scholarly journals Quantitative Analysis of Different Multi-Wavelength PPG Devices and Methods for Noninvasive In-Vivo Estimation of Glycated Hemoglobin

2021 ◽  
Vol 11 (15) ◽  
pp. 6867
Author(s):  
Shifat Hossain ◽  
Chowdhury Azimul Haque ◽  
Ki-Doo Kim
Keyword(s):  
Glycated Hemoglobin ◽  
Estimation Error ◽  
Realistic Model ◽  
Blood Oxygenation ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Diabetes Diagnosis ◽  
Sensor Noise ◽  
Glucose Levels

Diabetes is a serious disease affecting the insulin cycle in the human body. Thus, monitoring blood glucose levels and the diagnosis of diabetes in the early stages is very important. Noninvasive in vivo diabetes-diagnosis procedures are very new and require thorough studies to be error-resistant and user-friendly. In this study, we compare two noninvasive procedures (two-wavelength- and three-wavelength-based methods) to estimate glycated hemoglobin (HbA1c) levels in different scenarios and evaluate them with error level calculations. The three-wavelength method, which has more model parameters, results in a more accurate estimation of HbA1c even when the blood oxygenation (SpO2) values change. The HbA1c-estimation error range of the two-wavelength model, due to change in SpO2, is found to be from −1.306% to 0.047%. On the other hand, the HbA1c estimation error for the three-wavelength model is found to be in the magnitude of 10−14% and independent of SpO2. The approximation of SpO2 from the two-wavelength model produces a lower error for the molar concentration based technique (−4% to −1.9% at 70% to 100% of reference SpO2) as compared to the molar absorption coefficient based technique. Additionally, the two-wavelength model is less susceptible to sensor noise levels (max SD of %error, 0.142%), as compared to the three-wavelength model (max SD of %error, 0.317%). Despite having a higher susceptibility to sensor noise, the three-wavelength model can estimate HbA1c values more accurately; this is because it takes the major components of blood into account and thus becomes a more realistic model.

scholarly journals Derivation and Validation of Gray-Box Models to Estimate Non-Invasive In-vivo Percentage Glycated Hemoglobin Using Digital Volume Pulse Waveform

2020 ◽  
Author(s):  
Shifat Hossain ◽  
Shantanu Sen Gupta ◽  
Tae-Ho Kwon ◽  
Ki-Doo Kim
Keyword(s):  
Blood Vessel ◽  
Glycated Hemoglobin ◽  
Blood Oxygenation ◽  
Diabetic Patients ◽  
Physiological Basis ◽  
Glucose Levels ◽  
Finger Model ◽  
Volume Pulse ◽  
Digital Volume Pulse

Abstract Glycated hemoglobin and blood oxygenation are the two most important factors for monitoring a patient’s oxygen levels in the blood and the amount of average blood glucose levels. Digital Volume Pulse acquisition is a convenient method, even for a person with no previous training or experience, can be utilized to estimate the two abovementioned physiological parameters. The physiological basis assumptions are utilized to develop two-finger models for estimating the percent glycated hemoglobin and blood oxygenation levels. The first model consists of a blood vessel only hypothesis, while the second model is based on a whole-finger model system. We validated our two gray-box systems on diabetic and non-diabetic patients and obtained the mean absolute errors for the percent glycated hemoglobin (%HbA1c) and percent oxygen saturation (%SpO2) of 0.375 and 1.676, respectively, for the blood vessel model and 0.271 and 1.395, respectively, for the whole-finger model. The precision analysis indicated that these models resulted in 2.08% and 1.74% mean %CV for %HbA1c and 0.54% and 0.49% mean %CV for %SpO2 in the respective models. Herein, both models exhibit close performances to each other (HbA1c estimation Pearson R values are 0.92 and 0.96, respectively), even though the model assumptions greatly differed between them. Both of the models have a very high potential to be used in real-world scenarios. The whole-finger model performs better in terms of higher precision and accuracy compared to the blood vessel model.

