Accuracy of a Non-Invasive Home Glucose Monitor for Measurement of Blood Glucose

Introduction

Patients with diabetes mellitus monitor their blood glucose at home with monitors that require a drop of blood or use a continuous glucose monitoring device that implants a small needle in the body. However, both cause discomfort to the patients which may inhibit them for regular blood glucose checks. Photoplethysmogram (PPG) sensing technology is an approach for non-invasive blood glucose measurement and PPG sensors can be used to predict hypoglycaemic episodes. InChcek is a PPG-based non-invasive glucose monitor. However, its accuracy has not been checked yet. Hence, this study aimed to evaluate the accuracy of InCheck, a non-invasive glucose monitor for the estimation of blood glucose.

Methods

In a tertiary care hospital, patients who came for blood glucose estimation were tested for blood glucose non-invasively on the InCheck device and then by the laboratory method (glucose oxidase-peroxidase). These two readings were compared. We used International Organization for Standardization (ISO) 15197:2013 (95% of values should be within ± 15 mg/dL of reference reading if reference glucose <100 mg/dL or within ± 15% of reference reading if reference glucose ≥100 mg/dL and 99% of the values should be within zones A and B in consensus error grid), and Surveillance Error Grid for analyzing the accuracy.

Results

A total of 1223 samples were analyzed. There was a significant difference between the reference method glucose level (135 [Q1-Q3: 97 - 179] mg/dL) and monitor-measured glucose level (188.33 [Q1-Q3: 167.33-209.33] mg/dL) (P < 0.0001). A total of 18.5% of readings were following ISO 15197:2013 criteria and 67.25% of coordinates were within zone A and zone B of the consensus error grid. In the surveillance error grid analysis, about 29.4% of values were in the no-risk zone, 51.8% in slight risk, 18.6% in moderate risk, and 0.2% were in the severe risk zone.

Conclusion

The accuracy of the InCheck device for the estimation of blood glucose by PPG signal is not following the recommended guidelines. Hence, further research is necessary for programming or redesigning the hardware and software for a better result from this optical sensor-based non-invasive home glucose monitor.

Hypoglycemic Effect of Nelumbo Nucifera Seed Extract on GLUT-4 mRNA and GLUT-4 Protein in Streptozotocin-Induced Diabetic Rats

Background

To investigate the effect of N. nucifera hydroalcoholic seed extract on fasting blood glucose (FBG) levels, glucose transporter (GLUT)-4 mRNA, and GLUT-4 protein in the adipose tissue of streptozotocin (STZ)-induced diabetic rats.

Materials and Methods

Male Sprague Dawley (SD) rats were first fed with a high-fat diet (HFD) for three weeks, and then, diabetes was induced by intraperitoneal injection of STZ at a dose of 35 mg/kg bw. Rats were divided into four groups: group 1: normal rats (NC), group 2: STZ-induced diabetic rats (DC), group 3: diabetic rats with N. nucifera hydroalcoholic seed extract at a dose of 400 mg/kg bw (NN), and group 4: diabetic rats with metformin at a dose of 100 mg/kg bw (MET) for 28 days.

Results

FBG level was significantly lower in the NN group than in the DC group (P < 0.05). Also, the NN group increased GLUT-4 mRNA expression and GLUT-4 protein in the adipose tissue when compared to the diabetic group.

Conclusion

We conclude that the observed hypoglycemic effect of N. nucifera seed extraction in STZ-induced diabetic rats could be due to insulinomimetic activity.

The Capability of ChatGPT in Predicting and Explaining Common Drug-Drug Interactions

Background Drug-drug interactions (DDIs) can have serious consequences for patient health and well-being. Patients who are taking multiple medications may be at an increased risk of experiencing adverse events or drug toxicity if they are not aware of potential interactions between their medications. Many times, patients self-prescribe medications without knowing DDI. Objective The objective is to investigate the effectiveness of ChatGPT, a large language model, in predicting and explaining common DDIs. Methods A total of 40 DDIs lists were prepared from previously published literature. This list was used to converse with ChatGPT with a two-stage question. The first question was asked as "can I take X and Y together?" with two drug names. After storing the output, the next question was asked. The second question was asked as "why should I not take X and Y together?" The output was stored for further analysis. The responses were checked by two pharmacologists and the consensus output was categorized as "correct" and "incorrect." The "correct" ones were further classified as "conclusive" and "inconclusive." The text was checked for reading ease scores and grades of education required to understand the text. Data were tested by descriptive and inferential statistics. Results Among the 40 DDI pairs, one answer was incorrect in the first question. Among correct answers, 19 were conclusive and 20 were inconclusive. For the second question, one answer was wrong. Among correct answers, 17 were conclusive and 22 were inconclusive. The mean Flesch reading ease score was 27.64±10.85 in answers to the first question and 29.35±10.16 in answers to the second question, p = 0.47. The mean Flesh-Kincaid grade level was 15.06±2.79 in answers to the first question and 14.85±1.97 in answers to the second question, p = 0.69. When we compared the reading levels with hypothetical 6th grade, the grades were significantly higher than expected (t = 20.57, p < 0.0001 for first answers and t = 28.43, p < 0.0001 for second answers). Conclusion ChatGPT is a partially effective tool for predicting and explaining DDIs. Patients, who may not have immediate access to the healthcare facility for getting information about DDIs, may take help from ChatGPT. However, on several occasions, it may provide incomplete guidance. Further improvement is required for potential usage by patients for getting ideas about DDI.