Assessment of the applicability of a low-cost sensor-based methane monitoring system for continuous multi-channel sampling.
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ABSTRACT:
SUBMITTER: Nagahage ISP
PROVIDER: S-EPMC8302541 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:This publication revises the deteriorated performance of field calibrated low-cost sensor systems after spatial and temporal relocation, which is often reported for air quality monitoring devices that use machine learning models as part of their software to compensate for cross-sensitivities or interferences with environmental parameters. The cause of this relocation problem and its relationship to the chosen algorithm is elucidated using published experimental data in combination with techniques from data science. Thus, the origin is traced back to insufficient sampling of data that is used for calibration followed by the incorporation of bias into models. Biases often stem from non-representative data and are a common problem in machine learning, and more generally in artificial intelligence, and as such a rising concern. Finally, bias is believed to be partly reducible in this specific application by using balanced data sets generated in well-controlled laboratory experiments, although not trivial due to the need for infrastructure and professional competence.
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Project description:In vivo continuous glucose monitoring has posed a significant challenge to glucose sensor development due to the lack of reliable techniques that are non- or at least minimally-invasive. In this proof-of-concept study, we demonstrated the development of a new glucose sensor protein, AcGFP1-GBPcys-mCherry, and an optical sensor assembly, capable of generating quantifiable FRET (fluorescence resonance energy transfer) signals for glucose monitoring. Our experimental data showed that the engineered glucose sensor protein can generate measurable FRET signals in response to glucose concentrations varying from 25 to 800 ?M. The sensor developed based on this protein had a shelf-life of up to 3 weeks. The sensor response was devoid of interference from compounds like galactose, fructose, lactose, mannose, and mannitol when tested at physiologically significant concentrations of these compounds. This new glucose sensor protein can potentially be used to develop implantable glucose sensors for continuous glucose monitoring.
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Project description:Attention to Black Carbon (BC) has been rising due to its effects on human health as well its contribution to climate change. Measurements of BC are challenging, as currently used devices are either expensive or impractical for continuous monitoring. Here, we propose an optoacoustic sensor to address this problem. The sensor utilizes a novel ellipsoidal design for refocusing the optoacoustic signal with minimal acoustic energy losses. To reduce the cost of the system, without sacrificing accuracy, an overdriven laser diode and a Quartz Tuning Fork are used as the light source and the sound detector, respectively. The prototype was able to detect BC particles and to accurately monitor changes in concentration in real time and with very good agreement with a reference instrument. The response of the sensor was linearly dependent on the BC particles concentration with a normalized noise equivalent absorption coefficient (NNEA) for soot equal to 7.39 × 10-9 W cm-1 Hz-1/2. Finally, the prototype was able to perform NO2 measurements, demonstrating its ability to accurately monitor both particulate and gaseous pollutants. The proposed sensor has the potential to offer a significant economic impact for BC environmental measurements and source appointment technologies.
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Project description:BackgroundThe current development of sensor technologies towards ever more cost-effective and powerful systems is steadily increasing the application of low-cost sensors in different horticultural sectors. In plant in vitro culture, as a fundamental technique for plant breeding and plant propagation, the majority of evaluation methods to describe the performance of these cultures are based on destructive approaches, limiting data to unique endpoint measurements. Therefore, a non-destructive phenotyping system capable of automated, continuous and objective quantification of in vitro plant traits is desirable.ResultsAn automated low-cost multi-sensor system acquiring phenotypic data of plant in vitro cultures was developed and evaluated. Unique hardware and software components were selected to construct a xyz-scanning system with an adequate accuracy for consistent data acquisition. Relevant plant growth predictors, such as projected area of explants and average canopy height were determined employing multi-sensory imaging and various developmental processes could be monitored and documented. The validation of the RGB image segmentation pipeline using a random forest classifier revealed very strong correlation with manual pixel annotation. Depth imaging by a laser distance sensor of plant in vitro cultures enabled the description of the dynamic behavior of the average canopy height, the maximum plant height, but also the culture media height and volume. Projected plant area in depth data by RANSAC (random sample consensus) segmentation approach well matched the projected plant area by RGB image processing pipeline. In addition, a successful proof of concept for in situ spectral fluorescence monitoring was achieved and challenges of thermal imaging were documented. Potential use cases for the digital quantification of key performance parameters in research and commercial application are discussed.ConclusionThe technical realization of "Phenomenon" allows phenotyping of plant in vitro cultures under highly challenging conditions and enables multi-sensory monitoring through closed vessels, ensuring the aseptic status of the cultures. Automated sensor application in plant tissue culture promises great potential for a non-destructive growth analysis enhancing commercial propagation as well as enabling research with novel digital parameters recorded over time.
