\r\n\t• Role of technological innovation and corporate risk management \r\n\t• Challenges for corporate governance while launching corporate environmental management among emerging economies \r\n\t• Demonstrating the relationship between environmental risk management and sustainable management \r\n\t• Contemplating strategic corporate environmental responsibility under the influence of cultural barriers \r\n\t• Risk management in different countries – the international management dimension \r\n\t• Global Standardization vs local adaptation of corporate environmental risk management in multinational corporations. \r\n\t• Is there a transnational approach to environmental risk management? \r\n\t• Approaches towards Risk management strategies in the short-term and long-term.
",isbn:"978-1-83968-906-2",printIsbn:"978-1-83968-905-5",pdfIsbn:"978-1-83968-907-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"9b65afaff43ec930bc6ee52c4aa1f78f",bookSignature:"Dr. Muddassar Sarfraz and Prof. Larisa Ivascu",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10226.jpg",keywords:"Global Risk Management, Risk Assessment, Climate Risk, Environmental Management, International Business, Business Sustainability, Corporate Governance, Financial Market, Financial Risks, Sustainable Economic Environment, Business Valuation, Organizational Behavior",numberOfDownloads:131,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 24th 2020",dateEndSecondStepPublish:"October 22nd 2020",dateEndThirdStepPublish:"December 21st 2020",dateEndFourthStepPublish:"March 11th 2021",dateEndFifthStepPublish:"May 10th 2021",remainingDaysToSecondStep:"4 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Muddassar Sarfraz focuses on corporate social responsibility, human resource management, strategic management, and business management. He is a member of the British Academy of Management (UK), Chinese Economists Society (USA), World Economic Association (UK), American Economic Association (USA), and an Ambassador of the International MBA program of Chongqing University, PR China, for Pakistan.",coeditorOneBiosketch:"Dr. Larisa Ivascu's area of research includes sustainability, management, and strategic management. She has published over 190 papers in international journals. She is vice-president of the Society for Ergonomics and Work Environment Management, Timisoara, and a member of the World Economics Association (WEA), International Economics Development and Research Center (IEDRC), Engineering, and Management Research Center (CCIM).",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"260655",title:"Dr.",name:"Muddassar",middleName:null,surname:"Sarfraz",slug:"muddassar-sarfraz",fullName:"Muddassar Sarfraz",profilePictureURL:"https://mts.intechopen.com/storage/users/260655/images/system/260655.jpeg",biography:"Dr Muddassar Sarfraz is working at the Binjiang College, Nanjing University of Information Science and Technology, Wuxi, Jiangsu, China. He has obtained his PhD in Management Sciences and Engineering from the Business School of Hohai University. He holds an International Master of Business Administration (IMBA) from Chongqing University (China) and Master of Business Administration (HR) from The University of Lahore. He has published tens of papers in foreign authoritative journals and academic conferences both at home and abroad.\nHe is the Book Editor of Sustainable Management Practices, Analyzing the Relationship between Corporate Governance, CSR, Sustainability, and Cogitating the Interconnection between Corporate Social Responsibility and Sustainability. He is the Associate and Guest Editor of Frontiers in Psychology, International Journal of Humanities and Social Development Research and the Journal of Science and Innovative Technologies. He is an Editorial Board Member of the International Journal of Human Resource as well as a member of the British Academy of Management (UK), Chinese Economists Society (USA), World Economic Association (UK), American Economic Association (USA), and an Ambassador of the International MBA program of Chongqing University, PR China, for Pakistan. \nHis research focuses on corporate social responsibility, human resource management, strategic management, and business management.",institutionString:"Binjiang College, Nanjing University of Information Science &Technology, Wuxi, Jiangsu",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"1",institution:null}],coeditorOne:{id:"288698",title:"Prof.",name:"Larisa",middleName:null,surname:"Ivascu",slug:"larisa-ivascu",fullName:"Larisa Ivascu",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRfMOQA0/Profile_Picture_1594716735521",biography:"Dr Larisa IVAȘCU is currently an associate professor at the Politehnica University of Timisoara. 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\n
1. Introduction
\n
Spinel ferrite nanoparticles (NPs) are in the spotlight of current nanoscience due to immense application potential. Very interesting aspects of the spinel ferrite NPs are their excellent magnetic properties often accompanied with other functional properties, such as catalytic activity. Moreover, the magnetic response of the NPs can be tuned by particle size and shape up to some extent. Consequently, various spinel ferrite NPs are suggested as universal and multifunctional materials for exploitation in biomedicine [1–4], magnetic recording, catalysis [5–8] including magnetically separable catalysts [9–12], sensing [13–16] and beyond (MgFe2O4 in Li ion batteries [17, 18], or investigation of dopamine [19]). Thus it is of ultimate interest to get control over their functional properties, which requires in‐depth understanding of the correlation between their structural and magnetic order. For example, the particle size and shape are extremely important both in biomedical imaging using Magnetic Resonance Imaging (MRI) [20] and therapies by means of magnetic field‐assisted hyperthermia [21].
\n
The chapter aims to summarize the most important aspects of magnetism of cubic spinel ferrite nanoparticles (MFe2O4, M = Mg, Mn, Fe, Co, Ni, Cu, and Zn) in context of their crystal and magnetic structure. The factors that drive magnetic performance of the spinel ferrite NPs can be recognized on three levels: on the atomic level (degree of inversion and the presence of defects), at single‐particle level as balance between the crystallographically and magnetically ordered fractions of the NP (single‐domain and multi‐domain NPs, core‐shell structure, and beyond), and at mesoscopic level by means of mutual interparticle interactions and size distribution phenomena. All these effects are strongly linked to the preparation routes of the NPs. In general, each preparation method provides rather similar NPs by means of morphology and crystalline order. Thus the “three‐level” concept, which is the focal motif of the chapter, can be applied to all cubic spinel ferrite NPs.
\n
\n
\n
2. Brief overview of preparation methods of spinel ferrite nanoparticles
\n
In this section, selected methods of the NP preparation are summarized. Explicitly, the wet methods yielding well‐defined NPs, either isolated or embedded in a matrix, are accented. The reason is that only such samples can be sufficiently characterized and the factors defined within the “three level” concept can be disentangled. Outstanding reviews on the specific method(s) with further details and references are also included [22–25].
\n
Coprecipitation method is the archetype route, which can be used for preparation of all cubic spinel ferrite NPs: Fe3O4/γ‐Fe2O3 [24, 26], MgFe2O4 [27], etc. In general, two water‐soluble metallic salts are coprecipitated by a base. The reaction can be partly controlled in order to improve characteristics of the NPs [26, 28, 29]; however, it is generally reported as a facile method yielding polydispersed NPs with lower crystallinity and consequently less significant magnetic properties.
\n
The family of decomposition routes includes wet approaches based on decomposition of metal organic precursors in high‐boiling solvents, typically in the presence of coating agents (all [30], Fe3O4/γ‐Fe2O3 [24, 31], CoFe2O4 [32, 33], NiFe2O4 [34], ZnFe2O4 [35]). The most common organic complexes used for decomposition are metal oleates and acetylacetonates. The decomposition methods yield highly crystalline particles close to monodisperse limit with very good magnetic properties. However, the reaction conditions must be controlled in correspondence of the growth model suggested by Cheng et al. [22]. They can be also tailored to produce NPs of different shapes (CoFe2O4 [36, 37], Fe3O4/γ‐Fe2O3 [38, 39]). Higher‐order assemblies of the NPs can be also achieved by varying the ratio of the precursor and reaction temperature (CoFe2O4 [40]). Alternatively, the decomposition takes place in high‐pressure vessel (autoclave) [41, 42].
\n
A large group of preparation protocols is based on solvothermal treatments, in aqueous conditions termed as hydrothermal. The preparation can be carried out either in a simple single‐solvent system (MgFe2O4 [43], NiFe2O4 [44, 45]), mixture of solvents (MnFe2O4 [46], ZnFe2O4 [47]), surfactant‐assisted routes (ZnFe2O4 [48]), or in multicomponent systems, such as water‐alcohol‐fatty acid (Fe3O4/γ‐Fe2O3 [49], CoFe2O4 [50]). The solvothermal routes are often carried out at elevated pressure and can be also maintained in supercritical conditions (MgFe2O4 [51]). The NPs prepared by this class of methods are in general of very good crystallinity, in some cases competitive to the NPs obtained by the decomposition routes.
\n
Spinel ferrite NPs can be also obtained with the help of normal or reverse micelle methods, often referred as microemulsion routes (all [52, 53], Fe3O4/γ‐Fe2O3 [54], MgFe2O4 [55–57], CoFe2O4 [58], NiFe2O4 [59], CoFe2O4 and MnFe2O4 [60], ZnFe2O4 [61]). This approach takes advantage of the defined size of the micelle given by the ratio of the microemulsion components (water‐organic phase‐surfactant) according to the equilibrium phase diagram [62]. The micelles serve as nano‐reactors, which exchange the constituents dissolved in the water phase during the reaction and self‐limit the maximum size of the NPs. The as‐prepared NPs are often subjected to thermal posttreatment, which improves the NP crystallinity and enhances magnetic properties. However, such NPs are no more dispersible in liquid phase and their applications are thus limited. On the other hand, the microemulsion technique can be used for preparation of the NPs of a defined shape [63] and mixed ferrites [64].
\n
A modified polyol method is also used for preparation of spinel ferrite NPs [22, 24, 65–67]. While in the standard route the polyol acts as a solvent and sometimes reducing or complexing agent for metal ions, for preparation of the ferrite NPs, the reaction of 1,2‐alkanediols and metal acetylacetonates in high‐boiling solvents is the most common variant.
\n
Sol‐gel chemistry is a handy approach to produce spinel ferrite NPs. The common tactic is the growth of the NPs in porous silica matrix yielding well‐developed NPs embedded in the transparent matrix. The route requires annealing of the gel; however, the particle size can be sufficiently varied by the annealing temperature. Different spinel ferrites can be prepared (Fe3O4/γ‐Fe2O3 [68], CoFe2O4 [69–71], MnFe2O4 [72], NiFe2O4 [73–75]).
\n
NPs of spinel ferrites are also prepared with assistance of microwaves [76, 77], ultrasound (CoFe2O4 [78], MgFe2O4 [79]), combustion routes (MgFe2O4 [80]), or mechanical treatments (MgFe2O4 [81], MnFe2O4 [82], NiFe2O4 [83]); however, the as‐prepared NPs often require heat treatment, and the resulting samples with sufficient crystallinity are better classified as fine powders. Less common methods such as the use of electrochemical synthesis for the γ‐Fe2O3 NPs [84] or NiFe2O4 [85] and synthesis employing ionic liquids for cubic magnetite NPs [86] were recently reported. NPs with size in the multicore limit were obtained by disaccharide‐assisted seed growth [87]. Recently, combination of stop‐flow lithography and coprecipitation was reported [88]. Typical TEM images of spinel ferrite NPs prepared by the most common routes are shown in Figure 1.
\n
Figure 1.
TEM images of the spinel NPs prepared by different methods, with distributions of particle diameters in the inset. (a) γ‐Fe2O3 NPs prepared by coprecipitation technique. (b) CoFe2O4 NPs prepared by decomposition of oleic precursor. (c) ZnFe2O4 NPs prepared by sol‐gel method.
\n
The preparation methods described above can be successfully applied to preparation of core‐shell NPs: CoFe2O4@MFe2O4; M = Ni, Cu, Zn or γ‐Fe2O3 [89], and MnFe2O4@γ‐Fe2O3 [90]; CoFe2O4@ZnFe2O4 [91]; CuFe2O4@MgFe2O4 [92]; or other mixed ferrites NPs [93, 94]. A natural core‐shell structure is obtained for magnetite NPs due to topotactic oxidation to maghemite, which is mirrored, for example, in varying heating efficiency [95]. As a final remark, the selection of a particular preparation route yielding either a single core or multicore NPs is crucial and must be considered in the context of a specific application [96].
\n
\n
\n
3. Characterization of magnetic nanoparticles: parameters and methods
\n
In this section, the most important parameters characterizing structural and magnetic properties of NPs are introduced. Overview of the key experimental methods used for their evaluation is also included. For straightforwardness, details on the theoretical models and related formalism are not given, but relevant references are included. More details on the topic can be found in a comprehensive work by Koksharov [97].
\n
\n
3.1. Basic structural and magnetic characterization
\n
The most important parameter is the particle size itself, usually attributed to the diameter of a single NP. The first‐choice technique for determination of the particle size is the transmission electron microscopy (TEM), which gives the real (or physical) particle size, dTEM. As the NPs of spinel ferrites are usually spherical, cubic, octahedral, or symmetric star‐like objects, the value is a reasonable measure of the NP dimension as it gives information on the principal dimensions of those objects. Analysis of the TEM images also provides particle‐size distribution, sometimes expressed as polydispersity index (PDI = σ(dTEM)/<dTEM>). The direct TEM observation gives information on aggregation, chaining of particles, and other morphological specifics. Using high‐resolution TEM (HR TEM), internal structure of the NPs can be inspected, for example, the thickness of disordered surface layer and defects can be identified.
\n
Particle size can be also determined using powder X‐ray diffraction (XRD). The profile of the diffraction peak contains information about the so‐called crystallite size, Dhkl, and the microstrain (arise from the presence of vacancies, dislocation, stacking faults, or poor crystallinity of the material). Generally, the experimental profile is the convolution of the instrumental profile caused by the experimental setup and the physical profile caused by the intrinsic properties of the measured material [98].
