\\n\\n
Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\\n\\nSeeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\\n\\nOver these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\\n\\nWe are excited about the present, and we look forward to sharing many more successes in the future.
\\n\\nThank you all for being part of the journey. 5,000 times thank you!
\\n\\nNow with 5,000 titles available Open Access, which one will you read next?
\\n\\nRead, share and download for free: https://www.intechopen.com/books
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Preparation of Space Experiments edited by international leading expert Dr. Vladimir Pletser, Director of Space Training Operations at Blue Abyss is the 5,000th Open Access book published by IntechOpen and our milestone publication!
\n\n"This book presents some of the current trends in space microgravity research. The eleven chapters introduce various facets of space research in physical sciences, human physiology and technology developed using the microgravity environment not only to improve our fundamental understanding in these domains but also to adapt this new knowledge for application on earth." says the editor. Listen what else Dr. Pletser has to say...
\n\n\n\nDr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\n\nSeeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\n\nOver these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\n\nWe are excited about the present, and we look forward to sharing many more successes in the future.
\n\nThank you all for being part of the journey. 5,000 times thank you!
\n\nNow with 5,000 titles available Open Access, which one will you read next?
\n\nRead, share and download for free: https://www.intechopen.com/books
\n\n\n\n
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"5064",leadTitle:null,fullTitle:"Smart Drug Delivery System",title:"Smart Drug Delivery System",subtitle:null,reviewType:"peer-reviewed",abstract:"This contribution book collects reviews and original articles from eminent experts working in the interdisciplinary arena of novel drug delivery systems and their uses. 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Whizar-Lugo",coverURL:"https://cdn.intechopen.com/books/images_new/6221.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"169249",title:"Prof.",name:"Víctor M.",middleName:null,surname:"Whizar-Lugo",slug:"victor-m.-whizar-lugo",fullName:"Víctor M. Whizar-Lugo"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},ofsBook:{item:{type:"book",id:"10692",leadTitle:null,title:"Critical Systems - Towards Antifragility",subtitle:null,reviewType:"peer-reviewed",abstract:"
\r\n\tAs the world becomes ever more complex, connected and automated, the challenge of designing and operating critical systems increases exponentially. The Covid-19 pandemic has demonstrated that many critical systems – from healthcare to just-in-time supply chains to societal lockdown compliance – are remarkably fragile. If we are to learn anything from the pandemic it is that our critical systems need to become a lot more resilient. The ability of complex critical systems to survive unpredicted stresses and perturbations is one thing, but when solutions are responsible for the wellbeing of potentially millions of people, what is really needed, and this book proposes, are systems that are antifragile. That is, the more they are stressed the stronger they become.
\r\n\r\n\tAntifragility is a property found in many natural systems, but almost never in today’s human-engineered systems. Achieving antifragility demands new and better ways of specifying, designing and operating the world’s critical systems. More specifically, it demands the management and resolution of three overarching contradictions:
\r\n\r\n\t1) The divergence between system complexity of operating environments and the design capability of those tasked with creating such systems
\r\n\t2) The divergence between the levels of reliability required (twelve-9’s are not uncommon requirements) and the ability to identify or test failure modes that are increasingly unknown and unknowable
\r\n\t3) The divergence between the vulnerability of critical systems and the amount of damage that an individual ‘bad actor’ is able to inflict.
\r\n\t
\r\n\tThe book examines pioneering work to address these challenges and to ensure the timely arrival of antifragile critical systems into a world that currently sees humanity at the edge of a precipice.
Millimeter-wave CMOS RF circuits have received substantial attention in recent years, motivated by advances in CMOS processing. Figure 1 shows on-wafer measurement using probes, which is commonly used in research and development of RF front-end circuits. De-embedding is necessary to remove the effect of pads in on-wafer measurements of RF circuits. Thru-Reflect-Line (TRL) calibration technique [1][2][3] and the de-embedding technique using open and short patterns [4] have been used conventionally. The authors applied the Thru-Line (TL) de-embedding technique [5] to remove the effect of pads from the measured S-parameters of RF circuits on a Si CMOS substrate. The TL de-embedding technique requires two patterns (Thru and Line) while the TRL de-embedding requires three patterns (Thru, Reflect and Line). The TL de-embedding technique can characterize left and right pads under the assumption that left and right pads have the same characteristics while TRL de-embedding cannot characterize pads, without knowing characteristic impedance of the line used for example. Other de-embedding methods, such as double delay [6], through-only [7], and multi line (or L-2L) de-embedding [8][9], have been proposed. However, these all use approximation of pads, or parasitic component, by an equivalent circuit model while the TL de-embedding method treats pads rigorously with S-parameters. The effectiveness of TL de-embedding has been investigated in [10].
It is very difficult to keep repeatability of measurement in such high frequencies over millimeter-wave band. Hence, the electromagnetic (EM) simulation technology becomes important. This paper explains EM simulation modeling for on-wafer measurement using a GSG probe. By utilizing the result of EM simulation, the accuracy of de-embedding techniques (open-short, TRL, and TL) are compared and discussed.
The chapter is organized as follows. Section 2 describes structure of pads and transmission line considered in the chapter. Section 3 presents open-short, TRL and TL de-embedding techniques. Section 4 presents EM simulation modeling for on-wafer measurement using a GSG probe. The gap between ground (G) and signal (S) pads is excited by a lumped source. Section 5 discusses the accuracy of de-embedding techniques (open-short, TRL, and TL). The accuracy degradation of open-short de-embedding technique is quantitatively investigated via numerical simulation, which is verified in section 4.
On-wafer measurement using probes.
