Alteration in lymphocytes subsets in severe and moderate COVID-19 patients on hospital admission. Results are expressed as mean ± SEM.
\r\n\tSynthetic zeolites can be formed from different raw materials and among these many wastes represent some interesting sources due to their chemical and mineralogical composition. Today, a large number of different types of waste resulting from many human activities are produced in the world (e.g. industrial, municipal, agricultural waste) and most of them are deposed of in landfills thus determining a great environmental problem.
\r\n\r\n\tThis book intends to provide the reader with a comprehensive overview of the current state-of-the-art on the possibility to transform the different types of waste materials into useful products, zeolites, through conventional processes and innovative methods. The aim is to demonstrate that waste can be a problem or a resource depending on how it is managed.
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She is responsible for the research activity on zeolite synthesis from waste materials and natural sources which has allowed her to be the inventor of an International Patent, publish numerous scientific articles in peer-reviewed journals, and carry out scientific research in national and international projects.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"61457",title:"Dr.",name:"Claudia",middleName:null,surname:"Belviso",slug:"claudia-belviso",fullName:"Claudia Belviso",profilePictureURL:"https://mts.intechopen.com/storage/users/61457/images/system/61457.jpg",biography:"Claudia Belviso is a researcher at the Institute of Methodologies of Environmental Analysis (IMAA) of CNR. After graduating in Geological Sciences and qualifying as a professional geologist, she earned a Ph.D. in Earth Sciences. 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Virus pathogenicity, comorbidities of the infected individual, and the ability of the host immune system to respond to the induced cytopathic effect have significant impact on the progress and outcome of the disease. The virus SARS-CoV-2 has so far affected more than 400 million people globally, with over 5 million deaths. The largest number of deaths, approximately 73%, is in the population over 65 years of age [3].
Cytokine storm syndrome has been widely discussed and proposed as one of the underlying etiologies of respiratory failure in patients infected with SARS-CoV-2. Cytokines present a group of polypeptides signaling molecules responsible for regulation of number of biological processes by using cell surface receptors. Proinflammatory cytokines and chemokines play an important role in respiratory infections caused by viruses including SARS-CoV-2 by activation of the adaptive immune response. When infected with influenza, an excessive amount of reactive oxygen species (ROS) is generated in several tissues including the alveolar endothelium and epithelium [4] where induced cytokines expression by activation of Toll-like receptors (TLRs) is reported to be responsible for the pathogenesis [5, 6]. Increased oxidative stress is also present during infections with human respiratory syncytial virus [7], rhinoviruses [8], and many other viruses, which has been a subject of discussion in many previously published reviews [9, 10, 11, 12, 13, 14, 15, 16], and several other experimental studies propose that the so-called cytokine storm correlated with direct tissue injury, which afterward results in unfavorable prognosis in patients with severe COVID-19 [10]. In patients with severe COVID-19, increased levels of several cytokines, namely IL-6, IL-10, IL-2R, and TNF-α, have been reported in recently published articles on this subject [17, 18]. However, other authors suggest that more cytokines, such IL-1β, IL-1RA, IL-8, and IL-18, are also included in the pathogenesis of SARS-CoV-2 infection [10, 17, 18].
In general, patients infected with SARS-CoV-2 have either normal or reduced white blood cells (WBC) count and lymphocytopenia, and patients with severe form of the disease have additional presence of significant increase of elevated neutrophil levels, D-dimers, accompanied with continuous decrease of lymphocytes and increase of levels of certain cytokines and chemokines.
The infection with SARS-CoV-2 follows the same pathway as the innate immune response as suggested by several authors [4, 14]. That is to say, reactive oxygen species present a strong ligand and a direct mediator in the inflammasome (NLP3) trigger. Furthermore, the reactive oxygen species activate the NF-κB (nuclear factor kappa-light-chain-enhancer of activated B cells), and this further elicits the transcriptional levels of NLPR3, which are additionally enhanced by TLR and NLR (nod-like receptor) ligands. As a final outcome, this means that the ROS can increase inflammasome either directly or indirectly [12, 16]. In addition of ROS, H2O2 activates the NF-κB, which contributes to additional production of inflammatory cytokines [19]. Increased levels of the following cytokines: IL-6, TNF-α, IL-1β, IP-10, GCSF, MCP-1, MIP1-α/CCL3, followed by elevated blood ferritin levels, are also observed in patients infected with SARS-CoV-2 [11, 20]. Literature data has also discussed that the imbalance in the T-helper cell subsets (Th1/Th2/Th7) and regulatory T-cells also contribute to the COVID-19 pathogenesis. CD4+ T cells are divided into different subtypes based on their cytokine production, namely Th1 is producing IFN-γ, IL-2, and TNF-α, Th2: IL-4, IL-5, IL-9, IL-13, whereas the regulatory T-cells are producing TGF-β and IL-10, among others.
