A novel technique to assess the status of the body's regulatory system.

Abstract

Biological regulation is characterized by physiological parameters that do not follow completely random probability distributions. Rather, they reflect the non-random cooperative mutual interactions of all the functions of the body. As a result, the probability regarding the statistical frequencies of physiological values follows a Log-Normal distribution instead of a Normal or Gaussian one. The theory underlying this is explained. The technique is demonstrated in respect of electrical conductivity measurements on the skin of healthy subjects, and on patients with tumors and basilar thrombosis conditions. It is shown to provide a powerful tool to judge the auto-regulatory capacity of patients.

Introduction

All the common diagnoses search for individual and specific aberrations from normal physiological and biological parameters. Usually the measured abnormalities can be assigned then to known diseases. Thus, the Roentgen diagnosis enables us to uncover fractures or cancer by detecting particular optical features in the x-ray scan. In cases of biochemical abnormalities, the blood or urine analysis points to perturbations of the metabolism, poisoning, diabetes or AIDS. Actually, the modern medical diagnosis progressed during the last century to the most powerful tool for revealing almost every kind of disease by sometimes-intensive technical efforts. However, it can never be excluded that latent diseases are overlooked even if no troubles are documented. Moreover, there is no way to measure the power of the patient for overcoming diseases by self-healing forces. A well-known example is the spontaneous regression of cancer. It is no means a rare event, but it cannot be evoked in a purposeful way since the causal factors are not known or measurable up the present. In addition to and as supplement to the more basic theoretical reasoning, we like to describe here a new method that hopefully fulfills these requirements, at least in a pioneering way. (1) It has been investigated for more than 20 years now. In view of technical progress, this diagnosis is ripe for clinical applications.

Principle of the Method

This Regulation-Diagnosis, as we call it, is able to detect regulatory deficiencies in terms of particular measurable distances from the ideal state of health as compared to random chance. This method provides a rather reliable, fast, and noninvasive tool of demonstrating such regulation. It is based on the well-known central law of statistics that states the probability of measuring completely random values consequently follows a completely random (Gaussian or Normal-) distribution, whereas an ideal regulatory system probability distribution follows a Log-Normal (instead of Normal-) distribution.

In 1948, Schuerman and Schirduan published the first confirmation of its validity in medicine regarding penicillin intolerability of patients. (2) In 1950 this observation was confirmed and generalized by Gebelein and Heite. (3) The authors interpreted the results in terms of a "Multiplicative Gestaltungs-Principle" (MGP) defined here as an organizational principle of biological systems. They emphasized that the Log-Normal distribution of physiological parameters reflects the mutual non-random interactions of all the physiological and biological functions of the body. Today, this principle, which also plays a role in the Weber-Fechner law (fundamental psycho-physical law, the living system's logarithmic response to a stimulus), belongs to the generally accepted knowledge of medical statistics. (4) A more careful analysis traces these couplings back to the coherent character of a regulatory field behind the mutually linked functions of the body. (5-9) The most simple derivation of this principle for mathematically interested readers is given in the Appendix. In addition, an electrophysiological analysis of the body confirms the validity and significance of the Log-Normal distribution of the electric (or magnetic) body fields. (10,11) The diagnostic relevance is obvious: If a sufficiently high number of physiological parameters such as resistance or conductivity values of the body are measured, the probability of measuring definite values in small preset ranges between the lowest and the highest resistance (or conductivity) values will follow the random Gaussian distribution, only if the regulation has been completely broken down. In contrast, in the case of an ideal functioning of the regulatory activities, a Log-Normal distribution of the measured resistance (or conductivity) values will be registered. Deviations from these ideal distribution functions can be interpreted in terms of the kind and the degree of immanent and eminent diseases.

It is worthwhile to note that these measurements of resistance or conductivity values of the skin are not the same as commonly known in the field of "electro-acupuncture," but rather on ordinary physical registration of the resistance or conductivity values on arbitrary points on the skin. One might prefer points on the hands, feet or face in order to get enough values in a short time and with sufficient comfort. It doesn't matter what kind of resistance measurement is chosen to get the measurement values. We worked with the method of Croon, which uses ac-voltages as well as with that of Voll, where dc-voltages are applied. (12,13) With the exception of quantitative values, we got the same results in both cases. (14, 15) The method itself is not essential for evaluation. It is only important is that physiological parameters--here conductivity values of the skin--are measurable with sufficient accuracy and reproducibility.

