The author is an engineer who has conducted medical research work over the past 13 years in the fields of endocrinology, metabolic disorder-induced chronic diseases (especially diabetes), and their resulting various medical complications. Thus far, he has written and published nearly 700 research papers in various medical journals using math-physical medicine methodology (MPM). Beginning with paper No. 578 dated 1/8/2022, he has written 90 medical papers and 4 economics papers using viscoelasticity and viscoplasticity theories (VGT) tools from physics and engineering disciplines. These papers aim to explore some hidden physical behaviors and provide a deeper quantitative understanding of the inter-relationships of a selected output (symptom) versus singular input or multiple inputs (root causes, risk factors, or influential factors). In the field of medical research, the hidden biophysical behaviors and possible inter-relationships exist among lifestyle details, medical conditions, chronic diseases, and certain medical complications, such as heart attacks, stroke, kidney failure, cancers, dementia, and even longevity concerns. He has noticed that most medical sub- jects with their associated data, multiple symptoms, and influential factors are “time-dependent” which means that all biomedical variables change from time to time because body living cells are dynamically changing. This is what Professor Norman Jones, the author’s adviser at MIT, suggested to him in December 2021 and why he utilizes the VGT tools from physics and engineering to conduct his medical research work since then. Papers No. 671 through No. 674 were focused on the input of COVID infectious disease versus three key US economic outputs, GDP, Inflation, and CPI. From these 4 economics exercises, he further realized that the established theory of viscoelasticity and viscoplasticity (from engineering and physics) should not only be limited to the scope of engineering applications. Its ability to link certain time-dependent variables and their physical characteristics and associated energy estimation via the hysteresis loop area are equally powerful for applications in many other research fields, such as economics, psychology, social science, and medicine. Of course, one of the major challenges of VGT analysis is always related to data mining, selection, and prepara- tion. This particular Paper No. 685 discussed his inter-relationship findings regarding chronic kidney diseases (CKD) risk percentage versus type 2 diabetes situation via HbA1C (A1C), kidney damage assessment via urine albumin to creatinine ratio (ACR), and bladder or prostate conditions checking via a symptom of wakeup times for urination at night.
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