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Hormones Regulating Blood Glucose01:16

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Insulin is released by beta cells of the pancreas when blood glucose levels are high. It facilitates glucose absorption and utilization in insulin-dependent cells with insulin receptors on their plasma membranes. Insulin promotes glucose uptake by increasing the number of glucose transport proteins in the cell membrane, allowing glucose to enter the cell. As a result, glucose utilization and ATP production are enhanced.
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Glucose Absorption Into the Small Intestine01:26

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Complex carbohydrates consumed cannot be absorbed into the small intestine in their original form. First, they must be hydrolyzed to a monosaccharide form such as glucose or galactose. These monosaccharides are then transported across the intestinal membrane and into the blood via transcellular transport. The intestinal epithelial cells allow the movement of these monosaccharides with a defined 'entry' through membrane transporter proteins present on their apical membrane and...
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Diabetes Mellitus: Type 2 and Gestational01:22

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Type 2 diabetes, characterized by insulin resistance, arises when the insulin receptors on cells lose responsiveness to insulin, diminishing the cell's capacity to take up glucose, resulting in elevated blood glucose levels. To receive a diagnosis of Type 2 diabetes, a series of blood glucose tests are necessary to assess whether the blood glucose falls within normal parameters. If the result is out of the normal range, a patient may be diagnosed as prediabetic or diabetic, depending on the...
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Glucose Homeostasis: Pancreatic Islets and Insulin Secretion01:27

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The pancreatic islets comprising only 1%-2% of the volume are highly vascularized and innervated mini-organs. They contain five endocrine cell types, including β cells that secrete insulin, which is synthesized as a single polypeptide chain, preproinsulin, processed to proinsulin, and finally to insulin and C-peptide. This process is complex and regulated, involving the Golgi complex, the endoplasmic reticulum, and the secretory granules of the β cell.
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Improving IV Insulin Administration in a Community Hospital
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Predictive Framework Development for User-Friendly On-Site Glucose Detection.

Vinay Kishnani1, Ankur Gupta1

  • 1Department of Mechanical Engineering, Indian Institute of Technology, Jodhpur 342030, Rajasthan, India.

ACS Applied Bio Materials
|September 8, 2023
PubMed
Summary
This summary is machine-generated.

This study presents a smartphone-based system for rapid glucose detection using paper analytical devices (PADs) and machine learning. The framework offers accurate glucose level estimation, enhancing initial disease screening capabilities.

Keywords:
color spacesglucose detectionmachine-learningoptical biosensorspaper-based sensorsmartphonesmartphone color-readoutuser-friendly bio sensing framework

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Area of Science:

  • Biomedical Engineering
  • Analytical Chemistry
  • Machine Learning in Healthcare

Background:

  • Accurate glucose monitoring is crucial for diabetes management.
  • Existing methods can be complex, costly, or require specialized equipment.
  • Paper-based analytical devices (PADs) offer a promising low-cost alternative.

Purpose of the Study:

  • To develop a user-friendly, smartphone-based framework for glucose detection using PADs.
  • To evaluate the impact of chitosan and varying illumination conditions on detection accuracy.
  • To apply machine learning algorithms for enhanced signal processing and glucose concentration estimation.

Main Methods:

  • Utilized smartphone cameras for colorimetric analysis of glucose-spotted PADs.
  • Compared detection mixtures with and without chitosan (C vs. WC).
  • Investigated effects of diverse illumination and smartphone optics.
  • Employed machine learning classifiers (SVM, neural network, linear/quadratic discriminant) with color space combinations.

Main Results:

  • Machine learning models achieved high accuracy (up to 94.1%) for glucose detection.
  • Chitosan inclusion generally improved accuracy for specific classifiers.
  • Cubic SVM demonstrated consistent performance across both C and WC samples (∼93% accuracy).
  • A blind test confirmed the system's performance.

Conclusions:

  • The developed smartphone-PAD framework provides a simple, rapid, and affordable method for glucose detection.
  • Machine learning integration enhances accuracy and reliability for point-of-care applications.
  • The system holds potential for initial disease screening and remote healthcare monitoring.