Non-invasive Monitoring Method Research Articles

Testbed for Simulating Pulsewaveform for Development of Wearable Piezoresistive Pressure Sensors

Due to their non-invasive method of continuous monitoring of weak human physiological data, flexible and skin-mountable wearable health monitoring devices have attracted a lot of attention in recent years. According to their mode of operation, ultra-sensitive pressure sensors can generally be divided into piezoresistive, capacitive, triboelectric, and piezoelectric sensors. Particularly, piezoresistive sensors have received a lot of attention due to its rapid response, wide detection range, straightforward structure, and comparatively easy signal measuring system. Although previous attempts to obtain the complete pulse waveform of the human heartbeat were successful, their signal capture required the use of unwearable measuring equipment. In order to resolve this shortcoming of the current supposedly wearable systems, we outline three requirements for a sensor to be wearable (i.e. could be paired with a simple and compact signal acquisition device). The first of these is the sensitivity; through a complete literature review we determine the lower limit of sensitivity that if obtained, the sensor can acquire the pulsewaveform from the body peripheries such as wrist or ankle. Second is the response time of the sensor. The piezoresistive sensor must have a shorter response time than the period of fastest heartbeat. And lastly is the cyclability; the durability tests of the sensor must show ample stability and consistency in the signal output after operation for months.To verify that the sensors pass the requirements above, we have custom-built a testbed capable of measuring these three characteristics. The testbed specifically makes use of a secondary pressure sensor to confirm the sensitivity. Secondly, by using an encoder instead of merely relying on a sensor's rise or fall time, it is capable to more accurately determine the response time of the sensor. Furthermore, because the testbed is motorized, it is streamlined to mimic operation over an extended cycles.Finally, in this study we fabricated an exemplary piezoresistive pressure sensor using environmentally friendly materials and tested it in the testbed and then mounted it on the wrist of a human subject and acquired the pulsewaveform with a simple and wearable signal acquisition device to confirm the capability as a wearable pulsewave monitoring device. The details of this study will be presented in the meeting.

High-Entropy Oxides as Nanozymes for Non-Immunological Electrochemical Monitoring of Cortisol

High entropy oxides (HEOs) are a class of materials that have garnered significant attention in recent years due to their potential use as catalysts in energy-related applications[1]. These materials are derived from high entropy compounds, which consist of equimolar or near equimolar ratios of multiple primary elements and have configurational entropy in the formation of a single-phase structure. The cocktail effect of HEOs gives rise to various catalytically active sites due to the synergistic interaction of the different elements, and lattice distortion during the formation of a single-phase solid solution results in the generation of abundant oxygen vacancies, which enhances the adsorption activity of HEOs and improves their catalytic performance[2]. HEOs can also be cost-efficient by utilizing low-cost transition metal elements as their active centers. Despite their widespread use as catalysts, the application of HEOs in electrocatalysis for molecule monitoring and early diagnosis remains largely unexplored.Cortisol, a glucocorticoid hormone that regulates many aspects of metabolism, stress, and immune response, is closely related to various diseases and disorders[3]. Current methods for noninvasive monitoring of cortisol relies upon immunological assays and typically limited to labor-intensive and time-consuming laboratory techniques. As a consequence, detecting cortisol in a label-free manner that is low cost and offers significant sensor stability and high sensitivity is desirable for many emerging applications, particularly for on-body monitoring.Here, we present work that examines the catalytic activities of HEOs as nanozymes for the electrocatalysis of cortisol. The electrochemical cortisol sensor is based on a novel one-pot microwave-assisted reaction synthesized (Fe, Co, Mn, Ni, Cu) Ox. The catalytic mechanism of electrochemical sensors depends on the formation of various valence metal redox species pairs, where the higher valence state oxidizes cortisol species present on the electrode surface, accompanied by a reduced lower valence state. The one-pot microwave-assisted synthesized HEOs exhibited ultra-high catalytic activity towards cortisol species as measured by cyclic voltammetry and chronoamperometry. Experimental results show a superior sensing performance including a detection limit as low as 0.5 µM (s/N=3), a good linear detection range between 0.01 ~ 4.01 µM, and a sensitivity of 46.5 µA µM-1 cm-2.

