Continuous glucose monitoring devices: A brief presentation (Review)

Tailored diffusion limiting membrane for microneedle glucose sensors with wide linear range

Continuous glucose monitoring is very important to daily blood glucose control in diabetic patients, but its accuracy is limited by the narrow linear range of the response of biosensor to the glucose concentration because of the oxygen starvation in tissue and the limited maximum conversion rate of glucose oxidase. In this work, a biocompatible diffusion limiting membrane based on two medical-grade polyurethanes is developed via blending modification to restrict the diffusion flux of glucose to match the oxygen concentration and the maximum conversion rate. The expansiveness of the linear range for the nanomaterials-modified electrode in the glucose biosensor can be achieved through the regulation of two polyurethanes, the solvent, and the thickness of the membrane. In addition, the mass transport of hydrogen peroxide and interfering substances is also limited of the membrane. The in vitro experiments demonstrated that the membrane-modified microneedle biosensor exhibited a rapid response to the concentration variation of glucose, a wide linear range that is sufficient to cover the blood concentration of healthy and diabetic people, the ability to resist the oxygen concentration fluctuation and interfering substances, good reproducibility and long-term stability. The custom wearable electrochemical system, possessing these characteristics, has been proven to accurately monitor the blood concentration in a living rat in real time. This demonstrates a significant potential for application in both daily and clinical blood glucose monitoring.

Targeting PI3K/AKT/mTOR and MAPK Signaling Pathways in Gastric Cancer

Gastric cancer (GC) is the fourth leading cause of death worldwide, with more than 1 million cases diagnosed every year. Helicobacter pylori represents the main risk factor, being responsible for 78% of the cases. Increased amounts of salt, pickled food, red meat, alcohol, smoked food, and refined sugars negatively affect the stomach wall, contributing to GC development. Several gene mutations, including PIK3CA, TP53, ARID1A, CDH1, Ras, Raf, and ERBB3 are encountered in GC pathogenesis, leading to phosphatidylinositol 3-kinase (PI3K) protein kinase B (AKT)/mammalian target of rapamycin (mTOR)-PI3K/AKT/mTOR-and mitogen-activated protein kinase (MAPK) signaling pathway activation and promoting tumoral activity. Helicobacter pylori, growth factors, cytokines, hormones, and oxidative stress also activate both pathways, enhancing GC development. In clinical trials, promising results have come from monoclonal antibodies such as trastuzumab and ramucirumab. Dual inhibitors targeting the PI3K/AKT/mTOR and MAPK signaling pathways were used in vitro studies, also with promising results. The main aim of this review is to present GC incidence and risk factors and the dysregulations of the two protein kinase complexes together with their specific inhibitors.

Effects of Digitization of Self-Monitoring of Blood Glucose Records Using a Mobile App and the Cloud System on Outpatient Management of Diabetes: Single-Armed Prospective Study

BACKGROUND

In recent years, technologies promoting the digitization of self-monitoring of blood glucose (SMBG) records including app-cloud cooperation systems have emerged. Studies combining these technological interventions with support from remote health care professionals have reported improvements in glycemic control.

OBJECTIVE

To assess the use of an app-cloud cooperation system linked with SMBG devices in clinical settings, we evaluated its effects on outpatient management of diabetes without remote health care professional support.

METHODS

In this multicenter, open-label, and single-armed prospective study, 48 patients with diabetes (including type 1 and type 2) at 3 hospitals in Japan treated with insulin or glucagon-like peptide 1 receptor agonists and performing SMBG used the app-cloud cooperation system for 24 weeks. The SMBG data were automatically uploaded to the cloud via the app. The patients could check their data, and their attending physicians reviewed the data through the cloud prior to the patients' regular visits. The primary outcome was changes in glycated hemoglobin (HbA1c) levels.

RESULTS

Although HbA1c levels did not significantly change in all patients, the frequency of daily SMBG following applying the system was significantly increased before induction at 12 (0.60 per day, 95% CI 0.19-1.00; P=.002) and 24 weeks (0.43 per day, 95% CI 0.02-0.84; P=.04). In the subset of 21 patients whose antidiabetic medication had not been adjusted during the intervention period, a decrease in HbA1c level was observed at 12 weeks (P=.02); however, this significant change disappeared at 24 weeks (P=.49). The Diabetes Treatment Satisfaction Questionnaire total score and "Q4: convenience" and "Q5: flexibility" scores significantly improved after using the system (all P<.05), and 72% (33/46) patients and 76% (35/46) physicians reported that the app-cloud cooperation system helped them adjust insulin doses.

