Set Of Biomarkers Research Articles

Association of baseline soluble immune checkpoints with the risk of relapse in PR3-ANCA vasculitis following induction of remission

ObjectivesWe investigated whether soluble immune checkpoints (sICPs) predict treatment resistance, relapse and infections in patients with antineutrophil cytoplasm antibody (ANCA)-associated vasculitis (AAV).MethodsPlasma sICP concentrations from available samples obtained during conduct...

Putative Biomarkers of Environmental Enteric Disease Fail to Correlate in a Cross-Sectional Study in Two Study Sites in Sub-Saharan Africa.

Environmental enteric dysfunction (EED) is an elusive, inflammatory syndrome of the small intestine thought to be associated with enterocyte loss and gut leakiness and lead to stunted child growth. To date, the gold standard for diagnosis is small intestine biopsy followed by histology. Several putative biomarkers for EED have been proposed and are widely used in the field. Here, we assessed in a cross-sectional study of children aged 2–5 years for a large set of biomarkers including markers of protein exudation (duodenal and fecal alpha-1-antitrypsin (AAT)), inflammation (duodenal and fecal calprotectin, duodenal, fecal and blood immunoglobulins, blood cytokines, C-reactive protein (CRP)), gut permeability (endocab, lactulose-mannitol ratio), enterocyte mass (citrulline) and general nutritional status (branched-chain amino acids (BCAA), insulin-like growth factor) in a group of 804 children in two Sub-Saharan countries. We correlated these markers with each other and with anemia in stunted and non-stunted children. AAT and calprotectin, CRP and citrulline and citrulline and BCAA correlated with each other. Furthermore, BCAA, citrulline, ferritin, fecal calprotectin and CRP levels were correlated with hemoglobin levels. Our results show that while several of the biomarkers are associated with anemia, there is little correlation between the different biomarkers. Better biomarkers and a better definition of EED are thus urgently needed.

Predicting treat-and-extend outcomes and treatment intervals in neovascular age-related macular degeneration from retinal optical coherence tomography using artificial intelligence.

PurposeTo predict visual outcomes and treatment needs in a treat & extend (T&E) regimen in neovascular age-related macular degeneration (nAMD) using a machine learning model based on quantitative optical coherence tomography (OCT) imaging biomarkers.Materials and methodsStudy eyes of 270 treatment-naïve subjects, randomized to receiving ranibizumab therapy in the T&E arm of a randomized clinical trial were considered. OCT volume scans were processed at baseline and at the first follow-up visit 4 weeks later. Automated image segmentation was performed, where intraretinal (IRF), subretinal (SRF) fluid, pigment epithelial detachment (PED), hyperreflective foci, and the photoreceptor layer were delineated using a convolutional neural network (CNN). A set of respective quantitative imaging biomarkers were computed across an Early Treatment Diabetic Retinopathy Study (ETDRS) grid to describe the retinal pathomorphology spatially and its change after the first injection. Lastly, using the computed set of OCT features and available clinical and demographic information, predictive models of outcomes and retreatment intervals were built using machine learning and their performance evaluated with a 10-fold cross-validation.ResultsData of 228 evaluable patients were included, as some had missing scans or were lost to follow-up. Of those patients, 55% reached and maintained long (8, 10, 12 weeks) and another 45% stayed at short (4, 6 weeks) treatment intervals. This provides further evidence for a high disease activity in a major proportion of patients. The model predicted the extendable treatment interval group with an AUROC of 0.71, and the visual outcome with an AUROC of up to 0.87 when utilizing both, clinical and imaging features. The volume of SRF and the volume of IRF, remaining at the first follow-up visit, were found to be the most important predictive markers for treatment intervals and visual outcomes, respectively, supporting the important role of quantitative fluid parameters on OCT.ConclusionThe proposed Artificial intelligence (AI) methodology was able to predict visual outcomes and retreatment intervals of a T&E regimen from a single injection. The result of this study is an urgently needed step toward AI-supported management of patients with active and progressive nAMD.

Accuracy of EEG Biomarkers in the Detection of Clinical Outcome in Disorders of Consciousness after Severe Acquired Brain Injury: Preliminary Results of a Pilot Study Using a Machine Learning Approach.

