Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
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We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\n
Throughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\n
We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
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\n
1. Introduction
\n
Cannabis sativa L. (Cannabaceae) is one of the first plants cultivated by man and one of the oldest plant sources of fibre, food and remedies. It has a long history of medical use in the Middle East and Asia, dating back to the sixth century BC. During a period of colonial expansion in the early nineteenth century, cannabis found a way to Western Europe as a medicine to alleviate a variety of conditions, such as pain, spasms, dysentery, depression, sleep disturbance and loss of appetite. In the beginning of the twentieth century, due to the availability of substitute drugs, absence of quality control and the risk of abuse and intoxication, cannabis medication fell into disuse. Moreover, following the UN Single Convention on Narcotic Drugs in 1961, cannabis and its products were classified as narcotics. Phytochemical analysis of cannabis in the 1940s and 1960s led to the discovery of a unique group of terpenophenolic secondary metabolites, known as cannabinoids, of which trans‐(−)‐delta‐9‐tetrahydrocannabinol (THC) was shown to be the primary active constituent which is responsible for the plant’s psychoactive effect [1–3]. Many natural products besides cannabinoids have been isolated from cannabis, including terpenes, flavonoids, steroids and nitrogenous compounds. Up to date, 750 constituents have been identified from cannabis, out of which over 100 are classified as cannabinoids [4, 5]. Research of the cannabis medical properties has gained worldwide interest after the discovery of two types of cannabinoid receptors, which are G‐protein coupled receptors specifically responding to endocannabinoids and phytocannabinoids, and related synthetic cannabimimetic compounds. Therefore, the term cannabinoids now includes not only the plant‐derived compounds (phytocannabinoids), but also in laboratory synthesised derivatives (synthetic cannabinoids) and a family of endogenously produced compounds (endocannabinoids) [6]. The therapeutic properties of cannabis have been much debated from scientific and regulatory points of view over the years. The medical use of cannabis is still controversial and strongly limited by unavoidable psychotropic effects. However, solid scientific data indicated the potential of therapeutic value of cannabis in controlling some forms of pain, relieving chemotherapy‐induced nausea and vomiting, treating cachexia and anorexia in AIDS patients and combating muscle spasms in multiple sclerosis with no evidence that giving cannabis to the patients would increase illicit drug use in the general population [7]. Nowadays, many countries legalised cannabis for medical purposes. To avoid abuse, numerous centres for cannabis therapy are founded worldwide and usually organised as clinics where cannabis can be prescribed in various forms, including dried plant material and cannabis extract. So far, only three cannabis‐based medicines have been registered for certain indications. In the context of cancer, dronabinol (synthetically generated THC) and nabilone (a synthetic THC analogue) can be prescribed to prevent chemotherapy‐induced nausea and vomiting. Nabiximols, plant extract enriched in THC and cannabidiol (CBD) at an approximate 1:1 ratio, are approved for the treatment of cancer‐associated pain [8]. Apart from these palliative effects, recent preclinical studies suggest that various cannabinoids exert anti‐tumour effects in different experimental cancer models [1]. In this chapter, we will focus on phytochemistry and pharmacology of cannabinoids as well as their current and potential roles in symptom management and cancer therapy.
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2. The cannabis plant
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The concept of Cannabis as a monotypic genus containing just a single highly polymorphic species is widely accepted, although there has been a long‐standing debate among taxonomists regarding classification of the existing varieties. Other previously described species, including C. indica Lam. and C. ruderalis Janisch., are now recognised as varieties of C. sativa L. based on morphological, anatomical, phytochemical and genetic studies [9, 10]. C. sativa L. is an annual, herbaceous, taprooted and predominantly dioecious plant. Its height (0.2–6 m) and degree of branching depend on both genetic and environmental factors. Staminate (male) plants are usually taller but less robust than pistillate (female) plants. The leaves are petiolate, palmately compound, with an odd number (3–13) of coarsely serrate, lanceolate leaflets. The male inflorescence is a lax panicle or compound cyme composed of many individual, yellowish green, pedicellate flowers containing five pendulous anthers. The pistillate flowers are green, sometimes purple to red, sessile, grouped in apical leaf axils or terminals of branches. They form short, congested pseudospikes among leaf‐like bracts and bracteoles. Each flower has a small green bract enclosing the ovary with two long, slender pistils projecting well above the bract. The male plants commence flowering slightly before the females. When mature, the sepals on the male flowers are open to enable passing air currents to transfer the released pollens to the pistillate flowers. Soon after pollination, the male plants wither and die in order to secure more space, nutrients and water to the females so that they could produce a healthy crop of viable seeds. Following fertilisation, the ovary develops into an achene, a fruit containing a single seed with a hard shell [11–13]. The surface of aerial plant parts is covered in trichomes. These are either covering (non‐glandular) trichomes or glandular trichomes containing a resin (Figure 1). Non‐glandular trichomes are numerous, unicellular, rigid and curved hairs, with a slender pointed apex. Cystolithic trichomes found on the upper surface of the cannabis leaves are swollen at the base and have calcium carbonate crystals (cystoliths), while slender non‐cystolithic trichomes occur mainly on the lower side of the leaves, bracts and bracteoles. Three morphologically distinct types of glandular trichomes have been identified: (1) a long multi‐cellular stalk and a multi‐cellular head with approximately eight radiating club‐shaped cells (capitate‐stalked); (2) sessile with a multi‐cellular head (capitate‐sessile); (3) a short unicellular stalk and a bi‐cellular, rarely four‐cell, head (bulbous). These are mainly associated with the female inflorescences, but they can also be found on the underside of the leaves and occasionally on the stems of young plants. Bulbous and capitate‐sessile trichomes occur on all parts of vegetative and flowering shoots. In contrast, capitate‐stalked trichomes are restricted to flowering regions. The glandular trichomes are secretory structures, where the cannabinoid‐laden resin is produced and stored. Besides cannabinoids, these trichomes produce terpenes, which are responsible for the typical plant aroma. The extreme variations in cannabinoid contents of the different tissues are due to markedly different distributions of glandular trichomes on the surface of the plant [14, 15]. The unfertilised flower heads and flower bracts of the female plant are the primary source of cannabinoids (Figure 1).
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Figure 1.
Cannabis sativa L. – dried pistillate inflorescences and trichomes on their surface. (a) dried pistillate inflorescences (50% of the size); (b) non‐cystolithic trichome; (c) cystolithic trichome; (d) capitate‐sessile trichome; (e) simple bulbous trichome; (f) capitate‐stalked trichome (400×).
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3. Biosynthesis and structure of phytocannabinoids
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Phytocannabinoids represent a group of terpenophenolic compounds predominantly produced in the cannabis plant. These secondary metabolites are biosynthesised as prenylated aromatic carboxylic acids, and while almost no neutral forms can be found in fresh plants. However, cannabinoid acids may convert to their neutral homologues by spontaneous decarboxylation under the influence of light, heat or prolonged storage. The precursors of phytocannabinoids originate from two distinct biosynthetic pathways: the polyketide pathway, giving rise to olivetolic acid (OA) or divarinic acid (DA), and methylerythritol phosphate pathway, leading to the synthesis of geranyl pyrophosphate (GPP). The biogenesis of phytocannabinoids containing n‐pentyl side chain starts with the condensation of OA and GPP into cannabigerolic acid (CBGA), catalysed by geranyl pyrophosphate—olivetolate geranyl transferase (GOT). The isoprenylation step is next followed by activity of three corresponding oxidative cyclases that generate tetrahydrocannabinolic acid (THCA), cannabidiolic acid (CBDA) and cannabichromenic acid (CBCA) from CBGA as the key intermediate. The phytocannabinoid acids are non‐enzymatically decarboxylated into cannabigerol (CBG), delta‐9‐tetrahydrocannabinol (delta‐9‐THC), cannabidiol (CBD) and cannabichromene (CBC) [16, 17]. Figure 2 shows the cannabinoid biosynthetic pathway and the structures of the major constituents. The biosynthesis of phytocannabinoids with C3 side‐chain (propyl cannabinoids) from DA probably follows a similar pathway yielding cannabigerovarinic acid [18].
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Figure 2.
Biosynthesis and degradation of the major phytocannabinoids. OA—olivetolic acid; GPP—geranyl pyrophosphate; GOT—geranyl pyrophosphate—olivetolate geranyl transferase; CBGA—cannabigerolic acid; CBG—cannabigerol; CBCAS—cannabichromenic acid synthase; THCAS—tetrahydrocannabinolic acid synthase; CBDAS—cannabidiolic acid synthase; CBCA—cannabichromenic acid; THCA—tetrahydrocannabinolic acid; CBDA—cannabidiolic acid; CBC—cannabicromene; delta‐9‐THC—delta‐9‐tetrahydrocannabinol; CBD—cannabidiol; CBL—cannabicyclol; CBN—cannabinol; CBE—cannabielsoin.
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Over 100 various phytocannabinoids have been found so far, but many of them are produced in trace quantities or represent auto‐oxidation artefacts [16, 19]. The structural diversity of naturally occurring cannabinoids is the result of differences in the nature of their isoprenyl residue, resorcinyl core and side chain. Based on the structural variation, Hanuš and coworkers [4] have classified phytocannabinoids as follows: cannabigerol, cannabichromene, cannabidiol, tetrahydrocannabinol, cannabinol, thymyl, cannabielsoin, cannabicyclol and 8,9‐secomenthyl types. The Cannabigerol type compounds are one of the most structurally diversified classes of phytocannabinoids. A linear isoprenyl residue is their main feature, as exemplified by CBG, which was the first structurally elucidated and also the first natural cannabinoid to be synthesised. The isoprenyl residue of CBG is non‐oxygenated, indicating its early biogenetic stage within phytocannabinoids. Other components of this type are propyl side‐chain analogues (cannabigerovarin) and monomethyl ether derivative. The isoprenyl residue is oxidatively fused to the resorcinyl ring in the cannabichromene type. Cannabichromene (CBC) is the simplest natural cannabinoid to obtain by synthesis and the only major phytocannabinoid that shows a bluish fluorescence under UV light. CBD, as the main representative of the cannabidiol type compounds, was isolated in 1940, but the correct structure elucidation was reported more than two decades later. CBD and its corresponding acid are the most abundant cannabinoids in the fibre‐type of cannabis (non‐psychotropic). Ten CBD type phytocannabinoids with C1–C5 side‐chains have been described. The tetrahydrocannabinol type compounds contain several bis‐reduced forms of cannabinol (CBN), differing in location of the remaining double bond, the configuration of the chiral centres, or both isomeric options. The most prominent constituent of this subclass is delta‐9‐THC, the main psychoactive ingredient of cannabis plant, isolated in 1942, but structurally elucidated only in 1964. Other representative of this type is delta‐8‐THC, most likely to be generated from delta‐9‐THC or CBD. It is easier to synthesise and more thermodynamically stable than delta‐9‐THC. CBN and its derivatives and analogues (cannabinol type) are considered artefacts derived from oxidative aromatisation of the corresponding THC type compounds. Their concentration in cannabis products depends on age and storage condition. CBN is highly stable towards oxidative degradation and so has been used as a marker for the identification of narcotic cannabis in archaeological findings. The structural hallmark of thymyl type represented by cannabinodiol and cannabifuran is the presence of thymyl group obtained by aromatisation of the menthyl moiety of CBD. The Cannabielsoin type compounds are the result of the intra‐molecular opening of cannabidiol‐type epoxides and could be isolated artefacts. Cannabielsoin (CBE) is the major pyrolytic product of CBD and therefore expected to be present in cannabis smoke. Other artefacts formed during storage of the plant material in the presence of light are cannabicyclol (CBL) and its derivatives, characterised by a five‐atom ring and C1 bridge instead of a typical six‐membered ring in the cannabinoid structure. 8,9‐Secomenthyl cannabidiols are formed by splitting of the endocyclic double bonds of delta‐9‐THC (cannabicoumaronone) and CBD (cannabimovone) [4, 19, 20].
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4. Phytochemical characterisation of cannabinoids
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Various scientific attempts have been made to classify Cannabis taxa based on their cannabinoid composition, which is under strong environmental influences and also depends on plant sex and maturity. The most important classification of cannabis types in forensics and legislation is that into drug type (marijuana) and fibre type (hemp). A high amount of psychoactive THC characterises the drug type, while particularly low content defines the fibre type [21, 22]. Nowadays, cannabis is divided mainly into three chemotypes (i.e. chemical phenotypes) on the basis of the content ratio of the two major cannabinoids, THC and CBD, in dried inflorescence: (1) THC > 0.3% and CBD < 0.5% (THC predominant); (2) THC ≥ 0.3% and CBD > 0.5% (intermediate); (3) THC < 0.3% and CBD > 0.5% (CBD predominant). Two rare chemotypes with prevalence of CBG and cannabinoid‐free, respectively, have also been found [23, 24]. Apart from these chemotypes, de Meijer [25] has additionally described CBC, delta‐9‐tetrahydrocannabivarin (THCV) and other propyl cannabinoid‐rich chemotypes. A large variation of cannabis strains have been developed during a long period of breeding and selection. Over 700 different cultivars of cannabis have been catalogued and many more varieties are thought to exist [26]. With the increasing use of cannabis for medical purposes, the need for a clear chemotaxonomic distinction between varieties has become even more important. Phytocannabinoids were chosen as chemotype markers as they are considered to be the main pharmacologically active constituents in cannabis [27].
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Because of the complex chemistry of cannabis, advanced separation techniques, such as gas chromatography (GC) or high performance liquid chromatography (HPLC), often coupled with mass spectrometry detection (MS), are necessary for the determination of the typical phytochemical profiles of cannabis constituents [28, 29]. Thin layer chromatography (TLC) is suitable only for identification of cannabis plant material, detection of its principal cannabinoids and distinguishing between main chemotypes. The separation of phytocannabinoids is mainly achieved by using silica gel as stationary phase, reversed phase for the non‐polar system and normal phase for the polar system. Two different reagents for the visualisation of cannabinoids, fast blue and vanillin‐sulphuric acid, can be used [11, 30, 31]. Figure 3 shows high performance thin layer chromatography (HPTLC) chromatogram of cannabis ethanolic extracts, representing THC and CBD predominant types, respectively.
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Figure 3.
HPTLC chromatogram of phytocannabinoids in the concentrated ethanolic extracts of cannabis inflorescence. Cs1—THC predominant type of Cannabis sativa extract; Cs2—CBD predominant type of Cannabis sativa extract; stationary phase: HPTLC silica gel C18 F254; mobile phase: methanol‐water with 0.1% glacial acetic acid 75:25 (V/V); detection: Fast blue reagent; Rf (THC) = 0.25; Rf (CBD) = 0.38.
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Gas chromatography, commonly coupled to flame ionisation detection (FID) or MS, provides data only on neutral cannabinoids. Due to the high temperature of the injection port, the rapid decarboxylation of the acidic cannabinoids to the neutral forms occurs, thus the real cannabinoid profile of the plant material does not correspond to the results obtained. Derivatisation of phytocannabinoid acids to their trimethylsilyl esters before injection is one approach that can allow the separation and detection of the acidic and neutral forms. Identification of the phytocannabinoids is most readily performed by GC‐MS, method of choice for creating cannabis profiles and metabolic fingerprints [12, 28, 32]. GC‐FID is suitable for routine identification and quantification of the major phytocannabinoids as illustrated in Figure 4, representing THC and CBD predominant types, respectively.
