Home About us Editorial board Search Ahead of print Current issue Archives Submit article Instructions Subscribe Contacts Login 
  • Users Online: 34
  • Home
  • Print this page
  • Email this page

 Table of Contents  
Year : 2016  |  Volume : 2  |  Issue : 3  |  Page : 93-96

Your health: Analog or digital?

Mimex Montague Healthcare Limited, Cotgrave, Nottinghamshire NG12 3TU, UK

Date of Web Publication24-Nov-2016

Correspondence Address:
Graham Ewing
Graham Ewing, Mulberry House, 6 Vine Farm Close, Cotgrave, Nottinghamshire, NG12 3TU
Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/2226-8561.194690

Rights and Permissions

How to cite this article:
Ewing G. Your health: Analog or digital?. Digit Med 2016;2:93-6

How to cite this URL:
Ewing G. Your health: Analog or digital?. Digit Med [serial online] 2016 [cited 2023 Mar 29];2:93-6. Available from: http://www.digitmedicine.com/text.asp?2016/2/3/93/194690

The demand for healthcare around the world now exceeds the ability of the contemporary biomedical paradigm to diagnose and treat many and various medical conditions, which adversely influence our health. Moreover, a critical evaluation of the current paradigm illustrates the fundamental limitations and failings of techniques which are used to diagnose and treat the health of the patient, e.g. drugs are now recognized as the third major cause of death [1] and/or the effectiveness of drugs is at a lower level than expected. [2]

Despite the immense body of knowledge regarding how drugs function, there is not yet an accepted understanding of the following:

  • The basic processes which lead to pathological onset or the fundamental components of each pathology
  • The fundamental mechanism which regulates how the body functions
  • The fundamental mechanisms by which complementary and alternative medicine (CAM) techniques can have some effect upon the health of the patient
  • The relationship between health and wellbeing
  • The significance of genotype, phenotype, the influence of the environment, stress, etc.
  • Why the effectiveness of drugs declines over a period;
  • Or
  • Why drugs are rarely more than 50% effective. [3]
In recognition of the limitations of biomedicine, neuroscientist Henry Markram [4] convinced the European Commission to invest EUR1.2BN in a complex multidisciplinary research project-the Human Brain Project, [5] - which has the objectives: (i) to determine what the brain does and how it does it, (ii) to develop a new generation of cognitively-based diagnostic technologies which are able to determine the pathological correlates of complex medical conditions such as Alzheimer's disease, and (iii) to understand and adapt with therapeutic effect the multilevel nature of brain function.

The challenge for the managers of his project is to integrate the research of the 14 sub projects (SP1-14) to come up with a finished solution. Almost inevitably, the project has been controversial and has encountered political managerial and technological problems. [6] The project has assumed that it can use contemporary methods of diagnosing disease and that BIG DATA will enable researchers to reveal statistical relationships which will ultimately lead to a finished solution to the key aims and objectives of this enormously complex and expensive project.

In an astonishing and unexpected twist, it has been demonstrated that the Russian researcher Dr. Grakov has developed a technology [7],[8] which meets, almost in its entirety, the key aims and objectives of the Human Brain Project. It comprises strannik virtual scanning (SVS) [9],[10],[11],[12],[13] and strannik light therapy (SLT) [13],[14],[15],[16],[17],[18] and is based upon the fundamental observation that changes of brain function, in particular of sense perception, [19],[20],[21] have pathological correlates; there are fundamental processes which are regulated by the brain (which can be mathematically modeled); and knowledge of this relationship can be adapted and used as the basis of a biofeedback technology which optimizes the brain's ability to regulate the stability of the autonomic nervous system, [22],[23],[24],[25],[26],[27],[28],[29],[30],[31],[32] hence depending upon their pathological components, reducing or eliminating the onset or progression of pathologies.

The technique is based upon the observation that pathological reaction emits biophoton(s) of light influences our perception of color. [19],[20],[21] It serves as the basis of a theoretically sound scientific "digital" principle which measures the rate at which proteins are expressed and/or at which they react; by contrast with the measurement of biochemical markers; and it has been transcribed into a computer-based digital technique of immense medical, commercial, and political significance.