The structure of pads and transmission lines are shown in Figure 2. The GSG pad is used for touching with the GSG probe. A guided microstrip line (G-MSL) on a Si CMOS substrate [11], which has metal walls on both sides of the signal line, is used as the transmission line between the GSG pads as shown in Figure 2. The G-MSL with dummy metal fills is shown in Figure 3. The transmission line consists of several metal (Aluminum) layers and vias which connect them. SiO2 is used as an insulator between the metal layers. The ground plane and signal line are realized by the bottom and top metal layers, respectively. The width and thickness of the signal line are 10 μm and 1 μm, respectively. The characteristic impedance is designed to be about 50 Ohm. There are metal walls, or guides, on both sides of the signal line which consists of the metal layers and vias [11]. The role of the guide is to increase the metal density to satisfy design rules and to suppress unwanted leakage at corners. The distance from the guide walls to the edges of the signal line is 20 μm to ensure that the guide does not affect transmission characteristic of the microstrip mode.
Structures of pads and guided microstrip line (G-MSL).
Thru pattern and structure of a guided microstrip line.
Algorithms of open-short, TRL and TL de-embedding techniques are introduced in this section. Table 1 shows the patterns used in each de-embedding method.
Pattern | Method | |||
TRL | TL | Open/Short | ||
Thru | ||||
Reflect (~Open) | ||||
Line | ||||
Short |
The open-short de-embedding technique [4] is reviewed and outlined here. In the open-short de-embedding technique, the parasitic component of pads is approximated by the equivalent circuit topology shown in Figure 4.
(i) Three measurements are made to obtain the transmission line characteristics shown in Figure 4(i). The first measurement is done for the open-pattern, resulting in the open two-port Y-parameters
(ii) The thru-pattern is approximated by the equivalent circuit topology shown in Figure 4(ii). Parasitic elements
By comparing matrix elements,
(iii) Parasitic elements
By comparing matrix elements,
(iv) The Y-parameters for thru, only transmission line characteristic,
It is noted that a lumped element can be the DUT although the transmission line is assumed as the DUT in this paper. De-embedding technique using electromagnetic (EM) simulator [12], with higher accuracy than the open/short de-embedding technique, is also proposed when the DUT is a lumped element.
The Thru-Reflect-Line (TRL) calibration technique [1][2][3], which is widely used for network analyzer calibration, can be used for deembedding of pads directly.
The Thru-Line (TL) de-embedding technique [5] uses Thru (T) and Line (L) patterns, which have different lengths as shown in Figure 5. The line pattern is longer (by
where
De-embedding technique using open and short patterns.
where
It must be noted that there are two sets of solutions to (6) indicated by the double sign. The propagation constant can be calculated from
Selecting the correct solution from the two sets of solutions in (6) and (7) is uncomplicated. If the transmission line is a right-handed waveguide, the phase constant is positive, and a set of solutions which gives a positive phase constant
The effect of the pads can be de-embedded from a structure under test (SUT) by the following procedure:
(i) Measure the S-matrix of the SUT
(ii) Transform
(iii)
A sample Mathematica source code is given in Appendix.
Thru and line patterns.
This section verifies the modeling and accuracy of the electromagnetic (EM) simulator. The FEM-based EM simulator, Ansoft HFSS Ver.11 [14], was used for EM analysis in the chapter.
Figure 7 shows the model for the analysis of the thru-pattern in HFSS. Due to the symmetry of the structure and excitation, the model for the analysis of the Figure 3 thru-pattern can be reduced to half of the whole structure, as suggested in Figure 7. A magnetic wall, or perfect magnetic conductor (PMC), is assumed at the center of symmetry. The absorbing boundary, or radiation boundary in HFSS, conditions are applied to the other outer boundary walls. The gap between ground (G) and signal (S) pads is excited by a lumped source. Lumped ports with 100 Ohm intrinsic impedances, which is double of probe impedance because of image theory, were used for the excitations.
To verify the accuracy of the EM simulation, the calculated value is compared with the measured one. A micrograph of a fabricated chip is shown in Figure 8. The chip is 2.5 mm square and an 0.18 μm CMOS process is used. In the measurements, 100 μm-pitch GSG probes were used, and the system was calibrated using the impedance standard substrate (ISS). Smith charts of the S-parameters for the thru, line, and reflect patterns are shown in Figures 9, 10, and 11, respectively. The calculated and measured results agreed very well for all three patterns.
Then, sensitivity of lumped port position and size is investigated. Figure 12 shows position and size of lumped port in the GSG pad. Reflection coefficient S11 and transmission coefficient S12 (=S21) are shown in Figure 13 and Figure 14, respectively, with various sets of width (w), left pad offset (dl) and right pad offset (dr) (both offsets are prescribed in a similar manner). w is varied from 10 μm to 30 μm. dl and dr are varied from -15 μm to 15 μm. It is found that there are no significant differences in results. These results suggest that the probe positioning error is not serious in measurement. Results indicated by “edge1” and “edge2” are obtained with the excitation model in which lumped port is arranged at the edge of the GSG pad as shown in Figure 15. wa in Figure 15 is 20 μm for “edge1” while it is 50 μm for “edge2”. The phase of S11 begins to show different value in high frequency region. The result indicated by “vertical” is obtained with the excitation model in Figure 16. The phase of S12 begins to show different value in high frequency region. However, the results of these excitation models show good agreement.
Cascading expression of the line pattern.
Analysis model of thru-pattern in HFSS.