In this chapter, authors would like to share some experimental data, which were obtained in the last 2 years since the beginning of the COVID-19 pandemic. First, we will present results obtained by a highly standardized cytokine assay where we have measured plasma levels of IL-2, IL-4, IL-6, IL-8, IL-10, VEGF, IFN-γ, TNF-α, IL-1α, MCP-1, and EGF potentially associated as key factors with the cytokine storm syndrome in critically ill COVID-19 patients. Afterward, we have investigated which of these cytokines involved in the cytokine storm of COVID-19 show good association/correlation with the oxidative stress markers determined with fast and inexpensive photometric analytical method [20, 21]. Also we will present results in regard to the relation between the cytokines, oxidative stress markers, and the most commonly used inflammation-related biomarkers (CRP, D-dimers, PLR, NLR, and LDH) in severe form of the disease will be discussed. Additionally, the readers can obtain information for which we believe could enhance the knowledge of the altered lymphocyte subsets and their correlation with the oxidative stress markers as a tool with prognostic power to differentiate between moderate and severe COVID-19.
Our results obtained from 50 hospitalized COVID-19 patients who were hospitalized at the University Clinic for Infectious Diseases and Febrile Conditions, Skopje, Republic of North Macedonia, within a period of 2 months at the beginning of the pandemic were included in this study. All patients were confirmed to have SARS-CoV-2 infection by real-time reverse transcriptase–polymerase chain reaction assay from nasal and pharyngeal swab specimen. The diagnosis and classification of COVID-19 were based on the Interim Guidance for Clinical Management of COVID-19 issued by the World Health Organization. Patients with moderate form of the disease were adults from both sexes with clinical signs of pneumonia with no signs of severe pneumonia and SpO2 > 90% on room air. In terms of the severe cases, they additionally had at least one of the following conditions: SpO2 < 90% on room air, respiratory rate more than 30 breaths per minute, or presence of severe respiratory distress.
The work was performed by a multidisciplinary group, including clinical experts in COVID-19 management.
When admitted to hospital, patients classified with the moderate form of the disease demonstrated abnormal values for CRP, LDH, glucose, and NLR. Namely, on hospital discharge, these clinical laboratories were improved significantly, namely the mean value for C-reactive protein (CRP) was 44.1 mg/L versus 7.16 mg/L, LDH was 280.3 IU/L versus 283.4 IU/L, and neutrophil to leukocyte ratio (NLR) was 6.5 versus 5.1 at admission and discharge, respectively. Whereas patients with severe form of the disease had more prominent abnormal values of CRP, LDH, creatinine kinase (CK), ALT, AST, glucose, WBC, and NLR levels. It is worth noting that the mean values of CRP, LDH, CK, ALT, and NLR in the patients with severe COVID-19 were higher when compared with the patients with the moderate form of the disease upon admission and discharge. In general, our results are added value to data obtained by other research groups where lymphopenia can be used as reliable marker of the severity of the disease and the necessity for hospitalization. Meta-analyses by Lagunas-Rangel [22] demonstrated that patients with severe form of COVID-19 had increased NLR and decreased lymphocyte-CRP ratio when compared with moderate and mild form of the disease. Also, large number of studies, including our results, had showed that in the patients with severe form of the disease and the ones that did not survive had low platelet counts. Namely, the results from our hospitalized patients with severe COVID-19 had increased WBC, neutrophils, NLR, and platelet to lymphocyte ratio (PLR). The coagulation profile revealed elevated levels for D-dimer, prolonged PT with normal aPPT and TT. Patients with severe form of the disease that have recovered had a statistically significant difference for NLR and PLR (p < 0.01), and the values were decreased to 70% and 67%, respectively, when compared with the values of these parameters on admission. Additionally we have observed increase in the lymphocytes for 53%. Also, WBC decreased to 18%, neutrophils to 11%, platelets to 19%, PT to 22%, and D-dimers to 85%, but the difference was not statistically significant when compared between admission and hospital discharge (p > 0.05). Patients that had deterioration of their condition and died had continuous increase in WBC and neutrophils (34%), NLR (55%), decrease in lymphocytes (42%), PT (18%), and D-dimers (50%). We have only observed statistically significant difference only for the lymphocytes as parameter compared with the admission values. Reported results from meta-analysis [23] with 1779 patients demonstrated that thrombocytopenia is associated with fivefold increased risk for disease complications and death. D-dimer has also been applied as predictor for developing complications of the disease such as requiring mechanical ventilation, developing of acute respiratory distress syndrome, etc. Experiences from different clinical centers worldwide suggest that patients who had significant increase in the D-dimers should be considered for hospitalization even if there is an absence of symptoms.
