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The host immune responses to SARS-CoV-2 and therapeutic strategies in the treatment of COVID-19 cytokine storm

  • Received: 12 August 2021 Accepted: 01 November 2021 Published: 12 November 2021
  • The ravaging pandemic caused by SAR-CoV-2, a member of β-coronaviruses, marks the end of the year 2019. Despite being identified and classified at the earliest stage, the virus records worldwide soaring transmissibility, morbidity, and mortality. Global data have shown the infection with SARS-CoV-2 to be severe among at least 15% of the infected; the aged and those with premorbid conditions like cancer, cardiovascular, and respiratory diseases. The highest prevalence and mortality are seen in the Americas, with African countries least affected. Severe respiratory distress and multiorgan failure are the usual findings in severe cases. A hyperinflammatory, fulminant, hypercytokinemia that is often further complicated by hypercoagulopathy and multiorgan failure has been reported extensively among severely infected patients. Scientists describe hyper-activated immune response mediated by macrophages secreting copious amounts of interleukin (IL)-6 forming the epicenter of cytokine storm (CS), thereby perpetuating signaling cascade through JAK/Kinase pathway that yields a hypercytokinemia. Researchers globally are exploring JAK/kinase inhibitors, immunomodulatory (immunosuppressive) therapy, cytokines, and cytokine receptor blockers for CS management. In which interestingly some of these agents possess antiviral activity. Here, we reviewed published studies with their respective outcome. However, a lot needs to be done to address the CS of COVID-19 to avert its fatal outcome.

    Citation: Mansur Aliyu, Sayed-Hamidreza Mozhgani, Omid Kohandel Gargari, Mustapha Ahmed Yusuf, Ali Akbar Saboor-Yaraghi. The host immune responses to SARS-CoV-2 and therapeutic strategies in the treatment of COVID-19 cytokine storm[J]. AIMS Allergy and Immunology, 2021, 5(4): 240-258. doi: 10.3934/Allergy.2021018

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  • The ravaging pandemic caused by SAR-CoV-2, a member of β-coronaviruses, marks the end of the year 2019. Despite being identified and classified at the earliest stage, the virus records worldwide soaring transmissibility, morbidity, and mortality. Global data have shown the infection with SARS-CoV-2 to be severe among at least 15% of the infected; the aged and those with premorbid conditions like cancer, cardiovascular, and respiratory diseases. The highest prevalence and mortality are seen in the Americas, with African countries least affected. Severe respiratory distress and multiorgan failure are the usual findings in severe cases. A hyperinflammatory, fulminant, hypercytokinemia that is often further complicated by hypercoagulopathy and multiorgan failure has been reported extensively among severely infected patients. Scientists describe hyper-activated immune response mediated by macrophages secreting copious amounts of interleukin (IL)-6 forming the epicenter of cytokine storm (CS), thereby perpetuating signaling cascade through JAK/Kinase pathway that yields a hypercytokinemia. Researchers globally are exploring JAK/kinase inhibitors, immunomodulatory (immunosuppressive) therapy, cytokines, and cytokine receptor blockers for CS management. In which interestingly some of these agents possess antiviral activity. Here, we reviewed published studies with their respective outcome. However, a lot needs to be done to address the CS of COVID-19 to avert its fatal outcome.


    Abbreviations

    CS:

    cytokine storm; 

    COVID-19:

    noble coronavirus pneumonia disease 2019; 

    SARS-CoV-2:

    severe acute respiratory syndrome coronavirus 2; 

    ACE2:

    angiotensin-converting enzyme receptor 2; 

    TMSPRS2:

    transmembrane serine protease 2; 

    NAK:

    numb-associated kinases; 

    JAK-STAT:

    Janus kinase-signal transducer and activator of transcription; 

    NF-κβ:

    nuclear factor-kappa beta; 

    IFN-γ:

    interferon gamma; 

    TNF-α:

    tumor necrosis factor alpha;; 

    NLRP3:

    NLR (nod-like receptor) family pyrin domain containing 3

    The study of optical solitons has gained increasing relevance in modern communication technologies due to their potential applications in optical fiber communications and their significant role in driving technological advancement in this field. A range of mathematical frameworks, including the nonlinear Schrödinger equation (NLSE) [1,2,3], the complex Ginzburg-Landau equation [4,5,6], the Gerdjikov–Ivanov equation [7], and the Lakshmanan-Porsezian-Daniel model [8,9], have been employed to explore this fascinating subject. These models have been crucial in advancing our understanding of the intricate dynamics involved, emphasizing the significance of mathematical approaches in studying soliton propagation. This collective effort contributes to the exploration of the critical roles of optical solitons, both in providing essential support for the needs of the modern telecommunications industry and in enhancing the frameworks of social media communications, making these theoretical tools indispensable in the field.

    The Chen-Lee-Liu (CLL) equation, a significant nonlinear wave equation, was introduced in applied mathematics and theoretical physics to describe certain wave phenomena. It was first presented in the paper by Chen, Lee, and Liu [10]. This work laid the foundation for further exploration and analysis in the propagation of waves under specific conditions and has been influential in studies related to soliton solutions and integrable systems. The CLL equation exemplifies a significant milestone in nonlinear wave dynamics, with a particular focus on soliton solutions, representing a critical evolution or refinement of the NLSE to more accurately reflect certain physical conditions, notably in the fields of optics and hydrodynamics. Understanding the CLL equation necessitates a preliminary appreciation of the NLSE's capacity to model wave packets in nonlinear media, where dispersion and nonlinearity collectively foster soliton formation, stable, localized wave packets that retain their shape while traveling at a constant velocity, applicable across a broad spectrum from fiber optics to quantum condensates, and tsunami analysis. While the original CLL article is a key resource for foundational understanding and applications, subsequent studies have expanded this work, providing analytical solutions, numerical simulations, and experimental validations highlighting the equation's significance in elucidating complex wave phenomena in nonlinear mediums [11,12,13,14]. It's imperative to note that the specifics of the citation for the original CLL work are not provided here, but for those interested, academic databases and journals focusing on nonlinear science, applied mathematics, and optical physics are recommended resources. This consolidation underscores the CLL equation's pivotal role in enhancing our understanding of nonlinear wave dynamics, emphasizing the ongoing impact of Chen, Lee, and Liu's contributions to physics and applied mathematics.

    In recent years, researchers have been growing interest in studying the effects of adding multiplicative white noise to various nonlinear evolution equations [15,16,17,18,19]. Examining the influence of multiplicative white noise on solitons presents an intriguing area of exploration within the broader realm of nonlinear science and soliton theory. Multiplicative white noise, a stochastic process in which the noise intensity can vary with the system's state, adds a layer of complexity to the dynamics and stability of solitons. Recent works [20,21] have investigated the modulation instability and dynamical behavior of nonlinear evolution equations under the influence of noise, offering insights into the resilience of solitons in stochastic environments. Solitons are self-reinforcing solitary wave packets that maintain their shape while traveling at a constant velocity and are inherently resilient against perturbations. However, when exposed to multiplicative white noise, these waveforms experience random fluctuations in their propagation characteristics. This can result in amplitude, velocity, and shape variations, potentially affecting their stability and coherence. Understanding these effects is vital, as it can shed light on the resilience of solitons under realistic conditions where noise is an inevitable factor. This, in turn, expands the potential applications of solitons in fields such as fiber optic communications, quantum field theory, and fluid dynamics.

