Frequent Measurements Research Articles

A rationale for higher ratios of CH4 to CO2 production in warmer anoxic freshwater sediments and soils

A rationale for higher ratios of <scp>CH₄</scp> to <scp>CO₂</scp> production in warmer anoxic freshwater sediments and soils

Estimation of coniferous shoot structure by high precision blue light 3D photogrammetry scanning

Clumping describes the spatial distribution of foliage elements (leaves or needles) within a vegetation canopy. Clumping information is important for determining the radiation transfer through canopies, photosynthesis, and hydrological processes. Clumping of needles in shoots in conifer stands has posed a challenge because optical instruments have generally been incapable of measuring gaps between needles within a shoot. Previous methods for estimating the needle-to-shoot-area ratio have had in common destructive and/or highly labor-intensive aspects. We introduce blue light 3D photogrammetry scanning as a highly efficient technique for estimating shoot-level clumping, which significantly reduces the labor intensity aspect of the previous approaches. We validate the approach by comparing it to the combined photographic/volume displacement method – an established methodology for quantifying shoot-level clumping. We used shoots of two species - Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies L. Karst.) - collected from trees in the Järvselja RAdiation transfer Model Intercomparison (RAMI) pine stand in Estonia. The needle-to-shoot area ratio values were similar to those measured using the traditional combined photographic/volume displacement method. The demonstrated effectiveness and performance of the blue light 3D photogrammetry scanning method shall lead to more frequent actual measurements of 3D shoot structures. Growth in knowledge about this most elementary yet often overlooked level of foliage clumping in canopies shall improve coniferous forest 3D radiative transfer modeling.

Associations between education level, blood-lipid measurements and statin treatment in a Danish primary health care population from 2000 to 2018

Objective To examine whether education level influences screening, monitoring, and treatment of hypercholesterolemia. Design Epidemiological cohort study. Setting Department of Clinical Biochemistry, Copenhagen University Hospital Hvidovre. Subjects Cholesterol blood test results ordered by general practitioners in Greater Copenhagen were retrieved from 2000-2018. Using the International Standard Classification of Education classification, the population was categorized by length of education in three groups (basic education; up to 10 years, intermediate education; 11-12 years, advanced education; 13 years or more). The database comprised 13,019,486 blood sample results from 653,903 patients. Main Outcome Measures Frequency of lipid measurement, prevalence of statin treatment, age and comorbidity at treatment initiation, total cholesterol threshold for statin treatment initiation, and achievement of treatment goal. Results The basic education group was measured more frequently (1.46% absolute percentage difference of total population measured [95% CI 0.86%–2.05%] in 2000 and 9.67% [95% CI 9.20%–10.15%] in 2018) over the period compared to the intermediate education group. The advanced education group was younger when receiving first statin prescription (1.87 years younger [95% CI 1.02–2.72] in 2000 and 1.06 years younger [95% CI 0.54–1.58 in 2018) compared to the intermediate education group. All education groups reached the treatment goals equally well when statin treatment was initiated. Conclusion Higher education was associated with earlier statin prescription, although the higher educated group was monitored less frequently. There was no difference in reaching treatment goal between the three education groups. These findings suggest patients with higher education level achieve an earlier dyslipidemia prevention intervention with an equally satisfying result compared to lower education patients. Key Points Little is known about the role of social inequality as a possible barrier for managing hypercholesterolemia in general practice. Increasing education level was associated to less frequent measurement and less frequent statin treatment. Patients with higher education level were younger, and less comorbidity at first statin prescription. Education level had no effect on frequency of statin treatment-initiated patients reaching the treatment goal was found.

