Non-invasive Sensor Research Articles

Is the ICP pulse waveform P2/P1 ratio during -6° head-down tilt associated with relative VO2 peak? A non-invasive intracranial compliance monitoring approach

BackgroundSpaceflights influence intracranial compliance (ICC). P2/P1 ratio, from the intracranial pressure (ICP) waveform, provides information about ICC. Additionally, non-invasive methods for ICC monitoring are needed for spaceflights. Furthermore, astronauts try to maintain good levels of cardiorespiratory fitness before and during spaceflights, not only to sustain exploratory missions, but also to prevent diseases in extreme environments. Objectiveto correlate cardiorespiratory fitness levels with the P2/P1 ratio during a microgravity analog [-6° head-down tilt (HDT)]. Method34 individuals (11 women), mean age of 31.7 (±6.3) years and BMI 24.2 (±3.2) performed a cardiopulmonary exercise testing (CPET) with an incremental protocol on a cycle ergometer to determine the cardiopulmonary fitness through peak relative oxygen uptake (VO2 peak) of each individual. On the second test, which was conducted in an interval of 15 days of the CPET, participants remained for 30 min at HDT with P2/P1 ratio acquired using a non-invasive strain gauge sensor. The average of the last 5 min was used for analysis. The mean P2/P1 ratio and relative VO2 peak were correlated using the Spearman test. ResultsVolunteers presented 1.05 ± 0.2 of P2/P1 ratio and VO2 peak of 47.5 ± 7.6 mL/kg/min. The Spearman test indicated a negative and low correlation between the P2/P1 ratio and VO2 peak (ρ = -0.388; p = 0.023). ConclusionThe study suggests that the better the cardiorespiratory fitness, the better ICC in a weightlessness simulation.

Auto-LIA: The Automated Vision-Based Leaf Inclination Angle Measurement System Improves Monitoring of Plant Physiology.

Plant sensors are commonly used in agricultural production, landscaping, and other fields to monitor plant growth and environmental parameters. As an important basic parameter in plant monitoring, leaf inclination angle (LIA) not only influences light absorption and pesticide loss but also contributes to genetic analysis and other plant phenotypic data collection. The measurements of LIA provide a basis for crop research as well as agricultural management, such as water loss, pesticide absorption, and illumination radiation. On the one hand, existing efficient solutions, represented by light detection and ranging (LiDAR), can provide the average leaf angle distribution of a plot. On the other hand, the labor-intensive schemes represented by hand measurements can show high accuracy. However, the existing methods suffer from low automation and weak leaf-plant correlation, limiting the application of individual plant leaf phenotypes. To improve the efficiency of LIA measurement and provide the correlation between leaf and plant, we design an image-phenotype-based noninvasive and efficient optical sensor measurement system, which combines multi-processes implemented via computer vision technologies and RGB images collected by physical sensing devices. Specifically, we utilize object detection to associate leaves with plants and adopt 3-dimensional reconstruction techniques to recover the spatial information of leaves in computational space. Then, we propose a spatial continuity-based segmentation algorithm combined with a graphical operation to implement the extraction of leaf key points. Finally, we seek the connection between the computational space and the actual physical space and put forward a method of leaf transformation to realize the localization and recovery of the LIA in physical space. Overall, our solution is characterized by noninvasiveness, full-process automation, and strong leaf-plant correlation, which enables efficient measurements at low cost. In this study, we validate Auto-LIA for practicality and compare the accuracy with the best solution that is acquired with an expensive and invasive LiDAR device. Our solution demonstrates its competitiveness and usability at a much lower equipment cost, with an accuracy of only 2. 5° less than that of the widely used LiDAR. As an intelligent processing system for plant sensor signals, Auto-LIA provides fully automated measurement of LIA, improving the monitoring of plant physiological information for plant protection. We make our code and data publicly available at http://autolia.samlab.cn.