Chip photo (process: CMOS 0.18μm, chip size: 2.5 mm x 2.5 mm)
Smith chart of thru-pattern. (Solid line: measurements, broken line: simulation)
Smith chart of line-pattern in HFSS.
Smith chart of reflect-pattern in HFSS.
Position and size of lumped port in the GSG pad.
Position sensitivity of lumped port for S11 of thru-pattern.
Position sensitivity of lumped port for S12 of thru-pattern.
Lumped port arranged at the edge of the GSG pad.
Lumped port arranged vertically in the GSG pad.
In this section, excitation modeling is extended in order to treat more general problem. Figure 17 shows EM excitation-modeling for GSG pad include asymmetric pattern while symmetric pattern is considered in the previous section. Port 1, 2, 3 and 4 have 100 Ohm intrinsic impedance when the impedance of the GSG probe is 50 Ohm. The objective of the following discussion is to convert
Port 1 and Port 2 are identically excited (
Because of the symmetry of the GSG pad,
If only one GSG probe is used to measure reflection, the reflection coefficient can be obtained by
To verify the formulation of (12), the EM excitation-modeling for thru-pattern shown in Figure 18 is compared with the modeling shown in Figure 7. Figure 19 shows the frequency characteristic of reflection and transmission coefficient for the thru-pattern. The result indicated by “Cal” is obtained by the model in Figure 7, and the result indicated by “Cal (4-ports to 2-ports)” is obtained by the model in Figure 18. They agreed very well each other. Figure 19 shows two short-circuited lines, which have asymmetric structure as an example. Figure 20 shows the frequency characteristic of S-parameters for the structure shown in Figure 19. Calculated results agreed very well with measured results, and (12) is validated.
EM excitation-modeling for GSG pad include asymmetric pattern.
Four port excitation model for thru-pattern.
Comparison between two-port and four-port excitation model for thru-pattern.
Two short-circuited lines.
Frequency characteristic of S-parameters for two short-circuited lines shown in Figure 20.
The frequency characteristic of the propagation constant for the G-MSL is extracted by de-embedding techniques, and shown in Figure 22. The solid and broken lines represent results without and with dummy metal fills (5 m square; w=5 m and p=10 m in Figure 3) in the G-MSL, respectively. The loss of the G-MSL with dummy metal fills is slightly larger than that without dummy metal fills. The phase constant of the G-MSL with dummy metal fills is slightly larger than that without dummy metal fills because the dummy metal fills result in an effect like an artificial dielectric compound. The line with “Cal.” is the calculated result with the method in [15]. The measured results agreed well with the calculations. Figure 22 shows that the accuracy of TL de-embedding technique is as good as that of the TRL de-embedding technique.
Figure 23 shows the characteristic impedance of the transmission lines. The characteristic impedance was obtained from the ratio of the voltage V to the current I. The voltage V is calculated by the tangential line integral of the electric field from the ground plane to the signal line. One half of the current I/2 is calculated by the tangential line integral of the magnetic field around the signal line. The characteristic impedance is obtained using the de-embedding technique [16] together with a characterization of the pads using the TL de-embedding technique. Very good agreement between the calculated and measured results was obtained. As the frequency increases, the real part of the characteristic impedance approaches 50 Ohm and the imaginary part of the characteristic impedance approaches 0 Ohm.
Extracted propagation constant of the G-MSL.
Extracted Characteristic impedance of the G-MSL.
The accuracy of de-embedding methods using thru-line patterns, thru-reflect-line patterns and open/short patterns will be discussed numerically in this section. Open and short patterns used in the simulation are shown Figure 24. The lengths from left and right pads to the open or short ends are
Extracted Characteristic impedance of the G-MSL.
Extracted transmission coefficient S21 of the 600 μm-length G-MSL (l=420 μm in Figure 24).
Extracted transmission coefficient S21 of the 600 μm-length G-MSL for several length of open/short patterns.
To investigate the accuracy of the open-short de-embedding technique, numerical simulation was performed to extract S21 of a G-MSL with 600 μm-length. Figure 26 shows extracted S21 for the 600 μm-length G-MSL using calibration patterns with l=20 μm, 120 μm, 220 μm, 420 μm, respectively. A line of “3-D FEM (HFSS)” is the result obtained by S21 of the 3-D FEM analysis for the 600 μm-length G-MSL. The result indicated by “2-D FEM (HFSS)” is port analysis solution. Results indicated by “TRL”, “TL” and “OS” are obtained after applying TRL, TL, and open-short de-embedding techniques, respectively. Results are obtained by simulation using HFSS. The accuracy of the TRL and TL de-embedding techniques is very good while that of open-short de-embedding technique differs in high frequency. The accuracy becomes higher as the length l of the open and short patterns becomes shorter. Figure 27 shows the error of the extracted S21 defined by
The result indicated by “3-D FEM (HFSS)” is used for
Error of the extracted transmission coefficient S21 of the 600 μm-length G-MSL with open-short de-embedding technique.
In this paper, EM simulation modeling for on-wafer measurement using a GSG probe was presented. The gap between ground (G) and signal (S) pads is excited by a lumped source. Transformation formula from 4-port to 2-port S-matrix expression was derived. The accuracy of EM simulation was verified by comparing with measurements. Results of EM simulation by changing excitation model suggest that the probe positioning error is not serious in measurement.
TL de-embedding technique was applied for on-wafer measurement using a GSG probe. The accuracy of de-embedding techniques (open-short, TRL, and TL) were compared and discussed. It was found that the accuracy of TRL and TL de-embedding technique is approximately the same. Degradation of accuracy in open-short de-embedding technique was quantitatively investigated via numerical simulation. In the open-short de-embedding technique, the accuracy becomes higher as the lengths of the open and short patterns become shorter.