[FIGURE 1 OMITTED]

Figure 1 confirms the validity of this basic law regarding the Log-Normal distribution of ideal regulatory activities in living matter, in contrast to a random (Gaussian) distribution. Eighteen thousand registrations of skin conductivity on 200 healthy people were recorded and then subjected to probability distribution. The bar chart displays a significance level greater than 99.9 percent--clear deviation from the random (Gaussian) distribution (dashed curve), and at the same time a rather high agreement with the ideal Log-Normal distribution (full curve). For this purpose, it is possible to take the values of many subjects, since the folding of Normal, as well as of Log-Normal, distributions may again provide a Normal and a Log-Normal distribution, respectively, as long as the overlap takes place within a common physiological range. However, usually the measurements are performed on one patient, where about 1000 values are taken for evaluation.

For more than 20 years now, a manifold of tests have been performed and they confirmed the correctness of these principles. However, technicalities prohibited for a long time its introduction into daily clinical practice because of the high numbers of measurements required in a practical amount of time. Recently, there has been some success in utilizing an electrode that facilitates approximately 1000 conductivity recordings on arbitrary points of the skin within 10 minutes. This provides a sufficiently high number of reliable data to calculate the probability distribution.

Practical Application for Diagnosis

A rather small piece of equipment that can be located on a corner of a table is connected to a PC. This equipment contains the registration program and the measuring electrode (similar to a penholder) is utilized to register conductivity values of the skin under conditions of stable pressure. These values are recorded in the computer and the probability distribution regarding the frequencies of values within preset small ranges between the smallest and the highest conductivity value are evaluated. The software quickly calculates: (a) the mean value and the standard deviation of all the measurement values; (b) the Log-Normal distribution; (c) the Gaussian distribution; (d) the deviations from the Log-Normal and Gaussian distributions in terms of the Kolmogoroff-Smirnov and Chi-square tests; (e) skewness; and (f) excess of the measured distribution.

[FIGURE 2 OMITTED]

These values provide the comparative assessment of the regulatory capacity of the patient regarding: (1) ideal regulation, vis-a-vis, the Log-Normal distribution; (2) the complete lack of regulatory capacity, visavis, the distance to the randomness of a Gaussian distribution; (3) the relative position between ideal and random regulation; and (4) the energy capacity and variability of regulatory activities. Only significant values are evaluated. Data that are not significantly above the 95 percent level are not displayed. The practitioner might record statistically significant results by adding further measurements.

[FIGURE 3 OMITTED]

These results provide a tool to judge the regulatory capacity of a patient's physiology and physics. Data provides answers to questions such as regulatory and energy capacity, self-healing potential, and the potential for serious disease such as cancer or multiple sclerosis. Future investigation of the deviation pattern from the Log-Normal distribution might identify differential diagnoses.

Fig. 2 and Fig.3 display two typical cases for a rather healthy patient and a cancer patient, respectively.

[FIGURE 4 OMITTED]

Fig. 4 demonstrates the rather big changes of the probability distribution p(C) during the development of mortal diseases, here in cases of cancer.

[FIGURE 5 OMITTED]

Fig. 5 displays an example of the reproducibility of the results.

In Fig. 6, the changes of the pattern are shown in case of a basilar thrombosis patient who was treated successfully during a long-term therapy.