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Figure 4.
GC‐FID chromatograms of two concentrated ethanolic extracts of cannabis inflorescence. (a) THC predominant type of cannabis extract (THC/CBD = 87;2). (b) CBD predominant type of cannabis extract (THC/CBD = 0.08). Agilent 7890A gas chromatograph equipped with FID; HP‐5MS column (15 m x 0.25 mm i.d., 0.25 µm film thickness); carrier gas: helium at a constant flow rate of 2.0 mL/minute; temperature program: initial temperature 200°C for 2 minutes, increased by 10°C/minute to final temperature 240°C and held for further 2 minutes; detector temperature 300°C; injector temperature 280°C with split ratio of 20:1; injection volume 1.5 µL; i.s. – tribenzylamine (TBA).
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Both acidic and neutral forms of phytocannabinoids can be directly analysed by means of HPLC without any derivatisation step. In contrast to GC, no decomposition occurs during HPLC analysis, which is the main advantage for obtaining the complete cannabinoid profiles. Analytical methods based on reversed‐phase chromatography with gradient elution are commonly used. Detection of phytocannabinoids is usually performed by UV and diode array detectors (DAD), but high sensitivity can best be achieved through the use of thermospray MS. Apart from several HPLC methods, ultra performance liquid chromatography (UPLC) method has also been validated for the analysis of a wide range of phytocannabinoids in plant material [13, 29]. Moreover, a novel method of ultra‐high performance supercritical fluid chromatography (UHPSFC) coupled with DAD/MS for the separation and discrimination of cannabinoids in complex matrices has been developed and validated [33]. Giese et al. [5] highlighted that typical concentration ranges for the cannabinoids vary from 0.1 to 40% of inflorescence dry weight. These data show how extreme the variations of phytocannabinoids between plant specimens can get, indicating that the cannabis for medical use should always be thoroughly profiled. Therefore, the previously mentioned analyses are of interest given the probability that both the therapeutic and adverse effects of cannabis may be dictated by the concentrations and interactions of certain phytocannabinoids.
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5. The endocannabinoid system
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The endocannabinoid system (ECS) consists of endogenous cannabinoids, their receptors and the enzymes responsible for their biosynthesis, transport and degradation. The endocannabinoids are lipophilic mediators, which include amides, esters and ethers of long‐chain polyunsaturated fatty acids, mostly arachidonic acid. The first two identified and most studied endocannabinoids are N‐arachidonylethanolamide called anandamide (AEA) and 2‐arachidonoylglycerol (2‐AG) (Figure 5). AEA and 2‐AG are not pre‐synthesised and stored in vesicles like classical neurotransmitters, but rather released from the cells immediately after biosynthesis. They are synthesised via enzymatic pathways from phospholipid precursors in the plasma membrane of post‐synaptic cells on demand upon relevant physiological or pathological stimuli. After release, acting as retrograde messengers, AEA and 2‐AG travel backwards to stimulate receptors on the pre-synaptic membrane. The main intermediate in the synthesis of AEA is N-acyl‐phosphatidylethanolamine (NArPE), transformed into anandamide by several possible pathways among which the most investigated is the direct conversion catalysed by an enzyme of phospholipase D family. 2-AG is produced primarily by the hydrolysis of diacylglycerols (DAGs) via DAG lipases α and β. The endocannabinoids act on their receptors only locally, possibly because of their high lipophilicity, and are immediately inactivated under physiological conditions. The suggested mechanisms of endocannabinoid transport across the plasma membrane (facilitated transport, passive diffusion and/or endocytosis) are still not fully elucidated. After their cellular re‐uptake, AEA is rapidly degraded by the enzyme fatty acid amide hydrolase (FAAH) while 2‐AG is hydrolysed by monoacylglycerol lipase (MAGL) forming arachidonate and ethanolamine or glycerol, respectively [34, 35]. Apart from hydrolytic degradation, endocannabinoids may also be oxidised by cyclooxygenase‐2, lipoxygenases and cytochrome P450 [36].
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Figure 5.
The structures of main endocannabinoids anandamide (AEA) and 2‐arachidonoylglycerol (2‐AG).
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The cannabinoids exert their effects by binding to specific receptors, among which the most important are cannabinoid receptors CB1 and CB2 encoded by different genes and exhibiting 44% homology in their primary structure. They belong to the large rhodopsin family of G‐protein‐coupled receptors (GPCRs) with seven transmembrane domains connected by three extracellular and three intra‐cellular loops, an extracellular N‐terminal tail and an intra‐cellular C‐terminal tail. There is increasing evidence supporting the existence of additional targets for cannabinoids like transient receptor potential (TRP) ligand‐gated cation channels (vanilloid type 1, TRPV1, melastatin type 8, TRPM8 and ankyrin type 1, TRPA1), certain orphan GPCRs (GPR55, GPR119 and GPR18), 5‐hydroxytryptamine receptor subtype 1A (5‐HT1A) and peroxisome proliferator‐activated receptors (PPARs). The functions of cannabinoid receptors can be modulated by endo‐, phyto‐ or synthetic‐cannabinoids which target the orthosteric or allosteric binding sites on the receptors. The cannabinoid receptors modulate adenylyl cyclase (AC) activity depending on its isoform expressed in the cells and, consequently, alter the cellular production of second messenger cyclic adenosine monophosphate (cAMP). The activation of CB1 and CB2 receptors mainly causes inhibition of AC and the subsequent reduction of intra‐cellular cAMP levels leads to the inactivation of the protein kinase A (PKA) phosphorylation pathway. Studies have shown that cannabinoid receptors can also be coupled to other types of intra‐cellular signals, such as the protein kinase B, phosphoinositide 3‐kinase and phospholipase C pathway. Also, activation of CB1 and CB2 receptors leads to the downstream activation of mitogen‐activated protein kinase (MAPK), p44/42, p38 and c‐JUN amino terminal kinase as signalling pathways to regulate nuclear transcription factors. Unlike the activation of CB2 receptor, which generally has no effect on ion channels, CB1 receptors inhibit calcium channels and activate potassium channels. The cannabinoid receptors are widely distributed in the human body. CB1 receptors are localised predominantly in the CNS and mainly expressed in areas that are involved in the control of motor coordination and movement, memory, learning, emotions, sensory perception and autonomic and endocrine functions. In addition, CB1 receptors are present to a lesser extent in some organs and peripheral tissues, including endocrine glands, leukocytes, adipocytes, spleen, liver, heart and part of the reproductive, urinary and gastrointestinal systems. By contrast, the CB2 receptor was initially described as present in the immune system, but more recently it has also been shown to be expressed in additional cell types [37–40]. Since elevated expression of CB1 and CB2 receptors and higher levels of endocannabinoids have been found in many types of cancer, compared to normal tissues, the ECS has been recognised as attractive potential target for cancer therapy. The growing evidence over the past decade suggests that cannabinoids affect multiple signalling pathways involved in the development of cancer, displaying an anti‐proliferative, proapoptotic, anti‐angiogenic and anti‐metastatic activity on a wide range of cell lines and animal models of cancer [41].
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6. Preclinical evidence on cannabinoids as anti‐cancer agents
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Despite remarkable advances in understanding and treating cancer, finding new, more effective pharmacotherapeutics still remains a key challenge for scientists worldwide. The first study suggesting that plant‐derived cannabinoids might be potential anti‐cancer agents, demonstrating their ability to inhibit tumour growth in vitro and in vivo and to increase the survival of lung cancer‐bearing animals, was published more than 40 years ago [42]. Later discoveries of the ECS in the human body, combined with the development of numerous preclinical testing models, have paved the way for a renaissance in the study of anti‐cancer properties of cannabinoids in the last two decades. A large body of in vitro data has been accumulated demonstrating that cannabinoids affect a wide spectrum of tumour cells, including gliomas, neuroblastomas, lymphomas, hepatocarcinoma as well as thyroid, skin, prostate, pancreatic, breast, cervical, colon, gastric, lung and some other cancers [6, 41, 43]. Several plant‐derived (THC and CBD), synthetic (e.g. JWH‐133, WIN‐55,212‐2 and KM‐233) and endogenous cannabinoids (AEA and 2‐AG) were found to be potent inhibitors of both cancer growth and spreading due to their ability of modulating various cell‐signalling pathways [6, 37, 43, 44]. Their anti‐neoplastic action mainly relies on the activation of cannabinoid CB1 and/or CB2 receptors, although some other non‐CB1/CB2 receptors, like TRPV1 and PPARs, as well as mechanisms unrelated to receptor stimulation may also be involved [43, 45, 46]. Cannabinoids might stop the uncontrolled growth of cancer cells by several different mechanisms, including inhibition of cell‐cycle progression, inhibition of cell proliferation as well as induction of autophagy and apoptosis [41, 43, 44]. Due to their modulatory actions on various cell cycle regulatory molecules, like cyclin A and cyclin dependent kinase (CDK) 2, cannabinoids have been shown to cause arrest of cell cycle progression in different phases (e.g. G0/G1, G2/M), leading to growth inhibition and/or apoptotic death of cancer cells [43]. The anti‐proliferative activity is based on their ability to inhibit proliferative and oncogenic pathways in cancer cells, such as adenylyl cyclase and cyclic adenosine monophosphate/protein kinase A (cAMP/PKA) pathway leading to the activation of mitogen‐activated protein kinase (MAPK) pathway as well as cell cycle blockade with induction of the CDK inhibitor (CDKI) p27Kip1 and p21waf, decrease in epidermal growth factor (EGF) receptor (EGFR) expression and/or attenuation of EGFR tyrosine kinase activity, decrease in the activity and/or expression of nerve growth factor (NGF), prolactin, or vascular endothelial growth factor (VEGF) tyrosine kinase receptors. The MAPK signalling cascades, consisting of the extracellular signal‐regulated kinase (ERK1/2), c‐Jun N‐terminal kinase (JNK) and p38 MAPK, as well as phosphatidylinositol 3 kinase (PI3K)‐Akt pathways seems to have a prominent role in the control of tumour cell fate by cannabinoids [43, 45]. Cancer cell death‐inducing activity of cannabinoids relies greatly on the apoptosis and, among several molecular mechanisms, the stimulation of endoplasmic reticulum (ER) stress and subsequent autophagy has been recently suggested as the most common one. Cannabinoids can induce accumulation of de novo–synthesised ceramide and thereby activate an ER stress‐related response through up‐regulation of the stress‐regulated protein p8 and several of its downstream targets, like activating transcription factor 4 (ATF4), C/EBP homologous protein (CHOP) and pseudokinase tribbles‐homologue 3 (TRIB3), leading to the inhibition of the AKT–mammalian target of rapamycin complex 1 (mTORC1) signalling, and autophagy‐mediated apoptosis. Cannabinoid‐evoked and ER stress‐dependent activation of calcium/calmodulin‐dependent protein kinase β (CaCMKKβ) and AMP‐activated protein kinase (AMPK) lead, together with the p8/TRIB3 pathway, to autophagy and apoptosis [1, 46]. Tumour angiogenesis represents additional important target for cancer therapy affected by cannabinoids. They can directly inhibit vascular endothelial cell migration and survival or act indirectly by modulating the expression of pro‐angiogenic factors, like VEGF, matrix metalloproteinase‐2 (MMP‐2) or anti‐angiogenic factors like tissue inhibitor of matrix metalloproteinase 1 (TIMP‐1) as well as their receptors in tumours [41, 44]. Besides influencing the growth of different cancer cells, cannabinoids may exert their anti‐cancer effects by inhibiting all the steps of tumour progression. The inhibitory effect on migration, adhesion and invasion through CB receptors is related to the blocking of key pathways such as EGF‐EGFR, RhoA‐RhoA kinase (ROCK), focal adhesion kinase (FAK)‐Src and of MMPs and TIMP‐1, which are fundamental for the invasiveness and spread of tumours [41, 43, 44]. Non‐CB receptors mediated anti‐metastasic effects may rely on the down‐regulation of the helix‐loop‐helix (bHLH) transcription factor inhibitor of DNA binding 1 (ID1) [46]. Tables 1 and 2 summarise preclinical evidence collected during the last decade about the role of two most‐investigated phytocannabinoids, THC and CBD, in different type of cancers and their associated cell signalling pathways.
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Recent in vivo studies demonstrated that cannabinoids of plant, synthetic and endogenous origin are able to decrease tumour growth and metastasis of different experimental cancers [47]. Preclinical assessments have mainly been conducted using human tumour engraft models, where human cancer cells were subcutaneously injected (ectopic model) or transplanted into the same origin site of the tumour (orthotropic model) in immunodeficient mice. The syngeneic (allograft) models, established by transplantation of mice cancer cells in immunocompetence animals, as well as carcinogen‐induced spontaneous tumour models and genetically engineered mouse models (GEMM) have also been used, but rarely [47, 48]. An overview of last decades’ discoveries revealed the effectiveness of THC against experimental glioma, liver, pancreatic, breast and lung cancers (Table 1) while CBD was found to be effective against glioma and neuroblastoma, melanoma, colon, breast, prostate and lung cancers (Table 2). Among other phytocannabinoids, CBG could be considered as a candidate for colon cancer prevention and treatment [49]. Beside these findings, the potential clinical interest of cannabinoids is additionally strongly suggested by their selectivity for tumour cells (and even ability to protect the non‐transformed cells) as well as by their good tolerance in animal studies and the absence of normal tissue toxicities that are still the major limitations of most conventional chemotherapeutics [45]. However, several studies reported that THC and some other cannabinoids can inhibit apoptosis in the transformed‐cell lines, exhibit proangiogenic effect and stimulate cancer cell proliferation or show a biphasic effect in cancer cells by increasing their proliferation at lower concentrations and decreasing at higher concentrations [37, 41]. The ability to promote the tumours growth was found in two experimental animal model cancers and attributed to their suppression of anti‐tumour immune response [37]. Despite the few mentioned conflicting data, the majority of recent preclinical studies provide the supporting evidence on cannabinoids as promising anti‐cancer agents, thus encouraging further clinical investigations.