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Project description:BackgroundThe level of continuous glucose monitoring (CGM) accuracy needed for insulin dosing using sensor values (i.e., the level of accuracy permitting non-adjunct CGM use) is a topic of ongoing debate. Assessment of this level in clinical experiments is virtually impossible because the magnitude of CGM errors cannot be manipulated and related prospectively to clinical outcomes.Materials and methodsA combination of archival data (parallel CGM, insulin pump, self-monitoring of blood glucose [SMBG] records, and meals for 56 pump users with type 1 diabetes) and in silico experiments was used to "replay" real-life treatment scenarios and relate sensor error to glycemic outcomes. Nominal blood glucose (BG) traces were extracted using a mathematical model, yielding 2,082 BG segments each initiated by insulin bolus and confirmed by SMBG. These segments were replayed at seven sensor accuracy levels (mean absolute relative differences [MARDs] of 3-22%) testing six scenarios: insulin dosing using sensor values, threshold, and predictive alarms, each without or with considering CGM trend arrows.ResultsIn all six scenarios, the occurrence of hypoglycemia (frequency of BG levels ≤50 mg/dL and BG levels ≤39 mg/dL) increased with sensor error, displaying an abrupt slope change at MARD =10%. Similarly, hyperglycemia (frequency of BG levels ≥250 mg/dL and BG levels ≥400 mg/dL) increased and displayed an abrupt slope change at MARD=10%. When added to insulin dosing decisions, information from CGM trend arrows, threshold, and predictive alarms resulted in improvement in average glycemia by 1.86, 8.17, and 8.88 mg/dL, respectively.ConclusionsUsing CGM for insulin dosing decisions is feasible below a certain level of sensor error, estimated in silico at MARD=10%. In our experiments, further accuracy improvement did not contribute substantively to better glycemic outcomes.
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Project description:BackgroundThe within-person and between-sensor variability of metrics from different interstitial continuous glucose monitoring (CGM) sensors in adults with type 2 diabetes not taking insulin is unclear.MethodsSecondary analysis of data from 172 participants from the Hyperglycemic Profiles in Obstructive Sleep Apnea randomized clinical trial. Participants simultaneously wore Dexcom G4 and Abbott Libre Pro CGM sensors for up to 2 weeks at baseline and again at the 3-month follow-up visit.ResultsAt baseline (up to 2 weeks of CGM), mean glucose for both the Abbott and Dexcom sensors was approximately 150 mg/dL (8.3 mmol/L) and time in range (70180 mg/dL [3.910.0 mmol/L]) was just below 80. When comparing the same sensor at 2 different time points (two 2-week periods, 3 months apart), the within-person coefficient of variation (CVw) in mean glucose was 17.4 (Abbott) and 14.2 (Dexcom). CVw for percent time in range: 20.1 (Abbott) and 18.6 (Dexcom). At baseline, the Pearson correlation of mean glucose from the 2 sensors worn simultaneously was r 0.86, root mean squared error (RMSE), 13 mg/dL (0.7 mmol/L); for time in range, r 0.88, RMSE, 8 percentage points.ConclusionsSubstantial variation was observed within sensors over time and across 2 different sensors worn simultaneously on the same individuals. Clinicians should be aware of this variability when using CGM technology to make clinical decisions.ClinicalTrials.gov Identifier: NCT02454153.
| S-EPMC9898170 | biostudies-literature
Project description:This work reports a first-of-its-kind hybrid wearable physicochemical sensor suite that we call PlantFit for simultaneous measurement of two key phytohormones, salicylic acid, and ethylene, along with vapor pressure deficit and radial growth of stem in live plants. The sensors are developed using a low-cost and roll-to-roll screen printing technology. A single integrated flexible patch that contains temperature, humidity, salicylic acid, and ethylene sensors, is installed on the leaves of live plants. The strain sensor with in-built pressure correction capability is wrapped around the plant stem to provide pressure-compensated stem diameter measurements. The sensors provide real-time information on plant health under different amounts of water stress conditions. The sensor suite is installed on bell pepper plants for 40 days and measurements of salicylic acid, ethylene, temperature, humidity, and stem diameter are recorded daily. In addition, sensors are installed on different parts of the same plant to investigate the spatiotemporal dynamics of water transport and phytohormone responses. Subsequent correlation and principal component analyses demonstrate the strong association between hormone levels, vapor pressure deficit, and water transport in the plant. Our findings suggest that the mass deployment of PlantFit in agricultural settings will aid growers in detecting water stress/deficiency early and in implementing early intervention measures to reduce stress-induced yield decline.