    This study explores the impact of multiplicative white noise on the perturbed generalized CLL equation, analyzed within the framework of Itô calculus. The incorporation of multiplicative white noise introduces stochastic perturbations that significantly alter the dynamics of the system. The modified structure of the perturbed generalized CLL equation, reflecting the influence of this stochastic noise, is expressed as follows:

    iqt+aqxx+bqxt+ic|q|2nqx+σ(qibqx)dW(t)dt+i{αqx+λ(|q|2nq)x+β(|q|2n)xq}=0. (1)

    The complex-valued function q=q(x,t) describes the wave profile, encapsulating the essential characteristics of the wave's behavior over space and time. The coefficients a, b, and c play critical roles, representing the effects of chromatic dispersion, spatiotemporal dispersion, and nonlinear dispersion, respectively. These coefficients govern the evolution of the wave and dictate how various dispersion phenomena influence its shape and dynamics. The parameter α is specifically associated with inter-modal dispersion, capturing the interactions between different modes within the medium. Meanwhile, λ is a key parameter that characterizes the self-steepening effect observed in short pulses, a phenomenon where the pulse shape becomes increasingly asymmetric as it propagates. Additionally, the coefficient β is tied to nonlinear dispersion, further contributing to the complex interplay between the wave's nonlinear and dispersive properties. The noise level within the system is quantified by the parameter σ, which introduces stochastic fluctuations into the model through the use of W(t), a standard Wiener process. The Wiener process, often referred to as Brownian motion, is a fundamental concept in stochastic processes. It is defined by its initial condition W(0)=0, and for any 0s<t, the increment W(t)W(s) follows a normal distribution with a mean of zero and a variance proportional to ts (W(t)W(s)N(0,ts)). This implies that the process has independent increments, meaning that for any sequence 0t1<t2<<tn, the increments W(t2)W(t1), W(t3)W(t2), ..., W(tn)W(tn1) are mutually independent. A notable feature of the Wiener process is its continuous trajectory, ensuring that the noise introduced into the system evolves smoothly over time. The magnitude of the noise can be controlled through the derivative of W(t) with respect to time, denoted by dWdt. This derivative represents the instantaneous rate of change of the Wiener process, allowing for precise modulation of the noise's impact on the wave profile. As highlighted in several studies [22,23,24], these stochastic components are essential for modeling real-world scenarios where noise and randomness play significant roles in the dynamics of wave propagation.

    The study unfolds with a systematic exploration of the methodologies and results, structured across five key sections to ensure clarity and logical progression. In the second section, the study introduces the improved extended tanh-function method, delving into its mathematical foundations and operational mechanics. This advanced analytical tool builds upon the conventional tanh-function method, incorporating enhancements that allow for the efficient handling of nonlinear differential equations, especially in the presence of complex terms such as stochastic perturbations. Detailed explanations are provided to showcase how this method facilitates the derivation of exact solutions with improved accuracy and generality. Illustrative examples and step-by-step derivations demonstrate its robustness and adaptability, emphasizing its suitability for analyzing the stochastic generalized CLL equation. The third section transitions to a critical step in the analysis: the introduction of a wave transformation scheme designed to incorporate the effects of white noise. This transformation is pivotal as it enables the conversion of the stochastic partial differential equation into a nonlinear ordinary differential equation (ODE). The section outlines the mathematical rationale behind the transformation, explaining how the inclusion of white noise, modeled in the Itô sense, impacts the governing dynamics. By simplifying the problem to an ODE form, the study creates a framework that makes the system more tractable for soliton analysis while retaining the essential influence of stochastic perturbations. The technical details of the transformation are meticulously discussed, highlighting its role in bridging the stochastic and deterministic aspects of the governing equation. The fourth section forms the core of the study, where the focus shifts to a comprehensive investigation of how white noise influences the soliton dynamics of the governing model. Through an extensive set of visual representations, including Figures 140, this section explores the intricate interplay between noise, nonlinearity, and soliton structures. The figures are categorized and analyzed in detail, depicting a variety of soliton solutions such as singular, bright, and dark solitons under different parameter settings. Each figure is accompanied by an explanation of the underlying physics and the role of white noise in shaping the observed dynamics. For instance, surface plots illustrate the three-dimensional evolution of solitons, contour plots reveal phase variations, and 2D slices highlight temporal and spatial profiles. This section underscores the critical insights gained from the visual analysis, showing how noise intensity and system parameters influence soliton stability, amplitude, and propagation characteristics. Finally, the fifth section serves as a concluding summary, synthesizing the findings of the study and reflecting on their broader implications. It recapitulates the key contributions, emphasizing the novel formulation of the stochastic generalized CLL equation and the diversity of soliton solutions uncovered through the improved extended tanh-function method. The implications of these findings are discussed in the context of nonlinear optics, with a particular focus on their potential applications in optical communication, signal processing, and interdisciplinary research. The section also outlines avenues for future exploration, suggesting that further studies could expand on the current work by examining higher-dimensional systems, alternative forms of stochastic perturbations, and experimental validation of the theoretical results.

    By systematically progressing through these sections, the study ensures a cohesive narrative that integrates methodological innovation, rigorous analysis, and insightful interpretations, laying a robust foundation for future advancements in the field.

    In our analysis, we initiate by exploring the methodology through an examination of a primary stochastic model characterized by the structure:

    G(u,ux,ut,uxt,uxx,...)+σudW(t)dt=0, (2)

    wherein u=u(x,t) expresses the wave solution, and t and x sequentially denote the temporal and spatial dimensions.

    Applying the change of variables

    u(x,t)=U(ξ)e(σW(t)σ2t),ξ=μ(xυt), (3)

    allows Eq (3) to be rewritten as the following ODE:

    P(U,μυU,μU,μ2U,...)=0, (4)

    with μ referring to the wave width, ξ representing the new wave variable, and υ specifying the wave velocity.

    Step 1: Equation (4) is proposed to be of the form:

    U(ξ)=η0+Ni=1{ηiϕ(ξ)i+μiϕ(ξ)i}, (5)

    with

    ϕ(ξ)2=4l=0τlϕ(ξ)l, (6)

    which results in a comprehensive set of solutions [25].

    Case 1: τ0=τ1=τ3=0.

    Bright and singular solitons are sequentially derived as follows:

    ϕ(ξ)=τ2τ4sech[τ2ξ],τ2>0,τ4<0, (7)

    and

    ϕ(ξ)=τ2τ4csch[τ2ξ],τ2>0,τ4>0. (8)

    Case 2: τ0=τ224τ4,τ1=τ3=0.

    Dark and singular solitons are achieved as follows:

    ϕ(ξ)=τ22τ4tanh[τ22ξ],τ2<0,τ4>0, (9)

    and

    ϕ(ξ)=τ22τ4coth[τ22ξ],τ2<0,τ4>0. (10)

    Case 3: τ2=τ4=0,τ00,τ10,τ3>0.

    Thus, Eq (6) holds the Weierstrass elliptic function (WEF):

    ϕ(ξ)=(τ32ξ,g2,g3), (11)

    where g2=4τ1τ3 and g3=4τ0τ3 are referred to as invariants of the WEF.

    Case 4: τ0=τ214τ2,τ3=τ4=0.

    An exponential-type solution is obtained:

    ϕ(ξ)=τ12τ2+exp[ετ2ξ],τ2>0. (12)

    Case 5: τ0=τ3=τ4=0.

    A solitary wave solution is obtained:

    ϕ(ξ)=±τ0τ2sinh[τ2ξ],τ0>0,τ2>0. (13)

    Case 6: τ0=τ1=0,τ2,τ4>0,τ3±2τ2τ4.

    Bright-dark and singular-singular solitons are retrieved as follows:

    ϕ(ξ)=τ2sech2[12τ2ξ]±2τ2τ4tanh[12τ2ξ]+τ3, (14)

    and

    ϕ(ξ)=τ2csch2[12τ2ξ]±2τ2τ4coth[12τ2ξ]+τ3. (15)

    Step 2: The determination of the positive integer N in Eq (5) is carried out by balancing the highest-order derivatives with the nonlinear terms in Eq (4).

