Persistent High Glucose Research Articles

The protective role of endothelial GLUT1 in ischemic stroke.

To provide thorough insight on the protective role of endothelial glucose transporter 1 (GLUT1) in ischemic stroke. We comprehensively review the role of endothelial GLUT1 in ischemic stroke by narrating the findings concerning biological characteristics of GLUT1 in brain in depth, summarizing the changes of endothelial GLUT1 expression and activity during ischemic stroke, discussing how GLUT1 achieves its neuroprotective effect via maintaining endothelial function, and identifying some outstanding blind spots in current studies. Endothelial GLUT1 maintains persistent high glucose and energy requirements of the brain by transporting glucose through the blood-brain barrier, which preserves endothelial function and is beneficial to stroke prognosis. This review underscores the potential involvement of GLUT1 trafficking, activity modulation, and degradation, and we look forward to more clinical and animal studies to illuminate these mechanisms.

Oxidative Stress: A Culprit in the Progression of Diabetic Kidney Disease.

Diabetic kidney disease (DKD) is the principal culprit behind chronic kidney disease (CKD), ultimately developing end-stage renal disease (ESRD) and necessitating costly dialysis or kidney transplantation. The limited therapeutic efficiency among individuals with DKD is a result of our finite understanding of its pathogenesis. DKD is the result of complex interactions between various factors. Oxidative stress is a fundamental factor that can establish a link between hyperglycemia and the vascular complications frequently encountered in diabetes, particularly DKD. It is crucial to recognize the essential and integral role of oxidative stress in the development of diabetic vascular complications, particularly DKD. Hyperglycemia is the primary culprit that can trigger an upsurge in the production of reactive oxygen species (ROS), ultimately sparking oxidative stress. The main endogenous sources of ROS include mitochondrial ROS production, NADPH oxidases (Nox), uncoupled endothelial nitric oxide synthase (eNOS), xanthine oxidase (XO), cytochrome P450 (CYP450), and lipoxygenase. Under persistent high glucose levels, immune cells, the complement system, advanced glycation end products (AGEs), protein kinase C (PKC), polyol pathway, and the hexosamine pathway are activated. Consequently, the oxidant-antioxidant balance within the body is disrupted, which triggers a series of reactions in various downstream pathways, including phosphoinositide 3-kinase/protein kinase B (PI3K/Akt), transforming growth factor beta/p38-mitogen-activated protein kinase (TGF-β/p38-MAPK), nuclear factor kappa B (NF-κB), adenosine monophosphate-activated protein kinase (AMPK), and the Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling. The disease might persist even if strict glucose control is achieved, which can be attributed to epigenetic modifications. The treatment of DKD remains an unresolved issue. Therefore, reducing ROS is an intriguing therapeutic target. The clinical trials have shown that bardoxolone methyl, a nuclear factor erythroid 2-related factor 2 (Nrf2) activator, blood glucose-lowering drugs, such as sodium-glucose cotransporter 2 inhibitors, and glucagon-like peptide-1 receptor agonists can effectively slow down the progression of DKD by reducing oxidative stress. Other antioxidants, including vitamins, lipoic acid, Nox inhibitors, epigenetic regulators, and complement inhibitors, present a promising therapeutic option for the treatment of DKD. In this review, we conduct a thorough assessment of both preclinical studies and current findings from clinical studies that focus on targeted interventions aimed at manipulating these pathways. We aim to provide a comprehensive overview of the current state of research in this area and identify key areas for future exploration.