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Cancer type
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Experimental model
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Findings [reference]
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Brain (Glioma)
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in vitro U251MG, U87MG C6.9, U87MG C6.9, U87MG U87MG in vivo C6.9 xenograft U87MG xenograft
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Inhibited cell cycle progression (G0/1 arrest) by down‐regulation of E2F transcription factor 1 and cyclin A [54] Inhibition of migration by inhibition of TIMP‐1 expression via ceramide and stress protein p8 [55] Inhibition of invasion by down‐regulating MMP‐2 via ceramide and p8 [56] Induced autophagy‐mediated cell death through ER stress–evoked stimulation of ceramide synthesis de novo, eIF2α phosphorylation and up‐regulation of p8/TRIB3 pathway leading to inhibition of Akt/mTORC1 pathway; autophagy leads to apoptosis [57] Decreased tumour growth and tumoural TIMP‐1 expression [55] Decreased tumour grow and tumoural MMP‐2 expression [56] Decreased tumour growth and activated autophagic mediated cell death pathway (↑TRIB3, ↑LC3‐II, ↑caspase 3, ↓rpS6) [57]
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Lung
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in vitro A549, SW‐1573 in vivo A549 xenograft LL2 allograft
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Inhibited proliferation, migration and invasion of tumour cells by inhibition of EGFR‐mediated activation of MAPKs (ERK1/2, JNK1/2) [58] Reduced tumour growth and metastasis through inhibition of proliferation (↓Ki67), vascularisation (↓CD31) and decreased phosphorylation of FAK, ERK1/2 and Akt [58] No significant effect on tumour growth [59]
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Liver
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in vitro HepG2, HuH‐7 HepG2 in vivo HepG2 xenograft HuH‐7 xenograft HepG2 orthotopic HepG2 xenograft
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Induced cancer cell death through autophagy stimulation via CB2 receptors by (i) inhibition of the Akt/mTORC1 axis via ER stress with TRIB3 up‐regulation and (ii) stimulation of AMPK via CaMKKβ; autophagy leads to apoptosis [60] Anti‐proliferative action modulated by up‐regulation of PPARγ‐dependent pathways through TRIB3 [61] Reduced tumour growth relies on decreased mTORC1 activation, enhanced AMPK phosphorylation and increased autophagy and apoptosis [60] Decreased hepatomegaly and ascites, ↓α‐fetoprotein, in tumour ↑pAMPK, ↓pAkt, ↓pS6, ↓procaspase‐3 [60] Reduced tumour growth via PPARγ activation [61]
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Pancreas
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in vitro MiaPaCa2, Panc1 in vivo MiaPaCa2 xenograft
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Induced cancer cell death by apoptosis via activation of the p8‐ATF‐4‐TRIB3 pathway (↑caspase‐3, ↑ceramide) [62] Reduced tumour growth [62]
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Breast
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in vitro EVSA‐T, HMEC in vivo MMTV‐neu N202.1 xenograft
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Reduced cancer cell proliferation through apoptosis and cell cycle blockade (G2‐M arrest) by CDK1 down‐regulation [63] Reduced tumour growth, tumour number and metastases by cell proliferation inhibition (↓Ki67), apoptosis (↑caspase 3), decreased angiogenesis and ↓MMP2 [64] Decreased tumour growth via Akt inhibition [64]
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Skin
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in vitro CHL‐1, A375, SK‐MEL‐28 in vivo CHL‐1 xenograft HCmel12 xenograft
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Induced cancer cell death by activating non‐canonical autophagy‐mediated apoptosis dependent on Atg7 but not Beclin‐1 or Ambra1 [65] Inhibited tumour growth via autophagy and apoptosis (↓Ki67, ↑TUNEL, ↑LC3) [65] Reduced tumour growth in CB receptor‐dependent manner and decreased inflammatory immune cell infiltrates in the tumour microenvironment [66]
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Table 1.
Effects of THC on different types of cancer.
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Cancer type
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Experimental model
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Findings [reference]
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Brain
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In vitro U87 in vivo U87 xenograft U251 orthotopic xenograft 3832, 387 orthotopic xenograft SK‐N‐SH xenograft
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Induced apoptosis of cancer cells through caspase activation (↑caspase‐8, ‐9 and ‐3) and oxidative stress (↑ROS, ↓GSH, ↑GPx, ↑GRed) [67] Reduced tumour growth through inhibition of 5‐LOX (↓LTB4) and ECS—activation of FAAH (↓AEA) [68] Reduced tumour progression and cancer cell invasion through down‐regulation of Id‐1 expression [69] Initial inhibition of tumour growth (↓Ki67, ↓pAkt, (↑caspase‐3) followed by tumour resistance [70] Suppressed neuroblastoma tumour growth via apoptosis induction (↑caspase‐3) [71]
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Lung
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In vitro A549, H460 in vivo A549 xenograft
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Anti‐invasive and anti‐metastatic action via up‐regulation of ICAM‐1 which leads to enhanced cancer cell adhesion to LAK cells and subsequent enhance of LAK cell‐mediated cancer cell lysis [72] Decreased tumour growth and inhibited tumour cell invasion via down‐regulation of PAI‐1 [73] Decreased tumour metastasis [74] Inhibited cancer cell invasion and metastasis by stimulation of TIMP‐1 via up‐regulation of ICAM‐1 [75] Decreased tumour growth via apoptosis caused by up‐regulation of COX‐2 and PPAR‐γ [76]
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Colon
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In vivo Azoxymethane‐induced cancer
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Reduced preneoplastic lesions, number of polyps and tumours through apoptosis by inhibition of the PI3K‐Akt pathway (↓pAkt, ↑caspase 3) [77]
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Prostate
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In vitro LNCaP, DU‐145 LNCaP in vivo LNCaP xenograft
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Induced cell death through apoptotic pathways (↑caspase 3, ↑PUMA, ↑CHOP, ↑intra‐cellular Ca2+, down‐regulation of AR, p53 activation, ↑ROS) [53] Induced phosphatase‐dependent apoptosis in cancer cells via CB1/CB2 [78] Decreased tumour growth [53]
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Breast
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In vitro MCF‐7, KiMol, C6, MDA‐MB‐231 MDA‐MB‐231, 4T1 MDA‐MB‐231 SUM159, 4T1.2 In vivo MDA‐MB xenograft 4T1 orthotopic 4T1 allograft 4T1.2 orthotopic MVT‐1 orthotopic
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Inhibited cancer cell proliferation through proapoptotic effect and by cell cycle blockade at G1/S phase, acting directly via CB2 and TRPV1 receptors and indirectly via elevation of intra‐cellular Ca2+ and ROS [79] Anti‐proliferative and anti‐invasive effect by up‐regulation of ERK and ROS pathways leading to down‐regulation of Id‐1 protein expression [80] Induced cancer cell death by both apoptosis (↑PARP) and autophagy (↑LC3‐II) through induction of ER stress and inhibition of Akt/mTOR/4EBP1 signalling independently of receptor activation; important role of ROS and Beclin‐1 [81] Inhibited tumour cell proliferation, migration and invasion through EGF/EGFR pathway inhibiting EGF‐induced activation of EGFR, ERK, Akt and NF‐kB signalling and actin stress fibre formation and focal adhesion formation; Anti‐metastatic effect also by decreasing secretion of MMP-2 and MMP-9 as well as chemokines CCL3, GM‐CSF, MIP‐2 [82] Decreased tumour growth and lung metastasis [79] Reduced tumour growth and metastasis. Anti‐metastatic effect by down‐regulation of tumoural Id1 expression [80, 83] Inhibited tumour growth and lung metastasis due to anti‐proliferative (↓Ki67) and angiogenic (↓CD31) effects and inhibition of EGFR, Akt and ERK activation [82]
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Table 2.
Effects of CBD on different types of cancer.
AEA—anandamide; Akt—serine/threonine protein kinase; AMPK—adenosine monophosphate‐activated protein kinase; AR—androgen receptor; ATF‐4—activating transcription factor 4; Atg7—autophagy‐related protein 7; CaMKKβ—calcium/calmodulin‐dependent protein kinase β; CCL3—chemokine (C‐C motif) ligand 3; CD31—cluster of differentiation 31, syn. platelet endothelial cell adhesion molecule (PECAM‐1); CDK1—Cyclin‐dependent kinase 1; CHOP—transcription factor CAAT/enhancer binding homologous protein; COX‐2—cyclooxygenase‐2; 4EBP1—eukaryotic translation initiation factor 4E binding protein 1; ECS—endocannabinoid system; EGF—epidermal growth factor; EGFR—epidermal growth factor receptor; eIF2α—α subunit of eukaryotic initiation factor 2; ER—endoplasmic reticulum; ERK—extracellular signal‐regulated kinase; FAAH—fatty acid amide hydrolase; FAK—focal adhesion kinase; GM‐CSF—granulocyte‐macrophage colony‐stimulating factor; GPx—glutathione peroxidase; GRed—glutathione reductase; GSH—glutathione; ICAM‐1—intercellular adhesion molecule 1; Id‐1—helix‐loop‐helix protein inhibitor of DNA binding‐1; JNK1/2—c‐Jun N‐terminal protein kinases 1 and 2; Ki67—biomarker of cancer cells proliferation LAK cells - lymphokine‐activated killer cells; LC3—microtubule‐associated protein 1 light chain 3; 5‐LOX—arachidonate 5‐lipoxygenase; LTB4—leukotriene B4; MAPK—mitogen‐activated protein kinase; MIP‐2—macrophage inflammatory protein 2; MMP—matrix metalloproteinase; mTOR—mechanistic target of rapamycin; mTORC1—mammalian target of rapamycin complex 1; NF‐kB—nuclear factor‐kappa B; p53—tumour protein 53; p8—stress‐regulated protein; PAI‐1—plasminogen activator inhibitor‐1; pAkt—phosphorylated Akt; pAMPK—phosphorylated adenosine monophosphate‐activated protein kinase; PARP—poly (ADP‐ribose) polymerase; PI3K—phosphoinositide 3‐kinase; PPARγ—peroxisome proliferator‐activated receptor γ; pS6—phosphorylated‐ribosomal protein S6; PUMA—p53 up‐regulated modulator or apoptosis; ROS—reactive oxygen species; rpS6—ribosomal protein S6; TIMP‐1—tissue inhibitor of matrix metalloproteinase 1; TRIB3‐—tribbles pseudokinase 3; TUNEL—terminal deoxynucleotidyl transferase dUTP nick end labelling.
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Considering the possibilities for therapeutic use of cannabinoids in cancer, their combination with traditional chemotherapy or radiotherapy seems to be an interesting option. The possible advantages of combination therapy may be a synergistic effect evident as improved efficiency, lowered doses and consequently attenuated toxic side effect or reduced drug resistance. Accordingly, γ‐irradiation was found to enhance CBD‐induced apoptotic death in cultured leukaemia cells [50]. Synergism of plant‐derived cannabinoids and radiation was confirmed in vivo, where the simultaneous treatment with THC and CBD enhanced the cancer‐killing effects of the radiation in murine glioma model [51]. Preclinical evidence also supports the combination of phytocannabinoids and chemotherapy drug temozolomide (TMZ), commonly used in patients with glioblastoma. Torres et al. [52] proved that co‐administration of TMZ with THC reduces the growth of glioma xenograft to a much higher extent than the treatment with the individual agents, observing effect in the TMZ‐resistant tumours also. Interestingly, combined treatment with TMZ and submaximal doses of THC and CBD (approximate 1:1 ratio) produced similar anti‐tumoural effect in both TMZ‐sensitive and TMZ‐resistant tumours. Usage of main cannabis constituents together may be therapeutically very attractive, since CBD has the ability to potentiate anti‐cancer properties of THC and, as a non‐psychotropic cannabinoid, can mitigate adverse psychoactive effects of THC that limit its clinical use [46, 52]. Recent data also revealed that CBD‐enriched cannabis extract can significantly enhance the efficacy of bicalutamide or docetaxel, two standard drugs used in the treatment of prostate cancer, and taken together even prolong the survival of treated animals [53]. Overall, recent findings provide promising evidence on the benefits of cannabinoid‐based combinational therapy in cancer, and suggest novel therapeutic opportunities that need to be clinically proven in future.
7. Clinical studies of cannabinoids in cancer care
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7.1. Clinical anti‐cancer studies
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The promising preclinical data have encouraged the development of clinical studies aimed at investigating the potential therapeutic value of cannabinoids as anti‐cancer agents. The only clinical study published up to date was a pilot phase I trial in which nine patients with recurrent glioblastoma multiforme (GBM) that have previously failed standard therapy underwent intracranial THC administration. The study showed that THC delivery was safe without evident psychoactive effects and that THC neither facilitates tumour growth nor decreases patients’ survival. Additionally, THC inhibited tumour‐cell proliferation and induced apoptosis in samples obtained from two patients before and after treatment. However, evaluation of patients’ survival requires a larger study with a different design and preferably oral or oromucosal application [46, 84]. According to the register of clinical trials [85], there are several on‐going clinical trials evaluating anti‐cancer activity of cannabinoids. Two phase I/II clinical studies in recurrent GBM patients are being conducted to assess the safety and effectiveness of the administration of an oromucosal spray containing cannabis extract (2.7 mg THC and 2.5 mg CBD in 100 µL) in combination with dose‐intense TMZ (NCT01812603 and NCT01812616). These studies have passed their completion date, but the status has not yet been verified. Evaluation of pure CBD as a single‐agent for solid tumour (NCT02255292) started in 2014 as a phase II clinical trial and still did not reveal any results. Dexanabinol, a synthetic cannabinoid, is currently undergoing phase I trial for the treatment of advanced solid tumours (NCT01489826). This non‐psychotropic cannabinoid was applied in different doses with the intention to determine the maximum safe dose, to understand interactions between the body and the drug and to measure any reduction in size of patients’ tumour. Data on tumour response and the number of adverse events have not yet been reported.
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7.2. Studies on chemotherapy‐induced nausea and vomiting
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In contrast to rare clinical anti‐cancer studies, clinical trials evaluating efficacy of cannabinoids in cancer symptom management have a long history. The 1970s and 1980s mark a period of intensive clinical trials dealing with chemotherapy‐induced nausea and vomiting (CINV), but the interest in these investigations is not decreasing due to the influence of CINV on patients’ life quality and compliance with future treatment [86, 87]. Modern anti‐emetic treatment includes corticosteroids, serotonin receptor antagonists (5‐HT3) and neurokinin (NK1) receptor antagonists, while cannabinoids (dronabinol and nabilone) are prescribed to the patients who have failed to respond to conventional anti‐emetic therapy [88, 89]. Majority of clinical studies have compared efficacy of cannabinoids to dopamine receptor antagonist and neuroleptics [87], yet some recent studies have been focusing on newer generation agents such as 5‐HT3 and NK1 receptor antagonists. Meiri and coworkers [90] have design randomised, double‐blind, placebo‐controlled, parallel group, five‐day study for evaluating dronabinol alone and in combination with ondansetron, a 5‐HT3 receptor antagonist. They recruited 61 patients with delayed CINV, which is defined as nausea and vomiting occurring more than 24 hours after chemotherapy and lasting up to one week. Obtained results indicated that dronabinol or ondansetron was similarly effective and well tolerated, but combination of these two drugs was not more effective than either drug alone. Duran and coworkers [91] conducted a pilot, double‐blind, parallel, placebo‐controlled phase II clinical trial with standardised oromucosal cannabis extract containing a mixture of THC and CBD (2.7 mg THC and 2.5 mg CBD per spray) in patients with CINV. To be recruited in the study, patients had to have moderately emetogenic cancer therapy caused CINV lasting more than 24 hours despite standard anti‐emetic therapy. During five days patients were allowed to add up to eight sprays per day along with their standard therapy. Combination of cannabis extract with standard anti‐emetic therapy was well tolerated and provided better protection against delayed CINV. The benefits of cannabinoids in CINV are undoubtedly confirmed in numerous clinical studies, but there is lack of studies dealing with cannabis plant [92]. First scientific article about use of smoked cannabis reported it as a rescue drug in case of vomiting episodes [93]. In 2001, Musty and Rossi [94] published the review about effects of smoked cannabis and oral THC based on previously unpublished USA clinical trials with cannabis and/or THC. The investigation included 748 patients who smoked cannabis prior to and/or after cancer chemotherapy and 345 patients who used the oral THC capsules. Patients who smoked cannabis experienced 70–100% relief from nausea and vomiting, while those on THC capsules reported 76–88% relief. Although it is clear that cannabinoids can serve as anti‐emetic agents in cancer therapy, clinical studies on their effectiveness on nausea and vomiting in advanced cancer and metastasis are needed since there are case‐reports in which cannabinoids showed potential therapeutic use for these indications [95].