CONCLUSIONS

The digitization of SMBG records and sharing of the data by patients and attending physicians during face-to-face visits improved self-management in patients with diabetes.

TRIAL REGISTRATION

Japan Registry of Clinical Trials (jRCT) jRCTs042190057; https://jrct.niph.go.jp/en-latest-detail/jRCTs042190057.

Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data

Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these issues by transferring knowledge from a previously trained task to develop high-performance prediction models for a new task. This survey paper provides a comprehensive study of the effectiveness of transfer learning for digital health applications to enhance the accuracy and efficiency of diagnoses and prognoses, as well as to improve healthcare services. The first part of this survey paper presents and discusses the most common digital health sensing technologies as valuable data resources for machine learning applications, including transfer learning. The second part discusses the meaning of transfer learning, clarifying the categories and types of knowledge transfer. It also explains transfer learning methods and strategies, and their role in addressing the challenges in developing accurate machine learning models, specifically on digital health sensing data. These methods include feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning. This survey paper highlights the key features of each transfer learning method and strategy, and discusses the limitations and challenges of using transfer learning for digital health applications. Overall, this paper is a comprehensive survey of transfer learning methods on digital health sensing data which aims to inspire researchers to gain knowledge of transfer learning approaches and their applications in digital health, enhance the current transfer learning approaches in digital health, develop new transfer learning strategies to overcome the current limitations, and apply them to a variety of digital health technologies.

Factory-Calibrated Continuous Glucose Monitoring Systems in Type 1 Diabetes: Accuracy during In-Clinic Exercise and Home Use

Continuous glucose monitors (CGMs) are valuable tools for improving glycemic control, yet their accuracy might be influenced by physical activity. This study sought to assess the accuracy of the three latest factory-calibrated CGM systems available in Europe at the time the study was conducted, both during aerobic exercise and in typical daily scenarios. The accuracy evaluation, based on metrics such as the median absolute relative difference (MARD) and point and rate error-grid analyses (PEGA and REGA), involved 13 adults with type 1 diabetes. Participants wore all sensors during a 1 h in-clinic exercise session followed by a subsequent 3-day home period, with blood glucose measurements serving as reference values in both contexts. During exercise, no statistically significant differences in MARD were observed (Dexcom G6: 12.6%, Guardian 4: 10.7%, and Freestyle Libre 2: 17.2%; p = 0.31), and similarly, no significant differences emerged in PEGA-zone-AB (100%, 100%, 96.8%; p = 0.37). Nevertheless, Freestyle Libre 2 showed comparatively diminished accuracy in estimating glucose trends during exercise (REGA-zone-AB: 100%, 93.0%, 73.3%; p = 0.0003). In the home environment, Freestyle Libre 2 exhibited a significantly higher MARD when compared to the other systems (10.2%, 11.9%, 16.7%, p = 0.02). Overall, Dexcom G6 and Guardian 4 demonstrated superior accuracy in both exercise and daily life scenarios compared to Freestyle Libre 2.