Accurate outcome detection in neuro-rehabilitative settings is crucial for appropriate long-term rehabilitative decisions in patients with disorders of consciousness (DoC). EEG measures derived from high-density EEG can provide helpful information regarding diagnosis and recovery in DoC patients. However, the accuracy rate of EEG biomarkers to predict the clinical outcome in DoC patients is largely unknown. This study investigated the accuracy of psychophysiological biomarkers based on clinical EEG in predicting clinical outcomes in DoC patients. To this aim, we extracted a set of EEG biomarkers in 33 DoC patients with traumatic and nontraumatic etiologies and estimated their accuracy to discriminate patients’ etiologies and predict clinical outcomes 6 months after the injury. Machine learning reached an accuracy of 83.3% (sensitivity = 92.3%, specificity = 60%) with EEG-based functional connectivity predicting clinical outcome in nontraumatic patients. Furthermore, the combination of functional connectivity and dominant frequency in EEG activity best predicted clinical outcomes in traumatic patients with an accuracy of 80% (sensitivity = 85.7%, specificity = 71.4%). These results highlight the importance of functional connectivity in predicting recovery in DoC patients. Moreover, this study shows the high translational value of EEG biomarkers both in terms of feasibility and accuracy for the assessment of DoC.

NMR Metabolomics in Serum Fingerprinting of Schizophrenia Patients in a Serbian Cohort.

Schizophrenia is a widespread mental disorder that leads to significant functional impairments and premature death. The state of the art indicates gaps in the understanding and diagnosis of this disease, but also the need for personalized and precise approaches to patients through customized medical treatment and reliable monitoring of treatment response. In order to fulfill existing gaps, the establishment of a universal set of disorder biomarkers is a necessary step. Metabolomic investigations of serum samples of Serbian patients with schizophrenia (51) and healthy controls (39), based on NMR analyses associated with chemometrics, led to the identification of 26 metabolites/biomarkers for this disorder. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) models with prediction accuracies of 0.9718 and higher were accomplished during chemometric analysis. The established biomarker set includes aspartate/aspartic acid, lysine, 2-hydroxybutyric acid, and acylglycerols, which are identified for the first time in schizophrenia serum samples by NMR experiments. The other 22 identified metabolites in the Serbian samples are in accordance with the previously established NMR-based serum biomarker sets of Brazilian and/or Chinese patient samples. Thirteen metabolites (lactate/lactic acid, threonine, leucine, isoleucine, valine, glutamine, asparagine, alanine, gamma-aminobutyric acid, choline, glucose, glycine and tyrosine) that are common for three different ethnic and geographic origins (Serbia, Brazil and China) could be a good start point for the setup of a universal NMR serum biomarker set for schizophrenia.

Analyzing biomarker discovery: Estimating the reproducibility of biomarker sets

Many researchers try to understand a biological condition by identifying biomarkers. This is typically done using univariate hypothesis testing over a labeled dataset, declaring a feature to be a biomarker if there is a significant statistical difference between its values for the subjects with different outcomes. However, such sets of proposed biomarkers are often not reproducible – subsequent studies often fail to identify the same sets. Indeed, there is often only a very small overlap between the biomarkers proposed in pairs of related studies that explore the same phenotypes over the same distribution of subjects. This paper first defines the Reproducibility Score for a labeled dataset as a measure (taking values between 0 and 1) of the reproducibility of the results produced by a specified fixed biomarker discovery process for a given distribution of subjects. We then provide ways to reliably estimate this score by defining algorithms that produce an over-bound and an under-bound for this score for a given dataset and biomarker discovery process, for the case of univariate hypothesis testing on dichotomous groups. We confirm that these approximations are meaningful by providing empirical results on a large number of datasets and show that these predictions match known reproducibility results. To encourage others to apply this technique to analyze their biomarker sets, we have also created a publicly available website, https://biomarker.shinyapps.io/BiomarkerReprod/, that produces these Reproducibility Score approximations for any given dataset (with continuous or discrete features and binary class labels).

Reliable epithelial-mesenchymal transition biomarkers for colorectal cancer detection.

Aims: To combat increases in colorectal cancer (CRC) incidence and mortality, biomarkers among differentially expressed genes (DEGs) have been identified to objectively detect cancer. However, DEGs are numerous, and additional parameters may identify more reliable biomarkers. Here, CRC DEGs were filtered into a prioritized list of biomarkers. Materials & methods: Two independent datasets (COAD-READ [n=698] and GSE50760 [n=36]) were input alternatively to the recently published data-driven reference method. Results were filtered based on epithelial-mesenchymal transition enrichment (χ-square statistic: 919.05; p =2.2e-16) to produce 37 potential CRC biomarkers. Results: All 37 genes reliably classified CRC samples and ETV4, CLDN1 and CA2 together were top-ranked by DDR (accuracy: 89%; F1 score: 0.89). Conclusion: Biological and statistical information were combined to produce a better set of CRC detection biomarkers.