\n
The physical profile is the convolution of the two dominant contributions caused by the small Dhkl and by the microstrain. The Dhkl is defined as a coherently diffracting length in a crystallographic direction [hkl] that is parallel to the diffraction vector (surface normal) [98]. Assuming the spherical NPs with random orientation of individual [hkl] directions, the Dhkl determines the diameter of the coherently diffracting domain; in other words it is the diameter of the crystalline part of the NP, the dXRD. For highly symmetric shapes expected for spinel ferrite NPs, the coherently diffracting domain can be sufficiently described by a sphere or an ellipsoid in the case of flat crystallites.
\n
Other important parameters characterizing magnetic NPs are related to formation of a single‐domain state. In order to decrease the magnetostatic energy that is associated with the dipolar fields, the ferromagnetic (or ferrimagnetic)‐ordered crystal is divided into the magnetic domains. Within each of the domain, the magnetization, M reaches the saturation. The domain creation depends on the competition between the reduction of the magnetostatic energy and the energy required to form the domain walls separating the adjacent domains. The size of the domain wall is a balance between the exchange energy that tries to unwind the domain wall and the magnetocrystalline anisotropy with the opposite effect.
\n
In the magnetic NPs, the typical dimensions are comparable with the thickness of the domain; thus, at some critical size, it is energetically favorable for the NP to become single domain. The critical dimension ranging from 10-7 to 10-8 m is strictly specific to each magnetic spinel ferrite [99].
\n
In small magnetic NPs reaching the single‐domain regime, the paramagnetic‐like behavior can be observed even below the Curie temperature, Tc. The state is therefore called the superparamagnetism (SPM) as the whole particle behaves as one giant spin (superspin) consisting of the atomic magnetic moments; thus, the magnetic moment of the whole NP is 102 to 105 times larger than the atomic moment. The magnetization follows the behavior of the Langevin function. The theory of SPM and superspin relaxation of the NPs was treated by C. P. Bean, J.D. Livingston and M. Knobel. et al. [100, 101]. The key parameters representing the magnetic properties of single‐domain NPs are blocking temperature, TB, and superspin or NP magnetic moment, μm. The TB is related to the particle size through its volume, V as:\n
TB=KeffV/(akB)E1
\n
where Keff is the effective anisotropy constant. Parameter a is given by the measurement time, τm as a = ln(τm / τ0), a = 25 for the SPM systems with relaxation time τ0 = 10-12 s (see the following paragraphs) and τm = 100 s [101, 102].
\n
The μm is related to the saturation magnetization, Ms, which is defined as the maximum allowed magnetization at given temperature (all spins are aligned along the field direction) and often deviates from a theoretical bulk value. For ideal NPs (physical volume is identical with the volume where the magnetic structure is like in the bulk spinel), the dependence of μm on Ms can be written as μm = Ms V.
\n
Another important parameter is the relaxation time, τ of the NP superspin. For a particle with uniaxial anisotropy, the superspin relaxation corresponds to the flip between two equilibrium states separated by an energy barrier KeffV, which can be overcome by the thermal fluctuations at the TB. The superspin relaxation in the SPM systems is described by the Néel‐Arrhenius law as [103, 104]:\n
τ=τ0exp(EA/kBT)E2
\n
where EA is the anisotropy energy and other variables and constants have usual meaning.
\n
Below the TB, the NPs are in the so‐called blocked state analogous to the ordered state (such as ferromagnetic or ferrimagnetic), and the magnetic moments are fixed into the direction of the easy axis and can only fluctuate around these directions. The TB also depends on the time window of the measurements, τm. If the τm > τ, the NPs have enough time to fluctuate and the SPM state can be observed. On the other hand, if τm < τ, the blocked regime is observed. Thus, determination of the TB is dependent on the used experimental technique (10-8 s for Mössbauer spectroscopy (MS), 1 s for magnetic measurements, 10–10-3 s for a.c. susceptibility measurements).
\n
A very important parameter characterizing the blocked state is the coercivity, Hc (or coercive field) as it gives information on opening of the hysteresis loop. Depending on the dominant anisotropy term, the Hc value reaches values in fractions of 2Keff/Ms [101, 105]. In general, the coercivity (and also remanence) of NPs with nonspherical shapes shows complex angular dependence due to the shape anisotropy [106]. In very small particles, the coercivity is an interplay between the surface disorder and surface anisotropy [107].
\n
The typical magnetic measurements of the NP magnetic parameters yielding the above‐described parameters can be summarized as follows: temperature dependence of the magnetization in low external applied field, the so‐called zero field cooled curve (ZFC) and field cooled curve (FC); field dependence of the magnetization at fixed temperatures, the so‐called magnetization isotherm (or hysteresis loop in the blocked state); and the a.c. susceptibility measurement. The ZFC‐FC protocol reveals the value of the TB, while the analysis of the magnetization isotherms in the SPM state serves for determination of the μm. From this value, the so‐called magnetic size of a NP, dmag (size of the magnetically ordered part), can be determined.
\n
A unique tool used in characterization of spinel ferrite NPs is the Mössbauer spectroscopy. It is a dual probe both for structure and magnetism at local level based on recoilless resonant absorption of γ radiation. In general, information on coordination surroundings of the iron cations, their valence, degree of inversion of the spinel structure, and orientation of spins on the cubic spinel sub‐lattices can be obtained [108, 109].
\n
The small spinel ferrite NPs exhibit relaxation time in order of 10-9 s that is close to the time window of the MS (10-8 s) allowing the study of relaxation of the NPs by means of MS [109, 110]. Furthermore, the big advantage of the MS is that it is not restricted to the well crystalline samples; thus, a non‐well crystalline NP can be also investigated using MS. Finally, the so‐called spin canting angle, usually attributed to the presence of the surface spins, can be estimated [111].
\n
\n
\n
3.2. Real effects in magnetic nanoparticles
\n
\n
3.2.1. Size distribution
\n
All real systems of the NPs exhibit an intrinsic size distribution, which must be considered in evaluation and interpretation of structural and magnetic data. The most common is the log‐normal distribution (see Figure 1); however, Gaussian distribution has been also reported [112–114]. In the case of the TEM observation for the dTEM, the NPs can be termed depending on the value of the PDI as monodisperse (PDI < 0.05–0.1), highly uniform (PDI < 0.2), and polydisperse (PDI > 0.2). Similar classification might be applied to the distribution of dXRD; however, such in‐depth analysis is usually not included in common Rietveld treatment of the XRD data. On the other hand, the role of size distribution by means of magnetic size, dmag, and superspin values is extremely important for evaluation of magnetic properties. The mean magnetic moment per single NP, μm, and distribution width, σ, can be derived from the experimental data, μm = μ0exp(σ2 / 2), as the magnetization as a function of the applied field, H, and temperature, T, in SPM state can be described as a weighted sum of Langevin functions [69, 115, 116]:\n
M(H,T)=∫0∞μL(μHkBT)fL(μ)dμE3
\n
where L(x) represents the Langevin function and fL(μ) is the log‐normal distribution of magnetic moments μ. The NP size distribution also affects the character of ZFC and FC curves as it is mirrored in distribution of the TB and Keff, and suited models must be applied to obtain median values, TBm and distribution width σ as relevant parameters [117–120].
\n
One of the possible approaches evaluating the TB distribution is based on refinement of the ZFC temperature dependence of magnetization, MZFC(T) which is given by equation [101, 121, 122]:\n
MZFC(T)∝MS2H3Keff[25t∫0ttBf(tB)dtB+∫t∞f(tB)dtB]E4
\n
where tB = TB/TBm is the reduced blocking temperature of individual NPs and f(tB) is the log-normal distribution function of reduced blocking temperatures. The first term in Eq. (4) represents contribution of the NPs in the SPM state, whereas the second term belongs to the NPs in blocked state.
\n
Typical examples of magnetization isotherms and ZFC‐FC curves influenced by the particle‐size distribution are shown in Figure 2, presenting unhysteretic magnetization isotherms (Langevin curves) for different values of μ and σ and ZFC‐FC curves for different values of TB and σ.
\n
Figure 2.
Model Langevin and ZFC‐FC curves for selected NP magnetic moments and blocking temperature. (a) Langevin curves for magnetic moments with different orders of magnitude and σ = 0.5. (b) Evolution of Langevin curve for different magnetic moment distributions visualized in (c). (d) Ideal ZFC‐FC curves for NP without size distribution. (e) Evolution of the ZFC‐FC curve for fixed TB and different distribution widths (f).
\n
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3.2.2. Spin canting phenomenon and surface effects
\n
Decreasing the NP size, the number of atoms located at the surface dramatically increases. Thus the surface spins become dominant in the magnetic properties of the whole NP. The atoms at the surface exhibit lower coordination numbers originating from breaking of symmetry of the lattice at the surface.
\n
Moreover, the exchange bonds are broken resulting in the spin disorder and frustration at the surface leading to the undesirable effects such as low saturation magnetization of the NP and the unsaturation of the magnetization in the high magnetic applied field [123]. To explain these effects, J. M. D. Coey proposed the so‐called core‐shell model in which the NP consists of a core with the normal spin arrangement and the disordered shell, where the spins are inclined at random angles to the surface, the so‐called spin canting angle [123] (see Figure 3). The spin canting angle in general depends on the number of the magnetic nearest neighbors connecting with the reduced symmetry and dangling bonds. Other effects such as the interparticle interactions play role [124]. The spin canting angle can be determined with the help of in‐field Mössbauer spectroscopy (IFMS); an example is given in Figure 4.
\n
Figure 3.
Scheme of the internal structure of ideal (a) and magnetic core‐shell (b) structure of NP. (c) Scheme of the ideal and core‐shell NP with the model of NP diameters determined by TEM, X‐ray diffraction, and magnetic measurements. (d and e) Model Langevin curves for the ideal and core‐shell NP with paramagnetic contribution due to the disordered spins in the NP shell.
\n
Figure 4.
Schematic representation of the typical zero field cooled curve (ZFC) and field cooled curves (FC) for different types of NP ensembles. a) uniform NPs with negligible interparticle interactions, b) uniform NPs with “intermediate” interparticle interactions, c) NPs with non-negligible particle size distribution and strong interparticle interactions. The blocking temperature, TB and the irreversibility temperature, TDIFF typical for strongly interacting regime are shown.
\n
However, the spin canting is not a unique property of the surface spins, and several works point to the volume nature of the effect [125–127]. Thus the surface effects in the NPs together with the origin of the spin canting angle are still discussed within the scientific community [109, 128–130].
\n
Another consequence of the increased number of the surface atoms is the dominance of surface term to the anisotropy energy, usually expressed as a sum: Kv + (6/d)Ks, where Kv is the bulk value of the Keff and Ks describes the contribution from the surface spins originated by structural deviations and spin frustration on the surface. Depending on the NP shape, the surface anisotropy may contain non‐negligible admixture of higher‐order Néel terms [130]. In real systems, the Keff is additionally modified by the presence of other effects, mainly interparticle interactions described below.
\n
\n
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3.2.3. Interparticle interactions
\n
The interparticle interactions play a very important role in the magnetic response of the NPs, because they are usually not enough spatially separated to follow the behavior of an ideal SPM system. In general, two types of interaction can be observed: 1) the exchange interaction that affects mainly the surface spins of the NPs in close proximity thus can be neglected in most cases and 2) the long-range order dipolar interaction that is the dominant due to the high magnetic moment of the NPs [131].
\n
The NP systems can be tentatively divided into the weakly interacting systems (the representatives are much diluted ferrofluids or NPs embedded in matrix in small concentration) and strongly interacting system with the powder samples as representatives. The strength of the interparticle interactions is given by the magnitude of the superspins and interparticle distance, in reality by the concentration of the NPs in ferrofluids, thickness of the NP coating, or matrix‐to‐NP ratio. The interparticle interactions affect all parameters characterizing the single‐domain state. Furthermore, the strong interparticle interactions can result in the collective magnetic state at low temperature that resembles the typical physical properties of spin glasses [104], termed as superspin glasses in the case of strongly interacting SPM species [132–134].
\n
In weakly interacting system, the dipolar interaction is treated as a perturbation to the SPM model within the Vogel‐Fulcher law [104], the NP relaxation time is then written as:\n
τ=τ0exp(EA/kB(T−T0))E5
\n
The effect on the TB is described by two models giving contradictory results on the relaxation times—the Hansen-Morup model (HM) [135] and the Dormann-Bessais-Fiorani model (DBF) [129]. The decrease of the TB is predicted by the HM model, while its increase was obtained by the DBF model. So far, there have been no clear experimental evidences for a preference of one of these models. Some authors suggested that a phenomenological correction to the TB in the weakly interacting systems could be used in the same way as it is done fore the relaxation time by adding the phenomenological constant T0 to the TB of the SPM system [102, 136, 137]. A different approach treats weak interparticle interactions as additional magnetic field acting on a single NP, when correction to the external magnetic field, (1-H/HK)a is added to Eq. (1) [101].
