Comparative Analysis of Predictive Interstitial Glucose Level Classification Models.

BACKGROUND: New methods of continuous glucose monitoring (CGM) provide real-time alerts for hypoglycemia, hyperglycemia, and rapid fluctuations of glucose levels, thereby improving glycemic control, which is especially crucial during meals and physical activity. However, complex CGM systems pose challenges for individuals with diabetes and healthcare professionals, particularly when interpreting rapid glucose level changes, dealing with sensor delays (approximately a 10 min difference between interstitial and plasma glucose readings), and addressing potential malfunctions. The development of advanced predictive glucose level classification models becomes imperative for optimizing insulin dosing and managing daily activities. METHODS: The aim of this study was to investigate the efficacy of three different predictive models for the glucose level classification: (1) an autoregressive integrated moving average model (ARIMA), (2) logistic regression, and (3) long short-term memory networks (LSTM). The performance of these models was evaluated in predicting hypoglycemia (<70 mg/dL), euglycemia (70-180 mg/dL), and hyperglycemia (>180 mg/dL) classes 15 min and 1 h ahead. More specifically, the confusion matrices were obtained and metrics such as precision, recall, and accuracy were computed for each model at each predictive horizon. RESULTS: As expected, ARIMA underperformed the other models in predicting hyper- and hypoglycemia classes for both the 15 min and 1 h horizons. For the 15 min forecast horizon, the performance of logistic regression was the highest of all the models for all glycemia classes, with recall rates of 96% for hyper, 91% for norm, and 98% for hypoglycemia. For the 1 h forecast horizon, the LSTM model turned out to be the best for hyper- and hypoglycemia classes, achieving recall values of 85% and 87% respectively. CONCLUSIONS: Our findings suggest that different models may have varying strengths and weaknesses in predicting glucose level classes, and the choice of model should be carefully considered based on the specific requirements and context of the clinical application. The logistic regression model proved to be more accurate for the next 15 min, particularly in predicting hypoglycemia. However, the LSTM model outperformed logistic regression in predicting glucose level class for the next hour. Future research could explore hybrid models or ensemble approaches that combine the strengths of multiple models to further enhance the accuracy and reliability of glucose predictions.

The oxygenation module: the missing link in using sleep apnea devices to treat COVID-19 pneumonia at home.

BACKGROUND: The study aims at solving the problem with the limitations of the homecare CPAP equipment such as sleep apnea devices in the treatment of COVID-19 pneumonia. By adding an advanced, rapid-to-produce oxygenation module to existing CPAP devices we allow distributing healthcare at all levels, reducing the load on intensive care units, promoting treatment in the early stages at homecare. A significant part of the COVID-19 pneumonia patients requires not only an oxygen supply but also additional air pressure. Existing home care devices are able to create precise positive airway pressure, but cannot precisely measure supplied oxygen concentration. Either uses uncertified and potentially unsafe mechanisms. RESULTS: The developed system allows using certified and widely available CPAP (constant positive airway pressure) devices to perform the critical function of delivering pressure and oxygen to airways. CPAP device is connected to the designed add-on module that can provide predefined oxygen concentration in a precise and stable manner. Clinical test results include data from 12 COVID-19 positive patients. The device has been compared against certified NIV (non-invasive) equipment under 6-20 hPa pressure and 30-70% FiO2. Tests have proved that the developed system can achieve the same SaO2 (p = 0.93) and PaO2 (p = 0.80) levels as NIV with clinically insignificant differences. Test results show that the designed system can substitute NIV equipment for a significant part of COVID-19 patients while leaving existing NIV devices for unstable and critical patients. The system has been designed to be mass-produced while having medically certified critical components. CONCLUSION: The clinical testing of the new device for oxygen supplementation of patients treated using simple CPAP devices looks promising and could be used for the treatment of COVID-19 pneumonia.

