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Applied Sciences
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Modelling, Investigating and Treating Type 1 Diabetes
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Modelling, Investigating and Treating Type 1 Diabetes

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: closed (20 June 2024) | Viewed by 2365

Special Issue Editor

Dr. Mária Bagyánszki

E-Mail Website
Guest Editor
Department of Physiology, Anatomy and Neuroscience, Faculty of Science and Informatics, University of Szeged, H-6726 Szeged, Hungary
Interests: enteric nervous system; pathological animal models; type 1 diabetes; histology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Although type 1 diabetes has been known for a long time, the harmful effects of chronic hyperglycemia on various organs require further investigation. The aim of this Special Issue is to highlight diabetes-related changes in the whole body as vascular, gastrointestinal, microbial, and neurological alterations.

This Special Issue is open for original research articles as well as review articles focusing on modelling, investigating or treating type 1 diabetes. The understanding of different aspects of chronic hyperglycemia will help to develop new therapeutic and treatment strategies which can prevent or care diabetes-related conditions.

Dr. Mária Bagyánszki
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • type 1 diabetes
  • hyperglicemia
  • insulin
  • animal models for diabetes
  • diabetic vasculopathy
  • diabetic gastroenteropathy
  • microbial dysbiosis
  • diabetic neuropathy
  • diabetic nephropathy
  • diabetic retinopathy

Published Papers (2 papers)

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Research

20 pages, 15726 KiB  
Article
Oxidative-Stress-Related Alterations in Metabolic Panel, Red Blood Cell Indices, and Erythrocyte Morphology in a Type 1 Diabetic Rat Model
by Zita Szalai, Anikó Magyariné Berkó, Nikolett Bódi, Edit Hermesz, Ágnes Ferencz and Mária Bagyánszki
Appl. Sci. 2023, 13(17), 9920; https://doi.org/10.3390/app13179920 - 1 Sep 2023
Viewed by 1109
Abstract
Diabetes mellitus is often associated with vascular complications in which hyperglycemia-induced oxidative stress may be the cause of the impaired vessels and circulating blood cells. The aim of this study was to follow the hyperglycemia-related metabolic and morphological changes in blood and urine [...] Read more.
Diabetes mellitus is often associated with vascular complications in which hyperglycemia-induced oxidative stress may be the cause of the impaired vessels and circulating blood cells. The aim of this study was to follow the hyperglycemia-related metabolic and morphological changes in blood and urine samples of Wistar rats. Animals were divided into streptozotocin-induced diabetic (acute and chronic), insulin-treated diabetic, reversed diabetic, and control groups. In chronic diabetic rats, decreases in albumin, total protein, and antioxidant glutation concentration were measured, while glutamic-pyruvic transaminase, alkaline phosphatase, red blood cell (RBC) count, hematocrit, and hemoglobin levels were increased. Moreover, an increased level of the phenotypic variants was detected in the RBC population of the diabetic animals. In conclusion, we verified the sensitivity of RBCs to long-lasting hyperglycemia, and to insulin deficiency, which were both accompanied with an increased level of RBC-derived parameters and the presence of eccentrocytes, hemolyzed RBCs, and codocytes. Moreover, our results show that the response of the RBC glutation system to oxidative stress depends on the duration of hyperglycemia, and that the short-term activation of this defense system is exhausted in a long-lasting oxidative environment. Insulin therapy was effective in the case of most parameters, which clearly emphasizes the importance of maintaining blood glucose at physiological level. Full article
(This article belongs to the Special Issue Modelling, Investigating and Treating Type 1 Diabetes)
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15 pages, 326 KiB  
Article
On the Use of Population Data for Training Seasonal Local Models-Based Glucose Predictors: An In Silico Study
by Antonio Aslan, José-Luis Díez, Alejandro José Laguna Sanz and Jorge Bondia
Appl. Sci. 2023, 13(9), 5348; https://doi.org/10.3390/app13095348 - 25 Apr 2023
Viewed by 831
Abstract
Most advanced technologies for the treatment of type 1 diabetes, such as sensor-pump integrated systems or the artificial pancreas, require accurate glucose predictions on a given future time-horizon as a basis for decision-making support systems. Seasonal stochastic models are data-driven algebraic models that [...] Read more.
Most advanced technologies for the treatment of type 1 diabetes, such as sensor-pump integrated systems or the artificial pancreas, require accurate glucose predictions on a given future time-horizon as a basis for decision-making support systems. Seasonal stochastic models are data-driven algebraic models that use recent history data and periodic trends to accurately estimate time series data, such as glucose concentration in diabetes. These models have been proven to be a good option to provide accurate blood glucose predictions under free-living conditions. These models can cope with patient variability under variable-length time-stamped daily events in supervision and control applications. However, the seasonal-models-based framework usually needs of several months of data per patient to be fed into the system to adequately train a personalized glucose predictor for each patient. In this work, an in silico analysis of the accuracy of prediction is presented, considering the effect of training a glucose predictor with data from a cohort of patients (population) instead of data from a single patient (individual). Feasibility of population data as an input to the model is asserted, and the effect of the dataset size in the determination of the minimum amount of data for a valid training of the models is studied. Results show that glucose predictors trained with population data can provide predictions of similar magnitude as those trained with individualized data. Overall median root mean squared error (RMSE) (including 25% and 75% percentiles) for the predictor trained with population data are {6.96[4.87,8.67], 12.49[7.96,14.23], 19.52[10.62,23.37], 28.79[12.96,34.57], 32.3[16.20,41.59], 28.8[15.13,37.18]} mg/dL, for prediction horizons (PH) of {15,30,60,120,180,240} min, respectively, while the baseline of the individually trained RMSE results are {6.37[5.07,6.70], 11.27[8.35,12.65], 17.44[11.08,20.93], 22.72[14.29,28.19], 28.45[14.79,34.38], 25.58[13.10,36.60]} mg/dL, both training with 16 weeks of data. Results also show that the use of the population approach reduces the required training data by half, without losing any prediction capability. Full article
(This article belongs to the Special Issue Modelling, Investigating and Treating Type 1 Diabetes)
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