All hospitalized patients whether classified with the moderate or the severe form of the disease had low partial pressure of oxygen, although it did not show statistically significant difference between the two groups. However, this observation had clinical importance indicating the need for supplemental oxygen therapy in both groups with moderate and with severe disease. The measured oxygen saturation was lower in the severe group of patients (86.26%), and this parameter had strong statistical significance, supporting the clinical indication for application of oxygen therapy. The analysis showed that in both groups, the partial pressure of carbon dioxide and pH values were within the reference values, the levels of bicarbonates were slightly increased, but without a significant difference between the patients with moderate and severe form of the disease. The base excesses were increased in the group of patients with moderate disease, and it was statistically significant, but no disturbances in acid balance ratio occurred within any of the group. Similar results were reported by Mumoli et al. [24] at hospital admission of 88 COVID-19 patients, as well as by Doaei et al. [25], who measured similar values of the blood gas parameters in critically ill patients with COVID-19 to those reported in our study. On the other hand, Deniz et al. [26] detected a mild increase of pH and bicarbonate and relatively low pCO2 in COVID-19 patients compared with non-COVID-19 individuals.
Cytokines as well as chemokines and growth factors together with the lipid metabolites present one of the key players in the immune cell function and their differentiation, which on the other hand, means that following a dysregulation in the process, various diseases can arise [11, 12, 13, 16]. In this chapter, we present some of our recently obtained results as an add-on to the clinical and scientific evidences in terms of oxidative stress, which is increased in patients with severe COVID-19. We have also obtained results that the measured oxidative stress parameters in these patients show a good correlation with the level of cytokines and with some of the commonly used laboratory biomarkers. This was a case–control pilot study focused primarily on the possibility to apply the oxidative stress parameters (d-ROM, PAT, and OS index) measured on a spectrophotometric system as a fast and low-cost prognostic tool for disease progression and potentially predict the outcome of COVID-19 in patients. Abnormal levels of several cytokines involved in the adaptive immunity (IL-2, IL-4) or proinflammatory cytokines and interleukins (IFNs, IL-1, IL-6, IL-10 IL-17, and TNF-α) were reported [11, 17, 27, 28] mainly as results from retrospective studies and reviews.
In our case–control pilot study, we have measured the levels of 11 cytokines (IL-2, IL-4, IL-6, IL-8, IL-10, VEGF, IFN-γ, TNF-α, IL-1α, MCP-1, and EGF) in 14 critically ill patients. Afterward, these values were compared with the levels of the same cytokines in individuals that were not infected with the SARS-CoV-2 virus. By using t-test, we have obtained statistically significant increase (p < 0.05), which was observed in regard to the levels of IL-6, IL-8, IL-10, VEGF, MCP-1, and EGF in the SARS-CoV-2 patients. Additionally, the levels of IL-2, IFN-γ, TNF-α, and IL-1α were also increased, but, however, this difference was not considered to be significant when compared with the noninfected individuals (p > 0.05) [20, 21]. Important finding of our case–control pilot study was that the oxidative stress parameters, d-ROM (448.8 U.Carr), OS index (107.7), and PAT (3048 U.Corr), were significantly higher (p < 0.05) in the infected patients when compared with those not infected. Additionally, we have also explored the correlation among the abovementioned cytokines (IL-2, IL-4, IL-6, IL-8, IL-10, VEGF, IFN-γ, TNF-α, IL-1α, MCP-1, and EGF), the oxidative stress parameters (d-ROM, PAT, OSI), and some of the commonly used laboratory parameters (CRP, LDH, PLR, D-dimer, and NLR) by using the Spearman r calculation. These calculations were generated as heat map, and we have obtained a significant positive correlation between all investigated cytokines and the parameters of the oxidative stress (d-ROM, PAT, and OSI), except between IL-10 and the total antioxidant capacity, PAT, for which a negative a correlation was obtained. According to the performed analysis, we have obtained a nonsignificant correlation between OS index and the IL-8 (p = 0.8552) and also between d-ROM and VEGF (p = 0.999). The strongest correlation was demonstrated in the case of IL-6, which was estimated as significant with all of the markers of the oxidative stress, d-ROM (r = 0.9725, p < 0.01), PAT (r = 0.5000, p < 0.01), and the oxidative stress index (r = 0.9593, p < 0.05). Also, our results present evidence for similar behavior between IFN-γ and d-ROM (r = 0.4006, p < 0.01), PAT (r = 0.6030, p < 0.01) and the oxidative stress index (r = 0.4298, p < 0.05). Additionally, statistically we have investigated the correlation between the commonly used inflammation biomarker, CRP, and the levels of the investigated cytokines. In this case we have observed a strongest correlation with several of the investigated cytokines, namely with IL-6, IL-8, MCP-1, and IFN-γ. Furthermore, in terms of correlation, a strong correlation was obtained between the investigated inflammatory cytokines IL-2, IL-4, IL-6, IL-8, IL-10, VEGF, IFN-γ, TNF-α, IL-1α, and MCP-1, except between IL-6 and EGF where negative correlation was obtained. It has been demonstrated that the high expression of IL-6 in COVID-19 patients can hasten the inflammatory process, which can on the other hand contribute to the cytokine storm and can contribute to disease worsening. The importance of the IL-6 has been potentiated by using tociluzimab, a monoclonal antibody, which can block the receptor of IL-6 for which it has been reported that the cytokine hyperproduction has been reversed [10].
