Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.2
3.9
Journals
Biomedicines
Special Issues
Diabetes: Pathogenesis, Therapeutics and Outcomes
share announcement

Diabetes: Pathogenesis, Therapeutics and Outcomes

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Molecular and Translational Medicine".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 1797

Special Issue Editor

Dr. Tadesse Melaku Abegaz

E-Mail Website
Guest Editor
Outcomes and Translational Science, College of Pharmacy, The Ohio State University, Parks Hall, 217 Lloyd M, 500 W 12th Ave, Columbus, OH 43210, USA
Interests: outcome research; pharmacoepidemiology; health economics; artificial intelligence

Special Issue Information

Dear Colleagues,

This Special Issue of Biomedicines will publish peer-reviewed articles in the field of diabetes and its complications. This Special Issue focuses on the pathogenesis of diabetes, treatment strategies and treatment outcomes. It accepts papers related to the complications of diabetes including cardiovascular, kidney, eye, cerbrovascular, and other diseases. The specific research areas include molecular and cellular mechanisms, patient-reported outcomes, comparative effectivness, medication safety, and the application of artifical intelligence in diabetes. The journal invites authors to submit original articles and reviews.

Dr. Tadesse Melaku Abegaz
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomedicines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • diabetes
  • outcomes
  • treatment
  • pathoenesis
  • complications

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

12 pages, 704 KiB  
Article
Predictors of Hepatic Fibrosis in Type 2 Diabetes Patients with Metabolic-Dysfunction-Associated Steatotic Liver Disease
by Joana D’Arc Matos França de Abreu, Rossana Sousa Azulay, Vandilson Rodrigues, Sterffeson Lamare Lucena de Abreu, Maria da Glória Tavares, Flávia Coelho Mohana Pinheiro, Clariano Pires de Oliveira Neto, Caio Andrade, Alexandre Facundo, Adriana Guimarães Sá, Patrícia Ribeiro Azevedo, Ana Gregória Pereira de Almeida, Debora Camelo de Abreu Costa, Rogério Soares Castro, Marcelo Magalhães, Gilvan Cortês Nascimento, Manuel dos Santos Faria and Adalgisa de Souza Paiva Ferreira
Biomedicines 2024, 12(11), 2542; https://doi.org/10.3390/biomedicines12112542 - 7 Nov 2024
Viewed by 490
Abstract
Background/Objectives: Approximately 25% of the world’s population and more than 60% of patients with type 2 diabetes (T2D) have metabolic-dysfunction-associated steatotic liver disease (MASLD). The association between these pathologies is an important cause of morbidity and mortality in Brazil and worldwide due to [...] Read more.
Background/Objectives: Approximately 25% of the world’s population and more than 60% of patients with type 2 diabetes (T2D) have metabolic-dysfunction-associated steatotic liver disease (MASLD). The association between these pathologies is an important cause of morbidity and mortality in Brazil and worldwide due to the high frequency of advanced fibrosis and cirrhosis. The objective of this study was to determine the epidemiologic and clinical-laboratory profile of patients with T2D and MASLD treated at an endocrinology reference service in a state in northeastern Brazil, and to investigate the association of liver fibrosis with anthropometric and laboratory measurements. Methods: A cross-sectional study was performed in a specialized outpatient clinic with 240 patients evaluated from July 2022 to February 2024, using a questionnaire, physical examination, laboratory tests, and liver elastography with FibroScan®. Results: Estimates showed that women (adjusted OR = 2.69, 95% CI = 1.35–5.35, p = 0.005), obesity (adjusted OR = 2.23, 95% CI = 1.22–4.07, p = 0.009), high GGT (adjusted OR = 3.78, 95% CI = 2.01–7.14, p < 0. 001), high AST (adjusted OR = 6.07, 95% CI = 2.27–16.2, p < 0.001), and high ALT (adjusted OR = 3.83, 95% CI = 1.80–8.11, p < 0.001) were associated with the risk of liver fibrosis even after adjusted analysis. Conclusions: The study findings suggested that female sex and BMI were associated with an increased risk of liver fibrosis, highlighting the importance of comprehensive evaluation of these patients. In addition, FIB-4 and MAF-5 provided a good estimate of liver fibrosis in our population and may serve as a useful tool in a public health setting with limited resources. Full article
(This article belongs to the Special Issue Diabetes: Pathogenesis, Therapeutics and Outcomes)
►▼ Show Figures

Figure 1

21 pages, 909 KiB  
Article
Reinforcement Learning: A Paradigm Shift in Personalized Blood Glucose Management for Diabetes
by Lehel Dénes-Fazakas, László Szilágyi, Levente Kovács, Andrea De Gaetano and György Eigner
Biomedicines 2024, 12(9), 2143; https://doi.org/10.3390/biomedicines12092143 - 21 Sep 2024
Viewed by 886
Abstract
Background/Objectives: Managing blood glucose levels effectively remains a significant challenge for individuals with diabetes. Traditional methods often lack the flexibility needed for personalized care. This study explores the potential of reinforcement learning-based approaches, which mimic human learning and adapt strategies through ongoing interactions, [...] Read more.
Background/Objectives: Managing blood glucose levels effectively remains a significant challenge for individuals with diabetes. Traditional methods often lack the flexibility needed for personalized care. This study explores the potential of reinforcement learning-based approaches, which mimic human learning and adapt strategies through ongoing interactions, in creating dynamic and personalized blood glucose management plans. Methods: We developed a mathematical model specifically for patients with type IVP diabetes, validated with data from 10 patients and 17 key parameters. The model includes continuous glucose monitoring (CGM) noise and random carbohydrate intake to simulate real-life conditions. A closed-loop system was designed to enable the application of reinforcement learning algorithms. Results: By implementing a Policy Optimization (PPO) branch, we achieved an average Time in Range (TIR) metric of 73%, indicating improved blood glucose control. Conclusions: This study presents a personalized insulin therapy solution using reinforcement learning. Our closed-loop model offers a promising approach for improving blood glucose regulation, with potential applications in personalized diabetes management. Full article
(This article belongs to the Special Issue Diabetes: Pathogenesis, Therapeutics and Outcomes)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop