n-order and h-hexagon f-benzenoid with r inlets, Then

    (i) m22=n2hr;

    (ii) m23=2r;

    (iii) m33=3hr.

    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.

    Figure 4.  Some f-benzenoids in Γ42.

    At first, we try to obtain the maximum and minimum number of hexagons in any FΓm.

    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.

    Figure 5.  The spiral benzenoid system Th with maximal number of internal vertices.

    As a matter of convenience, let SHh (h3) represent the collection of f-benzenoids formed by two spiral benzenoids X and Y. Particularly, a f-spiral benzenoid is a f-benzenoid FSHh in which X=Th1 and Y=T1 (as shown in Figure 6). It is easy to see that that

    ni(F)=2h12(h1)3.
    Figure 6.  f-benzenoid FSHh whose two pieces X and Y are both spiral benzenoid systems, and f-spiral benzenoid FSHh with two pieces X=Th1 and Y=T1.

    In [40], we proved that for every FSHh (h3), 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 FSHh can be obtained (as shown in Figure 7).

    Figure 7.  f-benzenoid FSHh is obtained from F by applying Operation 1 to it.

    For any h-hexagon f-benzenoid F, when h=3, it is easily checked that

    ni(F)=1=2×312(31)3. (2.2)

    When h4, let h1=h(X) and h2=h(Y). Another FSHh (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)+1ni(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(h3)-hexagon f-benzenoid. Then

    ni(F)2h12(h1)3, (2.4)

    and the equality is established when F is F.

    For any FΓm, h(F) over Γm is variable. Sharp bounds for h(F) in Γm is given below.

    Theorem 2.1. For any f-benzenoid FΓm,

    15(m4)h(F)m113(2m+4m31), (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)+5ni(F). Combining the fact that ni(F)1 for any FΓm, we get

    h(F)15(m4).

    On the other hand, by Lemma 2.1 we know that ni(F)ni(F). Consequently, from m=5h(F)+5ni(F) we have

    m3h(F)512(h(F)1)312(h(F)1)3.

    Hence,

    (3h(F)+(3m))24m31.

    Due to the fact that 3h(F)+(3m)<0, we deduce

    3h(F)+(3m)4m31,

    i.e., h(F)m113(2m+4m31).

    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 FΓm 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 HHSh. Under the below cases, H is definitely not a CBS:

    (i) If h4 and ni=1;

    (ii) If h5 and ni=2;

    (iii) If h6 and ni=3.

    Lemma 2.3. [52] Let HHSh 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.

    Figure 8.  Benzenoid systems with 1, 2, 3 and 4 internal vertices, respectively.

    Lemma 2.4. Let SHSh. If h7 and ni(S)=4, then S is not a CBS.

    Proof. Let S be an h (h7)-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 h7, 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 h7, 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)=2h3 (h3);

    2) If ni=2, then r(F)r(Gh)=2h4 (h4);

    3) If ni=3, then r(F)r(Rh)=2h5 (h5);

    4) If ni=4, then r(F)r(Zh)=2h6 (h6).

    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.

    Figure 9.  Three types of benzenoid systems.
    Figure 10.  f-benzenoids G4, and Gh (h5).
    Figure 11.  f-benzenoids R5, and Rh (h6).
    Figure 12.  f-benzenoids Z6, and Zh (h7).
    Figure 13.  f-benzenoids U7, and Uh (h8).

    Lemma 2.6. [41] Let F be an h-hexagon f-benzenoid. Then

    r(F)r(FLh)=2h3.

    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=4h2ni2b, respectively.

    From Lemmas 1.2 (ii) and 8, we get

    r=2hnib (2.6)

    Furthermore, by Lemma 1.1 (ii) and Eq (2.6), we deduce

    r=m3h5b (2.7)

    Theorem 2.2. Let F be an h-hexagon f-benzenoid. If ni=5, then r(F)r(Uh)=2h7 (h7).

    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)=82h7, r(F)=r(D2)=72h7 or r(F)=r(D3)=82h7.

    Figure 14.  f-benzenoids D1, D2, D3, D4 and D5.

    Subcase 1.1.2. If h26, 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=2hnib2h8<2h7.

    Subcase 1.2. If h14, 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 r2h9<2h7.

    Subcase 1.2.2. If h26, 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 r2h10<2h7.

    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)=82h7, r(F)=r(D5)=72h7, r(F)=r(D6)=82h7, r(F)=r(D7)=72h7.

    Figure 15.  f-benzenoids D7, D8 and D9.

    Subcase 2.1.2. If h24, 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 r2h9<2h7.

    Subcase 2.2. If h16, 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 r2h9<2h7.

    Subcase 2.2.2. f h24, 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), r2h10<2h7.

    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<2h7 or r(F)=r(D9)=7<2h7.

    Subcase 3.1.2. If h25, 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, r2h9<2h7.

    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)=82h7 or r(F)=r(D9)=72h7.

    Subcase 3.2.2. If h15, 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 r2h9<2h7.

    Subcase 3.3. If h15, h25, 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 r2h10<2h7.

    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)=62h7, r(F)=r(D11)=62h7, r(F)=r(D12)=62h7, r(F)=r(D13)=62h7 or r(F)=r(U7)=7=2h7.

    Figure 16.  f-benzenoids D10, D11 and D12.
    Figure 17.  f-benzenoids D13, D14, D15, D16, D17, D18, D19, D20 and D21.

    Subcase 4.1.2. If h22, we have b(F)2, by Eq (2.6), r2h7.

    Subcase 4.2. If h17, 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 r2h8<2h7 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)=72h7, r(F)=r(D15)=82h7, r(F)=r(D16)=82h7, r(F)=r(D17)=72h7, r(F)=r(D18)=72h7, r(F)=r(D19)=82h7, r(F)=r(D20)=62h7 or r(F)=r(D21)=62h7.

    Subcase 5.1.2. If h13, 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 h13, 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, r2h9<2h7 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 h13, 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 r2h9<2h7.

    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 h13, 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 r2h9<2h7.

    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 h13, 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), r2h9<2h7.

    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 h13, 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), r2h9<2h7.

    Subcase 5.2. If h27, 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 r2h7.

    Subcase 5.2.2. If h13, 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), r2h7.

    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 FΓm. Then

    1) If m0(mod5), then r(F)2m355=r(Um5);

    2) If m1(mod5), then r(F)2m325=r(Zm15);

    3) If m2(mod5), then r(F)2m295=r(Rm25);

    4) If m3(mod5), then r(F)2m265=r(Gm35);

    5) If m4(mod5), then r(F)2m235=r(FLm45).

    Proof. We know by Eq (2.5) that

    15(m4)h(F)m113(2m+4m31).

