Continuous Sensor Research Articles

Belief-Rule-Based System with Self-organizing and Multi-temporal Modeling for Sensor-based Human Activity Recognition.

Smart environment is an efficient and cost- effective way to afford intelligent supports for the elderly people. Human activity recognition (HAR) is a crucial aspect of the research field of smart environments, and it has attracted widespread attention lately. The goal of this study is to develop an effective sensor-based HAR model based on the belief-rule-based system (BRBS), which is one of representative rule-based expert systems. Specially, a new belief rule base (BRB) modeling approach is proposed by taking into account the self- organizing rule generation method and the multi-temporal rule representation scheme, in order to address the problem of combination explosion that existed in the traditional BRB modelling procedure and the time correlation found in continuous sensor data in chronological order. The new BRB modeling approach is so called self-organizing and multi-temporal BRB (SOMT-BRB) modeling procedure. A case study is further deducted to validate the effectiveness of the SOMT-BRB modeling procedure. By comparing with some conventional BRBSs and classical activity recognition models, the results show a significant improvement of the BRBS in terms of the number of belief rules, modelling efficiency, and activity recognition accuracy.

Efficacy and Safety of a Self-Improved Continuous Bladder Irrigation Sensor Device in Patients after Transurethral Resection of the Prostate: A Prospective Study.

To explore the efficacy and safety of a self-improved continuous bladder irrigation (CBI) sensor device after transurethral resection of the prostate (TURP). A total of 160 patients with benign prostatic hyperplasia who received TURP from June 2021 to May 2022 were selected. According to the envelope randomization method, patients were divided into a control group (80 cases) and study group (80 cases). In the control group, the speed of bladder flushing fluid was adjusted according to the clinical experience of nurses. On the basis of the control group, the self-improved CBI sensor device was used in the study group to observe the postoperative comfort and complication rate in the two groups. The comfort of patients in the study group was significantly higher than that of patients in the control group (97.50% vs. 88.75%, P = .023), and the number of postoperative complications in the control group was significantly higher than that in the study group (8.75% vs. 1.25%, P = .021). Meanwhile, the average amount of irrigation fluid in the study group was obviously lower than that in the control group (26.4 L vs. 27.8 L, P = .011). In addition, patients in the study group had a significantly shorter hospital stay than the controls (3.3 days vs. 3.6 days, P = .005). Implementation of the new self-improved CBI sensor device for patients after TURP can improve their awareness regarding disease-related knowledge, alleviate their fear and anxiety, improve their compliance and comfort with treatment and nursing, and reduce the incidence of complications.

Feasibility of Fall-Risk Detection in Older Adults: Real-World Use of Sensor Data With Machine Learning.

To use machine learning techniques with sensor data to predict fall risk in older adults aging in place. We tested the feasibility of using anomaly detection on a dataset comprising 315 days of continuous unobtrusive sensor data obtained from a single participant to predict fall risk within a 10-day window. Predictions were validated with performance metrics, including accuracy, F1 score, and receiver operating characteristic-area under curve (ROC-AUC), using actual falls documented in the electronic health record. The model resulted with accuracy = 0.96 (95% confidence interval [CI] [0.94, 0.99]), F1 = 0.78 (95% CI [0.73, 0.83]), and ROC-AUC = 0.89 (95% CI [0.85, 0.93]). The application of anomaly detection on sensor data may provide a timely and valid indication of fall risk in older adults within a 10-day window. Further research and validation are warranted to confirm these findings and expand the scope of application in the domain of older adult care and health care support. [Journal of Gerontological Nursing, 50(10), 7-10.].

DDLA: a double deep latent autoencoder for diabetic retinopathy diagnose based on continuous glucose sensors.

The current diagnosis of diabetic retinopathy is based on fundus images and clinical experience. However, considering the ineffectiveness and non-portability of medical devices, we aimed to develop a diagnostic model for diabetic retinopathy based on glucose series data from the wearable continuous glucose monitoring system. Therefore, this study developed a novel method, i.e., double deep latent autoencoder, for exploring glycemic variability influence from multi-day glucose data for diabetic retinopathy. Specifically, the model proposed in this research could encode continuous glucose sensor data with non-continuous and variable length via the integration of a data reorganization module and a novel encoding module with fragmented-missing-wise objective function. Additionally, the model implements a double deep autoencoder, which integrated convolutional neural network, long short-term memory, to jointly capturing the inter-day and intra-day glucose latent features from glucose series. The effectiveness of the proposed model is evaluated through a cross-validation method to clinical datasets of 765 type 2 diabetes patients. The proposed method achieves the highest accuracy value (0.89), precision value (0.88), and F1 score (0.73). The results suggest that our model can be used to remotely diagnose and screen for diabetic retinopathy by learning potential features of glucose series data collected by wearable continuous glucose monitoring systems.