Pearl shape classification using deep convolutional neural networks from Tahitian pearl rotation in Pinctada margaritifera

Tahitian pearls, artificially cultivated from the black-lipped pearl oyster Pinctada margaritifera, are renowned for their unique color and large size, making the pearl industry vital for the French Polynesian economy. Understanding the mechanisms of pearl formation is essential for enabling quality and sustainable production. In this paper, we explore the process of pearl formation by studying pearl rotation. Here we show, using a deep convolutional neural network, a direct link between the rotation of the pearl during its formation in the oyster and its final shape. We propose a new method for non-invasive pearl monitoring and a model for predicting the final shape of the pearl from rotation data with 81.9% accuracy. These novel resources provide a fresh perspective to study and enhance our comprehension of the overall mechanism of pearl formation, with potential long-term applications for improving pearl production and quality control in the industry.

Dynamic evaluation of blood immune cells predictive of response to immune checkpoint inhibitors in NSCLC by multicolor spectrum flow cytometry.

Immune checkpoint inhibitors (ICIs) only benefit a subset of cancer patients, underlining the need for predictive biomarkers for patient selection. Given the limitations of tumor tissue availability, flow cytometry of peripheral blood mononuclear cells (PBMCs) is considered a noninvasive method for immune monitoring. This study explores the use of spectrum flow cytometry, which allows a more comprehensive analysis of a greater number of markers using fewer immune cells, to identify potential blood immune biomarkers and monitor ICI treatment in non-small-cell lung cancer (NSCLC) patients. PBMCs were collected from 14 non-small-cell lung cancer (NSCLC) patients before and after ICI treatment and 4 healthy human donors. Using spectrum flow cytometry, 24 immune cell markers were simultaneously monitored using only 1 million PBMCs. The results were also compared with those from clinical flow cytometry and bulk RNA sequencing analysis. Our findings showed that the measurement of CD4+ and CD8+ T cells by spectrum flow cytometry matched well with those by clinical flow cytometry (Pearson R ranging from 0.75 to 0.95) and bulk RNA sequencing analysis (R=0.80, P=1.3 x 10-4). A lower frequency of CD4+ central memory cells before treatment was associated with a longer median progression-free survival (PFS) [Not reached (NR) vs. 5 months; hazard ratio (HR)=8.1, 95% confidence interval (CI) 1.5-42, P=0.01]. A higher frequency of CD4-CD8- double-negative (DN) T cells was associated with a longer PFS (NR vs. 4.45 months; HR=11.1, 95% CI 2.2-55.0, P=0.003). ICIs significantly changed the frequency of cytotoxic CD8+PD1+ T cells, DN T cells, CD16+CD56dim and CD16+CD56- natural killer (NK) cells, and CD14+HLDRhigh and CD11c+HLADR + monocytes. Of these immune cell subtypes, an increase in the frequency of CD16+CD56dim NK cells and CD14+HLADRhigh monocytes after treatment compared to before treatment were associated with a longer PFS (NR vs. 5 months, HR=5.4, 95% CI 1.1-25.7, P=0.03; 7.8 vs. 3.8 months, HR=5.7, 95% CI 169 1.0-31.7, P=0.04), respectively. Our preliminary findings suggest that the use of multicolor spectrum flow cytometry helps identify potential blood immune biomarkers for ICI treatment, which warrants further validation.

Identification of salivary metabolic biomarker signatures for oral tongue squamous cell carcinoma

ObjectiveTo identify the salivary metabolites associated with squamous cell carcinoma of the tongue to develop easy and non-invasive potential biomarkers for disease diagnosis. DesignInitially, the study utilized untargeted metabolomics to analyze 20 samples of tongue squamous cell carcinoma and 10 control samples. The objective was to determine the salivary metabolites that exhibited differential expression in tongue squamous cell carcinoma. Then the selected metabolites were validated using targeted metabolomics in saliva samples of 100 patients diagnosed with squamous cell carcinoma of the tongue, as well as 30 healthy control individuals. ResultsFrom the analysis of untargeted metabolomics, 10 metabolites were selected as potential biomarkers. In the subsequent targeted metabolomics study on these selected metabolites, it was observed that N-Acetyl-D-glucosamine, L-Pipecolic acid, L-Carnitine, Phosphorylcholine, and Deoxyguanosine exhibited significant differences. The receiver operating characteristic curve analysis indicates a combination of three important metabolites such as N-Acetyl-D-glucosamine, L-Pipecolic acid and L-Carnitine provided the best prediction with an area under the curve of 0.901. ConclusionsThe present result reveals that the N-Acetyl-D-glucosamine, L-Pipecolic acid and L-Carnitine are the signature diagnostic biomarkers for oral tongue squamous cell carcinoma. These findings can be used to develop a rapid and non-invasive method for disease monitoring and prognosis in oral tongue cancer.

