115 Babelyuk Valeriy, Gozhenko Anatoliy, Dubkova Galyna, Zukow Walery, Hubyts’kyi Viktor, Ruzhylo Sofiya, Fedyayeva Svitlana, Kovalchuk Galyna, Popovych Igor. Causal relationships between the parameters of gas discharge visualization and immunity. Pedagogy and Psychology of Sport. 2021;7(1):115-134. elSSN 2450-6605. DOI http://dx.doi.org/10.12775/PPS.2021.07.01.008 https://apcz.umk.pl/czasopisma/index.php/PPS/article/view/PPS.2021.07.01.008 https://zenodo.org/record/4603448 The journal has had 5 points in Ministry of Science and Higher Education parametric evaluation. § 8. 2) and § 12. 1. 2) 22.02.2019. © The Authors 2021; This article is published with open access at Licensee Open Journal Systems of Nicolaus Copernicus University in Torun, Poland Open Access. This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author (s) and source are credited. This is an open access article licensed under the terms of the Creative Commons Attribution Non commercial license Share alike. (http://creativecommons.org/licenses/by-nc-sa/4.0/) which permits unrestricted, non commercial use, distribution and reproduction in any medium, provided the work is properly cited. The authors declare that there is no conflict of interests regarding the publication of this paper. Received: 17.02.2021. Revised: 17.02.2021. Accepted: 04.03.2021. CAUSAL RELATIONSHIPS BETWEEN THE PARAMETERS OF GAS DISCHARGE VISUALIZATION AND IMMUNITY Valeriy Y. Babelyuk1,2, Anatoliy I. Gozhenko1, Galyna I. Dubkova2, Walery Zukow3, Viktor Y. Hubyts’kyi2, Sofiya V. Ruzhylo4, Svitlana I. Fedyayeva5, Galyna Y. Kovalchuk4, Igor L. Popovych1,6 1State Enterprise Ukrainian Research Institute for Medicine of Transport, Ministry of Health, Odesa, Ukraine prof.gozhenko@gmail.com 2Clinical Sanatorium „Моldоvа”, Truskavets’, Ukraine san.moldova.tr@ukr.net 3Nicolaus Copernicus University, Torun, Poland w.zukow@wp.pl 4Ivan Franko Pedagogical University, Drohobych, Ukraine doctor-0701@ukr.net 5Danylo Halyts’kyǐ National Medical University, L’viv, Ukraine svfedyaeva@ukr.net 6OO Bohomolets’ Institute of Physiology NAS, Kyїv, Ukraine i.popovych@biph.kiev.ua Background. Earlier, we found a close canonical correlation between parameters of gas discharge visualization (GDV) and principal neuroendocrine factors of adaptation. The purpose of this study is to elucidate the relationship between GDV and immunity parameters. Material and research methods. We observed twice 10 women and 10 men aged 33-76 years without clinical diagnose. In the morning in basal conditions at first registered kirlianogram by the method of GDV by the device “GDV Chamber” (“Biotechprogress”, SPb, RF). For further analysis the following parameters were selected: Area, Shape Coefficient as ratio Square Length of outward contour gas discharge image to its Area as well as Entropy of contour in Right, Frontal and Left projections registered both with and without polyethylene filter. Estimated also Energy and Asymmetry of virtual Chakras. Then registered routine parameters of cellular and humoral Immunity. Results processed by method of canonical analysis, using the software package “Statistica 5.5”. Results. According to the value of the canonical correlation coefficient R with GDV parameters, the immunity parameters are arranged in the following order: IgA (0,716; p=0,005), CD8+CD3+ Tc-lymphocytes (0,646; p=0,004), IgG (0,645; p=0,002), IgM (0,622; p=0,0001), “active” T-lymphocytes (0,572; p=0,007), CD4+CD3+ Th-lymphocytes (0,566; p=0,003), CIC (0,491; p=0,018), 0- lymphocytes (0,457; p=0,036), CD16+ NK-lymphocytes (0,396; p=0,043), CD22+ B- lymphocytes (0,439; p=0,105). The integral canonical correlation between the parameters of GDV and Immunity was very strong (R=0,994; p<10-4). Conclusion. 