HPLC analysis of catechin compositions in six green tea extract solutions (2 g%, w/v).
\r\n\tTo sum up, there are numerous engineering applications of diamond which are yet to be realized and this book will address some of the mentioned and hopefully open some new topics.
\r\n\t
Brewed tea has been the most widely consumed beverage throughout history. Attractively, this is attributed to its present taste, aromatic odor and healthful effects to the human body. Tea (Camellia sinensis) is used for the production of green tea, oolong tea and black tea, depending on the fermentation process. Without fermentation, green tea can be made from fresh tea shoots at a high temperature, thereby inactivating the oxidizing enzymes such as polyphenol oxidase (PPO) and leaving the intact polyphenols. Many ingredients persisting in green tea products are flavonols, flavanols, chlorogenic acid, coumarylquinic acid, theogallin (3-galloylquinic acid), vitamin P (flavonoids), alkaloids, caffeine, theophylline, theobromine, theanine, volatile compounds, fluoride, minerals (e.g. aluminium and magnesium), trace elements and other unidentified compounds. During fermentation, these polyphenolic compounds undergo PPO-catalyzed oxidative polymerization giving rise to the formation of theaflavins and thearubigins which are the major antioxidants in black tea [1]. Besides being a world-wide, well-known beverage, green tea has many benefits for health, including anti-oxidative, free radical scavenging, iron-chelating, anti-hyperglycemic and anti-diabetic, weight-lowering, anti-aging, neuro-protective and rescue, thrombosis-inhibitory, anti-inflammatory, exercise-endurating, hepatoprotective, hepatic phase II enzyme activity-inducing, cardioprotective, neoangioprotective, anti-mutagenic, anti-carcinogenic and cancer-preventive, anti-microbial, as well as immunomodulatory activities. Tea products generally provide refreshment and diuretic benefits, as well as contributing to feelings of alertness; however; green tea extraordinarily exhibits such biological and pharmacological properties depending on several specific active phytochemical constituents; particularly catechins. Nowadays, crude extracts, purified catechin fractions and synthetic catechin derivatives of green tea are applicable in alternative and complementary medicines for the prevention, treatment and co-treatment of many diseases and disorders.
Tea has traditionally been cultivated across four continents in the manufacturing of a refreshing drink. The tea products that are available are directly related to the process used at the origin, and can be classified as black tea, green tea, yellow tea, red tea, green pressed tea, as well as instant tea and tea dyes [1]. Green tea is manufactured by using conventional and modified methods. One of these methods involves a 2-3-day process of to drying fresh tea leaves and then rolling the dry leaves with a commercial machine. Alternatively, another method involves the very rapid process of baking the tea leaves in a house-hold microwave oven (800 watt, 3 minutes) at a working temperature of 115 ºC. This process will shock the persisting PPO enzyme and result in higher catechins content [2]. A typical green tea beverage is normally prepared at a proportion of 1 g dry weight of tea leaves in 100 ml of hot water in a 3-minute brew (an approximate temperature of 80 ºC). This brew usually contains 250 – 350 mg tea solids, 30 – 42% catechins (74 mg) and 3–6% caffeine [3]. HPLC analysis shows the green tea extract (GTE) is comprised of at least six major catechins, including (-)-epicatechin (EC), (-)-epicatechin 3-gallate (ECG), (-)-epigallocatechin (EGC), (-)-epigallocatechin 3-gallate (EGCG), (+)-catechin (C) and (-)-gallocatechin (GC), of which EGCG is the major isomer followed by ECG, EGC and EC (Figure 1) [4]. Gallic acid (GA) is derivatized to one of the hydroxyl groups of the catechins, which has been directly attributed to the biological activities of the catechin species.
Chemical structures of catechins in green tea (Redrawn from [4])
Moreover, tea products have different amounts and compositions of catechins, which is possibly due to biodiversity, different processing methods, different planting areas and different varieties of tea strains. For instance, Khokhar and colleagues reported that 1 g dry weight of tea product contained 48.4 mg total catechins (9.1 mg EGC, 7.9 mg EC, 22.9 mg EGCG, and 8.5 mg ECG) for Ceylon (NL) black tea; 5.6 mg total catechins (<0.5 mg EGC, 3.1 mg EC, 1.8 mg EGCG, and 0.8 mg ECG) for Yule (India) black tea; 7.5 mg total catechins (<0.5 mg EGC, 4.0 mg EC, 2.6 mg EGCG, and 1.0 mg ECG) for PG tips (UK) black tea;5.15 mg total catechins (16.3 mg EGC, 4.7 mg EC, 26.3 mg EGCG, and 4.4 mg ECG) for Chinese green tea; 84.9 mg total catechins (28.7 mg EGC, 9.4 mg EC, 40.8 mg EGCG, and 5.9 mg ECG) for Japanese green tea; and 21.1 mg total catechins (7.7 mg EGC, 1.7 mg EC, 11.5 mg EGCG, and 0.5 mg ECG) for Chinese oolong tea [5]. EGCG has so far received the most attention because it represents approximately 59% catechin content, EGC at approximately 19%, ECG at approximately 13.6%, and EC at approximately 6.4% of the total catechin content [6]. Green tea also contains other phenolic acids, such as chlorogenic acid and caffeic acid, as well as other flavonols, such as kaempferol, myricetin and quercetin [7].
Green tea catechins have proven to be quite stable in water that is room temperature; however, they can be destroyed at a rate of 20% under brewing at 98 ºC for 7 hours and epimerized to other derivatives (e.g. EGCG → GCG) under autoclaving at 120 ºC for 20 minutes [8]. Fresh tea leaves are unusually rich in polyphenolic catechins that may constitute up to 30% of the dry leaf weight [9]. As a result of the immediate inactivation of PPO enzyme, the compositions of catechins in green tea are very similar to those of the fresh tea leaves. Various quinines, which are subsequently produced by the enzymatic oxidations, undergo condensation reactions to produce bis-flavanols, theaflavins, epitheaflavic acids and thearubigens. These compounds impart the characteristic taste and color properties of black and oolong tea [10].
In our Thai green tea, EGCG was found to be the most abundant catechin (at about 60% of the total catechin content), followed by EGG, ECG, and EC, respectively and the assay yield of the total green tea catechin content was found to be 26-29 g/100 g dry tea leaves (Table 1) [2]. In comparison, Taiwan green tea (1 g dry weight) contained 93.3 mg total catechin content, followed by 43.2 mg EGCG, 33.6 mg ECG, 0.7 mg C, 3.3 mg EC and 2.7 mg ECG [11].
\n\t\t\t\tAmounts\n\t\t\t | \n\t\t\t\n\t\t\t\tEGCG\n\t\t\t | \n\t\t\t\n\t\t\t\tEGC\n\t\t\t | \n\t\t\t\n\t\t\t\tECG\n\t\t\t | \n\t\t\t\n\t\t\t\tEC\n\t\t\t | \n\t\t\t\n\t\t\t\tTotal catechins\n\t\t\t | \n\t\t
mg/g dry weight | \n\t\t\t169.4±7.2 | \n\t\t\t60.1±6.2 | \n\t\t\t19.3±2.6 | \n\t\t\t16.3±5.6 | \n\t\t\t279.8±15.2 | \n\t\t
% (w/w) | \n\t\t\t60.6±2.2 | \n\t\t\t21.5±1.4 | \n\t\t\t6.9±1.0 | \n\t\t\t5.9±2.1 | \n\t\t\t100 | \n\t\t
HPLC analysis of catechin compositions in six green tea extract solutions (2 g%, w/v).
In general, an antioxidant refers to any substance capable of preventing the oxidation catalyzed by reactive oxygen species (ROS)/reactive nitrogen species (RNS). Thus, an antioxidant that protects against iron toxicity is a substance that can: i) chelate iron and prevent the reaction with oxygen or peroxides; ii) chelate iron and maintain it in a redox state that makes iron unable to reduce molecular oxygen; iii) trap already formed radicals, which is a putative action of any substance that can scavenge free radicals in biological systems, regardless of whether they have originated from iron-dependent reactions or not. Not only can natural products chelate iron, but also synthetic compounds are capable of chelating iron in vivo, thereby limiting its participation in free radical reactions. Thus, iron chelators also serve as antioxidants by suppressing iron-mediated oxidation in biological systems. Surprisingly, thiol compounds (e.g. glutathione) that are synthesised by mammals can afford significant antioxidant protection. This protection is related to the ability of glutathione to trap radicals, reduce peroxides, as well asits ability to work to maintain the redox state of the cells [12].
The potential of green tea to prevent or ameliorate chronic diseases is currently the subject of considerable scientific investigations. Although a number of mechanisms have been proposed for their beneficial effects, the radical scavenging and antioxidant properties of green tea catechins are frequently cited as important contributors. Emerging evidence has also shown that catechins and their metabolites possessmany additional mechanisms of action [13] by affecting numerous sites, potentiating endogenous antioxidants and eliciting dual actions during oxidative stress. Much of the evidence supporting an antioxidant function for green tea catechins is derived from assays of their antioxidant activity in vitro. However, the evidence that green tea catechins are acting either directly or indirectly as antioxidants in vivo is limited [14]
Green tea catechins stoichiometrically bind ferric ion to form a redox-inactive iron-phenolic complex [15, 16] and potentially protect vital biomolecules from oxidative damage. Incredibly, the catechins could be capable of chelating excessive redox iron in iron overloaded diseases, such as thalassemia, and play important preventive or/and protective roles in this unpleasant condition [2, 17-19]. The phytochemical compounds therefore play a double role in reducing the rate of oxidation because they can participate in: i) iron chelation [19], and ii) trapping radicals [2, 20]. Catechins can protect culture cells from iron-mediated damage [21, 22], ameliorate iron accumulation [17] and inhibit hepatic iron-induced lipid oxidation [23], and also play a dual effect in decreasing labile plasma iron (LPI) in iron-loaded rats [18]. Animal studies offer a unique opportunity to assess the contribution of green tea administration to the physiological effects on different models of oxidative-related diseases. In a combination of free-radical scavenging activity with iron-chelating properties, green tea may have a capacity of chelating excess iron in iron-overloaded conditions and play important preventive and/or protective roles in this unpleasant condition.
