How Insulin Works with Glucose | Kaiser Permanente Washington
Diabetes mellitus is a major metabolic disease in which the blood glucose level in all regions, with incomplete correlation between A1C and average glucose level the mobilebased diabetes diagnosis system with detailed illustration of the. Learn how insulin works in the body, how it's made, and which types are pills or insulin shots to help their bodies use glucose for energy. Your body's regulation of blood glucose is an amazing metabolic feat. However, for some people, the process doesn't work.
Humulin R, Novolin R Intermediate-acting insulin generally reaches the bloodstream about 2 to 4 hours after injection, peaks 4 to 12 hours later, and is effective for about 12 to 18 hours. NPH Humulin N, Novolin N Long-acting insulin reaches the bloodstream several hours after injection and tends to lower glucose levels fairly evenly over a hour period. Insulin detemir Levemir and insulin glargine Lantus Premixed insulin can be helpful for people who have trouble drawing up insulin out of two bottles and reading the correct directions and dosages.
It is also useful for those who have poor eyesight or dexterity and is convenient for people whose diabetes has been stabilized on this combination. In an inhaled insulin product, Afrezza, became available in the U.
Afrezza is a rapid-acting inhaled insulin that is administered at the beginning of each meal and can be used by adults with type 1 or type 2 diabetes. Afrezza is not a substitute for long-acting insulin.
Afrezza must be used in combination with injectable long-acting insulin in patients with type 1 diabetes and in type 2 patients who use long-acting insulin. Inhaled insulin begins working within 12 to 15 minutes, peaks by 30 minutes, and is out of your system in minutes. Technosphere insulin-inhalation system Afrezza Characteristics of Insulin Insulin has 3 characteristics: Onset is the length of time before insulin reaches the bloodstream and begins lowering blood glucose.
Peaktime is the time during which insulin is at maximum strength in terms of lowering blood glucose. Duration is how long insulin continues to lower blood glucose. Insulin Strength All insulins come dissolved or suspended in liquids. The standard and most commonly used strength in the United States today is U, which means it has units of insulin per milliliter of fluid, though U insulin is available for patients who are extremely insulin resistant.
The proposed diagnosis system collects the real time data from the glucometer through the Bluetooth device in the mobile phones. The collected input data are transmitted to the operating system through Wi-Fi and are processed.
The retinal images are captured using the mobile phone cameras and then are transmitted to the operating system in a similar fashion. Likewise the multiple measurement clinical data are also input into the processing system. Similarly the statistical measures are computed for the time series data features and are fused with the optimal features. Then the samples are trained using back propagation NN but it has infinitesimal step size problem that cannot be resolved in standard back propagation algorithm.
In order to determine the presence of diagnosis, the association rules are generated using Apriori algorithm but it is very costly due to the candidate selection process. Hence we use the FP-growth algorithm for generating association rules with which the relation between the features is estimated.
Thus the diabetes can be diagnosed accurately using the proposed mobile based diabetes diagnosis system. The remainder of the paper is organized as follows: Section 2 describes the previous research methods for the strategies utilized in the diagnosis of diabetes that form the basic motivation of our research.
Section 3 explains the mobilebased diabetes diagnosis system with detailed illustration of the research methodologies. Section 4 presents the performance evaluation results of the proposed diagnosis systems. Section 5 concludes the research work and provides directions for the future research.
Related Works Bourouis et al. The proposed work used Artificial Neural Network algorithm for analyzing the retinal images to identify the condition of the retinal diseases.
Insulin Basics: American Diabetes Association®
The proposed work presented the initial results for a simple client and server two-tier architecture for healthcare. However it had higher computational complexity.
In Andrea et al. The smart phones were used to register patients eating behavior to emotions thrice a day by also registering their fasting blood glucose level. This work is high of cost and architecture. The system generated a severity grade for diabetic retinopathy DR using machine learning method. But the implemented system was not applicable for data corresponding to increased coverage.
There has been a great deal of controversy regarding the definition and screening of hypoglycemia. A cutoff value for blood glucose level in glucometer readings were proposed based on the study conducted on newborns at risk of hypoglycaemia. Thirty patients with gingivitis or periodontitis and bleeding on probing BOP were chosen. The following clinical periodontal parameters were noted: The designed system provides a daily monitoring and monthly services.
The result of this visit entered into the system and then synchronized with the central database. Finally, the endocrinologist can remotely monitor the patient record and adjust the treatment plan and the insulin doses if need. The proposed work modified AIRS2 called MAIRS2 where the K- nearest neighbors algorithm was replaced with the fuzzy K-nearest neighbors in order to improve the diagnostic accuracy of diabetes diseases. Bob Zhang et al.
- Normal Regulation of Blood Glucose
- Insulin Basics
- The Liver and Blood Glucose Levels
Initially three groups of features were extracted from the images of retina. A color gamut was established with 12 colors representing the features of the images. Low Power Bluetooth was used in conjunction with harvesting RF methods which allowed the development of new wireless battery-less sensors may work for years without battery replacement. Mobile-Based Diabetes Diagnosis System In this section, the development of the proposed mobile-based diabetes diagnosis system is illustrated in detail.
The proposed diagnosis system utilizes the features from the multiple measurements clinical data, real time input from the glucometer and the retinal image features. These features are extracted using different approaches Figure 1. The multiple measurements clinical data are collected from the patients for different time periods in between a specified time interval.
Different parameters are considered from the clinical data for the diabetes diagnosis. From the previous medical researches it is found that the factors influencing the Type I and Type II diabetes are similar with the lifestyle behaviors being the only difference. Type II depends upon the lifestyle changes while the Type I diabetes does not depend upon it. Block diagram of proposed mobile-based diabetes diagnosis system.
Type I diabetes mellitus considers the parameters such as age, gender, family history of diabetes, Hemoglobin A1C HbA1csystolic blood pressure, platelet parameters, cholesterol, fast plasma glucose FPGimpaired fasting glycaemia, impaired glucose tolerance, plasma glucose level, for the diagnosis. Data from glucometer The inputs are also taken from the glucometer and also from the retinal images apart from the multiple measurements clinical data.
How Diabetes Works
The glucometer is utilized for the home glucose monitoring HBGM for patients whom are to be diagnosed with diabetes mellitus. A small drop of blood obtained by pricking of skin in fingers and is placed on a disposable test strip.
The readings from the glucometer are collected and are fed into the operating system through a mobile device. The glucometer test strips are made up of chemicals like glucose oxidase and when the blood of 0.
The data collected by the glucometer are transmitted to the mobile phones through the Bluetooth and from the mobiles the data is transmitted to the operating systems through Wi-Fi.
Retinal images The retinal images are usually captured using the Fundus camera which captures high quality and high sensitive images with the microscopic lens. The image database is generated by capturing images using the zoom lens mobile cameras so that the images can be captured at home itself and transmitted to the operating systems. The retinal images are pre-processed to remove the noise and various texture backgrounds.