Continuous cuffless blood pressure monitoring with a wearable ring bioimpedance device

Smart rings provide unique opportunities for continuous physiological measurement. They are easy to wear, provide little burden in comparison to other smart wearables, are suitable for nocturnal settings, and can be sized to provide ideal contact between the sensors and the skin at all times. Continuous measuring of blood pressure (BP) provides essential diagnostic and prognostic value for cardiovascular health management. However, conventional ambulatory BP measurement devices operate using an inflating cuff that is bulky, intrusive, and impractical for frequent or continuous measurements. We introduce ring-shaped bioimpedance sensors leveraging the deep tissue sensing ability of bioimpedance while introducing no sensitivity to skin tones, unlike optical modalities. We integrate unique human finger finite element model with exhaustive experimental data from participants and derive optimum design parameters for electrode placement and sizes that yields highest sensitivity to arterial volumetric changes, with no discrimination against varying skin tones. BP is constructed using machine learning algorithms. The ring sensors are used to estimate arterial BP showing peak correlations of 0.81, and low error (systolic BP: 0.11 ± 5.27 mmHg, diastolic BP: 0.11 ± 3.87 mmHg) for >2000 data points and wide BP ranges (systolic: 89–213 mmHg and diastolic: 42–122 mmHg), highlighting the significant potential use of bioimpedance ring for accurate and continuous estimation of BP.

Is frequent measurement of tumor markers beneficial for postoperative surveillance of colorectal cancer?

To determine whether frequent measurement of tumor markers triggers early detection of colorectal cancer recurrence. Of 1,651 consecutive patients undergoing colorectal cancer surgery between 2010 and 2016, 1,050 were included. CEA and CA 19-9 were considered to be postoperative tumor markers and were measured every 3months for 3years, and then every 6months for 2years. Sensitivity analysis of elevated CEA and CA19-9 levels and multivariate analysis of factors associated with elevated CEA and CA19-9 levels were performed. The proportion of triggers for detecting recurrence was determined. The median follow-up period was 5.3years. After applying the exclusion criteria, 1,050 patients were analyzed, 176 (16.8%) of whom were found to have recurrence. After excluding patients with persistently elevated CEA and CA19-9 levels before and after surgery from the 176 patients, 71 (43.6%) of 163 patients had elevated CEA levels and 35 (20.2%) of 173 patients had elevated CA19-9 levels. Sensitivity/positive predictive values for elevated CEA and CA19-9 levels at recurrence were 43.6%/32.3% and 20.2%/32.4%, respectively. Lymph node metastasis was a factor associated with both elevated CEA and CA19-9 levels at recurrence. Of the 176 patients, computed tomography triggered the detection of recurrence in 137 (78%) and elevated tumor marker levels in 13 (7%); the diagnostic lead interval in the latter 13 patients was 1.7months. Tumor marker measurements in surveillance after radical colorectal cancer resection contribute little to early detection, and frequent measurements are unnecessary for stage I patients with low risk of recurrence.

Internet-of-Things (IoT) Platform for Road Energy Efficiency Monitoring

The road transportation sector is a dominant and growing energy consumer. Although investigations to quantify the road infrastructure's impact on energy consumption have been carried out, there are currently no standard methods to measure or label the energy efficiency of road networks. Consequently, road agencies and operators are limited to restricted types of data when managing the road network. Moreover, initiatives meant to reduce energy consumption cannot be measured and quantified. This work is, therefore, motivated by the desire to provide road agencies with a road energy efficiency monitoring concept that can provide frequent measurements over large areas across all weather conditions. The proposed system is based on measurements from in-vehicle sensors. The measurements are collected onboard with an Internet-of-Things (IoT) device, then transmitted periodically before being processed, normalized, and saved in a database. The normalization procedure involves modeling the vehicle's primary driving resistances in the driving direction. It is hypothesized that the energy remaining after normalization holds information about wind conditions, vehicle-related inefficiencies, and the physical condition of the road. The new method was first validated utilizing a limited dataset of vehicles driving at a constant speed on a short highway section. Next, the method was applied to data obtained from ten nominally identical electric cars driven over highways and urban roads. The normalized energy was compared with road roughness measurements collected by a standard road profilometer. The average measured energy consumption was 1.55 Wh per 10 m. The average normalized energy consumption was 0.13 and 0.37 Wh per 10 m for highways and urban roads, respectively. A correlation analysis showed that normalized energy consumption was positively correlated to road roughness. The average Pearson correlation coefficient was 0.88 for aggregated data and 0.32 and 0.39 for 1000-m road sections on highways and urban roads, respectively. An increase in IRI of 1 m/km resulted in a 3.4% increase in normalized energy consumption. The results show that the normalized energy holds information about the road roughness. Thus, considering the emergence of connected vehicle technologies, the method seems promising and can potentially be used as a platform for future large-scale road energy efficiency monitoring.