Artificial Intelligence-Driven Prognosis of Respiratory Mechanics: Forecasting Tissue Hysteresivity Using Long Short-Term Memory and Continuous Sensor Data.

Tissue hysteresivity is an important marker for determining the onset and progression of respiratory diseases, calculated from forced oscillation lung function test data. This study aims to reduce the number and duration of required measurements by combining multivariate data from various sensing devices. We propose using the Forced Oscillation Technique (FOT) lung function test in both a low-frequency prototype and the commercial RESMON device, combined with continuous monitoring from the Equivital (EQV) LifeMonitor and processed by artificial intelligence (AI) algorithms. While AI and deep learning have been employed in various aspects of respiratory system analysis, such as predicting lung tissue displacement and respiratory failure, the prediction or forecasting of tissue hysteresivity remains largely unexplored in the literature. In this work, the Long Short-Term Memory (LSTM) model is used in two ways: (1) to estimate the hysteresivity coefficient η using heart rate (HR) data collected continuously by the EQV sensor, and (2) to forecast η values by first predicting the heart rate from electrocardiogram (ECG) data. Our methodology involves a rigorous two-hour measurement protocol, with synchronized data collection from the EQV, FOT, and RESMON devices. Our results demonstrate that LSTM networks can accurately estimate the tissue hysteresivity parameter η, achieving an R2 of 0.851 and a mean squared error (MSE) of 0.296 for estimation, and forecast η with an R2 of 0.883 and an MSE of 0.528, while significantly reducing the number of required measurements by a factor of three (i.e., from ten to three) for the patient. We conclude that our novel approach minimizes patient effort by reducing the measurement time and the overall ambulatory time and costs while highlighting the potential of artificial intelligence methods in respiratory monitoring.

NOVEL ADAPTIVE AI-POWER INSULIN NAVIGATOR FOR DIABETES USING MACHINE LEARNING

Adaptive AI-powered insulin delivery systems represent a significant advancement in diabetes management, offering enhanced precision and safety for patients with diabetes. This paper examines the development and implementation of AI-driven insulin delivery systems that dynamically adjust insulin dosages based on real-time glucose monitoring and predictive analytics. The system integrates continuous glucose sensors with adaptive AI algorithms to analyze blood glucose trends, activity levels, and dietary inputs, enabling the automatic adjustment of insulin delivery to meet individual patient needs. The study evaluates the performance of these systems in optimizing glycemic control, reducing the incidence of hypoglycemia and hyperglycemia, and improving overall patient outcomes. Key features include the ability to learn from historical data to refine dosing algorithms and the incorporation of safety mechanisms to prevent adverse events. Additionally, the paper explores user experiences and feedback, highlighting the system’s potential to improve quality of life and provide more personalized diabetes management. The integration of adaptive AI in insulin delivery systems promises to revolutionize diabetes care by offering a more responsive, accurate, and user-centric approach to insulin therapy.