This work was supported in part by Semiconductor Technology Academic Research Center (STARC). The chip in this study has been fabricated in the chip fabrication program of the VLSI Design and Education Center (VDEC), the University of Tokyo in collaboration with Rohm Corporation and Toppan Printing Corporation. The authors would like to express their deepest gratitude to Toshihiko Mori of FUJITSU, Osamu Kobayashi of STARC, Naoko Ono of TOSHIBA, Toshiakira Ando of STARC/PANASONIC, and Ryuichi Fujimoto of STARC /TOSHIBA for their fruitful discussion.
Tumor necrosis factor (TNF) alpha is one of the first discovered cytokines shown by Carswell [1] in 1975 and was named for tumor regression activity induced in the serum of mice treated with Serratia marcescens polysaccharide [2]. Cytokines are low-molecular-weight peptides secreted by activated immune cells as well as stromal cells and exerting biological activities through binding to cognate receptors on cell surface. Cytokines are produced by a number of cell types, predominantly leukocytes that regulate a number of physiological and pathological functions including innate immunity, acquired immunity, and a plethora of inflammatory responses [3]. Cytokines excite or hinder the generation, propagation, and differentiation of different associated target cells positive on antigen induction, thus leading to mediation in the activity of diverse other cells involved in the immune response especially the more pronounced macrophages, mast cells, B cells, T cells, and natural killer (NK) cells. Thus, cytokine is regarded as secreted proteins with growth, differentiation, and activation functions that regulate and determine the nature of immune responses [4]. The broad classification of cytokines are termed in a group as follows: interleukin (IL), interferon (IFN), tumor necrosis factor (TNF), colony stimulating factor (CSF), and chemokine and growth factor (GF), and these exerts biological functions through action mode and characteristics as paracrine, autocrine, and endocrine. TNF being one of the prominent cytokine has about 19 different members of the TNF superfamily that includes tumor necrosis factor alpha (TNF-α), tumor necrosis factor beta (TNF-β), TNF-related weak inducer of apoptosis (TWEAK), TNF-related apoptosis-inducing ligand (TRAIL), lymphotoxin-β (LT- β), CD40L, CD30L, 4-1BBL, CD27L, glucocorticoid-induced TND receptor ligand (GITRL), fibroblast-associated ligand (FasL), OX40 ligand (OX40L), LIGHT, A proliferation-inducing ligand (APRIL), B-cell-activating factor (BAFF), receptor activator of NFκB ligand (RANKL), vascular endothelial cell-growth inhibitor (VEGI), and ectodysplasin A ((EDA)–A1, EDA-A2) [2].
TNF-α is a potent mediator of inflammation, as well as many normal physiological functions in homeostasis and health and antimicrobial immunity [5]. Inflammation is a classical host defense response of vascularized living tissue to infection and injury, and in the central nervous system (CNS), the term neuroinflammation is used to denote cellular and inflammatory responses of vascularized neuronal tissue through activation of resident cells in the brain (microglia, astrocytes, and endothelial cells), the recruitment of blood-derived leukocytes including neutrophils, lymphocytes, and macrophages, and a plethora of humoral factors [6, 7]. More appropriately, neuroinflammation is a term used to denote inflammation associated with the brain and is characterized by the activation of microglia and expression of major inflammatory mediators without typical features of peripheral inflammation such as edema and neutrophil infiltration [8]. Neuroinflammation in the brain supposedly has a positive effect such as increasing blood flow and removal of damaged tissue by phagocytosis, but in a disease state, the resulting inappropriate inflammation caused negative effects which by far out weight the positive effect [6].
Nuclear factor kappa-light-chain enhancer of activated B cells (NFκB) otherwise called nuclear factor kappa B is a heterodimer and one of the most important transcription factors that activate transcription of many proinflammatory genes. It is well documented that TNF-α induces at least five different types of signals that include activation of NFκB, apoptosis pathways, extracellular signal-regulated kinase (ERK), p38 mitogen-activated protein kinase (p38MAPK), and c-Jun N-terminal kinase (JNK) [2]. These biological functions of TNF-α makes its role in neuroinflammation critically prominent. It is therefore expedient to elucidate and expand the rational basis of TNF-α as major cytokine of neuroinflammation. Hence, this review discusses summary of literature on unique role of TNF-α in neuroinflammation and various agents that mediate neuroinflammation via TNF-α modulation.
Microglia being major immune cells involved in defense in the central nervous system, its activation is considered to be the hallmark of neuroinflammation [7, 9]. Activation of microglia cells constitutes the first key acute response in the brain to external aggression such as acute brain ischemia, traumatic brain injury, or microbial pathogen, and this microglial activation is coupled with subsequent activation of blood-borne monocytes/macrophages to yield a full-blown neuroinflammatory thick rim around ischemia infarct that becomes observable after 1 week in both human and animal models [10]. Microglia in the CNS constitutes 5–15% of total brain population; having share common precursor with peripheral macrophages, they produced transient inflammatory changes like macrophages such as phagocytosis, inflammatory cytokine production, and antigen presentation, normally returning to their basal state when the activation stimulus is resolved [11]. In a disease state such as in the onset of focal cerebral ischemia or traumatic brain injury, however, the microglia response becomes inappropriately more reactive and exaggerated to produce plethora of inflammatory mediators that trigger apoptosis and exaggerate neuronal damage [12]. Therefore, microglia/macrophages are the key immune cells concerned with the protection of brain against injury. Their architectural and functional changes are linked with the liberation of injury signals induced by pathology. These cells are usually responsible for clearance of demised neural cells and allow for restoration of lost neuronal functions. However, when markedly activated by the damage-associated molecular patterns subsequent to a disease state, they can generate a huge amount of proinflammatory cytokines that are capable of interrupting neural cells and the blood-brain barrier, and manipulate neurogenesis [9].