\n\n
In the case of strong interactions, the collective state of the NP condensates below a characteristic temperature—the so‐called glass‐transition temperature, Tg—and the equation for the relaxation time is usually given by scaling law for critical spin dynamics [131, 132]:\n
τ=τ0(Tm/Tg−1)zvE6
\n
where Tm is the temperature of the maximum at the a.c. susceptibility curve and zv is the dynamical critical component. However; strongly interacting systems do not necessarily fulfill criteria for the so‐called superspin‐glass systems obeying Eq. (6). Then, one of the approaches dealing with the effect of strong interactions on shift of the TB is treated within the random anisotropy model (RAM) [101, 138–140]. RAM predicts the increase of interparticle interactions with decreasing correlation length, L which is a measure of average distance at which the magnetization fluctuations within the NP system are correlated. Then the Keff and particle volume V are averaged to the number N of the NP involved in the interactions, introducing new KL and VL variables, and consequently, the formula for the TB is modified to:\n
TB=KLVLN1/2E7
\n\n
The heart of the problem of calculating the TB for interacting systems within the RAM model is the correct evaluation of the KL and VL of NP system.
\n
Figure 5.
Typical MS spectra of almost ideal and core-shell NPs. The first column shows comparison of room-temperature MS spectra for the perfectly crystalline and ordered 7nm -Fe2O3 NPs (a) and core-shell 15 nm with 7 nm crystalline cores (d). The pink line is the fit of the spectra attributed to the fraction of NPs in SPM state. The middle and right column shows evolution of MS at low temperatures (4 K) in 0 T (b and e) and 6 T(c and f), respectively. Splitting of the lines attributed to the octahedral and tetrahedral positions can be disentangled after application of external magnetic field (c and f). Peak widening due to the disordered magnetic spins in the NP shell (d) is observable on the 4 K spectra (e and f), especially for the 1st and 6th lines.
\n
The presence of interparticle interactions (as well as the particle‐size distribution) is usually evidenced on the ZFC‐FC curves; typical examples of medium and strongly interacting ensembles of NPs in comparison to the ideal noninteracting case are given in Figure 5. In real samples, all effects are present with variable contribution, and in some cases, both the size distribution and interparticle interactions must be addressed to describe the magnetic response of the samples properly [141, 142] (Figure 5).
\n\n
\n
\n
\n
\n
4. Synergy of structural and magnetic probes
\n
In order to provide complete insight into properties of magnetic NPs, synergy of structural and magnetic probes is essential. At the atomic and single‐particle level, the complementarity of the (HR) TEM and XRD provides information on phase composition, the presence and type of defects, and particle sizes: dXRD and dTEM. The analysis of MS gives important knowledge on the degree of inversion and spin canting, which is then considered for interpretation of the magnetization data. Moreover, the particle‐size distribution obtained from the TEM should be confronted with the superspin distribution obtained by the analysis of the Langevin curves; this analysis also yields the magnetic size, dmag. Using the three different particle‐size parameters (dmag, dXRD, dTEM), the concept of the core‐shell model of NP can be extended as the core‐shell structure of the spins is often not identical with the crystallographically ordered‐disordered part of the NP. The reason is that the spin frustration and disorder usually occur at volume larger than the size of the crystalline part. Comparing the dmag, dXRD, and dTEM values, a very good estimate of the particle crystallinity and degree of spin order is obtained. A schematic representation of the crystallographic (structural) and magnetic core‐shell model structures together with typical magnetization isotherms in the SPM state are shown in Figure 3.
\n
At the mesoscopic level, the influence of interparticle interactions should not be neglected. For that purpose, morphology of the NP ensembles observed by the TEM gives estimate of mutual interparticle distance. The relevance of the interaction regime can be corroborated by a.c. susceptibility experiments, which yields characteristic relaxation times of the superspins, τ. As discussed above, those are strongly reformed because of the interactions. Finally, the effect of the μ, TB, and Keff distribution must be then carefully disentangled in order to estimate the pure contribution of the interaction.
\n
\n
\n
5. Impact of preparation and strategies of tuning magnetic properties
\n
The intrinsic NP parameters at all levels are imprinted during the preparation process. In this section, a brief discussion of this issue is given in the view of the “three‐level” concept considering the structural and spin order in the unit cell and coordination polyhedra, single‐particle, and NP ensemble level. Strategies profiting from the control over the imprint of the real effects by substitution or formation of artificial core‐shell structures are also mentioned.
\n
The degree of inversion, δ of the spinel structure is found to be significantly influenced by the preparation of the spinel ferrite NPs. In bulk, the normal or inverse spinel structure usually dominates. However, the degree of inversion in the NPs is often close to 0.5 and the mixed spinel structure is the most common. For example, the NiFe2O4 is a typical inverse spinel, while in NPs obtained by the sol‐gel method, the δ value of 0.6 was reported [143]. A very similar values were observed for sol‐gel‐prepared NPs of CoFe2O4 [69] (inverse spinel in bulk) and of ZnFe2O4 with normal spinel bulk structure [144]. The cation distribution in NPs prepared by coprecipitation method also often corresponds to mixed spinel structure as was demonstrated for ZnFe2O4 [145] and MnFe2O4 [146]. Moreover, the δ value can be controlled in the NPs prepared by the polyol method [65] and using tailored solvothermal protocols [147]. In addition, the stoichiometry of the NPs is not always matching the expected M2+/M3+/O2- ratio (1:2:4), e.g., as reported for NPs prepared by hydrothermal method [50].
\n
The presence of defects, mainly by means of oxygen vacancies, is believed to be another important factor driving magnetic properties of the NPs. It was shown that they dominate the properties of the NPs obtained by mechanochemical processes [148], and it was also demonstrated that the level of defects can be influenced by vacuum annealing [149–151]. A specific issue is related to the presence of the Verwey transition in the Fe3O4 NPs [152] as the topotactic oxidation from magnetite to maghemite is a rapid process in common environments. Consequently, experimental investigations of the iron oxide NPs with size below 20 nm do not evidence the transition [26, 153]. Recently, the Verwey transition was observed in the NPs with a size of 6 nm, which were kept under inert atmosphere, and thus their oxidation was prevented [154].
\n
The most significant and discussed issue is the spin order at single‐particle level and its surface to volume nature. Most works report the dominance of surface spin frustration and suggest the presence of the magnetically dead layer. The increased contribution of the frustrated spins is attributed mainly to size effect, low crystallinity, and surface roughness, dominating in the NPs obtained by coprecipitation method [26, 155–165]. The spin canting in the surface layer was also observed in diluted ferrofluids, which confirms the nature of the effect on single‐particle level [166]. However, the surface spin structure can be reformed when the NPs are in close proximity [131]. Significant increase of the amount of disordered spins was reported for hollow NPs of NiFe2O4 thanks to the additional inner surface [167]. On the other hand, the spin canting was also considered as volume effect, which occurs due to ion order‐disorder in the spinel structure [127, 168] or pinning of the spins on internal defects in single NPs [125].
\n
Focusing on the mesoscopic effects, the NP size distribution and interparticle interactions will be addressed. The particle‐size distribution is found to be very sensitive to the preparation method used. The NPs with almost monodisperse character are obtained by the decomposition route; however, the parameters of the reaction must be carefully controlled. For example, the prolongation of the reaction time leads both to larger NPs but also increased size distribution [33, 169]. Similar effect was observed for increasing concentration of the oleic acid or oleylamine [33]. Other techniques provide NPs with PDI over 0.2, and the size distribution must be then considered in analysis of the magnetic measurements [69, 116, 142]. However, it is worth mentioning that the narrow‐size distribution of the dTEM does not automatically imply the same value of the dXRD or dmag, as shown, e.g., for maghemite NPs [125]. In majority of real samples, interparticle interactions contribute to the magnetic properties. In most cases, the samples are studied in form of powders, which contain NPs in very close contact. Consequently, the response of such systems is always in the limit of the medium to strong interactions and is almost invariant to the preparation route used, and the interaction strength for a given NP size is given by minimum distance between the NPs, in other words by the thickness of the surface coating [42, 170–172]. Upon specific conditions, well‐defined aggregates are formed [173], as reported, e.g., for preparations in microemulsion [174], by decomposition method [175] and by controlled encapsulation into phospholipides [176]. Such assemblies attracted interest due to considerably enhanced heating properties in hyperthermia [177], which is associated with the enhancement of the single‐object (aggregate) anisotropy. In dense ensembles of the NPs, the onset of collective relaxation is also corroborated by significant increase of the relaxation time [178–184]; analogous consequence was observed in the aggregates [185]. However, the influence of the intra‐ and inter‐aggregate interactions is not explicitly decoupled. Recent studies also suggest a strong influence of the reformed particle energy barrier on the details of the aging dynamics, memory behavior, and apparent superspin dimensionality of the particles [132].
\n
In spite of the fact that the surface effects, defects, and interparticle interactions are believed to be contra‐productive factors as they in general decrease the value of saturation magnetization [26], they were recognized as potential enhancers of effective magnetocrystalline anisotropy, reflected, for example, in increase of the hysteresis losses [186]. Consequently, attempts to prepare smart NPs based on artificial core‐shell structure, e.g., NiFe2O4@γ‐Fe2O3 [187], ZnFe2O4@γ‐Fe2O3 [188], and Co,Fe2/Ni,Fe2O4 [189], appeared recently. Tri‐magnetic multi‐shell structures prepared by high‐temperature decomposition of the metal oleates were also reported [190].
\n
Alternative strategy is the tuning of magnetic properties of the spinel ferrite NPs via site‐specific occupation of the spinel lattice. This is a straightforward approach as the relevant metal ions can substitute each other in the spinel structure easily. In this case, however, the site occupation must be carefully evaluated and controlled. Successful preparation and basic investigation of structure and magnetic properties of the NPs of Mn‐doped CuFe2O4 ferrite [191], Zn‐doped MnFe2O4 [192] and NiFe2O4 [193, 194], Co‐doped NiFe2O4 [195] and ZnFe2O4 [196], and Cr‐doped CoFe2O4 [197] were reported. Recently, doping of spinel ferrites by large cations was suggested as a promising way to increase the effective magnetic anisotropy. La‐doped CoFe2O4 [198], Sr‐doped MgFe2O4 [199], and Ce‐doped NiFe2O4 [200] or ZnFe2O4 [201] were prepared. For the doped samples, the most promising are the polyol, sol‐gel, or microemulsion methods as they do not require identical decomposition temperatures of metal precursors like the organic‐based routes, allow rather good control over homogeneity of doping, and yield samples with sufficiently low particle‐size distribution.
\n
\n
\n
6. Conclusions and outlooks
\n
The core message of the chapter is to emphasize the importance of structural and spin order mirrored in magnetic properties of well‐defined spinel ferrite nanoparticles (NPs). The correlation between the specific preparation route to the typical structural and magnetic parameters of the particles is given, and the suitability of the resulting NPs in the context of possible applications is evaluated. Explicitly the meaning of different particle sizes obtained by different characterization methods, related to the degree of structural and spin order, is emphasized in the context of the magnetic properties. In order to wrap up the given subject, let\'s outline future outlooks in the field. The research of fine magnetic particles is progressively developing thanks to high demand on their practical exploitation, mainly in biomedicine. The forthcoming trend in customization of the magnetic NPs is obviously converging to control of the required magnetic properties at single‐particle level by adjustment of the synthetic protocols, which lead to fine tuning of the particle size, shape, and degree of order [169, 202]. For example, enhancement of the specific absorption rate in NPs can be achieved in natural or arbitrary core‐shell structures [203], via coupling of magnetically soft and hard ferrites for maximization of hysteresis losses [204] or by doping‐driven enhancement of heat generation [205]. Finally, smart self‐assembling strategies leading to superstructures [206], which can be even induced by magnetic field [207], seem to be a powerful tool for managing the magnetic response of the NPs at mesoscopic level.
\n
\n
Acknowledgments
\n
The authors gratefully acknowledge Dr. Puerto Morales from the Instituto de Ciencia de Materiales de Madrid for her generous support and for sharing his expertise in synthesis of uniform iron oxide nanoparticles and Prof. Carla Cannas from the Università degli studi di Cagliari for sharing her knowledge in synthesis of core‐shell spinel ferrite nanoparticles. The research was carried out thanks to the support of the Czech Science Foundation, project no. 15‐01953S, and 7FP program project MULTIFUN (no. 262,943), cofinanced by the Ministry of Education, Youth, and Sports (project no. 7E12057). Magnetic measurements were performed in MLTL (http://mltl.eu/), which is supported within the program of Czech Research Infrastructures (project no. LM2011025).