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Key words: Gas Discharge Visualization, Cellular and Humoral Immunity, Relationships. INTRODUCTION In 1996 KG Korotkov created a new scientific approach, based on the digital videotechnics, modern electronics and computer processing quantitative data, called as method gas discharge visualization (GDV bioelectrography). Parallel uses the terms kirlianography and electrophotonics [12,13]. Despite the initial obstruction of some conservative Western scientists, the GDV method is now considered quite relevant [8,14,21]. We are pleased to note that we are also a little involved in this recognition. In 2010 we launched a study on its verification. First, we found relationships between parameters GDV and HRV as well as blood pressure at 8 volunteers [23]. In the next study [1,25] we shown by means of 20 observations of 10 practically healthy men that between the eight basic parameters of GDV, on the one hand, and serum levels of triiodothyronine, testosterone and cortisol as well as mineralocorticoid activity, on the other hand, exist closely canonical correlation (R=0,947). A large contingent of 10 men and 10 women and broadened research methods confirmed the findings. Coefficient of canonical correlation parameters of GDV with Testosterone makes 0,81, with Cortisol 0,66, with Calcitonin 0,61, with Triiodothyronine 0,60, with Aldosterone 0,48. Among parameters HRV closely correlated with GDV constellation Bayevskiy’s Stress Index (R=0,80) and LF/HF Ratio (R=0,76). Coefficient of canonical correlation between Neuroendocrine constellation, on the one hand, and parameters of GDV, on the other hand, makes 0,970 [2,4]. The same contingent shows that entropy of GDV correlates with the entropies of EEG, immunocytogram and leukocytogram but not HRV [6]. In addition, the response of GDV parameters to the course of use of bioactive water Naftussya [10], electrostimulation by device “VEB” [7,11], the course of rehabilitation by VI Kozyavkin’s method [5,24,15,16], as well as the immediate response to Katas of Kyokushin Karate operator [3] were demonstrated. The purpose of this study is to elucidate the relationship between GDV and immunity parameters. MATERIAL AND RESEARCH METHODS The object of observation were members of the same contingent: 10 women and 10 men aged 33-76 years without clinical diagnose but with dysfunction of neuro-endocrine-immune complex and metabolism, characteristic for premorbid (intermediate between health and illness) state. In the morning on an empty stomach we registered kirlianogram by the method of GDV by the device of “GDV Chamber” (“Biotechprogress”, SPb, RF). The first base parameter of GDV is Area of gas discharge image (GDI) in Right, Frontal and Left projections registered both with and without polyethylene filter. The second base parameter is a Shape coefficient (ratio of square of length of external contour of GDI toward his area), which characterizes the measure of serration/fractality of external contour. The third base parameter of GDI is Entropy, id est measure of chaos. Program estimates also Energy and Asymmetry of virtual Chakras [12-14]. Immune status evaluated on a set of I and II levels recommended by the WHO as described in handbook [17]. For phenotyping subpopulations of lymphocytes used the methods of rosette formation with sheep erythrocytes on which adsorbed monoclonal antibodies against receptors