The primary function of microglia in the brain is to control any external aggression and neutralize its effect by a process of phagocytosis, which is a chronologically multistep system including oriented gradient motility (chemotaxis); identification of alien foreign agents by membrane lectins and receptors (recognition); encompassing flow round the injurious foreign agents into a vacuole/phagosome (engulfment); unraveling of intracellular secretor pools (granules); and liberation of innate antibiotics and enzymes into the phagosome, generation of reactive oxygen species by an intricate enzymatic system sequestrated on the phagocyte membrane and/or reactive nitrogen species by an inducible nitric oxide synthase, and decapitating and digestion of engulfed foreign substance in the multifaceted phagolysosomal medium (microbial killing) [13]. Therefore, there are four important events of phagocytosis: chemotaxis, recognition, engulfment, and microbial killing.
Chemotaxis is the immediate restricted, valuable host inflammatory reaction that is initiated by local tenant macrophages, demised cells and tissues, plasma factors, and microbial products. Specifically, the closely generated factors of inflammation (cytokines, activated complement protein, kinins, etc.) and microbial factors construct chemotactic gradients, alter endothelial cell membrane receptors, and encourage decrease of the blood flow. Blood-borne monocytes/macrophages that are rolling along the endothelial surface act in response to the chemotactic and cell-mediated signals and are primarily activated to definitely attach to the endothelium by way of their membrane integrins; the second pace is transendothelial migration, denoted as diapedesis, followed by tilting motility toward the inflammatory site (chemotaxis) [13].
Recognition involves identifying and attachment of particle to be ingested by the microglia/phagocytes. There are two methods of recognition: opsonin/opsonin dependent/receptor mediated and non-opsonin/opsonin independent. Opsonin/opsonin dependent is where microglia/phagocytes recognize pathogens via their membrane receptors for opsonins (e.g., complement factors C3b and iC3b and Fc component of immunoglobulins), which are present on the microbial surface, while non-opsonin/opsonin independent is where microglia/phagocytes recognize pathogens via microbial and phagocyte lectins [13]. Because microglia/phagocytes express high-affinity receptors for opsonin, the term opsonization is used to indicate a process, whereby injurious foreign particle becomes coated with substance, thereby enhancing its recognition by leukocyte and making it more open to phagocytosis. As aforementioned, the injurious foreign agents or microbes are usually opsonized by specific protein substances such as immunoglobulin G (IgG) antibodies, breakdown product of compliment (C3b), and fibrinogen all of which phagocytes express high-affinity receptors.
Engulfment refers to microglia/phagocyte extension of cytoplasm (pseudopods) flow around the injurious foreign agents or microbes after its binding with phagocyte and subsequent pinches off to form vesicles (phagosome) that enclose the injurious foreign agents or microbes. Phagocyte extensions (pseudopods) finally engulf the injurious foreign agent or microbe in a vacuole and trigger the activation of two functions: the release of granule contents into the phagosome and the oxidative burst. Coiling engulfment is the most frequent unusual uptake: unilateral pseudopods wrap around the microorganism in multiple turns, giving rise to largely self-apposed pseudopodial surfaces [13].
Microbial killing can be achieved through oxygen-dependent or oxygen-independent/non-oxygen-dependent method of pathogen or injurious agent killing. Oxygen dependent involves the use free radicals. A free radical is clearly referred to as atom or molecule having one or more unpaired electrons in valence shell or outer orbit and is competent for autonomous survival [14]. The strange quantities of odd electron(s) possess by a free radical make it unbalanced, short lived, and extremely reactive. This high reactivity makes free radical exert a pull on electrons from further compounds to reach steadiness. The newly pulled attacked molecule loses its electron and becomes a free radical itself, opening a chain of feedback cascade of reaction. Free radicals/oxidants derived from both endogenous sources and exogenous sources have gained importance in the field of biology due to their central role in various physiological conditions as well as their implication in a diverse range of diseases. They include reactive oxygen species (ROS) which are hydroxyl radicals (˙OH), superoxide (O2−), hydrogen peroxide (H2O2), and reactive nitrogen species (RNS) which are nitric oxide (NO) and peroxynitrite (OONO−). At reasonable or little concentrations, ROS/RNS encompasses desirable effects and engage in a variety of physiological purposes such as in immune function (i.e., guard in opposition to pathogenic microorganisms), in certain cellular signaling pathways, in mitogenic reaction, and in redox directive. Conversely, at excessively elevated concentrations, both ROS and RNS lead to oxidative stress and nitrosative stress, respectively, that potentially cause adverse effect to biological molecules [14].
The mechanism of oxygen-dependent microbial killing is initiated after engulfment where oxygen burst is activated to cause increase in oxygen consumption (50- to 100-fold increase) and metabolism; this leads to massive production of nicotinamide adenosine diphosphate (NADP) as by-product of adenosine triphosphate (ATP) generation by oxidative phosphorylation. The oxygen burst is unrelated to mitochondrial respiration and reflects the activity of the NADPH oxidase system in the cytosol and membrane constituents, which are separated in resting microglia/phagocytes and are reassembled upon microglia/phagocytes activation. The generated NADP through NADPH oxidase enzyme activity generates superoxide (O2−) which is further converted to hydrogen peroxide (H2O2) either spontaneously or through enzymatic catalysis of superoxide dismutase (SOD) enzyme by combining with hydrogen ion (H+). Both hydrogen peroxide (H2O2) and superoxide (O2−) can cause microbial killing. For instance, H2O2 in the presence of myeloperoxidase (MPO) released from microglia/phagocytes azurophilic (primary) granules and a halide generates very potent oxidizing agents such as hypochlorous acid (HOCl) and chloramines [13]. Other oxidative species such as singlet oxygen has been suggested to be important for microbial killing through the formation of ozone [15].
