Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
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This achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
\n\n
We are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
\n\n
Thank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
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\r\n\tPharmacodynamics (sometimes described as what a drug does to the body) is the study of the biochemical, physiologic, and molecular effects of drugs on the body involving receptor binding (including receptor sensitivity), post-receptor effects, effect on enzymes, ion channel, or carrier proteins and chemical interactions. Pharmacodynamics, with pharmacokinetics (what the body does to a drug, or the fate of a drug within the body), helps explain the relationship between the dose and response, in other words, the drug's effects. The pharmacologic response depends on the drug binding to its target. The concentration of the drug at the receptor site influences the drug’s effect.
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\r\n\tThis book intends to provide the reader with a comprehensive overview of the current insights in pharmacodynamics and drug safety, focusing on the most important evidence-based developments in this existing field.
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1. Introduction
In the last few decades, lipoprotein research has focused on the phenomenon of atherogenic and non-atherogenic lipoproteins, specifically, atherogenic and non-atherogenic lipoprotein profiles phenotype A and phenotype B [7, 18, 60] after it was reported that more than 75% of patients with an acute coronary syndrome or myocardial infarction had normal plasma values of cholesterol, Low Density Lipoprotein cholesterol (LDL cholesterol) and High Density Lipoprotein cholesterol (HDL cholesterol) [15 - 17].
Thus, it was necessary to look for other risks factors in plasma, the presence of which in relevant quantities could cause damage to endothelial cells and resultant endothelial dysfunction [59]. This called into question whether an increased total cholesterol level, or increased LDL-cholesterol, as a criterion for the degree of atherogenic risk, provided a universal explanation for the origin of atherogenesis. A reasonable explanation was found in atherogenic lipoprotein subpopulations, the presence of which in plasma, even in very low concentrations, could impair the integrity of the vessel wall and lead to endothelial dysfunction with its fatal consequences [Table 1]: formation of atherothrombotic plaques, acute myocardial infarction, ischemic stroke, or sudden death [39, 59, 64].
The predominance of atherogenic lipoproteins in plasma is characteristic for the atherogenic lipoprotein spectrum, phenotype B. When present in plasma in high concentrations, these lipoproteins contribute to ischemic vascular impairment [6, 8, 57, 60]. The process of degenerative changes in vessels results in the formation of atheromatous vascular plaques. These later play an important role in the formation of stable or unstable angina (pectoris), and critical ischemia of peripheral and/or cerebral arteries as well [39,56]. When atherogenic lipoproteins in plasma are present in small quantities, we obtain a picture of a non-atherogenic lipoprotein profile, phenotype A.
Various methods have been developed (gradient gel electrophoresis, ultracentrifugation, magnetic resonance spectroscopy, endothelial models for testing lipoprotein cytotoxicity) to identify atherogenic lipoproteins [2, 26, 45, 48], but because of technical and financial issues, long-term analyses and high operating costs, the previously mentioned methods were used primarily in basic research. Simple analytical procedures for routine distribution were lacking and the possibility of their implementation in every day laboratory practice remained limited.
An electrophoretic method by which to separate lipoproteins on polyacrylamide gel (PAG) with the use of Lipoprint LDL System [29, 41] has become a milestone in routine laboratory analysis and in diagnosing metabolism disorders of lipoproteins. It enables the analysis of 12 lipoprotein subfractions: VLDL, IDL 1-3, LDL 1-7, and HDL.
The Lipoprint LDL system identifies and quantifies
Atherogenic lipoproteins (VLDL, IDL1, IDL2, and LDL3-7, so-called small dense LDL)
Non-atherogenic lipoprotein entities (IDL3, HDL)
Lipoproteins with uncertain atherogenicity (LDL1, LDL2)
And determines
The atherogenic vs. non-atherogenic lipoprotein spectrum, phenotype B vs. phenotype A
Atherogenic lipoprotein spectrums are characterized according to the predominance of atherogenic lipoproteins: very low density (VLDL); intermediate density IDL1 and IDL2; and by the presence of small dense-low density lipoproteins (sd-LDL). The last represented small dense LDL are highly atherogenic LDL subfractions that form fractions LDL3-7. As the name implies, they are smaller than the other types of LDL with a diameter < 26.5 nm (265 Angströms) and they float within the density range of 1.048–1.065 g/ml, that is, higher than LDL1 and LDL2. On the separating polyacrylamide gel (PAG) sd-LDL are detected as subtle bands on the anodic end of the gel, right behind HDL, that migrate to the head of separated lipoproteins.
\n\t\t
\n\t\t
\n\t\t\n\t\t\t
\n\t\t\t\t
\n\t\t\t\t\tSmall dense LDL are highly atherogenic for [11, 14, 46 ]:\n\t\t\t\t
\n\t\t\t
\n\t\t\n\t\t
\n\t\t\t
* Low recognition by LDL-receptors (configuration alterations Apo B )
\n\t\t\t
→
\n
\n
\n\t
* Enhanced aptitude for oxidation and acetylation
\n\t
→
\n
\n
\n\t
* Oxide-LDL
\n\t
→ release of pro-inflammatory cytokines → muscle cell apoptosis
\n
\n
\n\t
* Oxide-LDL
\n\t
→ release of metalloproteinase → collagen degradation
\n
\n
\n\t
* Oxide-LDL
\n\t
→ enhanced aptitude for trapping by macrophages (scavenger-receptors) → stimulation of foam cell formation
\n
\n
\n\t
* Easier penetration into the subendothelial space and formation of cholesterol deposits
\n
\n
Table 1.
Atherogenicity of small dense LDL
In our studies were analyzed serum lipoprotein spectrums in patients with newly recognized a) arterial hypertension, b) coronary heart disease, c) lower extremity arterial disease, and d) in patients who survived a stroke. As mentioned earlier, an analytical method for a quantitative evaluation of lipoprotein fractions was used, and the incidence of an atherogenic lipoprotein spectrum phenotype B (vs. phenotype A) in these four representatives of cardiovascular diseases was identified. At the same time, a lipoprotein spectrum of a control group of healthy individuals was examined and tested for the incidence of phenotype B.
2. Arterial hypertension
Arterial hypertension (AH) (Fig. 3) is one of the most serious cardiovascular diseases. More than 20% of the adult population suffers from this disease. AH is one of the risk factors for atherosclerosis development of coronary, brain, and peripheral arteries, together with the main cardinal risk factors, that is, dyslipoproteinemia and tobacco smoking [14,20,38]. Atherogenic LDL subfractions also play a role in the development of the arterial hypertension [32, 43].
AH is a permanent, long-lasting increase in blood pressure of more than 140/90 mmHg in people of middle age. In people older than 70 years of age, values higher than 160/95 mmHg are considered increased. For more extensive guidelines see the Statement of WHO/ISH (International Society of Hypertension) on the management of hypertension [61].
Dyslipoproteinemia, which frequently accompanies AH and multiplies the risk of atherosclerosis development, can also be considered one of the multiple sources that give rise to AH [35, 64].
Atherogenic lipoproteins in plasma cause endothelial dysfunction, increase vessel tone, and support the development of AH, which terminates in organ ischemia [8,50,51,55,57,59].
2.1. Patients
In our study 107 patients with newly diagnosed arterial hypertension were examined. Repeated blood pressure (BP) examination confirmed an increased blood pressure more than 150 mmHg for systolic and more than 90 mmHg for diastolic blood pressure in all hypertensive patients. Average systolic blood pressure was 172 ±19 mmHg and average diastolic blood pressure was 102 ±10 mmHg. The group of hypertensive patients comprised 66 men and 41 women. The average age of the men was 50 ± 17.6 years and the average age of the women was 51.0 ± 13.4 years.
The control group consisted of 150 healthy normotensive and normolipemic volunteers, all non-smokers, without signs of cardiovascular disease and without biochemical signs of lipid metabolism disorders. The average age of the subjects was 21 years, and the control group involved 50 males and 100 females. Volunteers were recruited from medical students at the Medical Faculty, who gave written, informed consent, and the study was approved by the local ethics committee.
2.2. Methods
A blood sample from an antecubital vein was obtained in the morning after a 12-hour fasting period. Total cholesterol and triglycerides in serum were analyzed from lipid parameters, using the enzymatic CHOD-PAP method, Roche Diagnostics Germany. To determine the non-atherogenic lipoprotein phenotype A and the atherogenic lipoprotein phenotype B, the Lipoprint LDL System Quantimetrix CA, USA, was used.
The Score of the Anti-Atherogenic Risk (SAAR) was calculated as the ratio between non-atherogenic and atherogenic lipoproteins in serum [42]. SAAR values over 10.8 represented a non-atherogenic lipoprotein profile, whereas values under 9.8 represented an atherogenic lipoprotein profile. The cut off values for a non-atherogenic lipoprotein profile and an atherogenic lipoprotein profile were calculated from the results of 940 Lipoprint LDL analyses. Using the Quantimetrix Lipoprint LDL system interpretation, all 940 individuals were examined (general group of subjects) and tested for the occurrence of atherogenic vs. non-atherogenic lipoprotein profile and were divided into the two subgroups of subjects with an LDL profile:
Indicative of Type A, that is, a non-atherogenic lipoprotein profile phenotype A
Not indicative of Type A, that is, an atherogenic lipoprotein profile, phenotype B [29]
Statistical evaluation of obtained values was performed with an unpaired student’s t-test. The level of significance was accepted at p < 0.05.
2.3. Results
In the control group shown in Table 2, along with the individuals with non-atherogenic normolipidemia, that is, an ideal lipoprotein profile (Fig. 1), a subgroup of normolipidemic individuals with an atherogenic lipoprotein profile was also identified. This group represented people with an atherogenic normolipidemia (Fig. 2). These people are clinically healthy, without clinical or laboratory signs of cardiovascular diseases, but with a positive familial history for cardiovascular diseases (myocardial infarction) in the parents’ or grandparents’ generation. The triglycerides and LDL3-7 concentrations in the control group with the atherogenic profile, compared to the individuals with a non-atherogenic lipoprotein profile, were increased (p < 0.05, respectively, p < 0.0001). The Score of the Anti-Atherogenic Risk (SAAR) for a non-atherogenic lipoprotein profile is a sensitive indicator by which to differentiate between an atherogenic and non-atherogenic plasma lipoprotein constellation (non-atherogenic vs. atherogenic: p < 0.0001).
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t\n\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t\tChol\n\t\t\t
\n\t\t\t
\n\t\t\t\tTAG\n\t\t\t
\n\t\t\t
\n\t\t\t\tVLDL\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL1, 2\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL3-7\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL\n\t\t\t
\n\t\t\t
\n\t\t\t\tHDL\n\t\t\t
\n\t\t\t
\n\t\t\t\tScore\n\t\t\t
\n\t\t
\n\t\t
\n\t\t\t
\n\t\t\t
(mmol/l±SD)
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t
\n\t\n\t
\n\t\t
\n\t\t\tControl\n\t\t
\n\t\t
4.28
\n\t\t
1.15
\n\t\t
0.60
\n\t\t
1.29
\n\t\t
0.03
\n\t\t
2.31
\n\t\t
1.35
\n\t\t
37.8
\n\t
\n\t
\n\t\t
\n\t\t
±0.60
\n\t\t
±0.39
\n\t\t
±0.16
\n\t\t
±0.38
\n\t\t
±0.003
\n\t\t
±0.53
\n\t\t
±0.32
\n\t\t
±19.7
\n\t
\n\t
\n\t\t
(non atherogenic profile n =140)
\n\t
\n\t
\n\t\t
\n\t\t\tControl\n\t\t
\n\t\t
4.25
\n\t\t
1.44
\n\t\t
0.68
\n\t\t
1.16
\n\t\t
0.22
\n\t\t
2.24
\n\t\t
1.32
\n\t\t
6.0
\n\t
\n\t
\n\t\t
\n\t\t
±0.54
\n\t\t
±0.40
\n\t\t
±0.14
\n\t\t
±0.24
\n\t\t
±0.08
\n\t\t
±0.36
\n\t\t
±0.31
\n\t\t
±2.0
\n\t
\n\t
\n\t\t
(atherogenic profile n = 10)
\n\t
\n\t
\n\t\t
\n\t\t\tControl\n\t\t
\n\t\t
\n\t\t\t4.27\n\t\t
\n\t\t
\n\t\t\t1.17\n\t\t
\n\t\t
\n\t\t\t0.61\n\t\t
\n\t\t
\n\t\t\t1.28\n\t\t
\n\t\t
\n\t\t\t0.04\n\t\t
\n\t\t
\n\t\t\t2.30\n\t\t
\n\t\t
\n\t\t\t1.34\n\t\t
\n\t\t
\n\t\t\t35.8\n\t\t
\n\t
\n\t
\n\t\t
\n\t\t
±0.60
\n\t\t
±0.39
\n\t\t
±0.16
\n\t\t
±0.37
\n\t\t
±0.004
\n\t\t
±0.52
\n\t\t
±0.32
\n\t\t
±18.5
\n\t
\n\t
\n\t\t
\n\t\t\t(total number n=150)\n\t\t
\n\t
\n\t
\n\t\t
Non-atherogenic vs. atherogenic
\n\t
\n\t
\n\t\t
\n\t\t
\n\t\t
\n\t\t\tp<0.05\n\t\t
\n\t\t
\n\t\t
\n\t\t
\n\t\t\tp< 0.0001\n\t\t
\n\t\t
\n\t\t
\n\t\t
\n\t\t\tp< 0.0001\n\t\t
\n\t
\n
Table 2.
Serum concentration of lipids, lipoproteins, and SAAR-score in the control group
Non-atherogenic profile, 93.4 % vs. atherogenic profile, 6.6 %, in control group
Figure 1.
Non-atherogenic normolipidemia – Control group, SAAR score: 62.5
Figure 2.
Atherogenic normolipidemia – atherogenic subgroup of control group atherogenic small dense LDL are present in LDL 3,4 subfractions SAAR score: 2.7
A non-atherogenic lipoprotein profile in the control group was confirmed in 93.4% healthy normolipidemic individuals, and an atherogenic lipoprotein profile was found in 6.6%.
Table 3 shows high statistical significance for the analyzed lipid and lipoprotein parameters between the control group and the group of subjects with arterial hypertension (p < 0.0001, and for HDL, p <0.03).
In Table 4, 78.5% of patients with arterial hypertension have an atherogenic lipoprotein profile. There is a highly significantly increased concentration of small dense LDL (subfractions LDL3-7) in a subgroup of AH-patients, who have an atherogenic profile, compared to the concentration of small dense LDL in the subgroup of AH-patients with a non-atherogenic profile, which confirms the predominance of atherogenic lipoproteins in AH-patients and the creation of atherogenic lipoprotein profile, phenotype B, as well. SAAR in patients with AH is low, that is, 9.2 (cut off is 10.8), and confirms also the predominance of atherogenic lipoproteins in serum.
Figure 3.