\n
\n',keywords:"cubic spinel ferrite nanoparticles, magnetic properties, core‐shell structure, particle size, spin canting, Mössbauer spectroscopy, magnetic susceptibility, size effect, superparamagnetism, magnetic anisotropy",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/52889.pdf",chapterXML:"https://mts.intechopen.com/source/xml/52889.xml",downloadPdfUrl:"/chapter/pdf-download/52889",previewPdfUrl:"/chapter/pdf-preview/52889",totalDownloads:2925,totalViews:1851,totalCrossrefCites:9,totalDimensionsCites:18,hasAltmetrics:0,dateSubmitted:"May 9th 2016",dateReviewed:"September 29th 2016",datePrePublished:null,datePublished:"March 8th 2017",dateFinished:null,readingETA:"0",abstract:"This chapter focuses on the relationship between structural and magnetic properties of cubic spinel ferrite MFe2O4 (M = Mg, Mn, Fe, Co, Ni, Cu and Zn) nanoparticles (NPs). First, a brief overview of the preparation methods yielding well‐developed NPs is given. Then, key parameters of magnetic NPs representing their structural and magnetic properties are summarized with link to the relevant methods of characterization. Peculiar features of magnetism in real systems of the NPs at atomic, single‐particle, and mesoscopic level, respectively, are also discussed. Finally, the significant part of the chapter is devoted to the discussion of the structural and magnetic properties of the NPs in the context of the relevant preparation routes. Future outlooks in the field profiting from tailoring of the NP properties by doping or design of core‐shell spinel‐only particles are given.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/52889",risUrl:"/chapter/ris/52889",book:{slug:"magnetic-spinels-synthesis-properties-and-applications"},signatures:"Barbara Pacakova, Simona Kubickova, Alice Reznickova, Daniel\nNiznansky and Jana Vejpravova",authors:[{id:"191237",title:"Associate Prof.",name:"Jana",middleName:null,surname:"Vejpravova",fullName:"Jana Vejpravova",slug:"jana-vejpravova",email:"vejpravo@fzu.cz",position:null,institution:{name:"Institute of Physics",institutionURL:null,country:{name:"Czech Republic"}}},{id:"195334",title:"Dr.",name:"Daniel",middleName:null,surname:"Niznansky",fullName:"Daniel Niznansky",slug:"daniel-niznansky",email:"daniel.niznansky@natur.cuni.cz",position:null,institution:null},{id:"195336",title:"Dr.",name:"Barbara",middleName:null,surname:"Pacakova",fullName:"Barbara Pacakova",slug:"barbara-pacakova",email:"pacakova@fzu.cz",position:null,institution:null},{id:"195337",title:"Dr.",name:"Simona",middleName:null,surname:"Kubickova",fullName:"Simona Kubickova",slug:"simona-kubickova",email:"kubickova@fzu.cz",position:null,institution:null},{id:"195338",title:"Mrs.",name:"Alice",middleName:null,surname:"Reznickova",fullName:"Alice Reznickova",slug:"alice-reznickova",email:"reznickova@fzu.cz",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Brief overview of preparation methods of spinel ferrite nanoparticles",level:"1"},{id:"sec_3",title:"3. Characterization of magnetic nanoparticles: parameters and methods",level:"1"},{id:"sec_3_2",title:"3.1. Basic structural and magnetic characterization",level:"2"},{id:"sec_4_2",title:"3.2. Real effects in magnetic nanoparticles",level:"2"},{id:"sec_4_3",title:"3.2.1. Size distribution",level:"3"},{id:"sec_5_3",title:"3.2.2. Spin canting phenomenon and surface effects",level:"3"},{id:"sec_6_3",title:"3.2.3. Interparticle interactions",level:"3"},{id:"sec_9",title:"4. Synergy of structural and magnetic probes",level:"1"},{id:"sec_10",title:"5. Impact of preparation and strategies of tuning magnetic properties",level:"1"},{id:"sec_11",title:"6. Conclusions and outlooks",level:"1"},{id:"sec_12",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Bauer LM, Situ SF, Griswold MA, Samia ACS. 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Faraday Discuss 2015;181:403–21. doi:10.1039/c4fd00265b.\n'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Barbara Pacakova",address:null,affiliation:'
Department of Magnetic Nanosystems, Institute of Physics of the Czech Academy of Sciences, Prague, Czech Republic
Department of Magnetic Nanosystems, Institute of Physics of the Czech Academy of Sciences, Prague, Czech Republic
Department of Inorganic Chemistry, Faculty of Science, Charles University in Prague, Prague, Czech Republic
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Bhattarai, Dharmendra Neupane, Bishal Nepal, Vasilii\nMikhaylov, Alexei V. Demchenko and Keith J. Stine",authors:[{id:"192643",title:"Prof.",name:"Keith",middleName:null,surname:"Stine",fullName:"Keith Stine",slug:"keith-stine"},{id:"213383",title:"Dr.",name:"Jay",middleName:null,surname:"Bhattarai",fullName:"Jay Bhattarai",slug:"jay-bhattarai"},{id:"213384",title:"Mr.",name:"Dharmendra",middleName:null,surname:"Neupane",fullName:"Dharmendra Neupane",slug:"dharmendra-neupane"},{id:"213385",title:"Mr.",name:"Vasily",middleName:null,surname:"Mikhalov",fullName:"Vasily Mikhalov",slug:"vasily-mikhalov"},{id:"213386",title:"Prof.",name:"Alexei",middleName:null,surname:"Demchenko",fullName:"Alexei Demchenko",slug:"alexei-demchenko"}]},{id:"60746",title:"Colorimetric Detection of Copper Ion Based on Click Chemistry",slug:"colorimetric-detection-of-copper-ion-based-on-click-chemistry",signatures:"Lingwen Zeng, Zhiyuan Fang and Yunbo Wang",authors:[{id:"173972",title:"Dr.",name:"Lingwen",middleName:null,surname:"Zeng",fullName:"Lingwen Zeng",slug:"lingwen-zeng"},{id:"228491",title:"Dr.",name:"Zhiyuan",middleName:null,surname:"Fang",fullName:"Zhiyuan Fang",slug:"zhiyuan-fang"}]},{id:"61475",title:"Properties and Applications of Ruthenium",slug:"properties-and-applications-of-ruthenium",signatures:"Anil K. Sahu, Deepak K. Dash, Koushlesh Mishra, Saraswati P.\nMishra, Rajni Yadav and Pankaj Kashyap",authors:[{id:"204256",title:"Dr.",name:"Anil",middleName:"Kumar",surname:"Sahu",fullName:"Anil Sahu",slug:"anil-sahu"},{id:"211230",title:"Mr.",name:"Pankaj",middleName:null,surname:"Kashyap",fullName:"Pankaj Kashyap",slug:"pankaj-kashyap"},{id:"211868",title:"Ms.",name:"Rajni",middleName:null,surname:"Yadav",fullName:"Rajni Yadav",slug:"rajni-yadav"},{id:"221419",title:"Mr.",name:"Koushlesh",middleName:null,surname:"Mishra",fullName:"Koushlesh Mishra",slug:"koushlesh-mishra"},{id:"221420",title:"Mr.",name:"Sarawati Prasad",middleName:null,surname:"Mishra",fullName:"Sarawati Prasad Mishra",slug:"sarawati-prasad-mishra"},{id:"250558",title:"Dr.",name:"Deepak Kumar",middleName:null,surname:"Dash",fullName:"Deepak Kumar Dash",slug:"deepak-kumar-dash"}]},{id:"58968",title:"Extraction of Platinum Group Metals",slug:"extraction-of-platinum-group-metals",signatures:"Bongephiwe Mpilonhle Thethwayo",authors:[{id:"224083",title:"Dr.",name:"Bongephiwe",middleName:null,surname:"Thethwayo",fullName:"Bongephiwe Thethwayo",slug:"bongephiwe-thethwayo"}]},{id:"57050",title:"Rare Earth Extraction from NdFeB Magnets",slug:"rare-earth-extraction-from-ndfeb-magnets",signatures:"Jiro Kitagawa and Masami Tsubota",authors:[{id:"210570",title:"Prof.",name:"Jiro",middleName:null,surname:"Kitagawa",fullName:"Jiro Kitagawa",slug:"jiro-kitagawa"},{id:"220323",title:"Dr.",name:"Masami",middleName:null,surname:"Tsubota",fullName:"Masami Tsubota",slug:"masami-tsubota"}]}]}]},onlineFirst:{chapter:{type:"chapter",id:"74788",title:"SARS-CoV-2 and Coronavirus Ancestors under a Molecular Scope",doi:"10.5772/intechopen.95102",slug:"sars-cov-2-and-coronavirus-ancestors-under-a-molecular-scope",body:'\n
\n
1. Introduction
\n
The Coronaviridae have a wide variety of host species, which infect many mammalian and avian species and result in high respiratory, hepatic, and central nervous system diseases. Coronaviruses in humans and fowl mainly cause infections in the upper respiratory tract and enteric infections are caused by pig and bovine Coronavirus [1]. Coronaviruses CoVs are divided into four genera and in 1937 the first coronavirus was identified [2, 3]. Coronaviruses are a family of helical nucleocapsid and extremely large genomes enveloped positive-stranded RNA viruses. Coronaviruses are composed of: 1) Nucleocapsid Protein (N): helical nucleocapsid protein component and is supposed to bind genomic RNA in a bead-on-string mode. 2) spike protein (S): Viral envelope component that mediates binding to the receptor and merging of cell membranes if the virus and host. 3) Membrane Protein (M): the most present component and gives its form to the virion envelope. 4) Envelope Protein (E): A small, only minor component of virions and a small polypeptide between 8.4 and 12 kDa (76–109 amino acids). 5) Accessory Proteins: “Extra” genes may be interspersed with a group of canonical genes, replicase, S, E, M, and N with additional ORFs, or embedded in a separate ORF or heavily overlapped with another gene [4]. (Figure 1) Coronaviruses are also one of the few genomically proof-reading RNA viruses that avoid the virus developing mutations that could weaken it. Such capacity may have contributed to the failure of specific antivirals like ribavirin to subdue SARS-CoV-2 meanwhile, can thwart viruses like hepatitis C. Drugs kill viruses by mutations. However, the proofreader can eliminate these changes in coronaviruses. Coronaviruses have a special trick that is fatal: they often recombine, exchange pieces of RNA and other coronaviruses. This is usually an insignificant trade between viruses like parts [6]. Both mammals are affected by alphacoronaviruses and beta-coronaviruses. Alpha-Coronaviruses and beta-Coronaviruses typically cause human breathing diseases and animal gastroenteritis. Gamma and delta coronaviruses infect birds, but some can infect mammals as well. The SARS-CoV, MERS-CoV viruses, and the other four human coronaviruses (HCoV-NL-63, HCoV-229E, HCoV-OC43 and HKU1) are responsible for severe respiratory syndromes in people with mild conditions in immunocompetent hosts, although some infections are severe in infants and elderly people [7]. Coronavirus transcription is characterized by the development of several mRNAs containing the sequences corresponding to the two ends of the genome. The production of subgenomic mRNA requires discontinuous transcripts. Transcription is known as the process by which subgenomical mRNAs are generated, and replications are the process by which genomic-sized RNA, which also acts as mRNA, is generated [8]. Human coronaviruses (HCoV) were first detected and developed in the nasal cavities of common cold patients in the 1960s. Two human coronaviruses-OC43 and 229E are responsible for about 30% of common colds [9, 10]. Middle East Coronavirus Respiratory Syndrome (MERS-CoV) has also been a global health concern. The initial report for MERS-CoV was in 2012. More than 2000 civilians have been infected in 27 countries in the Middle East and 4 subcontinents. During the SARS outbreak in 26 countries, more than 8000 cases were recorded in 2003 [11]. The ongoing coronavirus disease outbreak (COVID-19), first reported in December 2019 in Wuhan, China. As of 5th of April 2020, the world health organization (WHO) announced this disease as a global public health emergency to extend to 206 countries and territories across the world, with two international correspondence performed on 3,090,445 confirmed cases reported cases, including 217,769 deaths [12]. SARS-CoV-2 virus, the cause of COVID-19 disease that lead to an emergency outbreak that has been going for several months, now it may as well continue to its spread until the finding of new treatments along with the implementation of effective countermeasures. The newly evolving coronavirus (SARS-CoV 2) is becoming increasingly largescale. In the last few weeks, complete genomic sequences were released in order to understand the development and molecular characteristics of the virus by the global scientific community. In this review we will discuss the genomic structure of the virus, the possible relations between several viruses of the same family and the suspected origins and spill over that might have led to such epidemic and molecular diagnostics used to detect.
\n
Figure 1.
Viral structure diagram showing the envelope, Centre and structure of the nucleoprotein. S, the spike protein and different drug candidates against the three coronaviruses [5].
\n
\n
\n
2. Emergence of a new virus
\n
The life of people over the centuries has been influenced by Zoonotic diseases. Many of these situations are especially variable in complexity, dynamics and shifts over time, as they emerge, and reappear. Transmission of the pathogen from an animal to human, also known as zoonotic spillovers, is a global public health issue and remains an ambiguous phenomenon, while associated with multiple outbreaks [13]. A mixture of many factors is needed to fulfill a zoonotic spillover, including ecological, epidemiological and behavioral determinants of pathogen transmission and inherent human factors influencing susceptible infection, as well as dietary and societal factors linked with foodborne zoonotic spillover [14]. A new virus is a virus that mutated and went through an evolution process to adapt to new kinds of hosts by a process called spillover. Spillover can happen in wild animals’ market as a virus can mutate and go on infecting a new host where it further mutates within new host until it adapts to this new host and become infectious [15]. Over the past two decades, many outbreaks of Zoonotic diseases such as SARS, the Hendra virus and the Nipah virus have been related to the bat-borne viruses. The most definitive proof was included from the separation of the CoV from bats in China, there was over 98% similarity in the genome sequence to SARS-CoV, and can use SARS-CoV-receptor ACE2 on cells of the human race. It is hard to evaluate the possibility for spillover of several similar SARS-CoV Bat CoVs as a result of infringing isolation of viruses, but it should be noted that a “consensus” virus developed through reverse genetics has high evidence of human infection it is clear that bats are the most likely original cause of the current 2019 CoV outbreak in Wuhan, China, which started in December 2019, continuing to spread to many city and province areas in China from a “wet market.” The probability of food transmission of derived animal products was also suggested, as it has recently been pointed out to affect the present epidemic as well as the chance of common near contact with animals (a not unusual scenario in these types of markets). Their possible adaptations may lead to new and stable reservoirs, such as human hosts. Those are ideas and problems arisen from the emerged SARS-CoV2, that immediately compares SARS-CoV and MERS-CoV with other beta-coronaviruses with similar natural, intermediate animal hosts with also the possibility of human-to-human transmission in comparison [13, 16].