Pharmacological Probes to Validate Biomarkers for Analgesic Drug Development.

There is an urgent need for analgesics with improved efficacy, especially in neuropathic and other chronic pain conditions. Unfortunately, in recent decades, many candidate analgesics have failed in clinical phase II or III trials despite promising preclinical results. Translational assessment tools to verify engagement of pharmacological targets and actions on compartments of the nociceptive system are missing in both rodents and humans. Through the Innovative Medicines Initiative of the European Union and EFPIA, a consortium of researchers from academia and the pharmaceutical industry was established to identify and validate a set of functional biomarkers to assess drug-induced effects on nociceptive processing at peripheral, spinal and supraspinal levels using electrophysiological and functional neuroimaging techniques. Here, we report the results of a systematic literature search for pharmacological probes that allow for validation of these biomarkers. Of 26 candidate substances, only 7 met the inclusion criteria: evidence for nociceptive system modulation, tolerability, availability in oral form for human use and absence of active metabolites. Based on pharmacokinetic characteristics, three were selected for a set of crossover studies in rodents and healthy humans. All currently available probes act on more than one compartment of the nociceptive system. Once validated, biomarkers of nociceptive signal processing, combined with a pharmacometric modelling, will enable a more rational approach to selecting dose ranges and verifying target engagement. Combined with advances in classification of chronic pain conditions, these biomarkers are expected to accelerate analgesic drug development.

BPTF inhibition antagonizes colorectal cancer progression by transcriptionally inactivating Cdc25A

As the largest subunit of the nuclear remodeling factor complex, Bromodomain PHD Finger Transcription Factor (BPTF) has been reported to be involved in tumorigenesis and development in several cancers. However, to date, its functions and related molecular mechanisms in colorectal cancer (CRC) are still poorly defined and deserve to be revealed. In this study, we uncovered that, under the expression regulation of c-Myc, BPTF promoted CRC progression by targeting Cdc25A. BPTF was found to be highly expressed in CRC and promoted the proliferation and metastasis of CRC cells through BPTF specific siRNAs, shRNAs or inhibitors. Based on RNA-seq, combined with DNA-pulldown, ChIP and luciferase reporter assay, we proved that, by binding to −178/+107 region within Cdc25A promoter, BPTF transcriptionally activated Cdc25A, thus accelerating the cell cycle process of CRC cells. Meanwhile, BPTF itself was found to be transcriptionally regulated by c-Myc. Moreover, BPTF knockdown or inactivation was verified to sensitize CRC cells to chemotherapeutics, 5-Fluorouracil (5FU) and Oxaliplatin (Oxa), c-Myc inhibitor and cell cycle inhibitor not just at the cellular level in vitro, but in subcutaneous xenografts or AOM/DSS-induced in situ models of CRC in mice, while Cdc25A overexpression partially reversed BPTF silencing-caused tumor growth inhibition. Clinically, BPTF, c-Myc and Cdc25A were highly expressed in CRC tissues simultaneously, the expression of any two of the three was positively correlated, and their expressions were highly relevant to tumor differentiation, TNM staging and poor prognosis of CRC patients. Thus, our study indicated that the targeted inhibition of BPTF alone, or together with chemotherapy and/or cell cycle-targeted therapy, might act as a promising new strategy for CRC treatment, while c-Myc/BPTF/Cdc25A signaling axis is expected to be developed as an associated set of candidate biomarkers for CRC diagnosis and prognosis prediction.

Lung cancer survival prediction and biomarker identification with an ensemble machine learning analysis of tumor core biopsy metabolomic data.