Non-oxygen-dependent/oxygen-independent microbial killing is mediated by protein molecule and other factors that are mostly found within the lysosome such as lysozyme, lactoferrin, and elastase. Lysozyme is an enzyme that hydrolyzes N-acetyl glucosamine bond found in glycopeptide coat of all bacterial cell wall. Thus, non-oxygen-dependent/oxygen-independent microbial killing is dependent on protein and peptide antibiotics such as bactericidal permeability-increasing protein, cationic antimicrobial protein 37, and defensins that are stored in peroxidase-positive (azurophilic, primary) granules where they are together localize with active proteases such as elastase, cathepsin G, and proteinase 3. The synergistic interaction of oxygen-dependent and non-oxygen-dependent/oxygen-independent microbial killing systems generally results in pathogen killing [13].
Pathological consequences that result from a disease state of the brain, however, make microglia response becomes inappropriately exaggerated. Microglia when transformed into phagocytes can release a variety of substances many of which are cytotoxic and/or cytoprotective. While cytoprotective substances include neurotrophic molecules such as brain-derived neurotrophic factor (BDNF), insulin-like growth factor I (IGF-I), several other growth factors, and anti-inflammatory factors, cytotoxic substances include proinflammatory cytokines such as TNF-α, IL-1β, and IL-6 as well as other potential cytotoxic molecules including nitric oxide (NO), reactive oxygen species (ROS), and prostanoids. The uniquely outburst cytokines extensively studied in acute ischemic stroke are tumor necrosis factor-α (TNF-α); the interleukins (IL), IL-1β, IL-6, IL-20, and IL-10; and transforming growth factor (TGF)-β. Although IL-1β and TNF-α are proinflammatory that appears to exacerbate cerebral injury, TGF-β and IL-10 are anti-inflammatory that may exert neuroprotective effects, and IL-6 has both pro- and anti-inflammatory effects [16]. Astrocytes, like microglia, are also capable of secreting inflammatory factors such as cytokines, chemotaxis cytokines (chemokines), and NO in response to brain pathological state.
Table 1 reveals researches of various agents that mediate neuroinflammation via TNF modulation.
Treatment | Experimental model | Related TNF finding | References |
---|---|---|---|
Puerarin | Stroke model of rat middle cerebral artery occlusion | Modulate neuroinflammation by mark reduction in mRNA expression of tumor necrosis factor-α (TNF-α) | [17] |
Edaravone and scutellarin | Stroke model of rat intraluminal middle cerebral artery occlusion | Modulate neuroinflammation by attenuating expression levels of TNF-α | [18] |
Matrix metalloproteinase-8 inhibitor (M8I) | Stroke model of rat middle cerebral artery occlusion | Modulate neuroinflammation by abrogating TNF-α expression | [19] |
Wogonin (5,7-dihydroxy-8-methoxyflavone) | Stroke model of rat middle cerebral artery occlusion | Modulate neuroinflammation by decrease in production of TNF-α | [20] |
Nicotine | Stroke model of rat global cerebral ischemia | Modulate neuroinflammation by significant reduction of enhanced expression of tumor necrosis factor alpha (TNF-α) | [21] |
Glycyrrhizin (GRZ) | Brain cognitive impairment and neuroinflammation of lipopolysaccharide treated Mice | Modulate neuroinflammation through inhibition of proinflammatory TNF-α | [8] |
Atorvastatin | Stroke model of rat intracerebral hemorrhage | Modulate neuroinflammation by dose-dependent reduction of TNF-α | [22] |
Angiotensin-(1–7) | Stroke model of mice intracerebral hemorrhage | Modulate neuroinflammation by decrease in levels of TNF-α | [23] |
Milk fat globule-EGF factor VIII (MFG-E8) | Stroke model of rat permanent middle cerebral artery occlusion | Modulate neuroinflammation through decrease in expression of cerebral TNF-α level | [24] |
Compound K (20-O-D-glucopyranosyl-20(S)-protopanaxadiol) | Stroke model of mice transient middle cerebral artery occlusion | Modulate neuroinflammation through inhibition of lipopolysaccharide-induced production of TNF-α | [25] |
Kaempferol glycosides | Stroke model of rat transient middle cerebral artery occlusion | Modulate neuroinflammation by inhibiting expression of tumor necrosis factor alpha | [26] |
Angiotensin-(1–7) | Stroke model of rat permanent middle cerebral artery occlusion | Modulate neuroinflammation by inhibiting increase in TNF-α | [27] |
Nicotine | Stroke model of rat global ischemia | Modulate neuroinflammation by reduction of enhanced expression of tumor necrosis factor alpha (TNF-α) induced by ischemia/reperfusion | [21] |
Propofol | Brain neuroinflammation of lipopolysaccharide-induced inflammation in activated microglia | Modulate neuroinflammation by inhibiting lipopolysaccharide-mediated production TNF-α | [28] |
Zileuton | Stroke model of rat permanent middle cerebral artery occlusion | Modulate neuroinflammation through attenuating release of TNF-α in the serum | [29] |
Caffeic acid ester fraction (Caf) | Stroke model of rat middle cerebral artery occlusion in vivo and lipopolysaccharide-induced microglial activation in vitro | Modulate neuroinflammation by inhibiting TNF-α induced by lipopolysaccharide treatment in primary microglia in a dose-dependent manner | [30] |
Telmisartan | Stroke model of rat intracerebral hemorrhage | Modulate neuroinflammation by decrease in tumor necrosis factor-α | [31] |
Setarud (IMOD™) | Human patients with acute ischemic stroke | Modulate neuroinflammation by decrease in TNF-α levels | [32] |