Like the deferiprone (DFP) treatment, oral administrations of GTE and EGCG significantly lowered levels of plasma non-transferrin bound iron (NTBI) and LPI in wild-type (WT), heterozygous β-globin gene knockout (BKO) thalassemic and double heterozygous β-globin gene knockout carrying human βE gene (DH) mice (strain C57BL/6J) with iron overload, when compared to the DW group (Table 2) (Sakaewan Ounjaijean and Somdet Srichairatanakool, unpublished data). Elimination of these two toxic irons by green tea extract and EGCG fraction would relieve redox iron-induced oxidative stress and tissue damage in the body. GTE treatment efficiently depleted plasma malondialdehyde (MDA) concentrations in the iron-loaded mice (approximately 50% in WT, 30% in BKO and 40% in DH mice), whereas EGCG treatment caused significantly lower plasma MDA levels (approximately 30% in all the mice), suggesting that GTE and EGCG are strong antioxidants and exert potent anti-plasma lipid peroxidation. Consistently, the GTE and EGCG increased levels of reduced glutathione (GSH) in the plasma of all the mice despite under iron overload. Thus, it can be said that green tea catechins, particularly EGCG, chelate the redox irons and consequently inhibit the iron-catalyzed lipid peroxidation reactions in plasma lipids, as well as membrane phospholipids, resulting in an improvement of a powerful antioxidants as reduced glutathione is reduced in the plasma compartment.
\n\t\t\t\tMice\n\t\t\t | \n\t\t\t\n\t\t\t\tN diet\n\t\t\t | \n\t\t\t\n\t\t\t\tFe diet (0.2% ferrocene, w/w)\n\t\t\t | \n\t\t||||
+DW | \n\t\t\t+DW | \n\t\t\t+GTE (90 mg/kg/day) | \n\t\t\t+EGCG (50 mg/kg/day) | \n\t\t\t+DFP (50 mg/kg/day) | \n\t\t||
\n\t\t\t\tPlasma NTBI concentrations (μM)\n\t\t\t | \n\t\t||||||
WT (n = 24) | \n\t\t\t-0.27±0.23 | \n\t\t\t11.17±0.26*\n\t\t\t | \n\t\t\t6.83±1.49†\n\t\t\t | \n\t\t\t6.80±1.75†\n\t\t\t | \n\t\t\t6.81±2.23†\n\t\t\t | \n\t\t|
BKO (n = 16) | \n\t\t\t0.34±0.26 | \n\t\t\t19.78±1.36*\n\t\t\t | \n\t\t\t10.20±1.92†\n\t\t\t | \n\t\t\t10.66±1.60†\n\t\t\t | \n\t\t\t11.75±1.21†\n\t\t\t | \n\t\t|
DH (n = 10) | \n\t\t\t-0.07±0.11 | \n\t\t\t13.41±1.84*\n\t\t\t | \n\t\t\t7.74±1.38†\n\t\t\t | \n\t\t\t7.81±0.71†\n\t\t\t | \n\t\t\t7.83±1.41†\n\t\t\t | \n\t\t|
\n\t\t\t | \n\t\t\t\tLPI concentrations (μM)\n\t\t\t | \n\t\t|||||
WT (n = 24) | \n\t\t\t-2.49±1.00 | \n\t\t\t1.19±0.42*\n\t\t\t | \n\t\t\t-2.26±1.98†\n\t\t\t | \n\t\t\t-2.47±1.25†\n\t\t\t | \n\t\t\t-2.01±2.76†\n\t\t\t | \n\t\t|
BKO (n = 16) | \n\t\t\t0.87±0.22 | \n\t\t\t2.30±1.08*\n\t\t\t | \n\t\t\t0.30±2.57†\n\t\t\t | \n\t\t\t-0.21±1.37†\n\t\t\t | \n\t\t\t0.35±1.05†\n\t\t\t | \n\t\t|
DH (n = 10) | \n\t\t\t-0.15±0.52 | \n\t\t\t0.81±0.26*\n\t\t\t | \n\t\t\t0.26±0.61†\n\t\t\t | \n\t\t\t0.47±0.63 | \n\t\t\t0.34±0.70 | \n\t\t|
\n\t\t\t | \n\t\t\t\tPlasma MDA concentrations (μM)\n\t\t\t | \n\t\t|||||
WT (n = 24) | \n\t\t\t13.39±5.10 | \n\t\t\t39.97±8.67*\n\t\t\t | \n\t\t\t23.26±8.30†\n\t\t\t | \n\t\t\t28.39±6.66†\n\t\t\t | \n\t\t\t32.58±8.73†\n\t\t\t | \n\t\t|
BKO (n = 16) | \n\t\t\t26.80±3.59 | \n\t\t\t54.34±9.88*\n\t\t\t | \n\t\t\t37.62±9.23†\n\t\t\t | \n\t\t\t41.85±11.8†\n\t\t\t | \n\t\t\t52.29±7.51†\n\t\t\t | \n\t\t|
DH (n = 10) | \n\t\t\t18.79±2.31 | \n\t\t\t46.89±4.56*\n\t\t\t | \n\t\t\t26.62±5.14†\n\t\t\t | \n\t\t\t32.72±2.46†\n\t\t\t | \n\t\t\t38.13±5.09†\n\t\t\t | \n\t\t|
\n\t\t\t | \n\t\t\t\tPlasma GSH concentrations (μM)\n\t\t\t | \n\t\t|||||
WT (n = 24) | \n\t\t\t11.53±2.50 | \n\t\t\t7.73±4.70*\n\t\t\t | \n\t\t\t15.15±7.72†\n\t\t\t | \n\t\t\t16.04±6.61†\n\t\t\t | \n\t\t\t15.89±8.43†\n\t\t\t | \n\t\t|
BKO (n = 16) | \n\t\t\t7.73±4.25 | \n\t\t\t6.24±3.89 | \n\t\t\t15.12±9.76*,†\n\t\t\t | \n\t\t\t15.63±7.74*,†\n\t\t\t | \n\t\t\t16.50±8.75*,†\n\t\t\t | \n\t\t|
DH (n = 10) | \n\t\t\t10.97±5.00 | \n\t\t\t5.81±1.44*\n\t\t\t | \n\t\t\t14.78±5.16†\n\t\t\t | \n\t\t\t17.03±7.27†\n\t\t\t | \n\t\t\t14.19±4.89†\n\t\t\t | \n\t\t
NTBI, LPI, MDA and GSH concentrations (mean+SD) in the WT, BKO and DH mice fed with a normal (N) diet (iron content 180 mg/kg) and an iron (Fe) diet (iron content 780 mg/kg) and treated with deionized water (DW), 90 mg/kg/day GTE, 50 mg/kg EGCG and 50 mg/kg DFP for 6 months.
*p <0.05 compared to N diet; †p <0.05 compared to DW.
Among these polyphenolic compounds, the hierarchy of antioxidant activity is ECG > EGCG > EGC > GA > EC ≈ C [24]. With the chelating activity of such prooxidant metals as iron (Fe2+), green tea is able to reduce dietary nonheme iron absorption [25]. The ratio of EGC, EGCG, ECG or EC to the iron was 3:2, 2:1, 2:1 and 3:1, respectively [26]. Unlike most flavonoids, tea catechins existing as aglycone are found in the blood following oral ingestion and subsequently metabolized in the liver by methylation, sulfation and glucoronidation reactions [27]. Structure-activity studies have shown that the presence of the galloyl ring in the 3-position and trihydroxyphenyl B ring are of significant importance in terms of the antioxidant properties of the catechins [26].
After green tea (25 mg/kg) and pure EGCG fractions (10 mg/kg) were intravenously administered into rats, a study of the pharmacokinetic (concentration-time curves) properties of the catechins in the plasma was conducted. Beta-elimination half-lives (T1/2β) were found to be 212, 45, and 41 minutes; clearances were 2.0, 7.0, and 13.9 ml•minute/kg; and apparent distribution volumes (Vd) were 1.5, 2.1, and 3.6 dl/kg for EGCG, EGC, and EC, respectively. In comparison, EGCG had a shorter T1/2β (135 minute), a larger clearance (72.5 ml•minute/kg), and a larger volume (Vd) (22.5 dl/kg) than the other two. When the green tea was intragastrically given (200 mg/kg), around 0.1, 13.7 and 31.2% of EGCG, EGC and EC were detected in the plasma compartment. The EGCG level was found to be the highest in the intestine samples and declined with a T1/2 of 173 minute. EGC and EC levels were found to be the highest in the kidneys and declined rapidly with T1/2 of 29 and 28 minute, respectively. EGCG, EGC, and EC levels in the liver and lungs were lower than those recorded in the intestine and the kidney [28]. This implies that EGCG is mainly excreted through bile, while EGC and EC are excreted through urine and bile. Inter-individual variations in the bioavailability of green tea catechins can be substantial and may be due, in part, to differences in colonic microflora and genetic polymorphisms among the enzymes involved in polyphenol metabolism [29]. The effect of green tea drinking may also differ by genotype [30].
Following oral administration of green tea catechin solutions (0.6%, w/v) to the rats, plasma levels of the catechins measured at 6:00 AM., 9:00 AM., 0:00 PM and 6:00 PM on the same day were found to be 983.9±114.2, 372.89±56.7, 186.89±34.5 and 548.1±221.6 ng/ml EGC; 1,527.3±163.7, 449.2±82.7, 224.6±58.0 and 845.6±374.0 ng/ml ECG; and 105.0±12.6, 85.5±15.8, 95.7±18.3 and 114.8±45.6 ng/ml EGCG, respectively, for which the plasma EGCG concentrations were found to be even lower than those of EGC and EC. There wasa gradual increase in plasma concentrations of EGC, EC and EGCG during Days 1-4. Levels of EGC and EC in the plasma on Day 14 were approximately three times higher than those on Day 1. Plasma levels of these three catechins decreased after Day 14, and by Day 28, plasma levels of the EGC and EC returned to the levels recorded on Day 1. On Days 4, 14 and 28, the catechins were mostly present in glucuronic acid (MW=194)/sulfuric acid (MW=98) conjugated-EGC (91.4, 95.6 and 86.4 ng/ml, respectively) and-EC (92.4, 95.5 and 89.4 ng/ml, respectively), while a much lower proportion of EGCG was found in the conjugated form (21.2, 60.7 and 39.3 ng/ml, respectively). The highest EGC concentration was found in the bladder; the highest EGCG concentration was found in the large intestine; very high concentrations of EGC and EGCG were found in the kidneys, prostate gland and lungs; and low levels of these three catechins were present in the liver, spleen, heart, and thyroid glands [31].