    Step 3: When Eq (5) along with (6) are substituted into Eq (4), the resulting system takes the form of a polynomial in ϕ. By grouping terms by their respective powers of ϕ and equating each group to zero, an over-determined algebraic system is obtained. Solving this system determines the unknown parameters, leading to the solutions of Eq (2).

    We employ a particular solution format, as outlined below, to solve Eq (1):

    q(x,t)=U(ξ)ei(κx+ωt+σW(t)σ2t+θ0), (16)

    where the definition of the wave parameter ξ is

    ξ=k(xvt). (17)

    The function U(ξ) plays a crucial role in describing the soliton dynamics, as it represents the real-valued amplitude of the soliton solution, which is a measure of the soliton's height or strength as it propagates through the medium. The parameter v is the soliton's propagation velocity, indicating the rate at which the soliton travels along the spatial coordinate. This velocity plays a significant role in understanding the stability and dynamics of the soliton as it interacts with the medium. The parameter κ is identified as the soliton's frequency, which determines the oscillatory nature of the soliton's profile and is closely related to its temporal evolution. Similarly, k defines the wave's width, which provides insight into the soliton's spatial spread or localization. A smaller k corresponds to a more localized soliton, while a larger k leads to a broader soliton profile. The parameter ω, referred to as the wave number, governs the spatial periodicity of the soliton, linking it to the underlying wave structure of the solution. Another key parameter, σ, represents the noise intensity, which captures the stochastic effects that influence the soliton's behavior in the presence of multiplicative white noise. This parameter is particularly important in exploring the stability and robustness of soliton solutions under random perturbations. The phase constant θ0 determines the initial phase of the soliton. By substituting the proposed solution from Eq (16) into the governing model, Eq (1), the relationship between these parameters is established, and a rigorous mathematical framework is developed. This substitution reduces the governing equation to a simpler form, allowing us to isolate the contributions of the real-valued amplitude U(ξ) and other parameters. The process leads to the derivation of a critical expression that captures the essence of the soliton solution. The result highlights the real part of the solution, which is given as:

    (abν)U+U(aκ2+ακ(bκ1)(σ2ω))+κ(c+λ)U2n+1=0, (18)

    and the imaginary part is:

    U(2aκ+α+bκνbσ2+bω+(c+λ+2n(β+λ))U2nν)=0. (19)

    This expression provides valuable insights into the soliton's characteristics, shedding light on how the amplitude, frequency, wave number, width, and noise intensity collectively shape the soliton's dynamics.

    From the imaginary part (19), we can derive the soliton speed

    ν=2aκα+bσ2bωbκ1, (20)

    with the constraint

    c+λ+2n(β+λ)=0. (21)

    In our discussion, we will examine the formula given by (18) under the specific constraints outlined in (20). To obtain a positive integer balance number N, we will conduct this analysis through the application of a transformation:

    U(ξ)=V(ξ)1n, (22)

    which transforms Eq (18) to

    n2V2(aκ2+ακ(bκ1)(σ2ω))+nV(abν)V(n1)(abν)V2+κn2(c+λ)V4=0. (23)

    We present a comprehensive description of the proposed technique, outlining an auxiliary equation that includes various solutions of diverse nature. The present study investigates the ability of these particular types to recover singular, dark, and bright solitons successfully.

    According to Step 2 in Section 2, we need to determine the positive integer N by balancing the highest derivative with the nonlinear terms in Eq (23). The highest derivative in this context is V, while the nonlinear terms include V2, V2, and V4. By balancing VV (which contributes N+N+2) with V4 (which contributes 4N) in Eq (23), we obtain N=1. Similarly, by balancing V2 (which contributes 2(N+1)) with V4 (which contributes 4N) in Eq (23), we again find that N=1. Since N must be the positive integer satisfying these conditions, we conclude that N=1. Consequently, the solution can be expressed as follows:

    V(ξ)=η0+η1ϕ(ξ)+μ1ϕ(ξ). (24)

    Inserting both Eqs (24) and (6) into Eq (23) leads to algebraic equations. The solutions to this system are given by:

    Case 1: For τ0=τ1=τ3=0:

    η0=0,μ1=0,τ2=n2(aκ2ακ+bκσ2bκωσ2+ω)abν,τ4=η21κn2(c+λ)(n+1)(abν). (25)

    The analysis yields the solutions to Eq (1):

    q(x,t)={(n+1)(aκ2ακ+bκσ2bκωσ2+ω)κ(c+λ)×sech[n2(aκ2ακ+bκσ2bκωσ2+ω)abν(xνt)]}1n×ei(κx+ωtσ2t+σw(t)+θ0), (26)

    and

    q(x,t)={(n+1)(aκ2ακ+bκσ2bκωσ2+ω)κ(c+λ)×csch[n2(aκ2ακ+bκσ2bκωσ2+ω)abν(xνt)]}1n×ei(κx+ωtσ2t+σw(t)+θ0). (27)

    Case 2: For τ0=τ224τ4,τ1=τ3=0:

    Result 1:

    η0=0,η1=(n+1)(aκ2+ακbκσ2+bκω+σ2ω)4κμ1(c+λ),τ2=n2(aκ2ακ+bκσ2bκωσ2+ω)2(abν),τ4=n2(n+1)(aκ2+ακbκσ2+bκω+σ2ω)216κμ21(c+λ)(abν). (28)

    The derived solutions for Eq (1) are presented below:

    q(x,t)={±(n+1)(aκ2+ακbκσ2+bκω+σ2ω)κ(c+λ)×csch[n2(aκ2ακ+bκσ2bκωσ2+ω)abν(xνt)]}1n×ei(κx+ωtσ2t+σw(t)+θ0). (29)

    Result 2:

    η0=±aκ2ακ+(bκ1)(σ2ω)2κ(c+λ),η1=0,τ2=2(aκ2ακ+(bκ1)(σ2ω))abν,τ4=(aκ2ακ+(bκ1)(σ2ω))24κμ21(c+λ)(abν),n=2. (30)

    The resulting solutions to Eq (1) are as follows:

    q(x,t)={aκ2ακ+(bκ1)(σ2ω)2κ(c+λ)(coth[aκ2ακ+(bκ1)(σ2ω)abν(xνt)]±1)}12×ei(κx+ωtσ2t+σw(t)+θ0), (31)

    and

    q(x,t)={aκ2ακ+(bκ1)(σ2ω)2κ(c+λ)(tanh[aκ2ακ+(bκ1)(σ2ω)abν(xνt)]±1)}12×ei(κx+ωtσ2t+σw(t)+θ0). (32)

    Result 3:

    η0=0,η1=0,τ2=aκ2ακ+(bκ1)(σ2ω)abν,τ4=(aκ2ακ+(bκ1)(σ2ω))22κμ21(c+λ)(abν),n=1. (33)

    Accordingly, the solutions to Eq (1) can be expressed as follows:

    q(x,t)={aκ2ακ+(bκ1)(σ2ω)κ(c+λ)coth[aκ2ακ+(bκ1)(σ2ω)2(abν)(xνt)]}×ei(κx+ωtσ2t+σw(t)+θ0), (34)

    and

    q(x,t)={aκ2ακ+(bκ1)(σ2ω)κ(c+λ)tanh[aκ2ακ+(bκ1)(σ2ω)2(abν)(xνt)]}×ei(κx+ωtσ2t+σw(t)+θ0). (35)

    Case 3: For τ1=τ3=τ4=0:

    η0=0,η1=0,τ0=κμ21n2(c+λ)(n+1)(abν),τ2=n2(aκ2ακ+bκσ2bκωσ2+ω)abν. (36)

