Non-invasive Monitoring Device Research Articles

Tear-Based Ocular Wearable Biosensors for Human Health Monitoring.

Wearable tear-based biosensors have garnered substantial interest for real time monitoring with an emphasis on personalized health care. These biosensors utilize major tear biomarkers such as proteins, lipids, metabolites, and electrolytes for the detection and recording of stable biological signals in a non-invasive manner. The present comprehensive review delves deep into the tear composition along with potential biomarkers that can identify, monitor, and predict certain ocular diseases such as dry eye disease, conjunctivitis, eye-related infections, as well as diabetes mellitus. Recent technologies in tear-based wearable point-of-care medical devices, specifically the state-of-the-art and prospects of glucose, pH, lactate, protein, lipid, and electrolyte sensing from tear are discussed. Finally, the review addresses the existing challenges associated with the widespread application of tear-based sensors, which will pave the way for advanced scientific research and development of such non-invasive health monitoring devices.

Sensitivity Approach for Blood Glucose Impedance Data Using the Geselowitz Theorem

The article presents a preclinical study focused on developing a non-invasive blood glucose monitoring device. Current glucose monitoring methods, such as capillary or venous tests, are inconvenient for frequent use, making continuous monitoring challenging. This study explores the BGP (Bruna Gabriela Pedro) model, which links electrical impedance to blood glucose levels, as a potential alternative. Using the Geselowitz Theorem and impedance spectroscopy in a 4-electrode configuration, the study investigates the relationship between blood conductivity and glucose concentration. Impedance spectra were recorded for five human blood samples with glucose concentrations between 106 and 188 mg/dL. These samples were housed in an insulating container with four stainless steel electrodes. Conductivity was calculated using the Geselowitz Theorem at 1.0, 3.9, and 24.9 kHz. A finite element model built in COMSOL was employed to assess electrode sensitivity, which had a maximum regression error of 3.75 %. The results demonstrated a sigmoid relationship between blood glucose concentration and conductivity, with impedance decreasing non-linearly as glucose levels increased. Sensitivity diminished for glucose concentrations exceeding 188 mg/dL. An increase of 82 mg/dL in glucose concentration led to conductivity changes of 1.14, 2.82, and 5.14 S/m at frequencies of 1.0, 3.9, and 24.9 kHz, respectively. The findings suggest that the BGP analytical model could be refined through the inclusion of additional data on glucose's impact on electrical impedance. These improvements could support the development of non-invasive glucose meters. The research holds significant clinical potential for the creation of a simple, cost-effective glucose monitoring device. Continuous glucose monitoring could enhance diagnostic accuracy and support better therapeutic decision-making, particularly in emergency care. The affordability and accessibility of such a device may benefit a broad patient base.

Artificial intelligence based real-time prediction of imminent heart failure hospitalisation in patients undergoing non-invasive telemedicine.

Remote patient management may improve prognosis in heart failure. Daily review of transmitted data for early recognition of patients at risk requires substantial resources that represent a major barrier to wide implementation. An automated analysis of incoming data for detection of risk for imminent events would allow focusing on patients requiring prompt medical intervention. We analysed data of the Telemedical Interventional Management in Heart Failure II (TIM-HF2) randomized trial that were collected during quarterly in-patient visits and daily transmissions from non-invasive monitoring devices. By application of machine learning, we developed and internally validated a risk score for heart failure hospitalisation within seven days following data transmission as estimate of short-term patient risk for adverse heart failure events. Score performance was assessed by the area under the receiver-operating characteristic (ROCAUC) and compared with a conventional algorithm, a heuristic rule set originally applied in the randomized trial. The machine learning model significantly outperformed the conventional algorithm (ROCAUC 0.855 vs. 0.727, p < 0.001). On average, the machine learning risk score increased continuously in the three weeks preceding heart failure hospitalisations, indicating potential for early detection of risk. In a simulated one-year scenario, daily review of only the one third of patients with the highest machine learning risk score would have led to detection of 95% of HF hospitalisations occurring within the following seven days. A machine learning model allowed automated analysis of incoming remote monitoring data and reliable identification of patients at risk of heart failure hospitalisation requiring immediate medical intervention. This approach may significantly reduce the need for manual data review.