Caffeine | Brain neuroinflammation of lipopolysaccharide (LPS)-stimulated murine BV2 microglial cells | Modulate neuroinflammation by suppressing generation of proinflammatory TNF-α | [33] |
SCH58261 | Stroke model of rat bilateral common carotid artery occlusion | Modulate neuroinflammation by reversing ischemia reperfusion injury induced elevation of TNF-α. | [34] |
Caffeine | Stroke model of rat bilateral common carotid artery occlusion | Modulate neuroinflammation by reduction of TNF-α activity | [35] |
Fluoxetine | Stroke model of rat subarachnoid hemorrhage | Modulate neuroinflammation by decreasing the expression of proinflammatory mRNA levels of TNF-α | [36] |
Matrix metalloproteinases 8 (MMP-8) inhibitor | Brain neuroinflammation of lipoteichoic acid (LTA)-stimulated rat primary astrocytes | Modulate neuroinflammation by inhibiting lipoteichoic acid (LTA) induced expression of TNF-α | [37] |
Sildenafil | Brain neuroinflammation and demyelination induced by cuprizone in Mice model of multiple sclerosis. | Modulate neuroinflammation by reduction in the expression of the proinflammatory cytokines TNF-α | [38] |
Various agents that mediate neuroinflammation via TNF modulation.
In a comprehensive review of agents that induce neuroinflammation, Nazeem [39] has classified models of neuroinflammation based on mechanism through which agents induce neuroinflammation into three as follows: immune challenge-based models which include lipopolysaccharide (LPS)-induced neuroinflammation and polyriboinosinic-polyribocytidilic acid (PolyI:C)-induced neuroinflammation; neurotoxin-induced models which consist of streptozotocin-induced neuroinflammation, okadaic acid-induced neuroinflammation, and colchicine-induced neuroinflammation; genetically manipulated models that contain interleukin-1β (IL-1β) overexpression model, p25 transgenic model, anti-nerve growth factor (NGF) transgenic models, and transforming growth factor-β (TGF-β)-deficient models.
The most commonly studied model of neuroinflammation is LPS-induced neuroinflammation which activates microglia in the brain [40]. LPS also termed endotoxin is a constituent of the external membrane of Gram-negative bacteria, and the mechanism of LPS-induced neuroinflammation is mediated through LPS binding with CD14 on microglia membranes. The LPS-CD14 complex then interacts with the Toll-like receptor-4 (TLR-4), which, in turn, activates microglia by initiating signal transduction cascades leading to rapid transcription and release of proinflammatory cytokines, chemokines, and the complement system proteins, as well as anti-inflammatory cytokines like IL-10 and transforming growth factor-β (TGF-β) [39].
Another popular emerging noninvasive, effective, and sterile method of induction neuroinflammation in animal model is MRI-guided pulsed focused ultrasound (pFUS) combined with systemic infusion of contrast agent microbubbles (MB). This MRI-guided pFUS+MB has advantage over all other methods of inducing neuroinflammation in a way that it induces neuroinflammation without systemic involvement [40].
Within the brain, TNF-α is produced and discharged in the brain predominantly by glial cells and neurons, with microglia and astrocytes being the major glial cells involved. Upon arrival of appropriate TNF-α production stimulus, TNF-α is formed as a 27-kDa (233 amino acids) precursor, which binds to cell membrane of producing cells. This precursor is cleaved by proteolysis to liberate a 17-kDa (157 amino acids) subunit by the action of TNF alpha-converting enzyme (TACE). TACE also known as ADAM17 is well-identified proteinase enzyme that mediates the process TNF-α production and is a member in the family of mammalian adamalysins (or ADAMs: A disintegrins and metalloproteinases) [41].
Upon cleavage by TACE/ADAM17, the free TNF-α forms a bioactive homotrimer that lead to biological effect of TNF-α. The actions of TNF-α is achieved through two distinct cell surface receptors: TNFR1 and TNFR2. TNF-α generates the activation of TNF receptors (TNFR1 and TNFR2), and the resultant TNF-induced TNFR signaling pathways are complex and wide ranging in different cell types, and precise circumstances, thereby accounting for TNF-α pleiotropic nature of action [5]. For instance, with TNFR1 signaling pathway, binding of TNF-α to the cognate receptor leads to the recruitment of TNF-α adaptor protein termed as TNF receptor-associated death domain (TRADD), which then creates a platform for binding of additional cytoplasmic adaptor proteins including TNF receptor-associated factor 2 (TRAF2), receptor-interacting protein (RIP), and FAS-associated death domain (FADD). The TRAF2 and RIP are concerned in escalating the transcriptional gene regulation; TRAF2 triggers the activation of a mitogen-activated protein kinase (MAPK) pathway, thereby leading to the activation of c-Jun N-terminal kinase (JNK), thus increasing its transcriptional activity; the RIP is a protein kinase vital to the activation of the transcription factor NFκB by phosphorylation of IκB kinase (IKK). On the other hand, FADD pathway leads to activation of caspase-8, thereby leading to initiate a caspase cascade of apoptosis cellular demise [41]. Although TNF-α binds to both TNFR1 and TNFR2 receptors with high affinity, there are some species specificity in terms of the receptor subtype and TNF-α binding [42]. TNF-α-induced p38 MAPK pathway transcription activity has been also implicated to induce proinflammatory IL-6 synthesis [43].