Non-invasive glucose detection with smart textiles

The increasing prevalence of diabetes worldwide has heightened the need for innovative and patient-friendly glucose monitoring technologies. Wearable devices that integrate non-invasive glucose sensors into everyday clothing can further enhance user comfort and seamlessly fit into daily routines, making health management more intuitive and less intrusive.

Influence of nanocarrier additives on biomechanical response of a rat skin.

Skin health monitoring focuses on identifying diseases through the assessment of the mechanical properties of the skin. These properties may degrade with time, which can alter the skin's natural frequencies and the form of the modes associated with those frequencies. Exploring the skin's mechanical properties can enhance our understanding of its dynamics, improving clinical trials and diagnostics. In this work, the dynamics of the skin were measured using a laser-based non-invasive optical sensor experiment. We measured the skin's mechanical properties over time by analyzing its resonant frequencies and mode shapes. A nanocarrier gel and ketoconazole cream were topically applied to keep the skin hydrated and facilitate deeper penetration of the additives in the skin. Time-based research was used to assess the effect of different formulations on skin elasticity. Experimental results for the modulus of elasticity were compared with those obtained using Finite Element Analysis (FEA) simulations. We observed a reduction in frequencies of cream and gel-treated skin by 29.98% and 44.029% respectively compared to normal skin (frequency: 263.3 ± 1.18 Hz and Modulus of elasticity: 7.56 ± 2.60 MPa). A decrease in stiffness (function of frequency) attributed to increased water content, was observed in cream- and nanocarrier gel-treated skin compared to normal skin. Experimental and numerical results are found to be consistent with one another. This optical sensor-based approach has the potential for studying diseased skin mechanics and its response to gel and cream treatments, aiming to reduce skin disorder morbidity and severity.

Design of Non-Invasive Blood Glucose Detection Microwave Sensor Based on CSRR

Design of Non-Invasive Blood Glucose Detection Microwave Sensor Based on CSRR

Learning a Hand Model from Dynamic Movements Using High-Density EMG and Convolutional Neural Networks.

Surface electromyography (sEMG) can sense the motor commands transmitted to the muscles. This work presents a deep learning method that can decode the electrophysiological activity of the forearm muscles into the movements of the human hand. We have recorded the kinematics and kinetics of the hand during a wide range of grasps and individual digit movements that cover 22 degrees of freedom of the hand at slow (0.5 Hz) and comfortable (1.5 Hz) movement speeds in 13 healthy participants. The input of the model consists of 320 non-invasive EMG sensors placed on the extrinsic hand muscles. Our network achieves accurate continuous estimation of both kinematics and kinetics, surpassing the performance of comparable networks reported in the literature. By examining the latent space of the network, we find evidence that it mapped EMG activity into the anatomy of the hand at the individual digit level. In contrast to what is observed from the low-pass filtered EMG and linear decoding approaches, we found that the full-bandwidth EMG (monopolar unfiltered) signals during synergistic and individual digit movements contain distinct neural embeddings that encode each movement of the human hand. These manifolds consistently represent the anatomy of the hand and are generalized across participants. Moreover, we found a task-specific distribution of the embeddings without any presence of correlated activations during multi- and individual-digit tasks. The proposed method could advance the control of assistive hand devices by providing a robust and intuitive interface between muscle signals and hand movements.

Interpretable machine learning comprehensive human gait deterioration analysis.