scholarly journals Derivation and validation of gray-box models to estimate noninvasive in-vivo percentage glycated hemoglobin using digital volume pulse waveform

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shifat Hossain ◽  
Shantanu Sen Gupta ◽  
Tae-Ho Kwon ◽  
Ki-Doo Kim
Keyword(s):  
Blood Vessel ◽  
Glycated Hemoglobin ◽  
Blood Oxygenation ◽  
Physiological Basis ◽  
Pearson's R ◽  
Finger Model ◽  
Volume Pulse ◽  
Digital Volume Pulse ◽  
Pearson’S R

AbstractGlycated hemoglobin and blood oxygenation are the two most important factors for monitoring a patient’s average blood glucose and blood oxygen levels. Digital volume pulse acquisition is a convenient method, even for a person with no previous training or experience, can be utilized to estimate the two abovementioned physiological parameters. The physiological basis assumptions are utilized to develop two-finger models for estimating the percent glycated hemoglobin and blood oxygenation levels. The first model consists of a blood-vessel-only hypothesis, whereas the second model is based on a whole-finger model system. The two gray-box systems were validated on diabetic and nondiabetic patients. The mean absolute errors for the percent glycated hemoglobin (%HbA1c) and percent oxygen saturation (%SpO2) were 0.375 and 1.676 for the blood-vessel model and 0.271 and 1.395 for the whole-finger model, respectively. The repeatability analysis indicated that these models resulted in a mean percent coefficient of variation (%CV) of 2.08% and 1.74% for %HbA1c and 0.54% and 0.49% for %SpO2 in the respective models. Herein, both models exhibited similar performances (HbA1c estimation Pearson’s R values were 0.92 and 0.96, respectively), despite the model assumptions differing greatly. The bias values in the Bland–Altman analysis for both models were – 0.03 ± 0.458 and – 0.063 ± 0.326 for HbA1c estimation, and 0.178 ± 2.002 and – 0.246 ± 1.69 for SpO2 estimation, respectively. Both models have a very high potential for use in real-world scenarios. The whole-finger model with a lower standard deviation in bias and higher Pearson’s R value performs better in terms of higher precision and accuracy than the blood-vessel model.

scholarly journals A Kinetic Model for Glucose Levels and Hemoglobin A1c Provides a Novel Tool for Individualized Diabetes Management

2020 ◽  
pp. 193229681989761 ◽  
Author(s):  
Yongjin Xu ◽  
Timothy C. Dunn ◽  
Ramzi A. Ajjan
Keyword(s):  
Kinetic Model ◽  
Rate Constants ◽  
Diabetes Management ◽  
Glycated Hemoglobin ◽  
Glucose Monitoring ◽  
Accurate Estimation ◽  
Glucose Levels ◽  
Kinetic Rate Constants ◽  
Patients With Diabetes ◽  
The Relationship

Background: Regular assessment of glycated hemoglobin (HbA1c) is central to the management of patients with diabetes. Estimated HbA1c (eHbA1c) from continuous glucose monitoring (CGM) has been proposed as a measure that reflects laboratory HbA1c. However, discrepancies between the two markers are common, limiting the clinical use of eHbA1c. Therefore, developing a glycemic maker that better reflects laboratory HbA1c will be highly relevant in diabetes management. Methods: Using CGM data from two previous clinical studies in 120 individuals with diabetes, we derived a novel kinetic model that takes into account red blood cell (RBC) turnover, cross-membrane glucose transport, and hemoglobin glycation processes to individualize the relationship between glucose levels and HbA1c. Results: Using CGM data and two laboratory HbA1c measurements, kinetic rate constants for RBC glycation and turnover were calculated. These rate constants were used to project future HbA1c, creating a new individualized glycemic marker, termed calculated HbA1c (cHbA1c). In contrast to eHbA1c, the new glycemic marker cHbA1c gave an accurate estimation of laboratory HbA1c across individuals. The model and data demonstrated a non-linear relationship between laboratory HbA1c and steady-state glucose and also showed that glycation status is modulated by age. Conclusion: Our kinetic model offers mechanistic insights into the relationship between glucose levels and glycated hemoglobin. Therefore, the new glycemic marker does not only accurately reflect laboratory HbA1c but also provides novel concepts to explain the mechanisms for the mismatch between HbA1c and average glucose in some individuals, which has implications for future clinical management.