Urine Parameters in Patients with COVID-19 Infection.

A urine test permits the measure of several urinary markers. This is a non-invasive method for early monitoring of potential kidney damage. In COVID-19 patients, alterations of urinary markers were observed. This review aims to evaluate the utility of urinalysis in predicting the severity of COVID-19. A total of 68 articles obtained from PubMed studies reported that (i) the severity of disease was related to haematuria and proteinuria and that (ii) typical alterations of the urinary sediment were noticed in COVID-19-associated AKI patients. This review emphasizes that urinalysis and microscopic examination support clinicians in diagnosing and predicting COVID-19 severity.

Automated Tongue Diagnosis: A Deep Autoencoder Neural Network and Clustering-Based Image Segmentation Approach

Automated tongue diagnosis has gained significant attention in recent years as a non-invasive and cost-effective method for disease detection and monitoring. In this project, propose a novel approach for automated tongue diagnosis using a combination of Deep Autoencoder Neural Network (DAENN) and clustering-based image segmentation techniques. The objective is to develop a reliable and efficient system that can accurately analyze tongue images and provide diagnostic information. The proposed methodology involves several key steps. First, high-resolution images of the tongue are acquired using a digital camera. Preprocessing techniques are then applied to enhance image quality and remove noise. Next, a clustering-based image segmentation algorithm is employed to extract the tongue region from the background. This step ensures that subsequent analysis is focused on the relevant area of interest. After tongue region extraction, relevant features are extracted from the segmented image. These features capture important visual characteristics such as color distribution, texture, and shape. The DAENN is then trained using these features to learn an efficient representation of the input data. The network's encoder-decoder architecture enables it to compress the features into a lower-dimensional latent space and reconstruct the original features. By leveraging advanced machine learning techniques, the system can accurately analyze tongue images and assist healthcare professionals in making informed decisions. The evaluation of the proposed system on a diverse dataset demonstrates promising results, showcasing its potential as an effective tool for automated tongue diagnosis. Keywords—deep autoencoder, Tongue segmentation, Pattern recognition, colour intensity

Optimizing the automated recognition of individual animals to support population monitoring.

Reliable estimates of population size and demographic rates are central to assessing the status of threatened species. However, obtaining individual-based demographic rates requires long-term data, which is often costly and difficult to collect. Photographic data offer an inexpensive, noninvasive method for individual-based monitoring of species with unique markings, and could therefore increase available demographic data for many species. However, selecting suitable images and identifying individuals from photographic catalogs is prohibitively time-consuming. Automated identification software can significantly speed up this process. Nevertheless, automated methods for selecting suitable images are lacking, as are studies comparing the performance of the most prominent identification software packages. In this study, we develop a framework that automatically selects images suitable for individual identification, and compare the performance of three commonly used identification software packages; Hotspotter, I3S-Pattern, and WildID. As a case study, we consider the African wild dog, Lycaon pictus, a species whose conservation is limited by a lack of cost-effective large-scale monitoring. To evaluate intraspecific variation in the performance of software packages, we compare identification accuracy between two populations (in Kenya and Zimbabwe) that have markedly different coat coloration patterns. The process of selecting suitable images was automated using convolutional neural networks that crop individuals from images, filter out unsuitable images, separate left and right flanks, and remove image backgrounds. Hotspotter had the highest image-matching accuracy for both populations. However, the accuracy was significantly lower for the Kenyan population (62%), compared to the Zimbabwean population (88%). Our automated image preprocessing has immediate application for expanding monitoring based on image matching. However, the difference in accuracy between populations highlights that population-specific detection rates are likely and may influence certainty in derived statistics. For species such as the African wild dog, where monitoring is both challenging and expensive, automated individual recognition could greatly expand and expedite conservation efforts.