    Accordingly, the solutions for Eq (1) are found:

    q(x,t)={±(n+1)(aκ2ακ+bκσ2bκωσ2+ω)κ(c+λ)×csch[n2(aκ2ακ+bκσ2bκωσ2+ω)abν(xνt)]}1n×ei(κx+ωtσ2t+σw(t)+θ0). (37)

    Case 4: For τ0=τ1=0:

    Result 1:

    η0=0,μ1=0,τ2=n2(aκ2ακ+(bκ1)(σ2ω))abν,τ3=0,τ4=η21κn2(c+λ)(n+1)(abν). (38)

    As a result, Eq (1) yields the following solutions:

    q(x,t)={±(n+1)(aκ2ακ+(bκ1)(σ2ω))κ(c+λ)×csch[n2(aκ2ακ+(bκ1)(σ2ω))abν(xνt)]}1n×ei(κx+ωtσ2t+σw(t)+θ0). (39)

    Result 2:

    η0=±aκ2ακ+(bκ1)(σ2ω)κ(c+λ),μ1=0,τ2=4(aκ2ακ+(bκ1)(σ2ω))abν,τ3=16η1κ(c+λ)aκ2ακ+(bκ1)(σ2ω)3(abν),τ4=4η21κ(c+λ)3(abν),n=2. (40)

    Following this, Eq (1) provides the following solutions:

    q(x,t)={(±aκ2ακ+(bκ1)(σ2ω)κ(c+λ)(sech2[aκ2ακ+(bκ1)(σ2ω)abν(xνt)]23tanh[aκ2ακ+(bκ1)(σ2ω)abν(xνt)]43+1)}12×ei(κx+ωtσ2t+σw(t)+θ0), (41)

    and

    q(x,t)={(±aκ2ακ+(bκ1)(σ2ω)κ(c+λ)(csch2[aκ2ακ+(bκ1)(σ2ω)abν(xνt)]23coth[aκ2ακ+(bκ1)(σ2ω)abν(xνt)]43+1)}12×ei(κx+ωtσ2t+σw(t)+θ0). (42)

    Result 3:

    η0=aκ2ακ+(bκ1)(σ2ω)κ(c+λ),μ1=0,τ2=2(aκ2ακ+(bκ1)(σ2ω))abν,τ3=±2η1κ(c+λ)aκ2ακ+(bκ1)(σ2ω)abν,τ4=η21κ(c+λ)2(abν),n=1. (43)

    This leads to the derivation of the solutions for Eq (1):

    q(x,t)=±aκ2ακ+(bκ1)(σ2ω)κ(c+λ)(sech2[aκ2ακ+(bκ1)(σ2ω)2(abν)(xνt)]tanh[aκ2ακ+(bκ1)(σ2ω)2(abν)(xνt)]+11)×ei(κx+ωtσ2t+σw(t)+θ0), (44)

    and

    q(x,t)=±aκ2ακ+(bκ1)(σ2ω)κ(c+λ)(csch2[aκ2ακ+(bκ1)(σ2ω)2(abν)(xνt)]coth[aκ2ακ+(bκ1)(σ2ω)2(abν)(xνt)]+1+1)×ei(κx+ωtσ2t+σw(t)+θ0). (45)

    Result 4:

    η0=aκ2ακ+(bκ1)(σ2ω)κ(c+λ),μ1=0,τ2=4(aκ2ακ+(bκ1)(σ2ω))abν,τ3=±8η1κ(c+λ)aκ2ακ+(bκ1)(σ2ω)abν,τ4=4η21κ(c+λ)abν,n=2. (46)

    The analysis yields the solutions to Eq (1):

    q(x,t)={aκ2ακ+(bκ1)(σ2ω)κ(c+λ)(sech2[aκ2ακ+(bκ1)(σ2ω)abν(xνt)]2(tanh[aκ2ακ+(bκ1)(σ2ω)abν(xνt)]+1)+1)}12×ei(κx+ωtσ2t+σw(t)+θ0), (47)

    and

    q(x,t)={aκ2ακ+(bκ1)(σ2ω)κ(c+λ)(csch2[aκ2ακ+(bκ1)(σ2ω)abν(xνt)]2(coth[aκ2ακ+(bκ1)(σ2ω)abν(xνt)]+1)+1)}12×ei(κx+ωtσ2t+σw(t)+θ0). (48)

    This section discusses the results obtained from the analysis of various types of optical solitons, including bright, dark, singular, straddled singular-singular, and straddled bright-dark solitons, as depicted in Figures 140. Each figure set corresponds to a specific soliton type and explores the influence of key parameters such as time evolution, multiplicative white noise, and power nonlinearity. The figures also investigate the soliton dynamics through surface plots, contour plots, and 2D plots, providing a comprehensive view of their behavior under varying conditions. In Figures 140, we set the parameters: b=1.2, κ=1, a=1, α=1.8, ω=0.8, θ0=1, c=1.3, and λ=2.

    Figure 1 addresses the bright soliton q(x,t) described by the complex-valued solution (26) without the effect of multiplicative white noise (σ=0). This figure presents nine subfigures focusing on the modulus, real part, and imaginary part of the soliton. The surface plot, contour plot, and 2D plot reveal that the bright soliton maintains a stable, localized shape as time evolves, showing a peak amplitude that is consistent across different time slices.

    Figures 26 introduce the effect of increasing multiplicative white noise (σ=2 to σ=6). These figures highlight the gradual broadening and deformation of the soliton's profile as noise intensity increases. Surface plots indicate that higher noise levels disrupt the soliton's localized nature, leading to a more dispersed structure. This behavior is also evident in the contour and 2D plots, where the soliton's peak diminishes, and the surrounding wave structure becomes more irregular.

    Figures 37 and 38 explore the influence of the power nonlinearity parameter n on the bright soliton both with and without noise. Figure 37 shows that as n increases (from n=3 to n=5), the soliton's peak becomes sharper, and its width decreases, indicating stronger confinement. Figure 38 demonstrates that under the influence of noise (σ=1 to σ=6), the soliton's structure deteriorates more rapidly with higher n, suggesting a heightened sensitivity to noise when the nonlinearity is stronger. Bright solitons exhibit robust stability in the absence of noise, characterized by a well-defined peak and a localized waveform. The introduction of multiplicative white noise disrupts this stability, leading to the broadening and eventual dispersion of the soliton. Additionally, stronger power nonlinearity enhances the soliton's confinement but also increases its vulnerability to noise-induced perturbations.

    Figure 7 examines the singular soliton q(x,t) described by the complex-valued solution (27) without noise (σ=0). The modulus, real part, and imaginary part reveal that singular solitons have a distinctive sharp peak and a steep gradient, with the wave structure rapidly approaching zero outside the soliton core. The surface and contour plots confirm the highly localized and singular nature of these solitons.

    Figures 812 assess the impact of increasing noise (σ=2 to σ=6) on the singular soliton. The results show that singular solitons are particularly sensitive to noise, with even moderate noise levels (σ=2) leading to significant deformation. Higher noise intensities cause the soliton's peak to flatten and the gradient to become less steep, indicating a loss of the soliton's singular characteristics.

    Figures 39 and 40 investigate the role of power nonlinearity n on the singular soliton, both with and without noise. Figure 39 shows that increasing n sharpens the soliton's peak and intensifies its singularity. Figure 40 illustrates that under noise, the soliton's peak becomes increasingly disrupted as n rises, with the singular structure breaking down more rapidly, similar to the observations made for bright solitons. Singular solitons are highly localized and characterized by sharp peaks and steep gradients. They are, however, more susceptible to noise compared to bright solitons, with even small amounts of noise leading to significant structural degradation. Increasing the power nonlinearity sharpens the singularity but also heightens the soliton's sensitivity to noise.