\n
\n
\n
3. Genomic characteristics
\n
During infection, the genome has many roles. It first functions as mRNA that is translated into a huge polyprotein called replicase that involves a ribosomal frameshifting event for complete synthesis. The replicase is the only genome-derived translation product; all downstream ORFs are expressed by Subgenomic RNAs. Next, the genome is the replication and transcription template. Finally, the genome is involved in assembly, as progeny genomes are found in progeny viruses [4]. The genomic RNA for coronavirus of about 30 000 nucleotides encodes structural virus proteins, non-structural proteins with a key part in viral RNA synthesis (which is understood to be replicase transcriptase proteins) and non-structural proteins that are not necessary for viral replication in cell culture but which in vivo tend to be a selective advantage (which is referred to in vivo) [8]. Cis-acting sequence and structural elements involved in the replication, transcription, translation, and packaging are incorporated within RNA virus genomes. Some of these signals are intended to enable the interaction of selective viral RNAs with RNA synthesis machines while some allow or modify events that happen meanwhile the synthesis or assembly of viral protein [17, 18]. Coronaviruses contain the hugest genomes of any RNA virus, and this has hindered the production of full-length coronavirus cDNAs along with the discovery that certain cDNAs originating in the replicase areas of genes are unstable in bacteria. However, the assembly of long-lasting cDNAs in porcine coronavirus transmissible gastroenteritis viral genomic RNA (TGEV) has reportedly been identified with two methods. First, a TGEV full-length cDNA was installed on a bacterial artificial chromosome (BAC). Second, the TGEV total cDNA was installed in-vitro using a series of adjacent cDNAs within it engineered unique restriction sites, cDNA of the RNA transcripts derived from bacteriophage T7-RNA polymerase have been then used for infectious virus production [19]. SARS-CoV-2 (Figure 2) has a long genome with ORF1ab polyprotein, along with four main structural proteins, involving Spike surface glycoprotein, small envelope protein, matrix protein and nucleocapsid protein, which is also the case in other beta-coronaviruses (Figure 3; Table 1). In the ORF1ab polyprotein there were two deletions (three nucleotides and 24 nucleotides) and also one at the 3′ end of the genome (ten nucleotides) [21] (Figure 4).
\n
Figure 2.
The structure of SARS-CoV-2 transcriptome [20].
\n
Figure 3.
SARS-CoV-2 binding by its spike protein to ACE2 receptor [12].
\n
Figure 4.
SARS-CoV-2 inner proteins illustration [12].
\n
\n
\n
\n\n
\n
\nGroup 1\n
\n
\n\n\n
\n
Human coronavirus 229E
\n
HCoV-229E
\n
\n
\n
Porcine enteric (transmisible gastroenteritis virus, TGEV; and porcine epidemic diarrhea virus, PEDV) and respiratory (PRCoV) coronavirus
\n
PCoV
\n
\n
\n
Canine coronavirus
\n
CCoV
\n
\n
\n
Feline coronavirus, including feline infectious peritonitis virus (FIPV)
\n
FCoV
\n
\n
\n
\nGroup 2\n
\n
\n
\n
Human coronavirus OC43
\n
HCoV-OC43
\n
\n
\n
Bovine coronavirus
\n
BCoV
\n
\n
\n
Turkey coronavirus BCoV related
\n
TCoV-B
\n
\n
\n
Murine coronaviruses including mouse hepatitis virus (MHV)
\n
MCoV
\n
\n
\n
Porcine hemagglutinating encephalomyelitis virus
\n
HEV
\n
\n
\n
Rat coronavirus including sialodacryoadenitis virus (SDAV)
\n
RtCoV
\n
\n
\n
\nGroup 3\n
\n
\n
\n
Avian coronavirus including infectious bronchitis virus (IBV)
The unusual variations in host diversity and tissue tropism between coronaviruses are primarily due to differences in the spike glycoprotein. The S protein is a broad, glycoprotein type I membrane containing disruptive functional fields near the amino (S1) and carboxy (S2) Termini. Via their receptor specificity and probably by their membrane fusion activities in the cell entry of viral tropism, these spikes can be identified [1]. ACE2 is a primary determinant for the SARS-CoV Host range [23, 24]. The life cycle of COVID-19 starts with the binding of its Angiotensin Converting Enzyme (ACE2) receptor expressed in various cell types in the body and other susceptible cells throughout the body. (ACE2), the membrane-associated enzyme Carboxypeptidase, is a crucial regulator for cardiac function. Now, recognized and characterized with a sudden second role for ACE2 in mediating viral entry and cell fusion in the form of SARS-CoV spike glycoprotein partner. The coronaviridae family includes this zoonotic virus. The virus has a healthy ssRNA genome and little structural and non-structural protein. Different points of view have been identified with great similarity to SARS-CoV. The Approach of the virus is through S1 protein, which then integrates to the virus membrane with endosomal membranes, possibly by S2 mediation. Then the viral genome is released into the cytoplasm of the cell [25, 26, 27, 28, 29, 30]. S-protein has two sub-units with one sub-unit directly binding to the receptor enabling the entrance of the virus into cells. The S-protein RNA binding domain in COVID-19 has a more advanced SARS-CoV homology. Although some of the residues essential to binding are not alike, the structural conformation was not changed in general by the non-identical residues [31]. CoV spike (S) is a key goal for vaccines, antibodies and diagnosis. A 3.5 angle-resolution cryo-electron microscopy structure for the SARS-CoV-2 S was developed cutting conformation in order to promote medical response. The prominent trimer ‘s state possesses rotation in a receptor-accessible conformation in one of the three receptor binding domains (RBDs). Biophysical and structural verification is also given that the SARS-CoV-2 S protein has more affinity than severe acute respiratory (SARS)-CoV S-binding enzyme 2, (ACE2) [32] (Figure 5).
\n
Figure 5.
Phylogenetic tree of 160 SARS-CoV-2 genomes [33].
\n
\n
\n
3.2 Replication
\n
Untranslated regions of RNA (UTRs) have 5′ and 3′ viruses that carry RNA-specific signals. The 5′ capped coronavirus genome compromise a 3’ UTR consisting of 300 to 500 nucleotides) in addition to a poly(A) tail. Host-related factors involving two class-II viruses bovine coronavirus (BCV) and Mouse Hepatitis Coronavirus (MHV) were studied, in order to better understand coronavirus replication. Using gel mobility shift assays unique host protein interactions were identified with BCV 3’ UTR [287 nt plus poly(A) tail]. The MHV 3′ -UTR [301 nt in addition to poly(A) tail] rivalry indicates that interactivity for the two viruses are preserved. UV cross-linking studies observed proteins with molecular masses of 99, 95 and 73 kDa. The ranges 40- to 50 and 30 kDa even contained less heavily labeled proteins. For binding the 73-kDa protein a poly(A) tail was needed. The 73 kDa proteins have been identified as cytoplasmic poly(A)-binding protein (PABP) by an Immuno-precipitation of UV-cross-linked proteins. To define the significance of the poly(A) tail, the replication of the impaired genomes BCV Drep and MHV MIDI-C was used alongside with several mutants. After transfection to the supporting virus-infected cells, the defect genomes with shortened, 5- or 10-A poly(A) tails have been replicated. BCV Drep RNA lacking a poly(A) tail did not replicate while MHV MIDI-C RNA replication was detected with a deleted tail after multiple mutations of the virus. The kinetics of replication is delayed in both mutants. Noticeable extension or addition of the poly(A) tail in mutants in the replication assay associated with the presence of these RNAs. RNAs exhibit less in vitro PABP binding in shorted Poly(A) tails, indicating decreased RNA replication interactions with protein. The data show strongly that the poly(A) tail is a significant indication for the replication of coronavirus [34]. The virus initiates replication and assembly of protein that is followed by the release of new infectious particles into novel target cells. These events are followed by proinflammation chemokines and cytokines producing and triggering which lead to significant pulmonary damage-causing atypical pneumonia with quick abnormalities and failure [35, 36].
\n
\n
\n
3.3 Transcription
\n
For differing coronaviruses, the number of mRNAs varies. The number of mRNAs in the coronavirus species is the number of functional genes. A few hours after infection with the virus in most viral cell systems, coronavirus mRNA synthesis can be identified and proceeded until cells have become invasive. Subgenomic mRNAs are found to be heterogeneous (M. [37]). A two-component support based on expression system was developed and individual genomes were created by selective recombination or by using infectious cDNA clones. Transcription sequences have mainly been characterized by helper-dependent expression systems and can now be validated via single genomes. The coronavirus genome was created through modification of infectious cDNA, resulting in efficient expression of the foreign gene (20 g ml − 1) and stable (20 passages) [22, 38]. In a Study, The creation of the full-length infectious cDNA clone and a functional duplicate of the Urbani strain as a bacterial artificial chromosome (BAC) of the extreme acute respiratory syndrome (SARS-CoV). Through this method, the viral RNA was expressed in the cytomegalovirus promoter’s cell nucleus and further multiplied by viral replicas in the cytoplasm. The Escherichia coli infectious clone and duplicate have been completely stable. The use of the SARS-CoV replica has been shown to be important in efficient coronavirus-RNA synthesis for the recent identification of RNA-processes enzyme exoribonuclease, endo-ribonucleases and 2-line-O-ribose that are found to be essential [39]. The RNA-dependent RNA synthesis is used for coronaviral transcription. The result is that a nested range of 6 to 8 mRNAs of different sizes is produced, depending on the strain of the coronavirus. The mRNAs are five prime and three prime genome -co- terminals. The most significant mRNA is the genomic RNA (gRNA) for both rep1a and rep1b genes. A discontinuous transcription process fuses a lead sequence of 93 nucleotides) (originating from the 5 prime at the end of a genome to 5 prime of the mRNA coding sequence (body) [40]. The RNA virus genomes are comprised of a series of cis-acting and structural elements involved in viral replication. A bulky secondary loop structure was previously established at the upstream end of the 3-way untranslated region (3 tablets of UTR) of the Mouse Hepatitis Virus (MHV) coronavirus genome. This element has proved to be important for viral replication, beginning immediately downstream of the nucleocapsid gene stop codon. A 3 UTR pseudoknot of the corresponding downstream closely related to the bovine coronavirus BCoV. It is an essential pseudoknot for replication and has a preserved counterpart for each coronavirus in groups 1 and 2 [17]. More than one ORF is comprised of 5 ‘unique regions within multiple mRNA s. For example, mRNA 5 of MHV, which has two ORFs in the coding region which can encode two p 1 3 and pl0 proteins, respectively. A negative-stranded RNA template that is represented in an only very small percentage (1–2%) of the intracellular virus-specific RNAs is clearly mediated for Coronavirus RNA synthesis. This negative strand was synthesized by the virus-encoded RNA from the inbound virion. This is likely because the positive-sequenced RNA exceeds the negative-stranded RNA for several rounds of mRNA synthesis. Thus, negative-stranded RNA has more stability. This stability is attributed to the presence in the coronavirus-infected cells of all of the negatively-stranded RNA as a double-stranded RNA [41, 42, 43]. The transcriptome Structure was unknown despite the SARS-CoV-2 genome being recorded recently. a high-resolution map was presented of the SARS-CoV-2 transcriptome and epitranscriptome using two complementary sequencing techniques. DNA nanoball sequencing reveals that due to discontinuous transcription occurrences the transcriptome is highly complexSARS-CoV-2 yields transcripts that code unknown ORFs with fusion, deletion and/or frameshift in addition to the canonical genomic and 9 subgenomic RNAs. 41 sites for RNA modification on viral transcripts were also found with the most common motif being AAGAA with nanopore direct RNA sequencing [20].
\n
\n
\n
3.4 Morphogenesis
\n
Expression studies showed that coronavirus envelope protein E and the more present membrane glycoprotein M were required and adequate to assemble virus particles into cells. Clustered charged-to-alanine Mutagenesis of the gene E was carried, which integrated mutations in mouse hepatitis virus E (MHV) E protein, as a step forward in our understanding of the role of the mouse hepatitis virus E (MHV) E protein. One was apparently lethal and one was a wild-type phenotype of four probable clustered charged-to-alanine E gene mutants. The other two mutants were partly affected by temperature, developing tiny plaques at a nonpermissive temperature. Reverting analyses of these two mutants showed that each mutation was the reason for the temperature-sensitive phenotype and promoted probable interactions among E protein monomers. In permissive temperature, both temperature-sensitive mutants have been substantially thermolabile, indicating that their assembly fails. In the case of the electron microscopy, virions of one of the mutants were discovered to have remarkably aberrant morphology when compared with the wild type: most mutant virions had pinched and extended forms that were seen seldom in the wild [44, 45, 46]. Specific recombination of RNA was utilized to create mutants containing chimeric nucleocapsid (N) protein genes in mouse hepatitis virus (MHV) that replace bovine coronavirus N gene segments in place of the correct MHV sequences. This described portions of the two N proteins which were functionally equivalent, given evolutionary divergences. These regions included mostly the RNA binding domain centrally located and two putative spacers connecting the three N protein domains. On the other hand, a bovine coronavirus cannot be transferred from the amino terminus N, the acidic carboxy-terminal region and the central domain serine and arginine-rich section, probably because these parts of a molecule are engaged in protein–protein interactions that are unique to each virus (or possibly each host). The results show that the recombination of the coronavirus genome can be used to produce extensive substitutions and recombinants that cannot otherwise be produced between two viruses separated by species barrier [47].