Gait analysis, an expanding research area, employs non-invasive sensors and machine learning techniques for a range of applications. In this study, we investigate the impact of cognitive decline conditions on gait performance, drawing connections between gait deterioration in Parkinson's Disease (PD) and healthy individuals dual tasking. We employ Explainable Artificial Intelligence (XAI) specifically Layer-Wise Relevance Propagation (LRP), in conjunction with Convolutional Neural Networks (CNN) to interpret the intricate patterns in gait dynamics influenced by cognitive loads. We achieved classification accuracies of 98% F1 scores for PD dataset and 95.5% F1 scores for the combined PD dataset. Furthermore, we explore the significance of cognitive load in healthy gait analysis, resulting in robust classification accuracies of 90% ± 10% F1 scores for subject cognitive load verification. Our findings reveal significant alterations in gait parameters under cognitive decline conditions, highlighting the distinctive patterns associated with PD-related gait impairment and those induced by multitasking in healthy subjects. Through advanced XAI techniques (LRP), we decipher the underlying features contributing to gait changes, providing insights into specific aspects affected by cognitive decline. Our study establishes a novel perspective on gait analysis, demonstrating the applicability of XAI in elucidating the shared characteristics of gait disturbances in PD and dual-task scenarios in healthy individuals. The interpretability offered by XAI enhances our ability to discern subtle variations in gait patterns, contributing to a more nuanced comprehension of the factors influencing gait dynamics in PD and dual-task conditions, emphasizing the role of XAI in unraveling the intricacies of gait control.

A wearable AuNP enhanced metal-organic gel (Au@MOG) sensor for sweat glucose detection with ultrahigh sensitivity.

The demand for sensitive and non-invasive sensors for monitoring glucose levels in sweat has grown considerably in recent years. This study presents the development of a wearable sensor for sweat glucose detection with ultrahigh sensitivity. The sensor was fabricated by embedding Au nanoparticles (AuNPs) and metal-organic gels (MOGs) on nickel foam (NF). A non-enzymatic electrocatalytic glucose sensor has been developed to combine the three-dimensional network of MOGs with more active sites favourable for glucose diffusion and the transfer of electrons from glucose to the electrode. These results show that the sensor has an ultrahigh sensitivity of 13.94 mA mM-1 cm-2, a linear detection range between 2 and 600 μM, and a lower detection limit as low as 1 μM (signal/noise = 3) with comparable accuracy and reliability under non-alkaline conditions to those of high-pressure ion chromatography (HPIC). Furthermore, a wearable sweat glucose sensor has been constructed by sputtering an Au conductive layer on a flexible polydimethylsiloxane (PDMS) substrate and coating it with Au@MOGs. Our work demonstrates that the combination of Au NPs and MOGs can enhance the sensitivity and activity of these materials, making them useful for electrocatalytic glucose monitoring with ultrahigh sensitivity.

Non-Contact Thermal and Acoustic Sensors with Embedded Artificial Intelligence for Point-of-Care Diagnostics.

This work involves exploring non-invasive sensor technologies for data collection and preprocessing, specifically focusing on novel thermal calibration methods and assessing low-cost infrared radiation sensors for facial temperature analysis. Additionally, it investigates innovative approaches to analyzing acoustic signals for quantifying coughing episodes. The research integrates diverse data capture technologies to analyze them collectively, considering their temporal evolution and physical attributes, aiming to extract statistically significant relationships among various variables for valuable insights. The study delineates two distinct aspects: cough detection employing a microphone and a neural network, and thermal sensors employing a calibration curve to refine their output values, reducing errors within a specified temperature range. Regarding control units, the initial implementation with an ESP32 transitioned to a Raspberry Pi model 3B+ due to neural network integration issues. A comprehensive testing is conducted for both fever and cough detection, ensuring robustness and accuracy in each scenario. The subsequent work involves practical experimentation and interoperability tests, validating the proof of concept for each system component. Furthermore, this work assesses the technical specifications of the prototype developed in the preceding tasks. Real-time testing is performed for each symptom to evaluate the system's effectiveness. This research contributes to the advancement of non-invasive sensor technologies, with implications for healthcare applications such as remote health monitoring and early disease detection.

Towards Non-Invasive and Continuous Blood Pressure Monitoring in Neonatal Intensive Care Using Artificial Intelligence: A Narrative Review.