    Figure 13 presents the dark soliton q(x,t) described by the complex-valued solution (32) without noise (σ=0). Unlike bright and singular solitons, dark solitons are characterized by a localized dip in the wave intensity rather than a peak. The surface and contour plots show that the soliton maintains a consistent trough depth and width over time, suggesting stable propagation.

    Figures 1418 explore the effects of increasing noise (σ=2 to σ=6) on dark solitons. As noise intensity increases, the soliton's trough becomes less defined, and the surrounding wave structure becomes more turbulent. The 2D plots show that the soliton's depth decreases, and the trough becomes wider, indicating a partial loss of the soliton's dark characteristics. Dark solitons are stable in the absence of noise, maintaining a well-defined trough that propagates without significant change. However, the introduction of noise disrupts this stability, leading to a shallower and broader trough. This suggests that dark solitons are less robust against noise compared to bright solitons but more stable than singular solitons.

    Figure 19 illustrates the behavior of the singular soliton q(x,t) as described by the complex-valued solution (31) in the absence of noise (σ=0). The figure presents the modulus, real part, and imaginary part of the singular soliton. These plots demonstrate that singular solitons exhibit a highly distinctive sharp peak and a steep gradient. The wave structure of the soliton rapidly decays to near zero outside its core, underscoring its highly localized and singular nature. The surface and contour plots further reinforce this observation, showing the soliton's localized nature and the pronounced singularity at its peak.

    Figures 2024 explore the effects of increasing noise levels, ranging from σ=2 to σ=6, on the singular soliton. The analysis reveals that singular solitons are particularly sensitive to the introduction of noise. Even at moderate noise levels (σ=2), the soliton undergoes substantial deformation. As the noise intensity increases, the peak of the soliton becomes progressively flattened, and the gradient of the soliton becomes less steep. This flattening effect indicates a significant degradation of the soliton's singular characteristics. Higher noise levels exacerbate this effect, leading to a notable loss of the soliton's distinctive sharp peak and steep gradient. Singular solitons are characterized by their highly localized nature, sharp peaks, and steep gradients. However, they exhibit a pronounced sensitivity to noise compared to bright solitons. Even small amounts of noise can significantly compromise the structural integrity of singular solitons. Furthermore, increasing the power nonlinearity sharpens the singularity of the soliton but also amplifies its sensitivity to noise, resulting in greater structural degradation with increasing noise intensity.

    Figures 2530 address the straddled bright-dark soliton q(x,t) described by the complex-valued solution (41). These solitons exhibit a combination of bright and dark soliton characteristics, with a localized peak (bright) adjacent to a localized trough (dark). The surface and contour plots reveal a complex structure where the bright and dark components maintain their respective identities but are coupled together. As noise increases (σ=2 to σ=6), the interaction between the bright and dark components becomes more pronounced. The bright peak broadens and loses height, while the dark trough becomes shallower and wider. The overall structure becomes less distinct, indicating a breakdown in the coupling between the bright and dark components. Straddled bright-dark solitons represent a hybrid soliton form that combines the properties of both bright and dark solitons. They maintain their structure under moderate noise but become increasingly unstable as noise intensity rises. The interaction between the bright and dark components becomes less coherent under high noise, leading to a loss of the soliton's hybrid nature.

    Figures 3136 explore the straddled singular-singular soliton q(x,t) described by the complex-valued solution (42). These solitons feature two closely spaced singularities, each characterized by a sharp peak and steep gradient. The surface plots show that the soliton retains its dual singular structure over time, with both peaks maintaining their sharpness. With increasing noise (σ=2 to σ=6), the dual singular structure becomes increasingly disrupted. The peaks begin to flatten, and the steep gradients become less pronounced. The contour and 2D plots indicate that the soliton's singular nature is compromised, with the two singularities becoming less distinct and eventually merging into a broader, less defined structure. Straddled singular-singular solitons are characterized by the presence of two distinct singularities in close proximity. These solitons are stable in the absence of noise but are highly sensitive to noise, with the dual singular structure breaking down rapidly under increasing noise intensity. The soliton's ability to maintain distinct singularities diminishes as noise increases, leading to a loss of its characteristic structure. The analysis of various types of optical solitons under different conditions reveals the following key insights: Bright solitons are stable and robust under noise-free conditions but become increasingly unstable as noise intensity rises. The power nonlinearity parameter n plays a crucial role in determining the soliton's confinement and sensitivity to noise. Singular solitons exhibit strong localization and steep gradients, making them highly sensitive to noise. Even small amounts of noise can significantly disrupt their structure. Dark solitons maintain stability in the absence of noise but are less robust against noise compared to bright solitons.

    The introduction of noise leads to a shallower and broader trough. Straddled bright-dark solitons combine the properties of bright and dark solitons. They are moderately stable under noise but lose their distinct structure as noise intensity increases. Straddled singular-singular solitons characterized by dual singularities are highly sensitive to noise. The distinct singular structure breaks down rapidly under noise, leading to a loss of the soliton's characteristic features. Overall, the study highlights the critical role of noise and nonlinearity in determining the stability and behavior of optical solitons. While bright and dark solitons exhibit some resilience to noise, singular and hybrid solitons are more vulnerable, with their structure deteriorating rapidly under increasing noise intensity. The findings emphasize the need for careful consideration of environmental factors, such as noise and nonlinearity, in the practical application and manipulation of optical solitons.

    Numerous experimental studies have confirmed the accuracy and physical relevance of soliton solutions in various nonlinear systems, particularly in nonlinear optics and fluid dynamics. Early pioneering works, such as Mollenauer et al. [26] and Hasegawa and Tappert [27], provided clear experimental evidence for soliton propagation in optical fibers, validating theoretical predictions in nonlinear optics. These studies demonstrated the fundamental properties of soliton behavior, such as pulse narrowing and stabilization in dispersive media. Similarly, Grimshaw [28] analyzed solitary waves in shallow water, highlighting their relevance in hydrodynamics and offering experimental insights that corroborate analytical soliton solutions in nonlinear evolution equations.

    More recently, Herr et al. [29] and Kippenberg et al. [30] reported the generation and stabilization of temporal solitons in optical microresonators, representing a significant advancement in nonlinear optics. These studies not only confirmed the existence of solitons in new physical systems but also underscored their potential for practical applications, such as optical frequency comb generation. In parallel, Chabchoub et al. [31] observed soliton-like rogue waves in a water tank experiment, demonstrating the physical manifestation of nonlinear wave dynamics in fluid systems.

    Although the current study has not directly verified the solutions of the Chen–Lee–Liu equation through experiments, the consistency between soliton solutions and experimental results in similar nonlinear systems provides strong evidence of their physical relevance. Therefore, the soliton solutions presented here can serve as reliable predictions for future experimental validation, further contributing to the understanding of nonlinear wave phenomena.

    This article has successfully introduced a groundbreaking formulation of the CLL equation, which represents a significant leap forward in the field of nonlinear optics. By integrating multiplicative white noise within the Itô calculus framework, this study has expanded the theoretical landscape of the CLL equation, enabling researchers to analyze and predict complex optical phenomena under stochastic influences. This advancement not only broadens the mathematical understanding of the equation itself but also enhances its applicability in modeling real-world scenarios where noise and randomness play critical roles, such as in fiber optics and photonics.

    The adoption of the improved extended modified tanh-function algorithm has proven instrumental in exploring the intricate soliton solutions of this stochastic system. This sophisticated method has enabled a comprehensive investigation, leading to the identification of a diverse spectrum of soliton types, including singular solitons, dark solitons, and bright solitons. These findings provide deep insights into the behavior and dynamics of solitons under the influence of stochastic perturbations. The detailed characterization of these solitons enhances our understanding of their stability, interaction properties, and potential for practical applications, such as in signal transmission and energy propagation in nonlinear media.