    1) If m0(mod5), then 15(m4)=m5. If h=m5, then by Lemma 1.1 (ii)

    m=5h(F)+5ni(F)=m+5ni(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)=m53h(F)b(F)m53(m5+1)b(F)
    2m510=2m5052m355=r(Um5).

    2) If m1(mod5), then 15(m4)=m15. If h(F)=m15, then by Lemma 1.1 (ii)

    m=5h(F)+5ni(F)=m+4ni(F),

    thus ni(F)=4. Then r(F)r(Zm15) by part 4 of Lemma 2.5. Otherwise h(F)m15+1, then by equality (2.7) and the obvious fact that b(F)2

    r(F)=m53h(F)b(F)m53(m15+1)b(F)
    2m+3510=2m4752m325=r(Zm15).

    3) If m2(mod5), then 15(m4)=m25. If h(F)=m25, then by Lemma 1.1 (ii)

    m=5h(F)+5ni(F)=m+3ni(F),

    and so ni(F)=3. Then r(F)r(Rm25) by part 3 of Lemma 2.5. So assume now that h(F)m25+1, then by Eq (2.7) and the fact that b(F)2

    r(F)=m53h(F)b(F)m53(m25+1)b(F)
    2m+6510=2m4452m295=r(Rm25).

    4) If m3(mod5), then 15(m4)=m35. If h(F)=m35, then by Lemma 1.1 (ii)

    m=5h(F)+5ni(F)=m+2ni(F),

    thus ni(F)=2. By Lemma 2.5, r(F)r(Gm35) and we are done. If h(F)m35+1, then by equality (2.7) and the fact that b(F)2

    r(F)=m53h(F)b(F)m53(m35+1)b(F)
    2m+9510=2m4152m265=r(Gm35).

    5) If m4(mod5), then 15(m4)=m45. Since hm45 and b(F)2, then by Eq (2.7), we have

    r(F)=m53h(F)b(F)m53m125b(F)
    2m+1257=2m235=r(FLm45).

    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(ψ33ψ22)h+(2ψ23ψ22ψ33)r, (3.2)

    If U,VΓm then clearly

    TI(U)TI(V)=3(ψ33ψ22)(h(U)h(V))          +(2ψ23ψ22ψ33)(r(U)r(V)). (3.3)

    For convenience, we set s=ψ33ψ22, q=2ψ23ψ22ψ33.

    Theorem 3.1. For any FΓm, we have the following results.

    a. If s0 and q0,

    TI(F){TI(Um5),if m0(mod 5)TI(Zm15),if m1(mod 5)TI(Rm25),if m2(mod 5)TI(Gm35),if m3(mod 5)TI(FLm45),if m4(mod 5)

    b. If s0 and q0,

    TI(F){TI(Um5),if m0(mod 5)TI(Zm15),if m1(mod 5)TI(Rm25),if m2(mod 5)TI(Gm35),if m3(mod 5)TI(FLm45),if m4(mod 5)

    Proof. Let FΓm. By Eq (2.5)

    h(F)15(m4)={h(Um5),if m0(mod 5)h(Zm15),if m1(mod 5)h(Rm25),if m2(mod 5)h(Gm35),if m3(mod 5)h(FLm45),if m4(mod 5)

    i.e., f-benzenoids Um5, Zm15, Rm25, Gm35 and FLm45 have minimal h over the set Γm. Meanwhile, by Theorem 2.3, we have

    r(F){r(Um5),if m0(mod 5)r(Zm15),if m1(mod 5)r(Rm25),if m2(mod 5)r(Gm35),if m3(mod 5)r(FLm45),if m4(mod 5)

    i.e., these five f-benzenoids have maximal number of inlets over Γm. Hence, for any f-benzenoids FΓm and V{Um5,Zm15,Rm25,Gm35,FLm45}, 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 s0 and q0, then TI(F)TI(V)0, i.e., V reaches the maximum value of TI over Γm. If s0 and q0, then TI(F)TI(V)0, i.e., V reaches the minimum value of TI over Γm. Furthermore, which V{Um5,Zm15,Rm25,Gm35,FLm45} 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:

    Table 2.  Values of s and q for six famous TI.
    ij 1ij 2iji+j 1i+j (ij)3(i+j2)3 i+j2ij
    q -1 -0.0168 -0.0404 -0.0138 -3.390 0.040
    s 5 -0.1667 0 -0.091 3.390 -0.040

     | Show Table
    DownLoad: CSV

    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 FΓm, then from the Eqs (2.3) and (2.6) and Lemma 1.1(ii) we have

    TI(F)=(2ψ23ψ33)m+6(ψ33ψ23)h(2ψ23ψ22ψ33)b               5(2ψ23ψ22ψ33). (3.4)

    Consequently, for f-benzenoids U,VΓm

    TI(U)TI(V)=6(ψ33ψ23)(h(U)h(V))          +(2ψ23+ψ22+ψ33)(b(U)b(V)). (3.5)

    Set u=6(ψ33ψ23) and keep in mind that q=2ψ23ψ22ψ33. 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 FΓm, We know that

    h(F)m113(2m+4m31),

    and the equality can be achieved precisely when F is the f-spiral benzenoid F [41].

    In [41], we proved that ni(F)=2h12(h1)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].

    Figure 18.  The general form of a CBS. The parameters li1,i=1,2,,6, count the number of hexagons on the respective side of CBS.

    The following lemma gave requirements that there exists CBS with maximal ni [53].

    Lemma 3.1. [53] Let hN. The conditions below are isovalent:

    (a) There is a CBS W containing h hexagons and 2h+112h3  number of internal vertices.

    (b) There exist l1,l2,l3,l4N satisfying the following equation

    h=l1l3+l1l4+l2l3+l2l4l2l312l1(l1+1)12l4(l4+1)+112h3 =l1+2l2+2l3+l43} (3.7)

    If for hN, Eq (3.7) has a solution l1,l2,l3,l4N, 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 h1N, supposing that the system below

    h1=l1l3+l1l4+l2l3+l2l4l2l312l1(l1+1)12l4(l4+1)+112(h1)3 =l1+2l2+2l3+l43 li{l1,l2,l3,l4,l5,l6}, li=2} (3.8)

    has a solution {l1,l2,l3,l4}, then a CBS Wh1 containing ni(Wh1)=2(h1)+112(h1)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 Wh1, let u,w,v in Figure 1 represent the three vertices of this fissure. Now, we obtain an f-spiral benzenoid F1 in which X=Wh1 and Y=L1. It is obvious that

    ni(F1)=2h12(h1)3 (3.9)

    and b(F1)=2. (as shown in Figure 19)

    Figure 19.  A f-spiral benzenoid F1 whose fragment X is a convex spiral benzenoid system Wh1.