Preterm birth is a live birth that occurs before 37 completed weeks of pregnancy. Approximately 11% of babies are born preterm annually worldwide. Blood pressure (BP) monitoring is essential for managing the haemodynamic stability of preterm infants and impacts outcomes. However, current methods have many limitations associated, including invasive measurement, inaccuracies, and infection risk. In this narrative review, we find that artificial intelligence (AI) is a promising tool for the continuous measurement of BP in a neonatal cohort, based on data obtained from non-invasive sensors. Our findings highlight key sensing technologies, AI techniques, and model assessment metrics for BP sensing in the neonatal cohort. Moreover, our findings show that non-invasive BP monitoring leveraging AI has shown promise in adult cohorts but has not been broadly explored for neonatal cohorts. We conclude that there is a significant research opportunity in developing an innovative approach to provide a non-invasive alternative to existing continuous BP monitoring methods, which has the potential to improve outcomes for premature babies.

Conductive and Eco-friendly Biomaterials-based Hydrogels for Noninvasive Epidermal Sensors: A Review.

As noninvasive wearable electronic devices, epidermal sensors enable continuous, real-time, and remote monitoring of various human physiological parameters. Conductive biomaterials-based hydrogels as sensor matrix materials have good biocompatibility, biodegradability, and efficient stimulus response capabilities and are widely applied in motion monitoring, healthcare, and human-machine interaction. However, biomass hydrogel-based epidermal sensing devices still need excellent mechanical properties, prolonged stability, multifunctionality, and extensive practicality. Therefore, this paper reviews the common biomass hydrogel materials for epidermal sensing (proteins, polysaccharides, polyphenols, etc.) and the various types of noninvasive sensing devices (strain/pressure sensors, temperature sensors, glucose sensors, electrocardiograms, etc.). Moreover, this review focuses on the strategies of scholars to enhance sensor properties, such as strength, conductivity, stability, adhesion, and self-healing ability. This work will guide the preparation and optimization of high-performance biomaterials-based hydrogel epidermal sensors.

Exploring the Gut-Brain Connection in Gastroparesis With Autonomic and Gastric Myoelectric Monitoring.

The goal of this study was to identify autonomic and gastric myoelectric biomarkers from throughout the day that differentiate patients with gastroparesis, diabetics without gastroparesis, and healthy controls, while providing insight into etiology. We collected 19 24-hour recordings of electrocardiogram (ECG) and electrogastrogram (EGG) data from healthy controls and patients with diabetic or idiopathic gastroparesis. We used physiologically and statistically rigorous models to extract autonomic and gastric myoelectric information from the ECG and EGG data, respectively. From these, we constructed quantitative indices which differentiated the distinct groups and demonstrated their application in automatic classification paradigms and as quantitative summary scores. We identified several differentiators that separate healthy controls from gastroparetic patient groups, specifically around sleep and meals. We also demonstrated the downstream utility of these differentiators in automatic classification and quantitative scoring paradigms. Even with this small pilot dataset, automated classifiers achieved an accuracy of 79% separating autonomic phenotypes and 65% separating gastrointestinal phenotypes. We also achieved 89% accuracy separating controls from gastroparetic patients in general and 90% accuracy separating diabetics with and without gastroparesis. These differentiators also suggested varying etiologies for different phenotypes. The differentiators we identified were able to successfully distinguish between several autonomic and gastrointestinal (GI) phenotypes using data collected while at-home with non-invasive sensors. Autonomic and gastric myoelectric differentiators, obtained using at-home recording of fully non-invasive signals, can be the first step towards dynamic quantitative markers to track severity, disease progression, and treatment response for combined autonomic and GI phenotypes.