SVS can determine the earliest onset of pathologies from their presymptomatic onset, each pathology reported in terms of its genotype and its phenotype, and the entire range of comorbidities in each and every organ (typically 15 per organ/30 organs). Moreover, the technique is entirely noninvasive and safe, and can be conducted in 20 min to the point where results are available in report format, at a cost which is typically 5%-25% of contemporary diagnostic tests; and it is entirely free from factors which could influence the reported test results.

By contrast, contemporary diagnostic tests are not generally based upon a statistically significant theoretical concept, but instead simplistic observations or phenomena, which can be adapted with diagnostic or therapeutic effect.

  • Genetic testing is based upon the assumption that a single gene is responsible for a single pathological process, yet it is recognized that it takes the coordinated function of a number of genes to express a protein
  • Biomarker tests are often based upon measuring the level of a protein or other biologically active material. They fail to take into account that proteins may be coiled and reactive, which is most significant, or that proteins may be uncoiled and unreactive.
As a result, most diagnostic tests are experiential, rarely precisely accurate, incorporate a range of limiting factors, which influence the accuracy and precision of reported outcomes, [33],[34] and do not consider the genetic and/or phenotypic nature of each pathology, i.e., that each pathology has both genetic and phenotypic components; [31],[35] that most medical conditions are polygenomic, multi-systemic and multi-pathological; and that pathological onset is the consequence of systemic dysfunction, not the fundamental stress-related/phenotypic cause.

In addition, drugs are based upon the same fundamental concept, i.e., that a pathology has single pathological onset, and therefore that a drug (or occasionally a combination of drugs) can be used to mask the symptoms of the pathology and provide relief; however, this has significant limitations. The drug(s) may be ineffective in the patient, the effectiveness of the drug(s) wears off after a period, the fundamental stress-related cause of the problem still exists and ultimately influences the stability of other body systems until other pathological issues develop, and other comorbidities are factors. The process, through its ignorance of how the brain regulates the body's function via the autonomic nervous system, merely perpetuates the disease process and leads to the onset of chronically stable pathologies/conditions without dealing with the fundamental neurological cause(s) of the problem.

This lack of understanding of how the brain regulates the body's function, and thereby maintains our health and wellbeing, is perpetuated in the new "digital" paradigm where it is assumed that all things digital must be good; however, most digital techniques such as APPS are merely using digital means to convey existing "analog" or "experiential" data sets around the medical system more quickly and at lower cost. There is nothing the matter with that. The principle is laudable and will improve the efficiency of the medical system to some extent but it does little to improve the fundamental need for better quality data and to make radical change to the cost of healthcare. More data, big data, will yield little of significance unless and until the fundamental principles by which the body functions are recognized and adapted. There is a need for better quality data. [36]

The market is optimistic that future "Point of Care" technologies will emerge-in a digital format-which can enable the patient to monitor their lifestyle and better manage their lives; however, genetic-based approaches have recently encountered significant regulatory issues which threaten their future viability. [37] The plethora of "wearables" is considered by some to be evidence of a short-lived fad which will attract a fitness-oriented clientele. Nevertheless, irrespective of their intended application, there is a need to illustrate that these "medical devices" perform at an acceptable level of performance and thereby justify their use in a medical context. Moreover, initial research illustrates that after an initial honeymoon period the end-user dispenses with their wearable. [38]

Several APPs are being used to measure key parameters such as vital signs and blood glucose levels. Nevertheless, the same basic issues apply. How relevant are the measured parameters? How accurate are the measured results? Are the technologies robust and reliable not just now but also in the future after a period of use? Perhaps, it is for such reasons that there is a body of sceptical opinion in the investment community which considers that the current enthusiasm for anything digital is a bubble which is going to burst. [39]

There needs to be a statistically significant scientific principle, which can be applied via the human-device interface, if digital techniques are to be used to diagnose any medical conditions, and there needs to be a reliable way of delivering and interpreting the measured parameters.