The research performed by our study group has contributed to the evidences that more than a few cytokines and other clinically relevant biomarkers were significantly increased in patients with severe form COVID-19 in comparison to the individuals that were not infected by the virus, which was followed with coagulopathy as determined by worsening of the platelet-related parameters (PLR, D-dimers, and IL-6) and the increased levels of MCP-1 as thrombosis-related indicator. MCP-1 levels were estimated to be much higher in critical hospitalized patients in the ICU accompanied by decrease of platelet count in patients that do not survive, which was reported also by Huang et al. [27]. In our case–control pilot study, unfortunately all patients had severe form of COVID-19 and all of them had died during hospitalization. Besides, in our study we have witnessed that the levels of several of the investigated cytokines had been increased more than 10 times above the levels of the noninfected, which we have considered as a baseline. It is worth noticing that the significant increase of the vascular endothelial growth factor (VEGF) levels more than 10 times can be related to the essential VEGF role in the endothelial cell activation by binding to cell surface VEGF receptors. This increase was considered to be statistically significant (p < 0.01). The upregulation of VEGF was also observed in several other viral infections, and also it has been investigated as a potential targeted therapy in viral diseases [28]. Higher levels of VEGF in hospitalized COVID-19 patients were also reported in a published study by Huang et al. [27]. The cytokine profile in COVID-19 pregnant women was investigated in a published study by Tanacan et al. [29], in which this research group has reported significantly higher values for IFN-γ and IL-6 with lower values of IL-2, IL-10, and IL-17. This situation was especially pronounced in those patients who had complications such as miscarriage and preterm delivery [29].
From the above presented results and the available literature data, we can conclude that the strong correlation between the investigated cytokines including chemokines and growth factors (IL-2, IL-4, IL-6, IL-8, IL-10, VEGF, IFN-γ, TNF-α, IL-1α, MCP-1, and EGF), the oxidative stress parameters (d-ROM, OSI, and PAT), and some of the commonly used clinical markers (CRP, D-dimers, NLR, PLR) is in agreement with the proposed “cytokine storm” as potential mechanism of the SARS-CoV-2 infection. In general, the so-called “cytokine storm” occurs when large numbers of white blood cells (leukocytes) are activated followed by release of a high concentration of proinflammatory cytokines, primarily IL-6, IL-10, IFN, MPC-1, IL-1, IL-2, and IL-8. Mostly, SARS-CoV-2 infection is related with increased oxidative stress, the proinflammatory state, cytokine production, and cell death confirmed by increase in the levels of the ROS and an alteration of antioxidant defense during the infection [15, 21, 30]. This interplay is depicted in Figure 1.
Interplay of factors in COVID-19.
As proposed previously [11, 27, 30], RNA viruses trigger the oxidative stress by disturbing the pro-antioxidant–antioxidant balance for which we believe that the same mechanism exists also during the infection with SARS-CoV-2. During the last 2 years, we have performed several investigations with COVID-19 patients and we have managed to demonstrate increased oxidative stress and increased cytokines levels in these patients. Actually, we have observed significantly higher level of the markers of the oxidative stress (d-ROM and OS index values) and decreased total antioxidant capacity (PAT) in the COVID-19 patients when compared with disease-free individuals, supporting the theory that viral infection will increase the oxidative stress in the organism and further complicate the course of the disease (Figure 1). While we consider that the oxidative stress index value presents a significant parameter, which can be used in clinical practice for patient classification, we can also try in future activities, to implement a certain principle of supplementation with antioxidants especially when there is relevant knowledge for more than a few vitamins (vitamin C, vitamin D, selenium, quercetin, and other polyphenols) with proven antioxidant, anti-inflammatory, and even antiviral capacity [31]. Also, information on several clinical trials for potential therapy in COVID-19, such as tocilizumab, a monoclonal antibody IL-6 receptor antagonist; sarilumab – IL-6 antagonist; and anakinra, a recombinant IL-1 receptor antagonist against selected cytokines is in progress, some of which have already been published [31, 32, 33, 34, 35, 36, 37, 38]. Even though, data on their efficacy are evolving and additional studies are needed for those treatments to be administered routinely in COVID-19.
In regard to the pathophysiology of the virus, SARS-CoV-2 enters into the cell through the angiotensin-converting enzyme-2 (ACE-2), mainly through the Toll-like receptor-7 (TLR-7) that is present in the endosomes (Figure 1). The activation of this receptor requires production of IL-6, IL-12, and TNF-a enabling cytotoxic CD8+ T cells generation. This process results in further formation of antigen-specific B- cells and antibodies production through CD4+ helper T cells [39]. The suppression of the immune response can occur as negative reaction from immune activation and that is one of the reasons we have additionally performed analysis on several lymphocyte subsets in one of our studies. Th1 cells, natural killer cells (NK cells), and CD8+ T cells are main sources of INF-γ, whose production has been increased in COVID-19 patients. The increase of this cytokine (INF-γ) triggers Th1 response to eradicate the viral infect as one of the human immune strategies. Results reported since the beginning of the COVID-19 pandemic stated that more than a half of the patients had low lymphocytes number, and moreover pathological findings suggested that the patients who died of COVID-19 had significant increase of the concentration of proinflammatory CCR6+ Th17 within the CD4+ cells [2]. This point toward that overactivation of T-cells in combination with high cytotoxicity of CD8 T cells is partially responsible for severe immune injuries in COVID-19 patients. Several reported clinical studies indicate that low CD8+ T cells counts and high neutrophil-to-lymphocyte ratios (NLR) are associated with increased risk for disease severity and mortality in critically ill COVID-19 patients [40, 41, 42, 43]. Namely, the high ratio between the neutrophils and leukocytes in COVID-19 patients leads to redox imbalance as a result of increased reactive oxygen species (ROS) production. In addition, the activation of macrophages and the polymorphonuclear cells also has effect on the oxidative damage to the tissues, and they can lead to organ failure. Patients with moderate form of COVID-19 had lower values of the measured concentration of free radicals (d-ROMs) and hence lower oxidative stress index (OSI) when compared with the patients classified with the severe form of the disease (p < 0.01). Additionally, the moderate group had increased total antioxidant capacity (PAT) in comparison with the severe group of patients, but however, this difference did not reach a statistical significance (p > 0.05). In our previous study, d-ROM (concentration of free radicals) and OSI demonstrated a good correlation with IL-6 and VEGF out of the 11 screened cytokines as predictors of disease worsening in severe COVID-19 patients [21]. In this study we have used them as parameters to potentially predict the so-called “cytokine storm,” and herein we report a statistically significant difference of IL-6 and VEGF levels between the two groups of patients (moderate vs. severe) (p < 0.01).
In terms of the alteration of the lymphocyte subsets, we have observed decreased levels of leukocytes and its subsets, namely CD4+, CD8+, CD3+, NK cells, and the absolute number of CD8 (p < 0.05). Also, CD19+ and CD45+ were decreased in the severe group in comparison to the moderate group of patients, but the difference did not reach a statistical significance (p > 0.05), presumably due to small sample size. These results are shown in Table 1.
Leukocytes | 12.28 ± 1.055 | 7.637 ± 0.8581 | |
CD4+ | 0.1711 ± 0.0184 | 0.6765 ± 0.0653 | |
CD8+ | 0.1034 ± 0.0191 | 0.3157 ± 0.0332 | |
NK | 0.06225 ± 0.01656 | 0.1498 ± 0.02519 | |
Absolute CD8 | 99.63 ± 10.78 | 317.4 ± 32.42 | |
CD45+ | 0.7382 ± 0.1188 | 1.266 ± 0.1428 | 0.7594 |
CD19+ | 0.1852 ± 0.0495 | 0.2686 ± 0.0861 | 0.4296 |
CD3+ | 0.4038 ± 0.0629 | 0.9057 ± 0.0114 |
Alteration in lymphocytes subsets in severe and moderate COVID-19 patients on hospital admission. Results are expressed as mean ± SEM.