A Smart Sensing Technologies-Based Intelligent Healthcare System for Diabetes Patients

An Artificial Intelligence (AI)-enabled human-centered smart healthcare monitoring system can be useful in life saving, specifically for diabetes patients. Diabetes and heart patients need real-time and remote monitoring and recommendation-based medical assistance. Such human-centered smart healthcare systems can not only provide continuous medical assistance to diabetes patients but can also reduce overall medical expenses. In the last decade, machine learning has been successfully implemented to design more accurate and precise medical applications. In this paper, a smart sensing technologies-based architecture is proposed that uses AI and the Internet of Things (IoT) for continuous monitoring and health assistance for diabetes patients. The designed system senses various health parameters, such as blood pressure, blood oxygen, blood glucose (non-invasively), body temperature, and pulse rate, using a wrist band. We also designed a non-invasive blood sugar sensor using a near-infrared (NIR) sensor. The proposed system can predict the patient’s health condition, which is evaluated by a set of machine learning algorithms with the support of a fuzzy logic decision-making system. The designed system was validated on a large data set of 50 diabetes patients. The results of the simulation manifest that the random forest classifier gives the highest accuracy in comparison to other machine learning algorithms. The system predicts the patient’s condition accurately and sends it to the doctor’s portal.

Carbon fiber-based multichannel solid-contact potentiometric ion sensors for real-time sweat electrolyte monitoring

Solid-contact ion-selective electrodes (SC-ISEs) feature miniaturization and integration that have gained extensive attention in non-invasive wearable sweat electrolyte sensors. The state-of-the-art wearable SC-ISEs mainly use polyethylene terephthalate, gold and carbon nanotube fibers as flexible substrates but suffer from uncomfortableness, high cost and biotoxicity. Herein, we report carbon fiber-based SC-ISEs to construct a four-channel wearable potentiometric sensor for sweat electrolytes monitoring (Na+/K+/pH/Cl−). The carbon fibers were extracted from commercial cloth, of which the starting point is addressing the cost and reproducibility issues for flexible SC-ISEs. The bare carbon fiber electrodes exhibited reversible voltammetric and stable impedance performances. Further fabricated SC-ISEs based on corresponding ion-selective membranes disclosed Nernstian sensitivity and anti-interface ability toward both ions and organic species in sweat. Significantly, these carbon fiber-based SC-ISEs revealed high reproducibility of standard potentials between normal and bending states. Finally, a textile-based sensor was integrated with a solid-contact reference electrode, which realized on-body sweat electrolytes analysis. The results displayed high accuracy compared with ex-situ tests by ion chromatography. This work highlights carbon fiber-based multichannel wearable potentiometric ion sensors with low cost, biocompatibility and reproducibility.

Taking Advantage of a Luminescent ESIPT-Based Zr-MOF for Fluorochromic Detection of Multiple External Stimuli: Acid and Base Vapors, Mechanical Compression, and Temperature.

Luminescent materials responsive to external stimuli have captivated great attention owing to their potential implementation in noninvasive photonic sensors. Luminescent metal-organic frameworks (LMOFs), a type of porous crystalline material, have emerged as one of the most promising candidates for these applications. Moreover, LMOFs constructed with organic linkers that undergo excited-state intramolecular proton-transfer (ESIPT) reactions are particularly relevant since changes in the surrounding environment induce modifications in their emission properties. Herein, an ESIPT-based LMOF, UiO-66-(OH)2, has been synthesized, spectroscopically and photodynamically characterized, and tested for detecting multiple external stimuli. First, the spectroscopic and photodynamic characterization of the organic linker (2,5-dihydroxyterephthalic acid (DHT)) and the UiO-66-(OH)2 MOF demonstrates that the emission properties are mainly governed by the enol → keto tautomerization, occurring in the organic linker via the ESIPT reaction. Afterward, the UiO-66-(OH)2 MOF proves for the first time to be a promising candidate to detect vapors of acid (HCl) and base (Et3N) toxic chemicals, changes in the mechanical compression (exercised pressure), and changes in the temperature. These results shed light on the potential of ESIPT-based LMOFs to be implemented in the development of advanced optical materials and luminescent sensors.

