Non-Invasive In Vivo Estimation of HbA1c Using Monte Carlo Photon Propagation Simulation: Application of Tissue-Segmented 3D MRI Stacks of the Fingertip and Wrist for Wearable Systems.

The early diagnosis of diabetes mellitus in normal people or maintaining stable blood sugar concentrations in diabetic patients requires frequent monitoring of the blood sugar levels. However, regular monitoring of the sugar levels is problematic owing to the pain and inconvenience associated with pricking the fingertip or using minimally invasive patches. In this study, we devise a noninvasive method to estimate the percentage of the in vivo glycated hemoglobin (HbA1c) values from Monte Carlo photon propagation simulations, based on models of the wrist using 3D magnetic resonance (MR) image data. The MR image slices are first segmented for several different tissue types, and the proposed Monte Carlo photon propagation system with complex composite tissue support is then used to derive several models for the fingertip and wrist sections with different wavelengths of light sources and photodetector arrangements. The Pearson r values for the estimated percent HbA1c values are 0.94 and 0.96 for the fingertip transmission- and reflection-type measurements, respectively. This is found to be the best among the related studies. Furthermore, a single-detector multiple-source arrangement resulted in a Pearson r value of 0.97 for the wrist. The Bland-Altman bias values were found to be -0.003 ± 0.36, 0.01 ± 0.25, and 0.01 ± 0.21, for the two fingertip and wrist models, respectively, which conform to the standards of the current state-of-the-art invasive point-of-care devices. The implementation of these algorithms will be a suitable alternative to the invasive state-of-the-art methods.

Optical Measurement of Molar Absorption Coefficient of HbA1c: Comparison of Theoretical and Experimental Results.

Diabetes can cause dangerous complications if not diagnosed in a timely manner. The World Health Organization accepts glycated hemoglobin (HbA1c) as a measure of diagnosing diabetes as it provides significantly more information on the glycemic behavior from a single blood sample than the fasting blood sugar reading. The molar absorption coefficient of HbA1c is needed to quantify the amount of HbA1c present in a blood sample. In this study, we measured the molar absorption coefficient of HbA1c in the range of 450 nm to 700 nm using optical methods experimentally. We observed that the characteristic peaks of the molar absorption coefficient of HbA1c (at 545 nm and 579 nm for level 1, at 544 nm and 577 nm for level 2) are in close agreement with those reported in previous studies. The molar absorption coefficient values were also found to be close to those of earlier reports. The average molar absorption coefficient values of HbA1c were found to be 804,403.5 M−1cm−1 at 545 nm and 703,704.5 M−1cm−1 at 579 nm for level 1 as well as 503,352.4 M−1cm−1 at 544 nm and 476,344.6 M−1cm−1 at 577 nm for level 2. Our experiments focused on calculating the molar absorption coefficients of HbA1c in the visible wavelength region, and the proposed experimental method has an advantage of being able to easily obtain the molar absorption coefficient at any wavelength in the visible wavelength region. The results of this study are expected to help future investigations on noninvasive methods of estimating HbA1c levels.

Noninvasive In Vivo Estimation of Blood-Glucose Concentration by Monte Carlo Simulation.

Continuous monitoring of blood-glucose concentrations is essential for both diabetic and nondiabetic patients to plan a healthy lifestyle. Noninvasive in vivo blood-glucose measurements help reduce the pain of piercing human fingertips to collect blood. To facilitate noninvasive measurements, this work proposes a Monte Carlo photon simulation-based model to estimate blood-glucose concentration via photoplethysmography (PPG) on the fingertip. A heterogeneous finger model was exposed to light at 660 nm and 940 nm in the reflectance mode of PPG via Monte Carlo photon propagation. The bio-optical properties of the finger model were also deduced to design the photon simulation model for the finger layers. The intensities of the detected photons after simulation with the model were used to estimate the blood-glucose concentrations using a supervised machine-learning model, XGBoost. The XGBoost model was trained with synthetic data obtained from the Monte Carlo simulations and tested with both synthetic and real data (n = 35). For testing with synthetic data, the Pearson correlation coefficient (Pearson's r) of the model was found to be 0.91, and the coefficient of determination (R2) was found to be 0.83. On the other hand, for tests with real data, the Pearson's r of the model was 0.85, and R2 was 0.68. Error grid analysis and Bland-Altman analysis were also performed to confirm the accuracy. The results presented herein provide the necessary steps for noninvasive in vivo blood-glucose concentration estimation.