Arterial hypertension with a borderline hypertriglyceridemia, small dense LDL are present in LDL3, 4 subfractions, SAAR score: 0.9
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t\n\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t\tChol\n\t\t\t
\n\t\t\t
\n\t\t\t\tTAG\n\t\t\t
\n\t\t\t
\n\t\t\t\tVLDL\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL1,2\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL3-7\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL\n\t\t\t
\n\t\t\t
\n\t\t\t\tHDL\n\t\t\t
\n\t\t\t
\n\t\t\t\tScore\n\t\t\t
\n\t\t
\n\t\t
\n\t\t\t
\n\t\t\t
(mmol/l SD)
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t
\n\t\n\t
\n\t\t
\n\t\t\tControl\n\t\t
\n\t\t
\n\t\t\t4.27\n\t\t
\n\t\t
\n\t\t\t1.17\n\t\t
\n\t\t
\n\t\t\t0.61\n\t\t
\n\t\t
\n\t\t\t1.28\n\t\t
\n\t\t
\n\t\t\t0.04\n\t\t
\n\t\t
\n\t\t\t2.30\n\t\t
\n\t\t
\n\t\t\t1.34\n\t\t
\n\t\t
\n\t\t\t35.8\n\t\t
\n\t
\n\t
\n\t\t
\n\t\t
±0.60
\n\t\t
±0.39
\n\t\t
±0.16
\n\t\t
±0.37
\n\t\t
±0.004
\n\t\t
±0.52
\n\t\t
±0.32
\n\t\t
±18.5
\n\t
\n\t
\n\t\t
(total number n=150)
\n\t
\n\t
\n\t\t
\n\t\t\tAH\n\t\t
\n\t\t
\n\t\t\t5.19\n\t\t
\n\t\t
\n\t\t\t2.28\n\t\t
\n\t\t
\n\t\t\t0.97\n\t\t
\n\t\t
\n\t\t\t1.54\n\t\t
\n\t\t
\n\t\t\t0.35\n\t\t
\n\t\t
\n\t\t\t3.00\n\t\t
\n\t\t
\n\t\t\t1.25\n\t\t
\n\t\t
\n\t\t\t9.2\n\t\t
\n\t
\n\t
\n\t\t
\n\t\t
±1.10
\n\t\t
±1.07
\n\t\t
±0.34
\n\t\t
±0.55
\n\t\t
±0.25
\n\t\t
±0.91
\n\t\t
±0.34
\n\t\t
± 4.5
\n\t
\n\t
\n\t\t
(total number n= 107)
\n\t
\n\t
\n\t\t
\n\t\t\tControl vs. AH\n\t\t
\n\t
\n\t
\n\t\t
\n\t\t
\n\t\t
\n\t\t
\n\t\t
\n\t\t\tp<0.0001\n\t\t
\n\t\t
\n\t\t
\n\t\t
\n\t\t
\n\t\t\tp< 0.03\n\t\t
\n\t\t
\n\t\t\tp< 0.0001\n\t\t
\n\t
\n
Table 3.
Serum concentration of lipids, lipoproteins, and SAAR-score in AH patients vs.
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t\n\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t\tChol\n\t\t\t
\n\t\t\t
\n\t\t\t\tTAG\n\t\t\t
\n\t\t\t
\n\t\t\t\tVLDL\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL1,2\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL3-7\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL\n\t\t\t
\n\t\t\t
\n\t\t\t\tHDL\n\t\t\t
\n\t\t\t
\n\t\t\t\tScore\n\t\t\t
\n\t\t
\n\t\t
\n\t\t\t
\n\t\t\t
(mmol/l SD)
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t
\n\t\n\t
\n\t\t
AH
\n\t\t
5.32
\n\t\t
1.56
\n\t\t
0.84
\n\t\t
1.78
\n\t\t
0.08
\n\t\t
3.02
\n\t\t
1.49
\n\t\t
24.2
\n\t
\n\t
\n\t\t
\n\t\t
±0.98
\n\t\t
±0.55
\n\t\t
±0.31
\n\t\t
±0.44
\n\t\t
±0.04
\n\t\t
±0.71
\n\t\t
±0.34
\n\t\t
±13.6
\n\t
\n\t
\n\t\t
(non-atherogenic profile n= 23)
\n\t
\n\t
\n\t\t
AH
\n\t\t
5.15
\n\t\t
2.48
\n\t\t
1.01
\n\t\t
1.47
\n\t\t
0.42
\n\t\t
2.99
\n\t\t
1.18
\n\t\t
5.1
\n\t
\n\t
\n\t\t
\n\t\t
±1.14
\n\t\t
±1.34
\n\t\t
±0.35
\n\t\t
±0.58
\n\t\t
±0.31
\n\t\t
±0.96
\n\t\t
±0.34
\n\t\t
± 2.0
\n\t
\n\t
\n\t\t
(atherogenic profile n= 84)
\n\t
\n\t
\n\t\t
\n\t\t\tAH\n\t\t
\n\t\t
\n\t\t\t5.19\n\t\t
\n\t\t
\n\t\t\t2.28\n\t\t
\n\t\t
\n\t\t\t0.97\n\t\t
\n\t\t
\n\t\t\t1.54\n\t\t
\n\t\t
\n\t\t\t0.35\n\t\t
\n\t\t
\n\t\t\t3.00\n\t\t
\n\t\t
\n\t\t\t1.25\n\t\t
\n\t\t
\n\t\t\t9.2\n\t\t
\n\t
\n\t
\n\t\t
\n\t\t
±1.10
\n\t\t
±1.07
\n\t\t
±0.34
\n\t\t
±0.55
\n\t\t
±0.25
\n\t\t
±0.91
\n\t\t
±0.34
\n\t\t
± 4.5
\n\t
\n\t
\n\t\t
\n\t\t\t(total number n=107)\n\t\t
\n\t
\n\t
\n\t\t
Non-atherogenic vs. atherogenic
\n\t\t
\n\t
\n\t
\n\t\t
\n\t\t
\n\t\t\tn.s. \n\t\t
\n\t\t
\n\t\t\tp<0.002 \n\t\t
\n\t\t
\n\t\t\tp<0.05 \n\t\t
\n\t\t
\n\t\t\tp<0.02\n\t\t
\n\t\t
\n\t\t\tp< 0.0001\n\t\t
\n\t\t
\n\t\t\tn.s.\n\t\t
\n\t\t
\n\t\t\tp< 0.001\n\t\t
\n\t\t
\n\t\t\tp< 0.0001\n\t\t
\n\t
\n
Table 4.
Serum concentration of lipids, lipoproteins, and SAAR-score in patients with arterial hypertension
Atherogenic 78.5% vs. non-atherogenic 21.5% – arterial hypertension
3. Coronary heart disease
Coronary heart disease (CHD) (Fig. 4) is a common manifestation of cardiovascular diseases and is frequently associated with lipid and lipoprotein metabolism disorders. Hypercholesterolemia and hypertriglyceridemia, as well as combined hyperlipoproteinemia are regular features that accompany CHD [22,50,51]. Pathophysiologically, the cause of myocardial ischemia is a disproportion, or imbalance, between myocardial oxygen supply and oxygen demand. Ischemia in stable angina is generally due to fixed atheromatous stenosis of one or more coronary arteries as a consequence of impaired lipoprotein metabolism and the formation of lipid atheromas in the coronary arteries [5, 33, 34,49].
Figure 4.
Coronary heart disease combined with an atherogenic hypercholesterolemia. High concentration of atherogenic small dense LDL in LDL 3,4 subfractions SAAR score: 5.1
However, clinically, stable angina is not the only form of manifestation of coronary heart disease. Stable angina, as an ischemia due to fixed atheromatous stenosis, can turn into a myocardial ischemia due to plaque rupture with thrombosis and spasm of the artery (instable angina). In addition, myocardial necrosis (myocardial infarction), caused by acute occlusion of a coronary artery (due to plaque rupture and thrombosis), can have fatal consequences for disabled persons. It can be supposed that the modified forms of lipoproteins can play an important role in any form of clinical manifestation of coronary heart disease. Recently, clinical studies reported that the atherogenic lipoprotein populations (lipoprotein subfractions), presented in the plasma lipoprotein spectrum in high concentrations, play an important role in the development of atherosclerotic changes in the arterial wall [14, 38, 39].
We distinguish facultative atherogenic very low density lipoproteins, VLDL, and their remnants, intermediate density lipoproteins, IDL, low density lipoproteins, LDL (considered a lipoprotein family with high atherogenicity), and high density lipoproteins, HDL. Modified lipoprotein entities in all these lipoprotein families can play a role in the formation of atherogenic lipoproteins, which accelerate the atherogenesis in the artery walls, including in the coronary arteries.
In our study, we focused on the determination of the incidence of an atherogenic lipoprotein phenotype in patients with coronary heart disease – in stable angina patients.
3.1. Patients
In our study, 104 patients with newly diagnosed coronary heart disease were examined. The diagnosis of CHD (stable angina pectoris grade I or II) was confirmed by medical examination, laboratory results, resting ECG, results of echocardiography, and duplex ultrasound of the carotid arteries.
3.2. Methods
See methods published in the section “Arterial hypertension (AH).”
3.3. Results
The results of lipid parameters presented in Table 5 confirm a highly significant increased concentration of analyzed lipid and lipoprotein parameters (p<0.0001) in CHD-patients, compared to control values and a low value of the SAAR. These low values (< 10.8) are regularly found in atherogenic lipoprotein phenotype B.
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t\n\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t\tChol \n\t\t\t
\n\t\t\t
\n\t\t\t\tTAG\n\t\t\t
\n\t\t\t
\n\t\t\t\tVLDL\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL1,2\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL3-7\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL\n\t\t\t
\n\t\t\t
\n\t\t\t\tHDL\n\t\t\t
\n\t\t\t
\n\t\t\t\tScore\n\t\t\t
\n\t\t
\n\t\t
\n\t\t\t
\n\t\t\t
(mmol/l SD)
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t
\n\t\n\t
\n\t\t
\n\t\t\tControl\n\t\t
\n\t\t
\n\t\t\t4.27\n\t\t
\n\t\t
\n\t\t\t1.17\n\t\t
\n\t\t
\n\t\t\t0.61\n\t\t
\n\t\t
\n\t\t\t1.28\n\t\t
\n\t\t
\n\t\t\t0.04\n\t\t
\n\t\t
\n\t\t\t2.30\n\t\t
\n\t\t
\n\t\t\t1.34\n\t\t
\n\t\t
\n\t\t\t35.8\n\t\t
\n\t
\n\t
\n\t\t
\n\t\t
±0.60
\n\t\t
±0.39
\n\t\t
±0.16
\n\t\t
±0.37
\n\t\t
±0.004
\n\t\t
±0.52
\n\t\t
±0.32
\n\t\t
±18.5
\n\t
\n\t
\n\t\t
(total number n=150)
\n\t
\n\t
\n\t\t
\n\t\t\tCHD\n\t\t
\n\t\t
\n\t\t\t5.25\n\t\t
\n\t\t
\n\t\t\t2.41\n\t\t
\n\t\t
\n\t\t\t0.99\n\t\t
\n\t\t
\n\t\t\t1.52\n\t\t
\n\t\t
\n\t\t\t0.41\n\t\t
\n\t\t
\n\t\t\t3.06\n\t\t
\n\t\t
\n\t\t\t1.18\n\t\t
\n\t\t
\n\t\t\t5.6\n\t\t
\n\t
\n\t
\n\t\t
\n\t\t
\n\t\t\t±1.15\n\t\t
\n\t\t
\n\t\t\t±1.72\n\t\t
\n\t\t
\n\t\t\t±0.43\n\t\t
\n\t\t
\n\t\t\t±0.47\n\t\t
\n\t\t
\n\t\t\t±0.29\n\t\t
\n\t\t
\n\t\t\t±0.85\n\t\t
\n\t\t
\n\t\t\t±0.29\n\t\t
\n\t\t
\n\t\t\t± 3.8\n\t\t
\n\t
\n\t
\n\t\t
(total number n= 104)
\n\t
\n\t
\n\t\t
Control vs. CHD
\n\t
\n\t
\n\t\t
\n\t\t
\n\t\t\tp<0.0001\n\t\t
\n\t
\n
Table 5.
Serum concentration of lipids, lipoproteins, and SAAR-score in CHD patients vs. control group
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t\n\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t\tChol\n\t\t\t
\n\t\t\t
\n\t\t\t\tTAG\n\t\t\t
\n\t\t\t
\n\t\t\t\tVLDL\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL1,2\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL3-7\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL\n\t\t\t
\n\t\t\t
\n\t\t\t\tHDL\n\t\t\t
\n\t\t\t
\n\t\t\t\tScore\n\t\t\t
\n\t\t
\n\t\t
\n\t\t\t
\n\t\t\t
(mmol/l±SD)
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t
\n\t\n\t
\n\t\t
CHD
\n\t\t
5.26
\n\t\t
1.44
\n\t\t
0.82
\n\t\t
1.73
\n\t\t
0.13
\n\t\t
3.11
\n\t\t
1.29
\n\t\t
12.7
\n\t
\n\t
\n\t\t
\n\t\t
±0.99
\n\t\t
±0.50
\n\t\t
±0.27
\n\t\t
±0.49
\n\t\t
±0.06
\n\t\t
±0.80
\n\t\t
±0.31
\n\t\t
±5.1
\n\t
\n\t
\n\t\t
(non-atherogenic profile = 19)
\n\t
\n\t
\n\t\t
CHD
\n\t\t
5.25
\n\t\t
2.63
\n\t\t
1.02
\n\t\t
1.47
\n\t\t
0.48
\n\t\t
3.05
\n\t\t
1.16
\n\t\t
4.0
\n\t
\n\t
\n\t\t
\n\t\t
±1.19
\n\t\t
±2.03
\n\t\t
±0.47
\n\t\t
±0.46
\n\t\t
±0.35
\n\t\t
±0.86
\n\t\t
±0.28
\n\t\t
±3.5
\n\t
\n\t
\n\t\t
(atherogenic profile n= 85)
\n\t
\n\t
\n\t\t
\n\t\t\tCHD\n\t\t
\n\t\t
\n\t\t\t5.25\n\t\t
\n\t\t
\n\t\t\t2.41\n\t\t
\n\t\t
\n\t\t\t0.99\n\t\t
\n\t\t
\n\t\t\t1.52\n\t\t
\n\t\t
\n\t\t\t0.41\n\t\t
\n\t\t
\n\t\t\t3.06\n\t\t
\n\t\t
\n\t\t\t1.18\n\t\t
\n\t\t
\n\t\t\t5.6\n\t\t
\n\t
\n\t
\n\t\t
\n\t\t
±1.15
\n\t\t
±1.72
\n\t\t
±0.43
\n\t\t
±0.47
\n\t\t
±0.29
\n\t\t
±0.85
\n\t\t
±0.29
\n\t\t
±3.8
\n\t
\n\t
\n\t\t
\n\t\t\t(total number n=104)\n\t\t
\n\t
\n\t
\n\t\t
Non-atherogenic vs. atherogenic profile
\n\t
\n\t
\n\t\t
\n\t\t
\n\t\t
\n\t\t\tp<0.01\n\t\t
\n\t\t
\n\t\t
\n\t\t\tp<0.05 \n\t\t
\n\t\t
\n\t\t\tp< 0.0001\n\t\t
\n\t\t
\n\t\t\tn.s.\n\t\t
\n\t\t
\n\t\t
\n\t\t\tp< 0.0001\n\t\t
\n\t
\n
Table 6.
Serum concentration of lipids, lipoproteins, and SAAR-score in coronary heart disease
Note: In the column without published p values, the differences in the evaluated parameter were not significant (n.s.)
Atherogenic 81.7% vs. non-atherogenic 18.3%, in coronary heart disease
In Table 6, an atherogenic lipoprotein phenotype B is present in 81.7% of patients with CHD. An increased concentration of small dense LDL (LDL3-7) in the CHD-patient subgroup with an atherogenic lipoprotein profile, compared to the results of the CHD-patient subgroup with a non-atherogenic lipoprotein profile (p<0.0001), confirms a predominance of atherogenic lipoproteins in the serum of patients with CHD.
4. Lower extremity arterial disease
Lower extremity arterial disease (LEAD) (Fig. 5) is a common atherogenic disease of the cardiovascular system. Patients with LEAD exhibit normal to high atherogenic dyslipoproteinemia [8, 31, 50 -52, 62].
Figure 5.
Lower extremity arterial disease with combined atherogenic hyperlipoproteinemia with high concentration of atherogenic small dense LDL (LDL3,4 subfractions) SAAR score: 1.5
Almost all lower extremity arterial disease is due to atherosclerotic changes in artery vessels, and the pathology of LEAD is also similar to coronary heart disease. The most important risk factor for the development and progression of atherosclerotic LEAD are tobacco smoking, arterial hypertension, and hyperlipidemia. Other risk factors include diabetes mellitus, low physical activity, and a diet rich in lipids and carbohydrates. However, dyslipidemia plays an important role. Increased lipid levels of cholesterol and triglycerides are generally accepted as important risk factors for the development of atherosclerosis [14,25, 47].
