Two Decades of Research in Artificial Pancreas: Insights from a Bibliometric Analysis

Machine Learning and Deep Learning Techniques Applied to Diabetes Research: A Bibliometric Analysis

BACKGROUND

The use of machine learning and deep learning techniques in the research on diabetes has garnered attention in recent times. Nonetheless, few studies offer a thorough picture of the knowledge generation landscape in this field. To address this, a bibliometric analysis of scientific articles published from 2000 to 2022 was conducted to discover global research trends and networks and to emphasize the most prominent countries, institutions, journals, articles, and key topics in this domain.

METHODS

The Scopus database was used to identify and retrieve high-quality scientific documents. The results were classified into categories of detection (covering diagnosis, screening, identification, segmentation, among others), prediction (prognosis, forecasting, estimation), and management (treatment, control, monitoring, education, telemedicine integration). Biblioshiny and RStudio were used to analyze the data.

RESULTS

A total of 1773 articles were collected and analyzed. The number of publications and citations increased substantially since 2012, with a notable increase in the last 3 years. Of the 3 categories considered, detection was the most dominant, followed by prediction and management. Around 53.2% of the total journals started disseminating articles on this subject in 2020. China, India, and the United States were the most productive countries. Although no evidence of outstanding leadership by specific authors was found, the University of California emerged as the most influential institution for the development of scientific production.

CONCLUSION

This is an evolving field that has experienced a rapid increase in productivity, especially over the last years with exponential growth. This trend is expected to continue in the coming years.

Scientific mapping of research on English as a foreign language (EFL) reading instruction: A thirty-year perspective

In recent years, English as a foreign language (EFL) reading instruction has emerged as a prominent and recurring research theme in the realm of education. Despite this growing interest, prior studies have not outlined the research status of EFL reading instruction from a bibliometric lens. Addressing this gap, the present study aims to provide valuable insights into the research landscape of EFL reading instruction by using scientific mapping analysis on documents sourced from the Web of Science Database between 1994 and 2023. The study found that: 1) The analysis of publication trends suggests that the field of EFL reading instruction continues to evolve, with a foreseeable ongoing increase in research output. 2) Asian countries, specifically Iran, China, and Saudi Arabia emerge as leading contributors and collaborators in terms of research on EFL reading instruction. 3) Authors mainly from Asian countries demonstrate the highest levels of productivity and citation impact. 4) The most productive and cited journals primarily originate from developed countries, notably the United Kingdom and the United States. 5) The research foci identified cover various aspects such as "technology-based instruction," "critical thinking," and "literacy skills." However, there is an emerging shift in research focus towards themes "English proficiency," "Covid-19," "technology," "blended learning" and "mobile-assisted language learning (MALL)." By shedding light on the significant concepts and trends of the research field, this study adds to the burgeoning body of literature and provides valuable guidance to new scholars seeking to enrich their knowledge and perspectives on this particular topic.

Use of Artificial Pancreas in the Management of Diabetes Mellitus: A Bibliometric Study

Objectives

To describe the trends and characteristics of the worldwide scientific production on the use of artificial pancreas (AP) in the management of diabetes mellitus (DM).

Materials and Methods

Scientific papers published between 2017 and 2022 were retrieved from the Scopus database using relevant keywords. Only original articles, reviews, and short surveys were included. The metadata were exported to the SciVal software for retrieving quantitative data and the main characteristics such as journals, authors, institutions, journal metrics by quartiles, subcategories, and collaborative networks were extracted.

Results

A total of 642 articles were included after applying the inclusion/exclusion criteria: original articles, 489; reviews, 151; and short surveys, 2. The most common type of collaboration was at the national level (38.3%; citations per publication: 22.3; field-weighted citation index [FWCI]: 2.2) followed by international collaboration (29.4%; citations per publication: 19.6; FWCI: 1.94). More than 70% of articles in each year were published in journals listed in Q1. Two journals, Diabetes Technology and Therapeutics and Journal of Diabetes Science and Technology, accounted for about 22% of all publications. Six of the top 10 universities were from the United States, with The University of Virginia having the most publications (n = 54; 59 authors; citations per publication: 38.4; FWCI: 3.73).

Conclusions

The findings of this study highlight that most research on this topic is published in high-quality journals and has a good citation impact. Notably, most research has been conducted in developed countries, thereby indicating the need for research efforts in this field from developing countries.