Moreover, we have also investigated the correlation by calculation of the Spearman r among all investigated parameters in both groups separately. We have demonstrated that in the moderate group, a good correlation between the levels of IL-6 and VEGF and NK cells was obtained (for IL-6, r = 0.6973, p < 0.05; and for VEGF, r = 0.6498, p < 0.05), whereas in the severe group only these cytokines correlated with CD45+ (for IL-6, r = 0.5610, p < 0.05; and for VEGF, r = 0.5462, p < 0.05). The results from our investigation had shown that both parameters CD45+ and the oxidative stress index can be considered as an applicable diagnostic standard in distinguishing severe form of the disease and disease complications.
The oxidative stress index can be used as potential marker with a diagnostic value since we have obtained very high values in patients with severe COVID-19 as well as OSI demonstrated a good correlation with IL-6, CD45+, CD4+, and absolute number of CD8 cells. In the severe group, we have observed a high level of the proinflammatory cytokine IL-6, which probably contributes to the T lymphocyte deficiency (CD4+, CD8+, CD3+) [17, 28]. Several authors have reported that during viral infections with the virus, CD4+ T lymphocytes are activated into T helper cells, and then they secret proinflammatory cytokines as IL-6. The activated immune cells enter into pulmonary circulation and can lead to serious lung injury [17, 21, 28]. In a study of Mudd et al. [44], gene expressions differences between influenza and COVID-19 patients were investigated. Namely, in comparison to the influenza state, INF-γ and IFN-α response pathways were downregulated within the patients with COVID-19 in terms of B-cells, CD8+ T cells, regulatory T cells, plasma blasts, and monocyte subsets. Hence, because of this interplay between the defect in the host immunity and the increased cytokines level, the evaluation of the adaptive and the innate immunity of the COVID-19 patient might be useful in terms of immunomodulatory therapies.
Upon available data and the results from our clinical experience in the last 2 years of the pandemic, we can conclude that most probably the interplay between the defect in the host immunity and the cytokines hyperproduction is an important factor in the immunopathology of the SARS-CoV-2 infection. Our experience demonstrated that the levels of certain cytokines, namely IL-6, IL-8, IL-10, VEGF, MCP-1, and EGF, were significantly increased in the critically ill COVID-19 patients. Moreover, we have proved a good correlation of the increased level of IL-6 with the oxidative stress index, which can be considered as evidence that the cytokine storm syndrome lies as an immune-pathogenesis during SARS-CoV-2 infection.
Authors would like to thank Nikola Labachevski for critical reading of the manuscript and to the technical staff at the Clinic for Infectious Diseases and Febrile Conditions.
The authors declare no conflict of interest.
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Metabolomics has several applications in health and disease including precision/personalized medicine, single cell, epidemiologic population studies, metabolic phenotyping, and metabolome-wide association studies (MWAS), precision metabolomics, and in combination with other omics disciplines as integrative omics, biotechnology, and bioengineering. Mass spectrometry (MS)-based metabolomics/lipidomics provides a useful approach for both identification of disease-related metabolites in biofluids or tissue and also encompasses classification and/or characterization of disease or treatment-associated molecular patterns generated from metabolites. Here, in this review, we provide a brief overview of the current status of promising MS-based metabolomics strategies and their emerging roles, as well as possible challenges.",book:{id:"8400",slug:"molecular-medicine",title:"Molecular Medicine",fullTitle:"Molecular Medicine"},signatures:"Sinem Nalbantoglu",authors:[{id:"147712",title:"Dr.",name:"Sinem",middleName:null,surname:"Nalbantoglu",slug:"sinem-nalbantoglu",fullName:"Sinem Nalbantoglu"}]},{id:"33119",doi:"10.5772/38349",title:"Additive Manufacturing Solutions for Improved Medical Implants",slug:"additive-manufacturing-solutions-for-improved-implants",totalDownloads:8038,totalCrossrefCites:7,totalDimensionsCites:21,abstract:null,book:{id:"1899",slug:"biomedicine",title:"Biomedicine",fullTitle:"Biomedicine"},signatures:"Vojislav Petrovic, Juan Vicente Haro, Jose Ramón Blasco and Luis Portolés",authors:[{id:"116774",title:"Dr.",name:"Vojislav",middleName:null,surname:"Petrovic",slug:"vojislav-petrovic",fullName:"Vojislav Petrovic"},{id:"116777",title:"MSc.",name:"Juan",middleName:"Vicente",surname:"Haro González",slug:"juan-haro-gonzalez",fullName:"Juan