Framework of the Alu Subfamily Evolution in the Platyrrhine Three-Family Clade of Cebidae, Callithrichidae, and Aotidae.

The history of Alu retroposons has been choreographed by the systematic accumulation of inherited diagnostic nucleotide substitutions to form discrete subfamilies, each having a distinct nucleotide consensus sequence. The oldest subfamily, AluJ, gave rise to AluS after the split between Strepsirrhini and what would become Catarrhini and Platyrrhini. The AluS lineage gave rise to AluY in catarrhines and to AluTa in platyrrhines. Platyrrhine Alu subfamilies Ta7, Ta10, and Ta15 were assigned names based on a standardized nomenclature. However, with the subsequent intensification of whole genome sequencing (WGS), large scale analyses to characterize Alu subfamilies using the program COSEG identified entire lineages of subfamilies simultaneously. The first platyrrhine genome with WGS, the common marmoset (Callithrix jacchus; [caljac3]), resulted in Alu subfamily names sf0 to sf94 in an arbitrary order. Although easily resolved by alignment of the consensus sequences, this naming convention can become increasingly confusing as more genomes are independently analyzed. In this study, we reported Alu subfamily characterization for the platyrrhine three-family clade of Cebidae, Callithrichidae, and Aotidae. We investigated one species/genome from each recognized family of Callithrichidae and Aotidae and of both subfamilies (Cebinae and Saimiriinae) of the family Cebidae. Furthermore, we constructed a comprehensive network of Alu subfamily evolution within the three-family clade of platyrrhines to provide a working framework for future research. Alu expansion in the three-family clade has been dominated by AluTa15 and its derivatives.

Noninvasive Estimation of Glycated Hemoglobin In-Vivo Based on Photon Diffusion Theory and Genetic Symbolic Regression Models.

The diagnosis and management of diabetes require frequent monitoring of blood sugar levels. Prolonged exposure of most of the monosaccharides in the bloodstream results in the glycation of hemoglobin. This glycated hemoglobin (HbA1c) based test plays an important role to avoid diabetic complications. However, noninvasive estimation of HbA1c is a very new, promising, and challenging topic in modern bioengineering scopes. The purpose of this study is to develop and verify mathematical models in order to quantify the glycated hemoglobin in-vivo percentage non-invasively. This research utilized photon diffusion theory to develop the finger models and genetic symbolic regression methods to solve the models to estimate the level of glycated hemoglobin in the blood. The validation of these models with human participants indicated a high degree of correlation (0.887 and 0.907 Pearson's r value), and high precision (2.56% and 2.96% coefficient of variation (%CV)) for transmission and reflection type noninvasive digital volume pulse-based signals. This research will be a breakthrough for the application of noninvasive HbA1c estimation.

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

Glycated 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.

Understanding the nonlinear behavior of EEG with advanced machine learning in artifact elimination.

Steady-state Visually Evoked Potential (SSVEP) based Electroencephalogram (EEG) signal is utilized in brain-computer interface paradigms, diagnosis of brain diseases, and measurement of the cognitive status of the human brain. However, various artifacts such as the Electrocardiogram (ECG), Electrooculogram (EOG), and Electromyogram (EMG) are present in the raw EEG signal, which adversely affect the EEG-based appliances. In this research, Adaptive Neuro-fuzzy Interface Systems (ANFIS) and Hilbert-Huang Transform (HHT) are primarily employed to remove the artifacts from EEG signals. This work proposes Adaptive Noise Cancellation (ANC) and ANFIS based methods for canceling EEG artifacts. A mathematical model of EEG with the aforementioned artifacts is determined to accomplish the research goal, and then those artifacts are eliminated based on their mathematical characteristics. ANC, ANFIS, and HHT algorithms are simulated on the MATLAB platform, and their performances are also justified by various error estimation criteria using hardware implementation.