scholarly journals Glycated Hemoglobin in the Pre and Diabetes Ranges as Related to Lipid Cardiovascular Risk

2021 ◽  
Vol 3 (3) ◽  
pp. 01-03
Author(s):  
Tania Leme da Rocha Martinez ◽  
Bernardo M Machado de Almeida ◽  
Carolina Queiroz Cardoso ◽  
Anita L R Saldanha ◽  
Marileia Scartezini ◽  
...  
Keyword(s):  
Cardiovascular Risk ◽  
Lipid Profile ◽  
Glycated Hemoglobin ◽  
Fasting Glucose ◽  
Statistical Tests ◽  
Accurate Estimation ◽  
Normal Range ◽  
Metabolic Markers ◽  
Glucose Levels ◽  
Hba1c Levels

The concomitance of diabetes metabolic markers, as Glycated Hemoglobin and blood glucose, together with lipid changes; Cholesterol and fractions and Triglycerides, occurs very frequently but not always in the same pairs of markers, being its peculiarities important factors for the estimation of the cardiovascular risk. Not only has the association of high glucose levels and high triglycerides pointed to an augmented risk. The study of the correlations of the parameter Glycated Hemoglobin with all the values of the lipid profile may help gain a broader insight as to the associated risks. A database of 548 individuals with concomitant results of HbA1C, triglycerides, CT and HDL-c were applied statistical tests of ANOVA and Tukey. Most of the 546 individuals tested for glycated hemoglobin (HbA1C) and lipid profile had HbA1C levels within normal range (49.8%), 15.4% were classified as prediabetic, and 34.8% had HbA1C levels above 6.4% (diabetics). The overall mean HbA1C observed was 6.3%, and triglycerides was 236.8 mg/dL. Data from HbA1C-lipid profile comparations are not superimposed, as expected, to the combinations of fasting glucose and triglycerides. In not accompanying lipids concomitantly with HbA1C, the correct assessment of the overall risk calculation for atherosclerosis can be omitted. In conclusion, HbA1C levels should be added to the lipid profile for a more accurate estimation of the cardiovascular risk.

scholarly journals Gpr1 is an active chemerin receptor influencing glucose homeostasis in obese mice

2014 ◽  
Vol 222 (2) ◽  
pp. 201-215 ◽  
Author(s):  
Jillian L Rourke ◽  
Shanmugam Muruganandan ◽  
Helen J Dranse ◽  
Nichole M McMullen ◽  
Christopher J Sinal
Keyword(s):  
Adipose Tissue ◽  
Glucose Homeostasis ◽  
Stromal Vascular Fraction ◽  
Tolerance Test ◽  
Glucose Levels ◽  
Brown Adipose ◽  
Elevated Glucose ◽  
And Function ◽  
G Protein Coupled

Chemerin is an adipose-derived signaling protein (adipokine) that regulates adipocyte differentiation and function, immune function, metabolism, and glucose homeostasis through activation of chemokine-like receptor 1 (CMKLR1). A second chemerin receptor, G protein-coupled receptor 1 (GPR1) in mammals, binds chemerin with an affinity similar to CMKLR1; however, the function of GPR1 in mammals is essentially unknown. Herein, we report that expression of murineGpr1mRNA is high in brown adipose tissue and white adipose tissue (WAT) and skeletal muscle. In contrast to chemerin (Rarres2) andCmklr1,Gpr1expression predominates in the non-adipocyte stromal vascular fraction of WAT. Heterozygous and homozygousGpr1-knockout mice fed on a high-fat diet developed more severe glucose intolerance than WT mice despite having no difference in body weight, adiposity, or energy expenditure. Moreover, mice lackingGpr1exhibited reduced glucose-stimulated insulin levels and elevated glucose levels in a pyruvate tolerance test. This study is the first, to our knowledge, to report the effects ofGpr1deficiency on adiposity, energy balance, and glucose homeostasisin vivo. Moreover, these novel results demonstrate that GPR1 is an active chemerin receptor that contributes to the regulation of glucose homeostasis during obesity.