Extended Kalman Filter State Estimation for Autonomous Competition Robots

Autonomous mobile robot competitions judge based on a robot’s ability to quickly and accurately navigate the game field. This means accurate localization is crucial for creating an autonomous competition robot. Two common localization methods are odometry and computer vision landmark detection. Odometry provides frequent velocity measurements, while landmark detection provides infrequent position measurements. The state can also be predicted with a physics model. These three types of localization can be “fused” to create a more accurate state estimate using an Extended Kalman Filter (EKF). The EKF is a nonlinear full-state estimator that approximates the state estimate with the lowest covariance error when given the sensor measurements, the model prediction, and their variances. In this paper, we demonstrate the effectiveness of the EKF by implementing it on a 4-wheel mecanum-drive robot simulation. The position and velocity accuracy of fusing together various combinations of these three data sources are compared. We also discuss the assumptions and limitations of an EKF.

Extended Kalman Filter State Estimation for Autonomous Competition Robots

Autonomous mobile robot competitions judge based on a robot’s ability to quickly and accurately navigate the game field. This means accurate localization is crucial for creating an autonomous competition. Two common localization methods are odometry and computer vision landmark detection. Odometry provides frequent velocity measurements, while landmark detection provides infrequent position measurements. The state can also be predicted with a physics model. These three types of localization can be “fused” to create a more accurate state estimate using an Extended Kalman Filter (EKF). The EKF is a nonlinear full-state estimator that approximates the state estimate with the lowest covariance error when given the sensor measurements, the model prediction, and their variances. In this paper, we demonstrate the effectiveness of the EKF by implementing it on a 4-wheel mecanum-drive robot simulation. The position and velocity accuracy of fusing together various combinations of these three data sources are compared. We also discuss the assumptions and limitations of an EKF.

Open-Source Strain Gauge System for Monitoring Pressure Distribution of Runner's Feet.

The objective of the research presented in this paper was to provide a novel open-source strain gauge system that shall enable the measurement of the pressure of a runner's feet on the ground and the presentation of the results of that measurement to the user. The system based on electronic shoe inserts with 16 built-in pressure sensors laminated in a transparent film was created, consisting of two parts: a mobile application and a wearable device. The developed system provides a number of advantages in comparison with existing solutions, including no need for calibration, an accurate and frequent measurement of pressure distribution, placement of electronics on the outside of a shoe, low cost, and an open-source approach to encourage enhancements and open collaboration.

Quantification of parasite clearance in Plasmodium knowlesi infections

BackgroundThe incidence of zoonotic Plasmodium knowlesi infections in humans is rising in Southeast Asia, leading to clinical studies to monitor the efficacy of anti-malarial treatments for knowlesi malaria. One of the key outcomes of anti-malarial drug efficacy is parasite clearance. For Plasmodium falciparum, parasite clearance is typically estimated using a two-stage method, that involves estimating parasite clearance for individual patients followed by pooling of individual estimates to derive population estimates. An alternative approach is Bayesian hierarchical modelling which simultaneously analyses all parasite-time patient profiles to determine parasite clearance. This study compared these methods for estimating parasite clearance in P. knowlesi treatment efficacy studies, with typically fewer parasite measurements per patient due to high susceptibility to anti-malarials.MethodsUsing parasite clearance data from 714 patients with knowlesi malaria and enrolled in three trials, the Worldwide Antimalarial Resistance Network (WWARN) Parasite Clearance Estimator (PCE) standard two-stage approach and Bayesian hierarchical modelling were compared. Both methods estimate the parasite clearance rate from a model that incorporates a lag phase, slope, and tail phase for the parasitaemia profiles.ResultsThe standard two-stage approach successfully estimated the parasite clearance rate for 678 patients, with 36 (5%) patients excluded due to an insufficient number of available parasitaemia measurements. The Bayesian hierarchical estimation method was applied to the parasitaemia data of all 714 patients. Overall, the Bayesian method estimated a faster population mean parasite clearance (0.36/h, 95% credible interval [0.18, 0.65]) compared to the standard two-stage method (0.26/h, 95% confidence interval [0.11, 0.46]), with better model fits (compared visually). Artemisinin-based combination therapy (ACT) is more effective in treating P. knowlesi than chloroquine, as confirmed by both methods, with a mean estimated parasite clearance half-life of 2.5 and 3.6 h, respectively using the standard two-stage method, and 1.8 and 2.9 h using the Bayesian method.ConclusionFor clinical studies of P. knowlesi with frequent parasite measurements, the standard two-stage approach (WWARN’s PCE) is recommended as this method is straightforward to implement. For studies with fewer parasite measurements per patient, the Bayesian approach should be considered. Regardless of method used, ACT is more efficacious than chloroquine, confirming the findings of the original trials.