Experimental research progress in traditional Chinese medicine prevention and treatment of diabetic peripheral neuropathy based on autophagy

Diabetic peripheral neuropathy(DPN) is a chronic complication resulted from peripheral nerve injury in the late stage of diabetes. It involves a variety of pathological changes such as oxidative stress, endoplasmic reticulum stress, neuroinflammation, and apoptosis of Schwann cells(SCs). DPN is the main factor leading to lower limb disability or amputation in diabetic patients, with high incidence, long disease course, and poor prognosis. The modern medicine treatment of DPN mainly focuses on controlling blood glucose and improving microcirculation and nerve nutrition, which can only mitigate the clinical symptoms and not fundamentally reverse the pathological changes of peripheral nerves. Autophagy is a self-clearing mechanism that maintains cellular homeostasis by removing excess metabolites. Traditional Chinese medicine(TCM), featuring the holistic concept and syndrome differentiation, can treat chronic diseases in a multi-target, multi-pathway, and wide-range manner. Modern studies have shown that the occurrence and development of DPN are related to a variety of pathological changes, and autophagy is a key mechanism associated with DPN. The environment with persistent high glucose can lead to the inhibition or over-activation of peripheral nerve cells, which causes irreversible damage of nerve cells and the occurrence and development of DPN. Therefore, restoring autophagy balance and reducing nerve damage is one of the key ways to treat DPN. The recent studies have confirmed that some active ingredients in traditional Chinese medicines and TCM compound prescriptions can inhibit the oxidative stress, endoplasmic reticulum stress, mitochondrial damage, inflammation, and apoptosis of SCs in DPN by regulating the autophagy pathway, thus playing a role in the prevention and treatment of DPN. However, the systematic induction in this field remains to be carried out. This paper reviewed the relevant literature, explained the mechanism of TCM in the prevention and treatment of DPN by regulating autophagy, and summarized the potential targets of TCM in the treatment of DPN, with a view to providing new ideas for clinical research and drug development.

Epidermal Stem Cell Derived Exosomes Alleviate Excessive Autophagy Induced Endothelial Cell Apoptosis by Delivering miR200b-3p to Diabetic Wounds

The dysfunction of endothelial cells caused by hyperglycemia is observed as a decrease in neovascularization in diabetic wound healing. Studies have found that epidermal stem cells (EpiSCs) can promote the angiogenesis of full-thickness wounds. To further explain the therapeutic effect of EpiSCs, EpiSC-derived exosomes (EpiSC-EXOs) are considered the main substance contributing to stem cell effectivity. In our study, EpiSCs and EpiSC-EXOs were supplied to the dorsal wounds of db/db mice. Results showed that EpiSCs could colonize in the wound area and both EpiSCs and EpiSC-EXOs could accelerate diabetic wound healing by promoting angiogenesis. Invitro, persistent high glucose led to the malfunction and apoptosis of endothelial cells. The apoptosis induced by high glucose is due to excessive autophagy and was alleviated by EpiSC-EXOs. RNA sequencing of EpiSC-EXOs showed that miR200b-3p was enriched in EpiSC-EXOs and alleviated the apoptosis of endothelial cells. Synapse defective rho GTPase homolog 1 was identified the target of miR200b-3p and affected the phosphorylation of ERK to regulate intracellular autophagy and apoptosis. Furthermore, animal experiments validated the angiogenic effect of miR200b-3p. Collectively, our results verified the effect of EpiSC-EXOs on apoptosis caused by hyperglycemia in endothelial cells through the miR200b-3p/synapse defective rho GTPase homolog 1 /RAS/ERK/autophagy pathway, providing a theoretical basis for EpiSC in treating diabetic wounds.

Emodin attenuates diabetic kidney disease by inhibiting ferroptosis via upregulating Nrf2 expression.

Diabetic kidney disease (DKD) poses a threat to people's health. The current treatments only provide partial relief of symptoms. Therefore, seeking a promising therapeutic medication for the prevention and control on DKD will benefit patients. Recently, a novel iron-dependent and non-apoptotic regulated mode of cell death, termed as ferroptosis, is expected to offer us a novel insight into the mechanism of DKD. We conducted experiments to investigate the role of ferroptosis in the development of DKD. Iron accumulation, weakened antioxidant capacity and ROS overproduction were observed in the renal tissues of STZ-induced diabetic rats. A persistent high glucose condition contributed to down regulated levels of Glutathione Peroxidase 4 (GPX4) and Solute Carrier Family 7 Member 11 (SLC7A11) which marked the occurrence of ferroptosis. Treatment of Emodin in DKD models could significantly attenuated these changes and reduced renal injury. Besides, NFE2-related factor 2 (Nrf2), an important antioxidant regulator, was inhibited in both in vivo and in vitro assay, which contributes to Reactive Oxygen Species (ROS) generation that further promoted the expression of ferroptosis related protein. These unwanted effects were offset by the intervention of Emodin. The specific Nrf2 knock out enhanced cell's sensitivity to ferroptosis by being exposed to high glucose culture, which was improved by treatment of Emodin via restoring activity of Nrf2. In conclusion, our research demonstrated that Emodin exerted renal protection against DKD via inhibiting ferroptosis and restoring Nrf2 mediated antioxidant capacity, which could be employed as a novel therapeutic medication against DKD.