Patent analysis of digital sensors for continuous glucose monitoring

The high need for optimal diabetes management among an ever-increasing number of patients dictates the development and implementation of new digital sensors for continuous glucose monitoring. The purpose of this work is to systematize the global patenting trends of digital sensors for continuous glucose monitoring and analyze their effectiveness in controlling the treatment of diabetes patients of different ages and risk groups. The Lens database was used to build the patent landscape of sensors for continuous glucose monitoring. Retrospective analysis showed that the patenting of sensors for continuous glucose monitoring had positive trend over the analyzed period (2000-2022). Leading development companies are Dexcom Inc., Abbott Diabetes Care Inc., Medtronic Minimed Inc., Roche Diabetes Care Inc., Roche Diagnostics Operations Inc., Roche Diabetes Care Gmbh, and Ascensia Diabetes Care Holdings Ag, among others. Since 2006, a new approach has emerged where digital sensors are used for continuous glucose monitoring, and smartphones act as receivers for the data. Additionally, telemedicine communication is employed to facilitate this process. This opens up new opportunities for assessing the glycemic profile (glycemic curve information, quantitative assessment of the duration and amplitude of glucose fluctuations, and so on), which may contribute to improved diabetes management. A number of digital sensors for minimally invasive glucose monitoring are patented, have received FDA approval, and have been on the market for over 10 years. Their effectiveness in the clinic has been proven, and advantages and disadvantages have been clarified. Digital sensors offer a non-invasive option for monitoring blood glucose levels, providing an alternative to traditional invasive methods. This is particularly useful for patients with diabetes who require frequent monitoring, including before and after meals, during and after exercise, and in other scenarios where glucose levels can fluctuate. However, non-invasive glucose measurements can also benefit patients without diabetes, such as those following a dietary treatment plan, pregnant women, and individuals during fasting periods like Ramadan. The availability of non-invasive monitoring is especially valuable for patients in high-risk groups and across different age ranges. New world trends have been identified in the patenting of digital sensors for non-invasive glucose monitoring in interstitial skin fluid, saliva, sweat, tear fluid, and exhaled air. A number of non-invasive devices have received the CE mark approval, which confirms that the items meet European health, safety, and environmental protection standards (TensorTip Combo-Glucometer, Cnoga Medical Ltd.; SugarBEAT, Nemaura Medical; GlucoTrack, GlucoTrack Inc.), but are not FDA-approved yet. The above-mentioned sensors have characteristics that make them popular in the treatment of diabetes: they do not require implantation, do not cause an organism reaction to a foreign body, and are convenient to use. In the EU, in order to increase clinical safety and the level of transparency about medical devices, manufacturers must obtain certificates in accordance with Regulation (EU) 2017/745, taking into account the transition period. The development of systems, which include digital sensors for continuous glucose monitoring, mobile applications, and web platforms for professional analysis of glycemic control and implementation of unified glycemic assessment principles in mobile healthcare, represent promising approaches for controlling glycaemia in patients.

Continuous Glucose Monitoring in Transient Neonatal Diabetes Mellitus-2 Case Reports and Literature Review

Neonatal diabetes mellitus is a rare genetic disease that affects 1 in 90,000 live births. The start of the disease is often before the baby is 6 months old, with rare cases of onset between 6 months and 1 year. It is characterized by low or absent insulin levels in the blood, leading to severe hyperglycemia in the patient, which requires temporary insulin therapy in around 50% of cases or permanent insulin therapy in other cases. Two major processes involved in diabetes mellitus are a deformed pancreas with altered insulin-secreting cell development and/or survival or faulty functioning of the existing pancreatic beta cell. We will discuss the cases of two preterm girls with neonatal diabetes mellitus in this research. In addition to reviewing the literature on the topic, we examined the different mutations, patient care, and clinical outcomes both before and after insulin treatment.

Phenylboronic acid conjugated poly(3,4-ethylenedioxythiophene) (PEDOT) coated Ag dendrite for electrochemical non-enzymatic glucose sensing

The fern leaf-like surface topography of poly(EDOT-PBA)/Ag/Cu/GCE increases the specific surface area of the sensor, thereby enhancing the glucose sensing performance.

Utilising a Real-Time Continuous Glucose Monitor as Part of a Low Glycaemic Index and Load Diet and Determining Its Effect on Improving Dietary Intake, Body Composition and Metabolic Parameters of Overweight and Obese Young Adults: A Randomised Controlled Trial

A randomised controlled trial to measure the effects of integrating real-time continuous glucose monitor (rtCGM) into a low glycaemic index (GI) and glycaemic load (GL) dietary intervention on dietary intake, body composition and specific metabolic parameters was carried out. A total of 40 overweight young adults [(means ± SD) age: 26.4 ± 5.3 years, BMI: 29.4 ± 4.7 kg/m2] were randomised into an intervention and control groups for a period of eight weeks. Both groups received nutrition education on low GI and GL foods. The intervention group also received an rtCGM system to monitor their glucose levels 24 h a day. While controlling for physical activities and GI and GL nutrition knowledge, the results indicated that the rtCGM system further improved body weight, BMI, fat mass, fasting plasma glucose, HbA1c, total cholesterol, HDL cholesterol and LDL cholesterol in the intervention group (p < 0.05). This trial unveils the robustness of the rtCGM where non-diabetic overweight and obese young adults can benefit from this device and utilise it as a management tool for overweight and obesity and a primary prevention tool for type 2 diabetes, as it provides real-time and personalised information on physiological changes.