    Theorem 3.2. Let h1N such that the Eq (3.8) has a solution, and m=3h+5+12(h1)3. Then for any FΓm

    1) TI(F1)TI(F), when u0 and q0;

    2) TI(F1)TI(F), when u0 and q0.

    Proof. From Lemma 1.1 (ii) and Eq (3.9), we have

    m(F1)=5h+5(2h12(h1)3)=3h+5+12(h1)3

    and so

    h=m113(2m+4m31).

    It is obvious that b(F1)=2 and b(F)2 for any FΓm. Hence by Eq (3.6), we have

    TI(F)TI(F1)=u(h(F)h(F1))q(b(F)b(F1))
    =u[h(F)(m113(2m+4m31))]q[b(F)2].

    And by Eq (2.5)

    h(F)m113(2m+4m31).

    If u0 and q0 then TI(F)TI(F1)0, i.e., F1 achieves maximal TI in Γm. Similarly, if u0 and q0 then TI(F)TI(F1)0, i.e., F1 obtains minimal TI in Γm.

    Example 2. The values of u and q for some famous TI are listed in the following Table 3:

    Table 3.  Values of u and q for six famous TI.
    ij 1ij 2iji+j 1i+j (ij)3(i+j2)3 i+j2ij
    q -1 -0.0168 -0.0404 -0.0138 -3.390 0.040
    u 18 -0.449 0.121 -0.233 20.344 -0.242

     | Show Table
    DownLoad: CSV

    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 F1 for those h such that Eq (3.8) holds.

    Example 3. Take consideration of the generalized Randć index

    Rα(G)=1ijn1mij(ij)α,

    where αR. Note that

    q=2(6α)4α9α=4α((32)α1)20

    for all αR. Moreover, s=9α4α0 if and only if α0 if and only if u=6(9α6α)0. Hence, by Theorem 3.1, the minimal value of Rα(G) is obtained for all α0, and for any α0, the minimal value of Rα(G) can be attained by the f-spiral benzenoid F1 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.



    Conflict of interest



    All authors declare no conflicts of interest in this paper.