Simple and rapid method for analysis of urinary vancomycin using solid phase extraction and fluorescence spectroscopy

Vancomycin (VCM) is an antimicrobial that is recommended for therapeutic drug monitoring (TDM) for maintaining the efficacy and safety of treatment. The trough monitoring has been used to guide VCM dosing regimens. However, newer guidelines recommend the use of area under the curve/minimum inhibitory concentration (AUC/MIC)-guided vancomycin dosing, and there is a need for easier and more frequent measurements of VCM concentrations. Therefore, in this study, we developed a simple and rapid analytical method for measuring urinary VCM by combining solid-phase extraction and fluorescence analysis. Urine samples are easier and less invasive than blood samples. In addition to the therapeutic range of blood VCM, this method was also able to measure 0.01–1 mg/mL, which is the concentration range of urinary VCM. The accuracy of 10, 20, and 30 μg/mL VCM solutions were between 93.18 and 109.76%. The relative standard deviation (RSD) of intra-day and inter-day analysis were less than 6.25% and 6.28%, respectively. Since this method does not use large equipment, it is expected to be better suited for clinical use.

Performance of home-based self-tonometry (iCare HOME (TA022)) for measuring intraocular pressure among healthy and glaucoma patients

Introduction: The purpose of this study was to compare iCare HOME (TA022) with Goldmann applanation tonometer and to evaluate the self-tonometry measurements among the Indian population. Methods: Eligible patients underwent iCare HOME training through guided demonstration (verbal, pictorial, video) and practised self-tonometry measures using iCare HOME. Certification for independent iCare HOME measure was provided if first iCare HOME intraocular pressure (IOP) measurement fell within ± 5 mmHg of Goldmann applanation tonometer (GAT) measurement which was measured by the trained clinician (principal investigator). Certified participants underwent simulated home self-tonometry measurements using iCare HOME, and agreement with GAT IOP measurements was assessed. Results: Seven of 83 participants (8.43%) failed to complete the study due to difficulty in performing the task, leading to non-certification. Patients who could use the iCare HOME had a mean age of 53 ± 15.55years (53% males; 46% females). Only one in 12 subjects did not qualify to use iCare HOME. The overall mean difference between iCare HOME and GAT was 0.83 mmHg (95%, 3.92 and -2.25). At various pressure ranges, 7-16 mmHg, 17-23 mmHg and &gt;23 mmHg, the mean difference between iCare HOME and GAT was 1.22 mmHg (95%, 4.32 and -1.86), 0.77 mmHg (95%,3.69 and -2.19), -0.11 mmHg (95%, 2.52 and -2.74) respectively. The intra-class correlation coefficient of the iCare HOME device was 0.997(95% CI,0.995-0.998). Conclusions: Patients were able to perform self- tonometry using iCare HOME with good reliability and safety. iCare HOME can be used to address the issue of difficulty in acquiring frequent and diurnal IOP measurements by patients doing self-tonometry from home.

Prognostic value of different anthropometric indices over different measurement intervals to predict mortality in 6-59-month-old children.