A further and more recent development of this method allows us to separate the different and independent factors of a system analysis for getting a rather reliable and remarkably reproducible pattern of the strengths and weaknesses concerning: (1) the regulatory capacity, (2) the energy availability, (3) the left-right symmetry, and (4) the degree of freedom in regulating the body. (10) Future research may be able to record in particular sclerotic or chaotic regulation even more accurately in terms of the probability distribution of further measurements such as capacity and resistance values at different frequencies of alternating voltages. The method provides, non-invasively, a possible way to evaluate a person's control of external and internal influences as well as the success or failure of therapeutic efforts. Concerning the practical results, two cancer hospitals --one in Frankfurt and one in Vienna--confirmed that in 90 percent of cases, there is a significant agreement between the evidence of clinically investigated cancer and the "break down" of the agreement of the frequency distribution to a LogNormal distribution. (16,17) We are not able to confirm these results at present due to the lack of possibilities of performing clinical tests. However, our experience shows that the systems analysis can clearly distinguish between "healthy" subjects and those who have "obvious" troubles, and there are significant correlations between the deviations from ideal Log-Normal frequency distribution and the kind and the degree of disease. A lot of patients visit us from time to time in order to get an impression about the development of their health state, and it turned out that the results are rather valuable for this "subjective" control. More detailed information is still the subject of collection of the data of practical applications and their evaluation. The most recent development makes use of the data in terms of a system analysis by factorization and additional rotation of the independent properties of the measurement values. This is a question of technical development, but already provides a rather interesting and powerful tool of opening a new and basically orthodox window in clinical diagnosis. We are convinced that there is already enough experience available for ample indication of the validity of the theory and its successful application.

[FIGURE 6 OMITTED]

Appendix

Take a system that is able to display m results [x.sub.1], [x.sub.2], ..., [x.sub.m], for instance the numbers one through six of a dice. Suppose that an event is dependent on n single measurements on these results, where all the values [x.sub.1], [x.sub.2], ...., [x.sub.m] could have been registered one or several times, and where they have to be connected in a definite way. The system becomes subject of an additive "Gestaltungs"-principle if the final event is dependent on whether either a definite value [x.sub.j] or another definite value [x.sub.k] has been registered. For instance, by throwing always two dice at the same time, the event may happen only if either number three or number six has been thrown. Then the probability W(3,6) of getting an event, where three or six appear, is the sum of the probabilities of W(3) and W(6). For always two throws, W is then 1/6+1/6 = 1/3. As a result, after three times throwing, it is always likely to get the number three or six. In general: An additive regulatory principle follows the law W([x.sub.j],[x.sub.k], .., [x.sub.r]) = W([x.sub.j]) + W([x.sub.k]) + ... + W([x.sub.r]). On the other hand, the system becomes subject of a multiplicative "Gestaltungs"-principle if the event can take place only if as well a definite [x.sub.j] as a definite [x.sub.k] has been registered. For instance, by throwing two dice at the same time, the event may happen only if as well number three as number six has been thrown. Then the probability of getting this event is the product of W(3) and W(6). This means that W(3/6) is then 1/6 x 1/6 = 1/18. After 18 times throwing two dice, it gets likely to throw as well the number three as six at the same time. In general, we have then for a multiplicative regulatory principle W([x.sub.j]/[x.sub.k]/.../[x.sub.r]) = W([x.sub.j]) x W([x.sub.k]) x ... x W([x.sub.r]). The sum of random variables is again a random variable. Thus, the probability distribution of a system that is subject of an additive "Gestaltungs-principle" follows a Gaussian (-Normal-) distribution. It is governed by completely random events. The probability of getting in n trials a deviation u = (hp) n from throwing N = np times a definite number, where n _ , is described by the well-known Gaussian distribution [P.sub.N](u) = [(2 Nq).sup.-1/2] exp ([-u.sup.2]/2Nq), where q = 1-p. Since, on the other hand, the logarithm of a product of m components is equal to the sum of the logarithms of all the components (i.e., log(A x B x C x ... x Z) = logA + logB + logC + ... + logZ), in case of the multiplicative "Gestaltungsprinciple", the logarithms of all the random variables now take the same role of random variables that are taken for a additive principle by the values themselves. Consequently, if instead of the original parameters the logarithms of the parameters follow a Gaussian distribution, now one gets the Log-Normal distribution instead of the normal (Gaussian) one. This is the case of the ideally ordered multiplicative "Gestaltungs"-principle. A corresponding example of throwing dice concerns the arrangement of a group of m people who agree in the acceptance of an event only if a definite series of numbers ([k.sub.1],[k.sub.2], ..., [k.sub.m]) is thrown. The probability of a successful event is then p = W[(k).sup.m], where for simplicity we provided that the probability of every single event (like by throwing dice) is always the same. Consequently, log p = m log W(k) takes the same role as p does in case of the additive principle. Consequently the probability of measuring in n trials a deviation logu = (logh-logp)n = n log(h/p) follows again a Gaussian distribution. This is the basic model of a multiplicative organizational principle, where the deviation from the mean value may originate by random fluctuations around an ideal cooperation of the parts, such as, for instance, variable collectiveness of cells in a cell population. In summary, as soon as a Log-Normal distribution becomes subject of the measurement, there is ample indication of cooperative effects within units of the system under study. It is evident that this may provide at the same time optimal selectiveness, certainty and sensitivity of the sensory organs under investigation, as it is expressed, for instance, in the fundamental psychophysical law (WeberFechner law). It is evident that this law can be taken at the same time as a control parameter of the regulatory functions of the system under investigation.