EC was not glucuronidated by uridine diphosphate-glucuronosyltransferases (UGT) and sulfotransferases (SULT) in human liver and small intestinal microsomes. However, the compound was efficiently glucuronidated in rat liver microsomes with the formation of two different glucuronides, and was also sulfated in human liver cytosol, mainly through the SULT1A1 isoform, as well as in the intestine through the SULT1A1 and SULT1A3 isoforms. In comparison, the EC was much less sulfated in the rat liver than in the human liver [32]. EGCG and ECG constituents in green tea drinks (10%, w/v) almost completely, competitively inhibited the activities of the SULT1A1 and SULT1A3 enzymes that play an important role in the presystemic inactivation of β2 agonists in the liver and intestine, respectively [33]. When EGCG was glucuronidated by the liver microsomal UGT enzymes; four EGCG-glucuronides were identified as EGCG-3-glucuronide, EGCG-3’-glucuronide, EGCG-4’-glucuronide and EGCG-7-glucuronide. Under the same conditions, EGC was metabolized into two EGC-glucuronides as EGC-7-glucuronide and EGC-3’-glucuronide [34]. Since rate of the glucuronidation of EGCG and ECG in liver microsomes is rather low (12.2±0.2 and 7.5±0.2%, respectively for 3 hours) due to the galloyl ring, the two potent catechins are therefore recognized to be circulating in the plasma in unconjugated forms [35]. One study claimed that catechins were toxic to rat liver cells in the order of EGCG (LD50 200±19 μM) > ECG (LD50 2,000±214 μM) > GA (LD50 3,000±298 μM), EGC (LD50 3,000±304 μM) > EC (LD50 >10,000 μM), and this was likely due to the mitochondrial membrane potential (∆ψm) collapse, and the depletion of GSH and ROS formation. The EGCG and GA dose dependently affected GSH conjugation, methylation, metabolism by NAD(P)H:quinoneoxidoreductase 1 (NQO1) in the hepatic detoxification step (Figure 2), as monitored by a significant increase of plasma alanine aminotransferase (ALT) activity in mice [36].
Possible mechanism for the hepaticgalloyl ring metabolism (Redrawn from [36]). Abbreviations: COMT=catechol-O-methyltransferase; GSH=reduced glutathione;NAD(P)+=nicotinamide adenine dinucleotide phosphate; NAD(P)H=reduced nicotinamide adenine dinucleotide phosphate; NQO1=NAD(P)H:quinone oxidoreductase-1; SAM=S-adenosyl methionine
Most polyphenolic catechins in green tea may not be absorbed in the small intestine. The nonabsorbed catechins will be converted by large bow bacterial flora into simpler phenolic compounds, such as hippuric acid, then absorbed into the blood and excreted in the urine (4.22±0.28 mmol/24 hours), when compared to non-consumption (1.89+0.28 mmol/24 hours) [37].
The potential health effects of green tea catechins depend not only on the amount consumed, but also on their bioavailability, which appears to be substantially varied. Following the oral administration of tea catechins to rats [38] and mice [39], the four principal catechins have been identified in the portal vein, indicating that tea catechins are absorbed intestinally. There appear to be species-related differences in the bioavailability of EGCG compared to other tea catechins [7]. The epicatechin isomers purified from green tea were effective agents in protecting human low-density lipoproteins (LDL) and red blood cell (RBC) membranes from oxidative modification [40]. These effects are partly due to their free-radical scavenging abilities [20] and their iron-chelating properties, which are capable of binding any available iron, thus greatly reducing their bioavailability. They also have many pharmacological properties, including anti-hypertensive [41], anti-atheroslcerotic [42], anti-carcinogenic [43-45] and hypocholesterolemic effects [46, 47]. Health benefits of green tea consumption were achieved in the rodents within relatively short periods: three weeks [48], four weeks [49, 50], five weeks [51, 52] and eight weeks [53, 54].
Surprisingly, catechins inhibited xanthine oxidase (XO) activity in vitro [55– 58] and the XO inhibition constant (Ki) of EGCG was comparable to that of the common XO inhibitor allopurinol (0.76 versus 0.30 µmol) [56]. GTE consumption was found to decrease levels of serum uric acid concentrations observed in human subjects [59-61]; for instance, by decreasing serum uric acid levels from 4.54 to 4.22 mg/dl (p<0.05) within 7 days [59]. Our group has currently found that the consumption of GTE (2, 4 and 6 g/day), of which the 2 g/day dose was found to be the most effective, lowered levels of serum uric acid (3.53%) and also increased serum trolox-equivalent antioxidant capacity (TEAC) values in healthy volunteers (n=11) (unpublished data), suggesting an hypouricemic effect by XO inhibition or an increase of urinary uric acid excretion.
In consideration of the evidence for its iron chelating properties, strong antioxidant capacity, low toxicity, and orally available administration, green tea has potential to be used as a pharmacological agent in the management of diseasesrelated to iron overload. Hypothetically, the removal of iron could reduce oxidative damage to the cellular biomolecules, including lipids, proteins, carbohydrates and nucleic acids, and this can consequently prevent, as well as improve the vital organ dysfunctions. DFP, known to readily permeate and mobilize nonheme iron from β-thalassemic patient erythrocytes, serves as a benchmark against which orally effective agents; catechins in GTE and purified EGCG products have been compared. Our studies have paid particular attention to the efficacy of GTE and EGCG in terms of the aspects of anti-oxidation and the potential for iron chelation in order to alleviate oxidative stress and iron overload in β-thalassemic mice in the past experiments, and in Thai β-thalassemia patients with regard to the near future.
Though the pathological role of redox iron in the hemosiderosis (such as hereditary hemochromatosis and thalassemia) is well characterized, excessive accumulation of the iron can result in significant organ dysfunction and abnormality. Recognition of the role of iron in conditions beyond transfusion-dependent iron overload may bring significant implications for the management of metabolic, infectious, and degenerative diseases. New findings obtained during the past years, especially in terms of the discovery of mutations in the genes associated with brain iron metabolism, have provided key insights into the mechanisms of brain iron homeostasis and the pathological mechanisms responsible for neurodegenerative diseases. Increased iron in the brain, which is rich in oxygen and fatty acids, provides an ideal environment for oxidative stress and possible irreparable tissue damage. Oxidative stress, resulting from increased brain iron levels, and possibly also from defects in antioxidant defense mechanisms, is widely believed to be associated with neuronal death in brain disorders.
The interaction between iron overload and dietary antioxidants has been well characterized; especially, with respect to vitamins E and C. Vitamin E has been extensively studied with respect to its capacity to protect molecules from the in vitro and in vivo effects of iron toxicity [62, 63]. Elevated levels of ROS tended to normalize in response to oral therapy with vitamin E, with patients exhibiting improvement in the antioxidant–oxidant balance in plasma and decreased lipid peroxidation in erythrocytes [64]. However, prolonged administration of vitamin E did not result in any significant changes in Hb levels in patients with β-thalassemia intermedia [65]. Therefore, vitamin E by itself is probably insufficient in inducing major changes in the rate of RBC hemolysis and in prolonging their survival, resulting in increased Hb levels.
The interaction of another dietary antioxidant, vitamin C (ascorbic acid) and iron is less clear. Ascorbic acid can reduce ‘free iron’ (ferric) to a ferrous form, promoting the initiation and propagation of free radical reactions [66, 67]. In people under a risk of iron overload, in which the elevated levels of iron could lead to higher ‘free iron’ concentrations, an excess of vitamin C could have deleterious effects. Plant flavonoids (including rutin and curcumin) are another group of antioxidants, which may have therapeutic potential in thalassemia. However, despite their apparent salutary effects on erythrocytes, antioxidants have not yet been shown to ameliorate the anemia of the patients [68]. Antioxidants may be more effective if used in combination with an iron chelator. This approach, if successful, could be particularly useful in countries with limited financial resources.
The potential of green tea to prevent or ameliorate chronic disease is currently the subject of considerable scientific investigation. Although a number of mechanisms have been proposed as being responsible for green tea’s beneficial effects, the radical scavenging and antioxidant properties of green tea catechins are frequently cited as important contributors. Emerging evidence has shown that catechins and their metabolites have many additional mechanisms of action [69] by affecting numerous sites, potentiating endogenous antioxidants and eliciting dual actions during oxidative stress. Much of the evidence supporting the antioxidant function for green tea catechins has been derived from assays of their antioxidant activity in vitro. However, evidence that green tea catechins are acting directly or indirectly as antioxidants in vivo is more limited [14]. Interestingly, green tea catechins can play a double role in reducing the rate of oxidation, as they can participate in: i) iron chelation [19]; and ii) trapping radicals [2, 20]. The catechins have been shown to protect culture cells from iron-mediated damage [21, 22]. In animal models of iron overload, they have been reported to ameliorate iron accumulation [17] and inhibit iron-induced lipid oxidation in the liver [23]. They also play a dual effect in decreased labile iron in the plasma and consequently depleting oxidative stress in iron-loaded rats [18]. Animal studies offer a unique opportunity to assess the contribution of the antioxidant properties of the catechins in terms of the physiological effects of tea administration in different models of oxidative-related diseases. Combining free-radical scavenging with iron-chelating properties, green tea catechins may have a capacity of chelating an excess of iron under iron-overloaded conditions and play the important preventive and/or protective roles in this unpleasant condition. None of the treatments over 2 months affected the levels of blood hemoglobin (Hb) in the WT, BKO and DH mice challenged by iron overload, there were fed a high iron-diet. Nonetheless, iron-induced oxidative stress as well as GSH content in the RBC cytoplasm, and lipid-peroxidation in the RBC plasma membrane were reversed by the GTE that was used in the testing, as well as the EGCG, when compared to the DW treatment (Table 3). We also found that these two green tea products increased the survival rate or half-life of the RBC of the WT and BKO mice significantly (Sakaewan Ounjaijen and Somdet Srichairatanakool, unpublished data). Our results suggest that GTE and EGCG treatment might directly increase the erythropoietic rate, either in normal mice or in thalassemic mice, but probably do protect red cells from ROS-induced hemolysis.