In the last few decades, there has been much discussion about which atherogenic lipoproteins participate in the formation of the atherogenic lipoprotein profile, phenotype B. Atherogenic lipoproteins in relevant concentration in the blood serum are responsible for the acceleration of the development of atherogenic cardiovascular diseases, including the development of LEAD. The LDL subpopulations of small dense LDL are considered to be strongly atherogenic lipoprotein entities in the plasma/serum lipoprotein spectrum [38,59] with consequent acceleration of endothelial dysfunction and formation of the atheromatous subendothelial plaques in the arteries [21]. In the present study, we have focused on determining the incidence of an atherogenic lipoprotein phenotype, along with determining the role of atherogenic serum lipoproteins, in patients with lower extremity arterial disease.
4.1. Patients
In the clinical study, 100 patients with newly diagnosed lower extremity arterial disease were examined. The study included 55 males and 45 females: the average age of males was 56.0 years ±11 years and the average age of females 52.5 years ± 14 years. The patients had C2a degree, according to the Claudication classification: [proximal type (AP), the first degree (P1) with dyslipidemia]. Patients were ex-smokers.
LEAD was diagnosed according to the history of disease, intermittent claudication, the medical examination, including physical examination (Ratschow’s test in the modification according to Linhart, see the Angiological Section of Slovak Medical Chamber) [23, 24, 27, 28] and examination of the ankle-brachial (pressure) index (ABPI) [40, 55, 57].
4.2. Methods
See methods published earlier in the section “Arterial hypertension (AH).”
4.3. Results
Results of lipid parameters presented in Table 7 confirm the highly significant increased concentration of analyzed lipid and lipoprotein parameters in LEAD-patients (p<0.0001), compared to control values. The low values of the SAAR, which is generally low (< 10.8) in the atherogenic lipoprotein phenotype, also confirms the atherogenic lipoprotein constellation in the serum of LEAD-patients.
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t\n\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t\tChol \n\t\t\t
\n\t\t\t
\n\t\t\t\tTAG\n\t\t\t
\n\t\t\t
\n\t\t\t\tVLDL\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL1,2\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL3-7\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL\n\t\t\t
\n\t\t\t
\n\t\t\t\tHDL\n\t\t\t
\n\t\t\t
\n\t\t\t\tScore\n\t\t\t
\n\t\t
\n\t\t
\n\t\t\t
\n\t\t\t
(mmol/l SD)
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t
\n\t\n\t
\n\t\t
\n\t\t\tControl\n\t\t
\n\t\t
\n\t\t\t4.27\n\t\t
\n\t\t
\n\t\t\t1.17\n\t\t
\n\t\t
\n\t\t\t0.61\n\t\t
\n\t\t
\n\t\t\t1.28\n\t\t
\n\t\t
\n\t\t\t0.04\n\t\t
\n\t\t
\n\t\t\t2.30\n\t\t
\n\t\t
\n\t\t\t1.34\n\t\t
\n\t\t
\n\t\t\t35.8\n\t\t
\n\t
\n\t
\n\t\t
\n\t\t
±0.60
\n\t\t
±0.39
\n\t\t
±0.16
\n\t\t
±0.37
\n\t\t
±0.004
\n\t\t
±0.52
\n\t\t
±0.32
\n\t\t
±18.5
\n\t
\n\t
\n\t\t
(total number n=150)
\n\t
\n\t
\n\t\t
\n\t\t\tLEAD\n\t\t
\n\t\t
\n\t\t\t5.29\n\t\t
\n\t\t
\n\t\t\t2.21\n\t\t
\n\t\t
\n\t\t\t0.96\n\t\t
\n\t\t
\n\t\t\t1.58\n\t\t
\n\t\t
\n\t\t\t0.39\n\t\t
\n\t\t
\n\t\t\t3.11\n\t\t
\n\t\t
\n\t\t\t1.21\n\t\t
\n\t\t
\n\t\t\t7.2\n\t\t
\n\t
\n\t
\n\t\t
\n\t\t
\n\t\t\t±1.21\n\t\t
\n\t\t
\n\t\t\t±1.05\n\t\t
\n\t\t
\n\t\t\t±0.37\n\t\t
\n\t\t
\n\t\t\t±0.51\n\t\t
\n\t\t
\n\t\t\t±0.28\n\t\t
\n\t\t
\n\t\t\t±0.96\n\t\t
\n\t\t
\n\t\t\t±0.31\n\t\t
\n\t\t
\n\t\t\t± 4.5\n\t\t
\n\t
\n\t
\n\t\t
(total number n= 100)
\n\t
\n\t
\n\t\t
Control vs. LEAD
\n\t
\n\t
\n\t\t
\n\t\t
\n\t\t\tp<0.0001\n\t\t
\n\t
\n
Table 7.
Serum concentration of lipids, lipoproteins, and SAAR-score in LEAD-patients vs. control group
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t\n\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t\tChol\n\t\t\t
\n\t\t\t
\n\t\t\t\tTAG\n\t\t\t
\n\t\t\t
\n\t\t\t\tVLDL\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL1,2\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL3-7\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL\n\t\t\t
\n\t\t\t
\n\t\t\t\tHDL\n\t\t\t
\n\t\t\t
\n\t\t\t\tScore\n\t\t\t
\n\t\t
\n\t\t
\n\t\t\t
\n\t\t\t
(mmol/l±SD)
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t
\n\t\n\t
\n\t\t
LEAD
\n\t\t
5.37
\n\t\t
1.81
\n\t\t
0.86
\n\t\t
1.82
\n\t\t
0.10
\n\t\t
3.18
\n\t\t
1.33
\n\t\t
17.4
\n\t
\n\t
\n\t\t
\n\t\t
±0.95
\n\t\t
±0.51
\n\t\t
±0.26
\n\t\t
±0.54
\n\t\t
±0.03
\n\t\t
±0.82
\n\t\t
±0.29
\n\t\t
± 6.5
\n\t
\n\t
\n\t\t
(non-atherogenic profile n= 20)
\n\t
\n\t
\n\t\t
LEAD
\n\t\t
5.28
\n\t\t
2.31
\n\t\t
0.98
\n\t\t
1.52
\n\t\t
0.46
\n\t\t
3.09
\n\t\t
1.18
\n\t\t
4.6
\n\t
\n\t
\n\t\t
\n\t\t
±1.28
\n\t\t
±1.18
\n\t\t
±0.39
\n\t\t
±0.50
\n\t\t
±0.34
\n\t\t
±0.99
\n\t\t
±0.32
\n\t\t
± 4.0
\n\t
\n\t
\n\t\t
(atherogenic profile n= 80)
\n\t
\n\t
\n\t\t
\n\t\t\tLEAD\n\t\t
\n\t\t
\n\t\t\t5.29\n\t\t
\n\t\t
\n\t\t\t2.21\n\t\t
\n\t\t
\n\t\t\t0.96\n\t\t
\n\t\t
\n\t\t\t1.58\n\t\t
\n\t\t
\n\t\t\t0.39\n\t\t
\n\t\t
\n\t\t\t3.11\n\t\t
\n\t\t
\n\t\t\t1.21\n\t\t
\n\t\t
\n\t\t\t7.2\n\t\t
\n\t
\n\t
\n\t\t
\n\t\t
±1.21
\n\t\t
±1.05
\n\t\t
±0.37
\n\t\t
±0.51
\n\t\t
±0.28
\n\t\t
±0.96
\n\t\t
±0.31
\n\t\t
± 4.5
\n\t
\n\t
\n\t\t
(total number n=100)
\n\t
\n\t
\n\t\t
Non-atherogenic vs. atherogenic
\n\t
\n\t
\n\t\t
\n\t\t
p<0.01
\n\t\t
p<0.001
\n\t\t
\n\t\t
p<0.05
\n\t\t
p< 0.0001
\n\t\t
\n\t\t
\n\t\t
p< 0.0001
\n\t
\n
Table 8.
Serum concentration of lipids, lipoproteins, and SAAR-score in lower extremity arterial disease
Atherogenic 80% vs. non-atherogenic 20% in lower extremity arterial disease
In 80% of patients (Table 8), LEAD was associated with an atherogenic lipoprotein phenotype. An increased concentration of small dense LDL (LDL3-7 subgroups) in the LEAD-patients with an atherogenic lipoprotein profile, compared to the results from the LEAD-patients with a non-atherogenic lipoprotein profile (p<0.0001), confirms the predominance of atherogenic lipoproteins in serum in the subgroup of patients with an atherogenic lipoprotein profile.
5. Stroke
Stroke (Fig. 6) is the leading cause of mortality and of long-term morbidity in the populations of developed industrialized countries in the world. The atherogenic serum lipoproteins in high concentrations create an atherogenic lipoprotein profile, which plays a key role in the acute onset of cardiovascular and cerebrovascular events, that is, stroke [54,55]. Cerebral stroke attack remains a frequent medical problem and is the third most frequent cause of mortality all over the world. It represents a heterogeneous group of diseases with more than 150 known causes. In 25–39% of strokes, the cause leading to the acute cerebrovascular event cannot usually be definitively explained [4].
Figure 6.
Patient survived an ischemic stroke with combined atherogenic hyperlipoproteinemia high concentration of VLDL, VLDL remnants and atherogenic small dense LDL, i.e. LDL 3, 4 subfractions. SAAR score: 2.4
*Reference ranges derived from 125 serum samples that met the NCEP ATPIII guidelines for desirable lipid status
**LDL-C comprised of the sum of cholesterol in Md bands C through A as well as all the subfractions
Dyslipidemia represents a risk factor for the development of cardiovascular disease, and thus dyslipidemia has been classified as an atherogenic phenomenon. The goal of the treatment of hyperlipoproteinemia, that is, of dyslipidemia, is to reduce the lipid concentration in serum to established target values of lipids (cholesterol and triglycerides), but the primary goal is to reduce the atherogenic potential of serum lipids [9, 21, 53]. Dyslipoproteinemia is also the key phenomenon in the pathogenesis of the onset of atherosclerotic alterations in brain vessels [64]. Accompanied by high cholesterol levels – a classic risk factor for the development of cardiovascular diseases – an increased concentration of triglycerides in the blood serum can also play an important role in atherogenesis [3,58].
There are several studies that have provided evidence for the relation between carotid artery stenosis and an ischemic cerebral event [55]. However, the causal inter-relation between dyslipidemia and stroke has not been explained sufficiently [3, 4, 63]. Relapsing ischemic strokes account for one-fourth of all strokes in a year and are a strong evidence for a failure of secondary prevention [10]. This hard reality leads rightly so to the idea of optimal stroke prevention through the selection of individuals, who are at risk of stroke [13]. The aim of this pilot study was to identify the atherogenic lipoproteins and determine the lipoprotein profile in subjects who had suffered an ischemic cerebrovascular event, that is, stroke.
5.1. Patients
The study included 55 patients, 23 men, with an average age of 64 years ± 13 years, and 32 women, average age 74 years ± 13 years, who survived an ischemic cerebrovascular event, that is, a large-artery atherosclerosis subtype of stroke. To determine the subtype of ischemic stroke, the original TOAST (Trial of ORG 10172 in Acute Stroke Treatment) [1] criteria were used. The diagnosis of subtype was based on the risk factor profiles, clinical features, and results of diagnostic tests, including CT scan/MRI, vascular imaging (carotid duplex, transcranial Doppler), EEG – electroencephalography, echocardiography (transesophageal/ transthoracic), assessment of prothrombotic syndromes [1,30], activated partial thromboplastin time (aPTT), and international normalized ratio (INR).
5.2. Methods
See methods published in the section “Arterial hypertension (AH).”
A blood sample from the antecubital vein was obtained throughout the 24 hours after the onset of cerebrovascular event.
5.3. Results
The results of lipid parameters presented in Table 9 confirm a highly significantly increased concentration of analyzed lipid and lipoprotein parameters (p<0.0001) in people who survive a stroke, compared to control values, and also a low value on the SAAR, which is generally low (< 10.8) in an atherogenic lipoprotein phenotype.
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t\n\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t\tChol \n\t\t\t
\n\t\t\t
\n\t\t\t\tTAG\n\t\t\t
\n\t\t\t
\n\t\t\t\tVLDL\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL1,2\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL3-7\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL\n\t\t\t
\n\t\t\t
\n\t\t\t\tHDL\n\t\t\t
\n\t\t\t
\n\t\t\t\tScore\n\t\t\t
\n\t\t
\n\t\t
\n\t\t\t
\n\t\t\t
(mmol/l SD)
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t
\n\t\n\t
\n\t\t
\n\t\t\tControl\n\t\t
\n\t\t
\n\t\t\t4.27\n\t\t
\n\t\t
\n\t\t\t1.17\n\t\t
\n\t\t
\n\t\t\t0.61\n\t\t
\n\t\t
\n\t\t\t1.28\n\t\t
\n\t\t
\n\t\t\t0.04\n\t\t
\n\t\t
\n\t\t\t2.30\n\t\t
\n\t\t
\n\t\t\t1.34\n\t\t
\n\t\t
\n\t\t\t35.8\n\t\t
\n\t
\n\t
\n\t\t
\n\t\t
±0.60
\n\t\t
±0.39
\n\t\t
±0.16
\n\t\t
±0.37
\n\t\t
±0.004
\n\t\t
±0.52
\n\t\t
±0.32
\n\t\t
±18.5
\n\t
\n\t
\n\t\t
(total number n=150)
\n\t
\n\t
\n\t\t
\n\t\t\tStroke\n\t\t
\n\t\t
\n\t\t\t5.19\n\t\t
\n\t\t
\n\t\t\t2.21\n\t\t
\n\t\t
\n\t\t\t1.08\n\t\t
\n\t\t
\n\t\t\t1.56\n\t\t
\n\t\t
\n\t\t\t0.29\n\t\t
\n\t\t
\n\t\t\t2.91\n\t\t
\n\t\t
\n\t\t\t1.09\n\t\t
\n\t\t
\n\t\t\t6.40\n\t\t
\n\t
\n\t
\n\t\t
\n\t\t
±1.10
\n\t\t
±0.91
\n\t\t
±0.34
\n\t\t
±0.57
\n\t\t
±0.24
\n\t\t
±0.82
\n\t\t
±0.32
\n\t\t
±4.22
\n\t
\n\t
\n\t\t
(total number n= 55
\n\t
\n\t
\n\t\t
Control vs. stroke
\n\t
\n\t
\n\t\t
\n\t\t
\n\t\t\tp<0.0001\n\t\t
\n\t
\n
Table 9.
Serum concentration of lipids, lipoproteins, and SAAR-score in stroke patients vs. control group.