Evaluation of Different Registration Algorithms to Reduce Motion Artifacts in CT-Thermography (CTT).

Computed tomography (CT)-based Thermography (CTT) is currently being investigated as a non-invasive temperature monitoring method during ablation procedures. Since multiple CT scans with defined time intervals were acquired during this procedure, interscan motion artifacts can occur between the images, so registration is required. The aim of this study was to investigate different registration algorithms and their combinations for minimizing inter-scan motion artifacts during thermal ablation. Four CTT datasets were acquired using microwave ablation (MWA) of normal liver tissue performed in an in vivo porcine model. During each ablation, spectral CT volume scans were sequentially acquired. Based on initial reconstructions, rigid or elastic registration, or a combination of these, were carried out and rated by 15 radiologists. Friedman's test was used to compare rating results in reader assessments and revealed significant differences for the ablation probe movement rating only (p = 0.006; range, 5.3-6.6 points). Regarding this parameter, readers assessed rigid registration as inferior to other registrations. Quantitative analysis of ablation probe movement yielded a significantly decreased distance for combined registration as compared with unregistered data. In this study, registration was found to have the greatest influence on ablation probe movement, with connected registration being superior to only one registration process.

Sensing System for Mixed Inorganic Salt Solution Based on Improved Double Label Coupling RFID

As a crucial part in sewage, high-salt wastewater contains abundant corrosive components and heavy metal elements, which makes it difficult for invasive monitoring. Therefore, based on coupling Radio Frequency Identification(RFID) tags, this paper proposes a non-invasive monitoring method for concentration of inorganic salt in sewage. In this method, Cole-Cole model and Slit Cylindrical Capacitance(SCC) model are combined to explore the relationship between capacitance of RFID and concentration of inorganic salt in sewage pipeline. A New Efficient Levenberg-Marquardt(NELM) algorithm is used for regression between relative permittivity and concentration of inorganic salt categorically. Then, measurement data is processed under modified relative permittivity mixing rule of electrolyte and solvent. Finally the concentration of each component in the multi-component inorganic salt wastewater are obtained. Experimental results show that the maximal relative error (MRE) and average relative error(ARE) of single inorganic salt solution are 1.1% and 0.5%, while mixed solution are 2.6% and 1.89%. Such results indicate significant improvement comparing with previous works. Great potential of RFID in working sewage monitoring is sufficiently confirmed.

Accuracy of genome-wide mutational analysis for tumor-informed ctDNA-guided MRD monitoring in bladder cancer.

4589 Background: Neoadjuvant chemotherapy (NAC) followed by radical cystectomy (RC) is the standard management of localized muscle-invasive bladder cancer (MIBC). However, about 45% of patients develop metastases within 2 years after RC. Biomarkers for treatment efficacy evaluation and early detection of minimal residual disease (MRD) are needed for earlier treatment initiation and monitoring of treatment response. Tumor-informed detection of mutations in cell-free DNA (cfDNA) has shown promising results to monitor MRD. However, the low tumor fraction after surgery and limited input material obtained from a typical plasma sample limits the probability of detecting low metastatic burden scenarios. Here we implemented and applied a whole-genome sequencing (WGS) approach to monitor circulating tumor DNA (ctDNA) for sensitive ctDNA detection. Methods: 110 MIBC patients undergoing NAC and RC were enrolled. cfDNA was extracted from ~1mL plasma (n=978) and procured from longitudinal plasma sampling during NAC and pre-RC and post-RC. WGS of tumor/germline pairs (30x/20x) and plasma cfDNA (>20x) was performed, facilitating detection of genome-wide genomic alterations and quantification of ctDNA using the C2inform method. Results: For each patient we developed a tumor-informed WGS model by integrating genome-wide mutation and copy number variation data coupled with advanced signal processing and AI-based error suppression. Patient-specific somatic variant patterns were used for detection and measuring ctDNA levels in low-input blood samples by WGS. Post-RC ctDNA analysis identified patients with recurrence with 78% sensitivity and 95% specificity and with a median lead time over radiographic imaging of 118 days (full follow-up included). When restricting follow-up time to 12 months following the latest ctDNA test, a sensitivity of 86% and specificity of 94% was achieved. ctDNA status was associated with recurrence-free (p<0.0001) and overall survival (p<0.0001). Furthermore, ctDNA clearance during NAC was also associated with recurrence-free survival (p=0.0051). The APOBEC associated signatures SBS2 and SBS13 were identified as the primary contributors to the mutational landscape of the primary tumors and TP53, KMT2D, PIK3CA, RB1 and KDM6A were the main affected driver genes. Half of the tumors displayed whole genome doubling. Moreover, our analysis of plasma samples after treatment showed the presence of chemotherapy-induced mutational signatures not present in the primary tumor, along with increases in copy numbers on chromosomes 19q and 20, and a focal amplification of the FGFR3 gene on chromosome 4. Conclusions: Our results highlight the clinical potential of personalized genome-wide mutation integration as an ultra-sensitive, non-invasive method for MRD detection and treatment response monitoring.