Solid-Phase microextraction on Self-Driven microfluidic chip using capillary micropump and microvalves for saliva electrochemical analysis

The real-time detection of biomarkers in saliva is of great significance for early non-invasive diagnostic of related diseases. In this work, molecular imprinting technology was used to prepare a lactic acid monolithic column, which could realize specific adsorption and elution of lactic acid, achieving solid phase microextraction (SPME) of lactic acid in samples, and purify the analytes. Lactic acid monolithic column was then embedded into the microfluidic channel, and passive microfluidic structures such as stop valve, retention valve, trigger valve and capillary pump were utilized to construct the SPME self-driven microfluidic chip. A series of complex operations such as lactic acid adsorption, rinse and elution on the monolithic column could be realized automatically without external power. Since the purification of the analytes was achieved by the monolithic column, the low-cost nano copper oxide modified electrode was used to achieve the specific electrochemical analysis of lactic acid. The screen-printed electrode was designed and fabricated, and the integrated electrochemical detection circuit was used to realize the portable analysis of Na+, K+ and lactic acid simultaneously. The microfluidic chip, screen-printed electrode and miniaturized detection circuit are integrated into a Point-of-care testing (POCT) device, which realizes the automatically real-time detection of saliva. By adjusting the template molecule in monolithic column polymerization, the application of this physiological fluid POCT device can be extended, and may provide some implications for the development of portable non-invasive health monitoring devices.

Prediction of intraoperative hypotension using deep learning models based on non-invasive monitoring devices.

Intraoperative hypotension is associated with adverse outcomes. Predicting and proactively managing hypotension can reduce its incidence. Previously, hypotension prediction algorithms using artificial intelligence were developed for invasive arterial blood pressure monitors. This study tested whether routine non-invasive monitors could also predict intraoperative hypotension using deep learning algorithms. An open-source database of non-cardiac surgery patients ( https://vitadb.net/dataset ) was used to develop the deep learning algorithm. The algorithm was validated using external data obtained from a tertiary Korean hospital. Intraoperative hypotension was defined as a systolic blood pressure less than 90 mmHg. The input data included five monitors: non-invasive blood pressure, electrocardiography, photoplethysmography, capnography, and bispectral index. The primary outcome was the performance of the deep learning model as assessed by the area under the receiver operating characteristic curve (AUROC). Data from 4754 and 421 patients were used for algorithm development and external validation, respectively. The fully connected model of Multi-head Attention architecture and the Globally Attentive Locally Recurrent model with Focal Loss function were able to predict intraoperative hypotension 5min before its occurrence. The AUROC of the algorithm was 0.917 (95% confidence interval [CI], 0.915-0.918) for the original data and 0.833 (95% CI, 0.830-0.836) for the external validation data. Attention map, which quantified the contributions of each monitor, showed that our algorithm utilized data from each monitor with weights ranging from 8 to 22% for determining hypotension. A deep learning model utilizing multi-channel non-invasive monitors could predict intraoperative hypotension with high accuracy. Future prospective studies are needed to determine whether this model can assist clinicians in preventing hypotension in patients undergoing surgery with non-invasive monitoring.

Intracranial compliance in patients with COVID-19: a multicenter observational study.

Patients with severe coronavirus disease-19 (COVID-19) may require the use of invasive mechanical ventilation (MV) for prolonged periods. Aggressive MV parameters have been associated with changes in intracranial pressure (ICP) in patients with acute intracranial disorders. Significant ICP elevation could compromise intracranial compliance (ICC) and cerebrovascular hemodynamics (CVH). However, the effects of these parameters in individuals without neurological disorders have not yet been evaluated. To evaluate ICC in patients on MV with COVID-19 infection compared to other diagnoses, to better characterize the effects of MV and COVID-19 upon ICC. We also compared between the ICC in patients with COVID-19 who did not require MV and healthy volunteers, to assess the isolated effect of COVID-19 upon ICC. This was an exploratory, observational study with a convenience sample. The ICC was evaluated with a noninvasive ICP monitoring device. The P2/P1 ratio was calculated by dividing the amplitude of these two points, being defined as "abnormal" when P2 > P1. The statistical analysis was performed using a mixed linear model with random effects to compare the P2/P1 ratio in all four groups on the first monitoring day. A convenience sample of 78 subjects (15 MV-COVID-19, 15 MV non-COVID-19, 24 non-MV-COVID-19, and 24 healthy participants) was prospectively enrolled. There was no difference in P2/P1 ratios between MV patients with and without COVID-19, nor between non-MV patients with COVID-19 and healthy volunteers. However, the P2/P1 ratio was higher in COVID-19 patients with MV use than in those without it. This exploratory analysis suggests that COVID-19 does not impair ICC.