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t
\n\t\n\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t\tChol \n\t\t\t
\n\t\t\t
\n\t\t\t\tTAG\n\t\t\t
\n\t\t\t
\n\t\t\t\tVLDL\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL1,2\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL3-7\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL\n\t\t\t
\n\t\t\t
\n\t\t\t\tHDL\n\t\t\t
\n\t\t\t
\n\t\t\t\tScore\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL1\n\t\t\t
\n\t\t\t
\n\t\t\t\tLDL2\n\t\t\t
\n\t\t
\n\t\t
\n\t\t\t
\n\t\t\t
(mmol/l±SD)
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t
\n\t\n\t
\n\t\t
Stroke
\n\t\t
5.54
\n\t\t
1.70
\n\t\t
0.93
\n\t\t
2.19
\n\t\t
0.14
\n\t\t
3.30
\n\t\t
1.31
\n\t\t
13.74
\n\t\t
1.22
\n\t\t
0.92
\n\t
\n\t
\n\t\t
\n\t\t
±1.30
\n\t\t
±0.44
\n\t\t
±0.14
\n\t\t
±0.86
\n\t\t
±0.07
\n\t\t
±1.01
\n\t\t
±0.35
\n\t\t
±1.36
\n\t\t
±0.45
\n\t\t
±0.44
\n\t
\n\t
\n\t\t
(non-atherogenic profile n= 8)
\n\t
\n\t
\n\t\t
Stroke
\n\t\t
5.14
\n\t\t
2.29
\n\t\t
1.11
\n\t\t
1.48
\n\t\t
0.31
\n\t\t
2.86
\n\t\t
1.06
\n\t\t
5.33
\n\t\t
0.72
\n\t\t
0.76
\n\t
\n\t
\n\t\t
\n\t\t
±1.11
\n\t\t
±0.94
\n\t\t
±0.37
\n\t\t
±0.41
\n\t\t
±0.23
\n\t\t
±0.72
\n\t\t
±0.29
\n\t\t
±3.32
\n\t\t
±0.26
\n\t\t
±0.26
\n\t
\n\t
\n\t\t
(atherogenic profile n = 47)
\n\t
\n\t
\n\t\t
\n\t\t\tStroke\n\t\t
\n\t\t
\n\t\t\t5.19\n\t\t
\n\t\t
\n\t\t\t2.21\n\t\t
\n\t\t
\n\t\t\t1.08\n\t\t
\n\t\t
\n\t\t\t1.56\n\t\t
\n\t\t
\n\t\t\t0.29\n\t\t
\n\t\t
\n\t\t\t2.91\n\t\t
\n\t\t
\n\t\t\t1.09\n\t\t
\n\t\t
\n\t\t\t6.40\n\t\t
\n\t\t
\n\t\t\t0.78\n\t\t
\n\t\t
\n\t\t\t0.78\n\t\t
\n\t
\n\t
\n\t\t
\n\t\t
±1.06
\n\t\t
±0.91
\n\t\t
±0.35
\n\t\t
±0.53
\n\t\t
±0.23
\n\t\t
±0.77
\n\t\t
±0.31
\n\t\t
± 4.22
\n\t\t
±0.33
\n\t\t
±0.28
\n\t
\n\t
\n\t\t
(total number n= 55)
\n\t
\n\t
\n\t\t
Non-atherogenic vs. atherogenic
\n\t
\n\t
\n\t\t
\n\t\t
\n\t\t\tn.s.\n\t\t
\n\t\t
\n\t\t\tn.s.\n\t\t
\n\t\t
\n\t\t\tn.s.\n\t\t
\n\t\t
\n\t\t\tp<0.002\n\t\t
\n\t\t
\n\t\t\tp<0.05\n\t\t
\n\t\t
\n\t\t\tn.s.\n\t\t
\n\t\t
\n\t\t\tn.s.\n\t\t
\n\t\t
\n\t\t\tp< 0.0001\n\t\t
\n\t\t
\n\t\t\tn.s.\n\t\t
\n\t\t
\n\t\t\tp< 0.0001\n\t\t
\n\t
\n
Table 10.
Serum concentration of lipids, lipoproteins, and SAAR-score in patients with stroke.
Atherogenic 85.5 % vs. non-atherogenic 14.5 % in stroke patients.
In Table 10, an atherogenic lipoprotein phenotype was identified in 85.5 % of the patients who survive a cerebral ischemic stroke. The increased concentration of small dense LDL (LDL3-7 subgroups) in the atherogenic lipoprotein profile of patients with stroke, compared to the results in a non-atherogenic lipoprotein profile, is mild, but significant (p<0.05). The difference in the SAAR between the two subgroups was highly significant (p<0.0001), which also confirmed the overwhelming atherogenic lipoprotein constellation in patients who survived a stroke. The concentration of LDL1 was significantly higher in the subgroup of stroke-patients with a non-atherogenic lipoprotein profile (p<0.0001), however, the difference in the LDL2 lipoprotein subfraction was not significant.
6. Discussion
In the last few decades, lipoprotein research has focused on the phenomenon of atherogenic and non-atherogenic lipoproteins, and on the phenotype A vs. phenotype B characterization, as a consequence of the published evidence that the majority of the patients with an acute coronary syndrome or patients who survive a myocardial infarction had normal plasma values of cholesterol, LDL-cholesterol, and HDL-cholesterol [15-17]. A reasonable explanation for this fact was to posit a new, active atherogenic substance in plasma, an atherogenic lipoprotein subfraction, the presence of which in plasma, even in very low concentrations, could impair the integrity of the vessel wall and lead to endothelial dysfunction with its fatal consequences. Several clinical studies reported observations that in the plasma of patients with coronary heart disease there are subfractions of lipoproteins, which could play a crucial role in atherodegenerative processes and form the atherothrombotic plaques [5, 33, 34, 37, 39,49]. The Quebec Cardiovascular Study, a prospective study of 2,103 men [33,34] concluded that “ a significant proportion of the risk for heart disease associated with small, dense LDL particles may be independent of variations in plasma lipid concentrations. Small LDL particles and elevated apo B levels were found to be the most predictive indications for ischemic heart disease ”.
For this reason, patients who were suffering from cardiovascular diseases were examined in order to quantify the atherogenic lipoproteins in serum and to determine the incidence of an atherogenic lipoprotein profile in patients who had a diagnosis of cardiovascular diseases.
The clinical studies included 366 patients with a diagnosis of arterial hypertension (n=107), coronary heart disease (n= 104), lower extremity arterial disease (n= 100), and ischemic stroke (n= 55). Patients were tested with the diagnostic method Lipoprint LDL System, which quantifies atherogenic lipoproteins and identifies an atherogenic and a non-atherogenic lipoprotein profile [29]. This was a fundamental methodological contribution of this new analytical and diagnostic method.
Our study confirmed that more than 80% of tested patents with cardiovascular diseases have an atherogenic lipoprotein profile, with a high level of strongly atherogenic small dense LDL. The atherogenic lipoprotein profile was found to be the overwhelming lipoprotein profile in tested cardiovascular diseases. Such a profile was found in arterial hypertension in 78.5%, in coronary heart disease in 81.7%, in lower extremity arterial disease in 80%, and in patients who survived an ischemic stroke in 85%. The average atherogenic lipoprotein profile in all these tested diagnoses in the study was found to be of 81.3%.
This study also highlights the observation that, in the atherogenic lipoprotein profiles, in all diagnoses, compared to the non-atherogenic profiles, the concentration of total cholesterol is lower (n.s.) and the concentration of triglycerides is higher (even statistically significant; in AH, CHD, LEAD, as well as in the control group, up to p<0.002). Hypertriglyceridemia accompanied the hypercholesterolemia in all tested diagnoses, that is, in AH, CHD, LEAD, and stroke). The concentration of triglycerides, compared to the control group, was significantly increased (p<0.0001) and proportionally even higher than cholesterol. From this result, it can be assumed that triglycerides/hypertriglyceridemia can play a much more important role than was generally accepted, as until now the most important role in the pathogenesis of vascular degenerative atherosclerotic injury was attributed to cholesterol and hypercholesterolemia. Our present results are in agreement with other authors, who have called attention to hypertriglyceridemia as a risk factor for cardiovascular diseases [8,12,19, 36,58], as triglyceride-rich lipoproteins can generate small dense LDL in high quantities [46].
The strong atherogenic lipoproteins – small dense LDL – have been found in the lipoprotein profile of all diagnostic groups [25,52,55,56]. Their presence is decisive for an atherogenic profile declaration. This is a rule that is valid not only for a hyperlipidemia, but also for a normolipidemia.
In the case of normolipidemia (see the atherogenic lipoprotein profile in the control group), a new phenomenon could be established – atherogenic normolipidemia [44] – as a risk factor for the development of cardiovascular disease. A special form of normolipidemia can also be atherogenic. This is new knowledge, and this new knowledge could help in the prevention and treatment of cardiovascular disease.
Acknowledgments
This study was supported by an EU structural research fund Interreg III AT-SR, project code: 1414-02-000-28 in years 2006-2008.
We would like to acknowledge the excellent technical assistance of MTA Barbara Reif, MTA Judith Trettler, and MTA Karin Waitz, Krankenanstalten Dr. Dostal, Vienna, Austria, and also to acknowledge the excellent technical assistance of MTA Olga Reinoldova, 2nd Department of Internal Medicine, Faculty of Medicine, Comenius University, Bratislava, Slovak Republic.
\n',keywords:"atherogenic lipoproteins, atherogenic lipoprotein profile, small dense LDL, cardiovascular diseases",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/49062.pdf",chapterXML:"https://mts.intechopen.com/source/xml/49062.xml",downloadPdfUrl:"/chapter/pdf-download/49062",previewPdfUrl:"/chapter/pdf-preview/49062",totalDownloads:1550,totalViews:161,totalCrossrefCites:0,totalDimensionsCites:0,totalAltmetricsMentions:0,impactScore:0,impactScorePercentile:16,impactScoreQuartile:1,hasAltmetrics:0,dateSubmitted:"October 31st 2014",dateReviewed:"June 8th 2015",datePrePublished:null,datePublished:"October 7th 2015",dateFinished:"September 3rd 2015",readingETA:"0",abstract:"Research focus: Identification of incidence of an atherogenic lipoprotein phenotype B in four representative diagnoses of cardiovascular diseases: a) arterial hypertension, b) coronary heart disease, c) lower extremity arterial disease, d) ischemic stroke Research methods: A clinical study included 366 patients with a diagnosis of arterial hypertension (n=107), coronary heart disease (n= 104), lower extremity arterial disease (n= 100) and ischemic stroke (n= 55). The control group consisted of 150 healthy normotensive and normolipemic volunteers, all non-smokers, without signs of cardiovascular disease. In all tested individuals (or subjects) lipid parameters in serum: cholesterol and triglycerides were analyzed, using the enzymatic CHOD-PAP method, Roche Diagnostics Germany. Lipoproteins in serum lipoprotein spectrum by Lipoprint LDL system were analyzed and an atherogenic and a non-atherogenic lipoprotein profile idetified. The Score of the Anti-Atherogenic Risk (SAAR) was calculated as the ratio between non-atherogenic and atherogenic lipoproteins. Results: More than 80 percent of tested patients with cardovascular diseases have an atherogenic lipoprotein profile, with a high level of strongly atherogenic small dense LDL. The atherogenic profile was found in arterial hypertension 78.5%, in coronary heart disease in 81.7%, in lower extremity arterial disease in 80 %, and in patients who survived an ischemic stroke in 85%. Main conclusion: The atherogenic lipoprotein profile was found to be the overwhelming lipoprotein profile in tested cardiovascular diseases A new phenomenon- atherogenic normolipidemia - as a risk factor for the development of cardiovascular disease, would be established as a new term used in the diagnostics of dyslipoproteinemias",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/49062",risUrl:"/chapter/ris/49062",book:{id:"4605",slug:"lipoproteins-from-bench-to-bedside"},signatures:"Stanislav Oravec, Kristina Gruber, Andrej Dukat, Peter Gavornik,\nLudovit Gaspar and Elisabeth Dostal",authors:[{id:"142445",title:"Prof.",name:"Stanislav",middleName:null,surname:"Oravec",fullName:"Stanislav Oravec",slug:"stanislav-oravec",email:"stanislavoravec@yahoo.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Comenius University",institutionURL:null,country:{name:"Slovakia"}}},{id:"175551",title:"Dr.",name:"Kristina",middleName:null,surname:"Gruber",fullName:"Kristina Gruber",slug:"kristina-gruber",email:"kristinagruber@hotmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"175552",title:"Prof.",name:"Andrej",middleName:null,surname:"Dukat",fullName:"Andrej Dukat",slug:"andrej-dukat",email:"andrej.dukat@uniba.fmed.sk",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"175553",title:"Prof.",name:"Peter",middleName:null,surname:"Gavornik",fullName:"Peter Gavornik",slug:"peter-gavornik",email:"peter.gavornik@uniba.fmed.sk",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"175554",title:"Prof.",name:"Ludovit",middleName:null,surname:"Gaspar",fullName:"Ludovit Gaspar",slug:"ludovit-gaspar",email:"ludovit.gaspar@uniba.fmed.sk",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"175555",title:"Dr.",name:"Elisabeth",middleName:null,surname:"Dostal",fullName:"Elisabeth Dostal",slug:"elisabeth-dostal",email:"office@labor-dostal.at",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. Arterial hypertension",level:"1"},{id:"sec_2_2",title:"2.1. Patients",level:"2"},{id:"sec_3_2",title:"2.2. Methods",level:"2"},{id:"sec_4_2",title:"2.3. Results",level:"2"},{id:"sec_6",title:"3. Coronary heart disease",level:"1"},{id:"sec_6_2",title:"3.1. Patients",level:"2"},{id:"sec_7_2",title:"3.2. Methods",level:"2"},{id:"sec_8_2",title:"3.3. Results",level:"2"},{id:"sec_10",title:"4. Lower extremity arterial disease",level:"1"},{id:"sec_10_2",title:"4.1. Patients",level:"2"},{id:"sec_11_2",title:"4.2. Methods",level:"2"},{id:"sec_12_2",title:"4.3. Results",level:"2"},{id:"sec_14",title:"5. Stroke",level:"1"},{id:"sec_14_2",title:"5.1. Patients",level:"2"},{id:"sec_15_2",title:"5.2. Methods",level:"2"},{id:"sec_16_2",title:"5.3. Results",level:"2"},{id:"sec_18",title:"6. Discussion",level:"1"},{id:"sec_19",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Adams HP, Benedixen BH, Kapelle LJ, Biller J, Love BB, Gordon DL, Marsh EE. Classification of subtype of acute ischemic stroke. Definition for use in a multicenter clinical trial:TOAST Trial of Org 10172 in Acute Stroke Treatment. Stroke 1993; 24(1): 35–41'},{id:"B2",body:'Alabakovska SB, Todorova BB, Labudovic DD, Tosheska KN. Gradient gel electrophoretic separation of LDL and HDL subclasses on BioRad Mini Protean II and size phenotyping in healthy Macedonians. Clin Chim Acta 2002; 317: 119–123'},{id:"B3",body:'Amarenco P, Labreuche J, Touboul PJ. High-density lipoprotein cholesterol and risk of stroke and carotid atherosclerosis. A systemic review. Atherosclerosis 2008; 196: 489–496'},{id:"B4",body:'Amarenco P, Bogousslavsky J, Caplan LR, Donnan GA, Hennerici MG. Classification of stroke subtypes. 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Circulation 2000; 102: 716–721'},{id:"B60",body:'Van J, Pan J, Charles MA, Krauss R, Wong N, Wu X. Atherogenic lipid phenotype in a general group of subjects. Arch Pathol Lab Med 2007; 131: 1679–1685'},{id:"B61",body:'Whitworth JA. 2003 World Health Organisation (WHO), International Society of hypertension (ISH) Statement on management of hypertension. J Hypertension 2003; 21: 1983–1992'},{id:"B62",body:'Wierzbicki AS. Quality as well as quantity? Beyond low-density lipoprotein-cholesterol – The role of particle size. Int J Clin Pract 2007; 61: 1780–1782'},{id:"B63",body:'Vrablik M. Prevence cévních mozkových príhod hypolipidemickou léčbou. Remedia. Interetové stránky ceského farmakoterapeutického dvoumesícniku, rubrika PND, 2. duben 2009 (The prevention of the stroke by hypolipidemic treatment. Remedia). Internet Website of Czech Pharmaco therapeutic two-monthly Journal, column PND, April 2, 2009'},{id:"B64",body:'Chun Xia Zhao, Ying Hua Cui, Qiao Fan, Pei Hua Wang, Ruitai Hui, Cianflone K, Dao Wen Wang. Small dense low-density lipoproteins and associated risk factors in patients with stroke. Cerebrovasc Dis 2009; 27: 99–104'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Stanislav Oravec",address:"stanislavoravec@yahoo.com",affiliation:'
2nd Department of Internal Medicine, Faculty of Medicine, Comenius University, Bratislava, Slovak Republic
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1. Introduction
Mushrooms, their fruiting bodies and mycelia have served as food and food supplements around the world. They are relatively less toxic and are rich in bioactive compounds, such as polysaccharides, proteins, minerals, and other nutrients [1]. Beneficial activities associated with mushroom fruiting bodies and mycelia include antitumor [2], antimutagenic [3], antiviral [4], and antioxidant activities [5]. Some mushrooms alleviate the risk of diseases, such as Parkinson’s and Alzheimer’s disease, and hypertension [6].