While prediction of short versus long term survival from lung cancer is clinically relevant in the context of patient management and therapy selection, it has proven difficult to identify reliable biomarkers of survival. Metabolomic markers from tumor core biopsies have been shown to reflect cancer metabolic dysregulation and hold prognostic value. Implement and validate a novel ensemble machine learning approach to evaluate survival based on metabolomic biomarkers from tumor core biopsies. Data were obtained from tumor core biopsies evaluated with high-resolution 2DLC-MS/MS. Unlike biofluid samples, analysis of tumor tissue is expected to accurately reflect the cancer metabolism and its impact on patient survival. A comprehensive suite of machine learning algorithms were trained as base learners and then combined into a stacked-ensemble meta-learner for predicting "short" versus "long" survival on an external validation cohort. An ensemble method of feature selection was employed to find a reliable set of biomarkers with potential clinical utility. Overall survival (OS) is predicted in external validation cohort with AUROCTEST of 0.881 with support vector machine meta learner model, while progression-free survival (PFS) is predicted with AUROCTEST of 0.833 with boosted logistic regression meta learner model, outperforming a nomogram using covariate data (staging, age, sex, treatment vs. non-treatment) as predictors. Increased relative abundance of guanine, choline, and creatine corresponded with shorter OS, while increased leucine and tryptophan corresponded with shorter PFS. In patients that expired, N6,N6,N6-Trimethyl-L-lysine, L-pyrogluatmic acid, and benzoic acid were increased while cystine, methionine sulfoxide and histamine were decreased. In patients with progression, itaconic acid, pyruvate, and malonic acid were increased. This study demonstrates the feasibility of an ensemble machine learning approach to accurately predict patient survival from tumor core biopsy metabolomic data.

Metabolomic profile of combined healthy lifestyle behaviours in humans: A systematic review

A combination of healthy lifestyle behaviours (i.e., regular physical activity, nutritious diet, no smoking, moderate alcohol, and healthy body mass) has been consistently associated with beneficial health outcomes including reduced risk of cardiometabolic diseases. Metabolomic profiles, characterized by distinct sets of biomarkers, have been described for healthy lifestyle behaviours individually and in combination. However, recent literature calls for systematic evaluation of these heterogenous data to identify potential clinical biomarkers relating to a combined healthy lifestyle. The objective was to systematically review existing literature on the metabolomic profile of combined healthy lifestyle behaviours. MEDLINE, EMBASE and Cochrane databases were searched through March 2022. Studies in humans outlining the metabolomic profile of a combination of two or more healthy lifestyle behaviours were included. Collectively, the metabolomic profile following regular adherence to combined healthy lifestyle behaviours points to a positive association with beneficial fatty acids and phosphocreatine, and inverse associations with triglycerides, trimethylamine N-oxide, and acylcarnitines. The findings suggest that a unique metabolomic profile is associated with combined healthy lifestyle behaviours. Additional research is warranted to further describe this metabolomic profile using targeted and untargeted metabolomic approaches along with uniform definitions of combined healthy lifestyle variables across populations.

Viscoelastic or Viscoplastic Glucose Theory (VGT#53) Applying VGT for Selecting an Appropriate Conversion Factor from Finger-Pierced Daily Estimated Average Glucose (eAG) to a Predicted Finger A1C (F.A1C) and using Collected Annual Average Data from Y2012 to Y2022 to Study the Inter-Relationships Among Lab-Tested A1C, Calculated Finger A1C, and Finger-Pierced eAG Based on the GH-Method: Math-Physical Medicine (No. 640)

The author was a professional engineer working in the fields of the space shuttle, naval battleships, nuclear power plant, computer hardware and software, artificial intelligence, and semiconductor chips. After retiring from his work, he initiated self-study and research on internal medicine with an emphasis on biomarker relationship exploration and disease prevention. Since 2010, he has utilized these disciplines learned from 7 different universities along with various work experiences to formulate his current medical research work during the past 13 years. One thing he has learned is that in engineering or medicine, we are frequently seeking answers, illustrations, or explanations for the relationships between the input variable (force applied on a structure or cause of a disease) and output variable (deformation of a structure or symptom of a disease). However, the multiple relationships between input and output could be expressed with many different matrix formats of 1 x 1, 1 x n, m x 1, or m x n (m or n means different multiple variables). In addition to these described mathematical complications, the output resulting from one or more inputs can also become an input of another output, i.e. a symptom of certain causes can become a cause of another different symptom. This phenomenon is a complex scenario with “chain effects”. In fact, engineering and biomedical complications are fundamentally mathematical problems that correlate or conform with many inherent physical laws or principles. Over the past 13 years, in his medical research work, he has encountered more than 100 different sets of biomarkers with almost equal amounts of cause/input variables versus symptom/output variables. For example, food and exercise influence both body weight and glucose lev el, where persistent high glucose can result in diabetes. When diabetes combines with hypertension (high blood pressure) and hyperlipidemia (high blood lipids), it can cause cardiovascular diseases. Furthermore, obesity and diabetes are also linked with various kinds of cancers. These multiple sets of biomedical input versus output have been researched by the author using different tools he has learned from academic fields of mathematics, physics, computer science, and engineering.