    The implications of these discoveries are far-reaching. By elucidating the intricate dynamics of solitons in the presence of stochasticity, this study paves the way for future research into their underlying mechanisms and properties. It highlights the importance of extending this work to more complex systems and different types of noise to uncover additional soliton behaviors and broaden the scope of their applications. Moreover, the results underscore the potential for leveraging these findings in designing advanced optical systems, such as noise-resilient communication networks and novel photonic devices.

    As we look ahead, the foundational work established by this study opens up new avenues for interdisciplinary research, offering exciting opportunities to explore the role of solitons in a variety of scientific domains. The potential applications extend beyond nonlinear optics, with promising implications for fields such as quantum computing, biological systems, and even the study of complex networks. By addressing the interplay between nonlinearity, stochasticity, and soliton dynamics, this research represents a significant step forward in our ability to manipulate and understand complex physical systems. It serves as a catalyst for innovation, inspiring future investigations that could revolutionize our approach to solving challenges in science and engineering.

    Ahmed M. Elsherbeny: Writing – original draft; Taher A. Nofal: Writing – original draft; Yakup Yıldırım: Writing – review & editing; Ahmed H. Arnous: Writing – review & editing. All authors have read and agreed to the published version of the manuscript.

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

    The authors extend their appreciation to Taif University, Saudi Arabia, for supporting this work through project number (TU-DSPP-2024-46).

    This research was funded by Taif University, Saudi Arabia, Project No. (TU-DSPP-2024-46).

    All authors declare no conflicts of interest in this paper.

    Figure 1.  Profile of a bright soliton given σ=0.
    Figure 2.  Profile of a bright soliton given σ=2.
    Figure 3.  Profile of a bright soliton given σ=3.
    Figure 4.  Profile of a bright soliton given σ=4.
    Figure 5.  Profile of a bright soliton given σ=5.
    Figure 6.  Profile of a bright soliton given σ=6.
    Figure 7.  Profile of a singular soliton given σ=0.
    Figure 8.  Profile of a singular soliton given σ=2.
    Figure 9.  Profile of a singular soliton given σ=3.
    Figure 10.  Profile of a singular soliton given σ=4.
    Figure 11.  Profile of a singular soliton given σ=5.
    Figure 12.  Profile of a singular soliton given σ=6.
    Figure 13.  Profile of a dark soliton given σ=0.
    Figure 14.  Profile of a dark soliton given σ=2.
    Figure 15.  Profile of a dark soliton given σ=3.
    Figure 16.  Profile of a dark soliton given σ=4.
    Figure 17.  Profile of a dark soliton given σ=5.
    Figure 18.  Profile of a dark soliton given σ=6.
    Figure 19.  Profile of a singular soliton given σ=0.
    Figure 20.  Profile of a singular soliton given σ=2.
    Figure 21.  Profile of a singular soliton given σ=3.
    Figure 22.  Profile of a singular soliton given σ=4.
    Figure 23.  Profile of a singular soliton given σ=5.
    Figure 24.  Profile of a singular soliton given σ=6.
    Figure 25.  Profile of a bright-dark soliton given σ=0.
    Figure 26.  Profile of a bright-dark soliton given σ=2.
    Figure 27.  Profile of a bright-dark soliton given σ=3.
    Figure 28.  Profile of a bright-dark soliton given σ=4.
    Figure 29.  Profile of a bright-dark soliton given σ=5.
    Figure 30.  Profile of a bright-dark soliton given σ=6.
    Figure 31.  Profile of a singular-singular soliton given σ=0.
    Figure 32.  Profile of a singular-singular soliton given σ=2.
    Figure 33.  Profile of a singular-singular soliton given σ=3.
    Figure 34.  Profile of a singular-singular soliton given σ=4.
    Figure 35.  Profile of a singular-singular soliton given σ=5.
    Figure 36.  Profile of a singular-singular soliton given σ=6.
    Figure 37.  Profile of a bright soliton given σ=0.
    Figure 38.  Profile of a bright soliton given σ=6.
    Figure 39.  Profile of a singular soliton given σ=0.
    Figure 40.  Profile of a singular soliton given σ=6.


    Conflict of interest



    All authors declare no conflicts of interest in this paper.

    Author contributions



    Conceptualization MA; literature search and review MA and AAS; drawing of Figures 1 and 2 MA, OKG and SHM; manuscript writing MA, MAY, AAS and SHM; supervision AAS; manuscript review and final version approval for submission MA, AAS, SHM, MAY and OKG.