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7.3. Studies on cancer‐related pain
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In the last decades, available clinical data on benefits of cannabinoids in chronic pain were scarce; however, currently there are many clinical studies, which include various cannabinoid preparations and test different chronic pain conditions [96]. Animal studies in a variety of nociceptive assays have confirmed that activation of CB1 receptors by exogenously applied agonists can reduce pain sensitivity, while activation of CB2 receptors may promote analgesia without psychoactive side effects usual for CB1 agonist [97]. Patients who are suffering from chronic cancer‐related pain usually are put on high doses of opiates, which alter their state of consciousness. It has been reported that cancer patients down‐sized opioid dose after adding cannabis in their pain regimen and when selecting cannabis extract, THC‐rich cannabis extract was the first choice, though many patients experienced pain relief after using CBD‐rich type [92]. In multi‐centre, double‐blind, randomised, placebo‐controlled, parallel‐group, two‐week study, THC:CBD extract and THC extract were evaluated in patients with intractable cancer‐related pain. Study included 177 patients with inadequate analgesia despite opioid dosing. During first week patients self‐titrated dose up to maximum of 48 actuations (each 100 µL containing 2.7 mg THC and 2.5 mg CBD or just 2.7 mg THC) per day and remained on that dose till the end of the study. The mean number of THC/CBD sprays was 9.26 and of THC 8.47 per day. Analysis of change from baseline in Numeral Rating Scale score was significantly in favour of THC/CBD extract, while THC extract showed non‐significant change. There was no change in dose of opioid background medication as well [98]. A long‐term, open‐label, follow‐up study investigated the long‐term tolerability of THC/CBD and THC oromucosal spray in 43 patients with terminal cancer‐related pain refractory to opioid who had participated in previously mentioned trial. Patients self‐administered the medication to their optimal dose, again with limitation to a maximum of 48 sprays per day. The duration of treatment with THC/CBD spray (39 patients) was from minimum of 2 and maximum of 579 days (median 25 days) while treatment with THC spray lasted from 4 up to 657 days (median 151.5 days). THC/CBD spray was found to be well tolerated in long‐term use, and patients did not ask for higher dose of spray or other pain‐relieving medication. Long‐term use of cannabinoids did not result with loss of relieving effect on cancer pain [99]. Another randomised, placebo‐controlled, graded‐dose trial evaluating THC/CBD extract was conducted among opioid‐treated patients with poorly‐controlled chronic pain who received placebo, low (1–4 sprays/day), medium (6–10 sprays/day) or high dose (11–16 sprays/day) of 2.8 mg THC/2.5 mg CBD extract. During period of five weeks average pain, worst pain and sleep disruption were measured among 360 patients, of which 263 completed the study. Low and medium dose group of patients showed greater analgesia than placebo group and could be assumed as effective and safe, while in high‐dose group dose medication was not well‐tolerated and had no analgesic effect [100]. Another type of pain that usually occurs in cancer patients is chemotherapy‐induced peripheral neuropathy caused by neurotoxicity of drugs such as platinum compounds, vinca alkaloids, taxols and suramin. Although chemotherapy is limited to a short period of use and to a specific tissue, there is no adequate medications for prophylaxis of this type of neuropathy and therapy is restricted to symptomatic treatment of paraesthesia and pain. Ion channel blockers and tricyclic anti‐depressants are first choice for treating neuropathy symptoms [101]. Being resistant to conventional treatments, neuropathy lowers life quality in affected patients and limits dosing and duration of chemotherapy, which is crucial for extending their life. Preclinical studies implied that cannabinoid agonists can suppress neuropathy caused by chemotherapeutics, namely vincristine, paclitaxel and cisplatin; moreover, they had better efficacy than conventional treatment. For effectiveness estimation of cannabinoid extract for treating neuropathy, a randomised, placebo‐controlled, cross‐over pilot study with 18 patients was conducted. Patients were experiencing neuropathic pain, which persisted for three months after chemotherapy with paclitaxel, vincristine or cisplatin, and were treated with maximum of 12 oromucosal sprays (each containing 2.7 mg THC and 2.5 mg CBD) per day. First study period lasted for four weeks with the result of five patients having a decrease of 2.6 on an 11‐point numeric rating scale for pain intensity, but in whole group, there was no significant difference between treated and placebo group. Ten patients have entered the extension phase for the next six months (five have completed the study), and confirmed pain reduction by average dose of 4.5 sprays per day. Despite inconsistent results, these findings support studying cannabinoids for chemotherapy‐induced neuropathic pain in larger randomised controlled trials [102].
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7.4. Cannabis and cancer associated anorexia/cachexia
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Many cancer patients experience cachexia, anorexia as well as progressive loss of adipose tissue and skeletal muscle mass. Poor chemotherapy response and decreased survival are often connected with cachexia, a syndrome characterised by systemic inflammation, negative protein and energy balance, and an involuntary loss of body mass [103]. Majority of clinical studies dealing with cachexia and anorexia are focused on AIDS patients and as a result dronabinol was approved for treatment of anorexia associated with weight loss in patients with AIDS. Still there are some clinical evidences that show that cannabinoids could be beneficial for patients with cancer‐associated anorexia/cachexia. One of earliest trials with cancer patients, in 1976, showed that oral THC in doses up to 15 mg per day stimulated appetite and produced significant weight gain [104]. Eighteen cancer patients with anorexia and life expectancy more than 4 weeks underwent a phase II study of THC under regime 2.5 mg tree times per day, one hour after meal. Thirteen patients responded positively to the appetite stimulating effects of THC, but rather surprising was the fact that nausea was common side‐effect [105]. In contrast, study conducted in 2006 did not confirm these results. Multi‐centre, phase II, randomised, double blind, placebo controlled clinical trial included 164 patients with advanced incurable cancer and involuntary weight loss more than 5%. Patients were divided in placebo, cannabis extract (2.5 mg THC and 1 mg CBD in a capsule) or THC (2.5 mg in a capsule) group, and they were assigned to take capsules twice per day, one hour before meal for six weeks. There were no significant differences between groups considering appetite, quality of life, cannabinoid related toxicity, mood and nausea [106]. It is rather unusual that in this large trial there were no side effects, which suggest that administrated dose of cannabinoids is suboptimal. Moreover, in case of the use of cannabinoids for anorexia and cachexia, European Palliative Care Research Collaborative noticed that dose‐regimen of THC used in clinical trials may be the reason for its lack of efficacy. They concluded that for future trials individual dose titration could be more efficient [107, 108]. These theses were confirmed in another randomised, double‐blind, placebo‐controlled pilot trial in which influence of THC on taste improvement, smell perception, appetite, caloric intake and quality of life was explored. Twenty‐one advanced cancer patients, with poor appetite and chemosensory alterations, received THC (2.5 mg, twice per day) and had the option to increase their drug dose to a maximum of 20 mg/day. Though study population was not specifically cachexic, THC‐treated patients had improvement in taste, appetite, protein consumption and sleep quality [109].
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To summarise, cannabinoids show positive results in various clinical trials considering treatment of nausea, vomiting, pain and anorexia/cachexia while clinical anti‐cancer studies are yet to be reported. The perspective of cannabis‐based therapy also depends on a paradigm shift from illicit drug to clinically proved medicine. Due to their acceptable safety profile, with side effects that are generally tolerable and reversible [92], clinical trials testing them as single drugs or in combination therapies in various types of cancer are needed, particularly with respect to their effects on tumour growth and patient survival.
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\n\n',keywords:"cannabinoids, phytochemistry, THC, CBD, cancer",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/54967.pdf",chapterXML:"https://mts.intechopen.com/source/xml/54967.xml",downloadPdfUrl:"/chapter/pdf-download/54967",previewPdfUrl:"/chapter/pdf-preview/54967",totalDownloads:2248,totalViews:634,totalCrossrefCites:1,totalDimensionsCites:2,totalAltmetricsMentions:0,impactScore:1,impactScorePercentile:62,impactScoreQuartile:3,hasAltmetrics:0,dateSubmitted:"September 28th 2016",dateReviewed:"February 6th 2017",datePrePublished:null,datePublished:"July 5th 2017",dateFinished:"April 23rd 2017",readingETA:"0",abstract:"Cannabinoids comprise the plant‐derived compounds and their synthetic derivatives as well as endogenously produced lipophilic mediators. Phytocannabinoids are terpenophenolic secondary metabolites predominantly produced in Cannabissativa L. The principal active constituent is delta‐9‐tetrahydrocannabinol (THC), which binds to endocannabinoid receptors to exert its pharmacological activity, including psychoactive effect. The other important molecule of current interest is non‐psychotropic cannabidiol (CBD). Since 1970s, phytocannabinoids have been known for their palliative effects on some cancer‐associated symptoms such as nausea and vomiting reduction, appetite stimulation and pain relief. More recently, these molecules have gained special attention for their role in cancer cell proliferation and death. A large body of evidence suggests that cannabinoids affect multiple signalling pathways involved in the development of cancer, displaying an anti‐proliferative, proapoptotic, anti‐angiogenic and anti‐metastatic activity on a wide range of cell lines and animal models of cancer. These findings have led to the development of clinical studies to investigate potential anti‐cancer activity in humans, but reliable clinical evidence for this therapeutic option is still missing.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/54967",risUrl:"/chapter/ris/54967",book:{id:"5767",slug:"natural-products-and-cancer-drug-discovery"},signatures:"Sanda Vladimir‐Knežević, Biljana Blažeković, Maja Bival Štefan and\nMarija Kindl",authors:[{id:"71793",title:"Dr.",name:"Biljana",middleName:null,surname:"Blazekovic",fullName:"Biljana Blazekovic",slug:"biljana-blazekovic",email:"bblazekovic@pharma.hr",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"University of Zagreb",institutionURL:null,country:{name:"Croatia"}}},{id:"76050",title:"MSc.",name:"Maja",middleName:null,surname:"Bival Štefan",fullName:"Maja Bival Štefan",slug:"maja-bival-stefan",email:"mbival@pharma.hr",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"University of Zagreb",institutionURL:null,country:{name:"Croatia"}}},{id:"196734",title:"Prof.",name:"Sanda",middleName:null,surname:"Vladimir-Knežević",fullName:"Sanda Vladimir-Knežević",slug:"sanda-vladimir-knezevic",email:"svladimir@pharma.hr",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"University of Zagreb",institutionURL:null,country:{name:"Croatia"}}},{id:"197944",title:"Dr.",name:"Marija",middleName:null,surname:"Kindl",fullName:"Marija Kindl",slug:"marija-kindl",email:"mkindl@pharma.hr",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. The cannabis plant",level:"1"},{id:"sec_3",title:"3. Biosynthesis and structure of phytocannabinoids",level:"1"},{id:"sec_4",title:"4. Phytochemical characterisation of cannabinoids",level:"1"},{id:"sec_5",title:"5. The endocannabinoid system",level:"1"},{id:"sec_6",title:"6. Preclinical evidence on cannabinoids as anti‐cancer agents",level:"1"},{id:"sec_7",title:"7. Clinical studies of cannabinoids in cancer care",level:"1"},{id:"sec_7_2",title:"7.1. Clinical anti‐cancer studies",level:"2"},{id:"sec_8_2",title:"7.2. Studies on chemotherapy‐induced nausea and vomiting",level:"2"},{id:"sec_9_2",title:"7.3. Studies on cancer‐related pain",level:"2"},{id:"sec_10_2",title:"7.4. Cannabis and cancer associated anorexia/cachexia",level:"2"}],chapterReferences:[{id:"B1",body:'Velasco G, Sánchez C, Guzmán M. Cancer. In: Pertwee E, editor. Handbook of Cannabis. 1st ed. Oxford: Oxford University Press; 2014. pp. 626-649. 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Nutrition Research Reviews. 2001;14:65-86. DOI: 10.1079/NRR200118\n'},{id:"B105",body:'Nelson K. A phase II study of delta‐9‐tetrahydrocannabinol for appetite stimulation in cancer‐associated anorexia. Journal of Palliative Care. 1994;10:14-18.\n'},{id:"B106",body:'Strasser F, Luftner D, Possinger, Gernot Ernst G, Ruhstaller T, Meissner W, Ko YD, Schnelle M, Reif M, Cerny T. Comparison of orally administered cannabis extract and delta‐9‐tetrahydrocannabinol in treating patients with cancer‐related anorexia‐cachexia syndrome: a multicenter, phase III, randomized, double‐blind, placebo‐controlled clinical trial from the Cannabis‐In‐Cachexia‐Study‐Group. Journal of Oncology. 2006;24:3394-3400. DOI: 10.1200/JCO.2005.05.1847\n'},{id:"B107",body:'Maccio A, Madeddu C, Mantovani G. Current pharmacotherapy options for cancer anorexia and cachexia. Expert Opinion on Pharmacotherapy. 2012;13:2453-2472. DOI: 10.1517/14656566.2012.734297\n'},{id:"B108",body:'Reuter SE, Martin JH. Pharmacokinetics of cannabis in cancer cachexia‐anorexia syndrome. Clinical Pharmacokinetics. 2016;55:807-812. DOI: 10.1007/s40262‐015‐0363‐2\n'},{id:"B109",body:'Brisbois TD, De Kock IH, Watanabe SM, Mirhosseini M, Lamoureux DC, Chasen M, MacDonald N, Baracos VE, Wismer WV. Delta‐9‐tetrahydrocannabinol may palliate altered chemosensory perception in cancer patients: Results of a randomized, double‐blind, placebo‐controlled pilot trial. Annals of Oncology. 2011;22:2086-2093. DOI: 10.1093/annonc/mdq727\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Sanda Vladimir‐Knežević",address:"svladimir@pharma.hr",affiliation:'
Department of Pharmacognosy, Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
Department of Pharmacognosy, Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
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1. Introduction
Coronavirus Disease 2019 (COVID-19), a pandemic has shaken the entire world. The outbreak which initiated in December 2019 from the city of Wuhan in China [1, 2] within four months has spread like wildfire to 210 countries infecting almost 6,403,001 people. (data as of 2nd June 2020).
The disease with an unknown etiology has clogged the healthcare machinery of even the most medically advanced nations [3]. COVID-19 spreads through human to human contact [1, 4]. The fact that the disease can even be transmitted through asymptomatic patients [5] makes it highly infectious [6]. With no pharmaceutical intervention yet, even the developed countries are at the mercy of social distancing [7] to protect their citizens from the fatal virus.