Event-Driven Non-Invasive Multi-Core Cable Current Monitoring Based on Sensor Array

Current monitoring of multi-core cables plays an important role in various applications since cables are widely utilized in distribution and transmission systems. However, existing current sensing devices which are mostly designed for the single conductor break the cable jacket to measure conductor currents in multi-core cables. Recently various non-invasive sensing devices with reconstruction algorithms are designed for rapid access to currents in multi-core cables but don't make full use of abundant information during current monitoring. Thus, this paper proposes an event-driven non-invasive multi-core cable current monitoring method using an array of magnetic field sensors. Real-time currents of the multi-core cable are determined by solving conductor localization and current measurement problems. Since the observed changes of the magnetic field around the cable are caused by current or position changes, conductor localization is driven by the events which are classified as electrical events and mechanical events respectively. The key idea is to improve the accuracy of conductor localization through effective electrical events. Then current monitoring is achieved by solving current measurement problems using more accurate conductor positions and the measured magnetic field. This method is utilized in the real cable current monitoring, a sustaining decrease of relative current errors is observed, which is below 1% in long-term monitoring.

Liquid metal-based plant electronic tattoos for in-situ monitoring of plant physiology

Flexible plant sensors, as a noninvasive and real-time monitoring method for plant physiology, are becoming crucial for precision agriculture. However, integrating flexible devices with plants are challenging due to their fragility and complex surfaces. In this study, we introduce a liquid metal-based plant electronic tattoo (LM-PET) that can harmlessly and continuously monitor the loss of water content and plant electrical signals, which are critical parameters for analyzing plant physiological status. The LM-PET achieves double-sided conductivity through soluble electrostatic spinning films and transferring technology, effectively addressing the issue of mismatch between the rigid interface of electronic devices and the surface of delicate plants. The fabricated tattoo electrode can adhere tightly to the leaf surface for a long time and can significantly broaden the scope of moisture monitoring, even in cases of severe wrinkling caused by water loss. At the optimum operating frequency of 100 kHz, the sensitivity of LM-PET can reach 25.4 kΩ%−1. Thus, LM-PETs can record the electrical signals generated when abiotic stresses threaten plants. They are also significant in providing a deeper understanding of the drought adaptation mechanisms of plants and developing drought-resistant varieties. They offer data-driven crop management and decision-making guidance, which is imperative for advancing precision agriculture. Overall, our findings provide valuable insights into the performance of agricultural inputs and facilitate real-time monitoring of plant growth and development.