scholarly journals Evidence for the Involvement of the Glc7-Reg1 Phosphatase and the Snf1-Snf4 Kinase in the Regulation of INO1 Transcription in Saccharomyces cerevisiae

Genetics ◽  
1999 ◽  
Vol 152 (1) ◽  
pp. 73-87 ◽  
Author(s):  
Margaret K Shirra ◽  
Karen M Arndt
Keyword(s):  
Rna Polymerase Ii ◽  
Glucose Repression ◽  
Snf1 Kinase ◽  
Glucose Levels ◽  
Recessive Mutations ◽  
Mutant Strains ◽  
Rna Polymerase Ii Transcription ◽  
Two Hybrid

AbstractBinding of the TATA-binding protein (TBP) to the promoter is a pivotal step in RNA polymerase II transcription. To identify factors that regulate TBP, we selected for suppressors of a TBP mutant that exhibits promoter-specific defects in activated transcription in vivo and severely reduced affinity for TATA boxes in vitro. Dominant mutations in SNF4 and recessive mutations in REG1, OPI1, and RTF2 were isolated that specifically suppress the inositol auxotrophy of the TBP mutant strains. OPI1 encodes a repressor of INO1 transcription. REG1 and SNF4 encode regulators of the Glc7 phosphatase and Snf1 kinase, respectively, and have well-studied roles in glucose repression. In two-hybrid assays, one SNF4 mutation enhances the interaction between Snf4 and Snf1. Suppression of the TBP mutant by our reg1 and SNF4 mutations appears unrelated to glucose repression, since these mutations do not alleviate repression of SUC2, and glucose levels have little effect on INO1 transcription. Moreover, mutations in TUP1, SSN6, and GLC7, but not HXK2 and MIG1, can cause suppression. Our data suggest that association of TBP with the TATA box may be regulated, directly or indirectly, by a substrate of Snf1. Analysis of INO1 transcription in various mutant strains suggests that this substrate is distinct from Opi1.

Automatic weaving method for three-dimensional composite preforms using vision system

2021 ◽  
pp. 004051752098238
Author(s):  
Siyuan Li ◽  
Zhongde Shan ◽  
Dong Du ◽  
Li Zhan ◽  
Zhikun Li ◽  
...  
Keyword(s):  
Relative Position ◽  
Vision System ◽  
Estimation Error ◽  
Three Dimensional ◽  
Fiber Reinforced Composites ◽  
Accurate Estimation ◽  
Digital Manufacturing ◽  
Relative Rotation ◽  
Reinforced Composites ◽  
Main Structure

Three-dimensional composite preform is the main structure of fiber-reinforced composites. During the weaving process of large-sized three-dimensional composite preform, relative rotation or translation between the fiber feeder and guided array occurs before feeding. Besides, the weaving needles can be at different heights after moving out from the guided array. These problems are mostly detected and adjusted manually. To make the weaving process more precise and efficient, we propose machine vision-based methods which could realize accurate estimation and adjustment of the relative position-pose between the fiber feeder and guided array, and make the needles pressing process automatic by recognizing the position of the weaving needles. The results show that the estimation error of relative position-pose is within 5%, and the rate of unrecognized weaving needles is 2%. Our proposed methods improve the automation level of weaving, and are conducive to the development of preform forming toward digital manufacturing.

scholarly journals Adaptive State-of-Charge Estimation for Lithium-Ion Batteries by Considering Capacity Degradation

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 122
Author(s):  
Peipei Xu ◽  
Junqiu Li ◽  
Chao Sun ◽  
Guodong Yang ◽  
Fengchun Sun
Keyword(s):  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Maximum Error ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Soc Estimation ◽  
Capacity Degradation ◽  
Battery Capacity