Solid-State Electrodes Towards a Real-Time Sensor Array for Health Monitoring

The healthcare industry has been transformed by advances in biotechnology, resulting in improved health outcomes. Central to this has been advances in medical diagnostics that can both monitor patients as well as provide actionable data at the point-of-care. However, further improvements are needed to maximize the utility of diagnostics at times where acute care may be required, such as when individuals are unstable after experiencing physical trauma. When medical resources are limited, the ability to rapidly and effectively triage patients that have experienced trauma is critical to maximizing health outcomes. Notably, individuals that have experienced significant trauma can degrade quickly. Therefore, continuous monitoring approaches are needed that can provide critical biometric and biomarker information that will enable updating of the triage in real-time.At present time, patients are assessed continuously using biometric monitoring. However, biometrics are often a lagging indicator to biochemical markers, and thus while easier to measure, less informative. Currently, the ISTAT-8 is a point-of-care unit to perform an automated, rapid biochemical panel that can assess the patient’s ability to maintain homeostasis by monitoring eight biomarkers. However, the ISTAT-8 captures biochemical data only at a point in time instead of continuously; this is a drawback, as the health of patients who have undergone trauma can improve or deteriorate rapidly. Therefore, a continuous biochemical monitoring solution is needed to allow for triage to happen in real-time.Advances in biotechnology have for the first time made real-time biochemical sensing a reality. Unfortunately, commercial continuous biochemical sensors to-date are limited to subcutaneous continuous glucose monitors (CGMs). CGMs monitor glucose concentrations in the interstitial fluid (ISF), which strongly correlates to blood values. While the CGM is now commonly used, the current devices are not easily multiplexed. Therefore, a scalable, easy to insert system is needed to perform multiplexed measurements; microneedle-based platforms have the promise to enable sensing in the ISF leveraging five or more distinct sensors.Here we present a solid-state platform that can enable multiplexed sensing leveraging hollow, wicking microneedles in fluidic contact with solid-state electrodes. Our approach leverages optimized sensor formulations and electrode materials fabricated onto a printed circuit board (PCB). The putative sensor array can be produced in a modular fashion leveraging scalable technologies and processes. Notably, this talk focuses on methods and approaches to fabricate an array of solid-state sensors that are capable of detecting ions and metabolites over extended periods (> 1 week) and can be readily integrated with microneedles for sensing interstitial fluid.

Continuous Glucose, Insulin and Lifestyle Data Augmentation in Artificial Pancreas Using Adaptive Generative and Discriminative Models.

Artificial pancreas requires data from multiple sources for accurate insulin dose estimation. These include data from continuous glucose sensors, past insulin dosage information, meal quantity and time and physical activity data. The effectiveness of closed-loop diabetes management systems might be hampered by the absence of these data caused by device error or lack of compliance by patients. In this study, we demonstrate the effect of output sequence length-driven generative and discriminative model selection in high quality data generation and augmentation. This novel generative adversarial network (GAN) based architecture automatically selects the generator and discriminator architecture based on the desired output sequence length. The proposed model is able to generate glucose, physical activity, meal information data for individual patients. The discriminative scores for Ohio T1DM (2018) dataset were 0.17 ±0.03 (Inputs: CGM, CHO, Insulin) and 0.15 ±0.02 (Inputs: CGM, CHO, Insulin, Heart Rate, Steps) and for Ohio T1D (2020) dataset was 0.16 ±0.02 (Inputs: CGM, CHO, Insulin) and 0.15 ±0.02 (Inputs: CGM, CHO, Insulin, acceleration). A mixture of generated and real data was used to test predictive scores for glucose forecasting models. The best RMSE and MARD achieved for OhioT1DM patients were 17.19 ±3.22 and 7.14 ±1.76 for PH=30 min with CGM, CHO, Insulin, heartrate and steps as inputs. Similarly, the RMSE and MARD for real+synthetic data were 15.63 ±2.57 and 5.86 ±1.69 respectively. Compared to existing generative models, we demonstrate that sequence length based architecture selection leads to better synthetic data generation for multiple output sequences (CGM, CHO, Insulin) and forecasting accuracy.

SRCNN: Stacked-Residual Convolutional Neural Network for Improving Human Activity Classification Based on Micro-Doppler Signatures of FMCW Radar

Current methods for daily human activity classification primarily rely on optical images from cameras or wearable sensors. Despite their high detection reliability, camera-based approaches suffer from several drawbacks, such as low-light conditions, limited range, and privacy concerns. To address these limitations, this article proposes the use of a frequency-modulated continuous wave radar sensor for activity recognition. A stacked-residual convolutional neural network (SRCNN) is introduced to classify daily human activities based on the micro-Doppler features of returned radar signals. The model employs a two-layer stacked-residual structure to reuse former features, thereby improving the classification accuracy. The model is fine-tuned with different hyperparameters to find a trade-off between classification accuracy and inference time. Evaluations are conducted through training and testing on both simulated and measured datasets. As a result, the SRCNN model with six stacked-residual blocks and 64 filters achieves the best performance, with accuracies exceeding 95% and 99% at 0 dB and 10 dB, respectively. Remarkably, the proposed model outperforms several state-of-the-art CNN models in terms of classification accuracy and execution time on the same datasets.

Accuracy and Feasibility of a Novel Glucose/Lactate Continuous Multi-Analyte Sensing Platform in Humans.