3D Prototyping of Medical Devices for Non-Invasive Glucose Monitoring

In recent years, as global diabetes rates have increased, a growing number of individuals have been experiencing pain and infections due to the invasive nature of conventional commercial glucose meters.

Comparison of Ensemble Method Performance in Classifying Blood Sugar Levels Output from Non-Invasive Device

Diabetes Mellitus (DM) is a persistent health issue in many countries and is a leading cause of heart disease, kidney failure, and blindness The International Diabetes Federation (IDF) estimated in 2019 that at least 463 million people worldwide aged 20-79 suffer from diabetes. This number is expected to rise to 578 million by 2030 and 700 million by 2045. Machine learning is a type of machine learning that is very helpful in various fields, including healthcare. In classification cases, ensemble methods classify by combining decisions from several other models, one way being through majority voting. Ensemble methods often produce more accurate classification or prediction results. Several ensemble methods include random forest, extra trees, rotation forest, and double random forest. The data used in this study is part of research on the development and clinical testing of a prototype non-invasive blood glucose monitoring device by the non-invasive biomarking team at IPB. The data includes both invasive and non-invasive blood glucose measurements collected in 2019. This study compares the performance of the random forest, extra trees, rotation forest, and double random forest models on blood glucose level data obtained from non-invasive devices. The research results show that the Rotation Forest algorithm is the best model, with the highest average accuracy compared to the other three algorithms, achieving an accuracy level of 0.7142857 (71.42%).

Surface-adaptive interfaces: Bioresorbable ultrasound biomedical devices for noninvasive monitoring and imaging of deep-tissue homeostasis

Surface-adaptive interfaces: Bioresorbable ultrasound biomedical devices for noninvasive monitoring and imaging of deep-tissue homeostasis

Noninvasive Real-Time Glucose Monitoring Is in the Near Future.

Objective: Since the introduction of continuous glucose monitoring (CGM) technology, developers have rigorously researched the feasibility of creating a noninvasive glucose monitoring device. In a recent pilot study, investigators reported a strong correlation between glucose values obtained from novel noninvasive monitoring device (GWave) values to venous and capillary glucose measurements. Research Design and Methods: We investigated whether the level of accuracy observed in the pilot study could be reproduced in a larger cohort, using a smaller third-generation manufacturable device (Gen III GWave) containing a standardized sensor chip that can be mass produced for commercial use. The evaluation assessed concordance with capillary blood glucose, reproducibility between two Gen III devices, and accuracy during insulin-induced hypoglycemia. Results: Assessment of samples from 75 subjects (type 2 diabetes, n = 6; type 1 diabetes, n = 28; nondiabetic pregnant subjects, n = 10; and nondiabetic, n = 31) showed that 97% of values were in Zone A with 3% in Zone B of the Clarke Error Grid, with a mean absolute relative difference of 6.7% from reference blood glucose. Comparison between two independent Gen III GWave devices demonstrated reproducibility between the sensors (R2 = 0.95), with 100% of values within Zone A. In the hypoglycemia assessment, measurements from the Gen III sensor tightly followed the capillary glucose measurements down to 42 mg/dL (2.3 mmol/L), whereas the CGM measurements from two different CGM only converged with the GWave and capillary glucose readings after 90 min of decreasing glucose levels. Conclusion: Our results show promise as potentially the first noninvasive technology. Future studies will focus on larger number of people in all glucose ranges. Real-time noninvasive blood glucose monitoring is possible using GWave technology.

Measuring congestion with a non-invasive monitoring device in heart failure and haemodialysis: CONGEST-HF.