\n\t\t\t\tMice\n\t\t\t | \n\t\t\t\n\t\t\t\tFe diet (0.2% ferrocene, w/w)\n\t\t\t | \n\t\t|||||||||||||||||
\n\t\t\t\t+DW\n\t\t\t | \n\t\t\t\n\t\t\t\t+GTE (90 mg/kg/day)\n\t\t\t | \n\t\t\t\n\t\t\t\t+EGCG (50 mg/kg/day)\n\t\t\t | \n\t\t||||||||||||||||
Month 0 | \n\t\t\tMonth 1 | \n\t\t\tMonth 2 | \n\t\t\tMonth 0 | \n\t\t\tMonth 1 | \n\t\t\tMonth 2 | \n\t\t\tMonth 0 | \n\t\t\tMonth 1 | \n\t\t\tMonth 2 | \n\t\t||||||||||
\n\t\t\t | \n\t\t\t\tBlood Hb concentration (g/dl)\n\t\t\t | \n\t\t|||||||||||||||||
WT (n = 24) | \n\t\t\t14.80±1.14 | \n\t\t\t15.19 ±1.11 | \n\t\t\t15.15±1.06 | \n\t\t\t15.22±1.23 | \n\t\t\t15.37±1.23 | \n\t\t\t15.32±1.14 | \n\t\t\t15.29±1.25 | \n\t\t\t15.40±1.13 | \n\t\t\t15.41±1.00 | \n\t\t|||||||||
BKO (n = 16) | \n\t\t\t9.45 ±0.56 | \n\t\t\t9.35 ±0.40 | \n\t\t\t9.28 ±0.31 | \n\t\t\t9.55 ±0.59 | \n\t\t\t9.60 ±0.45 | \n\t\t\t9.54 ±0.48 | \n\t\t\t9.45 ±0.56 | \n\t\t\t9.53 ±0.36 | \n\t\t\t9.56 ±0.39 | \n\t\t|||||||||
DH (n = 10) | \n\t\t\t15.97±1.09 | \n\t\t\t15.80±1.01 | \n\t\t\t15.85±0.88 | \n\t\t\t16.11±0.84 | \n\t\t\t16.25±0.85 | \n\t\t\t16.17±0.77 | \n\t\t\t16.33±0.76 | \n\t\t\t16.41±0.72 | \n\t\t\t16.29±0.71 | \n\t\t|||||||||
\n\t\t\t | \n\t\t\t\tErythrocyte ROS (Fluorescent intensity unit)\n\t\t\t | \n\t\t|||||||||||||||||
WT (n = 10) | \n\t\t\t38.46±9.17 | \n\t\t\t38.29 ±10.93 | \n\t\t\t42.62±9.63 | \n\t\t\t31.74±9.14 | \n\t\t\t23.29±3.99†\n\t\t\t | \n\t\t\t22.45±3.39†\n\t\t\t | \n\t\t\t33.07±8.08 | \n\t\t\t26.87±3.72†\n\t\t\t | \n\t\t\t23.26±3.02†\n\t\t\t | \n\t\t|||||||||
BKO (n = 10) | \n\t\t\t48.29±8.01 | \n\t\t\t53.92 ±9.69 | \n\t\t\t59.81±8.96 | \n\t\t\t35.07±7.68 | \n\t\t\t33.69±7.83†\n\t\t\t | \n\t\t\t38.50±6.90†\n\t\t\t | \n\t\t\t33.88±7.08 | \n\t\t\t31.47±8.93†\n\t\t\t | \n\t\t\t36.67±5.16†\n\t\t\t | \n\t\t|||||||||
DH (n = 10) | \n\t\t\t43.08±4.38 | \n\t\t\t44.38 ±3.78 | \n\t\t\t49.60±4.25 | \n\t\t\t31.97±4.23 | \n\t\t\t31.57±4.31 | \n\t\t\t27.88±3.65†\n\t\t\t | \n\t\t\t31.51±5.38 | \n\t\t\t31.50±4.38 | \n\t\t\t28.17±4.64†\n\t\t\t | \n\t\t|||||||||
\n\t\t\t | \n\t\t\t\tErythrocyte MDA (pmol/g Hb)\n\t\t\t | \n\t\t|||||||||||||||||
WT (n = 8) | \n\t\t\tND | \n\t\t\tND | \n\t\t\t48.23±6.15 | \n\t\t\tND | \n\t\t\tND | \n\t\t\t36.80±3.23†\n\t\t\t | \n\t\t\tND | \n\t\t\tND | \n\t\t\t36.82±2.71†\n\t\t\t | \n\t\t|||||||||
BKO (n = 8) | \n\t\t\tND | \n\t\t\tND | \n\t\t\t60.63±3.28 | \n\t\t\tND | \n\t\t\tND | \n\t\t\t44.26±2.87†\n\t\t\t | \n\t\t\tND | \n\t\t\tND | \n\t\t\t44.74±2.79†\n\t\t\t | \n\t\t|||||||||
DH (n = 8) | \n\t\t\tND | \n\t\t\tND | \n\t\t\t50.86±1.90 | \n\t\t\tND | \n\t\t\tND | \n\t\t\t42.00±2.85†\n\t\t\t | \n\t\t\tND | \n\t\t\tND | \n\t\t\t40.36±2.83†\n\t\t\t | \n\t\t|||||||||
\n\t\t\t | \n\t\t\t\tErythrocyte GSH (μmol/g Hb)\n\t\t\t | \n\t\t|||||||||||||||||
WT (n = 8) | \n\t\t\tND | \n\t\t\tND | \n\t\t\t2.80 ±0.52 | \n\t\t\tND | \n\t\t\tND | \n\t\t\t3.61 ±0.32†\n\t\t\t | \n\t\t\tND | \n\t\t\tND | \n\t\t\t3.73 ±0.29†\n\t\t\t | \n\t\t|||||||||
BKO (n = 8) | \n\t\t\tND | \n\t\t\tND | \n\t\t\t2.56 ±0.22 | \n\t\t\tND | \n\t\t\tND | \n\t\t\t3.31 ±0.40†\n\t\t\t | \n\t\t\tND | \n\t\t\tND | \n\t\t\t3.24 ±0.36†\n\t\t\t | \n\t\t|||||||||
DH (n = 8) | \n\t\t\tND | \n\t\t\tND | \n\t\t\t2.76 ±0.18 | \n\t\t\tND | \n\t\t\tND | \n\t\t\t3.49 ±0.22†\n\t\t\t | \n\t\t\tND | \n\t\t\tND | \n\t\t\t3.46 ±0.19†\n\t\t\t | \n\t\t
Levels of blood Hb, erythrocyte ROS, GSH and MDA (mean±SD) in WT, BKO and DH mice fed with an Fe diet (iron content 780 mg/kg) and treated with DW, GTE and EGCG for 2 months.†p <0.05 compared to DW treatment. ND=not done.
†p <0.05 compared to DW.
The inhibition of the growth of blast cells from patients with acute myelocytic leukemic (AML) by EGCG affected hematopoietic growth factors (HGF), granulocyte colony stimulating factor (G-CSF), granulocyte macrophage colony stimulating factor (GM-CSF) or interleukin-3 (IL-3), was useful in terms of the stimulation of leukemic cell proliferation. EGCG dosage and time dependently reduced the survival of NSF60 leukemic cell lines [69, 70]. These factors can lead to an induction of cell apoptosis, including endonuclease activation, p53 induction, caspase 3 protease activation via a Bcl-2-insensitive pathway, free-radical production and sphinganine accumulation. Chemopreventive effects in multi-step carcinogenesis by EGCG have also been mentioned [71]. From in vitro studies, green tea and EGCG (dose 9-27 μg/ml) displayed a significant level of inhibition of the peripheral blood T-lymphocytes of adult T-lymphocytes patients, along with a moderate level of inhibition of human T-cell lymphotropic virus type I (HTLV-I)-infected T-cell line, but not of T-lymphocytes in the healthy controls. This was possibly due to a suppression of HTLV-I pX gene expression and induction of cell apoptosis [72].Consistently, Japanese HTLV-1 carriers who had taken green tea extract powder (9 capsules/day equivalent to 10 cups of regular green tea drinking) for 5 months showed a significant decrease in the HTLV-1 provirus load, suggesting that green tea drinking could inhibit the proliferation of HTLV-1-infected lymphocytes [73].
Surprisingly, EGCG diminished the phosphorylation of vascular endothelial growth factor (VEGF) receptors, potent angiogenic cytokines that are essential for the survival of tumor cells in the autocrine pathway, in chronic lymphocytic leukemia B cells that have been isolated from leukemic patients, leading to cell apoptosis [74]. EGCG decreased human telomerase reverse transcriptase (hTERT) promoter methylation and histone H3 Lys9 acetylation, but increased hTERT repressor E2F-1 binding at the promoter of MCF-7 breast cancers and HL60 promyelocytic leukemia cell cultures [75]. EGCG treatment induced death-associated protein kinase 2 (DAPK2) in multiple myeloma cells and dose dependently increased levels of the DAPK2 in HL60 and NB4 AML cells, while a combined treatment of EGCG (0 – 40 μM) with all-trans retinoic acid (ATRA) (1 μM) cooperatively induced and potentially differentiated the DAPK2 enzyme [76]. After ten patients with stage-0 chronic lymphocytic leukemia (CLL) had been orally given GTE for 6 months; eight patients showed decreases in lymphocytosis and circulating regulatory T cells (Treg) numbers, while one patient revealed stable lymphocytosis with decreased Treg numbers, and one patient showed increased lymphocytosis and Treg numbers [77].
The results of the phase 2 clinical study demonstrated that patients with early stage CLL consumed GTE (Polyphenon E preparation, dose of 2000 mg/day, twice daily) for 6 months showed decreases in the absolute lymphocyte counts and lymphadenopathy [78].
Epigenetics, hypercholesterolemia, diabetes, hypertension, heavy smoking, physical inactivity, stress and obesity are all risk factors for atherosclerosis and cardiovascular diseases (CVD). Green tea catechins have been reported to exert anti-obesity effects, including a reduction of adipocyte differentiation and proliferation; decreases in lipogenesis, fat mass, body weight, fat absorption, plasma levels of triglycerides, free fatty acids, cholesterol, glucose, insulin and leptin; and increases in β-oxidation and thermogenesis.