Mushroom mycelia contain bioactive compounds as well as mushroom fruiting bodies, which have been investigated for their medicinal value. For example, oral administration of Sparassis crispa mycelia resulted in antitumor responses in tumor-bearing ICR mice [7]. The ability of erinacines, the bioactive compounds of H. erinaceum mycelia, to promote nerve health has been documented [8]. However, published data on the bioactivity of mushroom mycelia are limited compared with those of mushroom fruiting bodies.
In this chapter, we first discuss the antioxidant activity of 20 different species of wild mushroom mycelia [9]. These mushrooms are considered edible in the Tohoku area in northern Japan. Second, we present our findings on the ability of the comb tooth cap medicinal mushroom, H. ramosum mycelia, to promote NGF synthesis [9]. Finally, we discuss our results from using these mushroom mycelia in soybean fermentation [10] and discuss the prospects of utilizing H. ramosum mycelia in soybean fermentation for large-scale production of aglycons.
2. Characteristics of wild mushroom mycelia
2.1 Collection of mushrooms and separation of mycelia
We investigated the characteristics of 20 species of mushrooms: #1, A. brasiliensis (Agaricaceae); #2, Mycoleptodonoides aitchisonii (Climacodontaceae); #3, Ganoderma applanatum and #4, G. lusidum (Ganodermataceae); #5, H. erinaceum and #6, Hericium ramosum (Hericiaceae); #7, Inonotus obliquus (Hymenochaetaceae); #8, Lentinus edodes (Pleurotaceae); #9, Dendropolyporus umbellatus; #10, Grifola frondosa; #11, Laetiporus sulphureus; #12, Polyporellus badius and #13, Polyporus tuberaster (Polyporaceae); #14, Sparassis crispa (Sparassidaceae); #15, Pholiota aurivella and #16, Pholiota nameko (Strophariaceae); #17, Hypsizygus marmoreus, #18, Lepista nuda; #19, Lyophyllum shimeji and #20, Panellus serotinus (Tricholomataceae).
Nineteen of these (#2–20) wild mushroom fruiting bodies were collected from the Akita and Iwate prefectures in the Tohoku area in northern Japan. A. brasiliensis (#1) mycelia were provided by Dr. Makoto Yoneyama, I.M.C. Institution (Yamanashi Prefecture, Japan). Pieces of mushroom fruiting bodies collected from natural sites were plated in a 90-mm Petri dish containing potato dextrose agar (PDA) medium and incubated at 25°C for 2 days until the mycelia germinated. Mycelia were allowed to germinate and then cultured for 14 days at 25°C, after which period, they were maintained at 3°C on PDA medium. Mushroom mycelia were grown in submerged culture following the methods of A. brasiliensis mycelia cultivation, as described previously [11]. The culture was incubated at 25°C for 14 days with gentle shaking and the mycelia were lyophilized by freeze-drying after cultivation.
2.2 Ethanol extract preparation from mushroom mycelia
Mushroom mycelia extraction with ethanol was performed following methods described in previous reports [12, 13] with a few modifications. Lyophilized mushroom mycelia (0.1 g) were extracted with 80% ethanol (10 mL) at 25°C for 24 h and the resulting solutions were concentrated and lyophilized to a powder.
2.3 Antioxidant activity of wild mushroom mycelia
Free radicals exert tissue damage through reactive oxygen species (ROS)-induced oxidative stress, which can be counterbalanced by antioxidants [14, 15]. ROS, such as superoxide anion radicals, hydroxyl radicals, and hydrogen peroxide (H2O2), induce aging and cell damage [16, 17], and have been implicated in several diseases [18]. Recent epidemiological data indicate the association between inactivation of ROS and the disease-prevention benefits resulting from consuming food containing antioxidants, such as fruits, vegetables, and certain cereals [19]. As a result, there is an increasing trend worldwide in the incorporation of antioxidant compounds and foods into regular diet. We measured the antioxidant activity of the 20 wild mushrooms listed above by DPPH radical scavenging activity assay.
2.3.1 Methods
Measurement of 2,2-diphenyl-1-picryhydrazyl (DPPH) radical scavenging activity of mushroom mycelia was performed as previously described [20]. Ethanol extracts of mushroom mycelia (0.3 mL) were mixed with 0.6 mL of 100 mM MES buffer (pH 6.0)/10% ethanol solution, and 0.3 mL of 400 μM DPPH in ethanol. The absorbance of the reaction mixture was quantified at 520 nm after the reaction was set to complete for 20 minutes at RT. The DPPH radical scavenging activity of mushroom mycelia was calculated from assay lines of Trolox (0, 5, 10, 15, 20, and 25 μM) and expressed as μmol Trolox/g dry powder.
2.3.2 DPPH free radical scavenging activity of mushroom mycelia
Eighty-percent ethanol extracts of mushroom mycelia were used for antioxidant activity measurements using DPPH radical scavenging activity (Figure 1). Among the 20 mushroom mycelia analyzed, L. shimeji (#19), G. frondosa (#10), H. erinaceum (#5), and H. ramosum (#6) showed more robust DPPH radical scavenging activities. Among the mycelia tested, H. ramosum showed maximum antioxidant activity, followed by H. erinaceum, G. frondosa, and L. shimeji.
Figure 1.
DPPH radical scavenging activity of mycelial extracts from the 20 wild mushrooms [9]. 1, A. brasiliensis; 2, M. aitchisonii; 3, G. applanata; 4, G. lucidum; 5, H. erinaceum; 6, H. ramosum; 7, I. obliquus; 8, L. edodes; 9, D. umbellatus; 10, G. frondosa; 11, L. sulphureus; 12, P. badius; 13, P. tuberaster; 14, S. crispa; 15, P. aurivella; 16, P. nameko; 17, H. marmoreus; 18, L. nuda; 19, L. shimeji; and 20, P. serotinus. The DPPH radical scavenging activity of mushroom mycelia was calculated and expressed as the Trolox equivalent. Data represent the mean ± SD (n = 5).
2.4 Total phenolic content of the wild mushroom mycelia
Phenolic compounds are secondary metabolites of plants produced as defensive responses to threatening environments, including pathogen attack and UV radiation [21]. Generally, these polyphenols are classified as phenolic acids, flavonoids, lignans, and stilbenes [22]. These phenolic compounds possess antioxidant, antiglycemic, anticarcinogenic, and anti-inflammatory properties and can protect against bacterial and viral infections [23]. We analyzed the total phenolic content of the mushroom mycelia.
2.4.1 Methods
Folin & Ciocalteu method [24] with catechin as a standard was used for analysis. Ethanol extracts of mushroom mycelia (1 mL) were mixed with 0.5 mL of Folin & Ciocalteu solution and 5 mL of 0.4 M sodium carbonate solution. The absorbance of the reaction mixture was quantified at 660 nm after the reaction was set to complete for 30 minutes at 30°C. Methods are described in detail in Suruga et al. [9].
2.4.2 Measurement of total phenolic content
The phenolic contents of the samples were expressed as mg of catechin equivalent/g dry powder in Figure 2. H. ramosum (#6) showed the highest amount of phenol contents, followed by H. erinaceum (#5), G. frondosa (#10), A. brasiliensis (#1), L. shimeji (#19), E. applanata (#3), G. lucidum (#4), and H. marmoreus (#17).
Figure 2.
Total phenolic content of wild mushrooms mycelia extracts [9]. 1, A. brasiliensis; 2, M. aitchisonii; 3, G. applanata; 4, G. lucidum; 5, H. erinaceum; 6, H. ramosum; 7, I. obliquus; 8, L. edodes; 9, D. umbellatus; 10, G. frondosa; 11, L. sulphureus; 12, P. badius; 13, P. tuberaster; 14, S. crispa; 15, P. aurivella; 16, P. nameko; 17, H. marmoreus; 18, L. nuda; 19, L. shimeji; and 20, P. serotinus. Data represent the mean ± SD (n = 5).
2.5 Phenolic compounds enable the DPPH radical scavenging capacity of mushroom mycelia
DPPH radical scavenging activity showed a significant correlation (R2 = 0.7929) with the total phenolic content in the wild mushroom mycelia extracts (Figure 3). The Hericiaceae group, including H. erinaceum (#5) and H. ramosum (#6), which had a higher total phenolic content, showed stronger antioxidant potential. All these results suggest that the DPPH radical scavenging capacity of these extracts is driven by the phenolic compounds.
Figure 3.
Direct correlation between DPPH radical scavenging activity and phenolic content [9].
3. NGF synthesis of H. ramosum mycelia
Senile dementia, such as AD, is a severe problem, with no effective therapy [25]. Neurotrophic factors, including NGF, brain-derived neurotrophic factor (BDNF), neurotrophin 3, and glial-derived neurotrophic factor (GDNF), have been implicated in the prevention of neuronal death and promotion of neurite outgrowth [26]. Among them, NGF has been associated with AD [27], with decreased NGF levels in the basal forebrain of AD patients. Intracerebroventricular administration of NGF has been reported to eliminate degeneration and resultant cognitive deficits in rats after brain injury [28]. In rats, poor cognitive effects caused by neuronal degeneration have been shown to be eliminated by intracerebroventricular administration of NGF. However, since NGF cannot cross the blood-brain barrier, utilizing it for therapeutic application will be difficult. Several studies have investigated low-molecular-weight compounds, such as catecholamines [29], benzoquinones [30], hericenones [31], and erinacines [32], for their ability to promote NGF synthesis.
H. erinaceum is a common fungus found in the East Asian diet. Hericenones [33] have been isolated from the fruiting bodies of H. erinaceum and erinacines [34] have been identified in H. erinaceum mycelia. H. erinaceum may be valuable in the treatment and prevention of dementia [35, 36]. However, to our knowledge, no reports have shown the induction of NGF synthesis by H. ramosum mycelia. In this section, we show the results of our assessment on the ability of H. erinaceum and H. ramosum mycelia to induce NGF synthesis.
3.1 Methods
NGF synthesis was measured as described by Hazekawa et al. [37]. Male ddY mice (25–30 g weight) obtained from Kiwa Laboratory Animals Co., Ltd. (Wakayama, Japan), were housed under a 12-h light/dark cycle at room temperature and 55 ± 5% humidity. The lyophilized mycelia from H. erinaceum and H. ramosum were suspended in purified water and the samples were orally administrated to mice once a day for 14 days. NGF levels were analyzed in the cortex, striatum, and hippocampus 24 h after the last administration. Results are expressed as mean ± standard error of mean (SEM). The standard dose (300 mg/kg body weight) was determined based on Hazekawa et al. [37]. More detailed methodology can be found in Suruga et al. [9].
3.2 Stimulation of NGF synthesis by H. ramosum and H. erinaceum mycelia
The effects of 14-days of oral administration of 300 mg/kg H. erinaceum mycelia and H. ramosum mycelia on NGF levels in intact mouse brains are shown in Figure 4. H. ramosum mycelia were more potent than H. erinaceum mycelia in terms of NGF stimulation in the hippocampus of intact mice. Processing of H. ramosum mycelia over time significantly increased NGF levels in the hippocampus.
Figure 4.
Activation of NGF synthesis with wild H. erinaceum and H. ramosum mycelia [9]. NGF levels in various parts of the brain were measured after 14 days of repeated oral administration of H. erinaceum and H. ramosum mycelia (300 mg/kg). 1, Cortex; 2, striatum; and 3, hippocampus. Data are expressed as the mean ± SEM. *p < 0.05, compared with vehicle (Student’s test).
Different regions of the mouse brain responded differently to application of varying concentrations of H. ramosum mycelia on NGF synthesis [9]. The NGF levels in hippocampus increased with increased concentration of H. ramosum mycelia, while such dose-dependent response was not seen in cortex or striatum (Figure 5).
Figure 5.
Effect of varying concentrations of wild H. ramosum mycelia on NGF synthesis in different parts of mouse brain [9]. 1, Cortex; 2, striatum; and 3, hippocampus. Data are expressed as the mean ± SEM. *p < 0.05 and **p < 0.01, compared with vehicle (Student’s test).
4. Soybean fermentation using mushroom mycelia
The legume soybean is highly proteinaceous (36% protein in dried beans), rich in major nutrients essential for human nutrition and can potentially be a good replacement for animal-derived proteins [38, 39, 40, 41]. It can be used both in fermented and non-fermented forms [42]. While soybeans are rich in flavonoid groups such as genistein, daidzein, and glycitein isoflavones that have tremendous health benefits [43], they are not easily absorbed and incorporated in their natural glycosylated forms unless hydrolyzed by the microflora of the intestine through their beta-glucosidase production [44]. Isoflavones have health benefits against several diseases and hormone-related issues [45, 46, 47, 48]. The easily absorbable form of flavones is the aglycon form, which is abundant in fermented sources of soybean, such as tempeh, miso, and natto [49].
Mushroom mycelia can be used as a source of beta glucosidase to convert isoflavone glycosides to their aglycon form. For example, G. lusidum, belonging to the basidiomycetes group, has been shown to increase serum concentration of the aglycon form of isoflavones in soybeans [50].
Studies from our laboratory investigated the health effects of fermentation using mushrooms, such as G. lucidum, H. erinaceum, and H. ramosum [10]. We measured DPPH scavenging activity, total phenolic content, antioxidant activity, alpha glucosidase inhibition, and isoflavone concentration, few major health parameters of paramount importance, in soybeans fermented with different mushroom types and compared them with non-fermented soybeans.
Soybean fermentation was carried out as described in Suruga et al. [10]. We found that G. lucidum was more effective in quickly fermenting soybeans compared to the other two mushroom types (Figure 6).
Figure 6.
G. lucidum was faster in fermenting soybeans compared to other types (a) Control (non-fermented soybeans); (b) G. lucidum; (c) H. erinaceum; (d) H. ramosum [10].
4.1 Antioxidant activity of fermented soybean
4.1.1 Methods
The DPPH radical scavenging activity and total phenolic content of fermented soybeans were analyzed using the methods described in Subsections 2.3.1 and 2.4.1. Oxygen radical absorbance capacity (ORAC) was determined using the OxiSelect™ ORAC Activity Assay Kit (Cell Biolabs Inc., San Diego, CA, USA) [51]. The assay was performed as described in Suruga et al. [10]. Briefly, fluorescence activity of the reaction mixture with antioxidant and fluorescein solution was measured after adding the free radical initiator. Increasing Trolox concentrations were used for the standard curve, and extracts were quantified and expressed as μmol Trolox equivalents/g of dry fermented soybean powder.
4.1.2 Total phenolic content and antioxidant activity of fermented soybean by mushroom mycelia
Total phenolic content was higher in all the fermented extracts compared to non-fermented control soybeans. Both DPPH radical scavenging activity and antioxidant activity were higher in fermented soybeans than in non-fermented ones (Table 1).
Total phenolic content and antioxidant activity of soybeans fermented by mushroom mycelia [10].
4.2 Alpha-glucosidase inhibitory activity of soybeans fermented with mushroom mycelia
4.2.1 Methods
Yeast alpha-glucosidase inhibitory activity was measured using methods reported before [52] with modifications as described in Suruga et al. [10]. Briefly, yeast alpha-glucosidase was incubated with fermented soybean extract solutions and then p-nitrophenyl α-D-glucopyranoside (pNP-glucoside) was added and absorbance was determined at 400 nm. For the mammalian reaction, alpha glucosidase from rat intestinal acetone powder [53] was incubated with fermented soybean extract and the amount of glucose released was measured. We also used maltose as the substrate and calculated the % inhibition rate of alpha glucosidase [54].
4.2.2 Fermented soybean showed higher inhibition of alpha-glucosidase activity compared to non-fermented ones
Comparison of control (non-fermented soybeans) to soybeans fermented with mushroom mycelia showed that significant alpha-glucosidase inhibition could be achieved in the fermented soybeans using both pNP-glucoside and maltose (Figure 7A and B). Yeast alpha-glucosidase inhibition was the highest with H. ramosum compared to G. lucidum and H. erinaceum, while the mammalian alpha-glucosidase inhibition was significantly higher with G. lucidum fermentation (Figure 7A–C).