Continuous glucose monitoring systems (CGMs) have been commercially available since 1999. However, automated insulin delivery systems may benefit from real-time inputs in addition to glucose. Continuous multi-analyte sensing platforms will meet this area of potential growth without increasing the burden of additional devices. We aimed to generate pilot data regarding the safety and function of a first-in-human, single-probe glucose/lactate multi-analyte continuous sensor. The investigational glucose/lactate continuous multi-analyte sensor (PercuSense Inc, Valencia, California) was inserted to the upper arms of 16 adults with diabetes, and data were available for analysis from 11 of these participants (seven female; mean [SD] = age 43 years [16]; body mass index [BMI] = 27 kg/m2 [5]). A commercially available Guardian 3 CGM (Medtronic, Northridge, California) was also inserted into the abdomen for comparison. All participants underwent a meal-test followed by an exercise challenge on day 1 and day 4 of wear. Performance was benchmarked against venous blood YSI glucose and lactate values. The investigational glucose sensor had an overall mean absolute relative difference (MARD) of 14.5% (median = 11.2%) which improved on day 4 compared with day 1 (13.9% vs 15.2%). The Guardian 3 CGM had an overall MARD of 13.9% (median = 9.4%). The lactate sensor readings within 20/20% and 40/40% of YSI values were 59.7% and 83.1%, respectively. Our initial data support safety and functionality of a novel glucose/lactate continuous multi-analyte sensor. Further sensor refinement will improve run-in performance and accuracy.

Construction of Ag2O/WO3-based hollow microsphere p-n heterojunction for continuous detection of ppb-level H2S gas sensor

Three-dimensional (3D) hierarchical hollow microsphere WO3 and Ag2O/WO3 heterojunction materials were prepared by a simple hydrothermal method for a highly sensitive continuous response H2S gas sensor. The results show that compared with the original WO3 (Ra/Rg=12.5, 10 ppm, 70℃), the sensor based on Ag2O/WO3 has excellent H2S sensing performance at 60℃. Including ultra-high sensitivity (Ra/Rg=207.12, 10 ppm), continuous detection of different concentrations of H2S gas (0.05–50 ppm), fast response capability (15 s), low detection limit (50 ppb), high selectivity and reliable stability. The study of gas sensing mechanism shows that the synthesized 3D hierarchical hollow microsphere structure provides more channels for gas diffusion and transmission. The dispersion of Ag2O nanoparticles (NPs) on the surface of WO3 provides more reactive sites for gas adsorption. At the same time, the charge transfer is promoted by constructing Ag2O/WO3 p-n heterojunction. Sulfurization of Ag2O improved the response value and selectivity to H2S gas. These factors together determine the superior performance of the prepared Ag2O/WO3 H2S-based sensor.

Comparison of continuous temperature measurement methods in the intensive care unit: standard bladder catheter measurements versus non-invasive transcutaneous sensors.

The purpose of this study was to compare a wearable system for body core temperature measurement versus bladder and tympanic thermometers in an intensive care setting. The question was, if continuous non-invasive sensors in the intensive care unit represent an alternative to current standard methods of invasive continuous bladder temperature measurement methods?Between May and September 2023, a comparative investigation involving 112 patients was conducted in a 20-bed surgical intensive care unit to assess various temperature probes, including those placed in the tympanic tube, bladder, and skin. To achieve this, a wireless non-invasive sensor system provided by greenTEG AG, Switzerland, was affixed to different body locations (clavicular and lateral chest) of each catheterized patient (equipped with a temperature probe) admitted to the intensive care unit. Furthermore, tympanic temperatures were recorded at specified intervals. The measurement duration ranged from a minimum of six hours to a maximum of six days, resulting in the analysis of a total of 355 simultaneous temperature measurements.In this study, a wearable temperature measurement system attached to two different body sites revealed a consistent negative bias compared to bladder temperature. In addition, the measurements were particularly influenced by body constitution. The tested system in all patients showed a mean absolute error (MAE) of 0.45°C for the lateral chest and 0.50°C for the clavicular position. Tympanic measurements had a mean absolute error of 0.35°C. In patients with body mass index (BMI) ≥ 25 the MAE increased to 0.5°C for the lateral chest and 0.56°C for the clavicular position. In contrast, the tympanic measurement had a reduced MAE of 0.32°C, which is well below this threshold when compared to bladder measurements.In conclusion the investigated system did not meet the clinically relevant acceptance criteria and showed low precision in correctly identifying fever episodes compared to invasive temperature probes, however its main advantage lies in its continuity and non-invasiveness. This makes it a potential alternative to intermittent tympanic measurement devices. In this study we were able to show, that in at least one subset of patients, the non-invasive and continuous device demonstrated a precision comparable to tympanic measurements.The accuracy of all non-invasive methods was lower than in previous studies, suggesting that the use of bladder temperature as reference and user related variations may have introduced additional errors.