    [1] Eriksson E, Lysell J, Larsson H, et al. (2019) Geometric flow control lateral flow immunoassay devices (GFC-LFIDs): a new dimension to enhance analytical performance. Research . https://doi.org/10.34133/2019/8079561
    [2] Peeling RW, Wedderburn CJ, Garcia PJ, et al. (2020) Serology testing in the COVID-19 pandemic response. Lancet Infect Dis 20: e245-e249. https://doi.org/10.1016/S1473-3099(20)30517-X
    [3] Mathieu E, Ritchie H, Rodés-Guirao L, et al. Coronavirus Pandemic (COVID-19) (2020). Available from: https://ourworldindata.org/coronavirus
    [4] World Health OrganizationDiagnostic testing for SARS-CoV-2 (2020). Available from: https://www.who.int/publications/i/item/diagnostic-testing-for-sars-cov-2
    [5] (2022) European Centre for Disease Prevention and ControlConsiderations for the use of antibody tests for SARSCoV-2 – first update. Stockholm: ECDC.
    [6] Chisale MRO, Ramazanu S, Mwale SE, et al. (2022) Seroprevalence of anti-SARS-CoV-2 antibodies in Africa: a systematic review and meta-analysis. Rev Med Virol 32: e2271. https://doi.org/10.1002/rmv.2271
    [7] Mirica AC, Stan D, Chelcea IC, et al. (2022) Latest trends in lateral flow immunoassay (LFIA) detection labels and conjugation process. Front Bioeng Biotechnol 14: 922772. https://doi.org/10.3389/fbioe.2022.922772
    [8] Kosack CS, Page AL, Klatser PR, et al. (2017) A guide to aid the selection of diagnostic tests. Bull World Health Organ 95: 639-645. https://doi.org/10.2471/BLT.16.187468
    [9] Kumar M, Iyer SS (2021) ASSURED-SQVM diagnostics for COVID-19: addressing the why, when, where, who, what and how of testing. Expert Rev Mol Diagn 21: 349-362. https://doi.org/10.1080/14737159.2021.1902311
    [10] Azkur AK, Akdis M, Azkur D, et al. (2020) Immune response to SARS-CoV-2 and mechanisms of immunopathological changes in COVID-19. Allergy 75: 1564-1581. https://doi.org/10.1111/all.14364
    [11] Guo L, Ren L, Yang S, et al. (2020) Profiling early humoral response to diagnose novel coronavirus disease (COVID-19). Clin Infect Dis 28: 778-785. https://doi.org/10.1093/cid/ciaa310
    [12] Wang Y, Zhang L, Sang L, et al. (2020) Kinetics of viral load and antibody response in relation to COVID-19 severity. J Clin Invest 130: 5235-5244. https://doi.org/10.1172/JCI138759
    [13] Herroelen PH, Martens GA, De Smet D, et al. (2020) Humoral immune response to SARS-CoV-2. Am J Clin Pathol 154: 610-619. https://doi.org/10.1093/ajcp/aqaa140
    [14] Huang AT, Garcia-Carreras B, Hitchings MDT, et al. (2020) A systematic review of antibody mediated immunity to coronaviruses: kinetics, correlates of protection, and association with severity. Nat Commun 11: 4704. https://doi.org/10.1038/s41467-020-18450-4
    [15] Spicuzza L, Montineri A, Manuele R, et al. (2020) Reliability and usefulness of a rapid IgM-IgG antibody test for the diagnosis of SARS-CoV-2 infection: a preliminary report. J Infect 81: e53-e54. https://doi.org/10.1016/j.jinf.2020.04.022
    [16] Padoan A, Sciacovelli L, Basso D (2020) IgA-Ab response to spike glycoprotein of SARS-CoV-2 in patients with COVID-19: A longitudinal study. Clin Chim Acta 507: 164-166. https://doi.org/10.1016/j.cca.2020.04.026
    [17] Zhang Y, Chai Y, Hu Z, et al. (2022) Recent progress on rapid lateral flow assay-based early diagnosis of COVID-19. Front Bioeng Biotechnol 10: 866368. https://doi.org/10.3389/fbioe.2022.866368
    [18] Vabret N, Britton GJ, Gruber C, et al. (2020) Immunology of COVID-19: current state of the science. Immunity 52: 910-941. https://doi.org/10.1016/j.immuni.2020.05.002
    [19] Zhao J, Yuan Q, Wang H, et al. (2020) Antibody responses to SARS-CoV-2 in patients of novel coronavirus disease 2019. Clin Infect Dis 71: 2027-2034. https://doi.org/10.1093/cid/ciaa344
    [20] Martín J, Tena N, Asuero AG, et al. (2021) Current state of diagnostic, screening and surveillance testing methods for COVID-19 from an analytical chemistry point of view. Microchem J 167: 106305. https://doi.org/10.1016/j.microc.2021.106305
    [21] Al-Tamimi M, Tarifi AA, Qaqish A, et al. (2023) Immunoglobulins response of COVID-19 patients, COVID-19 vaccine recipients, and random individuals. PLoS One 18: e0281689. https://doi.org/10.1371/journal.pone.0281689
    [22] Zhang S, Xu K, Li C, et al. (2022) Long-term kinetics of SARS-CoV-2 antibodies and impact of inactivated vaccine on SARS-CoV-2 antibodies based on a COVID-19 patients cohort. Front Immunol 13: 829665. https://doi.org/10.3389/fimmu.2022.829665
    [23] Liang Y, Wang ML, Chien CS, et al. (2020) Highlight of immune pathogenic response and hematopathologic effect in SARS-CoV, MERS-CoV, and SARS-Cov-2 infection. Front Immunol 11: 1022. https://doi.org/10.3389/fimmu.2020.01022
    [24] Irani S (2022) Immune responses in SARS-CoV-2, SARS-CoV, and MERS-CoV infections: a comparative review. Int J Prev Med 13: 45. https://doi.org/10.4103/ijpvm.IJPVM_429_20
    [25] Chiereghin A, Zagari RM, Galli S, et al. (2021) Recent advances in the evaluation of serological assays for the diagnosis of SARS-CoV-2 infection and COVID-19. Front Public Health 8: 620222. https://doi.org/10.3389/fpubh.2020.620222
    [26] Vengesai A, Midzi H, Kasambala M, et al. (2021) A systematic and meta-analysis review on the diagnostic accuracy of antibodies in the serological diagnosis of COVID-19. Syst Rev 10: 155. https://doi.org/10.1186/s13643-021-01689-3
    [27] Wang MY, Zhao R, Gao LJ, et al. (2020) Structure, biology, and structure-based therapeutics development. Front Cell Infect Microbiol 10: 587269. https://doi.org/10.3389/fcimb.2020.587269
    [28] Rak A, Donina S, Zabrodskaya Y, et al. (2022) Cross-reactivity of SARS-CoV-2 nucleocapsid-binding antibodies and its implication for COVID-19 serology tests. Viruses 14. https://doi.org/10.3390/v14092041
    [29] Wang JJ, Zhang N, Richardson SA, et al. (2021) Rapid lateral flow tests for the detection of SARS-CoV-2 neutralizing antibodies. Expert Rev Mol Diagn 21: 363-370. https://doi.org/10.1080/14737159.2021.1913123
    [30] Jalkanen P, Pasternack A, Maljanen S, et al. (2021) A combination of N and S antigens with IgA and IgG measurement strengthens the accuracy of SARS-CoV-2 serodiagnostics. J Infect Dis 224: 218-228. https://doi.org/10.1093/infdis/jiab222
    [31] Gong F, Wei HX, Li Q, et al. (2021) Evaluation and comparison of serological methods for COVID-19 diagnosis. Front Mol Biosci 8: 682405. https://doi.org/10.3389/fmolb.2021.682405