To compare the prognostic value of mid-upper arm circumference (MUAC), weight-for-height Z-score (WHZ) and weight-for-age Z-score (WAZ) for predicting death over periods of 1, 3 and 6 months follow-up in children. Pooled analysis of twelve prospective studies examining survival after anthropometric assessment. Sensitivity and false-positive ratios to predict death within 1, 3 and 6 months were compared for three individual anthropometric indices and their combinations. Community-based, prospective studies from twelve countries in Africa and Asia. Children aged 6-59 months living in the study areas. For all anthropometric indices, the receiver operating characteristic curves were higher for shorter than for longer durations of follow-up. Sensitivity was higher for death with 1-month follow-up compared with 6 months by 49 % (95 % CI (30, 69)) for MUAC < 115 mm (P < 0·001), 48 % (95 % CI (9·4, 87)) for WHZ < -3 (P < 0·01) and 28 % (95 % CI (7·6, 42)) for WAZ < -3 (P < 0·005). This was accompanied by an increase in false positives of only 3 % or less. For all durations of follow-up, WAZ < -3 identified more children who died and were not identified by WHZ < -3 or by MUAC < 115 mm, 120 mm or 125 mm, but the use of WAZ < -3 led to an increased false-positive ratio up to 16·4 % (95 % CI (12·0, 20·9)) compared with 3·5 % (95 % CI (0·4, 6·5)) for MUAC < 115 mm alone. Frequent anthropometric measurements significantly improve the identification of malnourished children with a high risk of death without markedly increasing false positives. Combining two indices increases sensitivity but also increases false positives among children meeting case definitions.

The Official Journal of ATTD Advanced Technologies &amp; Treatments for Diabetes Conference 22‐25 February 2023 I Berlin &amp; Online

The Official Journal of ATTD Advanced Technologies &amp; Treatments for Diabetes Conference 22‐25 February 2023 I Berlin &amp; Online

Determination of lithium concentration in capillary blood using volumetric dried blood spots

BackgroundLithium is a cornerstone in the treatment of bipolar disorder and is considered one of the most effective treatments in psychiatry at large. Lithium treatment requires individual dosing with frequent serum concentration measurements due to the narrow therapeutic window and risk of toxicity. There is need for patient-centric methods for lithium monitoring and the use of dried blood spots has recently been proposed for determination of lithium concentration. The purpose of the current study was to assess feasibility of this method by introducing a volumetric technique developed for home-sampling. Materials and methodsLaboratory: Capillary blood was sampled by finger-prick using a volumetric device that collects 10 µL volumes as a dried blood spot. Lithium was measured in the dried blood spots using a validated atomic absorption spectroscopy method. Clinical: Thirty-nine lithium-treated patients were recruited, and dried blood spots and venous blood samples were collected. Routine serum analysis was performed for comparison. ResultsThe range of serum lithium concentrations was 0.41–1.22 mmol/L, and the dried blood spot/serum ratio was 0.78. A strong linear correlation between the two specimens was shown with Pearson’s R = 0.95 (r2 = 0.90). Adding hematocrit as a variable only minimally improved prediction. ConclusionVolumetric dried blood spots is a promising technique for measurement of lithium concentrations. This will enable home-sampling and could potentially save resources, improve compliance, and make treatment safer. This may facilitate the use of lithium treatment in regions where monitoring via venous blood sampling remains difficult. However, the usability of dried blood spots for monitoring lithium treatment longitudinally remains to be examined.

Implementation of five machine learning methods to predict the 52-week blood glucose level in patients with type 2 diabetes.

For the patients who are suffering from type 2 diabetes, blood glucose level could be affected by multiple factors. An accurate estimation of the trajectory of blood glucose is crucial in clinical decision making. Frequent glucose measurement serves as a good source of data to train machine learning models for prediction purposes. This study aimed at using machine learning methods to predict blood glucose for type 2 diabetic patients. We investigated various parameters influencing blood glucose, as well as determined the most effective machine learning algorithm in predicting blood glucose. 273 patients were recruited in this research. Several parameters such as age, diet, family history, BMI, alcohol intake, smoking status etal were analyzed. Patients who had glycosylated hemoglobin less than 6.5% after 52 weeks were considered as having achieved glycemic control and the rest as not achieving it. Five machine learning methods (KNN algorithm, logistic regression algorithm, random forest algorithm, support vector machine, and XGBoost algorithm) were compared to evaluate their performances in prediction accuracy. R 3.6.3 and Python 3.12 were used in data analysis. The statistical variables for which p< 0.05 was obtained were BMI, pulse, Na, Cl, AKP. Compared with the other four algorithms, XGBoost algorithm has the highest accuracy (Accuracy=99.54% in training set and 78.18% in testing set) and AUC values (1.0 in training set and 0.68 in testing set), thus it is recommended to be used for prediction in clinical practice. When it comes to future blood glucose level prediction using machine learning methods, XGBoost algorithm scores the highest in effectiveness. This algorithm could be applied to assist clinical decision making, as well as guide the lifestyle of diabetic patients, in pursuit of minimizing risks of hyperglycemic or hypoglycemic events.