    [1] Wu F, Zhao S, Yu B, et al. (2020) A new coronavirus associated with human respiratory disease in China. Nature 579: 265-269. doi: 10.1038/s41586-020-2008-3
    [2]  WHO Rolling Updates on Coronavirus Disease (COVID-19). WHO, 2020 Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/events-as-they-happen.
    [3] Li Q, Guan X, Wu P, et al. (2020) Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N Engl J Med 382: 1199-1207. doi: 10.1056/NEJMoa2001316
    [4] Wang D, Hu B, Hu C, et al. (2020) Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA 323: 1061-1069. doi: 10.1001/jama.2020.1585
    [5] Caly L, Druce J, Roberts J, et al. (2020) Isolation and rapid sharing of the 2019 novel coronavirus (SARS-CoV-2) from the first patient diagnosed with COVID-19 in Australia. Med J Australia 212: 459-462. doi: 10.5694/mja2.50569
    [6] Chan JFW, Kok KH, Zhu Z, et al. (2020) Genomic characterization of the 2019 novel human-pathogenic coronavirus isolated from a patient with atypical pneumonia after visiting Wuhan. Emerg Microbes Infect 9: 221-236. doi: 10.1080/22221751.2020.1719902
    [7] Ye Q, Wang B, Mao J (2020) The pathogenesis and treatment of the “Cytokine Storm” in COVID-19. J Infection 80: 607-613. doi: 10.1016/j.jinf.2020.03.037
    [8] 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. doi: 10.1172/JCI138759
    [9] Lyons-Weiler J (2020) Pathogenic priming likely contributes to serious and critical illness and mortality in COVID-19 via autoimmunity. J Transl Autoimmun 3: 100051-100051. doi: 10.1016/j.jtauto.2020.100051
    [10] Stasi C, Fallani S, Voller F, et al. (2020) Treatment for COVID-19: An overview. Eur J Pharmacol 889: 173644. doi: 10.1016/j.ejphar.2020.173644
    [11] Wrapp D, Wang N, Corbett KS, et al. (2020) Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation. Science 367: 1260-1263. doi: 10.1126/science.abb2507
    [12] Hoffmann M, Kleine-Weber H, Schroeder S, et al. (2020) SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor. Cell 181: 271-280. doi: 10.1016/j.cell.2020.02.052
    [13] Charoenlap S, Piromsopa K, Charoenlap C (2020) Potential role of Bacillus Calmette-Guérin (BCG) vaccination in COVID-19 pandemic mortality: Epidemiological and Immunological aspects. Asian Pac J Allergy 38: 150-161.
    [14] García LF (2020) Immune response, inflammation, and the clinical spectrum of COVID-19. Front Immunol 11: 1441. doi: 10.3389/fimmu.2020.01441
    [15] Mortaz E, Tabarsi P, Varahram M, et al. (2020) The immune response and immunopathology of COVID-19. Front Immunol 11: 2037. doi: 10.3389/fimmu.2020.02037
    [16] Hadjadj J, Yatim N, Barnabei L, et al. (2020) Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients. Science 369: 718-724. doi: 10.1126/science.abc6027
    [17] Bost P, Giladi A, Liu Y, et al. (2020) Host-viral infection maps reveal signatures of severe COVID-19 patients. Cell 181: 1475-1488. doi: 10.1016/j.cell.2020.05.006
    [18] Silvin A, Chapuis N, Dunsmore G, et al. (2020) Elevated calprotectin and abnormal myeloid cell subsets discriminate severe from mild COVID-19. Cell 182: 1401-1418. doi: 10.1016/j.cell.2020.08.002
    [19] Ni L, Ye F, Cheng ML, et al. (2020) Detection of SARS-CoV-2-specific humoral and cellular immunity in COVID-19 convalescent individuals. Immunity 52: 971-977. doi: 10.1016/j.immuni.2020.04.023
    [20] Sekine T, Perez-Potti A, Rivera-Ballesteros O, et al. (2020) Robust T cell immunity in convalescent individuals with asymptomatic or mild COVID-19. Cell 183: 158-168. doi: 10.1016/j.cell.2020.08.017
    [21] Kowitdamrong E, Puthanakit T, Jantarabenjakul W, et al. (2020) Antibody responses to SARS-CoV-2 in patients with differing severities of coronavirus disease 2019. PloS One 15: e0240502. doi: 10.1371/journal.pone.0240502
    [22] Lou B, Li TD, Zheng SF, et al. (2020) Serology characteristics of SARS-CoV-2 infection since exposure and post symptom onset. Eur Respir J 56: 2000763. doi: 10.1183/13993003.00763-2020
    [23] 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. doi: 10.1093/cid/ciaa344
    [24] Wang F, Nie J, Wang H, et al. (2020) Characteristics of peripheral lymphocyte subset alteration in COVID-19 pneumonia. J Infect Dis 221: 1762-1769. doi: 10.1093/infdis/jiaa150
    [25] Weiskopf D, Schmitz KS, Raadsen MP, et al. (2020) Phenotype and kinetics of SARS-CoV-2-specific T cells in COVID-19 patients with acute respiratory distress syndrome. Sci Immunol 5: eabd2071. doi: 10.1126/sciimmunol.abd2071
    [26] Remy KE, Mazer M, Striker DA, et al. (2020) Severe immunosuppression and not a cytokine storm characterizes COVID-19 infections. JCI Insight 5: e140329. doi: 10.1172/jci.insight.140329
    [27] Mehta P, McAuley DF, Brown M, et al. (2020) COVID-19: consider cytokine storm syndromes and immunosuppression. Lancet 395: 1033-1034. doi: 10.1016/S0140-6736(20)30628-0
    [28] Bhaskar S, Sinha A, Banach M, et al. (2020) Cytokine storm in COVID-19-immunopathological mechanisms, clinical considerations, and therapeutic approaches: The reprogram consortium position paper. Front Immunol 11: 1648. doi: 10.3389/fimmu.2020.01648
    [29] Chen LYC, Hoiland RL, Stukas S, et al. (2020) Confronting the controversy: interleukin-6 and the COVID-19 cytokine storm syndrome. Eur Respir J 56: 2003006. doi: 10.1183/13993003.03006-2020
    [30] Zhang D, Chen S (2020) Cytokine storms caused by novel coronavirus 2019 and treatment for cardiac injury. Eur Rev Med Pharmacol Sci 24: 12527-12535.
    [31] Yang L, Xie X, Tu Z, et al. (2021) The signal pathways and treatment of cytokine storm in COVID-19. Signal Transduct Target Ther 6: 255. doi: 10.1038/s41392-021-00679-0
    [32] Reeh H, Rudolph N, Billing U, et al. (2019) Response to IL-6 trans- and IL-6 classic signalling is determined by the ratio of the IL-6 receptor α to gp130 expression: fusing experimental insights and dynamic modelling. Cell Commun Signal 17: 46. doi: 10.1186/s12964-019-0356-0
    [33] Mogensen TH (2019) IRF and STAT transcription factors—From basic biology to roles in infection, protective immunity, and primary immunodeficiencies. Front Immunol 9: 3047. doi: 10.3389/fimmu.2018.03047
    [34] Wang C, Xie J, Zhao L, et al. (2020) Alveolar macrophage dysfunction and cytokine storm in the pathogenesis of two severe COVID-19 patients. EBioMedicine 57: 102833. doi: 10.1016/j.ebiom.2020.102833
    [35] Caricchio R, Gallucci M, Dass C, et al. (2020) Preliminary predictive criteria for COVID-19 cytokine storm. Ann Rheum Dis 80: 88-95. doi: 10.1136/annrheumdis-2020-218323
    [36] Zhang D, Guo R, Lei L, et al. (2020) COVID-19 infection induces readily detectable morphologic and inflammation-related phenotypic changes in peripheral blood monocytes. J Leukocyte Biol 2020: 1-10.
    [37] Huang C, Wang Y, Li X, et al. (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395: 497-506. doi: 10.1016/S0140-6736(20)30183-5
    [38] Yang Y, Shen C, Li J, et al. (2020) Plasma IP-10 and MCP-3 levels are highly associated with disease severity and predict the progression of COVID-19. J Allergy Clin Immun 146: 119-127. doi: 10.1016/j.jaci.2020.04.027
    [39] Quartuccio L, Sonaglia A, McGonagle D, et al. (2020) Profiling COVID-19 pneumonia progressing into the cytokine storm syndrome: Results from a single Italian Centre study on tocilizumab versus standard of care. J Clin Virol 129: 104444. doi: 10.1016/j.jcv.2020.104444
    [40] Zachariah P, Johnson CL, Halabi KC, et al. (2020) Epidemiology, clinical features, and disease severity in patients with coronavirus disease 2019 (COVID-19) in a children's hospital in New York City, New York. JAMA Pediatr 174: e202430-e202430. doi: 10.1001/jamapediatrics.2020.2430
    [41] Clinicaltrials.gov (2020)  A Phase 2 Trial of Infliximab in Coronavirus Disease 2019 (COVID-19) United States National Library of Medicine, Available from: https://clinicaltrials.gov/ct2/show/NCT04425538?term=TNF-%CE%B1+inhibitors&cond=COVID&draw=2&rank=1.