    [32] Yadegari H, Mohammadi M, Maghsood F, et al. (2023) Diagnostic performance of a novel antigen-capture ELISA for the detection of SARS-CoV-2. Anal Biochem 666: 115079. https://doi.org/10.1016/j.ab.2023.115079
    [33] Guarino C, Larson E, Babasyan S, et al. (2022) Development of a quantitative COVID-19 multiplex assay and its use for serological surveillance in a low SARS-CoV-2 incidence community. PLoS One 17: e0262868. https://doi.org/10.1371/journal.pone.0262868
    [34] Amanat F, Stadlbauer D, Strohmeier S, et al. (2020) A serological assay to detect SARS-CoV-2 seroconversion in humans. Nature Medicine 26: 1033-1036. https://doi.org/10.1038/s41591-020-0913-5
    [35] Ravi N, Cortade DL, Ng E, et al. (2020) Diagnostics for SARS-CoV-2 detection: a comprehensive review of the FDA-EUA COVID-19 testing landscape. Biosens Bioelectron 165: 112454. https://doi.org/10.1016/j.bios.2020.112454
    [36] Okba NMA, Müller MA, Li W, et al. (2020) Severe acute respiratory syndrome coronavirus 2-specific antibody responses in coronavirus disease patients. Emerg Infect Dis 26: 1478-1488. https://doi.org/10.3201/eid2607.200841
    [37] Ghaffari A, Meurant R, Ardakani A (2021) COVID-19 point-of-care diagnostics that satisfy global target product profiles. Diagnostics (Basel) 11: 115. https://doi.org/10.3390/diagnostics11010115
    [38] Li Z, Yi Y, Luo X, et al. (2020) Development and clinical application of a rapid IgM-IgG combined antibody test for SARS-CoV-2 infection diagnosis. J Med Virol 92: 1518-1524. https://doi.org/10.1002/jmv.25727
    [39] Andryukov BG (2020) Six decades of lateral flow immunoassay: from determining metabolic markers to diagnosing COVID-19. AIMS Microbiol 6: 280-304. https://doi.org/10.3934/microbiol.2020018
    [40] Ernst E, Wolfe P, Stahura C, et al. (2021) Technical considerations to development of serological tests for SARS-CoV-2. Talanta 224. https://doi.org/10.1016/j.talanta.2020.121883
    [41] Koczula KM, Gallotta A (2016) Lateral flow assays. Essays Biochem 60: 111-120. https://doi.org/10.1042/EBC20150012
    [42] U.S. Food and Drug Administration, Coronavirus (COVID-19) Update: FDA Authorizes First Point-of-Care Antibody Test for COVID-19 (2020). Available from: https://www.fda.gov/news-events/press-announcements/coronavirus-covid-19-update-fda-authorizes-first-point-care-antibody-test-covid-19
    [43] Jazayeri MH, Amani H, Pourfatollah AA, et al. (2016) Various methods of gold nanoparticles (GNPs) conjugation to antibodies. Sens Bio-Sens Res 9: 17-22. https://doi.org/10.1016/j.sbsr.2016.04.002
    [44] O'Farrell B (2013) Lateral flow immunoassay systems: evolution from the current state of the art to the next generation of highly sensitive, quantitative rapid assays. The Immunoassay Handbook . Elsevier 89-107.
    [45] Albaz AA, Rafeeq MM, Sain ZM, et al. (2021) Nanotechnology-based approaches in the fight against SARS-CoV-2. AIMS Microbiol 7: 368-398. https://doi.org/10.3934/microbiol.2021023
    [46] Hsiao WW, Le TN, Pham DM, et al. (2021) Recent advances in novel lateral flow technologies for detection of COVID-19. Biosensors (Basel) 11: 295. https://doi.org/10.3390/bios11090295
    [47] Karuppaiah G, Vashist A, Nair M, et al. (2023) Emerging trends in point-of-care biosensing strategies for molecular architectures and antibodies of SARS-CoV-2. Biosens Bioelectron X 13: 100324. https://doi.org/10.1016/j.biosx.2023.100324
    [48] Huang C, Wen T, Shi FJ, et al. (2020) Rapid detection of IgM antibodies against the SARS-CoV-2 virus via colloidal gold nanoparticle-based lateral-flow assay. ACS Omega 5: 12550-12556. https://doi.org/10.1021/acsomega.0c01554
    [49] Zhang JJY, Lee KS, Ong CW, et al. (2021) Diagnostic performance of COVID-19 serological assays during early infection: a systematic review and meta-analysis of 11516 samples. Influenza Other Respir Viruses 15: 529-538. https://doi.org/10.1111/irv.12841
    [50] Tessaro L, Aquino A, Panzenhagen P, et al. (2023) A systematic review of the advancement on colorimetric nanobiosensors for SARS-CoV-2 detection. J Pharm Biomed Anal 222: 15087. https://doi.org/10.1016/j.jpba.2022.115087
    [51] Rabiee N, Ahmadi S, Soufi GJ, et al. (2022) Quantum dots against SARS-CoV-2: diagnostic and therapeutic potentials. J Chem Technol Biotechnol 97: 1640-1654. https://doi.org/10.1002/jctb.7036
    [52] Wang C, Yang X, Gu B, et al. (2020) Sensitive and simultaneous detection of SARSCoV-2-specific IgM/IgG using lateral flow immunoassay based on dual-mode quantum dot nanobeads. Anal Chem 92: 15542-15549. https://doi.org/10.1021/acs.analchem.0c03484
    [53] Seo SE, Ryu E, Kim J, et al. (2023) Fluorophore-encapsulated nanobeads for on-site, rapid, and sensitive lateral flow assay. Sens Actuators B Chem 381: 133364. https://doi.org/10.1016/j.snb.2023.133364
    [54] Budd J, Miller BS, Weckman NE, et al. (2023) Lateral flow test engineering and lessons learned from COVID-19. Nat Rev Bioeng 1: 13-31. https://doi.org/10.1038/s44222-022-00007-3
    [55] Chen PY, Ko CH, Wang CJ, et al. (2021) The early detection of immunoglobulins via optical-based lateral flow immunoassay platform in COVID-19 pandemic. PLoS One 16. https://doi.org/10.1371/journal.pone.0254486
    [56] Pieri M, Nicolai E, Nuccetelli M, et al. (2022) Validation of a quantitative lateral flow immunoassay (LFIA)-based point-of-care (POC) rapid test for SARS-CoV-2 neutralizing antibodies. Arch Virol 167: 1285-1291. https://doi.org/10.1007/s00705-022-05422-w
    [57] Castrejón-Jiménez NS, García-Pérez BE, Reyes-Rodríguez NE, et al. (2022) Challenges in the detection of SARS-CoV-2: evolution of the lateral flow immunoassay as a valuable tool for viral diagnosis. Biosensors (Basel) 12: 728. https://doi.org/10.3390/bios12090728
    [58] Burbelo PD, Riedo FX, Morishima C, et al. (2020) Sensitivity in detection of antibodies to nucleocapsid and spike proteins of severe acute respiratory syndrome coronavirus 2 in patients with coronavirus disease 2019. J Infect Dis 222: 206-213. https://doi.org/10.1093/infdis/jiaa273
    [59] Ang GY, Chan KG, Yean CY, et al. (2022) Lateral flow immunoassays for SARS-CoV-2. Diagnostics (Basel) 12: 2854. https://doi.org/10.3390/diagnostics12112854
    [60] Smits VAJ, Hernández-Carralero E, Paz-Cabrera MC, et al. (2021) The Nucleocapsid protein triggers the main humoral immune response in COVID-19 patients. Biochem Biophys Res Commun 543: 45-49. https://doi.org/10.1016/j.bbrc.2021.01.073