Removing Human Bottlenecks in Bird Classification Using Camera Trap Images and Deep Learning

Birds are important indicators for monitoring both biodiversity and habitat health; they also play a crucial role in ecosystem management. Declines in bird populations can result in reduced ecosystem services, including seed dispersal, pollination and pest control. Accurate and long-term monitoring of birds to identify species of concern while measuring the success of conservation interventions is essential for ecologists. However, monitoring is time-consuming, costly and often difficult to manage over long durations and at meaningfully large spatial scales. Technology such as camera traps, acoustic monitors and drones provide methods for non-invasive monitoring. There are two main problems with using camera traps for monitoring: (a) cameras generate many images, making it difficult to process and analyse the data in a timely manner; and (b) the high proportion of false positives hinders the processing and analysis for reporting. In this paper, we outline an approach for overcoming these issues by utilising deep learning for real-time classification of bird species and automated removal of false positives in camera trap data. Images are classified in real-time using a Faster-RCNN architecture. Images are transmitted over 3/4G cameras and processed using Graphical Processing Units (GPUs) to provide conservationists with key detection metrics, thereby removing the requirement for manual observations. Our models achieved an average sensitivity of 88.79%, a specificity of 98.16% and accuracy of 96.71%. This demonstrates the effectiveness of using deep learning for automatic bird monitoring.

Assessment of increased intracranial pressure in patients with brain injury

Background: Monitoring and managing elevated intracranial pressure (ICP) is one of the core topics in neurocritical care. Although invasive methods are regarded as standard means, the recent development of non-invasive monitoring devices help clinicians handle ICP issues without additional risks of device-related complications.Current Concepts: According to the Monro–Kellie hypothesis, any brain injury that can cause a mass effect will lead to ICP elevation. Therefore, an ICP surge beyond the capacity of a compensatory reserve will decrease cerebral blood flow and may end up causing secondary brain damage. Indications for invasive ICP monitoring may vary according to the underlying conditions or the severity of brain damage. Regardless, ICP monitoring is considered when there is a risk of ICP elevation. In addition to pressure monitoring, external ventricular drainage catheters are used therapeutically to drain cerebrospinal fluid to reduce ICP. Several ICP monitoring probes are available based on pressure measurement types. Recently, non-invasive ICP monitoring methods have been developed and are increasingly used in patients with severe brain injuries. Pulsatility index from transcranial Doppler ultrasonography, quantitative pupillary light reflex from an automated pupillometer, and optic nerve sheath diameter using ultrasonography are commonly used surrogates for ICP surges in neurointensive care units.Discussion and Conclusion: ICP monitoring is essential for managing patients with severe brain injuries. Understanding the differences among the ICP monitors and determining the appropriate methods for ICP monitoring is necessary for optimizing patients’ care in the neurocritical care unit.

A deep learning method for continuous noninvasive blood pressure monitoring using photoplethysmography

Objective. The aim of this study is to investigate continuous blood pressure waveform estimation from a plethysmography (PPG) signal, thus providing more human cardiovascular status information than traditional cuff-based methods. Approach. The proposed method utilizes the feature extraction ability of a convolution neural network to estimate blood pressure (BP) from PPG signals without the need for waveform analysis and signal feature extraction. Main results. The network achieved mean absolute errors and standard deviations of 2.55 ± 3.92 mmHg for systolic BP (SBP), 1.66 ± 2.76 mmHg for diastolic BP (DBP), and 2.52 ± 3.02 mmHg for overall pressure waveform. The results meet the best levels of the protocols of the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). Significance. The proposed method shows promise for noninvasive continuous BP monitoring in hospital wards and daily life, which can assist in clinical diagnosis, disease treatment, and rehabilitation.

Integration of molecular testing for the personalized management of patients with diffuse large B-cell lymphoma and follicular lymphoma

Diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL) are the most common forms of aggressive and indolent lymphoma, respectively. The majority of patients are cured by standard R-CHOP immunochemotherapy, but 30%-40% of DLBCL and 20% of FL patients relapse or are refractory (R/R). DLBCL and FL are phenotypically and genetically hereterogenous B-cell neoplasms. To date, the diagnosis of DLBCL and FL has been based on morphology, immunophenotyping and cytogenetics. However, next-generation sequencing (NGS) is widening our understanding of the genetic basis of the B-cell lymphomas. In this review we will discuss how integrating the NGS-based characterization of somatic gene mutations with diagnostic or prognostic value in DLBCL and FL could help refine B-cell lymphoma classification as part of a multidisciplinary pathology work-up. We will also discuss how molecular testing can identify candidates for clinical trials with targeted therapies and help predict therapeutic outcome to currently available treatments, including chimeric antigen receptor T-cell, as well as explore the application of circulating cell-free DNA, a non-invasive method for patient monitoring. We conclude that molecular analyses can drive improvements in patient outcomes due to an increased understanding of the different pathogenic pathways affected by each DLBCL subtype and indolent FL vs R/R FL.