We examined the effectiveness of a novel cardiopulmonary management wearable sensor (worn for less than 5 mins) at measuring congestion and correlated the device findings with established clinical measures of congestion. We enrolled three cohorts of patients: (1) patients with heart failure (HF) receiving intravenous diuretics in hospital; (2) patients established on haemodialysis, and (3) HF patients undergoing right heart catheterization (RHC). The primary outcomes in the respective cohorts were a Spearman correlation between (1) change in weight and change in thoracic impedance (TI) (from enrolment, 24 h after admission to discharge) in patients hospitalized for HF; (2) lung ultrasound B-lines and volume removed during dialysis with device measured TI, and (3) pulmonary capillary wedge pressure (PCWP) and sub-acoustic diastolic, third heart sound (S3) in the patients undergoing RHC. A total of 66 patients were enrolled. In HF patients (n = 25), change in weight was correlated with both change in device TI (Spearman correlation [rsp] = -0.64, p = 0.002) and change in device S3 (rsp = -0.53, p = 0.014). In the haemodialysis cohort (n = 21), B-lines and TI were strongly correlated before (rsp = -0.71, p < 0.001) and after (rsp = -0.77, p < 0.001) dialysis. Volume of fluid removed by dialysis was correlated with change in device TI (rsp = 0.49, p = 0.024). In the RHC cohort (n = 20), PCWP measured at one time point and device S3 were not significantly correlated (rsp = 0.230, p = 0.204). There were no device-related adverse events. A non-invasive device was able to detect changes in congestion in patients with HF receiving decongestion therapy and patients having fluid removed at haemodialysis. The cardiopulmonary management device, which measures multiple parameters, is a potentially useful tool to monitor patients with HF to prevent hospitalizations.

Abstract PO1-07-05: Preliminary Evaluation in Breast Cancer of PointCheck: a Noninvasive AI-Based Neutropenia Monitoring Device

Abstract Background Febrile neutropenia (FN) is a common complication that can arise in breast cancer patients receiving myelosuppressive chemotherapy and increases the risk of fatal infections. Despite better preventive strategies there are between 140,000 to 200,000 FN-related hospitalizations yearly. Quick detection and awareness of grade III neutropenia (Absolute Neutrophil Count [ANC] &amp;lt; 1,000/µL) or greater can be critical to managing FN and treatment scheduling and dosing in patients with breast cancer. The current gold standard for neutropenia detection is a peripheral blood collection that requires patients to come into the clinic. PointCheck™ is a novel, noninvasive technology that can monitor for grade III neutropenia or greater and enable prompt detection in the home by acquiring microscopy videos of superficial capillaries through the nailfold skin and analyzing those videos with computer vision AI algorithms. To evaluate the usability, diagnostic performance, and preliminary clinical utility of PointCheck™ on FN detection we conducted a multi-center observational usability and diagnostic study. Methods The present study included an overall cohort of 175 diverse cancer patients, with 70 breast cancer patients. The primary endpoint was to achieve a score over 80.8 on a standardized System Usability Scale (SUS). Secondary endpoint was achievement of diagnostic performance with an area under the curve (AUC) above 0.80. We also performed an exploratory analysis on the device's clinical utility. Eligible participants were introduced to the device, watched a tutorial video and were given a user manual. They used the device autonomously under the supervision of the clinical team. Usability data was collected using the SUS, the scoring system that has a scale between 0-100. PointCheck™ measurements were analyzed using AI algorithms and compared to same-day Complete Blood Counts (CBCs) collected within 90 minutes of the measurement to assess accuracy in classifying patients as grade III neutropenic or greater (&amp;lt; 1,000/µL), or non-grade III neutropenic (≥1,000/µL). In the exploratory clinical utility analysis, clinicians provided narrative insights by completing a Likert survey assessing how PointCheck™ would support or impact their clinical decisions. Results 81.4% of breast cancer patients scored above 80.8 on the SUS scale across all sites, with a mean SUS score of 87.7 (SD=13.2). Furthermore, the AI-based PointCheck™ classifier accurately discriminated neutropenia patients with an AUC = 0.90 in the overall cohort. In the exploratory analysis, a majority of clinicians (65.2%) agreed that the device could have helped better evaluate their patients (Table 1). Whereas almost 3 out of 4 clinicians (72.4%), described that the use of PointCheck™ could have avoided preparing a chemotherapy infusion if the patient was not ready due to grade III neutropenia or greater. Conclusions The present study showed that there is a high perception of usability of PointCheck™, indicating an above average user experience and falling within the top 10% of systems. Furthermore, we found that PointCheck™ can accurately detect grade III neutropenia or greater in a large cohort of patients including breast cancer patients. Application of PointCheck™ to clinical practice as a monitoring system can contribute to the early detection of FN and antineoplastic therapy management. Table 1: Clinical Utility Survey Results Citation Format: Juan Garcia-Mosquera, Manuel Alva, Toshiaki Iwase, Ganimete Lamaj, Pablo Tolosa, Rodrigo Sánchez-Bayona, José Pelaez-Escobar, Alberto Pablo-Trinidad, Ryan Benasutti, Aurélien Bourquard, Ian Butterworth, Alvaro Sánchez-Ferro, Carlos Castro-González, José Manuel Pérez-García, Alejandro Martínez-Bueno, Eva Ciruelos, Azadeh Nasrazadani. Preliminary Evaluation in Breast Cancer of PointCheck: a Noninvasive AI-Based Neutropenia Monitoring Device [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-07-05.