Figure 7.
Inhibition of alpha-glucosidase activity soybeans fermented by mushroom mycelia. (A) Yeast alpha-glucosidase inhibition using pNP-glucoside as substrate, (B) mammalian alpha-glucosidase inhibition using maltose as substrate, and (C) mammalian alpha-glucosidase inhibition using sucrose as substrate. Results are expressed as mean ± SD (n = 3). N.D.: not detectable. 1: p < 0.01 vs. control, 2: p < 0.01 vs. G. lucidum, 3: p < 0.01 vs. H. erinaceum, 4: p < 0.01 vs. H. ramosum, and 5: p < 0.05 vs. H. ramosum [10].
4.3 Comparison of isoflavone concentrations in soybeans fermented with mycelia versus non-fermented soybeans
We followed the methods described in Kudou et al. [55] for measuring isoflavone concentrations in fermented and non-fermented soybeans. An LC-6A system (Shimadzu, Kyoto, Japan) equipped with a PEGASIL-ODS (4.6 mm i.d. × 250 mm) HPLC column (Senshu Scientific, Tokyo, Japan) was used for analysis. HPLC parameters used for the measurement of different isoflavones, such as genistein, daidzein, glycitein, daidzin, and glycitin, both in the glycosylated and in aglycone forms, are detailed in Suruga et al. [10].
An ACQUITY UPLC apparatus (Waters MS Technologies, Manchester, UK) equipped with a reversed-phase Acquity UPLC CHS C18 column with a particle size of 2.1 mm × 100 mm × 1.7 μm (Waters MS Technologies) was used for LC/MS analysis. Parameters of analysis are documented in detail in Suruga et al. [10].
4.3.3 Fermentation with mushrooms decreased the concentrations of glycosylated forms of isoflavones and increased the concentrations of aglycon forms
LC/MS profile was shown in Figure 8. The concentration of glycosylated forms of isoflavones, such as daidzin, glycitin, and genistin was about 95.6% in non-fermented soybeans, while it was reduced to 52.5, 15.8, and 17.6% in soybeans fermented by the G. lucidum, H. erinaceum, and H. ramosum mycelia, respectively. The aglycone forms of these isoflavones, on the other hand, increased from 4.4% in the non-fermented controls to 47.5, 84.2, and 82.4% in soybeans fermented by G. lucidum, H. erinaceum, and H. ramosum mycelia, respectively. LC/MS profile shown in Figure 8 corroborate these results. Based on the retention time and MS data, molecular formula and identity of compounds corresponding to 11 of the 12 peaks have been predicted: peak #1, daidzin; peak #2, glycitin; peak #3, 8-hydroxydaidzein; peak #4, genistin; peak #5, 6″-O-malonyldaidzin; peak #7, 6″-O-acetyldaidzin; peak #8, 6″-O-malonylgenistin; peak #9, daidzein; peak #10, glycitein; peak #11, 6″-O-acetylgenistin; and peak #12, genistein, respectively.
Figure 8.
LC/MS profile of soybean fermented using mushroom mycelia: a, control (non-fermented soybeans); b, G. lucidum; c, H. erinaceum; and d, H. ramosum [10].
5. Discussion
5.1 Characteristics of H. ramosum mycelia and other mushroom mycelia
While the beneficial effects of mushrooms in human health and nutrition have long been known and their pharmacological use has been studied in several types of mushrooms, including Pleurotus, Ganoderma, Cordyceps, Lentinus, Grifola, and Hericium [56], there are plenty of rare species of mushrooms that have not been investigated yet in terms of their biological functions, such as antioxidant activity, induction of NGF synthesis, etc. For example, there is only a single report that investigated the kappa opioid receptor binding activity of erinacine E on H. ramosum, indicating the rarity of this mushroom. Our analysis has provided a vast amount of data on the potential value of this mushroom. Given their vast health benefits and medicinal value, finding new mushrooms and analyzing their biological and pharmacological properties is of tremendous importance toward utilizing them in the development of new drugs and food supplements. We have investigated 20 mushroom types for their health benefits.
DPPH radical scavenging activity is a good indicator of antioxidant properties. Our study indicated that several mushrooms (L. shimeji #19, G. frondosa #10, H. erinaceum #5, and H. ramosum #6) were potent scavengers of DPPH (Figure 1). We also found a direct correlation (R2 = 0.7929) between total phenolic content and DPPH radical scavenging activity (Figure 3). A direct relationship between total phenolic content and DPPH scavenging activity has been demonstrated in several studies. For example, a direct correlation (R2 = 0.9788) between total phenolic content and total antioxidant activity has been shown in 11 kinds of fruits by Sun et al. [58]. A direct relationship (R2 = 0.8181) between total phenolics volume and DPPH radical scavenging activity has also been reported in the fruiting bodies of 14 different kinds of commercially available mushrooms by Abdullah et al. [59]. A direct relationship was reported between the high antioxidant activity observed in rice fermented by Monascus mycelia and its high total phenolic compound levels [60]. Our results corroborate the findings from these reports.
From our analyses, we have found that L. shimeji (#19) and G. frondosa (#10) had potent DPPH radical-scavenging activities. Several studies have investigated the applications of these and other mushrooms in various diseases and for other purposes. Pyranose oxidase, a flavoprotein from L. shimeji (Honshimeji in Japanese), has been studied and its heterologous expression is reported to be under the control of the T7 promoter in Escherichia coli [61]. L. connatum fruiting bodies (Oshiroishimeji) have been shown to contain new ceramides [62]. The antitumor activities of (1 → 3)-ß-D-glucan and (1 → 6)-ß-D-glucan from L. decastes (Hatakeshimeji) hot water extract against Sarcoma 180 have also been described [63].
Several compounds responsible for DPPH and antioxidant activity have been isolated from mushrooms and studied in detail. However, little information has been published regarding the DPPH scavenging activity of active compounds from mycelia of L. shimeji and G. frondosa. DPPH active compounds ergothioneine, N-hydroxy-N′,N′-dimethylurea, connatin, and ß-hydroxyergothioneine have been isolated from L. connatum fruiting bodies [64]. Yeh et al. described the antioxidant compounds β-tocopherol and flavonoids in ethanol extracts of G. frondosa fruiting bodies, which are edible mushrooms in Japan [65]. Zhang et al. isolated three analogues of ergosterol from G. frondosa mycelia as compounds with antioxidant activity [66]. Reis et al. investigated the effects of five kinds of mushroom mycelia (A. bisporus-white, A. bisporus-brown, P. ostreatus, P. eryngii, and L. edodes) on antioxidant activity. The authors reported that the antioxidant compounds of these mushroom mycelia were gallic acid, protocatechuic acid, p-hydroxybenzoic acid, and p-coumaric acid [67]. Considering these observations, the potent DPPH scavenging activity of L. shimeji, G. frondosa, and other mushrooms could be attributed to the polyphenols ergothioneine, N-hydroxy-N′,N′-dimethylurea, connatin, ß-hydroxyergothioneine ergosterol, α-tocopherol, flavonoids, gallic acid, protocatechuic acid, p-hydroxybenzoic acid, and p-coumaric acid.
The present findings indicate that the DPPH radical scavenging activity of the Hericiaceae group, including H. erinaceum (#5) and H. ramosum (#6), was stronger than those of other mushroom mycelia. The antioxidant activity of some phenolic compounds has been reported in the H. erinaceum and its mycelial extracts [68]. A strong antioxidant activity has also been shown in vitro in polysaccharides derived from an ethanol extract of H. erinaceum grown on tofu [69]. Thus, there has been minimal effect on H. ramosum mycelia which contain phenolic compounds and polysaccharides with strong antioxidant activity.
NGF plays a crucial role in nerve growth and neuronal cell function, and protection of neurons. NGF has been implicated in various diseases, including in Alzheimer’s disease, the most common type of dementia that affects language, memory, processing of visual cues, judgment, and mood [70]. Reduced levels of NGF or increased accumulation of β-amyloid peptide and tau protein have been suggested as causes of AD [71]. Given its importance, there has been a demand for finding natural inducers of NGF synthesis. Natural compounds such as hericenones and erinacines isolated from H. erinaceum have been shown to induce NGF synthesis [33, 72]. We have shown that H. ramosum mycelia induced stronger NGF synthesis activity compared to H. erinaceum mycelia in the hippocampus of intact mice, and that processing of H. ramosum mycelia over time elevated the levels of NGF levels (Figure 4). We also found a dose-dependent response of NGF with increasing concentrations of H. ramosum mycelia in the hippocampi of intact mice (Figure 5). However, we have not determined the active compounds in the mycelia responsible for NGF synthesis. There are mounting evidence suggesting that erinacine species could be responsible, with the isolation of erinacine E from H. ramosum [57], and the observation that active substances other than hericenones stimulated NGF synthesis through c-Jun N-terminal kinase activation in H. erinaceum [73]. This evidence indicates that erinacine species could be involved in the induction of NGF synthesis in H. ramosum mycelia. There may be involvement from other unknown compounds as well, as our data comes from mycelia and not the fruiting bodies.
5.2 Soybean fermentation of mushroom mycelia
Mushrooms are effective in combating issues caused by obesity, diabetes, and other health issues [74]. The medicinal value of mushrooms has been known for thousands of years [75, 76] and they have been incorporated in nutrition supplements [74] and in the production of fermented foods, such as soybean-based foods, bread and cheese, and in alcoholic beverages [77]. However, detailed analysis of soybeans fermented by mushroom mycelia has not been conducted, insofar as their oxidative properties or alpha-glucosidase inhibitory activity are concerned. Our study analyzed all these properties and the LC/MS profiles of the bioactive products to glean more insights into the medicinal value of fermented soybeans.
We found that soybeans fermented with mushroom mycelia had stronger DPPH radical scavenging activity and ORAC than the non-fermented control ones. We also found that H. ramosum mycelia were more potent in DPPH radical scavenging and oxygen radical absorbance compared to all the other 19 mushroom groups we had tested (Figure 1) [9]. While this result was consistent in our subsequent study, we also found that DPPH radical scavenging activity and total phenolic content of G. lucidum mycelia-fermented soybeans was higher than soybeans fermented with H. erinaceum and H. ramosum mycelia [10].
The compound 8-hydroxydaidzein (peak #3 in Figure 8b) and one unidentified compound (peak #6) were identified by LC/MS analysis in soybeans fermented using G. lucidum mycelia. While we are investigating the identity of this unknown compound, we believe that this could possibly be 6-hydroxydaidzein or 3-hydroxydaidzein based on mass spectrometry analysis results. 6-Hydroxydaidzein has been isolated from soybean koji fermented with Aspergillus oryzae [78] and was found to be more potent in terms of antioxidative properties compared to daidzein [79], suggesting that phenolic compounds such as hydroxydaidzeins could influence the antioxidant effects of soybeans fermented with G. lucidum mycelia. Since oxidative stress is linked to several diseases [80], mushroom mycelia showing antioxidant activity is of much relevance toward producing antioxidant foods and nutritional supplements. We have shown high antioxidant activity in G. lucidum mycelia-fermented soybeans [10], as well as in fermented soy residue (“okara”) with Rhizopus oligosporus [81, 82, 83].
Alpha-glucosidases are the primary enzymes responsible for hydrolyzing carbohydrates into glucose. Inhibition of alpha-glucosidase activity, therefore, is a strategy for controlling increase in blood glucose levels in diabetic conditions. We have shown that soybeans fermented with mushroom mycelia have significantly higher alpha-glucosidase activity than the non-fermented control groups. When pNP-glucoside was used as a substrate, the yeast alpha-glucosidase activity was inhibited in soybeans fermented with H. erinaceum, H. ramosum, and G. lucidum mycelia, with fermentation using the Hericiaceae members showing higher inhibition than with G. lucidum mycelia. Similar inhibition of alpha-glucosidase using pNP-glucoside has been achieved by the commercial soy isoflavone genistein by Lee et al. [84], suggesting that genistein might play a role. Despite pNP-glucoside’s wide usage in testing anti-diabetic agents, maltose and sucrose are biologically more relevant as substrates than pNP-glucoside for mammalian systems. Therefore, we used these two substrates for testing inhibition of alpha-glucosidase activity in soybeans fermented using H. erinaceum, H. ramosum, and G. lucidum mycelia. We found that all three were able to inhibit alpha-glucosidase activity with varying degrees, with G. lucidum mycelia exhibiting higher inhibition with both maltose and sucrose as substrates compared to the other mushroom species. We suspect that in addition to genistein, hydroxydaidzein in soybeans fermented using G. lucidum mycelia could facilitate this inhibition. The precise identification of the active compounds in fermented soybeans using mushroom mycelia is yet to be completed, but fermented soybeans have potential use as nutritional supplements for treating diabetes.
The beta-glucosidase enzyme (EC 3.2.1.21) produced by microbes facilitates the breakdown of glycosylated isoflavones to their aglycon form, which is more easily absorbable [85]. Several microbes, including Aspergillus niger [86], A. oryzae [87], Penicillium brasilianum [88], and Phanerochaete chrysosporium [89], are being tapped for this fermentation purpose. We found that the levels of aglycons (daidzein, glycitein, and genistein), were higher in soybeans fermented with mycelia compared to non-fermented soybeans. While one previous report has shown the conversion of isoflavone glucosides to aglycons using G. lucidum mycelia to ferment soybeans [50], not many studies have investigated soybean fermentation using H. erinaceum and H. ramosum mycelia. We have shown that fermentation using these mycelia increased the amount of the aglycon form compared to non-fermented ones. The amount of aglycons was higher with H. erinaceum and H. ramosum mycelia compared to that when G. lucidum mycelia were used, possibly because the former produces more beta-glucosidase enzyme than the latter.
Our mass spectrometry analysis data revealed that the aglycon form of isoflavones obtained in soybeans fermented with G. lucidum mycelia contained 8-hydroxydaidzein and an unidentified compound, which we assumed to be 6-hydroxydaidzein or 3-hydroxydaidzein based on m/z data and molecular formula derived from LC/MS analysis. 8-Hydroxydaidzein was first isolated from Streptomyces sp. fermentation broth [90] and has also been obtained from A. oryzae and recombinant Pichia pastoris, in addition to 6-hydroxydaidzein and 3-hydroxydaidzein [91]. This compound has been shown to have anti-proliferative, tyrosinase inhibition, aldose reductase inhibition, anti-inflammatory, and antioxidant activities [79, 92, 93, 94, 95], indicating that soybeans fermented with G. lucidum mycelia might also have these properties. Since the mechanism of conversion of hydroxydaidzein to daidzin is known [96], and given its valuable properties, synthetic hydroxydaidzein is produced at the commercial level, but the process has its own limitations, such as the formation of undesirable by-products, lengthy reaction steps and low yield [97]. Large-scale production of hydroxydaidzein using natural resources such as A. oryzae are being investigated [96, 98]. Our results have added several suitable candidates for this purpose. In particular, soybeans fermented using G. lucidum mycelia have enormous potential to be used as food, nutritional supplement and as a source for commercial production of hydroxydaidzein.
Acknowledgments
We are grateful to the Chairman and CEO Masahito Hoashi, Kibun Foods Inc., for his support of this study.
Conflict of interest
The authors declare that there are no conflicts of interest.