CD3, CD4, CD8, CD22 and CD16 from company "Granum" 117 (Kharkiv) with visualization under light microscope with immersion system. We carried out also test of “active” rosette formation. The state of humoral immunity judged by the concentration in serum of immunoglobulins classes G, A, M (ELISA, analyser “Immunochem”, USA) and circulating immune complicis (with polyethylene glycol precipitation method). We calculated also the Entropy (h) of Immunocytogram (relative contents subpopulations of lymphocytes) using IL Popovych's [22,26] formula, which is based on CE Shannon's [29] formula: h= - [CD4•log2 CD4 + CD8•log2 CD8 + CD22•log2 CD22 + CD16•log2 CD16]/log2 4 Every day four people were tested. A week later, all the tests were repeated. Results processed using the software package "Statistica 5.5". RESULTS AND DISCUSSION Educated in the spirit of Western medicine, we decided to look at the results through the eyes of adherents of Eastern medicine, ie the parameters of GDV were considered as an argument (cause), and the parameters of Immunity as a function (consequence). The association with geocentric and heliocentric concepts in astronomy, which equally served seafarers for orientation, comes to mind. According to the formula: |r|≥{exp[2t/(n-1,5)0,5] - 1}/{exp[2t/(n-1,5)0,5] + 1}, for a sample of 40 observations critical value of correlation coefficient module at p<0,05 (t>2,02) is 0,31, at p<0,01 (t>2,70) is 0,41, at p<0,001 (t>3,55) is 0,52. Among the parameters of cellular immunity, the relative level of T-killers in the blood correlates most closely with the parameters of GDV, in particular with Entropy of GDI in Left projection (Fig. 1). Fig. 1. Scatterplot of correlation between Entropy of GDI in Left projection (X-line) and CD8+CD3+ T-lymphocytes level (Y-line) By stepwise exclusion to reach the maximum value of adjusted R2, the regression model included six GDV parameters, the total impact of which on the level of T-killers is estimated at 31% (Table 1 and Fig. 2). 118 Table 1. Regression Summary for Dependent Variable: CD8+CD3+ Tc-lymphocytes R=0,646; R2=0,418; Adjusted R2=0,312; F(6,3)=3,9; χ2(6)=18,9; p=0,004; SE: 3,6 % Beta St. Err. of Beta B St. Err. of B t(33) p- lev- el r Intercpt -15,52 63,03 -,25 ,81 EL -0,44 -,265 ,148 -7,01 3,92 -1,79 ,08 EL f -0,37 -,146 ,157 -3,59 3,86 -,93 ,36 Ch5 E -0,33 -,126 ,143 -1,83 2,08 -,88 ,39 Ch4 A -0,30 -,199 ,148 -2,73 2,03 -1,35 ,19 Sym f 0,38 ,202 ,149 ,84 ,62 1,36 ,18 Ch1 A f 0,36 ,195 ,153 4,59 3,59 1,28 ,21 Abbreviations of GDV parameters: A – Area (pixels), E - Entropy, Sym – Symmetry (%), S – Shape coefficient, R – Right projection, F – Frontal projection, L – Left projection, f – with filter, ChE - Chakra Energy [E=(R+L)/2], ChA – Chakra Asymmetry (A=R-L). Fig. 2. Scatterplot of canonical correlation between GDV parameters (X-line) and CD8+CD3+ Tc-lymphocytes level (Y-line) The level of T-helpers is upregulated by the symmetry of the GDI registered with the filter and the asymmetry of the seventh chakra, while it is negatively regulated by the energy of the sixth chakra. The degree of determination is 26% (Table 2 and Fig. 3). Table 2. Regression Summary for Dependent Variable: CD4+ CD3+Th-lymphocytes R=0,566; R2=0,320; Adjusted R2=0,264; F(3,4)=5,7; χ2(3)=14,1; p=0,003; SE: 4,4 % Beta St. Err. of Beta B St. Err. of B t(36) p- level r Intercpt -127,1 63,6 -2,00 ,053 Sym f 0,35 ,361 ,140 1,75 ,68 2,57 ,014 Ch7 A f 0,30 ,351 ,139 8,42 3,32 2,53 ,016 Ch6 E f -0,32 -,265 ,139 -5,16 2,71 -1,91 ,065 119 Fig. 3. Scatterplot of canonical correlation between GDV parameters (X-line) and CD4+CD3+Th-lymphocytes