The Strannik technology appears to be the most advanced of this new generation of medical technology. [30] Nevertheless, the entry of such technologies to the market is fraught with political intrigue and practical issues, i.e., it is a radical, disruptive, and quite different way of determining and treating health. It treads the boundary of CAM, neurology, cognitive neuroscience, preventative, and integrative medicine as well as contemporary biomedicine, biofeedback, and all things digital. It incorporates an unprecedented level of understanding of how the brain regulates the autonomic nervous system and of the relationship between molecular biology, cellular biology, organ function, and the coherent function of the organ systems often referred to as physiological systems. [30]

  References Top

Makary MA, Daniel M. Medical error-the third leading cause of death in the US. BMJ 2016;353:i2139.  Back to cited text no. 1
Kaplan RM, Irvin VL. Likelihood of null effects of large NHLBI clinical trials has increased over time. PLoS One 2015;10:e0132382.  Back to cited text no. 2
Spear BB, Heath-Chiozzi M, Huff J. Clinical application of pharmacogenetics. Trends Mol Med 2001;7:201-4.  Back to cited text no. 3
Ted. com. A Brain in a Supercomputer. Available from: http://www.ted.com/talks/henry_markram_supercomputing_the_brain_s_secrets. [Last accessed on 2014 Jun 05].  Back to cited text no. 4
Available from: http://www.humanbrainproject.eu/en_GB.  Back to cited text no. 5
Ewing GW. Back to basics: Limitations of research influencing the human brain project. Comput Sci Syst Biol 2015;6:322-6.  Back to cited text no. 6
Grakov IG. Strannik Diagnostic and Treatment System: A Virtual Scanner for the Health Service. Minutes of Meeting No. 11 of the Praesidium of the Siberian of the Academy of Medical Sciences of the USSR (AMN) Held in Novosibirsk; 4 December, 1985.  Back to cited text no. 7
Ewing GW, Ewing EN. Virtual Scanning - A new generation of medical technology - Beyond biomedicine? Nottingham, England: Montague Healthcare Books; 2007. ISBN 978-0-9556213-0-7.  Back to cited text no. 8
Hankey A, Ewing E. New light on chromotherapy: Grakov′s ′Virtual Scanning′ system of medical assessment and treatment. Evid Based Complement Alternat Med 2007;4:139-44.  Back to cited text no. 9
Ewing G, Ewing E, Hankey A. Virtual Scanning - a new system of medical assessment and treatment: Part I. Assessment. J Altern Complement Med 2007;13:271-85.  Back to cited text no. 10
Ewing GW, Duran JC. A report of the ability of Strannik Virtual Scanning to screen the health of a randomly selected cohort of patients. Enliven Neurol Neurotechnol 2016;2:1.  Back to cited text no. 11
Ewing GW. Case study: The determination a complex multi-systemic medical condition by a cognitive, Virtual Scanning technique. Case Rep Clin Med 2015;4:209-21.  Back to cited text no. 12
Vysochin Yu V, Lukoyanov VV, Yaichnikov IK, Tkachuk MI, Chyev VA, Yemelyanenko VV, et al. Methodology and Technology of Invigoration of Different Population Orders. In: Consolidated 5 Year Research Plan of Physical Training, Sports and Tourism State Committee of the Russian Federation. 2000, 2001. Available from: http://www.montaguehealthcare.co.uk/files/Vysochin/Vysochin.pdf.  Back to cited text no. 13
Ewing GW. A theoretical framework for photosensitivity: Evidence of systemic regulation. J Comput Sci Syst Biol 2009;2:287-97.  Back to cited text no. 14
Montaguehealthcare.com. Strannik Virtual Scanning and Strannik Light Therapy Case Studies. Available from: http://www.montaguehealthcare.co.uk/CaseStudiesSummary2Rev1.pdf.  Back to cited text no. 15
Ewing GW. The successful treatment of Dysarthria using Strannik Light Therapy (Biofeedback): A case study. Case Rep Clin Med 2015;4:266-9.  Back to cited text no. 16
Nwose EU, Ewing GW, Ewing EN. Migraine can be managed with Virtual Scanning: Case report. Open Complement Med J 2009;1:16-8.  Back to cited text no. 17