    [61] Owen SI, Williams CT, Garrod G, et al. (2022) Twelve lateral flow immunoassays (LFAs) to detect SARS-CoV-2 antibodies. J Infect 84: 355-360. https://doi.org/10.1016/j.jinf.2021.12.007
    [62] Barnes CO, Jette CA, Abernathy ME, et al. (2020) SARS-CoV-2 neutralizing antibody structures inform therapeutic strategies. Nature 588: 682-687. https://doi.org/10.1038/s41586-020-2852-1
    [63] McCallum M, Marco AD, Lempp F, et al. (2021) N-terminal domain antigenic mapping reveals a site of vulnerability for SARS-CoV-2. Cell 184: 2332-2347. https://doi.org/10.1016/j.cell.2021.03.028
    [64] Piccoli L, Park YJ, Tortorici MA, et al. (2020) Mapping neutralizing and immunodominant sites on the SARS-CoV-2 spike receptor-binding domain by structure-guided high-resolution serology. Cell 183: 1024-1042. https://doi.org/10.1016/j.cell.2020.09.037
    [65] Khoury DS, Cromer D, Reynaldi A, et al. (2022) Neutralizing antibody levels are highly predictive of immune protection from symptomatic SARS-CoV-2 infection. Nat Med 27: 1205-1211. https://doi.org/10.1038/s41591-021-01377-8
    [66] Post N, Eddy D, Huntley C, et al. (2020) Antibo1dy response to SARS-CoV-2 infection in humans: a systematic review. PLoS One 15: e0244126. https://doi.org/10.1371/journal.pone.0244126
    [67] Kim Y, Bae JY, Kwon K, et al. (2022) Kinetics of neutralizing antibodies against SARS-CoV-2 infection according to sex, age, and disease severity. Sci Rep 12: 13491. https://doi.org/10.1038/s41598-022-17605-1
    [68] Lau EHY, Tsang OTY, Hui DSC, et al. (2021) Neutralizing antibody titres in SARS-CoV-2 infections. Nat Commun 12: 63. https://doi.org/10.1038/s41467-020-20247-4
    [69] Park JH, Cha MJ, Choi H, et al. (2022) Relationship between SARS-CoV-2 antibody titer and the severity of COVID-19. J Microbiol Immunol Infect 55: 1094-1100. https://doi.org/10.1016/j.jmii.2022.04.005
    [70] Tuyji Tok Y, Can Sarinoglu R, Ordekci S, et al. (2023) One-year post-vaccination longitudinal follow-up of quantitative SARS-CoV-2 anti-spike total antibodies in health care professionals and evaluation of correlation with surrogate neutralization test. Vaccines (Basel) 11: 355. https://doi.org/10.3390/vaccines11020355
    [71] Vidal SJ, Collier AY, Yu J, et al. (2021) Correlates of neutralization against SARS-CoV-2 variants of concern by early pandemic sera. J Virol 95: e0040421. https://doi.org/10.1128/JVI.00404-21
    [72] Chen Y, Zhao X, Zhou H, et al. (2023) Broadly neutralizing antibodies to SARS-CoV-2 and other human coronaviruses. Nat Rev Immunol 23: 189-199. https://doi.org/10.1038/s41577-022-00784-3
    [73] Suthar MS, Zimmerman M, Kauffman R, et al. (2020) Rapid generation of neutralizing antibody responses in COVID-19 patients. Cell Rep Med 1. https://doi.org/10.1016/j.xcrm.2020.100040
    [74] Carter LJ, Garner LV, Smoot JW, et al. (2020) Assay techniques and test development for COVID-19 diagnosis. ACS Cent Sci 6: 591-605. https://doi.org/10.1021/acscentsci.0c00501
    [75] Crawford KHD, Eguia R, Dingens AS, et al. (2020) Protocol and reagents for pseudotyping lentiviral particles with SARS-CoV-2 spike protein for neutralization assays. Viruses 12. https://doi.org/10.3390/v12050513
    [76] Hamid S, Tali S, Leblanc JJ, et al. (2021) Tools and techniques for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)/COVID-19 detection. Clin Microbiol Rev 34. https://doi.org/10.1128/CMR.00228-20
    [77] Liu KT, Han YJ, Wu GH, et al. (2022) Overview of neutralization assays and international standard for detecting SARS-CoV-2 neutralizing antibody. Viruses 14: 1560. https://doi.org/10.3390/v14071560
    [78] The Global Fund, List of SARS-CoV-2 Diagnostic test kits and equipments eligible for procurement according to Board Decision on Additional Support for Country Responses to COVID-19 (GF/B42/EDP11) (2023). Available from: https://www.theglobalfund.org/media/9629/covid19_diagnosticproducts_list_en.pdf
    [79] Foundation for Innovative New Diagnostics, COVID-19 test directory. Available from: https://www.finddx.org/tools-and-resources/dxconnect/test-directories/covid-19-test-directory/
    [80] Ochola L, Ogongo P, Mungai S, et al. (2022) Performance Evaluation of Lateral Flow Assays for Coronavirus Disease-19 Serology. Clin Lab Med 42: 31-56. https://doi.org/10.1016/j.cll.2021.10.005
    [81] Filchakova O, Dossym D, Ilyas A, et al. (2022) Review of COVID-19 testing and diagnostic methods. Talanta 244: 123409. https://doi.org/10.1016/j.talanta.2022.123409
    [82] Deshpande PS, Abraham IE, Pitamberwale A, et al. (2022) Review of clinical performance of serology based commercial diagnostic assays for detection of severe acute respiratory syndrome coronavirus 2 antibodies. Viral Immunol 35: 82-111. https://doi.org/10.1089/vim.2020.0313
    [83] Wang Z, Zheng Z, Hu H, et al. (2020) A point-of-care selenium nanoparticle-based test for the combined detection of anti-SARS-CoV-2 IgM and IgG in human serum and blood. Lab Chip 20: 4255-4261. https://doi.org/10.1039/d0lc00828a
    [84] Bastos M, Tavaziva G, Abidi S, et al. (2020) Diagnostic accuracy of serological tests for covid-19: systematic review and meta-analysis. BMJ . https://doi.org/10.1136/bmj.m2516
    [85] Novello S, Terzolo M, Paola B, et al. (2021) Humoral immune response to SARS-CoV-2 in five different groups of individuals at different environmental and professional risk of infection. Sci Rep 11: 24503. https://doi.org/10.1038/s41598-021-04279-4
    [86] Deeks JJ, Dinnes J, Takwoingi Y, et al. (2020) Antibody tests for identification of current and past infection with SARS-CoV-2. Cochrane Database Syst Rev 6: CD013652. https://doi.org/10.1002/14651858.CD013652
    [87] Pecoraro V, Negro A, Pirotti T, et al. (2022) Estimate false-negative RT-PCR rates for SARS-CoV-2. A systematic review and meta-analysis. Eur J Clin Invest 52: e13706. https://doi.org/10.1111/eci.13706
    [88] Ozturk A, Bozok T, Bozok TS, et al. (2021) Evaluation of rapid antibody test and chest computed tomography results of COVID-19 patients: A retrospective study. J Med Virol 93: 6582-6587. https://doi.org/10.1002/jmv.27209
    [89] Yurtsever I, Karatoprak C, Sumbul B, et al. (2022) Thorax computed tomography findings and anti-SARS-CoV-2 immunoglobulin G levels in polymerase chain reaction-negative probable COVID-19 cases. Rev Assoc Med Bras 68: 1742-1746. https://doi.org/10.1590/1806-9282.20220921
    [90] Chen W, Zhang J, Qin X, et al. (2020) SARS-CoV-2 neutralizing antibody levels are correlated with severity of COVID-19 pneumonia. Biomed Pharmacother 130: 110629. https://doi.org/10.1016/j.biopha.2020.110629