Application of optical analysis methods for non-invasive monitoring of blood oxygen saturation level

An intelligent optical system for medical express diagnostics has been developed and tested. A method for visualizing the oxygen status of biological tissues in the form of "digital images" describing the general functional state of the human body is demonstrated. It has been shown that the method of principal components and hierarchical clustering can be used in combination with optical methods for detecting hemoglobin forms in biological tissues to perform non-invasive monitoring and express diagnostics of the oxygen status of the human body. The results obtained show that it is possible to stratify the subjects into risk groups based on optical sensor readings. In comparison with pulse oximetry, the use of which is common for determining the oxygen saturation level of blood, the described method can be employed to estimate peripheral oxygen saturation, and thus thrombosis and ischemia of the extremities can be detected in time.

A non-invasive method of glucose monitoring using FR4 material based microwave antenna sensor

Abstract This work presents a unique non-invasive method for monitoring glucose levels in blood using a planar Yagi–Uda antenna as a microwave sensor. The proposed antenna, operating at 5.5 GHz, exhibits a directional radiation pattern with a peak gain of 6.74 dBi. A low-cost FR4 material of size 30 mm × 40 mm × 1.6 mm is used as a dielectric substrate. A human finger phantom, comprising layers of skin, fat, blood, and bone, is created at 5.5 GHz in EM simulation tool for mimicking a real human finger. The finger phantom is positioned at different locations around the antenna and corresponding frequency shifts are remarked to a variation in glucose concentration from 0–500 mg/dL. An exemplary frequency shift of maximum 26 MHz is recorded when the phantom is placed at the bottom of the antenna. Time domain analysis is also carried out to understand the effect of glucose concentration variation on the output signal amplitude and delay. Simulated antenna results are found to be in stupendous agreement with the measured results. An experiment of placing a real human finger around the fabricated antenna also presents a splendid correspondence with the simulated results. Hence, this mechanism can be expedient for monitoring glucose levels in blood.

330 Remote Monitoring of Pediatric Heart Failure: A Systematic Review

OBJECTIVES/GOALS: Pediatric heart failure is a complex disease presenting as an end-stage condition due to various etiologies and symptoms, causing 14,000 hospitalizations per year in the United States. Currently, there is a lack of objective metrics that are monitored non-invasively. This study explores tools to adapt remote monitoring technologies for pediatrics. METHODS/STUDY POPULATION: The task was determining commercially available and upcoming technologies for remote heart failure monitoring in the pediatric population. Literature and patent reviews were done in various databases with defined eligibility and key terms. Our inclusion criteria were: English peer-reviewed research or review articles, patents filed by cardiac monitoring companies, and independent groups focusing on non-invasive monitoring solutions. Key terms used for the literature search and patent review included pediatric heart failure’, at-home monitoring’, cardiac monitoring’, and non-invasive’. Based on a strong correlation between increased filling pressures and pediatric heart failure, the term filling pressures’ was also included in the search. RESULTS/ANTICIPATED RESULTS: Preliminary searches demonstrate an abundance of adult-use commercially available devices and patents for monitoring heart failure. Yet, there are no FDA-approved devices for use in the pediatric population. Current devices include monitoring metrics such as lung congestion and multi-parametric monitoring to capture heart rate, thoracic impedance, and oxygen saturation levels. In monitoring filling pressures, Abbott CardioMEMs is a leader in measuring pulmonary arterial pressure invasively in adults. Thus, there is a gap for non-invasive monitoring of filling pressures in both pediatric and adult populations. For further review, a larger focus will be placed on non-invasive methods for direct monitoring or extrapolation of cardiac filling pressures. DISCUSSION/SIGNIFICANCE: Current methods of heart failure monitoring are ineffective in serving the pediatric population. Thus, an identified gap exists in non-invasive filling pressure monitoring for pediatric heart failure. This review informs that innovation within this area would address inefficacies within the current paradigm of heart failure monitoring.