Non-invasive antenna sensor for dielectric material identification at a distance

A planar leaky wave antenna is used as a non-invasive microwave sensor for dielectric material identification at a distance. The antenna radiation pattern main lobe points to specific angles according to the dielectric material under test, placed above the antenna. In this work, two materials were used, and compared to the antenna with no material under test, namely quartz with a dielectric constant of 3.75 and borosilicate glass with a dielectric constant of 4.5. The antenna sensor described in this work, is designed to operate at 18 GHz and has a total dimension of 150 × 32 mm. The experimental methods used in this work consist of validating the sensor antenna design in Ansys HFSS®, electromagnetic simulation software and comparing simulated results with measured tests performed in an anechoic chamber. Dielectrics are placed over the sensor antenna and measured radiation patterns recorded. The sensor antenna presented displacement in the main lobe of its radiation pattern when in contact with a dielectric under test. In the presence of quartz, the maximum radiation angle is 33°, in the presence of borosilicate glass the angle shifted to 37°.

Comparing body temperature measurements using the double sensor method within a wearable device with oral and core body temperature measurements using medical grade thermometers-a short report.

Introduction: Body temperature is essential for diagnosing, managing, and following multiple medical conditions. There are several methods and devices to measure body temperature, but most do not allow continuous and prolonged measurement of body temperature. Noninvasive skin temperature sensor combined with a heat flux sensor, also known as the "double sensor" technique, is becoming a valuable and simple method for frequently monitoring body temperature. Methods: Body temperature measurements using the "double sensor" method in a wearable monitoring device were compared with oral and core body temperature measurements using medical grade thermometers, analyzing data from two prospective clinical trials of different clinical scenarios. One study included 45 hospitalized COVID-19 patients in which oral measurements were taken using a hand-held device, and the second included 18 post-cardiac surgery patients in which rectal measurements were taken using a rectal probe. Results: In study 1, Bland-Altman analysis showed a bias of -0.04°C [0.34-(-0.43)°C, 95% LOA] with a correlation of 99.4% (p < 0.001). In study 2, Bland-Altman analysis showed a bias of 0.0°C [0.27-(-0.28)°C, 95% LOA], and the correlation was 99.3% (p < 0.001). In both studies, stratifying patients based on BMI and skin tone showed high accordance in all sub-groups. Discussion: The wearable monitor showed high correlation with oral and core body temperature measurements in different clinical scenarios.

Accuracy of a Dual-Sensor Heat-Flux (DHF) Non-Invasive Core Temperature Sensor in Pediatric Patients Undergoing Surgery.

Accurate temperature measurement is crucial for the perioperative management of pediatric patients, and non-invasive thermometry is necessary when invasive methods are infeasible. A prospective observational study was conducted on 57 patients undergoing elective surgery. Temperatures were measured using a dual-sensor heat-flux (DHF) thermometer (Tcore™) and a rectal temperature probe (TRec), and the agreement between the two measurements was assessed. The DHF measurements showed a bias of +0.413 °C compared with those of the TRec. The limits of agreement were broader than the pre-defined ±0.5 °C range (-0.741 °C and +1.567 °C). Although the DHF sensors tended to overestimate the core temperature compared to the rectal measurements, an error grid analysis demonstrated that 95.81% of the DHF measurements would not have led to a wrong clinical decision, e.g., warming or cooling when not necessary. In conclusion, the low number of measurements that would have led to incorrect decisions suggests that the DHF sensor can be considered an option for continuous temperature measurement when more invasive methods are infeasible.

Abstract 14030: Hemodynamic Metrics Can Be Accurately Assessed by a Non-Invasive Sensor Patch: The Portable Technology for Wireless Cardiomyopathy Tracking (Patchwrk) Study

Background: Echocardiographic timing intervals provide valuable prognostic information in patients with pre-clinical cardiac dysfunction. Diastolic filling time (DFT) can identify left bundle branch block (LBBB) patients at higher risk for cardiomyopathy. The need for specialized equipment limits the utility of echo in longitudinal assessment. Hypothesis: A non-invasive multimodal sensor Sansa patch will enable accurate assessment of hemodynamic parameters such as DFT, ejection time (ET), and pre-ejection period (PEP). Methods: Fifty patients scheduled for clinically indicated echocardiography were prospectively enrolled and had simultaneous application of the Sansa patch at the time of echocardiography. Timing intervals were analyzed using continuous wave (CW), pulsed wave (PW), and tissue Doppler imaging (TDI). Data from the Sansa electrocardiogram (ECG), seismocardiogram (SCG), and phonocardiogram (PCG) were independently analyzed to identify valve openings/closures to estimate DFT, ET and PEP. Agreement between echo and Sansa estimates was assessed using the coefficient of determination (R 2 ) and compared with agreement between different echo views. Results: Forty-six of the 50 patients (92%) had data of sufficient quality for analysis. The mean ejection fraction was 53±8% and 11 patients (24%) had LBBB. By echo, the mean DFT, ET, and PEP were 408±137 milliseconds (ms), 297±35ms, and 107±32ms respectively. Sansa estimates for DFT, ET and PEP were 431±135ms, 286±43ms, and 89±34ms respectively. The R 2 for Sansa DFT compared to echo was 0.85. Comparing TDI and PW estimates of DFT within the same patients demonstrated an R 2 of 0.82. Conclusion: Non-invasive Sansa patch monitoring accurately identified key cardiac timing intervals within the error observed between standard echo views. These measures have prognostic value and monitoring them in the ambulatory setting may allow early cardiomyopathy detection and upstream intervention.

Abstract 370: Oral-Mucosal PCO 2 During Hemorrhagic Shock Closely Monitors Its Time Course, Severity, and Reversal Outperforming Blood Lactate Measurement

Objective: Oral-Mucosal PCO 2 (P OM CO 2 ) has been shown to closely correlate with the splanchnic mucosal PCO 2 and provide information on its microcirculation. We used a swine model of controlled hemorrhagic shock to assess the potential clinical relevance of monitoring P OM CO 2 to assess its clinical course, severity, and treatment effect. Methods: The study was performed on six male domestic pigs (37.9±1.4 kg). P OM CO 2 was measured using a non-invasive disposable sensor clipped to the cheek, developed by ExoStat Medical. Blood was removed over 120 minutes from the right atrium using a LabVIEW-controlled pump according to a mono-exponential decay curve modeling spontaneous bleeding and reinfused in 20 minutes observing the animal for a total of 180 minutes. Results: A total of 1,491±193 ml corresponding to 65.5% of the estimated blood volume were removed inducing the characteristic hemodynamic features of hemorrhagic shock including tachycardia, hypotension, reduced cardiac output, and reduced systemic oxygen delivery with increased systemic oxygen extraction. P OM CO 2 closely paralleled the time course of blood removal showing an early and steady increase from 85±5 mmHg at baseline to 144±32 mmHg after 120 minutes. Blood reinfusion rapidly reduced the P OM CO 2 to 130±17 mmHg after 20 minutes and 96±14 mmHg at the end of 180 minutes, coincident with reversal of hemorrhagic shock. P OM CO 2 also closely tracked changes in systemic oxygen extraction. Concurrent measurements of blood lactate demonstrated a slower increase during the hemorrhagic shock period (from 1.1±0.3 to 3.0±0.3 mmol/l) with further increase (not decrease) to 5.4±2.0 mmol/l after blood reinfusion, remaining at 4.3±0.8 mmol/l by the end of the 180-minute observation period. Conclusions: The study demonstrated capability of P OM CO 2 to closely and non-invasively track hemodynamic and metabolic changes occurring during hemorrhagic shock. P OM CO 2 outperformed blood lactate which exhibited a slower and delayed response. Thus, P OM CO 2 may provide a clinically practical and non-invasive means to monitor hemorrhagic shock assessing its severity, clinical course, and treatment effect.