Viscoelastic or Viscoplastic Glucose Theory (VGT #58): Using the VGT Energy and Frequency Energy to Estimate the Relative Energy Associated with Diabetic Retinopathy (DR) Risk Probability Percentages under the Influence of LabTested A1C (L.A1C) and Finger-Pierced A1C (F.A1C) over Two 6-Year Periods, Y2010-Y2015 Versus Y2016-Y2021 Based on GH-Method: Math-Physical Medicine (No. 646)

The author was a professional engineer working in the fields of space shuttle, naval battleships, nuclear power plants, computer hardware and software, artificial intelligence, and semiconductor chips. After his retirement, he started to self-study and research internal medicine, emphasizing biomarker relationships exploration and disease prevention. Since 2010, he has utilized the disciplines learned from 7 different universities along with various work experiences to formulate his current medical research work during the past 13 years. In engineering and medicine, he discovered that we frequently seek answers, illustrations, or explanations for the relationships between the input variable (force applied on a structure or cause of a disease) and output variable (deformation of a structure or symptom of a disease). However, the multiple relationships between input and output could be expressed with many different matrix formats of 1 x 1, 1 x n, m x 1, or m x n (m or n means different multiple variables). In addition to these described mathematical complications, the output resulting from one or more inputs can also become an input of another output, which is a symptom of certain causes that can become a cause of another different symptom. This phenomenon is a complex scenario with “chain effects”. In fact, engineering and biomedical complications are fundamentally mathematical problems that correlate or conform with many inherent physical laws or principles. Over the past 13 years, in his medical research, he has encountered more than 100 different sets of biomarkers with almost equal amounts of cause/input variables versus symptom/output variables. For example, food and exercise influence both body weight and glucose level, where persistent high glucose can result in diabetes. When diabetes combines with hypertension (high blood pressure) and hyperlipidemia (high blood lipids), it can cause diabetic retinopathy (DR), cardiovascular disease (CVD), or chronic kidney disease (CKD). Furthermore, obesity and diabetes are also linked with various kinds of cancers. Diabetic retinopathy (DR) occurs when too much blood sugar or glucose damages the blood vessels in the retina. As a result, the retina does not get enough oxygen and nutrients, and blood vessels can leak blood into the retina. DR is the leading cause of new cases of blindness in people 20 to 74 years of age in the US (Reference: Eylea, Regeneron Pharmaceuticals, Inc.).

Viscoelastic or Viscoplastic Glucose Theory (VGT #56): Using the VGT Tool to Estimate the Relative Energy Associated with Different Calculated Annual Cancer Risk Percentages Under the Influence of Estimated Average Glucose (eAG) and Body Weight in the Early Morning (BW) over two 5-Year Periods, Y2012-Y2016 and Y2017-Y2021, Based on GH-Method: Math-Physical Medicine (No. 644)

The author was a professional engineer working in the fields of the space shuttle, naval battleships, nuclear power plants, computer hardware and software, artificial intelligence, and semiconductor chips. After his retirement, he started to self-study and research internal medicine with an emphasis on biomarker relationships exploration and disease prevention. Since 2010, he has utilized the disciplines learned from 7 different universities along with various work experiences to formulate his current medical research work during the past 13 years. In engineering and medicine, he discovered that we frequently seek answers, illustrations, or explanations for the relationships between the input variable (force applied on a structure or cause of a disease) and output variable (deformation of a structure or symptom of a disease). However, the multiple relationships between input and output could be expressed with many different matrix formats of 1 x 1, 1 x n, m x 1, or m x n (m or n means different multiple variables). In addition to these described mathematical complications, the output resulting from one or more inputs can also become an input of another output, which is a symptom of certain causes that can become a cause of another different symptom. This phenomenon is a complex scenario with “chain effects”. In fact, engineering and biomedical complications are fundamentally mathematical problems that correlate or conform with many inherent physical laws or principles. Over the past 13 years, in his medical research, he has encountered more than 100 different sets of biomarkers with almost equal amounts of cause/input variables versus symptom/output variables. For example, food and exercise influence both body weight and glucose level, where persistent high glucose can result in diabetes. When diabetes combines with hypertension (high blood pressure) and hyperlipidemia (high blood lipids), it can cause cardiovascular diseases. Furthermore, obesity and diabetes are also linked with various kinds of cancers. These multiple sets of biomedical input versus output have been researched by the author using different tools he has learned from academic fields of mathematics, physics, computer science, and engineering. For example, he has applied a signal processing technique to separate 19 components from the combined postprandial plasma glucose (PPG) wave. He identified the carbs/sugar intake amount and post-meal exercise as the two most important contributing factors to PPG formation. Based on these findings, he then applied the theory of elasticity to develop a linear elastic glucose theory (LEGT) to predict PPG value with high prediction accuracy, using fasting plasma glucose (FPG), carbs/sugar intake grams, and post-meal walking k-steps as three major input components of predicted PPG formation.