Neuroinflammation involves activation of microglia and astrocytes as well as influx of hematogenous cells recruited by cytokines, adhesion molecules, and chemokines across the activated blood vessel wall [44]. Neuroinflammatory signaling involves a coordinated effort of different molecules and cells types and is largely coordinated by a ubiquitous transcription factor NFκB. This signal transduction pathway for the activation of the transcription factor NFκB leads to control the expression of numerous genes activated during inflammation (i.e., cytokines, chemokines, growth factors, immune receptors, cellular ligands, and adhesion molecules). Thus, NFκB regulates a number of genes (including those coding for key inflammatory cytokines, like IL-6, TNF-α, etc.) involved in inflammation, making it the most important transcription factor that plays a key role in the inflammatory response. The collective gene targets of NFκB include various adhesion molecules, cytokines and chemokines (involved in proinflammatory signaling and NFκB activation, e.g., IL-1β and TNF-α), metalloproteinases (e.g., MMP-9), immune receptors, acute phase proteins, cell surface receptors, and inflammatory enzymes [45]. Various stimuli, such as cytokines, viruses, and oxidants, result in the activation of the transcription factor NFκB by separating it from inhibitor of NFκB alpha (IκBα)-bound protein in the cytoplasm, which becomes degraded and allows NFκB to move to the nucleus, where it binds to the DNA of the genes for numerous inflammatory mediators, resulting in their increased production and secretion [46].
It is pertinent to note that neuroinflammatory microglia-/macrophage-mediated phagocytosis is instrumental in neutralizing injurious foreign agent and conducting brain cleanup, the process which must occur to allow for tissue repair and functional recovery. This fast and efficient removal of apoptotic, dislocated, and damaged cells, before the discharge of injurious and proinflammatory cell contents occur, may help to reduce secondary damage. But inappropriate inflammatory responses generated by microglia/macrophages in a disease state may aggravate brain injury [45].
Proinflammatory TNF-α being one of the most key important early initiators of neuroinflammation interacts with two receptors R1 and R2, to mediate extrinsic apoptotic death signal via Fas-associated death domain (FADD) and inflammation via nuclear factor kappa-light-chain enhancer of activated B cells (NFĸB), respectively [5]. NFκB is a major regulatory transcription factor with a pivotal role in inducing genes involved in inflammation [47]. In its dormant state, NFκB resides in the cytosol where it is bound to its inhibitory proteins known as inhibitors of NFκB (IκB), most commonly inhibitor of NFκB alpha (IκBα), making it unable to translocate into the nucleus [48]. Inflammatory stimuli resulting from wide range of brain pathological processes, such as cerebral ischemia, leads to degradation of these inhibitors upon their phosphorylation by the IκB kinase (IKK), which allows NFκB to migrate into the nucleus, where it binds with DNA, and activates transcription of many proinflammatory genes [49]. This includes increase in expression of the genes for proinflammatory cytokines, chemokines, enzymes that generate mediators of inflammation, and adhesion molecules [50]. Thus, TNF-α both activate and are activated by NFκB, creating a type positive regulatory loop that amplify and perpetuate local inflammation [50]. Hence, these pathways of TNF-α-induced NF-kB explain the ability of TNF-α to induce other inflammatory cytokines such as IL-6 and IL-8 and synergize with interferons [5].
Apart from IκB-NFκB pathway, another intracellular signaling pathway through which TNF-α induces other inflammatory cytokines is Janus family of tyrosine kinase (JAK)-signal transducer and activator of transcription (STAT) pathway. This JAK-STAT pathway can be initiated when there is TNF-α signaling after binding to its cognate receptors and consequently stimulates STATs. The STATs subsequently become activated and translocate to the nucleus to transmit transcriptional genetic expression of many cytokines, thereby leading to their synthesis [43].
Therefore, TNF-α is a proinflammatory cytokine that plays a critical role under both homeostatic and pathophysiological status within the central nervous system. Under healthy status, TNF-α has regulatory functions on vital physiological processes such as synaptic plasticity, learning and memory, sleep, food and water intake, and astrocyte-mediated synaptic amplification [51]. Under pathological status, astrocytes and mainly microglia excessively release massive concentration of TNF-α, thereby leading important constituent of neuroinflammatory response that marks a characteristic of several neurological disorders. Neuroinflammation itself at the first initial stage is a protective response in the brain, but excessively inappropriate inflammatory responses are detrimental, and in fact, it diminish the neuronal regeneration thereby leading to neurodegenerative diseases and other neurological disorders [52, 53].
Microglia is a pivotal brain endogenous protective mechanism against various injuries agents. If such an injury is tolerable, it triggers cellular responses that protect the brain and precondition the body against more severe stimuli. Beyond tolerable level, it triggers response that may potentially aggravate brain injury. TNF-α is released by microglia-induced NFκB activation, and activated NFκB in turn activates more TNF-α. The IκB-NFκB pathway together with other intracellular signaling pathway such as p38 MAPK pathway and JAK-STAT pathway that all orchestrate cascade of cytokine production makes TNF-α so-called master regulator of neuroinflammatory cytokine production. This phenomenon forms the basis of TNF-α as major cytokine of brain neuroinflammation.
The author declares no conflict of interest.
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