\n',keywords:"H. ramosum mycelia, antioxidant, NGF synthesis, soybean fermentation, isoflavone",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/80675.pdf",chapterXML:"https://mts.intechopen.com/source/xml/80675.xml",downloadPdfUrl:"/chapter/pdf-download/80675",previewPdfUrl:"/chapter/pdf-preview/80675",totalDownloads:27,totalViews:0,totalCrossrefCites:0,dateSubmitted:"November 29th 2021",dateReviewed:"January 7th 2022",datePrePublished:"April 12th 2022",datePublished:null,dateFinished:"March 2nd 2022",readingETA:"0",abstract:"The medicinal value of mushrooms is long known, but there is increasing awareness of their health benefits and interest in utilizing these in diet as food or nutritional supplement. In this chapter, we discuss the characteristics of 20 wild mushrooms and results from our work on their antioxidant activity, ability to promote nerve growth factor (NGF) synthesis and to convert the glycosylated forms of isoflavones to usable aglycon forms in soybeans fermented with their mycelia. Of the 20 mushroom types, we found that Hericium ramosum (H. ramosum) mycelia had higher antioxidant activity and showed greater capability for increasing the levels of aglycons, such as daidzein, glycitein, and genistein when used for fermentation of soybeans. In general, soybeans fermented with mushrooms increased the levels of aglycons compared to non-fermented ones. Taken together, all these results suggest that mushroom mycelia have a huge potential to be used as food and nutritional supplements for the health benefits they offer and present the prospects for utilizing them in soybean fermentation as natural resources for the large-scale production of aglycons.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/80675",risUrl:"/chapter/ris/80675",signatures:"Kohei Suruga, Tsuyoshi Tomita and Kazunari Kadokura",book:{id:"11363",type:"book",title:"Functional Food",subtitle:null,fullTitle:"Functional Food",slug:null,publishedDate:null,bookSignature:"Dr. Naofumi Shiomi and Ph.D. Anna Savitskaya",coverURL:"https://cdn.intechopen.com/books/images_new/11363.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-80355-793-9",printIsbn:"978-1-80355-792-2",pdfIsbn:"978-1-80355-794-6",isAvailableForWebshopOrdering:!0,editors:[{id:"163777",title:"Dr.",name:"Naofumi",middleName:null,surname:"Shiomi",slug:"naofumi-shiomi",fullName:"Naofumi Shiomi"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Characteristics of wild mushroom mycelia",level:"1"},{id:"sec_2_2",title:"2.1 Collection of mushrooms and separation of mycelia",level:"2"},{id:"sec_3_2",title:"2.2 Ethanol extract preparation from mushroom mycelia",level:"2"},{id:"sec_4_2",title:"2.3 Antioxidant activity of wild mushroom mycelia",level:"2"},{id:"sec_4_3",title:"2.3.1 Methods",level:"3"},{id:"sec_5_3",title:"2.3.2 DPPH free radical scavenging activity of mushroom mycelia",level:"3"},{id:"sec_7_2",title:"2.4 Total phenolic content of the wild mushroom mycelia",level:"2"},{id:"sec_7_3",title:"2.4.1 Methods",level:"3"},{id:"sec_8_3",title:"2.4.2 Measurement of total phenolic content",level:"3"},{id:"sec_10_2",title:"2.5 Phenolic compounds enable the DPPH radical scavenging capacity of mushroom mycelia",level:"2"},{id:"sec_12",title:"3. NGF synthesis of H. ramosum mycelia",level:"1"},{id:"sec_12_2",title:"3.1 Methods",level:"2"},{id:"sec_13_2",title:"3.2 Stimulation of NGF synthesis by H. ramosum and H. erinaceum mycelia",level:"2"},{id:"sec_15",title:"4. Soybean fermentation using mushroom mycelia",level:"1"},{id:"sec_15_2",title:"4.1 Antioxidant activity of fermented soybean",level:"2"},{id:"sec_15_3",title:"4.1.1 Methods",level:"3"},{id:"sec_16_3",title:"Table 1.",level:"3"},{id:"sec_18_2",title:"4.2 Alpha-glucosidase inhibitory activity of soybeans fermented with mushroom mycelia",level:"2"},{id:"sec_18_3",title:"4.2.1 Methods",level:"3"},{id:"sec_19_3",title:"4.2.2 Fermented soybean showed higher inhibition of alpha-glucosidase activity compared to non-fermented ones",level:"3"},{id:"sec_21_2",title:"4.3 Comparison of isoflavone concentrations in soybeans fermented with mycelia versus non-fermented soybeans",level:"2"},{id:"sec_21_3",title:"4.3.1 Methods: high-performance liquid chromatography (HPLC) analysis",level:"3"},{id:"sec_22_3",title:"4.3.2 Methods: liquid chromatography/mass spectrometry (LC/MS) analysis",level:"3"},{id:"sec_23_3",title:"4.3.3 Fermentation with mushrooms decreased the concentrations of glycosylated forms of isoflavones and increased the concentrations of aglycon forms",level:"3"},{id:"sec_26",title:"5. Discussion",level:"1"},{id:"sec_26_2",title:"5.1 Characteristics of H. ramosum mycelia and other mushroom mycelia",level:"2"},{id:"sec_27_2",title:"5.2 Soybean fermentation of mushroom mycelia",level:"2"},{id:"sec_29",title:"Acknowledgments",level:"1"},{id:"sec_32",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Wang Q, Wang F, Xu Z, Ding Z. Bioactive mushroom polysaccharides: A review on monosaccharide composition, biosynthesis and regulation. Molecules. 2017;22:955. DOI: 10.3390/molecules22060955'},{id:"B2",body:'Yang Y, He P, Li N. The antitumor potential of extract of the oak bracket medicinal mushroom Inonotus baumii in SMMC-7721 tumor cells. Evidence-based Complementary and Alternative Medicine. 2019;2019:1242784. DOI: 10.1155/2019/1242784'},{id:"B3",body:'Yang NC, Wu CC, Liu RH, Chai YC, Tseng CY. 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A colon-specific drug-delivery system based on drug glycosides and the glycosidases of colonic bacteria. Journal of Medicinal Chemistry. 1984;27:261-266. DOI: 10.1021/jm00369a005'},{id:"B45",body:'Messina M, Kucuk O, Lampe JW. An overview of the health effects of isoflavones with an emphasis on prostate cancer risk and prostate-specific antigen levels. Journal of AOAC International. 2006;89:1121-1134'},{id:"B46",body:'Carroll KK. Review of clinical studies on cholesterol-lowering response to soy protein. Journal of the American Dietetic Association. 1991;91:820-827'},{id:"B47",body:'Ye YB, Tang XY, Verbruggen MA, Su YX. Soy isoflavones attenuate bone loss in early postmenopausal Chinese women: A single-blind randomized, placebo-controlled trial. European Journal of Nutrition. 2006;45:327-334. DOI: 10.1007/s00394-006-0602-2'},{id:"B48",body:'Lethaby A, Marjoribanks J, Kronenberg F, Roberts H, Eden J, Brown J. Phytoestrogens for vasomotor menopausal symptoms. 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DOI: 10.1271/bbb1961.55.2227'},{id:"B56",body:'Lindequist U, Niedermeyer THJ, Jülish WD. The pharmacological potential of mushrooms. Evidence-based Complementary and Alternative Medicine. 2005;2:285-299. DOI: 10.1093/ecam/neh107'},{id:"B57",body:'Saito T, Aoki F, Hirai H, Inagaki T, Matsunaga Y, Sakakibara T, et al. Erinacine E as a kappa opioid receptor agonist and its new analogs from a Basidiomycete, Hericium ramosum. The Journal of Antibiotics. 1998;51:983-990. DOI: 10.7164/antibiotics.51.983'},{id:"B58",body:'Sun J, Chu YF, Wu X, Liu RH. Antioxidant and antiproliferative activities of common fruits. Journal of Agricultural and Food Chemistry. 2002;50:7449-7454. DOI: 10.1021/jf0207530'},{id:"B59",body:'Abdullash N, Ismail SM, Aminudin N, Shuib AS, Lau BF. Evaluation of selected culinary-medicinal mushrooms for antioxidant and ACE inhibitory activities. Evidence-based Complementary and Alternative Medicine. 2012;2012:464238. DOI: 10.1155/2012/464238'},{id:"B60",body:'Yang JH, Tseng YH, Lee YL, Mau JL. Antioxidant properties of methanolic extracts from monascal rice. LWT- Food Science and Technology. 2006;39:740-747. DOI: 10.1016/j.lwt.2005.06.002'},{id:"B61",body:'Salaheddin C, Takakura Y, Tsunashima M, Stranzinger B, Spadiut O, Yamabjai M, et al. Characterization of recombinant pyranose oxidase from the cultivated mycorrhizal basidiomycete Lyophyllum shimeji (hon-shimeji). Microbial Cell Factories. 2010;9:1-12. DOI: 10.1186/1475-2859-9-57'},{id:"B62",body:'Yaoita Y, Kohata R, Kakuda R, Machida K, Kikuchi M. Ceramide constituents from five mushrooms. Chemical & Pharmaceutical Bulletin. 2002;50:681-684. DOI: 10.1248/cpb.50.681'},{id:"B63",body:'Ukawa Y, Hisamitsu M, Ito H. Antitumor effects of (1→3)-β-D-glucan and (1→6)-β-D-glucan purified from newly cultivated mushroom, Hatakeshimeji (Lyophyllum decastes Sing). Journal of Bioscience and Bioengineering. 2000;90:98-104'},{id:"B64",body:'Barger G, Ewins AJ. CCLVII—The constitution of ergothioneine: A betaine related to histidine. Journal of the Chemical Society, Transactions. 1911;99:2336-2341'},{id:"B65",body:'Yeh JY, Hsieh LH, Wu KT, Tsai CF. Antioxidant properties and antioxidant compounds of various extracts from the edible basidomycete Grifola frondosa (Maitake). Molecules. 2011;16:3197-3211. DOI: 10.3390/molecules16043197'},{id:"B66",body:'Zhang Y, Mills GK, Nair MG. Cyclooxygenase inhibition and antioxidant compounds from the mycelia of the edible mushroom Grifola frondosa. Journal of Agricultural and Food Chemistry. 2002;50:7581-7585. DOI: 10.1021/jf0257648'},{id:"B67",body:'Reis FS, Martins A, Barros L, Ferreira ICFR. Antioxidant properties and phenolic profile of the most widely appreciated cultivated mushrooms: A comparative study between in vivo and in vitro samples. Food and Chemical Toxicology. 2012;50:1201-1207. DOI: 10.1016/jf2012.02.013'},{id:"B68",body:'Khan MA, Tania M, Liu R, Rahman MM. 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Hericenones C, D and E, stimulators of nerve growth factor (NGF)-synthesis, from the mushroom Hericium erinaceum. Tetrahedron Letters. 1991;32:4561-4564. DOI: 10.1016/0040-4039(91)80039-9'},{id:"B73",body:'Kawagishi H, Shimada A, Shizuki K, Ojima F, Mori H, Okamoto K, et al. Erinacines D, a stimulator of NGF-synthesis, from the mycelia of Hericium erinaceum. Heterocyclic Communications. 1996;2:51-54'},{id:"B74",body:'Chaturvedi VK, Agarwal S, Gupta KK, Ramteke PW, Singh MP. Medicinal mushroom: Boon for therapeutic applications. 3 Biotech. 2018;8:334. DOI: 10.1007/s13205-018-1358-0'},{id:"B75",body:'El Enshasy HA, Hatti-Kaul R. Mushroom immunomodulators: Unique molecules with unlimited applications. Trends in Biotechnology. 2013;31:668-677. DOI: 10.1016/j.tibtech.2013.09.003'},{id:"B76",body:'Muszyńska B, Grzywacz-Kisielewska A, Kała K, Gdula-Argasińska J. Anti-inflammatory properties of edible mushrooms: A review. Food Chemistry. 2018;243:373-381. DOI: 10.1016/j.foodchem.2017.09.149'},{id:"B77",body:'Matsui T. Development of functional foods by mushroom fermentation (in Japanese). Mushroom Science and Biotechnology. 2017;24(4):169-175'},{id:"B78",body:'Esaki H, Kawakishi S, Morimitsu Y, Osawa T. New potent antioxidative o-dihydroxyisoflavones in fermented Japanese soybean products. Bioscience, Biotechnology, and Biochemistry. 1999;63:1637-1639. DOI: 10.1271/bbb.63.1637'},{id:"B79",body:'Esaki H, Watanebe R, Onozaki H, Kawakishi S, Osawa T. Formation mechanism for potent antioxidative o-dihydroxyisoflavones in soybean fermented with Aspergillus saitoi. Bioscience, Biotechnology, and Biochemistry. 1999;63:851-858. DOI: 10.1271/bbb.63.851'},{id:"B80",body:'Willcox JK, Ash SL, Catignani GL. Antioxidants and prevention of chronic disease. Critical Reviews in Food Science and Nutrition. 2004;44:275-295. DOI: 10.1080/10408690490468489'},{id:"B81",body:'Suruga K, Akiyama Y, Kadokura K, Sekino Y, Kawagoe M, Komatsu Y, et al. Synergistic effect on reactive oxygen scavenging activity of fermented okara and banana by XYZ system. Food Science and Technology Research. 2007;13:139-144. DOI: 10.3136/fstr.13.139'},{id:"B82",body:'Suruga K, Kato A, Kadokura K, Hiruma W, Sekino Y, Buffinton CAT, et al. “Okara” a new preparation of food material with antioxidant activity and dietary fiber from soybean. In: El-Shemy H, editor. Soybean and Nutrition. 1st ed. London, UK: IntechOpen; 2011. pp. 311-326. DOI: 10.5772/18821'},{id:"B83",body:'Noguchi S, Suruga K, Nakai K, Murashima A, Koshino-Kimura Y, Kobayashi A. An exploratory study of okara product on postprandial blood glucose and serum insulin responses. The Japanese Journal of Nutrition and Dietetics. 2018;76:156-162. DOI: 10.5264/eiyogakuzashi.76.156'},{id:"B84",body:'Lee DS, Lee SH. Genistein, a soy isoflavone, is a potent α-glucosidase inhibitor. FEBS Letters. 2001;501:84-86. DOI: 10.1016/s0014-5793(01)02631-x'},{id:"B85",body:'Doan DT, Luu DP, Nguyen TD, Thi BH, Thi HMP, Do HN, et al. Isolation of Penicillium citrinum from roots of Clerodendron cyrtophyllum and application in biosynthesis of aglycone isoflavones from soybean waste fermentation. Food. 2019;8:554. DOI: 10.3390/foods8110554'},{id:"B86",body:'Baraldo JA, Borges DG, Tardioli PW, Farinas CS. Characterization of β-glucosidase produced by Aspergillus niger under solid-state fermentation and partially purified using MANAE-agarose. Biotechnology Research International. 2014;2014:317092. DOI: 10.1155/2014/317092'},{id:"B87",body:'Langston J, Sheehy N, Xu F. Substrate specificity of Aspergillus oryze family 3 beta-glucosidase. Biochimica et Biophysica Acta, Proteins and Proteomics. 2006;1764:972-978. DOI: 10.1016/j.bbapap.2006.03.009'},{id:"B88",body:'Krogh KBRM, Harris PV, Olsen CL, Johansen KS, Hojer-Pedersen J, Borjesson J, et al. Characterization and kinetic analysis of a thermostable GH3 beta-glucosidase from Penicillium brasilianum. Applied Microbiology and Biotechnology. 2010;86:143-154. DOI: 10.1007/s00253-009-2181-7'},{id:"B89",body:'Tsukada T, Igarashi K, Yoshida M, Samejima M. Molecular cloning and characterization of two intracellular β-glucosidase belonging to glycoside hydrolase family 1 from the basidiomycete Phanerochaete chrysosporium. Applied Microbiology and Biotechnology. 2006;73:807-814. DOI: 10.1007/s00253-006-0526-z'},{id:"B90",body:'Chang TS, Wang TY, Yang SY, Kao YH, Wu JY, Chiang CM. Potential industrial production of a well-soluble, alkaline-stable, and anti-inflammatory isoflavone glucoside from 8-hydroxydaidzein glucosylated by recombinant amylosucrase of Deinococcus geothermalis. Molecules. 2019;24:2236. DOI: 10.3390/molecules24122236'},{id:"B91",body:'Chiang CM, Ding HY, Tsai YT, Chang TS. Production of two novel methoxy-isoflavones from biotransformation of 8-hydroxydaidzein by recombinant Escherichia coli expressing O-methyltransferase SpOMT2884 from Streptomyces peucetius. International Journal of Molecular Sciences. 2015;16:27816-27823. DOI: 10.3390/ijms161126070'},{id:"B92",body:'Lo YL. A potential daidzein derivative enhances cytotoxicity of epirubicin on human colon adenocarcinoma Caco-2 cells. International Journal of Molecular Sciences. 2013;14:158-176. DOI: 10.3390/ijms14010158'},{id:"B93",body:'Tai SS, Lin CG, Wu MH, Chang TS. Evaluation of depigmenting activity by 8-hydroxydaidzein in mouse B16 melanoma cells and human volunteers. International Journal of Molecular Sciences. 2009;10:4257-4266. DOI: 10.3390/ijms10104257'},{id:"B94",body:'Fujita T, Funako T, Hayashi H. 8-Hydroxydaidzein, an aldose reductase inhibitor from okara fermented with Aspergillus sp. HK-388. Bioscience, Biotechnology, and Biochemistry. 2004;68:1588-1590. DOI: 10.1271/bbb.68.1588'},{id:"B95",body:'Wu PS, Ding HY, Yen JH, Chen SF, Lee KH, Wu MJ. Anti-inflammatory activity of 8-hydroxydaidzein in LPS-stimulated BV2 microglial cells via activation of Nrf2-antioxidant and attenuation of Akt/NF-κB-inflammatory signaling pathways, as well as inhibition of COX-2 activity. Journal of Agricultural and Food Chemistry. 2018;66:5790-5801. DOI: 10.1021/acs.jafc.8b00437'},{id:"B96",body:'Seo MH, Kim BN, Kim KR, Lee KW, Lee CH, Oh DK. Production of 8-hydroxydaidzein from soybean extract by Aspergillus oryzae KACC 40247. Bioscience, Biotechnology, and Biochemistry. 2013;77:1245-1250. DOI: 10.1271/bbb.120899'},{id:"B97",body:'Roh C, Seo SH, Choi KY, Cha M, Pandey BP, Kim JH, et al. Regioselective hydroxylation of isoflavones by Streptomyces avermitilis MA-4680. Journal of Bioscience and Bioengineering. 2009;108:41-46. DOI: 10.1016/j.jbiosc.2009.02.021'},{id:"B98",body:'Wu SC, Chang CW, Lin CW, Hsu YC. Production of 8-hydroxydaidzein polyphenol using biotransformation by Aspergillus oryzae. Food Science and Technology Research. 2015;21:557-562. DOI: 10.3136/fstr.21.557'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Kohei Suruga",address:"kouhei_suruga@kibun.co.jp",affiliation:'
Research and Development Division, Products Development Department, Kibun Foods Inc., Tokyo, Japan
Research and Development Division, Products Development Department, Kibun Foods Inc., Tokyo, Japan
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As a gold Open Access publisher, an Open Access Publishing Fee is payable on acceptance following peer review of the manuscript. In return, we provide high quality publishing services and exclusive benefits for all contributors. IntechOpen is the trusted publishing partner of over 140,000 international scientists and researchers.