When the control of a deadly disease like Covid-19 depends on the actions of the population, the quality of information the individuals are being exposed to becomes a vital concern. It is alarming to see that even at a time when the truth can become a matter of life and death a multitude of false information is being spread on social media [8].
Social Media Platforms (SMP) like Facebook, Twitter, WhatsApp, Instagram, Linkedin were originally developed with the intention to connect people [9]. However, with speedier data networks and cheaper smart phones a majority of individuals now use these platforms to gather news and information from across the globe. Thus, social media plays a fundamental role in the news fruition.
Social Media in the current times have become an essential publishing platform for journalists (Zubiaga, et, al.). The journalists use the platform not only to report breaking news [10] but also to determine public opinions and to ascertain potential stories [11]. According to Social Media today has become a central platform for both news dissemination and consumption by journalists and individuals. Citizens follow the development of breaking news and events either through the official social media handles of various news channels or through posts of their own network (e.g. friends, family, and public figures).
Indeed, social networks can be extremely useful particularly during crisis like Covid- 19, because of their inherent ability to spread vital news much faster than traditional media [12]. However, this positive impact of social media comes at a cost. The absence of fact-checking and control over posts/ messages makes social media a fertile ground for breeding of misinformation. People often publish posts or share other messages without verifying either the genesis or reliability of the information. Oftentimes, a catchy headline is sufficient for an article to be shared thousands of times, despite it possibly being incorrect.
COVID-19 spread into India through travelers from abroad in the first week of March 2020. Accompanying the disease was infodemic related to Covid-19, which entered the country with an even mightier force. Infodemic such as COVID-19 being a Bio-Weapon declaration of emergency due to the pandemic in India (Press Trust of India, 2020), the disease would not survive in summers, a particular community purposely spreading Corona, steam inhalation and drinking warm water will kill the virus; are just a few examples of the infodemic spread in India. The term infodemic has been coined to outline the hazard of misinformation during the management of pandemics like COVID-19. Since it could even speed up the virus spreads process by influencing and fragmenting social response, controlling an infodemic becomes an additional challenge for all governments.
Numerous studies provide evidence for the spread of misinformation through Social Media [11]. Studies have also been conducted to analyze the spread of infodemic during crises through Social Media platforms [13]; Leung, and Huang, 2007). Research has been conducted to understand the pattern, speed and impact of misinformation spread both during pandemics and otherwise too [12, 14, 15]. Most of the studies conducted on the spread of fake news through Social Media platforms are generic in nature. None of the research so far attempts to identify any one social media platform which has a higher possibility of spreading fake news.
Research Question: Is there any one platform which is more likely to spread Infodemic as compared to other SMP?
Several efforts are being made by the government and the various SMP to curb the spread of fake news in the country. However the increasing number of SMPs and a billion plus users in the country makes fact checking of post/ message a mammoth task. In such a scenario if the SMP which is most likely medium of maximum misinformation is identified, it will help authorities to control infodemic.
The paper further includes the theoretical background, motivation for the study, case study and analysis using Fuzzy-Technique of Order Preference by Similarity to Ideal Solution (F-TOPSIS) methodology. The paper ends with discussion and suggestions for future scope of studies.
2. Theoretical background
The growth of the online social media platforms, have facilitated both- communication and dissemination of real-time information among people across the world [15]. With the characteristics of low cost, ease-of-use, and rapid rate, social media platforms have become the major stage for online social interaction and information transmission [16].
However, in recent times the platforms which were created with the intention of connecting and informing have become a hub of misinformation and fake news [11]. According to [12] the social media platforms (SMP) are an ideal breeding ground for circulating misleading or false news, political statements, advertisements, and even rumors. These platforms become particularly more fertile and active during a crisis. During times of worry individuals are more susceptible in believing any information that they come across. COVID-19 is one such crisis that the misinformation reapers are taking advantage off.
According to a report published by the Reuters Institute for the Study of Journalism at the University of Oxford more than one third of the social media users across Europe and USA have come across a misleading information related to COVID-19. The misinformation, fake news that is spread specifically during epidemic and pandemics through the social media is known as infodemic.
Infodemic is particularly hazardous as it hampers with the government intervention and also fragments the social responses. Infodemic adds to the anxiety and panic of the individuals making them more exposed to the dangers of the pandemic. Often infodemic speeds up the rate of spread of disease.
Studies reveal that four out of five individuals have shared online at least one news story that they later found out was inaccurate or fake. A study conducted by Common Sense Media revealed that even Generation-Z who practically live their life of internet are confused when it comes to identifying true from fake [14]. Further, it is revealed by [17] that people get influenced even by that information on the internet that they perceive as unreliable. In a survey conducted on the Indian population during lockdown revealed that a majority Indians agree to reading, often, fake or incorrect information about Covid-19 online. What was flabbergasting was that the same set of people still considered online as their major source of information.
A large amount of online fake news has the potential to cause serious problems in society specifically during global crisis like Covid- 19 [11]. On social networks, the reach and effects of information spread occur at such a swift pace and so augmented that the fake, distorted or inaccurate information acquires a remarkable potential to cause a catastrophes within minutes, for millions of users [18]. Some studies pointed out that fake news and inaccurate information may spread faster and wider than fact-based news particularly during calamity [11].
In recent years, there has been extensive research on establishing an effective and automatic framework for online fake news detection [19]. Identifying credible social information from millions of messages, however, is challenging, due to the heterogeneous and dynamic nature of online social communication [20].
Controlling all the Social Media Platforms collectively is a challenge. Studies claim that all SMP do not spread fake news with the same vigor. Thus, a model to forecast that Social Media Platforms that is most likely to spread maximum fake news can help authorities to curb infodemic and misinformation.
3. Research gap and motivation
The studies conducted on social Media misinformation can be broadly classified into three categories. The first comprises ofstudies that attempt to comprehend the variety of misinformation, its pattern of spread and its interaction with individuals [9, 11, 21, 22]. The second category comprised of all the studies that either describe or design methods to detect online fake news [16, 17, 20, 23, 24]. Considering the threat misinformation can be during crises like Covid-19, the third category comprises of studies conducted to analyze the impact and spread of misinformation during crises and pandemics [13].
Across studies related to misinformation, all the Social Media Platforms (SMPs) are regarded as one. Even though studies agree that the effect and strength of misinformation might be platform-specific. To fill this gap, this study using of F-TOPSIS method attempts to create a mathematical model to identify those social media platforms which are most likely to spread Infodemic.
4. Methods of research
In this section we describe the methodology that is used in the paper for ranking of SMP in order of their ability to spread misinformation. Here, we have used Fuzzy Technique for Order Preference by Similarity to Ideal Solution (F-TOPSIS) method. This being the extension of TOPSIS to incorporate the real-life decision making. The alternatives (here SMPs) should be such that each alternative is at the shortest distance from the ideal (best) solution and farthest distance from the negative-ideal solution (worst) possible in the given problem scenario [25].
Considering the concept of TOPSIS, Chen [26] proposed the variant of TOPSIS method under fuzzy environment. The method was proposed to incorporate the real-life inability to make decisions on exact quantification. The basic structure of F-TOPSIS is described in Figure 1.
Figure 1.
Fuzzy-TOPSIS methodology. Source: Adapted from Chen [7].
Many researchers have discussed the applications of F-TOPSIS in various fields. Evaluation of initial training for aircraft in Taiwanese Air Force Academy was discussed by Wang and Chang using F-TOPSIS. In Amiri the selection of project for the National Iranian Oil company has been done through the hybrid methodology of AHP and F-TOPSIS. AHP is used to calculate the weights of the criteria and F-TOPSIS for the final ranking of the alternatives. Awasthi et al. discussed the selection of sustainable transport system under the case where there is incomplete information. They carried out the selection through the F-TOPSIS method. Ashrafzadeh et al. discuss the application of the method for the case of selection of warehouse. To the best of our knowledge there are no studies which have quantified the spread misinformation in F-MCDM. Moreover, F- TOPSIS has not been used. This technique is used due to its simplicity in understanding, computational comprehension, easy method of selection and ranking of alternatives under consideration.
5. Case study
It is evident from the extant literature that Social Media Platforms (SMPs) extensively contribute to spread of misinformation. The question that arises is that which out of the many available are most likely to increase the spread of misinformation. In this section the focus is to give the details of the mathematical methodology viz. F-TOPSIS, for ranking of SMPs in decreasing order of their ability to spread misinformation.
For a real case for a situation of decision making there are always conflicting conditions under which the decision has to be taken. To come up with best decision it is believed that quantitative methods are more accurate. On the other hand one cannot ignore the subjectivity in real life cases. A good blend of quantitative decision making with inclusion of subjectivity is the Fuzzy-Multi Criteria Decision Making (F-MCDM). In this research we try and rank the Social Media Platforms (SMPs) in order to understand the role of each platform in spreading misinformation.
5.1 Steps of F-TOPSIS for ranking of SMP
For basic conceptual understanding of fuzzy set evaluations the reader could refer Chen [26]. Moreover, the F-TOPSIS methodology used has also been proposed by Chen [26]. The steps that can be used for ranking of the SMPs through F-TOPSIS are as follows:
Step 1: In the first step a choice of criteria for evaluation (j = 1,2,...,n’) (here n’ = 9) for evaluating the alternatives (i = 1,2,...,m’) (here m’ = 7) in connection with ranking in decreasing order of ability of each SMP in spreading misinformation. These criteria for evaluation were finalized by experts in the field based on extensive research of extant literature. Table 1 below describes the criteria for evaluation.
Step 2: As proposed by Chen [26], linguistic scales for assigning weights to criteria(as defined in Table 1) and for performance evaluation of alternatives with respect to criteria are defined in the form of Triangular Fuzzy Numbers (TFNs) (Refer Table A1 and Table A2 in appendix).
Note: It must be noted that in this study triangular fuzzy numbers have been used since they are easier to use and calculations becomes easier for decision makers. Further it has proven to be more effective in situations where the information available is imprecise and subjective.
Step 3: A group of k decision makers (here assessment is based on SMP users) (k = 1,2,..., K) (K = 40). These 40 have been empanelled for study. This study is part of larger project. The panel will be used for future studies as well. The panel is asked to give suitable weights for the jthcriteria. The average weights for each criterion and performance of alternatives w.r.t. to each criterion from the group are obtained using the formula for average of K = 40 decision makers. The 40 people empanelled were people working in different organizations and having different experience in years ranging from 10 to 20 years. They were part of marketing, operations, human resource departments. The data was collected to take their opinion from the period May 2020 onwards during the lockdown period in most countries.
Step 4: Based on the average performance values of each SMP from the group of decision makers the final fuzzy decision matrix and the corresponding vector of weights can be defined as given in Table 2.
Step 5(a): To preserve the property of a TFN to lie in the range [0,1] normalization is carried out. The normalized matrix is given in Table A3 in appendix. The components of the normalized matrix are obtained by the formula given Chen [26].
Step 5(b): The weight of each criterion is multiplied to the normalized performance values of the SMP with respect to each criterion through multiplication rule of fuzzy numbers. The outcome matrix of this step is called the fuzzy normalized weighted matrix The components of the normalized weighted matrix are given in Table A4 in appendix (refer [26])
Step 6: Then the Fuzzy Positive Ideal Solution (FPIS) and the Fuzzy Negative Ideal Solution (FNIS) is calculated
FPIS = A* = (1,1,1).
FNIS = A* = (0,0,0)
Step 7: The distance from the FPIS (di∗) and that from FNIS (di−) for each of the SMPs is given in Table A5 in appendix.
Step 8(a): Closeness coefficient (CCi) for each SMP is calculated and given in Table 3.
Step 8 (b): The closer the value of CCiis to unity the better the alternative. Arranging (CCi) values in descending order we obtain the top ranked alternatives.
Criteria for evaluation
Working definition
Reference
Majority people in my circle use this SMP
To check belief in news based on friendly influence
The progress from type writer to smart phones, from newspapers to social media walls have contributed to the ease of freedom of speech in the true spirit. However, on the flip side this freedom and ease has also enabled the fabrication and spread of misinformation like never before [29].
On social platforms, misinformation disseminates at an extremely swift pace and with such an augmented impact that the fake, distorted or inaccurate information acquires a remarkable potential to cause a devastation catastrophe within minutes [16]. The production and circulation of fake news and inaccurate information becomes even more rampant and destructive during crisis like Covid-19 [30].
A crisis makes people hungry for information, hungry for certitude. More mysterious the crisis stronger is the hunger [30]. In a survey conducted across six countries in the month of April 2020 it was revealed that in all the countries people use social media platforms to satisfy the hunger of information and certitude about Covid 19 [31].
Like the world over even Indian use Social Media Platforms as their major source of information for Covid 19. According to a recent report by the fact checking website BOOM, COVID-19 related misinformation and rumors which began souring in the third week of March 2020 and took a colossal spike in early April 2020, the same time period when India went under Lockdown [32].
The aim of this paper was to identify and rank in descending order, Social Media Platforms (SMPs) ability to spread misinformation. The research attempted to identify the SMP which according to the users are most responsible for spread of misinformation. In the paper proxy measures in the form of criteria were used to rank the SMPs. The criteria were termed as proxy measures as the end users filled up the survey based on a blind review. The users were asked to analyze each of the SMP w.r.t. each criterion without biasing their responses towards spread of misinformation.
As discussed in the previous section, the users are asked to give their ranks based on fuzzy linguistic scale to establish the performance (ability to spread) w.r.t to criteria.
The results reveal Facebook followed by WhatsApp and Twitter as the three top most SMP most likely to spread misinformation. The results corroborate with previous research that identify Facebook as tailor-made for the spreading of fake news and for political manipulation [29]. The author further states that Facebook combines all the essential characteristics for the design, creation, targeting and promulgation of fake news. In another study conducted by Princeton University it was revealed that Facebook is the worst perpetrator when it comes to disseminating misinformation. It is much ahead of both YouTube and Twitter when it comes to spreading of fake news or infodemic [33]. The study further states that Facebook aids those wishing to spread ‘fake news’, by providing them with tools and incentives to do so.
Facebook-owned WhatsApp is the next media most likely to spread infodemic. According to a research [34] WhatsApp is most popular social media platform in India, with over 400 million users in the country, thus making it a significant platform for the spread of infodemic. It is worth noting that the three major catastrophes related to COVID-19 that took place in India during the lockdown phase, in all three infodemic were majorly spread through WhatsApp. First the Palghar mob lynching case on the 16th April 2020 [35]. In the next incident misunderstanding caused by infodemic led to a series of violent attacks on health care professionals in Indore [36]. The most precarious out of all the incidents instigated by fake news was the one that depicted an entire community as vector of disease. In all the three WhatsApp had a major role to play in the spread of infodemic. Thus, confirming the results of the study. Supporting the study is the research titled WhatsApp Vigilantes: An exploration of citizen reception and circulation of WhatsApp misinformation linked to mob violence in India conducted by London School of Economics and political Science. The study innumerate’s the role of WhatsApp in spreading of fake information leading to various mob lynching incidents in India.
Some other factors contributed to the likely hood of the two platforms spreading maximum fake news are the number and age group of users; degree of rural and urban user penetration and nature of posts.
Facebook and WhatsApp have the one of highest number of users in India, 300 million and 200 million respectively. Being the oldest social media website1 Facebook has users of all generations2 and even from rural India. Anyone who uses a smart phone has WhatsApp installed, thus is used by people of all age group and demographics [37].
Although, more than 50 percent of social media users in India use Twitter, however the platform is yet not very popular both among the rural and older age users. Moreover, there are restrictions on the number of words, size of the Video that can be tweeted making it less attractive to Indians. YouTube has the highest number of users in India, however unlike Facebook and WhatsApp where messages, videos, photographs all can be shared, on YouTube only videos can be shared. Similar is the case with Instagram, which is a Photo and video sharing App. LinkedIn though originally developed as a professional networking site is slowly gaining popularity among its users. Just like Facebook and WhastApp, any kind of content can be shared on this platform too. However this platform is not popular among the rural users.
The Indians who found it difficult to express themselves through neatly worded tweets or self-appreciating captions on Instagram posts found solace in Tik Tok. Reports suggest that a large section of India’s first-time internet users—some of them illiterate, others speaking in local dialects—find navigating video-based platforms Tik Tok easier and addictive. However being just two years old in India and with only 30 seconds video posts, as per the results of the study, the platform is less likely to spread fake news.
7. Conclusion
The crisis of COVID-19 has already killed millions across the globe. The pandemic has further left a large number of people jobless and almost the entire world hopeless. While the think tanks across nations are fighting hard to find a solution to Covid- 19, the spread of Infodemic is making the handling of crisis even more complicated.
When the control of a deadly disease like Covid-19 depends on the actions of the population, the quality of information the individuals are being exposed too becomes a vital concern. It is alarming to see that even at a time when the truth can become a matter of life and death a multitude of false information is being spread on social media making it all the more difficult for governments to control the crises.
Plethora of research agree to the an electronic grapevine being more destructive than anything previously imaginable, which Social Media Platform is most likely to activate and spread the grapevine is not addressed by any study.
This study using the Fuzzy-TOPSIS approach identifies Facebook and Whatsapp as the two medium most likely to spread infodemic in the country. The study has enabled to objectively quantify the Social Media Platforms which in turn will facilitate the government to take more accurate decisions to stop the spread of misinformation from the identified platforms.
The identification of the aforesaid media will also help the platforms to take decisions on how to change the features of their SMP so to ensure reduction of spread of misinformation. The changes in the features can be undertaken either by the platforms themselves or educating the platform users.
8. Limitations of the study
The limitations of the current study will lead to future scope. In the current study we have taken only a small group of decision makers based on whose opinion and judgment we have been able to come to an exploratory conclusion. Further, in this study we have only considered a limited number of SMPs which are considered which may be considered for the spread of misinformation, there could be other media which may also be responsible for spread of misinformation.
In future cross border studies can be carried on to identify if the same platforms are responsible in spreading misinformation in all the countries, or are there any variations. Also, the above study was carried out specifically during COVID-19, post the crisis another study can be carried out to see if the same platforms are responsible for the spread of misinformation yet or do the results vary. This can be a longitudinal study to the current research. We may also include different media for our analysis.
Linguistic variables for giving order of preferences of criteria
Corresponding TFN
Very Low (VL)
(0,0,0.1)
Low(L)
(0,0.1,0.3)
Medium Low(ML)
(0.1,0.3,0.5)
Medium(M)
(0.3,0.5,0.7)
Medium High(MH)
(0.5,0.7,0.9)
High(H)
(0.7,0.9,1.0)
Very High(VH)
(0.9,1.0,1.0)
Table A1.
Linguistic scale for weights of criteria.
Linguistic variables for determining performance of SMPw.r.t. criteria
Corresponding TFN
Very poor (VP)
(0,0,1)
Poor(P)
(0,1,3)
Medium Poor(MP)
(1,3,5)
Fair(F)
(3,5,7)
Medium Good(MG)
(5,7,9)
Good(G)
(7,9,10)
Very Good(VG)
(9,10,10)
Table A2.
Linguistic scale for performance of alternatives (SMP).
Weights
(0.79,0.935,0.99)
(0.8,0.95,1)
(0.78,0.94,1)
(0.58,0.78,0.94)
(0.69,0.865,0.97)
(0.6,0.8,0.95)
(0.72,0.94,0.94)
(0.79,0.92,1)
(0.6,0.8,0.95)
C1
C2
C3
C4
C5
C6
C7
C8
C9
Facebook
(0.64,0.78,0.86)
(0.45,0.60,0.74)
(0.48,0.65,0.80)
(0.61,0.82,1)
(0.378,0.57,0.77)
(0.62,0.77,0.87)
(0.63,0.83,1)
(0.48,0.66,0.81)
(0.62,0.83,0.98)
WhatsApp
(0.86,0.97,1)
(0.79,0.94,1)
(0.80,0.94,1)
(0.48,0.65,0.82)
(0.46,0.67,0.875)
(0.76,0.92,1)
(0.56,0.75,0.91)
(0.68,0.87,1)
(0.56,0.77,0.95)
Twitter
(0.12,0.21,0.30)
(0.18,0.28,0.375)
(0.15,0.24,0.33)
(0.17,0.28,0.39)
(0.22,0.33,0.44)
(0.10,0.17,0.25)
(0.23,0.34,0.44)
(0.13,0.22,0.3125)
(0.18,0.29,0.38)
LinkedIn
(0.40,0.54,0.64)
(0.42,0.57,0.68)
(0.34,0.48,0.61)
(0.30,0.46,0.63)
(0.621,0.78,0.88)
(0.25,0.36,0.49)
(0.45,0.61,0.76)
(0.31,0.44,0.57)
(0.57,0.73,0.83)
Instagram
(0.39,0.52,0.62)
(0.37,0.49,0.60)
(0.39,0.53,0.63)
(0.44,0.60,0.75)
(0.23,0.38,0.56)
(0.31,0.44,0.56)
(0.32,0.48,0.65)
(0.40,0.55,0.67)
(0.40,0.54,0.68)
Tik-Tok
(0.17,0.24,0.29)
(0.12,0.18,0.25)
(0.17,0.24,0.29)
(0.11,0.18,0.26)
(0.08,0.12,0.19)
(0.22,0.28,0.32)
(0.15,0.22,0.29)
(0.17,0.22,0.27)
(0.18,0.23,0.28)
YouTube
(0.63,0.74,0.79)
(0.38,0.53,0.65)
(0.49,0.65,0.76)
(0.38,0.55,0.72)
(0.74,0.90,1)
(0.60,0.76,0.85)
(0.55,0.72,0.85)
(0.56,0.73,0.84)
(0.75,0.92,1)
Table A3.
Normalized fuzzy decision matrix.
C1
C2
C3
C4
C5
C6
C7
C8
C9
Facebook
(0.51,0.73,0.85)
(0.36,0.57,0.74)
(0.37,0.61,0.80)
(0.35,0.64,0.94)
(0.26,0.49,0.75)
(0.37,0.61,0.83)
(0.45,0.78,0.94)
(0.38,0.61,0.81)
(0.37,0.67,0.93)
WhatsApp
(0.68,0.91,0.99)
(0.63,0.89,1)
(0.62,0.89,1)
(0.27,0.50,0.77)
(0.32,0.58,0.84)
(0.45,0.73,0.95)
(0.40,0.70,0.85)
(0.54,0.80,1)
(0.33,0.61,0.90)
Twitter
(0.099,0.20,0.29)
(0.14,0.26,0.375)
(0.12,0.23,0.33)
(0.10,0.22,0.37)
(0.15,0.28,0.42)
(0.06,0.14,0.24)
(0.16,0.32,0.42)
(0.10,0.20,0.31)
(0.11,0.23,0.36)
LinkedIn
(0.32,0.50,0.64)
(0.34,0.54,0.68)
(0.26,0.45,0.61)
(0.17,0.36,0.59)
(0.42,0.67,0.85)
(0.15,0.29,0.46)
(0.32,0.58,0.71)
(0.24,0.40,0.57)
(0.34,0.58,0.79)
Instagram
(0.31,0.49,0.61)
(0.3,0.47,0.60)
(0.30,0.50,0.63)
(0.25,0.47,0.70)
(0.15,0.33,0.54)
(0.18,0.35,0.53)
(0.23,0.45,0.61)
(0.32,0.50,0.67)
(0.24,0.43,0.64)
Tik-Tok
(0.13,0.22,0.29)
(0.1,0.17,0.25)
(0.13,0.22,0.29)
(0.06,0.14,0.25)
(0.05,0.10,0.19)
(0.13,0.22,0.30)
(0.11,0.20,0.27)
(0.13,0.20,0.27)
(0.11,0.18,0.27)
YouTube
(0.50,0.69,0.78)
(0.30,0.50,0.65)
(0.38,0.61,0.76)
(0.22,0.43,0.68)
(0.51,0.77,0.97)
(0.36,0.61,0.81)
(0.39,0.68,0.80)
(0.44,0.67,0.84)
(0.45,0.73,0.95)
Table A4.
Weighted normalized fuzzy decision matrix.
S. No
Social Media Platforms
di∗
di−
1.
Facebook
3.808774
5.878767
2.
WhatsApp
3.090532
6.661095
3.
Twitter
6.935459
2.286388
4.
LinkedIn
4.886222
4.54513
5.
Instagram
5.193706
4.211984
6.
Tik-Tok
7.311769
1.810359
7.
YouTube
3.810798
5.741397
Table A5.
Distance from FPIS and FNIS.
\n',keywords:"Covid-19, Crisis, Infodemic, Social Media Platforms, Fuzzy Topsis, Facebook, WhatsApp",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/78366.pdf",chapterXML:"https://mts.intechopen.com/source/xml/78366.xml",downloadPdfUrl:"/chapter/pdf-download/78366",previewPdfUrl:"/chapter/pdf-preview/78366",totalDownloads:113,totalViews:0,totalCrossrefCites:0,dateSubmitted:null,dateReviewed:"July 30th 2021",datePrePublished:"September 1st 2021",datePublished:"January 7th 2022",dateFinished:"September 1st 2021",readingETA:"0",abstract:"Coronavirus Disease 2019 (COVID-19), a pandemic has shaken the entire world. While the think tanks across nations are fighting hard to find a solution to Covid- 19, the spread of Infodemic is making the handling of crisis even more complicated. When the control of a deadly disease like COVID-19 depends on the actions of the population, the quality of information the individuals are being exposed too becomes a vital concern. It is alarming to see that even at a time when the truth can become a matter of life and death a multitude of false information is being spread on social media making it all the more difficult for governments to control the crisis. Plethora of research agrees to an electronic grapevine being more destructive than anything previously imaginable. However, identifying which Social Media Platform is most likely to activate and spread the grapevine is not addressed by any study. This study uses the Fuzzy TOPSIS approach and identifies Facebook followed by WhatsApp as the two platforms most likely to spread infodemic in the country. The quantification of evaluation of the Platforms in spreading misinformation will facilitate the government to take accurate measures to stop the spread of misinformation from the identified platforms.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/78366",risUrl:"/chapter/ris/78366",signatures:"Arshia Kaul and Ritika Guaba",book:{id:"10752",type:"book",title:"Communication Management",subtitle:null,fullTitle:"Communication Management",slug:"communication-management",publishedDate:"January 7th 2022",bookSignature:"František Pollák, Jakub Soviar and Roman Vavrek",coverURL:"https://cdn.intechopen.com/books/images_new/10752.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-83969-512-4",printIsbn:"978-1-83969-511-7",pdfIsbn:"978-1-83969-513-1",isAvailableForWebshopOrdering:!0,editors:[{id:"304707",title:"Associate Prof.",name:"František",middleName:null,surname:"Pollák",slug:"frantisek-pollak",fullName:"František Pollák"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"353971",title:"Dr.",name:"arshia",middleName:null,surname:"kaul",fullName:"arshia kaul",slug:"arshia-kaul",email:"arshia.kaul@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"353975",title:"Dr.",name:"Ritika",middleName:null,surname:"Guaba",fullName:"Ritika Guaba",slug:"ritika-guaba",email:"drritikagauba@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Theoretical background",level:"1"},{id:"sec_3",title:"3. Research gap and motivation",level:"1"},{id:"sec_4",title:"4. Methods of research",level:"1"},{id:"sec_5",title:"5. Case study",level:"1"},{id:"sec_5_2",title:"5.1 Steps of F-TOPSIS for ranking of SMP",level:"2"},{id:"sec_7",title:"6. Results and discussion",level:"1"},{id:"sec_8",title:"7. Conclusion",level:"1"},{id:"sec_9",title:"8. Limitations of the study",level:"1"},{id:"sec_11",title:"Table A1.",level:"1"}],chapterReferences:[{id:"B1",body:'Huang, C. et al. (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet. 39(5).497–506, doi:http://dx.doi.org/10.1016/S0140-6736 (20)30183-5'},{id:"B2",body:'Zhou P, Yang X, Wang X, Hu B, Zhang L, Zhang W, et al.(2020) A pneumonia outbreak associated with a new coronavirus of probable bat origin. The Nature'},{id:"B3",body:'Wilder-Smith,Annelies.,Chiew,Calvin,J., and Lee, Vernon, J.(2020). 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Vol.20 issue 6 631-633 june 1 2020'},{id:"B8",body:'Frenkel, S., Alba, D., & Zhong, R. (2020). Surge of Virus Misinformation Stumps Facebook and Twitter. The New York Times. Retrieved 19th April 2020 from https://www.nytimes.com/2020/03/08/technology/coronavirus-misinformation-social-media.html'},{id:"B9",body:'Bondielli Alessandro & Marcelloni Francesco. (2019).A survey on fake news and rumour detection techniques. Information Sciences, 49(2),38–55'},{id:"B10",body:'Starbird, K. (2017). Examining the Alternative Media Ecosystem through the Production of Alternative Narratives of Mass Shooting Events on Twitter. Eleventh International AAAI Conference on Web and Social Media (Icwsm), 230–239'},{id:"B11",body:'Vosoughi, S., Deb, R., & Aral, S. (2018). The Spread of True and False News Online. Science, 359(6380),1146–1151'},{id:"B12",body:'Thota, A., Tilak, P., Ahluwalia, S., &Lohia, N. (2018). Fake News Detection: A Deep Learning Approach. SMU Data Science Review, 1(3), 1–20'},{id:"B13",body:'Yu Liu, Bai Wang, Bin Wu, Suiming Shang, Yunlei Zhang, and Chuan Shi. (2016).Characterizing super-spreading in microblog: An epidemic-based in-formation propagation model. Physica A: Statistical Mechanics and its Applications, 46(3),202-218'},{id:"B14",body:'Jessica, T. Davis., Nicola Perra; Qian Zhang; Yamir Moreno and Alessan-dro Vespignani. (2020). Phase transitions in information spreading on structured Populations. Nature Physics, 1-7='},{id:"B15",body:'Matthew, D. Ancona. (2018). Post-Truth: The New War on Truth and How to Fight Back. London: Ebury Press'},{id:"B16",body:'Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017). Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter, 19(1), 22-36'},{id:"B17",body:'Beck, J. (2017). This article won’t change your mind: The fact on why facts alone can’t fight false beliefs. The Atlantic. Retrieved 12th May 2020 from https://www.theatlantic.com/science/archive/2017/03/this-article-wont-change-yourmind/519093/'},{id:"B18",body:'Derek, Ruths., (2019). The misinformation machine. Science, 363(6425),348-348'},{id:"B19",body:'Hunt, E. (2016). What is fake news? How to spot it and what you can do to stop it. The Guardian, 17'},{id:"B20",body:'Kumar, K. K., &Geethakumari, G. (2014). Detecting misinformation in online social networks using cognitivepsychology. Human-Centric Computing and Information Sciences, 4(1), 1–22. https://doi.org/10.1186/s13673-014-0014-x'},{id:"B21",body:'Bronstein, M., Pennycook, G., Bear, A., Rand, D. G., & Cannon, T. (2019). Belief in fake news is associated with delusionality, dogmatism, religious fundamentalism, and reduced analytic thinking. Journal of Applied Research in Memory and Cognition, 8, 108–117. https://doi.org/10.1016/j.jarmac.2018.09.005'},{id:"B22",body:'Shao, C., Ciampaglia, G. L., Varol, O., Flammini, A., &Menczer, F. (2017). The spread of fake news by social bots. arXiv preprint arXiv:1707.07592, 96-104'},{id:"B23",body:'Jin, Z., Cao, J., Guo, H., Zhang, Y., & Luo, J. (2017). Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs. 25th ACM International Conference on Multimedia, 795 816.https://doi.org/10.1145/3123266.3123454'},{id:"B24",body:'Khattar, D., Goud, J. S., Gupta, M., & Varma, V. (2019). MVAE: Multimodal Variational Autoencoder for Fake News Detection. The World Wide Web Conference. ACM, 2915–2921.https://doi.org/10.1145/3308558.3313552'},{id:"B25",body:'Hwang, C. L., & Lin, M. J. (1987). Lecture Notes in Economics and Mathematical Systems 281. Group Decision Making Under Multiple Criteria'},{id:"B26",body:'Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy sets and systems, 114(1), 1-9'},{id:"B27",body:'Khan, N. Z., Ansari, T. S. A., Siddiquee, A. N., & Khan, Z. A. (2019). Selection of E-learning websites using a novel Proximity Indexed Value (PIV) MCDM method. Journal of Computers in Education, 6(2), 241-256'},{id:"B28",body:'Shu, K., Mahudeswaran, D., Wang, S., Lee, D., & Liu, H. (2018). Fakenewsnet: A data repository with news content, social context and dynamic information for studying fake news on social media. arXivpreprint arXiv:1809.01286'},{id:"B29",body:'Bernal, Paul. (2018). Fakebook: why Facebook makes the fake news problem inevitable. The Northern Ireland legal quarterly. 69. 513-530'},{id:"B30",body:'Worrall, Robin. (2020).Battling the ‘pandemic of misinformation’ retrieved 22nd May 2020 from https://news.harvard.edu/gazette/story/2020/05/social-media-used-to-spread-create-covid-19-falsehoods/'},{id:"B31",body:'Nielsen, Kleis, Rasmus. et al. (2020).Navigating the ‘infodemic’: how people in six countries access and rate news and information about coronavirus retrieved 23rd May 2020 from https://reutersinstitute.politics.ox.ac.uk/infodemic-how-people-six-countries-access-and-rate-news-and-information-about-coronavirus'},{id:"B32",body:'Chowdhury, Archis. (2020). Fake News in the Time of Coronavirus: A BOOM Study retrieved on 17th May 2020 from https://www.boomlive.in/fact-file/fake-news-in-the-time-of-coronavirus-a-boom-study-8008/page-2'},{id:"B33",body:'Travers, Mark. (2020). Facebook Spreads Fake News Faster than Any Other Social Website, According To New Research retrieved 30th May 2020 from https://www.forbes.com/sites/traversmark/2020/03/21/facebook-spreads-fake-news-faster-than-any-other-social-website-according-to-new-research/#164e6a276e1a'},{id:"B34",body:'Sathe Gopal. (2019). A New Study Tells You How Fake News Is Spread on WhatsApp retrieved on 30th May 2020 from https://www.huffingtonpost.in/entry/whatsapp-lynching-fake-news-misinformation study_in_5da89bc9e4b0b5c9be4b067b'},{id:"B35",body:'Mishra, Siddhant. (2020). Breakthrough in Palghar lynching case: Probe reveals WhatsApp rumours on ‘child thieves’ weeks before attack retrieved on 17th May 2020 from https://www.timesnownews.com/india/article/breakthrough-in-palghar-lynching-case-probe-reveals-whatsapp-rumours-on-child-thieves-weeks-before-attack/591570'},{id:"B36",body:'Ghatwai, Milind. (2020). Fake videos behind attack on health team in Indore, 4 booked under NSA retrieved on 17th May 2020 from https://indianexpress.com/article/coronavirus/indore-coronavirus-video-mob-attacks-health-officials-6343475/'},{id:"B37",body:'Sahir. (2019). WhatsApp Usage, Revenue, Market Share and other Statistics (2019) Retrieved on 15th May 2020 from https://www.digitalinformationworld.com/2019/02/whatsapp-facts-stats.html'}],footnotes:[{id:"fn1",explanation:"Orkut was launched in 2008 followed by Facebook, Orkut was ultimately closed down in 2014."},{id:"fn2",explanation:"https://www.statista.com/statistics/376128/facebook-global-user-age-distribution/"}],contributors:[{corresp:null,contributorFullName:"Arshia Kaul",address:null,affiliation:'
'}],corrections:null},book:{id:"10752",type:"book",title:"Communication Management",subtitle:null,fullTitle:"Communication Management",slug:"communication-management",publishedDate:"January 7th 2022",bookSignature:"František Pollák, Jakub Soviar and Roman Vavrek",coverURL:"https://cdn.intechopen.com/books/images_new/10752.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-83969-512-4",printIsbn:"978-1-83969-511-7",pdfIsbn:"978-1-83969-513-1",isAvailableForWebshopOrdering:!0,editors:[{id:"304707",title:"Associate Prof.",name:"František",middleName:null,surname:"Pollák",slug:"frantisek-pollak",fullName:"František Pollák"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},profile:{item:{id:"194382",title:"Ms.",name:"Hafzaliza Erny",middleName:null,surname:"Zainal Abidin",email:"hafzalizaerny@yahoo.com",fullName:"Hafzaliza Erny Zainal Abidin",slug:"hafzaliza-erny-zainal-abidin",position:null,biography:null,institutionString:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",totalCites:0,totalChapterViews:"0",outsideEditionCount:0,totalAuthoredChapters:"1",totalEditedBooks:"0",personalWebsiteURL:null,twitterURL:null,linkedinURL:null,institution:null},booksEdited:[],chaptersAuthored:[{id:"52604",title:"Interdigitated MEMS Supercapacitor for Powering Heart Pacemaker",slug:"interdigitated-mems-supercapacitor-for-powering-heart-pacemaker",abstract:"Power MEMS can be defined as microelectromechanical systems for power generation and energy conversion. Energy harvesting has become an increasingly popular option for powering electronic devices as a long-lasting power source. Energy scavenging is defined as the process by which the energy is derived such as vibration, solar, wind, and thermal. Energy harvesting from the environment can prolong the life cycle and reduce the maintenance costs of electronic devices. Among the various sources of energy storage, Among the various of energy storage, supercapacitor has recently gained much interest in fields such as bioMEMS, biomedical implants and power electronic devices due to its advantages such as high power density, rapid charge and discharge and unlimited number of recharge cycles. In biomedical and bioMEMS systems, an energy storage device is needed to power other active biomedical devices within the system. For implantable devices such as a heart pacemaker, the power requirement is in the range of 30–100 μW. Microsupercapacitors play an important role in energy harvesting system, such as collecting energy from ambient energy sources. Human body is very resourceful in generating micropower in the form of heat dissipation, deformation of elastic tissue, and motion. Due to the advantages of MEMS energy harvesting system, the system can be use widely for biomedical implant devices, such as heart pacemakers and hearing aids, and can be used for a long time and without the need for battery replacement. In this work, planar and double-stacked interdigital electrode supercapacitor designs were modeled using Coventorware software. From simulation, it is observed that for planar structure, the specific capacitance is 0.22 mF/cm−2, and for double-stacked structure specific capacitance can be increased to 0.48 mF/cm−2. In terms of specific power, the planar structure produces 0.99 mW/cm−2, and the double-stacked structure produces 2.18 mW/cm−2. These results highlight the superiority of the double-stacked MEMS interdigital supercapacitor design compared with its planar counterpart in terms of charging capacity and electrical performance, thus making it favorable for powering heart pacemaker.",signatures:"Hafzaliza Erny Zainal Abidin, Azrul Azlan Hamzah, Jumril Yunas,\nMohd Ambri Mohamed and Burhanuddin Yeop Majlis",authors:[{id:"187754",title:"Dr.",name:"Azrul",surname:"Hamzah",fullName:"Azrul Hamzah",slug:"azrul-hamzah",email:"azlanhamzah@ukm.edu.my"},{id:"194382",title:"Ms.",name:"Hafzaliza Erny",surname:"Zainal Abidin",fullName:"Hafzaliza Erny Zainal Abidin",slug:"hafzaliza-erny-zainal-abidin",email:"hafzalizaerny@yahoo.com"},{id:"194384",title:"Dr.",name:"Jumril",surname:"Yunas",fullName:"Jumril Yunas",slug:"jumril-yunas",email:"jumrilyunas@ukm.edu.my"},{id:"194385",title:"Prof.",name:"Burhanuddin Yeop",surname:"Majlis",fullName:"Burhanuddin Yeop Majlis",slug:"burhanuddin-yeop-majlis",email:"burhan@ukm.edu.my"},{id:"194754",title:"Dr.",name:"Mohd Ambri",surname:"Mohamed",fullName:"Mohd Ambri Mohamed",slug:"mohd-ambri-mohamed",email:"ambri@ukm.edu.my"}],book:{id:"5374",title:"Supercapacitor Design and Applications",slug:"supercapacitor-design-and-applications",productType:{id:"1",title:"Edited Volume"}}}],collaborators:[{id:"186686",title:"Associate Prof.",name:"Jian-Gan",surname:"Wang",slug:"jian-gan-wang",fullName:"Jian-Gan Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Northwestern Polytechnical University",institutionURL:null,country:{name:"China"}}},{id:"186756",title:"Dr.",name:"Dewei",surname:"Chu",slug:"dewei-chu",fullName:"Dewei Chu",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"UNSW Sydney",institutionURL:null,country:{name:"Australia"}}},{id:"186903",title:"Dr.",name:"Fadhel",surname:"El Kamel",slug:"fadhel-el-kamel",fullName:"Fadhel El Kamel",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Hôpital Sahloul",institutionURL:null,country:{name:"Tunisia"}}},{id:"187155",title:"Dr.",name:"Kang",surname:"Sun",slug:"kang-sun",fullName:"Kang Sun",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"187257",title:"Dr.",name:"Yue",surname:"Zhou",slug:"yue-zhou",fullName:"Yue Zhou",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Pennsylvania State University",institutionURL:null,country:{name:"United States of America"}}},{id:"187443",title:"Prof.",name:"Masatoshi",surname:"Uno",slug:"masatoshi-uno",fullName:"Masatoshi Uno",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/187443/images/7037_n.jpg",biography:null,institutionString:null,institution:{name:"Ibaraki University",institutionURL:null,country:{name:"Japan"}}},{id:"187754",title:"Dr.",name:"Azrul",surname:"Hamzah",slug:"azrul-hamzah",fullName:"Azrul Hamzah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National University of Malaysia",institutionURL:null,country:{name:"Malaysia"}}},{id:"188176",title:"Dr.",name:"Hengzhao",surname:"Yang",slug:"hengzhao-yang",fullName:"Hengzhao Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"California State University, Long Beach",institutionURL:null,country:{name:"United States of America"}}},{id:"188203",title:"Dr.",name:"Ying",surname:"Zhang",slug:"ying-zhang",fullName:"Ying Zhang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"188355",title:"Dr.",name:"Can",surname:"Liu",slug:"can-liu",fullName:"Can Liu",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Macau",institutionURL:null,country:{name:"Macau"}}}]},generic:{page:{slug:"authorship-policy",title:"Authorship Policy",intro:'
',metaTitle:"Authorship Policy",metaDescription:"IN TECH's Authorship Policy is based on ICMJE criteria for authorship. In order to be identified as an Author, one must:",metaKeywords:null,canonicalURL:"/page/authorship-policy",contentRaw:'[{"type":"htmlEditorComponent","content":"
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Substantially contribute to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work
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All contributors who meet these criteria are listed as Authors. Their exact contributions should be described in the manuscript at the time of submission.
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Conversely, all contributors who do not meet these criteria should be listed in the Acknowledgments section of the manuscript, along with a short description of their specific contributions.
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If it is felt necessary to make changes to the list of Authors after a manuscript has been submitted or published, it is the responsibility of the Author concerned to provide a valid reason to amend the published list. Additionally, all listed Authors must verify and approve the proposed changes in order for any amendments to be made.
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Authors are responsible for ensuring all addresses and emails provided are correct. Under affiliation(s) all Authors should indicate where the research was conducted. Please note that no changes to the affiliation(s) can be made after the chapter has been published.
Substantially contribute to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work
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Participate in drafting or revising the work
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Approve the final version of the work to be published.
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All contributors who meet these criteria are listed as Authors. Their exact contributions should be described in the manuscript at the time of submission.
\n\n
Conversely, all contributors who do not meet these criteria should be listed in the Acknowledgments section of the manuscript, along with a short description of their specific contributions.
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CHANGES IN AUTHORSHIP
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If it is felt necessary to make changes to the list of Authors after a manuscript has been submitted or published, it is the responsibility of the Author concerned to provide a valid reason to amend the published list. Additionally, all listed Authors must verify and approve the proposed changes in order for any amendments to be made.
\n\n
AFFILIATION
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Authors are responsible for ensuring all addresses and emails provided are correct. Under affiliation(s) all Authors should indicate where the research was conducted. Please note that no changes to the affiliation(s) can be made after the chapter has been published.