DeepQC: A deep learning system for automatic quality control of in-situ soil moisture sensor time series data

Amidst changing climate, real-time soil moisture monitoring is vital for the development of in-season decision support tools to help farmers manage weather-related risks in agriculture. Precision Sustainable Agriculture (PSA) recently established a real-time soil moisture monitoring network across the central, Midwest, and eastern U.S., but continuous field-scale sensor observations often come with data gaps and anomalies. To maintain the data quality and continuity needed for developing decision tools, a quality control system is necessary.The International Soil Moisture Network (ISMN) introduced the Flagit module for anomaly detection in soil moisture time series observations. However, under certain conditions, Flagit's threshold and spectral based quality control approaches may underperform in identifying anomalies. Recently, deep learning methods have been successfully applied to detect time series anomalies in time series data in various disciplines. However, their use in agriculture for anomaly detection in time series datasets has not been yet investigated. This study focuses on developing a Bi-directional Long Short-Term Memory (LSTM) model, referred to as DeepQC, to identify anomalies in soil moisture time series data. Manual flagged PSA observations were used for training, validation, and testing the model, following an 80:10:10 split. The study then compared the DeepQC and Flagit-based estimates to assess their relative performance.Flagit correctly flagged 95.8 % of the correct observations and 50.3 % of the anomaly observations, indicating its limitations in identifying anomalies, particularly at sites consists of more than 30 % anomalies. On the other hand, the DeepQC correctly flagged 89.8 % of the correct observations and 99.5 % of the anomalies, with overall accuracy of 95.6 %, in significantly less time, demonstrating its superiority over Flagit approach. Importantly, the performance of the DeepQC remained consistent regardless of the number of anomalies in site observations. Given the promising results obtained with the DeepQC, future studies will focus on implementing and finetuning this model on national and global soil moisture networks.

Technology in the management of diabetes in hospitalised adults.

Suboptimal glycaemic management in hospitals has been associated with adverse clinical outcomes and increased financial costs to healthcare systems. Despite the availability of guidelines for inpatient glycaemic management, implementation remains challenging because of the increasing workload of clinical staff and rising prevalence of diabetes. The development of novel and innovative technologies that support the clinical workflow and address the unmet need for effective and safe inpatient diabetes care delivery is still needed. There is robust evidence that the use of diabetes technology such as continuous glucose monitoring and closed-loop insulin delivery can improve glycaemic management in outpatient settings; however, relatively little is known of its potential benefits and application in inpatient diabetes management. Emerging data from clinical studies show that diabetes technologies such as integrated clinical decision support systems can potentially mediate safer and more efficient inpatient diabetes care, while continuous glucose sensors and closed-loop systems show early promise in improving inpatient glycaemic management. This review aims to provide an overview of current evidence related to diabetes technology use in non-critical care adult inpatient settings. We highlight existing barriers that may hinder or delay implementation, as well as strategies and opportunities to facilitate the clinical readiness of inpatient diabetes technology in the future.

Too much ado about data: continuous remote monitoring of water temperatures, circulation and throughput can assist in the reduction of hospital-associated waterborne infections

National and international guidance provides advice on maintenance and management of water systems in healthcare buildings; however, healthcare-associated waterborne infections (HAWIs) are increasing. To identify parameters critical to water quality in healthcare buildings and to assess whether remote sensor monitoring can deliver safe water systems, thus reducing HAWIs. A narrative review was performed using the following search terms: (1) consistent water temperature AND waterborne pathogen control OR nosocomial infection; (2) water throughput AND waterborne pathogen control OR nosocomial infection; (3) remote monitoring of in-premises water systems AND continuous surveillance for temperature OR throughput OR flow OR use. Databases employed were PubMed, CDSR (Clinical Study Data Request) and DARE (Database of Abstracts of Reviews of Effects) from January 2013 to March 2024. Single ensuite-patient rooms, expansion of handwash basins, widespread glove use, alcohol gel and wipes have increased water system stagnancy resulting in amplification of waterborne pathogens and transmission risk of legionella, pseudomonas, and non-tuberculous mycobacteria. Manual monitoring does not represent temperatures across large complex water systems. This review deems that multiple-point continuous remote sensor monitoring is effective at identifying redundant and low use outlets, hydraulic imbalance and inconsistent temperature delivery across in-premises water systems. As remote monitoring becomes more common there will be greater recognition of failures in temperature control, hydraulics, and balancing in water systems, and there remains much to learn as we adopt this developing technology within our hospitals.