Economic Evaluations of Digital Health Interventions for Patients With Heart Failure: Systematic Review.

Digital health interventions (DHIs) have shown promising results in enhancing the management of heart failure (HF). Although health care interventions are increasingly being delivered digitally, with growing evidence on the potential cost-effectiveness of adopting them, there has been little effort to collate and synthesize the findings. This study's objective was to systematically review the economic evaluations that assess the adoption of DHIs in the management and treatment of HF. A systematic review was conducted using 3 electronic databases: PubMed, EBSCOhost, and Scopus. Articles reporting full economic evaluations of DHIs for patients with HF published up to July 2023 were eligible for inclusion. Study characteristics, design (both trial based and model based), input parameters, and main results were extracted from full-text articles. Data synthesis was conducted based on the technologies used for delivering DHIs in the management of patients with HF, and the findings were analyzed narratively. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed for this systematic review. The reporting quality of the included studies was evaluated using the CHEERS (Consolidated Health Economic Evaluation Reporting Standards) guidelines. Overall, 27 economic evaluations were included in the review. The economic evaluations were based on models (13/27, 48%), trials (13/27, 48%), or a combination approach (1/27, 4%). The devices evaluated included noninvasive remote monitoring devices (eg, home telemonitoring using digital tablets or specific medical devices that enable transmission of physiological data), telephone support, mobile apps and wearables, remote monitoring follow-up in patients with implantable medical devices, and videoconferencing systems. Most of the studies (24/27, 89%) used cost-utility analysis. The majority of the studies (25/27, 93%) were conducted in high-income countries, particularly European countries (16/27, 59%) such as the United Kingdom and the Netherlands. Mobile apps and wearables, remote monitoring follow-up in patients with implantable medical devices, and videoconferencing systems yielded cost-effective results or even emerged as dominant strategies. However, conflicting results were observed, particularly in noninvasive remote monitoring devices and telephone support. In 15% (4/27) of the studies, these DHIs were found to be less costly and more effective than the comparators (ie, dominant), while 33% (9/27) reported them to be more costly but more effective with incremental cost-effectiveness ratios below the respective willingness-to-pay thresholds (ie, cost-effective). Furthermore, in 11% (3/27) of the studies, noninvasive remote monitoring devices and telephone support were either above the willingness-to-pay thresholds or more costly than, yet as effective as, the comparators (ie, not cost-effective). In terms of reporting quality, the studies were classified as good (20/27, 74%), moderate (6/27, 22%), or excellent (1/27, 4%). Despite the conflicting results, the main findings indicated that, overall, DHIs were more cost-effective than non-DHI alternatives. PROSPERO CRD42023388241; https://tinyurl.com/2p9axpmc.

