Validating Bio-Well Technology for Medical Research: A Multi-Parameter Optimization Approach
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Abstract
This study validates Bio-Well technology (a commercial implementation of Gas Discharge Visualization) for distinguishing treatment effects from placebo responses in medical research. In a randomized crossover study of 50 participants aged 40-90, we analyzed three key parameters (Area, Normalized Area, and Inner Noise Percentage) across 95 finger-organ pairs following consumption of light-infused water versus placebo. A novel scoring system (0-100 points) evaluated parameter-finger-organ combinations based on statistical significance, effect sizes, and opposing directional changes between conditions. Results showed exceptional sensitivity in endocrine systems, particularly thyroid measurements (80-90 points). Inner Noise Percentage demonstrated opposing directional changes in 50% of measurements, significantly exceeding random probability and indicating genuine physiological responses. Left-hand measurements consistently outperformed right-hand counterparts. Strong correlation between statistical metrics validated the methodology. This multi-parameter optimization approach establishes Bio-Well as a viable assessment tool for non-invasive monitoring of treatment efficacy when utilizing specific parameter-finger-organ combinations.
Article Details
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
2. Oschman JL. The role of subtle energies in health and well-being. In: Consciousness and the Physical World. Springer; 2015:201-218.
3. Rubik B, Muehsam D, Hammerschlag R, Jain S. Biofield science and healing: history, terminology, and concepts. Glob Adv Health Med. 2015;4(Suppl):8-14.
4. Korotkov KG. Human Energy Field: study with GDV bioelectrography. Saint-Petersburg: Publishing house "Kultura"; 2002.
5. Korotkov KG, Williams B, Wisneski LA. Assessing biophysical energy transfer mechanisms in living systems: the basis of life processes. J Altern Complement Med. 2004;10(1):49-57.
6. McCraty R, Atkinson M, Tomasino D, Bradley RT. The coherent heart: Heart-brain interactions, psychophysiological coherence, and the emergence of system-wide order. Integral Rev. 2009;5(2):10-115.
7. Korotkov KG, Matravers P, Orlov DV, Williams BO. Application of electrophoton capture (EPC) analysis based on gas discharge visualization (GDV) technique in medicine: a systematic review. J Altern Complement Med. 2010;16(1):13-25.
8. Miraglia FE. Unreliability of the gas discharge visualization (GDV) device and the Bio-Well for biofield science: Kirlian photography revisited and investigated. Part I. J Anomalistics. 2024;24.
9. Miraglia FE. Unreliability of the Gas Discharge Visualization (GDV) Device and the Bio-Well for Biofield Science: Kirlian Photography Revisited and Investigated. Part II. J Anomalistics. 2025;24.
10. Connor CA, Connor MH, Eickhoff J, Horzempa DT, Schmidt K. Investigation into the physiological effects of nanometer light energized water study I. Med Res Arch. 2024;12(11).
11. Connor CA, Connor MH, Eickhoff J, Perry M. Investigation into the physiological effects of nanometer light energized water study 2: Physical data. Int J Nurs Health Care Res. 2025;8:1632. doi:10.29011/2688-9501.101632
12. Connor CA, Connor MH, Eickhoff J, Perry M. Investigation into the physiological effects of nanometer light energized water study 2: Meridian and Acupuncture data. HSOA J Altern Complement Integr Med. 2025;11:574. doi:10.24966/ACIM-7562/100574
13. Connor CA, Connor MH, Eickhoff J, Perry M, Shipione H. Investigation into the physiological effects of nanometer light energized water study 3: Meridian and acupuncture data. HSOA J Altern Complement Integr Med. 2025;11:573. doi:10.24966/ACIM-7562/100573
14. Faul F, Erdfelder E, Lang AG, Buchner A. G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. 2007;39:175-191.
15. Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res. 1999;8(2):135-160.
16. Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Hillsdale, NJ: Lawrence Erlbaum Associates; 1988.
17. Bianco AC, Dumitrescu A, Gereben B, et al. Paradigms of dynamic control of thyroid hormone signaling. Endocr Rev. 2019;40(4):1000-1047.
18. Sinha RA, Singh BK, Yen PM. Direct effects of thyroid hormones on hepatic lipid metabolism. Nat Rev Endocrinol. 2018;14(5):259-269.
19. Razvi S, Jabbar A, Pingitore A, et al. Thyroid hormones and cardiovascular function and diseases. J Am Coll Cardiol. 2018;71(16):1781-1796.
20. Teixeira PF, Santos PB, Pazos-Moura CC. The role of thyroid hormone in metabolism and metabolic syndrome. Ther Adv Endocrinol Metab. 2020;11:2042018820917869.
21. Sinha RA, Yen PM. Metabolic messengers: thyroid hormones. Nat Metab. 2024;6(4):639-650.
22. Cicatiello AG, Di Girolamo D, Dentice M. Metabolic effects of the intracellular regulation of thyroid hormone: old players, new concepts. Front Endocrinol. 2018;9:474.
23. Anteneodo C, Tsallis C. Multiplicative noise: A mechanism leading to nonextensive statistical mechanics. J Math Phys. 2003;44(11):5194-5203.
24. Kanis JA, Johnell O, Oden A, Johansson H, McCloskey E. FRAX and the assessment of fracture probability in men and women from the UK. Osteoporos Int. 2008;19(4):385-397