\n
\n
\n
\n
4. Mutations
\n
RNA viruses must establish an equilibrium between the adaptability to new environmental circumstances or the necessity to preserve the intact and replicative genome to ensure survival and propagation for the host cells. Various virus families with the biggest and most complex replicating RNA genomes identified, up to 32 kb of positive RNA, such as coronaviruses, can achieve these objectives. CoVs, including (MHV) and SARS-CoV, express 3 to 5′ of exoribonuclease (ExoN) activity in nsp14. The exoN genetic inactivation of alanine replacement with retained active DE-D Residues in Engineered SARS-CoV and MHV Genomes leads to viable mutants, which display 15 to 20 times higher mutation rates and up to 18 times higher than those endured for other RNA fidelity mutants. Nsp14-ExoN, therefore, is important for the fidelity of the replication and possibly acts as a direct mediator or regulator for a more complex RNA proof-reader, an exceptional process in RNA virus biology. The removal of nsp14-mediated proofreading mechanisms will have significant consequences for our interpretation of RNA virus evolution and will also provide a robust model to research the correlation between fidelity, diversity and pathogenesis [48, 49, 50, 51, 52]. COVID-19 is very related to SARS-CoV Middle East Respiratory Syndrome (MERS). Yet another human attack by coronaviruses. A research attempted to explore potential changes/developments in the ‘spike protein’ element that enables the virus to bind to cell receptor(s) and in the silicon design and discovery of B epitopes in which antibody synthesis is used to neutralize and block this connection. The findings show that this protein varies constantly between the sequences of proteins obtained worldwide. Some B epitopes (part of an antigen molecule to which an antibody attaches itself), 177-MDLEGKQGNFKNL-189-555- SNKKFLPF-562-656 -VNSYECDIPI-666, 1035- GQSKRVDFC-1043, from the Cons sequence constructed from global protein sequences released between 11 Feb and 06 April, have been found to meet most of the criteria required for real wet application [53]. SARS-CoV is well suited to cultural development and does not seem to be selected in humans. It was also assessed that, in late October 2002, the alleged root of the SARS outbreak was consistent with a previous report of case use from China. The higher structural and antigenic sequence divergence and significant deletions within 3 ‘– of much of the viral genome indicate that some selection pressures conflict along with the functional structure of these confirmed and suspected ORFs [54]. In three regions the SARS and SARSr of bats-CoVs are largely different: S, ORF8 and ORF3. SARSr-CoVs bats share high sequence with the SARS- COV in the S2 but are highly different in the S1 region. However, bat MERSr-CoVs bats and human and camel MERS-CoVs share similar genomics but are significantly different from their genomic sequences [7]. Comparison of COVID-19, SARS-CoV and MERS-CoV genome sequence showed that COVID-19 has better sequence similarity with SARS-CoV compared to MERS CoV. Nevertheless, the COVID-19 amino acid sequence differed from the other coronavirus in specific areas of 1ab polyprotein and surface glycoprotein or S-protein [31]. Considering the high rate of mutation that characterizes RNA viruses, it is clear that several more mutations will emerge in the viral genome to monitor the spread of SARS-CoV-2 knowing that also their mutations rate are lower than other RNA viruses due to their proofreading activity described above [55, 56](Tables 2–4).
Mutations of SARS-CoV-2 strains found throughout the whole genome. The number in the parentheses shows where amino acid is found in its protein [21].
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Accession
\n
Location-date
\n
Nucleotide variation
\n
Gene
\n
Amino acid change
\n
Mutation type
\n
\n\n\n
\n
MT240479
\n
04-03-2020/Pakistan Gilgit
\n
1 1497G > A
\n
Orf1ab
\n
\n
Synonymous mutation
\n
\n
\n
MN996527
\n
30/Dec/2019-China Wuhan
\n
21316G > A
\n
Orf1ab
\n
D7018N
\n
Missense
\n
\n
\n
MN996527
\n
30/Dec/2019-China Wuhan
\n
24292A > G
\n
S
\n
\n
Synonymous mutation
\n
\n
\n
LC528232
\n
10/Feb/2020-Japan
\n
11083 T > G
\n
Orf1ab
\n
L3606F
\n
Missense
\n
\n
\n
LC528232
\n
10/Feb/2020-Japan
\n
29642C > T
\n
ORF10
\n
\n
Synonymous mutation
\n
\n
\n
LR757995
\n
05/Jan/2020-China Wuhan
\n
28144 T > C
\n
ORF8
\n
L84S
\n
Missense
\n
\n
\n
LR757998
\n
12/26/2019-China Wuhan
\n
6968C > A
\n
Orf1ab
\n
L2235I
\n
Missense
\n
\n
\n
LR757998
\n
12/26/2019-China Wuhan
\n
11749 T > A
\n
Orf1ab
\n
\n
Synonymous mutation
\n
\n
\n
MN938384
\n
1/10/2020-China Shenzhen
\n
8782C > T
\n
Orf1ab
\n
\n
Synonymous mutation
\n
\n
\n
MN938384
\n
1/10/2020-China Shenzhen
\n
28144 T > C
\n
ORF8
\n
L84S
\n
Missense
\n
\n
\n
MN938384
\n
1/10/2020-China Shenzhen
\n
29095C > T
\n
N
\n
\n
Synonymous mutation
\n
\n
\n
MN975262
\n
11/Jan/2020-China
\n
8782C > T
\n
Orf1ab
\n
\n
Synonymous mutation
\n
\n
\n
MN975262
\n
11/Jan/2020-China
\n
9534C > T
\n
Orf1ab
\n
T3090I
\n
Missense
\n
\n
\n
MN975262
\n
11/Jan/2020-China
\n
29095C > T
\n
N
\n
\n
Synonymous mutation
\n
\n
\n
MN975262
\n
11/Jan/2020-China
\n
28144 T > C
\n
ORF8
\n
L84S
\n
Missense
\n
\n
\n
MN975262
\n
11/Jan/2020-China
\n
8782C > T
\n
Orf1ab
\n
\n
Synonymous mutation
\n
\n
\n
MN985325
\n
19/Jan/2020-USA WA
\n
28144 T > C
\n
ORF8
\n
L84S
\n
Missense
\n
\n
\n
MN994467
\n
23/Jan/2020-USA CA
\n
1548G > A
\n
Orf1ab
\n
S428N
\n
Missense
\n
\n
\n
MN994467
\n
23/Jan/2020-USA CA
\n
8782C > T
\n
Orf1ab
\n
\n
Synonymous mutation
\n
\n
\n
MN994467
\n
23/Jan/2020-USA CA
\n
26729 T > C
\n
M
\n
\n
Synonymous mutation
\n
\n
\n
MN994467
\n
23/Jan/2020-USA CA
\n
28077G > C
\n
ORF8
\n
V62L
\n
Missense
\n
\n
\n
MN994467
\n
23/Jan/2020-USA CA
\n
28144 T > C
\n
ORF8
\n
L84S
\n
Missense
\n
\n
\n
MN994467
\n
23/Jan/2020-USA CA
\n
28792A > C
\n
N
\n
\n
Synonymous mutation
\n
\n
\n
MN994467
\n
23/Jan/2020-USA CA
\n
1912C > T
\n
Orf1ab
\n
\n
Synonymous mutation
\n
\n
\n
GWHABKF00000001
\n
23/Dec/2019-China Wuhan
\n
3778A > G
\n
Orf1ab
\n
\n
Synonymous mutation
\n
\n
\n
GWHABKF00000001
\n
23/Dec/2019-China Wuhan
\n
8388A > G
\n
Orf1ab
\n
N2708S
\n
Missense
\n
\n
\n
GWHABKF00000001
\n
23/Dec/2019-China Wuhan
\n
8987 T > A
\n
Orf1ab
\n
F2908I
\n
Missense
\n
\n
\n
GWHABKK00000001
\n
30/Dec/2019-China Wuhan
\n
24325A > G
\n
S
\n
\n
Synonymous mutation
\n
\n
\n
GWHABKK00000001
\n
30/Dec/2019-China Wuhan
\n
21316G > A
\n
Orf1ab
\n
D7018N
\n
Missense
\n
\n
\n
GWHABKH00000001
\n
30/Dec/2019-China Wuhan
\n
6996 T > C
\n
Orf1ab
\n
I2244T
\n
Missense
\n
\n
\n
GWHABKJ00000001
\n
01/Jan/2019-China Wuhan
\n
7866G > T
\n
Orf1ab
\n
G2534V
\n
Missense
\n
\n
\n
GWHABKM00000001
\n
30/Dec/2019-China Wuhan
\n
21137A > G
\n
Orf1ab
\n
K6958R
\n
Missense
\n
\n
\n
GWHABKM00000001
\n
30/Dec/2019-China Wuhan
\n
7016G > A
\n
Orf1ab
\n
G2251S
\n
Missense
\n
\n
\n
GWHABKO00000001
\n
30/Dec/2019-China Wuhan
\n
8001A > C
\n
Orf1ab
\n
D2579A
\n
Missense
\n
\n
\n
GWHABKO00000001
\n
30/Dec/2019-China Wuhan
\n
9534C > T
\n
Orf1ab
\n
T3090I
\n
Missense
\n
\n
\n
MT188341
\n
05/Mar/2020-USA MN
\n
6035A > G
\n
Orf1ab
\n
\n
Synonymous mutation
\n
\n
\n
MT188341
\n
05/Mar/2020-USA MN
\n
8782C > T
\n
Orf1ab
\n
\n
Synonymous mutation
\n
\n
\n
MT188341
\n
05/Mar/2020-USA MN
\n
16467A > G
\n
Orf1ab
\n
\n
Synonymous mutation
\n
\n
\n
MT188341
\n
05/Mar/2020-USA MN
\n
18060C > T
\n
Orf1ab
\n
\n
Synonymous mutation
\n
\n
\n
MT188341
\n
05/Mar/2020-USA MN
\n
21386insT
\n
Orf1ab
\n
\n
Insertion
\n
\n
\n
MT188341
\n
05/Mar/2020-USA MN
\n
21388-21390insTT
\n
Orf1ab
\n
\n
Insertion
\n
\n
\n
MT188341
\n
05/Mar/2020-USA MN
\n
23185C > T
\n
S
\n
\n
Synonymous mutation
\n
\n
\n
MT188341
\n
05/Mar/2020-USA MN
\n
28144 T > C
\n
ORF8
\n
L84S
\n
Missense
\n
\n
\n
MT188339
\n
09/Mar/2020-USA MN
\n
8782C > T
\n
Orf1ab
\n
\n
Synonymous mutation
\n
\n
\n
MT188339
\n
09/Mar/2020-USA MN
\n
17423A > G
\n
Orf1ab
\n
Y5720C
\n
Missense
\n
\n
\n
MT188339
\n
09/Mar/2020-USA MN
\n
18060C > T
\n
Orf1ab
\n
\n
Synonymous mutation
\n
\n
\n
MT188339
\n
09/Mar/2020-USA MN
\n
21386C > T
\n
Orf1ab
\n
\n
Synonymous mutation
\n
\n
\n
MT188339
\n
09/Mar/2020-USA MN
\n
22432C > T
\n
S
\n
\n
Synonymous mutation
\n
\n
\n
MT188339
\n
09/Mar/2020-USA MN
\n
28144 T > C
\n
ORF8
\n
L84S
\n
Missense
\n
\n
\n
MT121215
\n
02/Feb/2020-China Shanghai
\n
6031C > T
\n
Orf1ab
\n
\n
Synonymous mutation
\n
\n
\n
MT123290
\n
05/Feb/2020-China Guangzhou
\n
15597 T > C
\n
Orf1ab
\n
\n
Synonymous mutation
\n
\n
\n
MT123290
\n
05/Feb/2020-China Guangzhou
\n
29095C > T
\n
N
\n
\n
Synonymous mutation
\n
\n
\n
MT126808
\n
2/28/2020-Brazil
\n
26144G > T
\n
ORF3a
\n
G251V
\n
Missense
\n
\n
\n
MT066175
\n
31/Jan/2020-Taiwan
\n
8782C > T
\n
Orf1ab
\n
\n
Synonymous mutation
\n
\n
\n
MT066175
\n
31/Jan/2020-Taiwan
\n
28144 T > C
\n
ORF8
\n
L84S
\n
Missense
\n
\n
\n
MT093571
\n
07/Feb/2020-Sweden
\n
13225C > G
\n
Orf1ab
\n
\n
Synonymous mutation
\n
\n
\n
MT093571
\n
07/Feb/2020-Sweden
\n
13226 T > C
\n
Orf1ab
\n
\n
Synonymous mutation
\n
\n
\n
MT093571
\n
07/Feb/2020-Sweden
\n
17423A > G
\n
Orf1ab
\n
Y5720C
\n
Missense
\n
\n
\n
MT093571
\n
07/Feb/2020-Sweden
\n
23952 T > G
\n
S
\n
\n
Synonymous mutation
\n