Agreement between three noninvasive temperature monitoring devices during spinal anaesthesia for caesarean delivery: a prospective observational study

Hypothermia during obstetric spinal anaesthesia is a common and important problem, yet temperature monitoring is often not performed due to the lack of a suitable, cost-effective monitor. This study aimed to compare a noninvasive core temperature monitor with two readily available peripheral temperature monitors during obstetric spinal anaesthesia. We undertook a prospective observational study including elective and emergency caesarean deliveries, to determine the agreement between affordable reusable surface temperature monitors (Welch Allyn SureTemp® Plus oral thermometer and the Braun 3-in-1 No Touch infrared thermometer) and the Dräger T-core© (using dual-sensor heat flux technology), in detecting thermoregulatory changes during obstetric spinal anaesthesia. Predetermined clinically relevant limits of agreement (LOA) were set at ± 0.5 °C. We included 166 patients in our analysis. Hypothermia (heat flux temperature < 36 °C) occurred in 67% (95% CI 49 to 78%). There was poor agreement between devices. In the Bland-Altman analysis, LOA for the heat flux monitor vs. oral thermometer were 1.8 °C (CI 1.7 to 2.0 °C; bias 0.5 °C), for heat flux monitor vs. infrared thermometer LOA were 2.3 °C (CI 2.1 to 2.4 °C; bias 0.4 °C) and for infrared vs. oral thermometer, LOA were 2.0 °C (CI 1.9 to 2.2 °C; bias 0.1 °C). Error grid analysis highlighted a large amount of clinical disagreement between methods. While monitoring of core temperature during obstetric spinal anaesthesia is clinically important, agreement between monitors was below clinically acceptable limits. Future research with gold-standard temperature monitors and exploration of causes of sensor divergence is needed.

A Wearable Electrochemical Biosensor Utilizing Functionalized Ti3C2Tx MXene for the Real-Time Monitoring of Uric Acid Metabolite.

Wearable, noninvasive sensors enable the continuous monitoring of metabolites in sweat and provide clinical information related to an individual's health and disease states. Uric acid (UA) is a key indicator highly associated with gout, hyperuricaemia, hypertension, kidney disease, and Lesch-Nyhan syndrome. However, the detection of UA levels typically relies on invasive blood tests. Therefore, developing a wearable device for noninvasive monitoring of UA concentrations in sweat could facilitate real-time personalized disease prevention. Here, we introduce 1,3,6,8-pyrene tetrasulfonic acid sodium salt (PyTS) as a bifunctional molecule functionalized with Ti3C2Tx via π-π conjugation to design nonenzymatic wearable sensors for sensitive and selective detection of UA concentration in human sweat. PyTS@Ti3C2Tx provides many oxidation-reduction active groups to enhance the electrocatalytic ability of the UA oxidation reaction. The PyTS@Ti3C2Tx-based electrochemical sensor demonstrates highly sensitive detection of UA in the concentration range of 5 μM-100 μM, exhibiting a lower detection limit of 0.48 μM compared to the uricase-based sensor (0.84 μM). In volunteers, the PyTS@Ti3C2Tx-based wearable sensor is integrated with flexible microfluidic sweat sampling and wireless electronics to enable real-time monitoring of UA levels during aerobic exercise. Simultaneously, it allows for comparison of blood UA levels via a commercial UA analyzer. Herein, this study provides a promising electrocatalyst strategy for nonenzymatic electrochemical UA sensor, enabling noninvasive real-time monitoring of UA levels in human sweat and personalized disease prevention.

Behavioral Disorders of Spatial Cognition in Patients with Mild Cognitive Impairment due to Alzheimer's Disease: Preliminary Findings from the BDSC-MCI Project.

(1) Background: Spatial cognition (SC) is one of the earliest cognitive domains to be impaired in the course of Alzheimer's disease (AD), resulting in spatial disorientation and becoming lost even in familiar surroundings as later dementia symptoms. To date, few studies have identified initial alterations of spatial navigation (SN) in the premorbid AD phase by real-world paradigms, and none have adopted an innovative technological apparatus to better detect gait alterations as well as physiological aspects correlated to spatial disorientation (SD). The present study aimed at exploring initial SN defects in patients with prodromal AD via a naturalistic task by using a sensory garment. (2) Methods: 20 community-dwelling patients with Mild Cognitive Impairment (MCI) due to AD and 20 age/education controls were assessed on their sequential egocentric and allocentric navigation abilities by using a modified version of the Detour Navigation Test (DNT-mv). (3) Results: When compared to controls, patients with MCI due to AD exhibited higher wrong turns (WT) and moments of hesitation (MsH) in the DNT-mv, reflecting difficulties both in sequential egocentric and allocentric navigation, depending on hippocampal deterioration. Moreover, they reported more complaints about their SN competencies and lower long-term visuospatial memory abilities than controls. Remarkably, WTs and MsH manifested in the allocentric naturalistic task of the DNT-mv were associated with autonomic nervous system alteration pertaining to cardiac functioning in the whole sample. (4) Conclusions: Naturalistic navigation tests of hippocampal function using a continuous non-invasive monitoring device can provide early markers of spatial disorientation in patients with MCI due to AD. Future studies should develop cognitive remediation techniques able to enhance SC residual abilities in patients at high risk of conversion into dementia and ecological paradigms to be replicated on a large scale.

Evaluation and patient experience of wireless noninvasive fetal heart rate monitoring devices.

In clinical practice, fetal heart rate monitoring is performed intermittently using Doppler ultrasound, typically for 30 minutes. In case of a non-reassuring heart rate pattern, monitoring is usually prolonged. Noninvasive fetal electrocardiography may be more suitable for prolonged monitoring due to improved patient comfort and signal quality. This study evaluates the performance and patient experience of four noninvasive electrocardiography devices to assess candidate devices for prolonged noninvasive fetal heart rate monitoring. Non-critically sick women with a singleton pregnancy from 24 weeks of gestation were eligible for inclusion. Fetal heart rate monitoring was performed during standard care with a Doppler ultrasound device (Philips Avalon-FM30) alone or with this Doppler ultrasound device simultaneously with one of four noninvasive electrocardiography devices (Nemo Fetal Monitoring System, Philips Avalon-Beltless, Demcon Dipha-16 and Dräger Infinity-M300). Performance was evaluated by: success rate, positive percent agreement, bias, 95% limits of agreement, regression line, root mean square error and visual agreement using FIGO guidelines. Patient experience was captured using a self-made questionnaire. A total of 10 women were included per device. For fetal heart rate, Nemo performed best (success rate: 99.4%, positive percent agreement: 94.2%, root mean square error 5.1 BPM, bias: 0.5 BPM, 95% limits of agreement: -9.7 - 10.7 BPM, regression line: y = -0.1x + 11.1) and the cardiotocography tracings obtained simultaneously by Nemo and Avalon-FM30 received the same FIGO classification. Comparable results were found with the Avalon-Beltless from 36 weeks of gestation, whereas the Dipha-16 and Infinity-M300 performed significantly worse. The Avalon-Beltless, Nemo and Infinity-M300 closely matched the performance of the Avalon-FM30 for maternal heart rate, whereas the performance of the Dipha-16 deviated more. Patient experience scores were higher for the noninvasive electrocardiography devices. Both Nemo and Avalon-Beltless are suitable devices for (prolonged) noninvasive fetal heart rate monitoring, taking their intended use into account. But outside its intended use limit of 36 weeks' gestation, the Avalon-Beltless performs less well, comparable to the Dipha-16 and Infinity-M300, making them currently unsuitable for (prolonged) noninvasive fetal heart rate monitoring. Noninvasive electrocardiography devices appear to be preferred due to greater comfort and mobility.

Photoplethysmography for Assessing Microcirculation in Hypertensive Patients After Taking Antihypertensive Drugs: A Review.

High blood pressure (BP) is a common disease and is associated with many chronic diseases. Measuring BP is essential for the treatment and management of many diseases, and therefore there is a growing need for a non-invasive, sleeveless and continuous BP monitoring device. With the development of technology, pulse waveform analysis using photoplethysmography (PPG) has become more feasible for evaluating BP. This study aimed to evaluate the changes of vascular elasticity and blood volume over time by using the characteristic parameters extracted by PPG. We reviewed the latest progress and literature on the observation of capillary network characteristics in hypertensive and non-hypertensive patients by PPG, the influence of different drugs on microcirculation characteristics in hypertensive patients with PPG, and further explored the key relationship between microcirculation and hypertension. We found that the PPG waveform produced by the fingertips of hypertensive patients is very different from that of healthy people, and the PPG waveform changes significantly during diastolic period after antihypertensive treatment. With the rapid development of medical technology, people can get more intuitive microcirculation image data, which provides beneficial help for the comprehensive understanding of hypertension.

Analysis of Non-Invasive Devices and Techniques Involved in Continuous Monitoring of Interstitial Glucose of Patients with Type 1 Diabetes

Abstract: There are 422 million diabetes patients worldwide according to 2023 census of WHO, and around 100 million cases in India. 8.4 million type 1 diabetes cases were recorded as of 2021. By 2040, it is projected that the number of people living with Type 1 Diabetes will reach 13.5–17.4 million. The surge in diabetes cases has created the critical need for regular glucose monitoring to effectively manage this prevalent health issue. Currently, glucose levels are predominantly measured invasively by pricking the finger for a blood sample and involves invasive methods, such as fingertip pricks, which can be uncomfortable and inconvenient, particularly for paediatric patients. This method utilizes blood glucose monitoring devices, which measure sugar levels in a small blood sample placed on a disposable test strip and have a certain degree of inaccuracy. Patients do not test as often as they should because of the pain and hassle of finger pricks and many do not achieve optimal glycaemic control. However, the shift toward non-invasive continuous glucose monitoring is becoming increasingly imperative and a promising alternative. With current biotechnological advances, many techniques offer a non-invasive, continuous means of monitoring glucose levels and have garnered significant attention in recent years. The objective is to consolidate and summarize recent advancements in non-invasive continuous glucose monitoring, emphasizing integration into wearable platforms for seamless and real-time monitoring. Furthermore, paediatric patients with Type 1 Diabetes face elevated risks of severe hypoglycaemic and hyperglycaemic events due to challenges in glycaemic control. Frequently, monitoring blood glucose is crucial to mitigate these risks and achieve optimal glycaemic management. The objective of ongoing research is to validate AI-based algorithms utilizing ECG signals collected through non-invasive devices to detect glycaemic events. These advancements, once commercially available, have the potential to revolutionize glucose monitoring, offering a convenient, efficient, and less intrusive approach, particularly benefiting paediatric patients and improving overall healthcare management. Despite substantial research and development, challenges persist, including standardizing sweat collection methods, addressing sample degradation, accounting for variations in sweat composition among individuals, optimizing diverse detection methods, improving glucose detection sensitivity, and ensuring overall commercial viability. Overcoming these challenges is paramount to bridge the gap between research and market availability, paving the way for a technological revolution in glucose monitoring. In this study, we compared potential non-invasive glucose monitoring devices and techniques to evaluate the most accurate, pain-free, fast, and cost-friendly device. Upon examination and survey, we have theorized the drawbacks persisting in the existing devices and hypothesized techniques. Looking forward this study has a broader application in not only understanding the limitations and creating more accurate devices but also in continuous glucose monitoring for diabetic patient care.

Electro-Structured Cu Distorted Nanopyramids for Superior Sweat Glucose Sensing

There is a rising global demand for non-invasive glucose monitoring devices, due to an increase in diabetes and its associated health problems. Nanostructures, including needles, fibers, and rods, have drawn considerable attention due to their unique structural-based physical and chemical features in non-enzymatic electrochemical sensing. Copper is a potential candidate for applied research because of its higher electrical conductivity, low cost, and strong reactivity. To create 3-D nano copper structures, researchers have investigated chemical, electrochemical, and hydrothermal techniques. The electrochemical method can better control the formation reaction for these structures. The electrochemical process has better control over the formation reaction for these structures.Distinctive and self-supported copper nanopyramids were electrochemically created on a copper substrate within a few seconds by electrochemical deposition in a one pot synthesis. The SEM image of the constructed nano copper pyramids are shown in figure 1a. The built nano pyramids have a length of ~200 nm. The generated nanostructures display excellent catalytic activity for non-enzymatic glucose sensing. The created nonenzymatic sensor was tested for glucose detection in synthetic sweat and it displayed a linear response in the range of 20 µM- 2000 µM, making it appropriate for practical applications. The chrono amperometry for glucose sensing was performed at 0.5V for 60s. Figure 1b shows the calibration plot for glucose sensing. Linear response for the glucose was observed in the range from 20µM – 2000µM. The device showed a very low interference for uric acid and ascorbic acid. The device is stable upto 60 days, highly reproducible and can be reused upto 5 times. The correlation coefficient was 0.9857 for the linear equation I(μA) = (9.52*10-3) *C (in µM) + 5.065 and a sensitivity of 1.17 nA/(μM-cm2) was observed Figure 1