Haro González"},{id:"116778",title:"BSc.",name:"José Ramón",middleName:null,surname:"Blasco Puchades",slug:"jose-ramon-blasco-puchades",fullName:"José Ramón Blasco Puchades"},{id:"116779",title:"BSc.",name:"Luís",middleName:null,surname:"Portolés Griñán",slug:"luis-portoles-grinan",fullName:"Luís Portolés Griñán"}]},{id:"33113",doi:"10.5772/33951",title:"Encapsulation and Surface Engineering of Pancreatic Islets: Advances and Challenges",slug:"encapsulation-and-surface-engineering-of-pancreatic-islets-advances-and-challenges-",totalDownloads:3498,totalCrossrefCites:5,totalDimensionsCites:9,abstract:null,book:{id:"1899",slug:"biomedicine",title:"Biomedicine",fullTitle:"Biomedicine"},signatures:"Veronika Kozlovskaya, Oleksandra Zavgorodnya and Eugenia Kharlampieva",authors:[{id:"97932",title:"Prof.",name:"Eugenia",middleName:null,surname:"Kharlampieva",slug:"eugenia-kharlampieva",fullName:"Eugenia Kharlampieva"},{id:"101333",title:"Dr.",name:"Veronika",middleName:null,surname:"Kozlovskaya",slug:"veronika-kozlovskaya",fullName:"Veronika Kozlovskaya"},{id:"135852",title:"MSc.",name:"Oleksandra",middleName:null,surname:"Zavgorodnya",slug:"oleksandra-zavgorodnya",fullName:"Oleksandra Zavgorodnya"}]},{id:"33114",doi:"10.5772/38852",title:"In-Situ Forming Biomimetic Hydrogels for Tissue Regeneration",slug:"in-situ-forming-biomimetic-hydrogels-for-tissue-regeneration",totalDownloads:4053,totalCrossrefCites:2,totalDimensionsCites:9,abstract:null,book:{id:"1899",slug:"biomedicine",title:"Biomedicine",fullTitle:"Biomedicine"},signatures:"Rong Jin",authors:[{id:"120160",title:"Dr.",name:"Rong",middleName:null,surname:"Jin",slug:"rong-jin",fullName:"Rong Jin"}]},{id:"61418",doi:"10.5772/intechopen.77154",title:"Effect of Infra-Low Frequency Neurofeedback on Infra-Slow EEG Fluctuations",slug:"effect-of-infra-low-frequency-neurofeedback-on-infra-slow-eeg-fluctuations",totalDownloads:2125,totalCrossrefCites:5,totalDimensionsCites:5,abstract:"Infra-low frequency neurofeedback (ILF NF) has been proposed as an alternative or complementary treatment method. Previous studies have reported a good effect of ILF training on the subjective perception of positive psychological changes after training. Here we study whether the objective physiological parameters reflecting the brain function also change under the influence of ILF NF. Eight participants 21–50 years of age with no history of neurological or psychiatric diseases, but reporting about some physiological or psychological complaints, performed 20 sessions of infra-low frequency neurofeedback training. EEG in visual Go/NoGo test was recorded before the course of Neurofeedback and after its completion. The spectral power of slow EEG oscillations in the post-training recording was compared with the pretraining baseline. Along with remission of the clinical complaints, significant increase of spectral power in 0–0.5 Hz frequency band was observed in all eight participants in the post-training EEG patterns compared to the pretraining EEG, which may be linked to the improvement in the metabolic balance in the brain tissue and increasing efficiency of compensatory mechanisms in the stress regulation systems.",book:{id:"6632",slug:"biofeedback",title:"Biofeedback",fullTitle:"Biofeedback"},signatures:"Vera A. Grin-Yatsenko, Valery A. Ponomarev, Olga Kara, Bernhard\nWandernoth, Mark Gregory, Valentina A. 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Here, in this review, we provide a brief overview of the current status of promising MS-based metabolomics strategies and their emerging roles, as well as possible challenges.",book:{id:"8400",slug:"molecular-medicine",title:"Molecular Medicine",fullTitle:"Molecular Medicine"},signatures:"Sinem Nalbantoglu",authors:[{id:"147712",title:"Dr.",name:"Sinem",middleName:null,surname:"Nalbantoglu",slug:"sinem-nalbantoglu",fullName:"Sinem Nalbantoglu"}]},{id:"62128",title:"Body Mass Index and Colorectal Cancer",slug:"body-mass-index-and-colorectal-cancer",totalDownloads:1133,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Colorectal cancer (CRC) is one of the most common cancers in the world. Obesity is an established risk factor for colorectal carcinogenesis. Many epidemiological and experimental studies support this link and tumor-promoting effects of obesity. Body mass index (BMI) is a marker of general obesity. Obesity is also a global health problem and is defined by World Health Organization as BMI > 30 kg/m2. In this chapter, we give a general review about the mechanisms of obesity on colorectal carcinogenesis and the effects of obesity on clinical outcomes such as disease-free survival (DFS), progression-free survival (PFS) and overall survival (OS), in adjuvant setting and metastatic