    [91] Jurenka J, Nagyová A, Dababseh M, et al. (2022) Anti-SARS-CoV-2 antibody status at the time of hospital admission and the prognosis of patients with COVID-19: a prospective observational study. Infect Dis Rep 14: 1004-1016. https://doi.org/10.3390/idr14060100
    [92] Jarrom D, Elston L, Washington J, et al. (2022) Effectiveness of tests to detect the presence of SARS-CoV-2 virus, and antibodies to SARS-CoV-2, to inform COVID-19 diagnosis: a rapid systematic review. BMJ Evid Based Med 27: 33-45. https://doi.org/10.1136/bmjebm-2020-111511
    [93] Wang H, Ai J, Loeffelholz MJ, et al. (2020) Meta-analysis of diagnostic performance of serology tests for COVID-19: impact of assay design and post-symptom-onset intervals. Emerg Microbes Infect 9: 2200-2211. https://doi.org/10.1080/22221751.2020.1826362
    [94] Mekonnen D, Mengist HM, Derbie A, et al. (2021) Diagnostic accuracy of serological tests and kinetics of severe acute respiratory syndrome coronavirus 2 antibody: a systematic review and meta-analysis. Rev Med Virol 31: e2181. https://doi.org/10.1002/rmv.2181
    [95] Gracienta TJ, Herardi R, Santosa F, et al. (2021) Diagnostic accuracy of antibody-based rapid diagnostic tests in detecting coronavirus disease 2019: systematic review. Arch Med Sci 18: 949-957. https://doi.org/10.5114/aoms/135910
    [96] Fox T, Geppert J, Dinnes J, et al. (2022) Antibody tests for identification of current and past infection with SARS-CoV-2. Cochrane Database Syst Rev 11: CD013652. https://doi.org/10.1002/14651858.CD013652.pub2
    [97] Kontou PI, Braliou GG, Dimou NL, et al. (2020) Antibody tests in detecting SARS-CoV-2 infection: a meta-analysis. Diagnostics (Basel) 10: 319. https://doi.org/10.3390/diagnostics10050319
    [98] Mohit E, Rostami Z, Vahidi H, et al. (2021) A comparative review of immunoassays for COVID-19 detection. Expert Rev Clin Immunol 17: 573-599. https://doi.org/10.1080/1744666X.2021.1908886
    [99] Fong Y, Huang Y, Benkeser D, et al. (2023) Immune correlates analysis of the PREVENT-19 COVID-19 vaccine efficacy clinical trial. Nat Commun 14: 331. https://doi.org/10.1038/s41467-022-35768-3
    [100] Tran TT, Vaage EB, Mehta A, et al. (2023) Titers of antibodies against ancestral SARS-CoV-2 correlate with levels of neutralizing antibodies to multiple variants. NPJ Vaccines 7: 174. https://doi.org/10.1038/s41541-023-00600-6
    [101] World Health OrganizationEstablishment of the 2nd WHO International Standard for anti-SARS-CoV-2 immunoglobulin and Reference Panel for antibodies to SARS-CoV-2 variants of concern (2022). Available from: https://www.who.int/publications/m/item/who-bs-2022.2427
    [102] Mulder L, Carrères B, Muggli F, et al. (2022) A comparative study of nine SARS-CoV-2 IgG lateral flow assays using both post-infection and post-vaccination samples. J Clin Med 11: 2100. https://doi.org/10.3390/jcm11082100
    [103] Peghin M, Bontempo G, De Martino M, et al. (2022) Evaluation of qualitative and semi-quantitative cut offs for rapid diagnostic lateral flow test in relation to serology for the detection of SARS-CoV-2 antibodies: findings of a prospective study. BMC Infect Dis 22: 810. https://doi.org/10.1186/s12879-022-07786-5
    [104] Findlater L, Trickey A, Jones HE, et al. (2022) Association of results of four lateral flow antibody tests with subsequent SARS-CoV-2 infection. Microbiol Spectr 10: e0246822. https://doi.org/10.1128/spectrum.02468-22
    [105] Choi HW, Jeon CH, Won EJ, et al. (2022) Performance of severe acute respiratory syndrome coronavirus 2 serological diagnostic tests and antibody kinetics in coronavirus disease 2019 patients. Front Microbiol 13: 881038. https://doi.org/10.3389/fmicb.2022.881038
    [106] Pan X, Kaminga AC, Chen Y, et al. (2022) Auxiliary screening COVID-19 by serology. Front Public Health 10: 819841. https://doi.org/10.3389/fpubh.2022.819841
    [107] Zhu L, Xu X, Zhu B, et al. (2021) Kinetics of SARS-CoV-2 specific and neutralizing antibodies over seven months after symptom onset in COVID-19 patients. Microbiol Spectr 9: e0059021. https://doi.org/10.1128/Spectrum.00590-21
    [108] Chansaenroj J, Yorsaeng R, Puenpa J, et al. (2022) Long-term persistence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein-specific and neutralizing antibodies in recovered COVID-19 patients. PLoS One 17: e0267102. https://doi.org/10.1371/journal.pone.0267102
    [109] Chansaenroj J, Yorsaeng R, Posuwan N, et al. (2021) Detection of SARS-CoV-2-specific antibodies via rapid diagnostic immunoassays in COVID-19 patients. Virol J 18: 52. https://doi.org/10.1186/s12985-021-01530-2
    [110] Spicuzza L, Sambataro G, Bonsignore M, et al. (2022) Point of care antibody detection assays for past SARS-CoV-2 infection are accurate over the time. Infect Dis (Lond) 54: 464-466. https://doi.org/10.1080/23744235.2022.2036810
    [111] Robertson LJ, Moore JS, Blighe K, et al. (2021) Evaluation of the IgG antibody response to SARS CoV-2 infection and performance of a lateral flow immunoassay: cross-sectional and longitudinal analysis over 11 months. BMJ Open 11: e048142. https://doi.org/10.1136/bmjopen-2020-048142
    [112] Ong DSY, Fragkou PC, Schweitzer VA, et al. (2021) How to interpret and use COVID-19 serology and immunology tests. Clin Microbiol Infect 27: 981-986. https://doi.org/10.1016/j.cmi.2021.05.001
    [113] Tong H, Cao C, You M, et al. (2022) Artificial intelligence-assisted colorimetric lateral flow immunoassay for sensitive and quantitative detection of COVID-19 neutralizing antibody. Biosens Bioelectron 213: 114449. https://doi.org/10.1016/j.bios.2022.114449
    [114] Pallett SJC, Rayment M, Heskin J, et al. (2022) Early identification of high-risk individuals for monoclonal antibody therapy and prophylaxis is feasible by SARS-CoV-2 anti-spike antibody specific lateral flow assay. Diagn Microbiol Infect Dis 104: 115788. https://doi.org/10.1016/j.diagmicrobio.2022.115788
    [115] Brownstein NC, Chen YA (2021) Predictive values, uncertainty, and interpretation of serology tests for the novel coronavirus. Sci Rep 11: 5491. https://doi.org/10.1038/s41598-021-84173-1
    [116] Van den Hoogen LL, Smits G, van Hagen CCE, et al. (2022) Seropositivity to nucleoprotein to detect mild and asymptomatic SARS-CoV-2 infections: a complementary tool to detect breakthrough infections after COVID-19 vaccination?. Vaccine 40: 2251-2257. https://doi.org/10.1016/j.vaccine.2022.03.009