    [42] Patel S, Saxena B, Mehta P (2021) Recent updates in the clinical trials of therapeutic monoclonal antibodies targeting cytokine storm for the management of COVID-19. Heliyon 7: e06158. doi: 10.1016/j.heliyon.2021.e06158
    [43] Sinha P, Matthay MA, Calfee CS (2020) Is a “cytokine storm” relevant to COVID-19? JAMA Intern Med 180: 1152-1154. doi: 10.1001/jamainternmed.2020.3313
    [44] Saha A, Sharma AR, Bhattacharya M, et al. (2020) Tocilizumab: A therapeutic option for the treatment of cytokine storm syndrome in COVID-19. Arch Med Res 51: 595-597. doi: 10.1016/j.arcmed.2020.05.009
    [45] Syam AF, Pitoyo CW, Suhendro S, et al. (2021) Tocilizumab as a treatment for “cytokine storm syndrome” in COVID-19: A case report. Acta Med Indones 53: 194-201.
    [46] Suresh K, Figart M, Formeck S, et al. (2021) Tocilizumab for the treatment of COVID-19-induced cytokine storm and acute respiratory distress syndrome: A case series from a rural level 1 Trauma Center in Western Pennsylvania. J Investig Med High Impact Case Rep 9: 1-5.
    [47] Kornitzky FW, Langen HJ, Held M (2021) Treatment of a patient with a pronounced cytokine storm in severe COVID-19 pneumonia using a hemoadsorption in combination with the administration of tocilizumab. Pneumologie 75: 644-650. doi: 10.1055/a-1458-4080
    [48] Farooqi F, Dhawan N, Morgan R, et al. (2020) Treatment of severe COVID-19 with tocilizumab mitigates cytokine storm and averts mechanical ventilation during acute respiratory distress: A case report and literature review. Trop Med Infect Dis 5: 112. doi: 10.3390/tropicalmed5030112
    [49] Chitturi KR, Thacker S, Al-Saadi MA, et al. (2020) Successful treatment of acute heart failure in COVID-19-induced cytokine storm with tocilizumab: a case report. Eur Heart J Case Rep 4: 1-6. doi: 10.1093/ehjcr/ytaa188
    [50] Borku Uysal B, Ikitimur H, Yavuzer S, et al. (2020) Tocilizumab challenge: A series of cytokine storm therapy experiences in hospitalized COVID-19 pneumonia patients. J Med Virol 92: 2648-2656. doi: 10.1002/jmv.26111
    [51] Langer-Gould A, Smith JB, Gonzales EG, et al. (2020) Early identification of COVID-19 cytokine storm and treatment with anakinra or tocilizumab. Int J Infect Dis 99: 291-297. doi: 10.1016/j.ijid.2020.07.081
    [52] Ramiro S, Mostard RLM, Magro-Checa C, et al. (2020) Historically controlled comparison of glucocorticoids with or without tocilizumab versus supportive care only in patients with COVID-19-associated cytokine storm syndrome: results of the CHIC study. Ann Rheum Dis 79: 1143-1151. doi: 10.1136/annrheumdis-2020-218479
    [53] Stebbing J, Phelan A, Griffin I, et al. (2020) COVID-19: combining antiviral and anti-inflammatory treatments. Lancet Infect Dis 20: 400-402. doi: 10.1016/S1473-3099(20)30132-8
    [54] Cantini F, Niccoli L, Matarrese D, et al. (2020) Baricitinib therapy in COVID-19: A pilot study on safety and clinical impact. J Infection 81: 318-356. doi: 10.1016/j.jinf.2020.04.017
    [55] Kulkarni S, Fisk M, Kostapanos M, et al. (2020) Repurposed immunomodulatory drugs for Covid-19 in pre-ICu patients—mulTi-Arm therapeutic study in pre-ICu patients admitted with Covid-19—repurposed drugs (TACTIC-R): A structured summary of a study protocol for a randomised controlled trial. Trials 21: 626. doi: 10.1186/s13063-020-04535-4
    [56] Convertino I, Tuccori M, Ferraro S, et al. (2020) Exploring pharmacological approaches for managing cytokine storm associated with pneumonia and acute respiratory distress syndrome in COVID-19 patients. Crit Care 24: 331. doi: 10.1186/s13054-020-03020-3
    [57] Cao Y, Wei J, Zou L, et al. (2020) Ruxolitinib in treatment of severe coronavirus disease 2019 (COVID-19): A multicenter, single-blind, randomized controlled trial. J Allergy Clin Immun 146: 137-146. doi: 10.1016/j.jaci.2020.05.019
    [58] Cavalli G, Dinarello CA (2015) Treating rheumatological diseases and co-morbidities with interleukin-1 blocking therapies. Rheumatology 54: 2134-2144.
    [59] Cavalli G, De Luca G, Campochiaro C, et al. (2020) Interleukin-1 blockade with high-dose anakinra in patients with COVID-19, acute respiratory distress syndrome, and hyperinflammation: a retrospective cohort study. Lancet Rheumatol 2: e325-e331. doi: 10.1016/S2665-9913(20)30127-2
    [60] Schlesinger N, Firestein BL, Brunetti L (2020) Colchicine in COVID-19: an old drug, new use. Curr Pharmacol Rep 6: 137-145. doi: 10.1007/s40495-020-00225-6
    [61] Clinicaltrials.gov (2020)  Colchicine and COVID-19 Clinical Trials Search United States National Library of Medicine, Available from: https://clinicaltrials.gov/ct2/results?cond=COVID&term=colchicine&cntry=&state=&city=&dist=.
    [62] Brunetti L, Diawara O, Tsai A, et al. (2020) Colchicine to weather the cytokine storm in hospitalized patients with COVID-19. J Clin Med 9: 2961. doi: 10.3390/jcm9092961
    [63] Jeyaraman M, John A, Koshy S, et al. (2021) Fostering mesenchymal stem cell therapy to halt cytokine storm in COVID-19. BBA-Mol Basis Diss 1867: 166014-166014. doi: 10.1016/j.bbadis.2020.166014
    [64] Farkhad NK, Reihani H, Moghadam AA, et al. (2021) Are mesenchymal stem cells able to manage cytokine storm in COVID-19 patients? A review of recent studies. Regen Ther 18: 152-160. doi: 10.1016/j.reth.2021.05.007
    [65] Clinicaltrials.gov (2020)  Stem Cells and COVID-19 Clinical Trials Search United States National Library of Medicine, Available from: https://clinicaltrials.gov/ct2/results?cond=COVID-19&term=stem+cell&cntry=&state=&city=&dist=.
    [66] Kapugi M, Cunningham K (2019) Corticosteroids. Orthop Nurs 38: 336-339. doi: 10.1097/NOR.0000000000000595
    [67] Deng CX (2020) Glucocorticoids save lives in COVID-19 patients. Int J Biol Sci 16: 2477-2478. doi: 10.7150/ijbs.49125
    [68] Ramiro S, Mostard RLM, Magro-Checa C, et al. (2020) Historically controlled comparison of glucocorticoids with or without tocilizumab versus supportive care only in patients with COVID-19-associated cytokine storm syndrome: results of the CHIC study. Ann Rheum Dis 79: 1143-1151. doi: 10.1136/annrheumdis-2020-218479
    [69] Sharun K, Tiwari R, Dhama J, et al. (2020) Dexamethasone to combat cytokine storm in COVID-19: Clinical trials and preliminary evidence. Int J Surg 82: 179-181. doi: 10.1016/j.ijsu.2020.08.038
    [70] Ahmadikia K, Hashemi SJ, Khodavaisy S, et al. (2021) The double-edged sword of systemic corticosteroid therapy in viral pneumonia: A case report and comparative review of influenza-associated mucormycosis versus COVID-19 associated mucormycosis. Mycoses 64: 798-808. doi: 10.1111/myc.13256
    [71] Pakdel F, Ahmadikia K, Salehi M, et al. (2021) Mucormycosis in patients with COVID-19: A cross-sectional descriptive multicentre study from Iran. Mycoses 64: 1238-1252. doi: 10.1111/myc.13334
    [72] Pal R, Singh B, Bhadada SK, et al. (2021) COVID-19-associated mucormycosis: An updated systematic review of literature. Mycoses In press.
    [73] Rodriguez-Morales AJ, Sah R, Millan-Oñate J, et al. (2021) COVID-19 associated mucormycosis: the urgent need to reconsider the indiscriminate use of immunosuppressive drugs. Ther Adv Infect Dis 8: 1-5.
    [74] Tzilas V, Manali E, Papiris S, et al. (2020) Intravenous immunoglobulin for the treatment of COVID-19: a promising tool. Respiration 99: 1085. doi: 10.1159/000506650
    [75] Bongomin F, Asio LG, Ssebambulidde K, et al. (2021) Adjunctive intravenous immunoglobulins (IVIg) for moderate-severe COVID-19: emerging therapeutic roles. Curr Med Res Opin 37: 903-905. doi: 10.1080/03007995.2021.1903849
    [76] Kohler H, Kaveri S (2021) How IvIg can mitigate Covid-19 disease: A symmetrical immune network model. Monoclon Antibodies Immunodiagn Immunother 40: 17-20. doi: 10.1089/mab.2020.0041
    [77] Galeotti C, Kaveri SV, Bayry J (2020) Intravenous immunoglobulin immunotherapy for coronavirus disease-19 (COVID-19). Clin Transl Immunol 9: e1198. doi: 10.1002/cti2.1198
    [78] Calabrese LH (2020) Cytokine storm and the prospects for immunotherapy with COVID-19. Clevel Clin J Med 87: 389-393. doi: 10.3949/ccjm.87a.ccc008
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