DISC1 inhibits GSK3β activity to prevent tau hyperphosphorylation under diabetic encephalopathy.

Diabetic encephalopathy (DE) is a common complication of type 2 diabetes (T2D), especially in those patients with long T2D history. Persistent high glucose (HG) stimulation leads to neuron damage and manifests like Alzheimer's disease's pathological features such as neurofilament tangle. However, the precise mechanism of high-glucose-induced tau hyperphosphorylation is not fully revealed. We here gave evidence that Disrupted in schizophrenia 1 protein (DISC1) could interact with glycogen synthase kinase 3β (GSK3β) and inhibit its activity to prevent tau hyperphosphorylation. By using DB/DB mice as animal model and HG-treated N2a cell as cell model, we found that DISC1 was downregulated both in vivo and in vitro, complicated with Tau hyperphosphorylation and GSK3β activation. Further, we identified DISC1 interacted with GSK3β by its 198th-237th amino acid residues. Overexpression of full length DISC1 but not mutated DISC1 lacking this domain could prevent HG induced tau hyperphosphorylation. Taken together, our work revealed DISC1 could be an important negative modulators of tau phosphorylation, and suggested that preservation of DISC1 could prevent HG induced neuron damage.

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 #48): Investigating the Role of Strain, Strain Rate, and the Viscosity Factor in Biomedical VGT Applications using Sensor FPG Versus Body Weight over 18 Months from 10/1/2020 to 3/23/2022, along with Finger FPG Versus Body Weight over 11 Years from 1/1/2012 to 3/23/2022, with Biomedical Illustrations of the Physics and Engineering Analysis Results Based

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 based in the disciplines of mathematics, physics, and engineering, he has initiated self-study and research on internal medicine with an emphasis on biomarker relationships exploration and disease prevention. Since 2010, he has utilized these disciplines learned from 7 different universities along with work experiences to formulate his current medical research work. One thing he has learned is that in engineering or medicine, we are seeking answers or illustrations for the relationships between the input variable (force on a structure or cause of a disease) and output variable (deformation on a structure or symptom of a disease). However, the relationships between input and output could be expressed with a matrix format of 1 x 1, 1 x n, m x 1, or m x n (m or n means different multiple variables). In addition to the described complications, the output resulting from one or more inputs can turn into another input of different outputs, i.e., a symptom of certain causes can be a cause of the different symptoms. This phenomenon becomes a complex chain “effect”. In other words, an engineering or biomedical issue is fundamentally a mathematical problem that correlates with many inherent physical laws or principles. Over the past 13 years, he has investigated approximately 100 different sets of cause/input variables versus symptom/ output variables in the biomedical field. For example, food and exercise influence the 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, diabetes is also linked with various kinds of cancers. These sets of biomedical input versus output scenarios and problems have been researched by the author using different tools from mathematics, physics, computer science, and engineering. Recently, he has applied theories of viscoelasticity and viscoplasticity to various biomedical problems and has written nearly 50 papers. In this article, he selected two datasets to investigate the role played by strain, stress, strain rate, viscosity factor, and relative energy. The first dataset is a symptom of the sensor collected fasting plasma glucose (FPG) versus a cause of body weight (BW) during the 18 months from October 2020 to March 2022. The second dataset is a symptom of the finger pierced FPG versus a cause of BW during the 11 years from Y2012 to Y2022. The appearance of the two waveforms in the time domain is quite different due to two selected time windows and two glucose measurement methods.