A Data-Driven Framework for Clinical Decision Support Systems in Positive Airway Pressure and Oxygen Titration.

BACKGROUND: Current obstructive sleep apnea treatment relies on manual PAP titration, but it has limitations. Complex interactions during titration and variations in SpO2 data accuracy pose challenges. Patients with co-occurring chronic hypercapnia may require precise oxygen titration. To address these issues, we propose a Clinical Decision Support System using Markov decision processes. METHODS: This study, compliant with data protection laws, focused on adults with OSA-induced hypoxemia utilizing supplemental oxygen and CPAP/BiPAP therapy. PAP titration, conducted over one night, involved vigilant monitoring of vital signs and physiological parameters. Adjustments to CPAP pressure, potential BiLevel transitions, and supplemental oxygen were precisely guided by patient metrics. Markov decision processes outlined three treatment actions for disorder management, incorporating expert medical insights. RESULTS: In our study involving 14 OSA patients (average age: 63 years, 27% females, BMI 41 kg m-2), significant improvements were observed in key health parameters after manual titration. The initial AHI of 61.8 events per hour significantly decreased to an average of 18.0 events per hour after PAP and oxygen titration (p < 0.0001), indicating a substantial reduction in sleep-disordered breathing severity. Concurrently, SpO2 levels increased significantly from an average of 79.7% before titration to 89.1% after titration (p < 0.0003). Pearson correlation coefficients demonstrated aggravation of hypercapnia in 50% of patients (N = 5) with initial pCO2 < 55 mmHg during the increase in CPAP pressure. However, transitioning to BiPAP exhibited a reduction in pCO2 levels, showcasing its efficacy in addressing hypercapnia. Simultaneously, BiPAP therapy correlated with a substantial increase in SpO2, underscoring its positive impact on oxygenation in OSA patients. Markov Decision Process analysis demonstrated realistic patient behavior during stable night conditions, emphasizing minimal apnea and good toleration to high CPAP pressure. CONCLUSIONS: The development of a framework for Markov decision processes of PAP and oxygen titration algorithms holds promise for providing algorithms for improving pCO2 and SpO2 values. While challenges remain, including the need for high-quality data, the potential benefits in terms of patient management and care optimization are substantial, and this approach represents an exciting frontier in the realm of telemedicine and respiratory healthcare.

From classical dualistic antagonism to hormone synergy: potential of overlapping action of glucagon, insulin and GLP-1 for the treatment of diabesity.

The increasing prevalence of 'diabesity', a combination of type 2 diabetes and obesity, poses a significant global health challenge. Unhealthy lifestyle factors, including poor diet, sedentary behaviour, and high stress levels, combined with genetic and epigenetic factors, contribute to the diabesity epidemic. Diabesity leads to various significant complications such as cardiovascular diseases, stroke, and certain cancers. Incretin-based therapies, such as GLP-1 receptor agonists and dual hormone therapies, have shown promising results in improving glycaemic control and inducing weight loss. However, these therapies also come with certain disadvantages, including potential withdrawal effects. This review aims to provide insights into the cross-interactions of insulin, glucagon, and GLP-1, revealing the complex hormonal dynamics during fasting and postprandial states, impacting glucose homeostasis, energy expenditure, and other metabolic functions. Understanding these hormonal interactions may offer novel hypotheses in the development of 'anti-diabesity' treatment strategies. The article also explores the question of the antagonism of insulin and glucagon, providing insights into the potential synergy and hormonal overlaps between these hormones.

Corrigendum: Laser speckle imaging for visualization of hidden effects for early detection of antibacterial susceptibility in disc diffusion tests.

[This corrects the article DOI: 10.3389/fmicb.2023.1221134.].

Laser speckle imaging for visualization of hidden effects for early detection of antibacterial susceptibility in disc diffusion tests.