Ewing GW, Nwose EU, Ewing EN. Obstructive sleep apnea management with interactive computer technology and nutrition: Two case reports. J Altern Complement Med 2009;15:1379-81.  Back to cited text no. 18
Ewing GW, Ewing EN. Cognition, the autonomic nervous system and the physiological systems. Biogenic Amines 2008;22:140-63.  Back to cited text no. 19
Ewing GW, Parvez SH, Grakov IG. Further observations on visual perception: The influence of pathologies upon the absorption of light and emission of bioluminescence. Open Syst Biol J 2011;4:1-7.  Back to cited text no. 20
Ewing GW, Parvez SH. Systemic regulation of metabolic function. Biogenic Amines 2008;22:279-94.  Back to cited text no. 21
Ewing GW, Parvez SH. The Dynamic Relationship between Cognition, the Physiological Systems, and Cellular and Molecular Biochemistry: A Systems-based Perspective on the Processes of Pathology. Act Nerv Super Rediviva 2010;52:29-36.  Back to cited text no. 22
Ewing GW, Ewing EN. Neuroregulation of the physiological systems by the autonomic nervous system - Their relationship to insulin resistance and metabolic syndrome. Biogenic Amines 2008;22:208-39.  Back to cited text no. 23
Ewing GW, Ewing EN, Parvez SH. Developmental dyslexia: The link with the autonomic nervous system and the physiological systems. Biogenic Amines 2009;23:115-90.  Back to cited text no. 24
Ewing GW. There is a need for an alternative or modified medical paradigm involving an understanding of the nature and significance of the physiological systems. N Am J Med Sci 2010;2:1-6.  Back to cited text no. 25
Ewing GW, Parvez SH. Mathematical modeling the systemic regulation of blood glucose: ′A top-down′ systems biology approach. Neurol Endocr Lett 2011;32:371-9.  Back to cited text no. 26
Ewing GW. Mathematical modelling the neuroregulation of blood pressure using a cognitive top-down approach. N Am J Med Sci 2010;2:341-52.  Back to cited text no. 27
Ewing GW. The regulation of pH is a physiological system. Increased acidity alters protein conformation and cell morphology and is a significant factor in the onset of diabetes and other common pathologies. Open Syst Biol J 2012;5:1-12.  Back to cited text no. 28
Ewing GW. A framework for a mathematical model of the autonomic nervous system and physiological systems using the neuroregulation of blood glucose as an example. J Comput Sci Syst Biol 2015;8:59-73.  Back to cited text no. 29
Ewing GW, Grakov IG. A comparison of the aims and objectives of the human brain project with Grakov′s mathematical model of the autonomic nervous system (Strannik Technology). Enliven Neurol Neurotechnol 2015;1:2.  Back to cited text no. 30
Ewing GW. Further perspectives on diabetes: Neuroregulation of blood glucose. Neurosci Biomed Eng 2016;4:1-9.  Back to cited text no. 31
Ewing GW. Science or Non-Science? The Challenge for Medical Research - To Explain Neuro-Regulation. IEEE Technically Sponsored SAI Computing Conference 2016, London, UK; 13-15 July, 2016.  Back to cited text no. 32
Ewing GW. A comparison of the diagnostic scope of biomarker techniques, genetic screening and Virtual Scanning. Immunol Endocr Metab Agents Med Chem 2013;13:35-45.  Back to cited text no. 33
Ewing GW, Parvez SH. The dynamic relationship between cognition, the physiological systems, and cellular and molecular biochemistry: A systems-based perspective on the processes of pathology. Act Nerv Super Rediviva 2010;52:29-36.  Back to cited text no. 34
Ewing GW, Parvez SH. The multi-systemic nature of diabetes mellitus: Genotype or phenotype? N Am J Med Sci 2010;2:444-56.  Back to cited text no. 35
Ewing GW. NHS must make greater use of information technology. The quality of data - Not the quantity. Br Med J 2008;337:a2303.  Back to cited text no. 36
Mddionline.com. Wearables: Fad or the Future? Available from: http://www.mddionline.com/article/wearables-fad-or-future-02-06-15<>.   Back to cited text no. 38


Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

  In this article

 Article Access Statistics
    PDF Downloaded259    
    Comments [Add]    

Recommend this journal