In recent years, the number of patients using continuous glucose monitoring (CGM) has increased. In addition to helping patients manage their disease, CGM produces time series data that can be used for integration in ...
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Type 2 diabetes (T2D) is a prevalent chronic illness with many different options for treatment management. Continuous glucose monitors (CGM) offer physiological data that clinicians can access when making treatment de...
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Homeostasis is a fundamental concept in biology and ensures the stability of life by maintaining the constancy of physiological processes. Recent years have witnessed a surge in research interest in these physiologica...
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In recent years, the number of patients using continuous glucose monitoring (CGM) has increased. In addition to helping patients manage their disease, CGM produces time series data that can be used for integration in ...
详细信息
ISBN:
(数字)9798350351552
ISBN:
(纸本)9798350351569
In recent years, the number of patients using continuous glucose monitoring (CGM) has increased. In addition to helping patients manage their disease, CGM produces time series data that can be used for integration in control algorithms, predictive models, and for retrospective analyses. Through feature extraction, many digital biomarkers can be derived from CGM. In this work, we provide a tool to extract features derived from the frequency domain. We first introduce a novel open-source Python library, CGM-Freq, for the analysis of CGM data in the frequency domain. We then test the library on real data. This work provides an open-source tool to further investigate the frequency domain of CGM signals.
Type 2 diabetes (T2D) is a prevalent chronic illness with many different options for treatment management. Continuous glucose monitors (CGM) offer physiological data that clinicians can access when making treatment de...
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ISBN:
(数字)9798350371499
ISBN:
(纸本)9798350371505
Type 2 diabetes (T2D) is a prevalent chronic illness with many different options for treatment management. Continuous glucose monitors (CGM) offer physiological data that clinicians can access when making treatment decisions. However, the utility of CGM in management of T2D remains an active area of research. In our work, we demonstrate the feasibility of exploiting raw daily CGM data to estimate the physiological parameters of insulin sensitivity and beta-cell function that correlate with estimates derived from laboratory findings. We use a peak extraction algorithm to extract peaks from daily CGM data and implement a model-based approach to infer physiological parameters. We demonstrate that the inferred parameter estimates of insulin sensitivity and beta-cell function correlate to the ground truth measurements as determined by an oral glucose tolerance test (OGTT).
Recent studies of genotype-phenotype maps have reported universally enhanced phenotypic robustness to genotype mutations, a feature essential to evolution. Virtually all of these studies make a simplifying assumption ...
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Recent studies of genotype-phenotype maps have reported universally enhanced phenotypic robustness to genotype mutations, a feature essential to evolution. Virtually all of these studies make a simplifying assumption that each genotype—represented as a sequence—maps deterministically to a single phenotype, such as a discrete structure. Here we introduce probabilistic genotype-phenotype (PrGP) maps, where each genotype maps to a vector of phenotype probabilities, as a more realistic and universal language for investigating robustness in a variety of physical, biological, and computational systems. We study three model systems to show that PrGP maps offer a generalized framework which can handle uncertainty emerging from various physical sources: (1) thermal fluctuation in RNA folding, (2) external field disorder in the spin-glass ground state search problem, and (3) superposition and entanglement in quantum circuits, which are realized experimentally on IBM quantum computers. In all three cases, we observe a biphasic robustness scaling which is enhanced relative to random expectation for more frequent phenotypes and approaches random expectation for less frequent phenotypes. We derive an analytical theory for the behavior of PrGP robustness, and we demonstrate that the theory is highly predictive of empirical robustness.
Self-supervised learning (SSL) for clinical time series data has received significant attention in recent literature, since these data are highly rich and provide important information about a patient’s physiological...
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Opponent Colors Theory advances that four colors have special status and are yoked in opponent fashion (yellow-versus-blue, and red-versus-green). Classic hue cancelation studies provide evidence for this theory: peop...
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In this paper, we present a continuous wave (CW) and time-domain (TD) diffuse correlation spectroscopy (DCS) for cerebral blood flow monitoring at 1064nm. Findings show high brain sensitivity and fast monitoring which...
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