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Policy last updated: 2017-05-29
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Shohel"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},subject:{topic:{id:"1297",title:"Machine Learning",slug:"robot-control-machine-learning",parent:{id:"257",title:"Robot Control",slug:"robot-control"},numberOfBooks:2,numberOfSeries:0,numberOfAuthorsAndEditors:65,numberOfWosCitations:43,numberOfCrossrefCitations:46,numberOfDimensionsCitations:66,videoUrl:null,fallbackUrl:null,description:null},booksByTopicFilter:{topicId:"1297",sort:"-publishedDate",limit:12,offset:0},booksByTopicCollection:[{type:"book",id:"5238",title:"Automation and Control Trends",subtitle:null,isOpenForSubmission:!1,hash:"d234bd6ef276fb1abcaca85c8de363f4",slug:"automation-and-control-trends",bookSignature:"Pedro Ponce, Arturo Molina Gutierrez and Luis M. 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Bouallègue, J. Haggège and M. Benrejeb",authors:[{id:"141915",title:"Dr.",name:"Soufiene",middleName:null,surname:"Bouallegue",slug:"soufiene-bouallegue",fullName:"Soufiene Bouallegue"}]},{id:"39435",doi:"10.5772/48529",title:"Fuzzy Control Systems: LMI-Based Design",slug:"fuzzy-control-systems-lmi-based-design",totalDownloads:4663,totalCrossrefCites:6,totalDimensionsCites:7,abstract:null,book:{id:"2229",slug:"fuzzy-controllers-recent-advances-in-theory-and-applications",title:"Fuzzy Controllers",fullTitle:"Fuzzy Controllers - Recent Advances in Theory and Applications"},signatures:"Morteza Seidi, Marzieh Hajiaghamemar and Bruce Segee",authors:[{id:"145408",title:"Mr.",name:"Morteza",middleName:null,surname:"Seidi",slug:"morteza-seidi",fullName:"Morteza Seidi"},{id:"147329",title:"Ms.",name:"Marzieh",middleName:null,surname:"Hajiaghamemar",slug:"marzieh-hajiaghamemar",fullName:"Marzieh Hajiaghamemar"},{id:"155198",title:"Dr.",name:"Bruce",middleName:null,surname:"Segee",slug:"bruce-segee",fullName:"Bruce Segee"}]},{id:"39429",doi:"10.5772/47798",title:"A Type-2 Fuzzy Model Based on Three Dimensional Membership Functions for Smart Thresholding in Control Systems",slug:"a-type-2-fuzzy-model-based-on-three-dimensional-membership-functions-for-smart-thresholding-in-contr",totalDownloads:3329,totalCrossrefCites:2,totalDimensionsCites:6,abstract:null,book:{id:"2229",slug:"fuzzy-controllers-recent-advances-in-theory-and-applications",title:"Fuzzy Controllers",fullTitle:"Fuzzy Controllers - Recent Advances in Theory and Applications"},signatures:"M.H. Fazel Zarandi, Fereidoon Moghadas Nejad and H. Zakeri",authors:[{id:"142009",title:"Prof.",name:"Mohammad Hossein",middleName:null,surname:"Fazel Zarandi",slug:"mohammad-hossein-fazel-zarandi",fullName:"Mohammad Hossein Fazel Zarandi"},{id:"160785",title:"Ph.D.",name:"H",middleName:null,surname:"Zakeri",slug:"h-zakeri",fullName:"H Zakeri"},{id:"160872",title:"Prof.",name:"Freeidon",middleName:null,surname:"Moghadas Nejad",slug:"freeidon-moghadas-nejad",fullName:"Freeidon Moghadas Nejad"}]},{id:"52178",doi:"10.5772/64951",title:"Adaptive Building Envelope: An Integral Approach to Indoor Environment Control in Buildings",slug:"adaptive-building-envelope-an-integral-approach-to-indoor-environment-control-in-buildings",totalDownloads:2091,totalCrossrefCites:5,totalDimensionsCites:4,abstract:"The problem of energy consumption of buildings is complex and multidimensional, as it is a cross section of building envelope performance, indoor environmental conditions and user demands and preferences. In order to fulfil the EU goal stated in the 2020 climate and energy package and beyond, the implementation of high-performance buildings is crucial. Part of the solution is properly designed, flexible and adequately controlled building envelope that can contribute to reduced energy consumption and to increased occupancy comfort. In the presented chapter first, a structured treatment of the indoor environment formation is proposed that can be used in order to define appropriate fields of interventions when designing building automation systems. Furthermore, interaction between adaptive building envelope elements, indoor and exterior environment is discussed and elaborated. Second, the conventional and artificial intelligence control approaches used in building automation are discussed and commented, whereas advantages and disadvantages of each group are discussed. At the end, an example of building automation system designed on the principles of a holistic treatment of indoor environment in buildings is presented. The discussed system was designed at the Faculty of Civil and Geodetic Engineering using a combination of conventional and artificial intelligence control methods.",book:{id:"5238",slug:"automation-and-control-trends",title:"Automation and Control Trends",fullTitle:"Automation and Control Trends"},signatures:"Mitja Košir",authors:[{id:"182476",title:"Dr.",name:"Mitja",middleName:null,surname:"Košir",slug:"mitja-kosir",fullName:"Mitja Košir"}]}],mostDownloadedChaptersLast30Days:[{id:"39423",title:"Output Tracking Control for Fuzzy Systems via Static-Output Feedback Design",slug:"output-tracking-control-for-fuzzy-systems-via-static-output-feedback-design",totalDownloads:2310,totalCrossrefCites:1,totalDimensionsCites:2,abstract:null,book:{id:"2229",slug:"fuzzy-controllers-recent-advances-in-theory-and-applications",title:"Fuzzy Controllers",fullTitle:"Fuzzy Controllers - Recent Advances in Theory and Applications"},signatures:"Meriem Nachidi and Ahmed El Hajjaji",authors:[{id:"25659",title:"Prof.",name:"Ahmed",middleName:null,surname:"El Hajjaji",slug:"ahmed-el-hajjaji",fullName:"Ahmed El Hajjaji"},{id:"141360",title:"Dr.",name:"Meriem",middleName:null,surname:"Nachidi",slug:"meriem-nachidi",fullName:"Meriem Nachidi"}]},{id:"51207",title:"Human Movement Control",slug:"human-movement-control",totalDownloads:2103,totalCrossrefCites:0,totalDimensionsCites:2,abstract:"Control theory is used to design automatic systems, which are able to maintain a desired behaviour despite of the disturbances. It is present in different machines we use every day; in fact, technical systems in our homes and all the industries are hard to imagine today without these concepts. Moreover, the same theories can be used for modelling life processes as a collection of inputs, outputs, plants and control loops. Feedback is one of the main concepts behind control; in particular, several examples of physiological control mechanisms for regulating life aspects can be found in the human anatomy, for example, blood pressure, cholesterol levels, body movements, the equilibrium, etc. Those processes can be damaged by the aging effects, diseases, accidents or when the mechanism has been broken and cannot be recovered naturally; consequently, it will be required external assistance. A relative new field in control theory is related with developing technology for helping with physiological and medicals problems. However, in comparison with machines, those physiological processes are highly nonlinear, with delays and slow responses. Another problem is when human becomes the operators using their capacities of decision making to close the control loop, as they are prone to errors and mistakes. For those reasons, the biomedical system needs to be carefully designed and several aspects have to be considered. This chapter gives a small review of some internal and external control processes within the human body and discusses how to interact with them for designing biomedical devices. Under this design scheme, a practical application of a smart electric wheelchair for assisting persons with strong disabilities is presented. These assistive robotic systems are in close contact with the user, and thus, it is determinant to have a user-friendly relation between the human and the interface. Therefore, intuitive interfaces were included in the design and an intelligent navigation assistant to guarantee a collision-free path.",book:{id:"5238",slug:"automation-and-control-trends",title:"Automation and Control Trends",fullTitle:"Automation and Control Trends"},signatures:"David Balderas and Mario Rojas",authors:[{id:"183076",title:"M.Sc.",name:"David",middleName:null,surname:"Balderas Silva",slug:"david-balderas-silva",fullName:"David Balderas Silva"},{id:"184877",title:"MSc.",name:"Mario",middleName:null,surname:"Rojas",slug:"mario-rojas",fullName:"Mario Rojas"}]},{id:"51070",title:"Fuzzy PD Controller in NAO System's Platform",slug:"fuzzy-pd-controller-in-nao-system-s-platform",totalDownloads:1565,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"Humanoid robotic platforms rarely achieve the desire trajectory because of the deviation generated during the robot walking. This problem is due to different circumstances such as robot manufacturing, wear and tear of mechanic parts, or variations of floor flatness. Currently, one of the humanoid robots on the market is the robotic platform developed by Aldebaran Robotics called NAO robot, and it is used for different purposes where the robot needs to navigate into controlled spaces. NAO presents the issue of deviation during walking; therefore, a Fuzzy PD Controller is developed and implemented for this platform to reduce the orientation error and to ensure reliability during navigation. Inertial sensors are used to get the orientation reference and for feedback of the closed-loop control. Consequently, a robust control was implemented and tested in different conditions of floor and velocity during the robot’s navigation such as robot races and maze resolution. Experimental results show that fuzzy controller achieves significant improvements in the trajectories of NAO.",book:{id:"5238",slug:"automation-and-control-trends",title:"Automation and Control Trends",fullTitle:"Automation and Control Trends"},signatures:"Edgar Omar López‐Caudana and César Daniel González Gutiérrez",authors:[{id:"26464",title:"Dr.",name:"Edgar",middleName:"Omar",surname:"Lopez-Caudana",slug:"edgar-lopez-caudana",fullName:"Edgar Lopez-Caudana"},{id:"185936",title:"Mr.",name:"César Daniel",middleName:null,surname:"González Gutiérrez",slug:"cesar-daniel-gonzalez-gutierrez",fullName:"César Daniel González Gutiérrez"}]},{id:"51186",title:"Aircraft Landing Control Using the H-inf Control and the Dynamic Inversion Technique",slug:"aircraft-landing-control-using-the-h-inf-control-and-the-dynamic-inversion-technique",totalDownloads:1604,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"The chapter presents the automatic control of aircraft during landing, taking into account the sensor errors and the wind shears. Both planes—longitudinal and lateral-directional—are treated; the new obtained automatic landing system (ALS) will consists of two subsystems—the first one controls aircraft motion in longitudinal plane, while the second one is for the control of aircraft motion in lateral-directional plane. These two systems can be treated separately, but in the same time, these can be put together to control all the parameters which interfere in the dynamics of aircraft landing. The two new ALSs are designed by using the H-inf control, the dynamic inversion, optimal observers, and reference models. To validate the new obtained ALS, one uses the dynamics associated to the landing of a Boeing 747, software implements the theoretical results and analyzes the accuracy of the results and the precision standards' achievement with respect to the requirements of the Federal Aviation Administration (FAA).",book:{id:"5238",slug:"automation-and-control-trends",title:"Automation and Control Trends",fullTitle:"Automation and Control Trends"},signatures:"Romulus Lungu and Mihai Lungu",authors:[{id:"181904",title:"Prof.",name:"Romulus",middleName:null,surname:"Lungu",slug:"romulus-lungu",fullName:"Romulus Lungu"}]},{id:"51936",title:"Models for the Reliability Analysis of Digital Instrumentation and Control Systems for Nuclear Power Plants",slug:"models-for-the-reliability-analysis-of-digital-instrumentation-and-control-systems-for-nuclear-power",totalDownloads:1779,totalCrossrefCites:3,totalDimensionsCites:3,abstract:"The objective of this chapter is to discuss two approaches for reliability analysis of digital instrumentation and control systems in nuclear power plants taking into account the regulatory side. Dynamic Flowgraph Methodology (DFM) and Markov/Cell-to-Cell Mapping Technique (CCMT) are discussed and case studies developed are presented. These case studies involve simplified control systems for a steam generator and a pressurizer of a Pressurized Water Reactor (PWR) plant for the purpose of evaluating each method. Advantages and limitations of each approach are addressed. For the DFM approach, three concerns in the literature are addressed: modeling of the system itself, incorporation of the methodology results into existing Probabilistic Safety Assessments (PSA), and identification of software failures. The Markov/CCMT, which has been used in dynamic probabilistic safety assessments, is approached by means of a simplified digitally controlled water volume control system. The Markov/CCMT methodology results in detailed data of the system reliability behavior in relation to time. However, it demands a higher computational effort than usual as the complexity (i.e., number of components and failure states) of the system increases. As a regulatory research conclusion, the methodologies presented can be used on PSA risk informed assessment, contributing to the regulatory side.",book:{id:"5238",slug:"automation-and-control-trends",title:"Automation and Control Trends",fullTitle:"Automation and Control Trends"},signatures:"Jonathan M. O. Pinto, Ian B. Gomes, Pedro L. C. Saldanha, Eustério\nB. Furieri and Paulo F. F. e Melo",authors:[{id:"26628",title:"Prof.",name:"Paulo",middleName:null,surname:"Frutuoso e Melo",slug:"paulo-frutuoso-e-melo",fullName:"Paulo Frutuoso e Melo"},{id:"30429",title:"Dr.",name:"Pedro Luiz da C.",middleName:null,surname:"Saldanha",slug:"pedro-luiz-da-c.-saldanha",fullName:"Pedro Luiz da C. 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\r\n\tScientists have long researched to understand the environment and man’s place in it. The search for this knowledge grows in importance as rapid increases in population and economic development intensify humans’ stresses on ecosystems. Fortunately, rapid increases in multiple scientific areas are advancing our understanding of environmental sciences. Breakthroughs in computing, molecular biology, ecology, and sustainability science are enhancing our ability to utilize environmental sciences to address real-world problems. \r\n\tThe four topics of this book series - Pollution; Environmental Resilience and Management; Ecosystems and Biodiversity; and Water Science - will address important areas of advancement in the environmental sciences. They will represent an excellent initial grouping of published works on these critical topics.
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He also has an honorary appointment to serve as a Collaborative Professor at Kanazawa University, Japan, from Mar 2015 to the present. \nFormerly, Dr. Rahman was a faculty member of the University of Chittagong, Bangladesh, affiliated with the Department of Chemistry (Oct 2002 to Mar 2012) and the Department of Applied Chemistry and Chemical Engineering (Mar 2012 to Sep 2015). Dr. Rahman was also adjunctly attached with Kanazawa University, Japan (Visiting Research Professor, Dec 2014 to Mar 2015; JSPS Postdoctoral Research Fellow, Apr 2012 to Mar 2014), and Tokyo Institute of Technology, Japan (TokyoTech-UNESCO Research Fellow, Oct 2004–Sep 2005). \nHe received his Ph.D. degree in Environmental Analytical Chemistry from Kanazawa University, Japan (2011). He also achieved a Diploma in Environment from the Tokyo Institute of Technology, Japan (2005). Besides, he has an M.Sc. degree in Applied Chemistry and a B.Sc. degree in Chemistry, all from the University of Chittagong, Bangladesh. \nDr. Rahman’s research interest includes the study of the fate and behavior of environmental pollutants in the biosphere; design of low energy and low burden environmental improvement (remediation) technology; implementation of sustainable waste management practices for treatment, handling, reuse, and ultimate residual disposition of solid wastes; nature and type of interactions in organic liquid mixtures for process engineering design applications.",institutionString:null,institution:{name:"Fukushima University",institutionURL:null,country:{name:"Japan"}}},editorTwo:{id:"201020",title:"Dr.",name:"Zinnat Ara",middleName:null,surname:"Begum",slug:"zinnat-ara-begum",fullName:"Zinnat Ara Begum",profilePictureURL:"https://mts.intechopen.com/storage/users/201020/images/system/201020.jpeg",biography:"Zinnat A. 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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. 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He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. 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Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. 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