Short-term prediction method of blood glucose based on temporal multi-head attention mechanism for diabetic patients

The hyperglycemic state of people with diabetes can lead to metabolic and healthy disturbances in the body. Diabetes is mainly treated clinically by conservative treatment, which requires frequent and continuous measurement of blood glucose concentration. Accurate blood glucose prediction plays an important role in the future blood glucose management of patients. To improve the accuracy of blood glucose prediction, this paper proposes a short-term prediction method of blood glucose based on temporal multi-head attention mechanism for diabetic patients (PBGTAM). Firstly, a detection algorithm of abnormal blood glucose based on Adaptive Density-Based Spatial Clustering of Applications with Noise is proposed by using an autonomous neighborhood parameter selection method. Secondly, the imputation algorithm based on feature engineering is proposed to fill the missing blood glucose values. Thirdly, we propose temporal multi-head attention model to obtain global and local spatiotemporal features from sequence data, in which a temporal series features module is designed to insight the detail feature so as to keep the useful information contained in the details, and an intensity correlation module is used to obtain global features of sequence data and then to achieve strong correlation between blood glucose data, and a sequence features module is employed to learn temporal sequence features between sequence data to guarantee continuous prediction of future blood glucose values. Finally, the comparative results show that PBGTAM outperforms six state-of-the-art methods in terms of the overall prediction root mean square error, forecasting accuracy for warning, and clinical consistency with 20.57, 84.35% and 85.18%, respectively.

Challenge in hyponatremic patients- the potential of a laboratory-based decision support system for hyponatremia to improve patient's safety.

Hyponatremia is the most frequent electrolyte disorder in hospitalized patients with increased mortality and morbidity. In this study, we evaluated the follow-up diagnostic, the risk of inadequate fast correction and the outcome of patients with profound hyponatremia (pHN), defined as a blood sodium concentration below 120mmol/L. The aim was to identify a promising approach for a laboratory-based clinical decision support system (CDSS). This retrospective study included 378,980 blood sodium measurements of 83,315 cases at a German tertiary care hospital. Hospitalized cases with pHN (n=211) were categorized into two groups by the time needed for a follow-up measurement to be performed (time to control, TTC) as either <12h (group 1: "TTC≤12h", n=118 cases) or >12h (group 2: "TTC>12h", n=93 cases). Length of hospital stay, sodium level at discharge, ward transfers, correction of hyponatremia, and risk of osmotic demyelination syndrome (ODS) due to inadequate fast correction were evaluated with regard to the TTC of sodium blood concentration. pHN was detected in 1,050 measurements (0.3%) in 211 cases. Cases, in which follow-up diagnostics took longer (TTC>12h), achieved a significantly lower sodium correction during their hospitalization (11.2 vs. 16.7mmol/L, p<0.001), were discharged more frequently in hyponatremic states (<135mmol/L; 58 (62.4%) vs. 43 (36.4%), p<0.001) and at lower sodium blood levels (131.2 vs. 135.0mmol/L, p<0.001). Furthermore, for these patients there was a trend toward an increased length of hospital stay (13.1 vs. 8.5days, p=0.089), as well as an increased risk of inadequate fast correction (p<0.001). Our study shows that less frequent follow-up sodium measurements in pHN are associated with worse outcomes. Patients with a prolonged TTC are at risk of insufficient correction of hyponatremia, reduced sodium values at discharge, and possible overcorrection. Our results suggest that a CDSS that alerts treating physicians when a control time of >12h is exceeded could improve patient care in the long term. We are initiating a prospective study to investigate thebenefits of our self-invented CDSS (www.ampel.care) for patients with pHN.