Yang and Koo reported that the ECG and EGCG that are present in Chinese green tea reduced lipid deposition-caused weight, as well as the cholesterol content of the liver, importantly lowered levels of serum cholesterol levels and the atherogenic index in the rats fed with a cholesterol-enriched diet [54]. Rats fed with the green tea powder (20 g/kg)-enriched diet showed a significant increase in the lag-phase oxidation of plasma with very low density in lipoproteins (VLDL) and LDL by 33% when compared to the control diet [48]. EGCG was proposed to be a competitive inhibitor of β-ketoacyl reductase of the FAS enzyme complex in the liver [79]. EGCG and ECG lowered FAS and malate enzymes in rat liver cytosol, resulting in a significant decrease in visceral fat deposition and hepatic triglyceride content [80]. Nonetheless, experiments showed that GTE lowered serum cholesterol and triglyceride concentrations in the hamsters fed with high fat diet (200 g lard and 1 g cholesterol/kg) in a concentration-dependent manner, for which the mechanism was most likely due to its influence on the absorption of dietary fat and cholesterol, but not on the inhibition of the synthesis of cholesterol or fatty acid [49]. Another study supports the evidence that Chinese green tea lowered plasma cholesterol levels by increasing fecal bile acids and cholesterol excretion, but not by inhibiting activities of three major lipid metabolizing enzymes, including 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-Co A) reductase, cholesterol 7α-hydroxylase and FAS [53]. More importantly, high plasma cholesterol levels, fatty liver, renal dysfunctions and severe atherosclerotic aorta were observed in the LDL knockout mice fed with a normal diet, but not in those fed with a 4% green tea catechins-enriched diet for 35 weeks [81].
GTE, which had greater effects than oolong tea and black tea, decreased high serum triglyceride (195 vs. 119 mg/dl) and cholesterol (104 vs. 73 mg/dl) levels to normal levels, and also decreased hepatic triglyceride content in sucrose-induced hyperlipidemic rats (8.4±1.5 vs. 27.7±7.9 mg/g wet weight (p<0.05); possibly by limiting the intestinal absorption of dietary fat and storage of fat in the liver [52]. EGCG (dose of 0.5 g/kg for 4 weeks) can interfere with the micellar solubilization of dietary cholesterol in the gastrointestinal tract and subsequently reduce cholesterol absorption in the rats fed with a high cholesterol diet [50].
Green tea catechins functioned against membrane phospholipid peroxidation [82], showed a strong synergistic anti-oxidative activity with α-tocopherol in micelles [83],as well as in human LDL levels [84], and had a protective effect of high-density lipoprotein (HDL) on endothelial cell-dependent vasodilatation [85]. They have many pharmacological activities, including anti-hypertensive [41] and anti-atherosclerotic [86, 87] effects. Clearly, green tea catechins; especially EGCG, has a potential hypolipidemic effect, possibly by interfering with the emulsification, digestion, and micellar solubilization of lipids in the critical steps of intestinal absorption of dietary lipids [88]. Bursill and coworkers found that GTE (2% catechins, w/w) decreased the levels of total cholesterol (-60%), VLDL-and intermediate density lipoprotein (IDL)-cholesterol (-70%), LDL-cholesterol (-80%) in plasma, total cholesterol (622 versus 800 μmol/g,-10%) and unesterified cholesterol (323 versus 399 μmol/g,-15%) in the liver, and cholesterol (-25%) in the thoracic aorta (0.73 versus 0.98 μmol/g,-25%) and aortic arch fatty streak (1.93 versus 2.35 μmol/g) of the rabbits fed with a high cholesterol (0.25%, w/w) diet compared with the placebo group, possibly due to a decrease in cholesterol synthesis and an upregulation of hepatic LDL receptor gene expression [89, 90]. Controversial data has indicated that the consumption of high doses of green tea polyphenols (714 mg/day) for 3 weeks resulted in decreases in the total cholesterol:HDL-cholesterol ratio, but had no effects on the risk biomarkers of CVD [91].
The errors in brain iron metabolism found in neurological disorders are found to be multifactorial, however treatment conditions seem to be particularly important. Epilepsy, an oxidative related disorder of the brain, has served as a model for the investigation in the role of iron, especially NTBI in neurological disorders. The effect of antiepileptic drug treatment on changes of iron status and oxidative stress parameters were determined [92]. Recent genetic and biochemical manipulations of iron overload have placed iron at the centre of research into neurodegenerative diseases. This is supported by the discovery of genetic and non-genetic misregulations of the iron metabolism in these disorders. In many neurodegenerative disorders, abnormally high levels of iron in specific regions of the brain have been reported [93]. Evidence for iron contribution to diseases by augmentation of oxidative stress have led to an examination of the contribution of iron to other diseases, in which oxidative stress may be involved, including epilepsy [94]. It has not been possible to determine whether the accumulated iron is in the labile iron pool (LIP), which can participate in the Fenton reaction to generate the reactive hydroxyl radical. The reduction in GSH during disease progression and in response to neurotoxins, together with increased iron accumulation and ROS generation, might be taken as evidence for the presence of free iron [95].
Oxidative stress, resulting from increased brain iron levels, and possibly also from defects in antioxidant defensive mechanisms, is widely believed to be associated with neuronal death in these disorders [96, 97], along with a reduced availability of GSH and other antioxidant substances in the brain. Changes in the integrity of the blood brain barrier (BBB) due to altered vascularization of the tissue or inflammatory events could be another initial cause. However, neuronal death by any initial cause could lead to large amounts of iron release and increased ROS formation [98]. Therefore, iron and iron-induced oxidative stress could possibly be a common mechanism involved in the development of neurodegeneration [99] (Figure 3). Available data strongly support this hypothesis [100, 101]
Mechanism of iron-induced neurodegeneration and its prevention [95]. Abbreviations: DMT1=divalent metal ion transporter-1; GSH=reduced glutathione; GSSG=oxidized glutathione; IRPs=iron regulatory proteins; MAO=monoamine oxidase; MPTP=1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine;NADP+=nicotinamide adenine dinucleotide phosphate; NADPH=reduced nicotinamide adenine dinucleotide phosphate; 6-OHDA=6-hydroxydopamine
Based on this hypothesis, therefore, therapeutic efforts should be devoted to reducing brain iron levels and inhibiting the generation of ROS. A few recent studies have shown that the iron chelator, deferrioxamine, may be a significant factor in an effective therapy for the prevention and treatment of brain disorders [102]. One possible non-toxic approach with natural metal chelators could make use of green tea catechins, especially EGCG. This compound comprising antioxidant, iron chelating and anti-inflammatory activities; has been shown to be neuroprotective in animal models of Parkinson’s disease (PD) and Alzheimer’s disease (AD), and also regulates the processing of amyloid precursor protein (APP) through a non-amyloidogenic pathway [103, 104]. Understanding the timing of iron mismanagement in relation to the progression of neuronal losses would provide important information on pathogenesis, and would raise the possibility of monitoring iron changes as a marker of disease progression, and perhaps even in a pre-clinical diagnosis in conditions where iron misregulation is an early event.
More in vitro and in vivo studies should be done to investigate how iron misregulation/accumulation synergizes with common endogenous and environmental toxins. Iron mis-regulation/accumulation alone can kill neurons only in genetic disorders where iron imbalance occurs rapidly and is extensive. However, in most cases, the amount of iron build-up is relatively low and the neurotoxic mechanism might involve a combination of iron and other toxins. Future investigations of iron in the brain, including how to best monitor iron deposition in vivo, the clinical relevance of excessive iron deposition, and the mechanistic relationship between iron deposition and disease pathophysiology, hold the promise of advancements in the field of neurotherapeutics.
Topical applications of green tea polyphenol fractions (such as EGCG, EGC and ECG) inhibitedbenz[a]-pyrene (BP)-and 7,12-dimethylbenz[a]-anthracene (DMBA)-initiated and 12-O-tetradecanoylphorbol-13-acetate (TPA)-promoted tumorigenesis of mouse skin, possibly by inhibiting ROS production, inflammation and hyperplasia [105]. Green tea catechins can counteract against tumorigenesis and DNA damage [106]. Mu and colleagues have identified an association between tea consumption and decreased cancer risk [107]. Surprisingly, EGCG in green tea exerted inhibitory effects on skin cancer, hepatocellular carcinoma and duodenal cancer, on metastasis of the melanoma cell line in lung cancer in mice, possibly by blocking the interaction of their tumor promoters with their own membrane receptors called sealing effects. Green tea significantly prevented growth of Namalwa, RAP1-EIO and HS-Sultan tumors transplanted intraperitoneally in the NOD/SCID mice; possibly by impairment of tumor invasion, anti-angiogenesis and by cell apoptosis induction [108]. EGCG decreased ornithine decarboxylase (ODC) and Ras and Jun oncogene levels, and also inhibited tyrosine kinase (TK) as well as mitogen-activated protein kinase (MAPK) activities in the transformed NIH-pATMras fibroblasts [109].
Green tea has long been considered a refreshing beverage that is prepared from tea (Camellia sinensis) shoots. It exerts many beneficial health effects, including anti-oxidant, anti-diabetic, hypolipidemic, anti-aging, anti-gout, neuroprotective, cardioprotective, hepatoprotective, anti-inflammatory, anti-carcinogenesis properties, among others. Variations of strain, source, geographic area, altitude, climate, cultivation, duration of fermentation and the preparation process can give rise to differences in the amounts of catechins derivatives, nutritional values, biological activities and the pharmacological properties of green tea products. EGCG is the most abundant type of catechin found in green tea and also the most relevant phytochemical displaying an influencing in several diseases and disorders. Some green tea catechins are not absorbed, while some catechins are absorbed from the intestine into the blood, and then are biologically transformed in liver microsomes by glucuronidation, sulfation and methylation reactions and ultimately excreted through the bile and urine. In this article, we have focused on the efficacy of green tea crude extract and catechins fractions, particularly EGCG in 1) depleting free radicals in normal cells, 2) removing chelatable iron from vital organs with iron overload, 3) lowering the levels of the risk factors of cardiovascular diseases, 4) inhibiting growth and proliferation of leukemic cells and solid malignant tumors, and 5) scavenging ROS and a repletion in the reducing power in neuronal tissues. The recognized benefits of green tea are that it is 1) potent in antioxidation, 2) effective in iron chelation, 3) beneficial in the inhibition of fat digestion, absorption and synthesis, 4) apoptotic induction, and 5) neuroprotection, respectively. Green tea has played a prominent role in the lives of many over time as a beverage, as a component of the diet, and now a substance that can be applied in drugs. The benefits of which have now been evidently documented.