Smart Home Automation

The Smart Home Concept responds to the increasing need for integrating smart appliances and systems within residential environments. It includes a growing array of devices, services, and applications designed to simplify daily tasks and enhance the quality of life. Utilizing various technologies and standards, numerous device suppliers offer a wide range of solutions, including meters, actuators, sensors, and micro systems, which are integrated into the home environment. This advanced system incorporates sensors, artificial intelligence, and machine learning algorithms to develop an intelligent, responsive, and personalized living space. Continuous sensor data collection on environmental conditions and user behaviors allows AI to autonomously manage various home functions. The system emphasizes interoperability and standardization to ensure compatibility with a wide range of devices. Improvements in natural language processing and voice recognition further enhance human-machine interactions. This comprehensive approach aims to optimize energy efficiency, bolster security, and streamline daily activities, providing residents with a more intuitive and adaptable smart home experience in the evolving field of home automation.

Using Graphs to Perform Effective Sensor-Based Human Activity Recognition in Smart Homes.

There has been a resurgence of applications focused on human activity recognition (HAR) in smart homes, especially in the field of ambient intelligence and assisted-living technologies. However, such applications present numerous significant challenges to any automated analysis system operating in the real world, such as variability, sparsity, and noise in sensor measurements. Although state-of-the-art HAR systems have made considerable strides in addressing some of these challenges, they suffer from a practical limitation: they require successful pre-segmentation of continuous sensor data streams prior to automated recognition, i.e., they assume that an oracle is present during deployment, and that it is capable of identifying time windows of interest across discrete sensor events. To overcome this limitation, we propose a novel graph-guided neural network approach that performs activity recognition by learning explicit co-firing relationships between sensors. We accomplish this by learning a more expressive graph structure representing the sensor network in a smart home in a data-driven manner. Our approach maps discrete input sensor measurements to a feature space through the application of attention mechanisms and hierarchical pooling of node embeddings. We demonstrate the effectiveness of our proposed approach by conducting several experiments on CASAS datasets, showing that the resulting graph-guided neural network outperforms the state-of-the-art method for HAR in smart homes across multiple datasets and by large margins. These results are promising because they push HAR for smart homes closer to real-world applications.

Development of DNA aptamers universally bound to single-chain fragment variables and their applications in bioprocess monitoring

Single-chain fragment variables (scFvs), composed of variable heavy and light chains joined together by a peptide linker, can be produced using a cost-effective bacterial expression system, making them promising candidates for pharmaceutical applications. However, a versatile method for monitoring recombinant-protein production has not yet been developed. Herein, we report a novel anti-scFv aptamer-based biosensing system with high specificity and versatility. First, anti-scFv aptamers were screened using the competitive systematic evolution of ligands by exponential enrichment, focusing on a unique scFv-specific peptide linker. We selected two aptamers, P1-12 and P2-63, with KD = 2.1 μM or KD = 1.6 μM toward anti-human epidermal growth factor receptor (EGFR) scFv, respectively. These two aptamers can selectively bind to scFv but not to anti-EGFR Fv. Furthermore, the selected aptamers recognized various scFvs with different CDRs, such as anti-4-1BB and anti-hemoglobin scFv, indicating that they recognized a unique peptide linker region. An electrochemical sensor for anti-EGFR scFv was developed using anti-scFv aptamers based on square wave voltammetry. Thus, the constructed sensor could monitor anti-EGFR scFv concentrations in the range of 10–500 nM in a diluted medium for bacterial cultivation, which covered the expected concentration range for the recombinant production of scFvs. These achievements promise the realization of continuous monitoring sensors for pharmaceutical scFv, which will enable the real-time and versatile monitoring of large-scale scFv production.