Viscoelastic or Viscoplastic Glucose Theory (VGT #49): Investigating the Contribution Margin of Carbs/Sugar Intake Amount and Post-Meal Walking k-Steps on PPG Formation using 4 Validations: VGT (Viscoelasticity/Plasticity Theory), LEGT (Linear Elastic Glucose Theory), EFT (Energy Theory via Fourier Transform), along with 8 Sub-Cellular Pathology Pathways with Sensor PPG, carbs, and k-Steps Data from an ~4-Year Period from 5/8/18 to 3/25/2022

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 on a structure or cause of a disease) and output variable (deformation on a structure or symptom of a disease). However, the 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 in a “chain effect”. In fact, engineering and biomedical complications are fundamentally mathematical problems which correlate with many inherent physical laws or principles. Over the past 13 years, in his medical research work, he has encountered more than 100 different 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 mathematics, physics, computer science, and engineering. Previously, he has applied signal processing techniques 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 carbs/sugar grams and post-meal walking k-steps as two major input components of PPG formation. Furthermore, he took a specific PPG waveform in the time domain (TD) and applied Fourier transform technique to convert it into a waveform in the frequency domain (FD). The y-axis value in the frequency diagram indicates the magnitude of energy corresponding to a certain frequency component on the x-axis, while the total area underneath the frequency-energy curve is the total relative energy associated with the specific PPG wave.

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.

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.

Viscoelastic or Viscoplastic Glucose Theory (VGT #59): Using the VGT Energy and Frequency Energy to Estimate the Relative Energy Associated with Cardiovascular Disease (CVD) Risk Probability Percentages under the Influence of the Lab-Tested A1C (L.A1C) and Metabolism Index (MI) over Two 6-Year Periods, Y2010-Y2015 Versus Y2016-Y2021 Based on GH-Method: Math-Physical Medicine (No. 647)

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), chronic kidney disease (CKD), or diabetic retinopathy (DR). 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. 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 (around 40% of contribution) 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 #50): Studying the Role and Impact of the Viscosity Factors (Body Weight and Body Temperature) on Sensor Collected FPG (Symptom or Strain) using a Stress-Strain Diagram from the Viscoelasticity/Plasticity Theory with Measured and Normalized Data over 18 Months from 10/1/2020 to 3/27/2022 Based on the GH-Method: Math-Physical Medicine (No. 635)

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 engineering job, he initiated his self-study and research on internal medicine with an emphasis on biomarker relationship exploration and disease prevention. Since 2010, he has utilized the academic disciplines learned from 7 different universities along with various work experiences to formulate his current medical research work over the past 13 years. One thing he has learned is that in engineering or medicine, people are frequently seeking answers, illustrations, or explanations for the relationships between the input variable (force on a structure or cause of a disease) and output variable (deformation on a structure or symptom of a disease). However, the 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 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 for a “chain effect”. In fact, engineering and biomedical complications are fundamentally mathematical problems which correlate with many inherent physical laws or principles. Over the past 13 years, in his medical research work, he has encountered more than 100 different biomarkers with various relationships of causes (input variables) versus symptoms (output variables). For example, food and exercise influence both body weight (BW) and postprandial plasma glucose (PPG), while the pancreatic health state, BW, body temperature (BT), and sleep conditions affect the fasting plasma glucose (FPG) in the early morning. A persistent high glucose condition, including FPG and PPG, can result in diabetes. When diabetes combines with hypertension (high blood pressure) and hyperlipidemia (high blood lipids), they would likely cause various cardiovascular diseases (heart attacks) or strokes where blood pressure (BP) and lipids are tightly connected with food. Furthermore, obesity and diabetes are also linked with various cancers. These multiple sets of lifestyle and biomedical input versus disease output have been researched by the author using different tools he has learned from mathematics, physics, computer science, and engineering.

A1C as a Prognosticator of Perioperative Complications of Diabetes: A Narrative Review.