level (Y-line) The level of T-lymphocytes with high affinity is subject to downregulation by the asymmetry of the fifth and fourth chakras, as well as the energy of the seventh chakra. The degree of determination is 25% (Table 3 and Fig. 4). Table 3. Regression Summary for Dependent Variable: “active” T Lymphocytes R=0,572; R2=0,327; Adjusted R2=0,250; F(4,3)=4,3; χ2(4)=14,3; p=0,007; SE: 3,7 % Beta St. Err. of Beta B St. Err. of B t(35) p- level r Intercpt 30,65 ,64 48,2 10-6 Ch5 A -0,37 -,255 ,147 -5,15 2,95 -1,74 ,090 Ch4 A f -0,34 -,289 ,140 -4,65 2,25 -2,07 ,046 Ch4 A -0,31 -,249 ,147 -3,30 1,95 -1,69 ,099 Ch7 E -0,25 -,235 ,141 -4,15 2,49 -1,67 ,105 120 Fig. 4. Scatterplot of canonical correlation between GDV parameters (X-line) and “active” T-lymphocytes level (Y-line) Downregulation of the third and sixth chakras of the level of natural killers is very weak (11%), but statistically significant (Table 4 and Fig. 5). Table 4. Regression Summary for Dependent Variable: NK-Lymphocytes R=0,396; R2=0,156; Adjusted R2=0,111; F(2,4)=3,4; p=0,043; SE: 4,5 % Beta St. Err. of Beta B St. Err. of B t(37) p- level r Intercpt 18,95 ,85 22,39 10-6 Ch3 A f -0,28 -,307 ,152 -6,19 3,06 -2,02 ,050 Ch6 E -0,25 -,280 ,152 -4,58 2,49 -1,84 ,073 Fig. 5. Scatterplot of canonical correlation between GDV parameters (X-line) and NK- lymphocytes level (Y-line) Instead, the relationship between B-lymphocyte levels and chakras is insignificant (Table 5 and Fig. 6). Table 5. Regression Summary for Dependent Variable: CD22+ B-lymphocytes R=0,438; R2=0,192; Adjusted R2=0,099; F(4,3)=2,1; χ2(4)=7,7; p=0,105; SE: 4,0 % Beta St. Err. of Beta B St. Err. of B t(35) p- lev- el r Intercpt 23,14 ,66 35,0 10-6 Ch4 A f -0,21 -,301 ,156 -4,84 2,52 -1,92 ,063 Ch7 A -0,16 -,270 ,158 -3,93 2,29 -1,71 ,096 Ch3 A 0,17 ,284 ,158 3,82 2,12 1,80 ,080 Ch2 E 0,16 ,227 ,154 3,05 2,07 1,48 ,149 121 Fig. 6. Scatterplot of canonical correlation between GDV parameters (X-line) and CD22+ B-lymphocytes level (Y-line) The level of 0-lymphocytes calculated by the balance method (100-Tc-Th-B-NK) was associated with the symmetry of GDI and the energy of the sixth chakra, determined by these parameters by 14% (Table 6 and Fig. 7). Table 6. Regression Summary for Dependent Variable: 0-Lymphocytes R=0,457; R2=0,209; Adjusted R2=0,143; F(3,4)=3,2; χ2(3)=8,5; p=0,036; SE: 12,1 % Beta St. Err. of Beta B St. Err. of B t(36) p- level r Intercpt 286, 9 181,5 1,58 ,123 Sym f -0,34 -,202 ,170 -2,52 2,11 -1,19 ,240 Sym -0,29 -,222 ,169 -,59 ,45 -1,32 ,196 Ch6 E 0,28 ,270 ,151 12,1 4 6,81 1,78 ,083 Fig. 7. Scatterplot of canonical correlation between GDV parameters (X-line) and 0- lymphocytes level (Y-line) 122 Immunocytogram entropy correlates with three GDV parameters at the significance limit (Table 7 and Fig. 8). Table 7. Regression Summary for Dependent Variable: Entropy of Immunocytogram R=0,429; R2=0,184; Adjusted R2=0,116; F(3,4)=2,7; χ2(3)=7,4; p=0,059; SE: 0,034 Beta St. Err. of Beta B St. Err. of B t(36) p- level r Intercpt ,162 ,520 ,31 ,757 Sym f 0,30 ,297 ,154 ,010 ,005 1,93 ,061 EL f -0,26 -,175 ,155 -,035 ,031 -1,13 ,268 Ch3 A f -0,23 -,225 ,155 -,034 ,023 -1,45 ,155 Fig. 8. Scatterplot of canonical correlation between GDV parameters (X-line) and Entropy of Immunocytogram (Y-line) At the next stage, the analysis of the canonical correlation between the registered parameters of cellular immunity, on the one hand, and the parameters of GDV selected at the previous stage, on the other hand, was performed. As a result, a pair of canonical roots were formed (Table 8). The program did not