References:

(1.) Popp, F.A., Beloussov, L., Klimek, W., Swain, J. and Yan, Y. (2006). "Coupling of Frohlich-Modes as a Basis of Biological Regulation." In Hyland, H. and Hardie, J. (Eds), Festschrift of the International Symposium on the Centenary of the Birth of Herbert Frohlich. University of Liverpool, ( 4th and 5th April, 2006). Liverpool: University Press.

(2.) Schuermann, H. and Schirduan, M. (1948). Untersuchungen uber Fragen der Penicillindosierung. Wein Klin Wochenschr, 33/34: 526-8.

(3.) Gebelein, H. and Heite, H.J. (1950). Uber die Unsymmetrie biologischer Haufigkeitsverteilungen. Wein Klin Wochenschr, 28: 41-5.

(4.) Sachs, L. (1969). Statistische Auswertungsmethoden. Berlin: Springer.

(5.) Zhang, C.L. and Popp, F-A. (1994). "Log-Normal Distribution of Physiological Parameters and the Coherence of Biological Systems." Medical Hypotheses, 43:11-6.

(6.) Popp, F-A. (1998). "About the Coherence of Biophotons." In Sassaroli, E., Srivastava, Y., Swain, J., & Widom, A., (Eds). Macroscopic Quantum Coherence. Singapore, New Jersey, London: World Scientific. pp 130-50.

(7.) Popp, F-A., Chang, J.J., Herzog, A., Yan, Z., and Yan, Y. (2002). "Evidence of Non-classical (squeezed) Light in Biological Systems." Phys. Lett. A, 293, 98-102.

(8.) Popp, F-A., Beloussov, L. (Eds). (2003). Integrative Biophysics. Dordrecht, London: Kluwer Academic Publishers.

(9.) Glauber, R.J. (1969). "Coherence and Quantum Detection." In: Quantum Optics. New York and London: Academic Press, pp.15-56 (in particular pp 22 and 23).

(10.) Klimek, W.(2004). "Die elektrische Hautleitfahigkeit als Spiegel des inneren Regulationszustandes." Erfahrungsheilkunde (EHK) 53, 419-422.

(11.) Patent DE 103 55 348 A1 2005.06.23, Application PYCO.

(12.) Mehlhardt, W. (1975). "Elektrophysikalische Grundgesetze der Elektroakupunkturpunkte." Theorie und Praxis 2, 51-65.

(13.) Kramer, F. (1990). Lehrbuch der Elektroakupunktur Bd.1, Heidelberg: Haug-Verlag.

(14.) Hollischer, E., Mehlhardt, W., Popp, F-A. and Schmidt, H.G. (1979). "Statistische Analyse von Widerstandswerten an besonderen Hautstellen." Physikalische Medizin und Rehabilitation 9, 472-475

(15.) Rossmann, H. and Popp, F-A. (1986). Arztezeitschrift fur Naturheilverfahren 9, 623-630.

(16.) Siebenhuner, B. Frankfurt, personal communication.

(17.) Kostler, W. Vienna, personal communication.

Fritz-Albert Popp 

International Institute of Biophysics

Ehemalige Raketenstation, Kapellenerstrasse 41472 Neuss, Germany