[The principles and research status of noninvasive glucose detection based on near-infrared spectrum].

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi

College of Bioengineering, Chongqing University, Chongqing 400030, China.

Published: February 2013


Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

It is of great clinical significance to investigate noninvasive glucose detection. As one of the most potential methods, the noninvasive glucose detection based on near-infrared has become a hot research area recently. In this paper the principles and research methods of noninvasive glucose detection based on near-infrared spectrum are introduced. With the comparison between the research status at home and abroad in recent years, we summarize and discuss crucial issues in near-infrared noninvasive glucose detection, such as the selection of measurement method, selection of measurement position and choice of wavelength, and, furthermore, setting up models.

Download full-text PDF

Source

Publication Analysis

Top Keywords

noninvasive glucose
20
glucose detection
20
detection based
12
based near-infrared
12
methods noninvasive
8
selection measurement
8
noninvasive
5
glucose
5
detection
5
[the principles
4

Similar Publications

Aims: The estimated glucose disposal rate (eGDR) is a simple, non-invasive measure of insulin resistance. In this exploratory analysis of FINEARTS-HF, we evaluated whether lower eGDR, reflecting greater insulin resistance, is associated with adverse outcomes in heart failure (HF).

Methods And Results: The eGDR was calculated at baseline using waist circumference, glycated haemoglobin, and hypertension status.

View Article and Find Full Text PDF

Background: Acute ischemic stroke (AIS) is characterized by high incidence, sudden onset, and often poor prognosis. Carotid atherosclerosis plays a crucial role in its pathogenesis, and ultrasound imaging offers a non-invasive method for evaluating carotid plaque characteristics. This study aimed to develop and validate a prediction model for AIS risk based on a novel ultrasound-based carotid plaque scoring system combined with clinical risk factors.

View Article and Find Full Text PDF

Predicting stimulated C-peptide in type 1 diabetes using machine learning: a web-based tool from the T1D exchange registry.

Diabetes Res Clin Pract

September 2025

Division of Endocrinology and Metabolism, Department of Internal Medicine, Faculty of Medicine, Canakkale Onsekiz Mart University, Canakkale, Turkey.

Aims: The mixed-meal tolerance test (MMTT), though considered the gold standard for evaluating residual beta-cell function in type 1 diabetes mellitus (T1D), is impractical for routine use. We aimed to develop and validate a machine learning (ML) model to predict MMTT-stimulated C-peptide categories using routine clinical data.

Methods: Data from 319 individuals in the T1D Exchange Registry with complete MMTT and clinical information were analyzed.

View Article and Find Full Text PDF

Purpose: The liver-brain axis regulates metabolic homeostasis, with glucose metabolism playing a key role. Liver dysfunction, such as fibrosis, may impact brain metabolism and consequently, brain function. Positron emission tomography (PET) imaging provides a non-invasive approach to study glucose metabolism in both organs.

View Article and Find Full Text PDF

Background: Diabetes is a global concern, with an estimated 2 million individuals expected to be affected by the condition by 2024. Non-invasive glucose monitoring devices can greatly enhance patient care and management.

Objective: This study aimed to develop an instrument capable of non-invasively measuring blood glucose levels using an infrared transmitter and receiver, with data processing performed by a dedicated processor.

View Article and Find Full Text PDF