Intermittent closed-loop blood glucose control for people with type 1 diabetes on multiple daily injections

BACKGROUND AND OBJECTIVES

Recent advances in Automated Insulin Delivery systems have been shown to dramatically improve glycaemic control and reduce the risk of hypoglycemia in people with type 1 diabetes. However, they are complex systems that require specific training and are not affordable for most. Attempts to reduce the gap with closed-loop therapies using advanced dosing advisors have so far failed, mainly because they require too much human intervention. With the advent of smart insulin pens, one of the main constraints (having reliable bolus and meal information) disappears and new strategies can be employed. This is our starting hypothesis, which we have validated in a very demanding simulator. In this paper, we propose an intermittent closed-loop control system specifically intended for multiple daily injection therapy to bring the benefits of artificial pancreas to the application of multiple daily injections.

METHODS

The proposed control algorithm is based on model predictive control and integrates two patient-driven control actions. Correction insulin boluses are automatically computed and recommended to the patient to minimize the duration of hyperglycemia. Rescue carbohydrates are also triggered to avoid hypoglycemia episodes. The algorithm can adapt to different patient lifestyles with customizable triggering conditions, closing the gap between practicality and performance. The proposed algorithm is compared with conventional open-loop therapy, and its superiority is demonstrated through extensive in silico evaluations using realistic cohorts and scenarios. The evaluations were conducted in a cohort of 47 virtual patients. We also provide detailed explanations of the implementation, imposed constraints, triggering conditions, cost functions, and penalties for the algorithm.

RESULTS

The in-silico outcomes combining the proposed closed-loop strategy with slow-acting insulin analog injections at 09:00 h resulted in percentages of time in range (TIR) (70-180 mg/dL) of 69.5%, 70.6%, and 70.4% for glargine-100, glargine-300, and degludec-100, respectively, and injections at 20:00 h resulted in percentages of TIR of 70.5%, 70.3%, and 71.6%, respectively. In all the cases, the percentages of TIR were considerably higher than those obtained from the open-loop strategy, being only 50.7%, 53.9%, and 52.2% for daytime injection and 55.5%, 54.1%, and 56.9% for nighttime injection. Overall, the occurrence of hypoglycemia and hyperglycemia was notably reduced using our approach.

CONCLUSIONS

Event-triggering model predictive control in the proposed algorithm is feasible and may meet clinical targets for people with type 1 diabetes.

Human Digital Twin for Personalized Elderly Type 2 Diabetes Management

Managing Elderly type 2 diabetes (E-T2D) is challenging due to geriatric conditions (e.g., co-morbidity, multiple drug intake, etc.), and personalization becomes paramount for precision medicine. This paper presents a human digital twin (HDT) framework to manage E-T2D that exploits various patient-specific data and builds a suite of models exploiting the data for prediction and management to personalize diabetes treatment in E-T2D patients. These models include mathematical and deep-learning ones that capture different patient aspects. Consequently, the HDT virtualizes the patient from different viewpoints using an HDT that mimics the patient and has interfaces to update the virtual models simultaneously from measurements. Using these models the HDT obtains deeper insights about the patient. Further, an adaptive patient model fusing this information and a learning-based model predictive control (LB-MPC) algorithm are proposed. The geriatric conditions are captured as model parameters and constraints while solving the LB-MPC to personalize the insulin infusion for E-T2D management. The HDT is deployed on and illustrated with 15 patients using clinical trials and simulations. Our results show that HDT helps improve the time-in-range from 3–75% to 86–97% and reduces insulin infusion by 14–29%.

Research progress, trends, and future prospects on hydrothermal liquefaction of algae for biocrude production: a bibliometric analysis

The rising demand to settle a sustainable energy source is guiding researchers in the production of biofuels. The liquefaction process is an alternative to obtaining biocrude from different types of renewable biomass and can mitigate environmental impacts. All papers published since 2000, which are related to the hydrothermal liquefaction process that aims to obtain biocrude are analyzed in the present study using the bibliometric approach to provide the selected database. Furthermore, the use of algae biomass in the liquefaction was also a discussed topic considering its high relevance in the process. The focus of the present study was to evaluate the evolution of the current state of the art in these topics and also to indicate trends and courses that it might be taken in the future. The database used in the bibliometric analysis was taken from the Web of Science (WoS) and the papers were selected by two different search equations. With the selected data, the use of BibExcel, VOSviewer, and PowerBi software was useful to guide the discussion and to create graphics and visual networks. As shown in the results, it was noticeable the influence of China and the USA on the field, considering the high number of publications from these countries. Moreover, the main authors were indicated considering their citation numbers, publications, and local h-index factor. Based on the author's keywords, the most significant and recent topics on liquefaction were listed. Among them, technical-economic analysis, nutrient, and energy recovery, response surface methodology, and kinetic model are highlighted. This may indicate a new direction being taken by researchers besides the operational parameters' studies.

Graphical Abstract