include B-lymphocytes in the structure of the immune root, apparently due to the insignificant coefficient of canonical correlation, but under the same conditions the entropy of the immunocytogram was still found in the factor structure of the root. 123 Table 8. Factor Structure of GDV and Cellular Immunity Canonical Roots Left set Root 1 Symmetry GDI f ,64 Ch7 Asymmetry f ,35 Symmetry GDI ,33 Ch6 Energy f -,50 Ch6 Energy -,49 Ch5 Energy -,40 Ch4 Asymmetry -,32 Ch4 Asymmetry f -,17 Ch3 Asymmetry f -,15 Ch5 Asymmetry -,15 Entropy Left -,11 Right set Root 1 CD4+CD3+ ,86 NK-Lymphocytes ,59 Entropy ICG ,46 CD8+CD3+ ,41 T “active” ,18 0-Lymphocytes -,65 The GDV-root represents 11 parameters, three of which give positive factor loads, which reflects their enhancing immunotropic effect, while the other 8 parameters have an immunosuppressive effect. Downregulation of 0-lymphocyte levels is physiologically favorable because it reflects the activation of receptor expression by immature immunocytes. In general, the canonical correlation between the parameters of GDV and cellular immunity was strong, but it is unclear why statistically insignificant (Fig. 9). R=0,809; R2=0,657; χ2(84)=104; p=0,07; Λ Prime=0,026 Fig. 9. Scatterplot of canonical correlation between GDV parameters (X-line) and Cellular Immunity parameters (Y-line) 124 Among the parameters of humoral immunity, IgA was most closely related to the parameters of GDV. First of all, it is the energy of the fifth and sixth chakras (Fig. 10 and 11). Fig. 10. Scatterplot of correlation between Chakra 5 Energy (f) (X-line) and IgA serum level (Y-line) Significant influence on the level of IgA also have other parameters of GDV, in total determining it by 37% (Table 9 and Fig. 12). Levels of Iggs of other classes are determined by the parameters of GDV less, in particular IgG by 33% (Table 10 and Fig. 13) and IgM by 35% (Fig. 14-15 and Table 11). Fig. 11. Scatterplot of correlation between Chakra 6 Energy (f) (X-line) and IgA serum level (Y-line) 125 Table 9. Regression Summary for Dependent Variable: IgA R=0,716; R2=0,512; Adjusted R2=0,366; F(9,3)=3,5; χ2(9)=24,0; p=0,005; SE: 0,16 g/L Beta St. Err. of Beta B St. Err. of B t(30) p- level r Intercpt 1,890 1,319 1,43 ,162 Ch6 E f 0,48 ,526 ,242 ,413 ,190 2,18 ,038 Ch1 E f 0,44 ,478 ,332 ,433 ,301 1,44 ,160 ER f 0,41 ,463 ,155 ,623 ,209 2,99 ,006 Ch3 E f 0,34 -,384 ,299 -,309 ,241 -1,28 ,210 Ch1 E 0,29 -,519 ,264 -,343 ,174 -1,97 ,058 AL f 0,28 -,501 ,296 -,00005 ,00003 -1,69 ,102 Ch7 E 0,27 ,287 ,208 ,246 ,178 1,38 ,177 SL f -0,27 -,319 ,258 -,046 ,037 -1,24 ,226 Fig. 12. Scatterplot of canonical correlation between GDV parameters (X-line) and IgA serum level (Y-line) Table 10. Regression Summary for Dependent Variable: IgG R=0,645; R2=0,416; Adjusted R2=0,331; F(5,3)=4,9; χ2(5)=19,1; p=0,002; SE: 2,7 g/L Beta St. Err. of Beta B St. Err. of B t(34) p- level r Intercpt 8,93 5,29 1,69 ,100 AF 0,40 ,319 ,220 ,0003 1 ,00021 1,45 ,156 Ch7 E 0,35 1,005 ,396 13,72 5,41 2,53 ,016 Ch1 A 0,35 ,313 ,136 4,50 1,96 2,29 ,028 Ch3 A 0,31 ,320 ,135 3,33 1,40 2,38 ,023 Ch2 E 0,28 -,960 ,434 -9,97 4,51 -2,21 ,034 126 Fig. 13. Scatterplot of canonical correlation between GDV parameters (X-line) and IgG serum level (Y-line) Fig. 14. Scatterplot of correlation between Chakra 1 Asymmetry (f) (X-line) and IgM serum level (Y-line) Table 11. Regression Summary for Dependent Variable: IgM R=0,622; R2=0,387; Adjusted R2=0,353; F(2,4)=11,7; χ2(2)=18,1; p=0,0001; SE: 0,18 g/L Beta St. Err. of Beta B St. Err. of B t(37) p- level r Intercpt 1,511 ,029 51,66 10-6 Ch1 A f -0,44 -,54 7 ,132 -,668 ,162 -4,13 ,000 2 Ch7 E f -0,32 -,44 7 ,132 -,546 ,162 -3,38 ,002 127 Fig. 15. Scatterplot of canonical correlation between GDV parameters (X-line) and IgM serum level (Y-line) And the weakest were the links between the parameters of GDV and the level of circulating immune complexes (Table 12 and Fig. 16). Table 12. Regression Summary for Dependent Variable: CIC R=0,491; R2=0,241; Adjusted R2=0,178; F(3,4)=3,8; χ2(3)=10,1; p=0,018; SE: 12 units Beta St. Err. of Beta B St. Err. of B t(36) p- level r Intercpt 148,9 46,7 3,19 ,003 EF -0,37 -,347 ,146 -29,45 12,39 -2,38 ,023 Ch6 A f -0,31 -,187 ,161 -8,68 7,51 -1,16 ,255 Ch1 E -0,28 -,192 ,161 -8,24 6,89 -1,20 ,240 128 Fig. 16. Scatterplot of canonical correlation between GDV parameters (X-line) and CIC serum level (Y-line) The result of the canonical correlation analysis shows that the determination of the GDV parameters of humoral immunity is much stronger than cellular: 81% vs 66% (Table 13 and Fig. 17). Table 13. Factor Structure of GDV and Humoral Immunity Canonical Roots Left set Root 1 Ch1 Asymmetry ,40 Area Frontal ,37 Ch7 Energy ,34 Ch3 Asymmetry ,34 Ch2 Energy ,25 Area Left f ,22 Ch1 Energy f ,19 Ch5 Energy ,18 Ch2 Energy f ,08 Ch6 Energy f ,08 Ch7 Energy f ,06 Ch3 Energy f ,03 Entropy Frontal -,32 Shape Left f -,15 Ch6 Asymmetry f -,10 Right set Root 1 IgG ,85 IgM ,57 CIC ,28 IgA ,24 129 R=0,897; R2=0,807; χ2(68)=104; p=0,003; Λ Prime=0,024 Fig. 17. Scatterplot of canonical correlation between GDV parameters (X-line) and Humoral Immunity parameters (Y-line) At the final stage, the analysis of the canonical correlation of cellular and humoral immunity parameters with GDV parameters was performed. As a preamble we set out the following provisions. The GDV method is based on the registration of stimulated emission of photons and electrons from the skin surface. Korotkov KG [13,14] believes that GDV method measures the distribution of electron densities in human systems and organs. These electron densities are the main basis of physiological energy, so there is reason to say that the GDV method allows us to measure the body's potential energy reserve. GDI, taken off without filter, characterizes the functional changes of organism, while taken with a filter characterizes organic changes. At the same time, the GDV method is a bridge between the logical science of the West and the intuitive science of the East. It allows us to represent the same phenomena in different languages, in different systems, to look at the same things from different points of view. According to Ayurvedic medicine, Chakras are power centers, related to the endocrine glands and neural plexus as well as to some organs. In Puchko LG [27] we read that the first Chakra is related to the testicles and sacral plexus, second Chakra to the ovaries, adrenals and kidneys, third Chakra to spleen, liver and solar plexus, fourth Chakra to thymus, heart and cardial plexus, fifth Chakra to thyroid and parathyroid glands, sixth Chakra to pituitary gland and brain, seventh Chakra to pineal gland. Chase CR [8] provides a table according to which the first Chakra is associated with adrenals, pelvic nerve plexus, spine, kidneys, bladder, large intestine; second Chakra with testes/ovaries, inferior mesenteric ganglion, ileum, organs of reproduction; third Chakra with [endocrine] pancreas, celiac plexus ganglion, liver, gall bladder, stomach, duodenum, pancreas, spleen; fourth Chakra with thymus, celiac plexus, heart, circulation, vagus nerve; fifth Chakra with thyroid and parathyroid glands, inferior cervical ganglion, lungs, bronchus, larynx, pharynx, large intestine, vagus nerve; sixth Chakra 130 with pituitary and pineal glands, thalamus, hypothalamus, superior cervical ganglion, left brain, lower brain, ears/nose, left eye; seventh Chakra with pineal gland, right brain, upper brain, right eye. Korotkov KG [12] put forward the concept that each Chakra is associated with a part of the finger. This approach is embodied in the “GDV Chakras” program, which allows us to quantify the state of virtual Chakras. According to the results of the canonical analysis, two pairs of roots are formed, which are almost identical in the coefficients R and R2, but differ significantly in the factor structure (Table 14). Contrary to expectations, the parameters of the fourth and third Chakras, which are associated with the thymus and spleen, respectively, give only moderate factor loads, while the top positions are occupied by the parameters of the seventh and second Chakras. However, our shock quickly turns into antishock, given that these Chakras represent the pineal gland and brain and adrenals or sexual glands, respectively. The immunomodulatory effect of adrenal and gonadal hormones has long been known, as well as thyroid and parathyroid hormones [26,30,33] associated with the fifth Chakra, also present in the factor structure of the root. Now it became known about the immunomodulatory activity of the pineal gland [9,18,19,28]. Finally, one of the trends in modern neuroimmunology is the role of immunomodulation of the vagus [20,26,30-32] associated with the fourth and fifth Chakras, the activity of which, in turn, is controlled by the neural network [31,32]. The presence in the factor structure of the parameters of symmetry and asymmetry is perfectly consistent with the lateralization of cortical regulatory structures. Table 14. Factor Structure of GDV and Immunity Canonical Roots Left set Root 1 Root 2 Ch7 Energy f -,48 -,10 Ch2 Energy f -,46 -,12 Ch7 Energy -,38 ,04 Ch2 Energy -,38 -,05 Ch4 Asymmetry f -,31 ,23 Symmetry -,27 -,22 Area Frontal -,26 ,17 Area Left f -,26 ,15 Ch5 Energy -,25 ,21 Ch4 Asymmetry -,23 ,21 Ch6 Energy f -,23 ,20 Ch3 Energy f -,21 -,03 Ch1 Energy f -,20 ,01 Entropy Left f ,28 ,24 Ch5 Asymmetry -,23 ,24 Ch6 Energy -,22 ,25 Ch1 Asymmetry -,16 ,37 Symmetry f ,10 -,43 Ch1 Asymmetry f -,30 -,34 Right set Root 1 Root 2 T “active” ,60 -,41 IgM ,43 ,37 CIC ,16 ,06 IgA -,33 -,01 CD4+CD3+ Th ,07 -,46 CD8+CD3+ Tc -,22 -,40 Entropy ICG -,17 -,36 CD16+ NK -,11 -,14 0-Lymphocytes ,11 ,44 IgG -,21 ,37 131 R=0,994; R2=0,988; χ2(280)=388; p<10-4; Λ Prime<10-6 Fig. 18. Scatterplot of canonical correlation between GDV parameters (X-line) and all Immunity parameters (Y-line). The first pair of roots R=0,992; R2=0,984; χ2(243)=341; p=0,007; Λ Prime<10-6 Fig. 19. Scatterplot of canonical correlation between GDV parameters (X-line) and all Immunity parameters (Y-line). The second pair of roots 132 Judging by the coefficients of determination (Fig. 18 and 19), the parameters of GDV almost totally regulate the state of immunity. We secretly hope that we have convinced our colleagues, adherents of Western medicine, that the method of GDV is quite relevant, and the Chakras are not fiction but reality. Subsequent publications will present data on the relationship of GDV parameters with the electrical conductivity of acupuncture points and EEG and metabolism parameters. ACKNOWLEDGMENT We express sincere gratitude to administration JSC “Truskavets’kurort” for help in carrying out immune analyzes. Special thanks to the volunteers. 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