PS-C08-6: COMPARING BLOOD PRESSURE MEASUREMENTS BETWEEN A NOVEL CUFF-LESS EARPHONE-TYPE DEVICE BASED ON GREEN REFLECTED PHOTOPLETHYSMOGRAPHY AND A STANDARD CUFF-BASED SPHYGMOMANOMETER

Objective: Blood pressure (BP) measurement with conventional sphygmomanometers often causes discomfort and pain to the users due to arterial occlusion pressure caused by the inflatable cuff, making it difficult to perform continuous or frequent measurements for rigid blood pressure control. Developing unconstrained wearable electronics that can measure BP continuously would be useful in the healthcare field. We have developed a novel earphone-type device for cuff-less BP monitoring based on photoplethysmography with green light. In the present study, we compared the measured BP values between this novel cuff-less BP monitoring device and a conventional cuff-based sphygmomanometer. Design and Methods: From August 2020 to October 2021, 20 healthy subjects and 40 hypertensive patients (outpatients attending Yokohama City University Hospital and Yokohama Minami Kyosai Hospital, Yokohama, Japan) were recruited. Each participant's BP was measured using the novel cuff-less BP monitoring device as well as a standard cuff-based sphygmomanometer under four different conditions: static, BP rising and falling, and reproducible after the one-month interval. The results of measurements under the four conditions were integrated and analyzed. The BP measurements were determined to be comparable between the standard cuff-based sphygmomanometer and the novel cuff-less earphone-type device, if the mean absolute difference (MAD) of the measurements was within 7 mmHg. We also performed Bland-Altman agreement analyses. Results: The participants’ characteristics were as follows. Male to female ratio was approximately 50% in both groups. Age was 27.5 [interquartile (IQR) 24.5–33.0] years old for the healthy subjects and 65.0 [IQR 49.5–74.0] years old for the patients with hypertension (HTN). BMI was 21.2 [IQR 20.1–22.5] kg/m 2 and 23.8 [IQR 21.8–24.9] kg/ m 2, respectively. MAD between the novel cuff-less device and the reference using the standard cuff-based sphygmomanometer among the healthy subjects was 5.8 mmHg for systolic BP (SBP) and 5.3 mmHg for diastolic BP (DBP). MAD in the patients with HTN was 7.8 mmHg for SBP and 4.4 mmHg for DBP. In Bland-Altman agreement analyses, mean difference in the healthy subjects was 2.7 (95% confidence interval [CI] −13.4, 16.8) mmHg for SBP and 2.4 (95% CI −10.7, 15.5) mmHg for DBP. Mean difference in the patients with HTN was 0.9 (95% CI −20.0, 21.9) mmHg for SBP and 0.7 (95% CI −10.9, 12.3) mmHg for DBP. Conclusions: Our newly developed cuff-less earphone-type device based on green reflected photoplethysmography able to predict BP in healthy subjects, but it was difficult to predict SBP in the patients with HTN.

Human Fatty Liver Monitoring Using Nano Sensor and IoMT

Malfunction of human liver happens due to non-alcoholic fatty liver. Fatty liver measurement is used for grading hepatic steatosis, fibrosis and cirrhosis. The various imaging techniques for measuring fatty liver are Magnetic Resonance Imaging, Ultrasound and Computed Tomography. Imaging modalities lead to the exposure of harmful radiation of electromagnetic waves because of frequent measurement. The continuous monitoring of fatty liver is never achieved through imaging techniques. In this paper, the human fatty liver measured through a Fatty Liver Sensor (FLS). The continuous monitoring of the fatty liver is achieved through the FLS. FLS is fabricated through the screen-printing with materials such as graphene and polyacrylic. The fatty liver sensor is placed around the liver surface for continuous measuring fatty liver. The signal acquired from the fatty liver sensor is processed using blind source separation, a filtering technique removes the random noise from the acquired signal. The denoised signal is processed with tunable Q wavelet transform (TQWT), of FLS based fatty liver measurement fatty liver signals. The continuous fatty liver volume measured and analysis are performed through Long-short term memory and internet of medical things (IoMT). The experimental results are validated with ultrasound lab values.