Lifestyle changes and glycemic control in type 1 diabetes mellitus: a trial protocol with factorial design approach

Designing of Nanomaterials-Based Enzymatic Biosensors: Synthesis, Properties, and Applications

Among the many biological entities employed in the development of biosensors, enzymes have attracted the most attention. Nanotechnology has been fostering excellent prospects in the development of enzymatic biosensors, since enzyme immobilization onto conductive nanostructures can improve characteristics that are crucial in biosensor transduction, such as surface-to-volume ratio, signal response, selectivity, sensitivity, conductivity, and biocatalytic activity, among others. These and other advantages of nanomaterial-based enzymatic biosensors are discussed in this work via the compilation of several reports on their applications in different industrial segments. To provide detailed insights into the state of the art of this technology, all the relevant concepts around the topic are discussed, including the properties of enzymes, the mechanisms involved in their immobilization, and the application of different enzyme-derived biosensors and nanomaterials. Finally, there is a discussion around the pressing challenges in this technology, which will be useful for guiding the development of future research in the area.

The Current and Forecasted Status of Type 2 Diabetes in the Arab Society of Israel

BACKGROUND

Diabetes mellitus (DM) is considered one of the main causes of mortality, morbidity, and health care expenditures. Effectively treating this disease is of crucial importance and imposes a global challenge. The incidence of Type 2 DM (T2DM) is rapidly rising in both developing and developed countries. The Arab community in Israel is a distinct ethnic group with unique characteristics. Recently, this community has undergone major changes in its lifestyle, adopting the Westernized one, which could have caused an increase in the T2DM incidence rate.

OBJECTIVE

This review aims to shed light on various studies undertaken to explore the prevalence of diabetes and determine its current status in the Arab society of Israel, resting on previous and current data. It is presented to highlight the status of diabetes globally and to focus on its current situation in the Arab society of Israel, attempting to forecast its direction in the upcoming decade.

METHODS

Data were obtained from our previous comprehensive socio-economic and health crosssectional surveys for successive periods from 2004 to 2017. These surveys were conducted on the Arab society of Israel by the Galilee Society.

RESULTS

Our results showed a progressive increase in the prevalence of T2DM from 3.4% to 7.6% in the Arab society of Israel. This trend is expected to continue rising in the coming decade, and based on our predictions, may exceed 12% in 2030.

CONCLUSION

Substantial and practical health-related actions must be initiated to prevent an increasing number of adults from developing diabetes and its complications.

Ensemble methods in combination with compartment models for blood glucose level prediction in type 1 diabetes mellitus

Backgroung: Type 1 diabetes is a disease that adversely affects the daily life of a large percentage of people worldwide. Daily glucose levels regulation and useful advices provided to patients regarding their diet are essential for diabetes treatment. For this reason, the interest of the academic community has focused on developing innovative systems, such as decision support systems, based on glucose prediction algorithms. The present work presents the predictive capabilities of ensemble methods compared to individual algorithms while combining each method with compartment models for fast acting insulin absorption simulation. Methods: An approach of combining widely used glycemia prediction algorithms is proposed and three different ensemble methods (Linear, Bagging and Boosting metaregressor) are applied and evaluated on their ability to provide accurate predictions for 30, 45 and 60 minutes ahead prediction horizon. Moreover, glycemia levels, long and short acting insulin dosages and consumed carbohydrates from six type one people with diabetes are used as input data and the results are evaluated in terms of root-mean square error and Clarke error grid analysis. Results: According to results, ensemble methods can provide more accurate glucose concentration in comparison to individual algorithms. Bagging metaregressor, specifically, performed better than individual algorithms in all prediction horizons for small datasets. Bagging ensemble method improved the percentage in zone A according to Clarkes error grid analysis by 4% and in some cases by 9%. Moreover, compartment models are proved to improve results in combination with any method at any prediction horizon. This strengthen the potential practical usefulness of the ensemble methods and the importance of building accurate compartment models.