AD=Alzheimer’s disease
ALT=alanine aminotransferase
AML=acute myelocytic leukemia
APP=amyloid precursor protein
ATRA=all-trans retinoic acid
BBB=blood brain barrier
BKO=β-globin gene knockout
BP=benz[a]-pyrene
C=catechin
CLL=chronic lymphocytic leukemia
COMT=catechol-O-methyltransferase
CVD=cardiovascular diseases
DAPK2=death-associated protein kinase 2
DFP=deferiprone
DH=double heterozygous β-globin gene knockout carrying human βE gene
DMBA=7,12-dimethylbenz[a]-anthracene
DMT1=divalent metal ion transporter-1
DW=deionized water
EC=epicatechin
ECG=epicatechin 3-gallate
EGC=epigallocatechin
EGCG=epigallocatechin 3-gallate
FAS=fatty acid synthase
GA=gallic acid
GC=gallocatechin
G-CSF=granulocyte colony stimulating factor
GM-CSF=granulocyte macrophage colony stimulating factor
GSH=reduced glutathione
GSSG=oxidized glutathione
GTE=green tea extract
Hb=hemoglobin
HDL=high-density lipoprotein
HGF=hematopoietic growth factors
HMG-Co A=3-hydroxy-3-methylglutaryl-coenzyme A
hTERT=human telomerase reverse transcriptase
HTLV-I=human T-cell lymphotropic virus type I
IDL=intermediate density lipoprotein
IL-3=interleukin-3
IRPs=iron regulatory proteins
Ki=inhibition constant
LD50=lethal dose at 50%
LDL=low-density lipoprotein
LIP=labile iron pool
LPI=labile plasma iron
MAO=monoamine oxidase
MAPK=mitogen-activated protein kinase
MDA=malondialdehyde
MPTP=1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine
MW=molecular weight
NAD(P)+=nicotinamide adenine dinucleotide phosphate
NAD(P)H=reduced nicotinamide adenine dinucleotide phosphate
ND=not determined
NQO1=NAD(P)H:quinoneoxidoreductase 1
NTBI=non-transferrin bound iron
ODC=ornithine decarboxylase
6-OHDA=6-hydroxydopamine
PD=Parkinson’s disease
PPO=polyphenol oxidase
RBC=red blood cell
RNS=reactive nitrogen species
ROS=reactive oxygen species
SAM=S-adenosyl methionine
SULT=sulfotransferases
T1/2β=beta-elimination half-lives
TEAC=trolox-equivalent antioxidant capacity
TK=tyrosine kinase
TPA=12-O-tetradecanoylphorbol-13-acetate
Treg=regulatory T cells
UGT=uridine diphosphate-glucuronosyltransferase
Vd=distribution volume
VEGF=vascular endothelial growth factor
WT=wild-type
XO=xanthine oxidase
This work was partially supported by the Office of Higher Education Commission and Mahidol University under the National Research University Initiative, Thailand Research Fund through Professor Suthat Fucharoen, MD. and by a Research Chair Grant from the National Science and Technology Development Agency (NSTDA) and Mahidol University through Professor Suthat Fucharoen, MD.
The Cognitive Dynamic System (CDS) is an organized physical model and research tool that is based on certain features of the brain. Following its first introduction in [1], it was later expanded in [2] leading to its first applications in cognitive radio [3] and cognitive radar [4]. Since then, CDS has progressed enormously to give rise to Cognitive Control (CC) [5] and Cognitive Risk Control (CRC) [6] as two of its particular functions. Using those principles, the CDS was first merged in [7] with the Smart Grid (SG) to form a new structure, based on the DC state estimation model, that shows tremendous potential for handling the possible problems that the SG will be facing in the near future. Furthermore, in [8], the construct presented in [7] was expanded to include a more complex CRC that is closer to the brain. In that paper, it was proven how this new approach can to be used to mitigate the problem of cyber-attack in the SG. From a neuroscience perspective, the CDS is founded on Fuster’s paradigm of cognition comprising of the following five principles: perception-action cycle (PAC), memory, attention, intelligence and language [9]. In its simplest form, the CDS is built on two main components: the perceptor, on one side, and the executive on the other with the feedback channel uniting them together. In [7], it was shown that the integration of the over-arching function of CDS, CC, with the SG, is well adapted for slowly progressing cyber-physical systems. In this chapter, the construct presented in [7], where the DC-estimation model was involved, will be re-engineered to be able to carry out AC state estimation optimally and also be able to detect cyber-attacks. In order to do so, the perceptor of the CDS will incorporate a generative model that will allow it to sense and control the environment indirectly. Moreover, in order to bring forward the cognitive ability of the CDS and make it compatible with the current nonlinear state estimation in SG, the steps involved in the state estimation process will be re-engineered in a novel way. It will also be shown how the entropic state, which is the objective function of the CDS, will be instrumental in implementing a control-sensing mechanism that is capable of identifying and handling bad measurements. We will also show how this entropic state serves as the basis for detecting False Data Injection attacks (FDI) in SG.
\nThe next generation of engineering systems consisting of the Internet of Things (IoT) and Cyber-physical systems (CPSs) are currently paving the way towards the fourth industrial revolution [10]. As those systems are gradually occupying a more prominent role in our daily lives, through applications in critical infrastructures such as electrical power grids or transportation systems, the cyber-security aspects of those systems will also grow in importance [11]. In the context of this chapter, emphasis will be laid upon the SG and its most dangerous threat known as False Data Injection (FDI) attacks. More specifically, compared to our previous research where the DC model for state estimation was investigated [7], focus will be laid upon on the AC model, which is more a realistic representation of the smart grid, and the introduction the CDS for a new way of control and FDI attack detection.
\nMaking use of all the new generation of sensing, monitoring and control strategies, the SG is forecasted to be a more powerful entity than the traditional power grid in many facets such as reliability and efficiency [12, 13]. In the SG, the Supervisory Control and Data Acquisition systems (SCADA) is responsible for monitoring and processing the main control actions by collecting meter measurements from remote terminal units (RTUs) consisting of different field devices or sensors. Through a process known as state estimation, those measurements are then processed and analyzed for errors and inconsistencies after being transmitted to a control center [14, 15]. The state variables that are calculated by this process usually consist of the voltage magnitudes and angles of the different busses in the system [16]. The measurements used for state estimation are the currents, real and reactive power flows, power injections and voltage magnitudes and angles. In the DC model, the state variables are the bus angles only while in the more complex AC model, the voltage magnitudes and angles of the different busses in the network are estimated. Weighted Least Squares (WLS), introduced by Schweppe [14], is the technique used for the power system state estimation using those measurements. In order to enhance the accuracy of the estimated states, another process, known as Bad Data Identification, is carried out to remove bad measurements. Bad measurements are erroneous measurement readings that will impact state estimation negatively. The most commonly applied bad data identification techniques are the Chi-Squared Tests and Largest Normalized Residual Test [15, 17]. Those statistical tests rely on the residuals between the estimated states and the measurement residuals to identify the bad data. In the case of an FDI attack, bad data, which can bypass the previously mentioned tests, is introduced into the system such that the estimated states can be modified stealthily. Those bad data are maliciously crafted offsets to measurements that are injected to the sensor readings so as the state estimation process is influenced in a particular way. Consequently, with the incorrect calculated states, bad control decisions will be applied.
\nAlthough FDI attacks have been a popular topic of research over the past years [18], most of the works, e.g., in [10, 11, 12, 13, 19], investigated the FDI attacks on the DC model. Few works have been published on the AC model and those attacks [18, 20, 21]. Nevertheless, the DC model is just a simplified representation of the nonlinear AC state estimation model. There are major differences between the two models that could explain why the AC model has been unpopular. Firstly, in the nonlinear state estimation model, the estimated states are obtained after undergoing iterations, while in the DC model, those states are obtained in closed-form. Moreover, the linear state estimation relies on active power flow analysis [16, 22, 23]. On the other hand, the AC model uses both active and reactive power flow analysis. Furthermore, the state variables in the DC model consist of the voltage angles only while the states in the AC model consist of both the voltage angles and magnitudes. Consequently, these differences raise the complexity and computational expense of nonlinear state estimation as a topic of research when it comes to FDI attacks [24]. In fact, DC based FDI attacks can be detected by AC-based data detection techniques [20]. Hence, since the AC model is commonly applied in power systems, finding a way to detect these attacks and mitigating them under that environment is going to be very important for the coming years.
\nThe main contributions of this chapter can be summarized as follows:
The architectural architecture of the CDS, tailored for AC state estimation and FDI attack detection in the SG, is presented. Compared to our earlier work in [7], which was based on the DC model, we will show how that construct can re-engineered with the goal of nonlinear state estimation and computational efficiency in mind. Consequently, it will be shown how the CDS allows for optimal state estimation with relatively less computations, using the principles of cognition rooted in the brain.
To expand on our previous research, the entropic state will be re-introduced for two purposes namely; (1) it serves as a metric of the grid’s health on a cycle to cycle basis and (2) it is used in the detection of FDI attacks. The optimization of the entropic state is the goal of the cognitive controller residing in the executive of the CDS. The latter does this by selecting the most optimal actions that will maximize the available information from one PAC to the next. Simulations are performed on the IEEE 14-bus network to show the efficiency of this new approach using the CDS. By learning which measurements to prioritize and which ones to neglect, the CDS showcases a new way of control for bad data correction and FDI attack detection with the SG being the topic of application.
The rest of this chapter is organized as follows: In Section 2, the basic concepts of state estimation and data detection for the AC model will be presented and contrasted. The mathematics of FDI attacks for this model will also be demonstrated. Section 3 expands on the structure of the CDS for the SG. Since this research is an extension of [7], the material presented in that paper will be re-engineered for this new application. In the context of the CDS, the SG is considered as the environment with which it interacts. Section 4 gives a discussion on the application and simulation results of this approach on the IEEE 14-bus network. It will be shown how this new structure is able to handle the two problems of bad data detection and FDI attack detection simultaneously. Finally, Section 5 concludes this paper by highlighting the key results and presenting new avenues of research for this novel construct.
\nIn order for the Energy Management System (EMS) to operate properly, it is important for the SCADA to provide the latter with the required measurement data so that correct control decisions can be applied in real-time. However, as those signals are often contaminated with noise, filtering is carried out by both the state estimator and the bad data detector to obtain the most accurate states. However, since power systems comprise of an overdetermined system whereby redundant measurements are taken, the filtering process allows the discarding of those erroneous measurements that will be detrimental for state estimation.
\nThe states of a power system refer to the bus voltages angle \n
In the AC model, the nonlinear power flow equations are fundamental for state estimation since they indicate the link between the measurements and the estimated states. In this model, the active and reactive power for the transmission line between busses k and m are given by
\nAdditionally, for each bus k, it is calculated using the following equations:
\nwhere \n
where
x is the n vector of the true states (voltage magnitudes and angles)
z is the m vector of measurements (active and reactive power flows, active and reactive power injections, voltage magnitudes and angles)
h is the m x n Jacobian matrix (relates measurements to states)
h(x) is the m vector of nonlinear function linking measurements to states
e is the m vector of measurement errors
\nm is the number of measurements
\nn is the number of variables
H in (5), also known as the Jacobian matrix, is a matrix that defines the theoretical calculations that relates the states to the measurement vector z and therefore serves as a mathematical description of the power system. These equations are also referred to as the power flow equations and are described as vectors inside H. While in the DC model, those entries consists of a set of linear functions of the state variables, those functions are nonlinear as far as the AC model is concerned. The determination of the state variables is done according to the following criteria:
\nW in (6), is a diagonal matrix that contains the measurement weights. These are based on the reciprocals of the measurement error variance \n
where Rz is the covariance matrix of the measurement. The performance index J(X) is then differentiated to obtain the first order optimal conditions which can be solved using iterative methods, such as Honest Gauss Newton method, Dishonest Gauss Newton method and Fast Decoupled State Estimator [23]. The first order optimality condition of (6) to be solved is then expressed as:
\nwhere \n
During the state estimation process, faulty measurements have to be detected and identified to be removed as they lead to erroneous calculated states. However, the statistical properties of these errors simplify their detection and identification. In order to determine those errors, the estimated measurements, \n
The individual estimated measurement error is then obtained using:
\nAs these errors follow a zero mean Gaussian distribution [16], techniques such as the Chi-Squares test and normalized residual have been the most common ones applied for their detection [27]. When Chi-squares test is applied, it is assumed that the state variables are mutually independent from each other and the errors follow a normal distribution. The test involves a number of iterative steps that depend on the number of degrees of freedom of the system, sum of squares \n
where k is the appropriate number of degrees of freedom and \n
FDI attacks (also known as Bad Injection attacks) is a special category of attacks targeting the SG, whereby bad measurements are injected such that they are able to bypass the bad data detection methods discussed previously. While FDI attacks can also target other cyber-physical systems, various forms of these attacks and consequences have been investigated in [11, 12, 15, 16, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38]. In this paper, FDI attacks will be simulated using assumptions from [26], whereby it is assumed that the system parameters and topology (system Jacobian) is known to the attackers, and [18], where a mathematical formulation for simulating the FDI attack in the AC model is provided. Additionally, FDI attacks satisfying the first assumption regarding prior knowledge of the system have been proven to result in more disastrous consequences. Moreover, in [17], the authors demonstrate how an attacker, using that knowledge of the system matrix \n
Hence, this will result in the calculation of an incorrect system state vector \n
For the AC model, it is shown in [18] that the attack vector will remain undetected when it satisfies the condition:
\nIt is then proven as follows:
\nConsequently, in the case of nonlinear state estimation, it is more complicated to implement the FDI attack. Compared to the attack in the DC case [17], where the attacker only required knowledge of the Jacobian matrix, in the AC model, the latter is now additionally required to have some prior knowledge of the current states of the system. While it is more complicated to meet those conditions, it is still shown in [18] that such an attack is possible and the consequences can be disastrous. In both the DC and AC model, the calculation of wrong state variables, caused by this attack, can start a domino effect of incorrect control decisions leading to dire consequences. As this type of attack targets state estimation in the SG predominantly, the vector \n
From a neuroscience perspective, the CDS is the entity that matches Fuster’s paradigm [9] the closest as far as cognition is concerned. Basically, the CDS is made up of four components namely; environment, perceptor, executive and feedback channel. Moreover they are arranged in a very particular way. The feedback channel links the perceptor and executive, which are situated on two opposite sides. The environment finally closes the global feedback channel whereby the entire CDS is contained within it. Since the focus of this chapter is the nonlinear state estimation and FDI attack in the SG, the AC state estimator will be considered as the environment with which the CDS interacts since it is the recipient of the measurements in the network. By acting as the supervisor of the network, the CDS empowers the state estimator, through CC, with the cognitive ability to learn during every PAC which measurements to prioritize for optimal state estimation and which to ones to discard. Figure 1 shows the complex diagram whereby the CDS and AC state estimator are brought together for meeting the goals mentioned previously. In the next subsections, it will be elaborated how the arrangement and the role of each constituent plays a major role for goal-oriented action on the SG.
\nArchitectural structure of CDS for the nonlinear SG.
When the environment is free of uncertainty, the PAC is responsible for updating the CDS with new information from the environment for every cycle. Thus, with the continuous acquisition of new information from this global feedback loop, the information extraction ability of the perceptor is constantly being improved with each successive cycles. Consequently, this sets up an uninterrupted cyclic directed flow of information from the perceptor to the executive to lead the PAC with the most optimal actions to be performed on the environment. As a result, this hypothesis for a goal-focused scenario is then modified with new information gained from the PAC to allow the executive to improve its current ability to achieve the primary goal that it was designed for.
\nSimilar to the concept of Percept in the agent of AI [39], both in the brain and CDS, a perception process is performed on incoming measurements. The perceptor of the CDS extracts useful information from the noisy measurements, which subsequently the executive uses to optimize its actions and improve the information gain for the next cycles. Those actions, performed by the executive under CC, are called cognitive actions. However unlike the role of the percept in AI, the perceptor perceives the environment directly and extracts relevant information from it which in turn the cognitive controller, residing in the executive, uses to sense the environment indirectly. In order to perform its function, the perceptor is made up of the generative model and the Bayesian filter, which are reciprocally coupled to each other.
\nAs defined in [6], the first component of the perceptor for the CDS is conceptually the Bayesian generative model [6], which acts as classifier for the observables received from the environment. However, in [7], it was argued that due to the dynamic nature of the SG, the Bayesian generative model would not be suitable for this specific application. Due to the complexity of the SG and its adoption for almost all applications, it is of upmost importance to detect anomalies or cyber-attacks as soon as possible before they can infect the network further, thereby starting a domino effect of cascaded problems throughout the entire network and end users. Therefore, inspired from quickest detection theory, the generative model proposed for the perceptor was based on cumulative sum (CUSUM) and is written as follows:
\nWhere \n
The second component of the perceptor is the Bayesian filter, which is coupled to the generative model. Although the equations describing the SG for state estimation are nonlinear in nature, we can linearize the state estimates using the Kalman filter and assuming that it is operating under additive white Gaussian noise [42]. Since we are assuming that the power system is quasi-static in nature in this paper [43, 44, 45], we can use the well-known Kalman filter as the Bayesian filter in the perceptor. The Kalman filter is based on the state-space model which operates on a pair of equations known as the Process equation and the Measurement equation respectively. Moreover, under quasi-static assumptions, we can assume that the state variables \n
where \n
and the covariance of matrix \n
As we are assuming that the system is operating under quasi-static conditions, a random walk model can be employed as the process equation as follows:
\nwhere \n
Referring to (18) and (20), the system matrix \n
The predicted estimated states of the generative model and predicted error covariance, \n
When the next cycle starts, those two estimates are then used for the measurement update stages to calculate the Kalman gain, \n
As a result, through the iteration of the time update and measurement update steps, the preceding a posteriori estimates are used to predict new a priori estimates.
\nThe feedback channel has very distinctive roles in the CDS as it completes the PAC by bringing together the perceptor and the executive. It is mainly related to control and cyber-attack detection in the SG. In order for the CDS to supervise the SG, the feedback channel holds the entropic-information processor, which is tasked with calculating the entropic state and internal rewards during reinforcement learning in the executive. This will be elaborated in sub-Section 3.4 (Executive) where it is more relevant to the role of the executive during planning.
\nThe directed cyclic flow of information from the perceptor to the executive is known as the entropic state of the perceptor. The entropic state is built on the principles of the perceptual posterior, which can be viewed as the incoming filtered posterior embodying the essence of the generative model, Kalman filter and entropy, which is derived from Shannon’s information theory [46]. The entropic state at time \n
where \n
When the environment is operating in the absence of uncertainty, \n
When uncertainties are present, \n
From a design perspective, the Executive is the most important entity of the CDS as it is responsible for control of the SG in the absence of uncertainty. With this goal in mind, it consists of Reinforcement Learning (RL) and Cognitive Control (CC), which can be further subdivided into the action space, planner, working memory and policy.