disease, respectively.",book:{id:"7159",slug:"body-mass-index-and-health",title:"Body-mass Index and Health",fullTitle:"Body-mass Index and Health"},signatures:"Nuri Faruk Aykan, Mehmet Artac and Tahsin Özatli",authors:[{id:"94089",title:"Prof.",name:"Nuri Faruk",middleName:null,surname:"Aykan",slug:"nuri-faruk-aykan",fullName:"Nuri Faruk Aykan"},{id:"257212",title:"Prof.",name:"Mehmet",middleName:null,surname:"Artaç",slug:"mehmet-artac",fullName:"Mehmet Artaç"},{id:"257213",title:"Dr.",name:"Tahsin",middleName:null,surname:"Özatlı",slug:"tahsin-ozatli",fullName:"Tahsin Özatlı"}]},{id:"65402",title:"Pharmacogenetics and Cancer Treatment: Progress and Prospects",slug:"pharmacogenetics-and-cancer-treatment-progress-and-prospects",totalDownloads:1587,totalCrossrefCites:2,totalDimensionsCites:3,abstract:"The response of cancer patients to chemotherapy follows a very heterogeneous pattern. Pharmacogenetics is the study of inherited differences in interindividual drug disposition and effects, with the goal of selecting the optimal drug therapy and dosage for each patient. Pharmacogenetics for cancer treatment is very significant, as cancer therapies exhibit severe systemic toxicity and unpredictable efficacy. There is presence of genetic polymorphisms in the genes which code for the metabolic enzymes and cellular targets for the majority of chemotherapy agents, but to predict the outcome of chemotherapy in patients is not currently possible for most treatments. A greater understanding of the genetic determinants of drug response can revolutionize the use of many medications. By identifying the patients at risk for severe toxicity, or those likely to benefit from a particular treatment, individualized cancer therapy can be achieved for most cancer patients. The prediction of cancer treatment outcome based on gene polymorphisms is becoming possible for many classes of chemotherapy agents, and the most clinically significant examples of chemotherapy agents are discussed in the chapter. However, further studies are needed in well characterized and larger cancer populations with proper validation of pharmacogenetic markers in experimental settings before application in clinical routine diagnostics.",book:{id:"8400",slug:"molecular-medicine",title:"Molecular Medicine",fullTitle:"Molecular Medicine"},signatures:"Munindra Ruwali",authors:[{id:"245866",title:"Dr.",name:"Munindra",middleName:null,surname:"Ruwali",slug:"munindra-ruwali",fullName:"Munindra Ruwali"}]},{id:"62138",title:"Body Mass Index (BMI) and Anthropometric Measurement of the Developing Fetus",slug:"body-mass-index-bmi-and-anthropometric-measurement-of-the-developing-fetus",totalDownloads:1040,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"Medical and scientific study of the measurements and size of the human body is known as anthropometry. In anthropometry, body mass index (BMI) is one of the best indirect methods for the estimation of body fat and mass. Other methods of indirect methods include weight, stature, and abdominal circumference. Direct methods include total body water, total body counting, and criterion methods include body density. Other factors like the size and weight of the mother also influence the size and mass of the body. An earlier work was conducted by K.L. Mukherjee on the systemic anthropometric measurements of the aborted human fetus. The following chapter will deal with the importance of parental and fetal BMI and its influence on the development of the fetus at varying stages of development and their relationship with anthropometric measurements.",book:{id:"7159",slug:"body-mass-index-and-health",title:"Body-mass Index and Health",fullTitle:"Body-mass Index and Health"},signatures:"Niranjan Bhattacharya and Priyodarshi Sengupta",authors:[{id:"245970",title:"Prof.",name:"Niranjan",middleName:null,surname:"Bhattacharya",slug:"niranjan-bhattacharya",fullName:"Niranjan Bhattacharya"},{id:"252992",title:"Mr.",name:"Priyodarshi",middleName:null,surname:"Sengupta",slug:"priyodarshi-sengupta",fullName:"Priyodarshi Sengupta"}]}],onlineFirstChaptersFilter:{topicId:"169",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:8,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:98,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:286,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:106,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:9,numberOfPublishedChapters:101,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:11,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. 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His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. 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We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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