Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Insulin: Dosing Regimen and Adverse Effects01:16

Insulin: Dosing Regimen and Adverse Effects

658
Insulin-replacement therapy usually includes both long-acting insulin (basal) and short-acting insulin (to cater to postprandial needs). In a diverse group of type 1 diabetes patients, the average daily insulin dose is typically 0.5-0.7 units/kg body weight. However, obese patients and pubertal adolescents may need more due to insulin resistance.
The basal dose constitutes about 40%-50% of the total daily dose, with the rest as premeal insulin. The mealtime insulin dose should mirror...
658
Diabetes Mellitus: Overview and Type I Subtype01:22

Diabetes Mellitus: Overview and Type I Subtype

4.8K
Diabetes mellitus is a chronic metabolic disorder characterized by high blood glucose levels due to inadequate insulin production, insulin resistance, or both. The condition affects millions worldwide and can significantly impact their health and quality of life.
Type 1 diabetes is an autoimmune disease in which the immune system mistakenly attacks and destroys the insulin-producing beta cells in the pancreas. As a result, the body is unable to produce sufficient insulin, and individuals with...
4.8K
Diabetes: Management and Pharmacotherapy01:15

Diabetes: Management and Pharmacotherapy

848
The therapy for diabetes aims to alleviate hyperglycemia-related symptoms, prevent acute metabolic decompensation, and reduce chronic end-organ complications. Glycemic control is evaluated through short-term (self-monitoring, continuous glucose monitoring) and long-term (A1c, fructosamine) metrics, enabling near real-time tracking of blood glucose levels and reflecting glycemic control over specific time frames.
Insulin remains the cornerstone of treatment for most patients with type 1 and many...
848
Insulin Formulations: Types and Delivery01:27

Insulin Formulations: Types and Delivery

622
Insulin preparations are categorized by their duration of action into short-acting and long-acting types. Two strategies are used to modify insulin's absorption and pharmacokinetic profile: slowing the absorption post-subcutaneous injection, or altering human insulin's amino acid sequence or protein structure. These changes retain the insulin's ability to bind to the insulin receptor, but alter its behavior in solution or after injection.
Short-acting insulins are divided into...
622
Diabetes Mellitus: Type 2 and Gestational01:22

Diabetes Mellitus: Type 2 and Gestational

4.3K
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...
4.3K
Carbohydrate Metabolism01:36

Carbohydrate Metabolism

13.7K
Carbohydrates are polymers composed of molecules containing atoms of carbon, hydrogen and oxygen. One gram of carbohydrate can provide four kilo-calories of energy, which makes it the most efficient instant energy source.
Starch accounts for approximately 60% of the carbohydrates consumed by humans. Since amylase enzymes cannot function in the stomach's acidic environment, starch can only be digested in the mouth and small intestine. Simple sugars are found naturally in milk and fruits in...
13.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The Value of Anti-Drug Antibody Detection in Discriminating Patients from Healthy Controls and Predicting the Gross Motor Functional State in Patients with Pompe Disease.

Iranian journal of allergy, asthma, and immunology·2026
Same author

Adaptation to glucose restriction and β-hydroxybutyrate supplementation is associated with metabolic flexibility in lung cancer cells.

Scientific reports·2026
Same author

An analytical method based on magnetic dispersive solid phase extraction combined with high performance liquid chromatography-tandem mass spectrometry for monitoring of apixaban plasma concentration in patients undergoing hip fracture surgery.

Journal of chromatography. B, Analytical technologies in the biomedical and life sciences·2026
Same author

Modeling roles and trade-offs in multiplex networks.

Nature communications·2026
Same author

Quantitative analysis of cyclosporine A in whole blood samples of liver transplant recipients using a stir bar-assisted magnetic dispersive solid phase extraction combined with HPLC-MS/MS.

Journal of chromatography. A·2026
Same author

Corrigendum to "Evaluating antimicrobial efficacy in medical devices: The critical role of simulating in use test conditions" [Biomater. Adv. 172 (2025), 214241].

Biomaterials advances·2026

Related Experiment Video

Updated: Jan 9, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K

Predicting Treatment Outcome of Patients with Type 2 Diabetes on Once-Daily Basal Insulin Injections using Machine

Ali Mohebbi, Niels-Kristian Kjoller, Alexander R Johansen

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary

    Machine learning and continuous glucose monitoring data show a slight trend toward improving predictions for basal insulin treatment success in type 2 diabetes. However, accurately predicting outcomes for clinical use remains challenging with current patient information.

    More Related Videos

    Improving IV Insulin Administration in a Community Hospital
    12:08

    Improving IV Insulin Administration in a Community Hospital

    Published on: June 11, 2012

    19.3K
    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
    07:15

    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

    Published on: August 16, 2020

    7.3K

    Related Experiment Videos

    Last Updated: Jan 9, 2026

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
    04:09

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

    Published on: October 10, 2018

    8.6K
    Improving IV Insulin Administration in a Community Hospital
    12:08

    Improving IV Insulin Administration in a Community Hospital

    Published on: June 11, 2012

    19.3K
    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
    07:15

    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

    Published on: August 16, 2020

    7.3K

    Area of Science:

    • Biomedical Informatics
    • Endocrinology
    • Machine Learning in Healthcare

    Background:

    • Basal insulin is a common intensification strategy for type 2 diabetes (T2D) inadequately controlled by oral antidiabetic drugs (OADs).
    • Individualized treatment intensification alternatives and machine learning (ML)-based decision support tools are emerging.
    • Predicting basal insulin treatment success is crucial for optimizing patient care.

    Purpose of the Study:

    • To explore if patient characteristics and continuous glucose monitoring (CGM) data improve prediction of successful basal insulin treatment outcomes.
    • To assess the added predictive value beyond hemoglobin A1c (HbA1c) alone using ML models.
    • To investigate the potential of ML and CGM for personalized basal insulin initiation support.

    Main Methods:

    • Utilized clinical data from 222 T2D patients on OADs initiating basal insulin.
    • Employed logistic regression and Gaussian process (GP) classification models.
    • Input features included HbA1c, patient characteristics, and 3-day CGM metrics; outcome was binarized HbA1c (<7% or ≥7%) at six months.

    Main Results:

    • CGM metrics showed a trend towards slightly improved prediction performance when combined with HbA1c.
    • Accurate prediction of binarized HbA1c outcome using available patient data proved difficult for clinical application.
    • The study highlights limitations in current predictive capabilities for basal insulin treatment success.

    Conclusions:

    • This pilot study demonstrates an initial effort to use ML and CGM for personalized basal insulin decision support.
    • Further research is needed to overcome limitations and enhance predictive accuracy for clinical utility.
    • Leveraging ML and CGM holds promise for more cost-effective and individualized T2D management.