Viscoelastic or Viscoplastic Glucose Theory (VGT #57): Using the VGT Tool to Estimate the Relative Energy Associated with Chronic Kidney Disease (CKD) Risk Probability Percentages Under the Influence of Hemoglobin A1C (A1C) and Urine Albumin to Creatinine Ratio (ACR) over Two 6-Year Periods, Y2010-Y2015 Versus Y2016-Y2021, Based on GH-Method: Math-Physical Medicine (No. 645)

The author was a professional engineer working in the fields of the space shuttle, naval battleships, nuclear power plants, computer hardware and software, artificial intelligence, and semiconductor chips. After his retirement, he started to self-study and research internal medicine, emphasizing biomarker relationships exploration and disease prevention. Since 2010, he has utilized the disciplines learned from 7 different universities along with various work experiences to formulate his current medical research work during the past 13 years. In engineering and medicine, he discovered that we frequently seek answers, illustrations, or explanations for the relationships between the input variable (force applied on a structure or cause of a disease) and output variable (deformation of a structure or symptom of a disease). However, the multiple relationships between input and output could be expressed with many different matrix formats of 1 x 1, 1 x n, m x 1, or m x n (m or n means different multiple variables). In addition to these described mathematical complications, the output resulting from one or more inputs can also become an input of another output, which is a symptom of certain causes that can become a cause of another different symptom. This phenomenon is a complex scenario with “chain effects”. In fact, engineering and biomedical complications are fundamentally mathematical problems that correlate or conform with many inherent physical laws or principles. Over the past 13 years, in his medical research, he has encountered more than 100 different sets of biomarkers with almost equal amounts of cause/input variables versus symptom/output variables. For example, food and exercise influence both body weight and glucose level, where persistent high glucose can result in diabetes. When diabetes combines with hypertension (high blood pressure) and hyperlipidemia (high blood lipids), it can cause cardiovascular disease (CVD) or chronic kidney disease (CKD). Furthermore, obesity and diabetes are also linked with various kinds of cancers. The author has researched these multiple sets of biomedical input versus output using different tools he has learned from academic fields of mathematics, physics, computer science, and engineering.

Viscoelastic or viscoplastic glucose theory (VGT #54): Estimating relative energy carried by glucose under the influence of body weight while using the collected data from the daily estimated average glucose and body weight in the early morning over three triple-year periods, Y2013-Y2015, and Y2016-Y2018, Y2019-Y2021, along with the application of a VGT tool and frequency domain energy method based on

The author was a professional engineer working in the fields of the space shuttle, naval battleships, nuclear power plants, computer hardware and software, artificial intelligence, and semiconductor chips. After retiring from his work, he started to self-study and research internal medicine with an emphasis on the exploration of various biomarker relationships and the prevention of metabolic disorder-induced chronic diseases. Since 2010, he utilized the disciplines learned from 7 different universities along with various work experiences to formulate his current medical research work during the past 13 years. One thing he discovered in engineering and medicine, we frequently seek answers, illustrations, or explanations for the relationships between the input variable (force applied on a structure or cause of a disease) and output variable (deformation of a structure or symptom of a disease). However, the multiple relationships between input and output could be expressed with many different matrix formats of 1 x 1, 1 x n, m x 1, or m x n (m or n means different multiple variables). In addition to these described mathematical complications, the output resulting from one or more inputs can also become an input of another output, i.e. a symptom of certain causes can become a cause of another different symptom. This phenomenon is a complex scenario with “chain effects”. Engineering and biomedical complications are fundamentally mathematical problems that correlate or conform with many inherent physical laws or principles. Over the past 13 years, in his medical research work, he has encountered more than 100 different sets of biomarkers with almost equal amounts of cause/input variables versus symptom/output variables. For example, food and exercise influence both body weight and glucose level, where persistent high glucose can result in diabetes. When diabetes combines with hypertension (high blood pressure) and hyperlipidemia (high blood lipids), it can cause cardiovascular diseases. Furthermore, obesity and diabetes are also linked with various kinds of cancers. These multiple sets of biomedical input versus output have been researched by the author using different tools he has learned from academic fields of mathematics, physics, computer science, and engineering. For example, he has applied a signal processing technique to separate 19 components from the combined postprandial plasma glucose (PPG) wave. He identified the carbs/sugar intake amount and post-meal exercise as the two most important contributing factors to PPG formation. Based on these findings, he then applied the theory of elasticity to develop a linear elastic glucose theory (LEGT) to predict PPG value with high prediction accuracy, using fasting plasma glucose (FPG), carbs/sugar grams, and post-meal walking k-steps as three major input components of predicted PPG formation.