Rapid identification of effective antibiotic treatment is crucial for increasing patient survival and preventing the formation of new antibiotic-resistant bacteria due to preventative antibiotic use. Currently utilized "gold standard" methods require 16-24 h to determine the most appropriate antibiotic for the patient's treatment. The proposed technique of laser speckle imaging with subpixel correlation analysis allows for identifying dynamics and changes in the zone of inhibition, which are impossible to observe with classical methods. Furthermore, it obtains the resulting zone of inhibition diameter earlier than the disk diffusion method which is recommended by the European Committee on Antimicrobial Susceptibility Testing (EUCAST). These results could improve mathematical models of changes in the diameter of the zone of inhibition around the disc containing the antimicrobial agent, thereby speeding up and facilitating epidemiological analysis.

Development of rapid antigen test prototype for detection of SARS-CoV-2 in saliva samples.

Background: The development of easy-to-perform diagnostic methods is highly important for detecting current coronavirus disease (COVID-19). This pilot study aimed at developing a lateral flow assay (LFA)-based test prototype to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus in saliva samples. Methods: Mice were immunized using the recombinant receptor-binding domain (rRBD) of SARS-CoV-2 virus spike protein. The combinations of the obtained mouse anti-receptor-binding domain (RBD) polyclonal antibodies (PAbs) and several commercial antibodies directed against the SARS-CoV-2 spike protein were used for enzyme-linked immunosorbent assay (ELISA) to select antibody pairs for LFA. The antibody pairs were tested in a LFA format using saliva samples from individuals with early SARS-CoV-2 infection (n = 9). The diagnostic performance of the developed LFA was evaluated using saliva samples from hospitalized COVID-19 patients (n = 111); the median time from the onset of symptoms to sample collection was 10 days (0-24 days, interquartile range (IQR): 7-13). The reverse transcription-polymerase chain reaction (rRT-PCR) was used as a reference method. Results: Based on ELISA and preliminary LFA results, a combination of mouse anti-RBD PAbs (capture antibody) and rabbit anti-spike PAbs (detection antibody) was chosen for clinical analysis of sample. When compared with rRT-PCR results, LFA exhibited 26.5% sensitivity, 58.1% specificity, 50.0% positive prediction value (PPV), 33.3% negative prediction value (NPV), and 38.7% diagnostic accuracy. However, there was a reasonable improvement in assay specificity (85.7%) and PPV (91.7%) when samples were stratified based on the sampling time. Conclusion: The developed LFA assay demonstrated a potential of SARS-CoV-2 detection in saliva samples. Further technical assay improvements should be made to enhance diagnostic performance followed by a validation study in a larger cohort of both asymptomatic and symptomatic patients in the early stage of infection.

Data-Driven Decision Making and Proactive Citizen-Scientist Communication: A Cross-Sectional Study on COVID-19 Vaccination Adherence.

Due to the severe impact of COVID-19 on public health, rollout of the vaccines must be large-scale. Current solutions are not intended to promote an active collaboration between communities and public health researchers. We aimed to develop a digital platform for communication between scientists and the general population, and to use it for an exploratory study on factors associated with vaccination readiness. The digital platform was developed in Latvia and was equipped with dynamic consent management. During a period of six weeks 467 participants were enrolled in the population-based cross-sectional exploratory study using this platform. We assessed demographics, COVID-19-related behavioral and personal factors, and reasons for vaccination. Logistic regression models adjusted for the level of education, anxiety, factors affecting the motivation to vaccinate, and risk of infection/severe disease were built to investigate their association with vaccination readiness. In the fully adjusted multiple logistic regression model, factors associated with vaccination readiness were anxiety (odds ratio, OR = 3.09 [95% confidence interval 1.88; 5.09]), feelings of social responsibility (OR = 1.61 [1.16; 2.22]), and trust in pharmaceutical companies (OR = 1.53 [1.03; 2.27]). The assessment of a large number of participants in a six-week period show the potential of a digital platform to create a data-driven dialogue on vaccination readiness.