\n
\n
MT066156
\n
30/Jan/2020-Italy
\n
11083 T > G
\n
Orf1ab
\n
L3606F
\n
Missense
\n
\n
\n
MT066156
\n
30/Jan/2020-Italy
\n
26144G > T
\n
ORF3a
\n
G251V
\n
Missense
\n
\n
\n
LC522975
\n
20/JAN/2020-JAPAN
\n
8782C > T
\n
Orf1ab
\n
\n
Synonymous mutation
\n
\n
\n
LC522975
\n
20/JAN/2020-JAPAN
\n
29095C > T
\n
N
\n
\n
Synonymous mutation
\n
\n
\n
LC522975
\n
20/JAN/2020-JAPAN
\n
28144 T > C
\n
ORF8
\n
L84S
\n
Missense
\n
\n
\n
LC522975
\n
20/JAN/2020-JAPAN
\n
2662C > T
\n
ORF1ab
\n
\n
Synonymous mutation
\n
\n
\n
LC522974
\n
20/JAN/2020-JAPAN
\n
8782C > T
\n
ORF1ab
\n
\n
Synonymous mutation
\n
\n
\n
LC522974
\n
20/JAN/2020-JAPAN
\n
29095C > T
\n
N
\n
\n
Synonymous mutation
\n
\n
\n
LC522974
\n
20/JAN/2020-JAPAN
\n
28144 T > C
\n
ORF8
\n
L84S
\n
Missense
\n
\n
\n
LC522974
\n
20/JAN/2020-JAPAN
\n
2662C > T
\n
ORF1ab
\n
\n
Synonymous mutation
\n
\n
\n
LC522973
\n
20/JAN/2020-JAPAN
\n
8782C > T
\n
ORF1ab
\n
\n
Synonymous mutation
\n
\n
\n
LC522973
\n
20/JAN/2020-JAPAN
\n
29095C > T
\n
N
\n
\n
Synonymous mutation
\n
\n
\n
LC522973
\n
20/JAN/2020-JAPAN
\n
3792C > T
\n
ORF1ab
\n
A1176V
\n
Missense
\n
\n
\n
LC522973
\n
20/JAN/2020-JAPAN
\n
29095C > T
\n
N
\n
\n
Synonymous mutation
\n
\n
\n
LC522973
\n
20/JAN/2020-JAPAN
\n
2662C > T
\n
ORF1ab
\n
\n
Synonymous mutation
\n
\n
\n
LC522973
\n
20/JAN/2020-JAPAN
\n
28144 T > C
\n
ORF8
\n
L84S
\n
Missense
\n
\n
\n
LC522972
\n
20/JAN/2020-JAPAN
\n
29303C > T
\n
N
\n
P344S
\n
Missense
\n
\n
\n
LC522972
\n
20/JAN/2020-JAPAN
\n
25810C > G
\n
ORF3a
\n
L140V
\n
Missense
\n
\n
\n
LC522972
\n
20/JAN/2020-JAPAN
\n
11557G > T
\n
ORF1ab
\n
E3764D
\n
Missense
\n
\n
\n
LC522972
\n
20/JAN/2020-JAPAN
\n
15324C > T
\n
ORF1ab
\n
\n
Synonymous mutation
\n
\n
\n
LC521925
\n
21/JAN/2020-JAPAN
\n
1912C > T
\n
ORF1ab
\n
\n
Synonymous mutation
\n
\n
\n
LC521925
\n
21/JAN/2020-JAPAN
\n
18512C > T
\n
ORF1ab
\n
P6083L
\n
Missense
\n
\n
\n
LC521925
\n
21/JAN/2020-JAPAN
\n
359_382del
\n
ORF1ab
\n
G32_L39del
\n
Deletion
\n
\n
\n
MN988713
\n
21/JAN/2020-USA Chicago
\n
24034C > T
\n
S
\n
\n
Synonymous mutation
\n
\n
\n
MN988713
\n
21/JAN/2020-USA Chicago
\n
26729 T > C
\n
M
\n
\n
Synonymous mutation
\n
\n
\n
MN988713
\n
21/JAN/2020-USA Chicago
\n
8782C > T
\n
ORF1ab
\n
\n
Synonymous mutation
\n
\n
\n
MN988713
\n
21/JAN/2020-USA Chicago
\n
490 T > A
\n
ORF1ab
\n
D75E
\n
Missense
\n
\n
\n
MN988713
\n
21/JAN/2020-USA Chicago
\n
3177C > T
\n
ORF1ab
\n
P971L
\n
Missense
\n
\n
\n
MN988713
\n
21/JAN/2020-USA Chicago
\n
28854C > T
\n
N
\n
S194L
\n
Missense
\n
\n
\n
MN988713
\n
21/JAN/2020-USA Chicago
\n
28077G > C
\n
ORF8
\n
V62L
\n
Missense
\n
\n
\n
MN988713
\n
21/JAN/2020-USA Chicago
\n
28144 T > C
\n
ORF8
\n
L84S
\n
Missense
\n
\n
\n
MN997409
\n
21/JAN/2020-USA Arizona
\n
8782C > T
\n
ORF1ab
\n
\n
Synonymous mutation
\n
\n
\n
MN997409
\n
21/JAN/2020-USA Arizona
\n
29095C > T
\n
N
\n
\n
Synonymous mutation
\n
\n
\n
MN997409
\n
21/JAN/2020-USA Arizona
\n
11083G > T
\n
ORF1ab
\n
L3606F
\n
Missense
\n
\n
\n
MN997409
\n
21/JAN/2020-USA Arizona
\n
28144 T > C
\n
ORF8
\n
L84S
\n
Missense
\n
\n
\n
MT072688
\n
26/JAN/2020-USA: Massachussetts
\n
24034C > T
\n
S
\n
\n
Synonymous mutation
\n
\n
\n
NMDC60013002–09
\n
01/JAN/2019-China Wuhan
\n
27493C > T
\n
ORF7a
\n
P34S
\n
Missense
\n
\n
\n
NMDC60013002–09
\n
01/JAN/2019-China Wuhan
\n
28253C > T
\n
ORF8
\n
\n
Synonymous mutation
\n
\n
\n
NMDC60013002–10
\n
30/Dec/2019-China Wuhan
\n
20679G > A
\n
ORF1ab
\n
\n
Synonymous mutation
\n
\n
\n
NMDC60013002–01
\n
30/Dec/2019-China Wuhan
\n
11764 T > A
\n
ORF1ab
\n
N3833K
\n
Missense
\n
\n
\n
NMDC60013002–06
\n
30/Dec/2019-China Wuhan
\n
24325A > G
\n
S
\n
\n
Synonymous mutation
\n
\n
\n
NMDC60013002–04
\n
05/Dec/2019-China Wuhan
\n
28144 T > C
\n
ORF8
\n
L84S
\n
Missense
\n
\n\n
Table 3.
Coding mutation list detected in SARS-CoV-2 genomes [57].
\n
\n
\n
\n
\n
\n\n
\n
Accession
\n
Location-date
\n
Nucleotide variation
\n
UTR type
\n
\n\n\n
\n
MT240479
\n
04-03-2020/Pakistan Gilgit
\n
241C > T
\n
5 UTR
\n
\n
\n
MT123290
\n
05/Feb/2020-China Guangzhou
\n
4A > T
\n
5 UTR
\n
\n
\n
MT007544
\n
25/Jan/2020-Australia Victoria
\n
29749-29759del
\n
3 UTR
\n
\n
\n
NMDC60013002–07
\n
07/JAN/2019-China Wuhan
\n
29869del
\n
3 UTR
\n
\n
\n
NMDC60013002–04
\n
05/Dec/2019-China Wuhan
\n
29856 T > A
\n
3 UTR
\n
\n
\n
NMDC60013002–04
\n
05/Dec/2019-China Wuhan
\n
29854C > T
\n
3 UTR
\n
\n
\n
NMDC60013002–04
\n
05/Dec/2019-China Wuhan
\n
16C > T
\n
5 UTR
\n
\n
\n
MT049951
\n
17/Jan/2019-China Yunnan
\n
75C > A
\n
5 UTR
\n
\n
\n
LC522975
\n
20/JAN/2020-JAPAN
\n
29705G > T
\n
3 UTR
\n
\n
\n
GWHABKG00000001
\n
30/Dec/2019-China Wuhan
\n
124G > A
\n
5 UTR
\n
\n
\n
GWHABKG00000001
\n
30/Dec/2019-China Wuhan
\n
120 T > C
\n
5 UTR
\n
\n
\n
GWHABKG00000001
\n
30/Dec/2019-China Wuhan
\n
119C > G
\n
5 UTR
\n
\n
\n
GWHABKG00000001
\n
30/Dec/2019-China Wuhan
\n
112 T > G
\n
5 UTR
\n
\n
\n
GWHABKG00000001
\n
30/Dec/2019-China Wuhan
\n
111 T > C
\n
5 UTR
\n
\n
\n
GWHABKG00000001
\n
30/Dec/2019-China Wuhan
\n
104 T > A
\n
5 UTR
\n
\n\n
Table 4.
Non-coding mutation list detected in SARS-CoV-2 genomes [57].
\n
\n
\n
5. Evolution & origin
\n
Most SARS-CoV strains are derived from bats. SARS-CoV bat is a probable progenitor for SARS – CoV that is contagious to humans and civets, and thus it is important to study ACE2 receptor for monitoring origins of SARS-CoV and avoiding and controlling the outbreak. Though palm civets were involved in SARS emergence, most early MERS index cases had contact with dromedary camels. Indeed, the MERS-CoV strains separated from camels were nearly matching to those from humans [7]. The virus shares 96% of its genetic material with a virus detected from a bat found in a cave in Yunnan in China. A persuasive argument that it comes from bats but there is a critical alteration. The coronaviral spike proteins have a unit called a receptor-binding domain that is essential to the successful entry of human cells. Especially powerful is the SARS-CoV-2 binding domain and it varies from the bat virus Yunnan which appears to not affect human. Another Complicating matter, a scaly anteater called the pangolin with a coronavirus which was almost similar to the human version with a receptor-binding domain. However, the majority of the coronavirus was genetically identical just 90%, and some researchers do not believe that pangolin was the intermediary. It is difficult to draw a family tree since both mutations and recombinations are involved [58, 59, 60]. An article identifies and uses a machine learning-based alignment-free approach to identify a COVID-19 intrinsic genomic signature for an ultra-fast, scalable, and extremely precise classification of all COVID-19 virus genomes. The technique presented incorporates supervised machine learning with MLDSP for genome analysis, improved by a machine learning component decision tree approach and a Spearman-leading correlation coefficient analysis of tests. These methods are used to examine a broad collection of more than 61.8 million bp, including the 29 COVID-19 virus sequences on 27 January 2020, with over 5,000 unique viral genomic sequences. The findings endorse a bat hypothesis and the COVID-19 virus is classed under Betacoronavirus as the Sarbecovirus. Without any advanced biological expertise, training or genome annotations, our method achieves a 100% precise classification of the COVID-19 virus sequences, and determines the most important relationships between more than 5000 genomes in minutes, from the beginning on, with the sole use of raw DNA sequence details [61]. In a recent research, they have developed a phylogenetic tree, including other members of coronaviridae including Bat coronavirus (BCoV) and extreme acute respiratory 2019 disease, taking advantage of all of the available genomic knowledge. The closest BCoV sequence, with a 96,2% sequence 2019 SARS-CoV2 identity, confirm that all available genomes of the sequence are of zoonotic origin. We have confirmed the high sequence similarity (> 99%) among all available genomes. Given the low 2019 SARS-CoV2 heterogeneity, at least two genomic hyper various hotspots were identified, including one of the Serine/Leucine variations in viral ORF8 Protein encoded, can be detected [62]. (Figures 6 and 7) In the study a Malayan pangolin-isolated coronavirus showed 100%, 98.6%, 97.8% and 90.7% SARS-CoV-2 amino acid identity in genes E, M, N, and S respectively. Particularly in the S protein of Pangolin-CoV, the receptor-binding domain is nearly the same as the SARS-CoV-2 with vital one-amino acid alteration. Results of comparative genomic analysis indicate that SARS-CoV-2 may have been the result of a Pangolin-CoV-like virus recombination with a Bat-CoV-RaTG13 virus [63].
\n
Figure 6.
A coronavirus phylogenetic tree based on full-length genome sequences. Both complete coronavirus genome sequences have been obtained from RefSeq, the NCBI reference sequence database [5].
\n
Figure 7.
A phylogenetic tree with all the sequences of SARS- CoV2 available from the 02–Feb-2020 sequence in the blue divisions, plus six Bat coronavirus sequences split in multiple taxa, six human SARS sequences (green) and two MERS sequences (orange); the bootstrap percentage of each branch is recorded [62].