Viscoelastic or Viscoplastic Glucose Theory (VGT#52): Applying VGT and using Collected the Annual Average Data from Y2015 to Y2022 to Study the InterRelationships among Carbs/Sugar Intake Amount in Grams, Total Food Intake Quantity in Calories, Postprandial Plasma Glucose, and Body Weight in the Early Morning Based on the GH-Method: Math-Physical Medicine (No. 639)

The author was a professional engineer working in the fields of the space shuttle, naval battleships, nuclear power plants, computer hardware and software, artificial intelligence, and semiconductor chips. After retiring from his work, he initiated self-study and research on internal medicine with an emphasis on biomarker relationship exploration and disease prevention. Since 2010, he has utilized these disciplines learned from 7 different universities along with various work experiences to formulate his current medical research work during the past 13 years. One thing he has learned is that in engineering or medicine, we are frequently seeking answers, illustrations, or explanations for the relationships between the input variable (force applied on a structure or cause of a disease) and output variable (deformation of a structure or symptom of a disease). However, the multiple relationships between input and output could be expressed with many different matrix formats of 1 x 1, 1 x n, m x 1, or m x n (m or n means different multiple variables). In addition to these described mathematical complications, the output resulting from one or more inputs can also become an input of another output, i.e. a symptom of certain causes can become a cause of another different symptom. This phenomenon is a complex scenario with “chain effects”. Engineering and biomedical complications are fundamentally mathematical problems that correlate or conform with many inherent physical laws or principles. Over the past 13 years, in his medical research work, he has encountered more than 100 different sets of biomarkers with almost equal amounts of cause/input variables versus symptom/output variables. For example, food and exercise influence both body weight and glucose level, where persistent high glucose can result in diabetes. When diabetes combines with hypertension (high blood pressure) and hyperlipidemia (high blood lipids), it can cause cardiovascular diseases. Furthermore, obesity and diabetes are also linked with various kinds of cancers. These multiple sets of biomedical input versus output have been researched by the author using different tools he has learned from academic fields of mathematics, physics, computer science, and engineering.

Viscoelastic or Viscoplastic Glucose Theory (VGT #55): Estimation of Relative Energy Associated with Different Calculated CVD risk % under the Influence of the Estimated Average Glucose and Body Weight in the Early Morning Over Three Triple-Year Sub-Periods, Y2013-Y2015, Y2016-Y2018, and Y2019-Y2021, with the Application of a VGT Tool and Frequency Energy Methods Based on GH-Method: Math-Physical Medicine (No. 643)

The author was a professional engineer working in the fields of the space shuttle, naval battleships, nuclear power plants, computer hardware and software, artificial intelligence, and semiconductor chips. After his retirement, he started to self-study and research internal medicine with an emphasis on biomarker relationships exploration and disease prevention. Since 2010, he has utilized the disciplines learned from 7 different universities along with various work experiences to formulate his current medical research work during the past 13 years. In engineering and medicine, he discovered that we frequently seek answers, illustrations, or explanations for the relationships between the input variable (force applied on a structure or cause of a disease) and output variable (deformation of a structure or symptom of a disease). However, the multiple relationships between input and output could be expressed with many different matrix formats of 1 x 1, 1 x n, m x 1, or m x n (m or n means different multiple variables). In addition to these described mathematical complications, the output resulting from one or more inputs can also become an input of another output, which is a symptom of certain causes that can become a cause of another different symptom. This phenomenon is a complex scenario with “chain effects”. In fact, engineering and biomedical complications are fundamentally mathematical problems that correlate or conform with many inherent physical laws or principles. Over the past 13 years, in his medical research, he has encountered more than 100 different sets of biomarkers with almost equal amounts of cause/input variables versus symptom/output variables. For example, food and exercise influence both body weight and glucose level, where persistent high glucose can result in diabetes. When diabetes combines with hypertension (high blood pressure) and hyperlipidemia (high blood lipids), it can cause cardiovascular diseases. Furthermore, obesity and diabetes are also linked with various kinds of cancers. These multiple sets of biomedical input versus output have been researched by the author using different tools he has learned from academic fields of mathematics, physics, computer science, and engineering. For example, he has applied a signal processing technique to separate 19 components from the combined postprandial plasma glucose (PPG) wave. He identified the carbs/sugar intake amount and post-meal exercise as the two most important contributing factors to PPG formation. Based on these findings, he then applied the theory of elasticity to develop a linear elastic glucose theory (LEGT) to predict PPG value with high prediction accuracy, using fasting plasma glucose (FPG), carbs/sugar grams, and post-meal walking k-steps as three major input components of predicted PPG formation.