Hemoglobin A1c (A1C) or glycated hemoglobin reflects the levels of blood glucose during the previous 8–12 weeks duration. It also helps us to diagnose diabetes in some cases, during the preoperative screening, who were initially missed out. Although the number of patients with diabetes undergoing various surgeries has increased many times, the role of A1C as a predictor for the complications during the perioperative phase remains intriguing. This could be due to various factors such as lack of best shreds of evidence, various cut-off levels of target A1C, variations of the patient population, presence of other comorbid conditions, and so on. This narrative review article presents the role of A1C as a reflector of perioperative adverse events in various surgeries and discusses the controversies surrounding it. We searched “PubMed Central” database with search criteria of “hemoglobin A1c, glycated hemoglobin, and perioperative complications” with publication date from January 01, 2010, to January 31, 2020, and found a total of 214 articles. We included only the relevant articles to our topic and added a few more articles that we found as “secondary references” from those articles to suit the structured headings of our narrative review and made it a total of fifty. To our knowledge, the majority of the studies published on this topic are of the “Retrospective analysis” type of study, besides no narrative review article available to date in the literature. We suggest that assessment of A1C levels preoperatively can be used as a routine practice for major procedures in patients with diabetes and for patients who have persistent high glucose values during preoperative screening regardless of whether a diagnosis of diabetes is established or not. We found that a cut-off of 8% is acceptable for the majority of the surgical procedures. However, it is better to have a cut-off of 7% or lower for procedures such as spine and joint replacement surgeries, cardiac surgeries, and so on. Further prospective studies involving a large population preferably with a multicenter design would provide us more clarity on this topic.

Persistent high glucose induced EPB41L4A-AS1 inhibits glucose uptake via GCN5 mediating crotonylation and acetylation of histones and non-histones.

BackgroundPersistent hyperglycemia decreases the sensitivity of insulin‐sensitive organs to insulin, owing to which cells fail to take up and utilize glucose, which exacerbates the progression of type 2 diabetes mellitus (T2DM). lncRNAs' abnormal expression is reported to be associated with the progression of diabetes and plays a significant role in glucose metabolism. Herein, we study the detailed mechanism underlying the functions of lncRNA EPB41L4A‐AS1in T2DM.MethodsData from GEO datasets were used to analyze the expression of EPB41L4A‐AS1 between insulin resistance or type 2 diabetes patients and the healthy people. Gene expression was evaluated by qRT‐PCR and western blotting. Glucose uptake was measured by Glucose Uptake Fluorometric Assay Kit. Glucose tolerance of mice was detected by Intraperitoneal glucose tolerance tests. Cell viability was assessed by CCK‐8 assay. The interaction between EPB41L4A‐AS1 and GCN5 was explored by RNA immunoprecipitation, RNA pull‐down and RNA‐FISH combined immunofluorescence. Oxygen consumption rate was tested by Seahorse XF Mito Stress Test.ResultsEPB41L4A‐AS1 was abnormally increased in the liver of patients with T2DM and upregulated in the muscle cells of patients with insulin resistance and in T2DM cell models. The upregulation was associated with increased TP53 expression and reduced glucose uptake. Mechanistically, through interaction with GCN5, EPB41L4A‐AS1 regulated histone H3K27 crotonylation in the GLUT4 promoter region and nonhistone PGC1β acetylation, which inhibited GLUT4 transcription and suppressed glucose uptake by muscle cells. In contrast, EPB41L4A‐AS1 binding to GCN5 enhanced H3K27 and H3K14 acetylation in the TXNIP promoter region, which activated transcription by promoting the recruitment of the transcriptional activator MLXIP. This enhanced GLUT4/2 endocytosis and further suppressed glucose uptake.ConclusionOur study first showed that the EPB41L4A‐AS1/GCN5 complex repressed glucose uptake via targeting GLUT4/2 and TXNIP by regulating histone and nonhistone acetylation or crotonylation. Since a weaker glucose uptake ability is one of the major clinical features of T2DM, the inhibition of EPB41L4A‐AS1 expression seems to be a potentially effective strategy for drug development in T2DM treatment.