\nAsides from its role in the calculation of the entropic state during each PAC, the feedback channel is also involved in the calculation of internal rewards during the planning stages of the RL [39] algorithm in the executive. RL in the CDS is based on the current entropic state at each cycle which is subsequently used to optimize an objective function for optimal control in the network. Before we elaborate on the pivotal role of RL with the other components of the executive, Bayes-UCB [47] RL algorithm will be covered briefly in order to give an overview on how it operates. Bayes-UCB represents the current state of the art from a class of multi-armed bandit algorithms called UCB algorithms [48], which are based on the principle of optimism in the face of uncertainty. In this approach to the multi-armed bandit problem, the algorithm updates the estimate of the reward distribution for each action using a Bayesian method. The action that will be applied is then chosen according to the one that will yield the highest reward. Consequently, Bayes-UCB algorithm is an index policy that uses the prior distribution to pick a dynamic quantile of the posterior estimates for the index for each action. Hence, at each discrete time \n
where \n
where \n
CC can be considered in many ways as the heart of the CDS as it brings together all the components, described so far, for goal oriented action on the SG. CC is made up of two important modules namely the planner and the policy. The planner is involved in the extraction of a set of prospective actions from the action-space \n
where \n
The presence of uncertainties in the environment, whether stochastic or probabilistic, will cause a deviation in the output of the generative model of the perceptor from the estimated hidden state of the Kalman filter. Hence, the goal of (31) is to reduce this divergence by finding the best configuration weights for the respective meters. This condition is satisfied whenever the \n
Moving forward with equations that describe the planning steps, the stage is now set to define the relationship between the previous steps and the calculation of the internal rewards during RL. The hypothesized internal rewards, \n
As it can be seen from (32), the objective of RL, when operating under CC, is to minimize the amount of uncertainty in the SG by searching for an improved weight configuration during every PAC that will result in a better entropic state than the previous cycle. In other words, RL attempts to restrict the amount of uncertainty or disturbance during the state estimation process to the range computed by the Kalman filter in the perceptor. Referring back to the steps described so far that led to (32), we can see that the CDS, as defined in this specific architecture, learns from the past and present actions to pick the best actions for the future. To assist in this task, after undergoing the shunt cycles during every PAC, the working memory holds temporarily the actions that have achieved the highest quantile from Bayes-UCB in (29) and applies them to the system before starting the next PAC. Thus, when the next PAC starts and a new set of prospective actions are evaluated according to their quantile values, if any of those actions achieves a higher quantile than the quantile of its respective meter in the working memory, then the higher achieving action will replace that previously considered best action. This way of performing control in the SG can also be viewed from a Bandit perspective, whereby it can be considered as a Contextual Bandit problem where every cycle presents new situations to be faced. According to those conditions, the actions performed on the SG will modify the system configuration to a new set point, from which the RL algorithm will have to adapt. This then continues on until the CDS is brought to rest. The complete algorithm of the methodology presented in this chapter can be found in [51] where it is integrated with a cyber-attack mitigation strategy known as Cognitive Risk Control, which was not discussed in this chapter. In [51], a greater discussion on the parameters and its selection is provided and contrasted with other popular cyber-attack detection methods.
\nIn this section, two different experiments are carried out to show the capability of CC in this new CDS architecture adapted for the smart grid. The first experiment shows CC’s potential for optimal state estimation by using the optimization of the entropic state as objective function. In the second experiment, the capability of the entropic state as an attack detector will be demonstrated in four different scenarios based on the amount of information an attacker has and his access to the sensors. As IEEE bus networks have generally been used as benchmarks for evaluation in the other papers previously referenced and relating to this topic, the IEEE 14-bus network will be used for assessing the architecture proposed in this chapter. Since this particular network comprises of a large number of measurements and states, the results for the two different experiments will focus on certain aspects of the network that are relevant to the actual simulation. For both experiments, the data used to simulate the network configuration comes from the 14-bus case file in MATPOWER [53] which is an Electric Power System Simulation and Optimization Tools for MATLAB and Octave. Moreover, in order to bring about the modification for the AC state estimation algorithm, the doSE function of MATPOWER was modified for the requirements of the architecture. Originally, the algorithm uses Honest Gaussian Newton method with a maximum number of iterations of 100 and error tolerance of \n
In the first experiment, the measurement signals relating to the state values were available from the case data in MATPOWER [52]. For this simulation, a noisy version of those signals was then generated with a signal-to-noise ratio (SNR) of 20 dB to create \n
Referring to Figure 2, it can be seen that CC makes the whole network dynamic, whereby the executive of the CDS is assigning the best weight values for the meters for optimal state estimation on a cycle to cycle basis. Consequently, the cognitive controller shows it ability to learn from the current and past cycles to choose the best actions for future. Moreover, the constant modification of the weight values adds another level of nonlinearity on top of the already very complex and nonlinear AC state estimator. While this may appear to be over-complicated at first, the results show that this is not only feasible but it also makes the SG more powerful. As it can be seen in Figure 2, at the first instance of meter malfunction for meter 2 at \n
Graphs of some affected states, weights and entropic state.
In this section, the dual property of the entropic state for FDI cyber-attack detection will be demonstrated. Previously, it was shown how the latter is an objective function for the normal running of CC under the absence of uncertainty whereby it is always positive. However, when the presence of uncertainties are no longer probabilistic, such as when an attack takes place, the entropic state will also enable early detection of such attacks. In all the cases, it is assumed that the attacker has knowledge of current states of the system. Although many specialized attacks such as replay attack or Distributed Denial of Service (DDoS) attack exist, four broad categories of FDI attacks will be considered as follows:
Case 1: Here we assume that the intruder has perfect knowledge of the network configuration \n
Case 2: In this scenario, the intruder still has full knowledge of \n
Case 3: Here the circumstances of case 2 are flipped around; the intruder has access to all the meters but incomplete knowledge of \n
Case 4: Finally, a rogue attack combining case 2 and 3 is considered. The attacker has both imperfect knowledge of \n
The mentioned attacks in those different situations were simulated on the IEEE 14 bus network as shown in Figures 3–6. In all of the mentioned cases, the hacker’s goal is to deflect the value of two of the voltage magnitudes by −0.3 and 0.4 units respectively and one voltage angle by 0.3 radians. Since attack data is not publicly available, the parameters in the MATPOWER package will be used to simulate the IEEE 14 bus network.
\nCase 1.
Case 2.
Case 3.
Case 4.
In all four attack cases, the attack is started at \n
If the CDS architecture proposed in this paper is applied in a medium or large-scale power system, the computational complexity will be lesser compared to the other current detection methods, such as the ones mentioned earlier. A greater elaboration of this technique compared to the other detection methods can be found in [7]. Moreover, the application of the CDS for an application such as the SG is revolutionary as it is a dual system catering to both the control and attack detection aspects of the SG. The main parameter of interest that needs to be scaled up for a more complex grid will be the number of shunt cycles since more meters will have to be evaluated. Nevertheless, it is recommended to keep the action space small so as to make planned rewards, during planning, distinguishable from each other. Another important hyper-parameter in the system, especially for FDI attack detection, are the values in the \n
As voltage fluctuations are common occurrence disturbances in power systems, the second simulation was designed to provide the reader a greater intuition on how the algorithm is able to distinguish between what constitutes a perturbation and the normal condition. When the states of the AC state estimator is experiencing important fluctuations, this is propagated to the generative model and therefore affects the entropic state as a result. Since \n
This chapter covered the following points:
This is the first time that a CDS structure has been proposed for handling the nonlinear version of the SG. While previous research in this field, which were focused on bringing the CDS and the SG together, were based on the DC model, the AC model is a more realistic approach to the SG. Consequently, the new construct, which was described in the chapter, shows a lot of potential at tackling the future problems that the grid will face in the coming years as it becomes increasingly interconnected with the other aspects IT such as IoT.
While there are some tradeoffs to be made due to the already inherent computational complexity of the AC state estimation algorithms, it was shown that the CC is revolutionary in the sense that it allows the application of multiple actions during every PAC while still maintaining the stability of state estimation.
The CDS tailored for the AC model of the SG, proposed in this chapter, is a unique architecture that is able to make the SG more powerful by providing a new kind of control and cyber-attack detection, that are both based on cognition from the brain’s perspective.
In this chapter, a new CDS based architecture was united with the SG in order to tackle the issues of nonlinear state estimation and cyber-attack detection through CC. Computational experiments were carried out to show the individual benefits of CC for optimal state estimation and FDI attack detection respectively. Moreover, it was also discussed how the algorithm and the parameters can be adjusted so that it can be scaled up to work with bigger networks. In those bigger networks comprising of a large number of meters, a function approximator such as a Neural Network [54] can employed to simplify some of the computations involved. Although this chapter focused on the problems of control on state estimation and cyber-attack in the SG, the architecture covered in this paper, can also be formulated to work for other similar applications where state estimation is critical such as Vehicular Radar Systems. In order, to adapt the CDS for other applications, the mathematics involving the perceptor and the executive will have to be adjusted accordingly depending on the final goal of the different intended systems.
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\n\nOver the years we have learned what is important. What makes a difference to the researchers that work with us, what they value. Something that is very high not only on their lists, but our own, is the quality of the published content.
\n\nOur books contain scientific content written by two Nobel Prize winners, two Breakthrough Prize winners and 73 authors who are in the top 1% Most Cited.
\n\nWith regular submission for coverage in the single most important database, the Book Citation Index in the Web of Science™ Core Collection (BKCI), and no rejected submissions to date, over 43% of all Open Access books indexed in the BKCI are IntechOpen published books.
\n\nIn addition to BKCI, IntechOpen covers a number of important discipline specific databases as well, such as Thomson Reuters’ BIOSIS Previews.
\n\nACCESS
\n\nThe need for up to date information available at the click of a mouse is one thing that sets IntechOpen apart. By developing our own technologies in order to streamline the publishing process, we are able to minimize the amount of time from initial submission of a manuscript to its final publication date, without compromising the rigor of the editorial and peer review process. This means that the research published stays relevant, and in this fast paced world, this is very important.
\n\nYOUR WORK, YOUR COPYRIGHT
\n\nThe utilization of CC licenses allow researchers to retain copyright to their work. Researchers are free to use, adapt and share all content they publish with us. You will never have to pay permission fees to reuse a part of an experiment that you worked so hard to complete and are free to build upon your own research and the research of others. The Edited Volume helps bring together research from all over the world and compiles that research into one book - accessible for all. The research presented in chapter one can inspire the author of chapter three to take his or her research to the next level. It is about sharing ideas, insights and knowledge.
\n\nCan collaboration be inspired by a publishing format? At IntechOpen, the answer is yes. The way the research is published, the way it is accessed, it’s all part of our mission to help academics make a greater impact by giving readers free access to all published work.
\n\nOur Open Access book collection includes:
\n\n3,332 OPEN ACCESS BOOKS
\n\n107,564 INTERNATIONAL AUTHORS AND ACADEMIC EDITORS
\n\n113+ MILLION DOWNLOADS
\n\nPUBLISHING PROCESS STEPS
\n\nSee a complete overview of all publishing process steps and descriptions here.
\n\nCURRENT PROJECTS
\n\nTo view current Open Access book projects that are Open for Submissions visit us here.
\n\nNot sure if this is the right publishing option for you? Feel free to contact us at book.department@intechopen.com.
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