Viscoelastic or Viscoplastic Glucose Theory (VGT #60): A Summary Report on Four Complication Risk Probability Percentages with Their Two Selective Causes and Using Both VGT Energy and Frequency Energy Methods to Estimate the Relative Energy Associated with Each Complication over Two 5-Year or 6-Year Periods Based on GH-Method: Math-Physical Medicine (No. 648)

The author was a professional engineer working in the fields of the space shuttle, naval battleships, nuclear power plants, computer hardware and software, artificial intelligence, and semiconductor chips. After his retirement, he started to self-study and research internal medicine, emphasizing biomarker relationships exploration and disease prevention. Since 2010, he has utilized the disciplines learned from 7 different universities along with various work experiences to formulate his current medical research work during the past 13 years. Relationships Between Causes and Symptoms In engineering and medicine, he discovered that people frequently seek answers, illustrations, or explanations for the relationships between the input variable (force applied on a structure or cause of a disease) and output variable (deformation of a structure or symptom of a disease). However, the multiple relationships between input and output could be expressed with many different matrix formats of 1 x 1, 1 x n, m x 1, or m x n (m or n means different multiple variables). In addition to these described mathematical complications, the output resulting from one or more inputs can also become an input of another output, which is a symptom of certain causes that can become a cause of another different symptom. This phenomenon is a complex scenario with “chain effects”. In fact, both engineering and biomedical complications are fundamentally mathematical problems that correlate or conform with many inherent physical laws or principles. Over the past 13 years, in his medical research work, he has encountered more than 100 different sets of biomarkers with almost equal or more amounts of causes (or input variables) and symptoms (or output variables)

Applying Linear Elastic Glucose Theory (LEGT) and Viscoelastic or Viscoplastic Glucose Theory (VGT) While using Data Collected over an ~7-Year Period, from 7/1/2015 to 3/30/2022, Organized into 8 Annual Data Points, 81 Monthly Data Points, and 2,463 Daily Data Points, along with a Biomedical Explanation of 8 Sub-Cellular Pathology Pathways of Chronic Diseases to Study the Relationship among Pancreatic Beta

The author was a professional engineer working in the fields of the space shuttle, naval battleships, nuclear power plants, computer hardware and software, artificial intelligence, and semiconductor chips. After retiring from his work, he initiated self-study and research on internal medicine with an emphasis on biomarker relationship exploration and disease prevention. Since 2010, he has utilized these disciplines learned from 7 different universities along with various work experiences to formulate his current medical research work during the past 13 years. One thing he has learned is that in engineering or medicine, we are frequently seeking answers, illustrations, or explanations for the relationships between the input variable (force applied on a structure or cause of a disease) and output variable (deformation of a structure or symptom of a disease). However, the multiple relationships between input and output could be expressed with many different matrix formats of 1 x 1, 1 x n, m x 1, or m x n (m or n means different multiple variables). In addition to these described mathematical complications, the output resulting from one or more inputs can also become an input of another output, i.e. a symptom of certain causes can become a cause of another different symptom. This phenomenon is a complex scenario with “chain effects”. Engineering and biomedical complications are fundamentally mathematical problems that correlate or conform with many inherent physical laws or principles. Over the past 13 years, in his medical research work, he has encountered more than 100 different sets of biomarkers with almost equal amounts of cause/input variables versus symptom/output variables. For example, food and exercise influence both body weight and glucose level, where persistent high glucose can result in diabetes. When diabetes combines with hypertension (high blood pressure) and hyperlipidemia (high blood lipids), it can cause cardiovascular diseases. Furthermore, obesity and diabetes are also linked with various kinds of cancers. These multiple sets of biomedical input versus output have been researched by the author using different tools he has learned from the fields of mathematics, physics, computer science, and engineering.