Continuous glucose monitoring and rates of hyperglycemia during chemotherapy for early-stage breast cancer.

e24134 Background: Diabetes (DM) and hyperglycemia (HG) during chemotherapy increase the risk of treatment-related toxicities. The prevalence of HG and daily patterns of HG over the course of chemotherapy in patients with early-stage breast cancer (ESBC) are currently unknown. Methods: We conducted a prospective single-arm pilot study of continuous glucose monitoring in patients with ESBC receiving chemotherapy from 12/2020 – 2/2022. Eligibility criteria included: diagnosis of Stage I-III BC, age ≥18, and planned chemotherapy with concurrent corticosteroid supportive care use. Patients were excluded if they were receiving other systemic corticosteroids or insulin. Patients wore FreeStyle Libre Pro continuous glucose monitoring sensors from the time of chemotherapy initiation (± 7 days) through completion. The sensors detected interstitial glucose levels every 15 minutes and were reapplied by the study team every 2-3 weeks. Primary endpoints were: (1) prevalence of HG, defined as number of patients with ≥1 glucose reading ≥140 mg/dL and (2) among those who developed HG, the proportion of time spent hyperglycemic, defined as number of readings ≥140 mg/dL divided by total number of readings. Key secondary endpoints included prevalence of prediabetes (pre-DM) and diabetes (DM) at baseline and changes in A1c from baseline to weeks 12 and 24. The analysis was stratified by baseline A1c (euglycemia [A1c < 5.7%], pre-DM [A1c 5.7%-6.4%], or DM [A1c ≥6.5% and clinical diagnosis of DM prior to study enrollment]). Results: Among 20 evaluable patients, the median age at baseline was 59 (range 34-78) and median body mass index (BMI) was 29.5 kg/m2 (range 19.5-41.6). The most common chemotherapy regimens included docetaxel/cyclophosphamide (25%), weekly paclitaxel ± trastuzumab (20%), paclitaxel followed by doxorubicin/cyclophosphamide (15%), docetaxel/carboplatin/trastuzumab/pertuzumab (15%), and cyclophosphamide/methotrexate/fluorouracil (15%). All 20 patients developed HG. Of 124,165 total readings, 17% were ≥140 mg/dL and mean glucose was 112.7 mg/dL. At baseline, n = 10 were euglycemic, n = 7 had pre-DM, and n = 3 had DM. By cohort, the proportion of time spent hyperglycemic was 3.9% (euglycemia group), 10% (pre-DM group), and 73.3% (DM group) (p < .0001). Mean glucose values were 95.5 mg/dL (euglycemia group), 104.5 mg/dL (pre-DM group), and 183.0 mg/dL (DM group) (p < .0001). Change in mean A1c from baseline to weeks 12 and 24 varied significantly across groups. Conclusions: Among patients receiving chemotherapy for ESBC, we found evidence of HG in all patients, and the proportion of time spent hyperglycemic during chemotherapy varied significantly by baseline A1c. Further interventions should evaluate the benefits of improved glucose control among patients with baseline A1c > 5.7%. Clinical trial information: NCT04473378 .

Psychometric validation of the hyperglycaemia avoidance scale UK (HAS-UK).

Hyperglycaemia aversion in type 1 diabetes can be associated with severe hypoglycaemia and impaired awareness of hypoglycaemia but is not routinely assessed clinically. This study aimed to undertake the first psychometric validation of the UK version of the Hyperglycaemia Avoidance Scale (HAS-UK). The HAS-UK was completed by adults with type 1 diabetes in three separate research studies. Psychometric properties were evaluated, using exploratory factor analysis, internal consistency, and convergent validity. Of the 431 participants who completed the HAS-UK in the three studies, mean age was 49.5 years, and 58.0% were women. Mean duration of diabetes was 29 years, with 192 (44.5%) using multiple daily injections and 229 (53.1%) using an insulin pump. Five participants were excluded from analyses due to incomplete HAS-UK responses. Exploratory factor analysis revealed a 3-factor solution, with acceptable internal consistency for 'worry' and 'blood glucose decisions' factors. HAS-UK total score was higher in those using insulin pumps versus multiple daily injections, and 'blood glucose decisions' score was higher in those using a continuous blood glucose sensor versus a meter. The HAS-UK is a reliable measure with acceptable structural validity and is likely to be useful for evaluating hyperglycaemia aversion in people with type 1 diabetes. Future research would benefit from investigating further psychometric properties including test-retest reliability, sensitivity to change, and clinical significance of scores.