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The Open Access Publishing Fee (OAPF) is payable only after your book chapter, monograph or journal article is accepted for publication.
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OAPF Publishing Options
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1,400 GBP Chapter - Edited Volume
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850 GBP Chapter - Book Series Topic (Annual Volume)
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10,000 GBP Monograph - Long Form
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During the launching phase journals do not charge an APC, rather they will be funded by IntechOpen.
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\n\n
Services included are:
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An online manuscript tracking system to facilitate your work
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Personal contact and support throughout the publishing process from your dedicated Author Service Manager
\n\t
Assurance that your manuscript meets the highest publishing standards
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English language copyediting and proofreading, including the correction of grammatical, spelling, and other common errors
\n\t
XML Typesetting and pagination - web (PDF, HTML) and print files preparation
\n\t
Discoverability - electronic citation and linking via DOI
\n\t
Permanent and unrestricted online access to your work
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What isn't covered by the Open Access Publishing Fee?
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If your manuscript:
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Exceeds the number of pages defined by the publishing guidelines, an additional fee per page may be required
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If a manuscript requires Heavy Editing or Language Polishing, this will incur additional fees.
\n
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Your Author Service Manager will inform you of any items not covered by the OAPF and provide exact information regarding those additional costs before proceeding.
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To explore funding opportunities and learn more about how you can finance your IntechOpen publication, go to our Open Access Funding page. IntechOpen offers expert assistance to all of its Authors. We can support you in approaching funding bodies and institutions in relation to publishing fees by providing information about compliance with the Open Access policies of your funder or institution. We can also assist with communicating the benefits of Open Access in order to support and strengthen your funding request and provide personal guidance through your application process. You can contact us at funders@intechopen.com for further details or assistance.
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Added Value of Publishing with IntechOpen
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Choosing to publish with IntechOpen ensures the following benefits:
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Indexing and listing across major repositories, see details ...
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Long-term archiving
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Visibility on the world's strongest OA platform
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Pharmacogenetics for cancer treatment is very significant, as cancer therapies exhibit severe systemic toxicity and unpredictable efficacy. There is presence of genetic polymorphisms in the genes which code for the metabolic enzymes and cellular targets for the majority of chemotherapy agents, but to predict the outcome of chemotherapy in patients is not currently possible for most treatments. A greater understanding of the genetic determinants of drug response can revolutionize the use of many medications. By identifying the patients at risk for severe toxicity, or those likely to benefit from a particular treatment, individualized cancer therapy can be achieved for most cancer patients. The prediction of cancer treatment outcome based on gene polymorphisms is becoming possible for many classes of chemotherapy agents, and the most clinically significant examples of chemotherapy agents are discussed in the chapter. However, further studies are needed in well characterized and larger cancer populations with proper validation of pharmacogenetic markers in experimental settings before application in clinical routine diagnostics.",book:{id:"8400",slug:"molecular-medicine",title:"Molecular Medicine",fullTitle:"Molecular Medicine"},signatures:"Munindra Ruwali",authors:[{id:"245866",title:"Dr.",name:"Munindra",middleName:null,surname:"Ruwali",slug:"munindra-ruwali",fullName:"Munindra Ruwali"}]},{id:"72817",title:"Clinical Validation of a Whole Exome Sequencing Pipeline",slug:"clinical-validation-of-a-whole-exome-sequencing-pipeline",totalDownloads:769,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Establishing whole exome sequencing (WES) in an accredited clinical diagnostic space is challenging. The validation (as opposed to verification) of an approach that will lead to clinical reports requires adhering to international guidelines and recommendations and developing a robust analytical pipeline that can scale due to the increasing clinical demand for comprehensive gene screening. This chapter will present a step-wise approach to WES validation that any laboratory can follow. The focus will be on highlighting the pivotal technical issues that must be addressed in validating WES and the analytical tools and QC metrics that must be considered before implementing WES in a clinical environment.",book:{id:"9569",slug:"methods-in-molecular-medicine",title:"Methods in Molecular Medicine",fullTitle:"Methods in Molecular Medicine"},signatures:"Debra O. Prosser, Indu Raja, Kelly Kolkiewicz, Antonio Milano and Donald Roy Love",authors:[{id:"319362",title:"Dr.",name:"Donald Roy",middleName:null,surname:"Love",slug:"donald-roy-love",fullName:"Donald Roy Love"},{id:"319363",title:"Mrs.",name:"Debra O.",middleName:null,surname:"Prosser",slug:"debra-o.-prosser",fullName:"Debra O. Prosser"}]},{id:"71751",title:"Molecular Medicines for Parasitic Diseases",slug:"molecular-medicines-for-parasitic-diseases",totalDownloads:630,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Being the cause for significant amount of morbidities and mortalities, parasitic diseases remain the major challenge for the healthcare community due to the limitations associated with the current chemotherapeutics. Drug discovery/invention can be achieved by collaborative efforts of biotechnologists and pharmacists for identifying potential candidates and successfully turn them into medicine for improving the healthcare system. Although molecular medicine for disease intervention is still in its infancy, however, significant research works and successful trials in short span of time have made it broadly accepted among the scientific community. This chapter identifies different molecular medicine approaches for dealing with parasites that have been coming up on the horizon with the new technological advances in bioinformatics and in the field of omics. With the better understanding of the genomics, molecular medicine field has not only raised hopes to deal with parasitic infections but also accelerated the development of personalized medicine. This will provide a targeted approach for identifying the druggable targets and their pathophysiological importance for disease intervention.",book:{id:"9569",slug:"methods-in-molecular-medicine",title:"Methods in Molecular Medicine",fullTitle:"Methods in Molecular Medicine"},signatures:"Bhawana Singh",authors:[{id:"315192",title:"Dr.",name:"Bhawana",middleName:null,surname:"Singh",slug:"bhawana-singh",fullName:"Bhawana Singh"}]}],onlineFirstChaptersFilter:{topicId:"981",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[],lsSeriesList:[],hsSeriesList:[],sshSeriesList:[],testimonialsList:[]},series:{item:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. In today's highly integrated world, AI promises to become a robust and powerful means for obtaining solutions to previously unsolvable problems. This Series is intended for researchers and students alike interested in this fascinating field and its many applications.",coverUrl:"https://cdn.intechopen.com/series/covers/14.jpg",latestPublicationDate:"June 11th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:9,editor:{id:"218714",title:"Prof.",name:"Andries",middleName:null,surname:"Engelbrecht",slug:"andries-engelbrecht",fullName:"Andries Engelbrecht",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRNR8QAO/Profile_Picture_1622640468300",biography:"Andries Engelbrecht received the Masters and PhD degrees in Computer Science from the University of Stellenbosch, South Africa, in 1994 and 1999 respectively. He is currently appointed as the Voigt Chair in Data Science in the Department of Industrial Engineering, with a joint appointment as Professor in the Computer Science Division, Stellenbosch University. Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). In addition to a number of research articles, he has written two books, Computational Intelligence: An Introduction and Fundamentals of Computational Swarm Intelligence.",institutionString:null,institution:{name:"Stellenbosch University",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:6,paginationItems:[{id:"22",title:"Applied Intelligence",coverUrl:"https://cdn.intechopen.com/series_topics/covers/22.jpg",isOpenForSubmission:!0,editor:{id:"27170",title:"Prof.",name:"Carlos",middleName:"M.",surname:"Travieso-Gonzalez",slug:"carlos-travieso-gonzalez",fullName:"Carlos Travieso-Gonzalez",profilePictureURL:"https://mts.intechopen.com/storage/users/27170/images/system/27170.jpeg",biography:"Carlos M. Travieso-González received his MSc degree in Telecommunication Engineering at Polytechnic University of Catalonia (UPC), Spain in 1997, and his Ph.D. degree in 2002 at the University of Las Palmas de Gran Canaria (ULPGC-Spain). He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null},{id:"27",title:"Multi-Agent Systems",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",isOpenForSubmission:!0,editor:{id:"148497",title:"Dr.",name:"Mehmet",middleName:"Emin",surname:"Aydin",slug:"mehmet-aydin",fullName:"Mehmet Aydin",profilePictureURL:"https://mts.intechopen.com/storage/users/148497/images/system/148497.jpg",biography:"Dr. Mehmet Emin Aydin is a Senior Lecturer with the Department of Computer Science and Creative Technology, the University of the West of England, Bristol, UK. His research interests include swarm intelligence, parallel and distributed metaheuristics, machine learning, intelligent agents and multi-agent systems, resource planning, scheduling and optimization, combinatorial optimization. Dr. Aydin is currently a Fellow of Higher Education Academy, UK, a member of EPSRC College, a senior member of IEEE and a senior member of ACM. In addition to being a member of advisory committees of many international conferences, he is an Editorial Board Member of various peer-reviewed international journals. He has served as guest editor for a number of special issues of peer-reviewed international journals.",institutionString:null,institution:{name:"University of the West of England",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:19,paginationItems:[{id:"82196",title:"Multi-Features Assisted Age Invariant Face Recognition and Retrieval Using CNN with Scale Invariant Heat Kernel Signature",doi:"10.5772/intechopen.104944",signatures:"Kamarajugadda Kishore Kumar and Movva Pavani",slug:"multi-features-assisted-age-invariant-face-recognition-and-retrieval-using-cnn-with-scale-invariant-",totalDownloads:6,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Pattern Recognition - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11442.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"82063",title:"Evaluating Similarities and Differences between Machine Learning and Traditional Statistical Modeling in Healthcare Analytics",doi:"10.5772/intechopen.105116",signatures:"Michele Bennett, Ewa J. Kleczyk, Karin Hayes and Rajesh Mehta",slug:"evaluating-similarities-and-differences-between-machine-learning-and-traditional-statistical-modelin",totalDownloads:6,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Machine Learning and Data Mining - Annual Volume 2022",coverURL:"https://cdn.intechopen.com/books/images_new/11422.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"81791",title:"Self-Supervised Contrastive Representation Learning in Computer Vision",doi:"10.5772/intechopen.104785",signatures:"Yalin Bastanlar and Semih Orhan",slug:"self-supervised-contrastive-representation-learning-in-computer-vision",totalDownloads:25,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Pattern Recognition - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11442.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"79345",title:"Application of Jump Diffusion Models in Insurance Claim Estimation",doi:"10.5772/intechopen.99853",signatures:"Leonard Mushunje, Chiedza Elvina Mashiri, Edina Chandiwana and Maxwell Mashasha",slug:"application-of-jump-diffusion-models-in-insurance-claim-estimation-1",totalDownloads:8,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Data Clustering",coverURL:"https://cdn.intechopen.com/books/images_new/10820.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}}]},overviewPagePublishedBooks:{paginationCount:9,paginationItems:[{type:"book",id:"7723",title:"Artificial Intelligence",subtitle:"Applications in Medicine and Biology",coverURL:"https://cdn.intechopen.com/books/images_new/7723.jpg",slug:"artificial-intelligence-applications-in-medicine-and-biology",publishedDate:"July 31st 2019",editedByType:"Edited by",bookSignature:"Marco Antonio Aceves-Fernandez",hash:"a3852659e727f95c98c740ed98146011",volumeInSeries:1,fullTitle:"Artificial Intelligence - Applications in Medicine and Biology",editors:[{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}}]},{type:"book",id:"7726",title:"Swarm Intelligence",subtitle:"Recent Advances, New Perspectives and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/7726.jpg",slug:"swarm-intelligence-recent-advances-new-perspectives-and-applications",publishedDate:"December 4th 2019",editedByType:"Edited by",bookSignature:"Javier Del Ser, Esther Villar and Eneko Osaba",hash:"e7ea7e74ce7a7a8e5359629e07c68d31",volumeInSeries:2,fullTitle:"Swarm Intelligence - Recent Advances, New Perspectives and Applications",editors:[{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",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}]},{type:"book",id:"7656",title:"Fuzzy Logic",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7656.jpg",slug:"fuzzy-logic",publishedDate:"February 5th 2020",editedByType:"Edited by",bookSignature:"Constantin Volosencu",hash:"54f092d4ffe0abf5e4172a80025019bc",volumeInSeries:3,fullTitle:"Fuzzy Logic",editors:[{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",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",institutionURL:null,country:{name:"Romania"}}}]},{type:"book",id:"9963",title:"Advances and Applications in Deep Learning",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/9963.jpg",slug:"advances-and-applications-in-deep-learning",publishedDate:"December 9th 2020",editedByType:"Edited by",bookSignature:"Marco Antonio Aceves-Fernandez",hash:"0d51ba46f22e55cb89140f60d86a071e",volumeInSeries:4,fullTitle:"Advances and Applications in Deep Learning",editors:[{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}}]}]},openForSubmissionBooks:{},onlineFirstChapters:{},subseriesFiltersForOFChapters:[],publishedBooks:{},subseriesFiltersForPublishedBooks:[],publicationYearFilters:[],authors:{}},subseries:{item:{},onlineFirstChapters:{},publishedBooks:{},testimonialsList:[]},submityourwork:{pteSeriesList:[],lsSeriesList:[],hsSeriesList:[],sshSeriesList:[],subseriesList:[],annualVolumeBook:{},thematicCollection:[],selectedSeries:null,selectedSubseries:null},seriesLanding:{item:null},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"chapter.detail",path:"/chapters/49062",hash:"",query:{},params:{id:"49062"},fullPath:"/chapters/49062",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var t;(t=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(t)}()