The accurate estimation of a lithium-ion battery’s state of charge (SOC) plays an important role in the operational safety and driving mileage improvement of electrical vehicles (EVs). The Adaptive Extended Kalman filter (AEKF) estimator is commonly used to estimate SOC; however, this method relies on the precise estimation of the battery’s model parameters and capacity. Furthermore, the actual capacity and battery parameters change in real time with the aging of the batteries. Therefore, to eliminate the influence of above-mentioned factors on SOC estimation, the main contributions of this paper are as follows: (1) the equivalent circuit model (ECM) is presented, and the parameter identification of ECM is performed by using the forgetting-factor recursive-least-squares (FFRLS) method; (2) the sensitivity of battery SOC estimation to capacity degradation is analyzed to prove the importance of considering capacity degradation in SOC estimation; and (3) the capacity degradation model is proposed to perform the battery capacity prediction online. Furthermore, an online adaptive SOC estimator based on capacity degradation is proposed to improve the robustness of the AEKF algorithm. Experimental results show that the maximum error of SOC estimation is less than 1.3%.

scholarly journals Glycemic variability and cardiovascular disease in patients with type 2 diabetes

2021 ◽  
Vol 9 (1) ◽  
pp. e002032
Author(s):  
Marcela Martinez ◽  
Jimena Santamarina ◽  
Adrian Pavesi ◽  
Carla Musso ◽  
Guillermo E Umpierrez
Keyword(s):  
Type 2 Diabetes ◽  
Blood Glucose ◽  
Glycated Hemoglobin ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Cardiovascular Complications ◽  
Glucose Levels ◽  
Increased Risk ◽  
Patients With Diabetes

Glycated hemoglobin is currently the gold standard for assessment of long-term glycemic control and response to medical treatment in patients with diabetes. Glycated hemoglobin, however, does not address fluctuations in blood glucose. Glycemic variability (GV) refers to fluctuations in blood glucose levels. Recent clinical data indicate that GV is associated with increased risk of hypoglycemia, microvascular and macrovascular complications, and mortality in patients with diabetes, independently of glycated hemoglobin level. The use of continuous glucose monitoring devices has markedly improved the assessment of GV in clinical practice and facilitated the assessment of GV as well as hypoglycemia and hyperglycemia events in patients with diabetes. We review current concepts on the definition and assessment of GV and its association with cardiovascular complications in patients with type 2 diabetes.

scholarly journals Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

Plant Methods ◽  
2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Shanjun Luo ◽  
Yingbin He ◽  
Qian Li ◽  
Weihua Jiao ◽  
Yaqiu Zhu ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Growth Stage ◽  
Estimation Error ◽  
Potato Yield ◽  
Hyperspectral Data ◽  
Single Stage ◽  
Accurate Estimation ◽  
Yield Estimation ◽  
Area Index ◽  
Red Edge

Abstract Background The accurate estimation of potato yield at regional scales is crucial for food security, precision agriculture, and agricultural sustainable development. Methods In this study, we developed a new method using multi-period relative vegetation indices (rVIs) and relative leaf area index (rLAI) data to improve the accuracy of potato yield estimation based on the weighted growth stage. Two experiments of field and greenhouse (water and nitrogen fertilizer experiments) in 2018 were performed to obtain the spectra and LAI data of the whole growth stage of potato. Then the weighted growth stage was determined by three weighting methods (improved analytic hierarchy process method, IAHP; entropy weight method, EW; and optimal combination weighting method, OCW) and the Slogistic model. A comparison of the estimation performance of rVI-based and rLAI-based models with a single and weighted stage was completed. Results The results showed that among the six test rVIs, the relative red edge chlorophyll index (rCIred edge) was the optimal index of the single-stage estimation models with the correlation with potato yield. The most suitable single stage for potato yield estimation was the tuber expansion stage. For weighted growth stage models, the OCW-LAI model was determined as the best one to accurately predict the potato yield with an adjusted R2 value of 0.8333, and the estimation error about 8%. Conclusion This study emphasizes the importance of inconsistent contributions of multi-period or different types of data to the results when they are used together, and the weights need to be considered.

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