\n
\n
\n
6. Immunopathology of SARS-CoV2
\n
Pneumonia, lymphopenia, drained lymphocytes and a cytokine storm are distinguishable symptoms of Extreme Coronavirus Disease 2019 (COVID-19). Major antibody development is detected, but it remains to be determined if this is defensive or pathogenic. Defining the immunopathological changes in COVID-19 patients presents future drug development targets and is critical for clinical management [64]. Asymptomatic condition is found in a large but generally unexplained proportion of the infected people, analogous to many other viral diseases. Usually, a 1-week, self-limiting viral respiratory disease develops in most patients, and ends with the production of neutralizing antiviral T cell and antibody immunity [65]. SARS-CoV-2 has been shown to weaken natural immune responses, resulting in a compromised immune system and an unregulated inflammatory response in extreme and vital COVID-19 patients. These patients display lymphopenia, stimulation and malfunction of lymphocytes, defects of granulocytes and monocytes, elevated levels of cytokines, and higher amounts of immunoglobulin G (IgG) and total antibodies [66] (Figure 8). Extreme and fatal COVID-19 is linked with lymphopenia and an elevated amount of blood neutrophils [67]. Lymphocyte counts of 800 cells/μl and a decreased probability of recovery are reported in ICU patients suffering from COVID-19. The mechanism of action and causes of lymphopenia in patients with COVID-19 are unclear, but SARS-like viral particles and SARS-CoV RNA have been observed in T cells, indicating that the SARS virus may have a detrimental influence on T cells via apoptosis [68]. Accumulating data proves the involvement of T cells in COVID-19 and possibly in the immunological memory that develops after recovering from infection with SARS-CoV-2. Many, but not always, hospitalized patients tend to have both CD8 + and CD4 + T cell responses, and research points to potential T cell responses consistent with extreme disease that are suboptimal, abnormal or otherwise inadequate [5]. In a report, a group of 452 patients with positive test results of COVID-19 in Wuhan, China shows dysregulated immune system. Boosts in NOD-like receptor (NLR) and T lymphopenia, especially a decline in CD4 + T cells, were prominent in COVID-19 patients, and was even more noticeable in extreme cases, but the number of CD8 + cells and B cells did not change significantly. On the basis of these results, it was proposed that COVID-19 may affect lymphocytes, especially T lymphocytes, and that the immune system is disrupted during the infection period [69]. COVID-19 will lead to defects in the routine of peripheral blood parameters. The most noticeable anomalies that are linked to the intensity of the condition and clinical classification are the reduction in lymphocytes and the rise in the NLR ratio. The lower count and delay in eosinophil development can be indicators of weak COVID-19 outcomes. Thus, complex analysis of peripheral blood routine parameters has a significant reference point for COVID-19 progression and prognosis evaluation [70]. Also, In the different stages of COVID-19, multiple cell morphological modifications can be seen. In fact, a strong granulocytic reaction with immaturity, dysmorphism and apoptotic-degenerative morphology was apparent in peripheral blood in the initial stage of symptom aggravation, typically correlating with hospital entry [71]. Cytokine storm plays a crucial role in infected individuals for the pathogenesis of many serious manifestations of the disease. Acute respiratory distress syndrome, thromboembolic disorders such as acute ischaemic strokes caused by myocardial infarction and large vessel occlusion, encephalitis, acute kidney damage, and vasculitis (childhood Kawasaki syndrome and adult renal vasculitis) [72]. Nonetheless, it is uncertain if serious illness is triggered by immune hyperactivity or inability to overcome an inflammatory reaction owing to continuing virus replication or immune dysregulation. However, records of elevated levels of thrombi production and endothelial cell death in patients with COVID-19 suggest disruption to the vascular endothelium and the participation of cytokine elevated activity and immunothrombosis [73]. In response to infection as well as other triggers, cytokine storm is a general term referring to maladaptive cytokine release. The pathogenesis is complicated, but requires the depletion of regulated control at both local and systemic levels of proinflammatory cytokine output. The disease is rapidly progressing, and mortality is elevated. Some data suggests that dysregulated and uncontrolled cytokine release in certain COVID19 patients has been directly correlated with significant deterioration [74].
\n
Figure 8.
COVID-19 immunopathology [66].
\n
\n
\n
7. Molecular diagnostics
\n
COVID-19 Test of SARS-CoV-2 is a real-time reverse transcription polymerase chain-reaction (PCR) in upper or lower respiratory samples for the qualitative identification of nucleic acid (such as nasal, nasopharyngeal or oropharyngeal swabs, sputum, lower respiratory tract aspirates, bronchoalveolar lavage, nasopharyngeal wash/aspirate or a nasal aspirate) that is individually obtained from those suspected of COVID-19 by their healthcare provider [75]. The latest COVID-19 outbreak can be detected using qPCR, but insufficient possession of reagents and equipment has hindered the identification of diseases. To assist in making COVID-19 more effective in our diagnostics, a new protocol was suggested for the application of the CRISPR-based SHERLOCK technique for detecting COVID-9. COVID-19 objectives were identified between 20 and 200 aM (10–100 copies per input microlitre) with the use of synthetic COVID- 19 virus RNA fragments. The test can be performed starting with patient-purified RNA as used in qRT-PCR trials and read in less than an hour with a dipstick, without the need for complex instrumentation [58, 59]. GolayMetaMiner, an in-house software, has identified four different regions over 50 nucleotides for the SARS-CoV-2 genome with 96 SARS-CoV-2 and 104 non-SARS-CoV-2 coronaviral genomes. Primers were made to target the longest and previously not targeted nsp2 region and tailored as a reverse transcription-polymerase chain reaction (RT-PCR) test without a probe. The new COVID- 19-nsp2 assay had a detection limit (LOD) of 1.8 TCID5 mL and did not intensify any human coronavirus pathogens and respiratory viruses. The process threshold reproducibility (Cp) values have been adequate and overall imprecision (%CV) values have dropped far below 5%. The latest assay evaluation using 59 clinical samples from 14 reported cases demonstrated a 100% compliance with COVID-19-RdRp/Hel reference assay, which has been previously established. A COVID-19-nsp2, fast sensitive RT-PCR test was developed for SARS-CoV-2 [76].
\n
\n
\n
8. Future perspectives of nucleic acid-based vaccines
\n
Since COVID-19 is new to humanity and the essence of defensive immune responses is incompletely understood, it is unknown which vaccination techniques are going to be most effective. Therefore, designing diverse vaccine platforms and methods in tandem is crucial. Indeed, researchers worldwide have been racing to produce COVID-19 vaccines since the epidemic started, with at least 166 vaccine candidates now in preclinical and clinical production (Draft landscape of COVID-19 candidate vaccines, 2020). A new pandemic vaccine developing framework has been suggested to address the immediate need for a vaccine, compacting the development period from 10 to 15 years to 1 to 2 years [77]. recombinant plasmid DNA has been investigated as a vaccine model, although lately, mRNA has appeared as a promising platform. Six mRNA-based COVID-19 vaccines and four DNA-based COVID-19 vaccines are currently in clinical trials, with 27 such vaccines (16 mRNA-based and 11 DNA-based) undergoing preclinical production [78]. (Draft landscape of COVID-19 candidate vaccines, 2020). For protein translation and post-translational modifications, antigen-encoding mRNA encapsulated with a carrier such as lipid nanoparticles can be effectively conveyed in vivo into the cytoplasm of host cells, which is a plus over vaccines of the recombinant protein subunit. The mRNA vaccines are non - pathogenic and are synthesized without microbial molecules by in vitro transcription [79]. While no mRNA vaccine has been approved for human use yet, recent reports of influenza, rabies and Zika virus infections in animals support its promise in the covid-19 vaccine development race [80]. Plasmid DNA vaccines share many features, such as safety, ease of development and scalability, with mRNA vaccines, but with the differences of having poor immunogenic and having to be administered in several doses coupled with the addition of an adjuvant. This review provides valuable information that can be redirected to the purpose of working on these nucleic acid-based vaccines which provides a new propitious platform of vaccine production.
\n
\n
\n
9. Conclusion
\n
Coronaviruses have proven themselves to be prevailing and a very high threat to our existence by their unique features and ambiguity that caused catastrophic effects in this pandemic. It is obvious that coronaviruses have high spillover abilities and adaptation to new hosts so that enables more appearances in future. For a vaccine to be made or an anti-viral drug to be produced, it is very mandatory that all is known about the virus family, virus genome and its own central dogma, how it differs from its predecessors, their similarities, mutation rates and screening methods. A treatment that will be effective, long-lasting and prepared for any mutation is needed to be able to fight this virus and eradicate this disease and prevent the emergence of a new pandemic by this family of potential and active killers (The Coronaviruses).
\n
\n\n',keywords:"Covid-19, SARS-COV-2, genome, evolution, immunopathology, phylogenetic",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/74788.pdf",chapterXML:"https://mts.intechopen.com/source/xml/74788.xml",downloadPdfUrl:"/chapter/pdf-download/74788",previewPdfUrl:"/chapter/pdf-preview/74788",totalDownloads:44,totalViews:0,totalCrossrefCites:0,dateSubmitted:"June 29th 2020",dateReviewed:"November 23rd 2020",datePrePublished:"January 13th 2021",datePublished:null,dateFinished:"January 13th 2021",readingETA:"0",abstract:"The Pandemic of COVID-19 has been thoroughly followed and discussed on many levels due to the high level of attention that it has brought by its effect on the world. While this disease might seem like to arise out of the blue, we will shed light on COVID-19 disease which is caused by the virus SARS-CoV2 and belong to family of coronaviruses. We will discuss current knowledge about SARS-CoV2 emergence, diagnosis, its mode of action, and genomic information, For an antiviral treatment to be used, it should be preceded by a foundation of information about the virus genome and its family as discussed in this review.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/74788",risUrl:"/chapter/ris/74788",signatures:"Maram Adel Abdelghany, Sarah Abdullah Gozai Alghamdi and Jehane Ibrahim Eid",book:{id:"8564",title:"Cell Interaction - Regulation of Immune Responses, Disease Development and Management Strategies",subtitle:null,fullTitle:"Cell Interaction - Regulation of Immune Responses, Disease Development and Management Strategies",slug:null,publishedDate:null,bookSignature:"Dr. Bhawana Singh",coverURL:"https://cdn.intechopen.com/books/images_new/8564.jpg",licenceType:"CC BY 3.0",editedByType:null,editors:[{id:"315192",title:"Dr.",name:"Bhawana",middleName:null,surname:"Singh",slug:"bhawana-singh",fullName:"Bhawana Singh"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Emergence of a new virus",level:"1"},{id:"sec_3",title:"3. Genomic characteristics",level:"1"},{id:"sec_3_2",title:"3.1 Transfection",level:"2"},{id:"sec_4_2",title:"3.2 Replication",level:"2"},{id:"sec_5_2",title:"3.3 Transcription",level:"2"},{id:"sec_6_2",title:"3.4 Morphogenesis",level:"2"},{id:"sec_8",title:"4. Mutations",level:"1"},{id:"sec_9",title:"5. Evolution & origin",level:"1"},{id:"sec_10",title:"6. Immunopathology of SARS-CoV2",level:"1"},{id:"sec_11",title:"7. Molecular diagnostics",level:"1"},{id:"sec_12",title:"8. Future perspectives of nucleic acid-based vaccines",level:"1"},{id:"sec_13",title:"9. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'\nGallagher, T. M., & Buchmeier, M. J. (2001). Coronavirus spike proteins in viral entry and pathogenesis. Virology, 279(2), 371-374. https://doi.org/10.1006/viro.2000.0757\n\n'},{id:"B2",body:'\nde Groot, R. J., Baker, S. C., Baric, R. S., Brown, C. S., Drosten, C., Enjuanes, L., Fouchier, R. A. M., Galiano, M., Gorbalenya, A. E., Memish, Z. A., Perlman, S., Poon, L. L. M., Snijder, E. J., Stephens, G. M., Woo, P. C. Y., Zaki, A. M., Zambon, M., & Ziebuhr, J. (2013). Middle East Respiratory Syndrome Coronavirus (MERS-CoV): Announcement of the Coronavirus Study Group. Journal of Virology, 87(14), 7790-7792. https://doi.org/10.1128/jvi.01244-13\n\n'},{id:"B3",body:'\nWoo, P. C. Y., Lau, S. K. P., Huang, Y., & Yuen, K. Y. (2009). Coronavirus diversity, phylogeny and interspecies jumping. In Experimental Biology and Medicine (Vol. 234, Issue 10, pp. 1117-1127). https://doi.org/10.3181/0903-MR-94\n\n'},{id:"B4",body:'\nMasters, P. S. (2006). The Molecular Biology of Coronaviruses. Advances in Virus Research, 65(06), 193-292. https://doi.org/10.1016/S0065-3527(06)66005-3\n\n'},{id:"B5",body:'\nChen, Z., & John Wherry, E. (2020). T cell responses in patients with COVID-19. Nature Reviews Immunology, 20(9), 529-536. https://doi.org/10.1038/s41577-020-0402-6\n\n'},{id:"B6",body:'\nCyranoski, D. (2020). Profile of a killer: the complex biology powering the coronavirus pandemic. Nature, 581(7806), 22-26. https://doi.org/10.1038/d41586-020-01315-7\n\n'},{id:"B7",body:'\nCui, J., Li, F., & Shi, Z. L. (2019). Origin and evolution of pathogenic coronaviruses. In Nature Reviews Microbiology (Vol. 17, Issue 3, pp. 181-192). Nature Publishing Group. https://doi.org/10.1038/s41579-018-0118-9\n\n'},{id:"B8",body:'\nSawicki, S. G., Sawicki, D. L., & Siddell, S. G. (2007). A Contemporary View of Coronavirus Transcription. Journal of Virology, 81(1), 20-29. https://doi.org/10.1128/jvi.01358-06\n\n'},{id:"B9",body:'\nTorres, J., Surya, W., Li, Y., & Liu, D. X. (2015). Protein-protein interactions of viroporins in coronaviruses and paramyxoviruses: New targets for antivirals? Viruses, 7(6), 2858-2883. https://doi.org/10.3390/v7062750\n\n'},{id:"B10",body:'\nTyrrell, D. 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Department of Zoology, Faculty of Science, Cairo University, Egypt
'},{corresp:null,contributorFullName:"Sarah Abdullah Gozai Alghamdi",address:null,affiliation:'
Bioscience Department, Faculty of Science, University of Jeddah, Saudi Arabia
'},{corresp:"yes",contributorFullName:"Jehane Ibrahim Eid",address:"jehaneeid@sci.cu.edu.eg",affiliation:'
Department of Biotechnology, Faculty of Science, Cairo University, Egypt
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