    [117] Liu L, Wang P, Nair MS, et al. (2020) Potent neutralizing antibodies against multiple epitopes on SARS-CoV-2 spike. Nature 584: 450-456. https://doi.org/10.1038/s41586-020-2571-7
    [118] Biby A, Wang X, Liu X, et al. (2022) Rapid testing for coronavirus disease 2019 (COVID-19). MRS Commun 12: 12-23. https://doi.org/10.1557/s43579-021-00146-5
    [119] Bradley T, Grundberg E, Selvarangan R, et al. (2021) Antibody responses boosted in seropositive healthcare workers after single dose of SARS-CoV-2 mRNA vaccine. MedRxiv . https://doi.org/10.1101/2021.02.03.21251078
    [120] Baldanti F, Ganguly NK, Wang G, et al. (2022) Choice of SARS-CoV-2 diagnostic test: challenges and key considerations for the future. Crit Rev Clin Lab Sci 59: 445-459. https://doi.org/10.1080/10408363.2022.2045250
    [121] Kitchin N Pfizer/BioNTech COVID-19 mRNA vaccine (2020). Available from: https://www.cdc.gov/vaccines/acip/meetings/downloads/slides-2020-08/Pfizer-COVID-19-vaccine-ACIP-presentation-508.pdf
    [122] Sami S, Tenforde MW, Talbot HK, et al. (2021) Adults hospitalized with coronavirus disease 2019 (COVID-19)-United States, March-June and October-December 2020: implications for the potential effects of COVID-19 Tier-1 vaccination on future hospitalizations and outcomes. Clin Infect Dis 73: S32-S37. https://doi.org/10.1093/cid/ciab319
    [123] Cann A, Clarke C, Brown J, et al. (2022) Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody lateral flow assay for antibody prevalence studies following vaccination: a diagnostic accuracy study. Wellcome Open Res 6: 358. https://doi.org/10.12688/wellcomeopenres.17231.2
    [124] Fulford TS, Van H, Gherardin NA, et al. (2021) A point-of-care lateral flow assay for neutralising antibodies against SARS-CoV-2. EBioMedicine 74: 103729. https://doi.org/10.1016/j.ebiom.2021.103729
    [125] Shurrab FM, Younes N, Al-Sadeq DW, et al. (2022) Performance evaluation of novel fluorescent-based lateral flow immunoassay (LFIA) for rapid detection and quantification of total anti-SARS-CoV-2 S-RBD binding antibodies in infected individuals. Int J Infect Dis 118: 132-137. https://doi.org/10.1016/j.ijid.2022.02.052
    [126] Moeller ME, Engsig FN, Bade M, et al. (2022) Rapid quantitative point-of-care diagnostic test for post COVID-19 vaccination antibody monitoring. Microbiol Spectr 10: e0039622. https://doi.org/10.1128/spectrum.00396-22
    [127] Greenland-Bews C, Byrne RL, Owen SI, et al. (2023) Evaluation of eight lateral flow tests for the detection of anti-SARS-CoV-2 antibodies in a vaccinated population. BMC Infect Dis 23: 110. https://doi.org/10.1186/s12879-023-08033-1
    [128] Lee W, Kurien P (2023) Evaluation of a point of care lateral flow assay for antibody detection following SARS CoV-2 mRNA vaccine series. J Immunol Methods 513: 113410. https://doi.org/10.1016/j.jim.2022.113410
    [129] Nickel O, Rockstroh A, Borte S, et al. (2022) Evaluation of simple lateral flow immunoassays for detection of SARS-CoV-2 neutralizing antibodies. Vaccines (Basel) 10: 347. https://doi.org/10.3390/vaccines10030347
    [130] Wang Q, Feng L, Zhang H, et al. (2022) Longitudinal waning of mRNA vaccine-induced neutralizing antibodies against SARS-CoV-2 detected by an LFIA rapid test. Antib Ther 5: 55-62. https://doi.org/10.1093/abt/tbac004
    [131] Sauré D, O'Ryan M, Torres JP, et al. (2023) COVID-19 lateral flow IgG seropositivity and serum neutralising antibody responses after primary and booster vaccinations in Chile: a cross-sectional study. Lancet Microbe 4: e149-e158. https://doi.org/10.1016/S2666-5247(22)00290-7
    [132] Akkız H (2022) The biological functions and clinical significance of SARS-CoV-2 variants of corcern. Front Med (Lausanne) 9: 849217. https://doi.org/10.3389/fmed.2022.849217
    [133] Khan K, Karim F, Ganga Y, et al. (2022) Omicron BA.4/BA.5 escape neutralizing immunity elicited by BA.1 infection. Nat Commun 13: 4686. https://doi.org/10.1038/s41467-022-32396-9
    [134] Cao Y, Wang J, Jian F, et al. (2022) Omicron escapes the majority of existing SARS-CoV-2 neutralizing antibodies. Nature 602: 657-663. https://doi.org/10.1038/s41467-022-32396-9
    [135] Groenheit R, Galanis I, Sondén K, et al. (2023) Rapid emergence of omicron sublineages expressing spike protein R346T. Lancet Reg Health Eur 24: 100564. https://doi.org/10.1016/j.lanepe.2022.100564
    [136] Ao D, He X, Hong W, et al. (2023) The rapid rise of SARS-CoV-2 Omicron subvariants with immune evasion properties: XBB.1.5 and BQ.1.1 subvariants. MedComm 4: e239. https://doi.org/10.1002/mco2.239
    [137] Thomas SJ, Moreira Jr ED, Kitchin N, et al. (2021) Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine through 6 months. N Engl J Med 385: 1761-1773. https://doi.org/10.1038/s41467-022-32396-9
    [138] Gao F, Zheng M, Fan J, et al. (2023) A trimeric spike-based COVID-19 vaccine candidate induces broad neutralization against SARS-CoV-2 variants. Hum Vaccin Immunother 19: 2186110. https://doi.org/10.1080/21645515.2023.2186110
    [139] Singh J, Samal J, Kumar V, et al. (2021) Structure-function analyses of new SARS-CoV-2 variants B.1.1.7, B.1.351 and B.1.1.28.1: clinical, diagnostic, therapeutic and public health implications. Viruses 13: 439. https://doi.org/10.3390/v13030439
    [140] Lu L, Chen LL, Zhang RR, et al. (2022) Boosting of serum neutralizing activity against the Omicron variant among recovered COVID-19 patients by BNT162b2 and CoronaVac vaccines. EBioMedicine 79: 103986. https://doi.org/10.1016/j.ebiom.2022.103986
    [141] Rössler A, Knabl L, Raschbichler LM, et al. (2023) Reduced sensitivity of antibody tests after omicron infection. Lancet Microbe 4: e10-e11. https://doi.org/10.1016/S2666-5247(22)00222-1
    [142] Springer DN, Perkmann T, Jani CM, et al. (2022) Reduced sensitivity of commercial spike-specific antibody assays after primary infection with the SARS-CoV-2 Omicron variant. Microbiol Spectr 10: e0212922. https://doi.org/10.1128/spectrum.02129-22
    [143] Heggestad JT, Britton RJ, Kinnamon DS, et al. (2023) COVID-19 diagnosis and SARS-CoV-2 strain identification by a rapid, multiplexed, point-of-care antibody microarray. Anal Chem 95: 5610-5617. https://doi.org/10.1021/acs.analchem.2c05180
    [144] Falzone L, Gattuso G, Tsatsakis A, et al. (2021) Current and innovative methods for the diagnosis of COVID-19 infection. Int J Mol Med 47. https://doi.org/10.3892/ijmm.2021.4933
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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