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Project description:ObjectiveRecording seizures using personal seizure diaries can be challenging during everyday life and many seizures are missed or mis-reported. People living with epilepsy could benefit by having a more accurate and objective wearable EEG system for counting seizures that can be used outside of the hospital. The objective of this study was to (1) determine which seizure types can be electrographically recorded from the scalp below the hairline, (2) determine epileptologists' ability to identify electrographic seizures from single-channels extracted from full-montage wired-EEG, and (3) determine epileptologists' ability to identify electrographic seizures from Epilog, a wireless single-channel EEG sensor.MethodsEpilog sensors were worn concurrently during epilepsy monitoring unit (EMU) monitoring. During standard-of-care review, epileptologists were asked if the electrographic portion of the seizure was visible on single channels of wired electrodes at locations proximal to Epilog sensors, and if focal-onset, which electrode was closest to the focus. From these locations, single channels of EEG extracted from wired full-montage EEG and the proximal Epilog sensor were presented to 3 blinded epileptologists along with markers for when known seizures occurred (taken from the standard-of-care review). Control segments at inter-ictal times were included as control. The epileptologists were asked whether a seizure event was visible in the single channel EEG record at or near the marker.ResultsA total of 75 seizures were recorded from 22 of 40 adults that wore Epilog during their visit to the EMU. Epileptologists were able to visualize known seizure activity on at least one of the wired electrodes proximal to Epilog sensors for all seizure events. Epileptologists accurately identified seizures in 71% of Epilog recordings and 84% of single-channel wired recordings and were 92% accurate identifying seizures with Epilog when those seizures ended in a clinical convulsion compared to those that did not (>55%).ConclusionsEpileptologists are able to visualize seizure activity on single-channels of EEG at locations where Epilog sensors are easily placed on the scalp below hairline. Manual review of seizure annotations can be done quickly and accurately (>70% TP and >98% PPV) on single-channel EEG data. Reviewing single-channel EEG is more accurate than what has been reported in the literature on self-reporting seizures in seizure diaries, the current standard of care for seizure counting outside of the EMU.SignificanceWearable EEG will be important for seizure monitoring outside of the hospital. Epileptologists can accurately identify seizures in single-channel EEG, better than patient self-reporting in diaries based on the literature. Automated or semi-automated seizure detection on single channels of EEG could be used in the future to objectively count seizures to complement the standard of care outside of the EMU without the overt burden upon epileptologist review.
| S-EPMC8220094 | biostudies-literature
Project description:Aims and hypothesisThe optimal duration and frequency of short-term continuous glucose monitoring (CGM) to reflect long-term glycemia have not been determined. The Juvenile Diabetes Research Foundation CGM randomized trials provided a large dataset of longitudinal CGM data for this type of analysis.MethodsThe analysis included 185 subjects who had 334 3-month intervals of CGM data meeting specific criteria. For various glucose indices, correlations (r²) were computed for the entire 3-month interval versus selected sampling periods ranging from 3 to 15 days. Other computed agreement measures included median relative absolute difference, values within ± 10% and ± 20% of full value, and median absolute difference.ResultsAs would be expected, the more days of glucose data that were sampled, the higher the correlation with the full 3 months of data. For 3 days of sampling, the r² value ranged from 0.32 to 0.47, evaluating mean glucose, percentage of values 71-180 mg/dL, percentage of values > 180 mg/dL, percentage of values ≤ 70 mg/dL, and coefficient of variation; in contrast, for 15 days of sampling, the r² values ranged from 0.66 to 0.75. The results were similar when the analysis intervals were stratified by age group (8-14, 15-24, and ≥ 25 years), by baseline hemoglobin A1c level (< 7.0% and ≥ 7.0%), and by CGM device type.Conclusions and interpretationOur data suggest that a 12-15-day period of monitoring every 3 months may be needed to optimally assess overall glucose control. Shorter periods of sampling can be useful, but the correlation with 3-month measures of glycemic control is lower.
| S-EPMC6468940 | biostudies-literature
Project description:ObjectiveContinuous glucose monitoring (CGM) has been found to improve glucose control in type 1 diabetic patients. We estimated the cost-effectiveness of CGM versus standard glucose monitoring in type 1 diabetes. RESEARCH DESIGN AND METHODS This societal cost-effectiveness analysis (CEA) was conducted in trial populations in which CGM has produced a significant glycemic benefit (A1C >or=7.0% in a cohort of adults aged >or=25 years and A1C <7.0% in a cohort of all ages). Trial data were integrated into a simulation model of type 1 diabetes complications. The main outcome was the cost per quality-adjusted life-year (QALY) gained.ResultsDuring the trials, CGM patients experienced an immediate quality-of-life benefit (A1C >or=7.0% cohort: 0.70 quality-adjusted life-weeks [QALWs], P = 0.49; A1C <7.0% cohort: 1.39 QALWs, P = 0.04) and improved glucose control. In the long-term, CEA for the A1C >or=7.0% cohort, CGM was projected to reduce the lifetime probability of microvascular complications; the average gain in QALYs was 0.60. The incremental cost-effectiveness ratio (ICER) was $98,679/QALY (95% CI -60,000 [fourth quadrant] to -87,000 [second quadrant]). For the A1C <7.0% cohort, the average gain in QALYs was 1.11. The ICER was $78,943/QALY (15,000 [first quadrant] to -291,000 [second quadrant]). If the benefit of CGM had been limited to the long-term effects of improved glucose control, the ICER would exceed $700,000/QALY. If test strip use had been two per day with CGM long term the ICER for CGM would improve significantly.ConclusionsLong-term projections indicate that CGM is cost-effective among type 1 diabetic patients at the $100,000/QALY threshold, although considerable uncertainty surrounds these estimates.
| S-EPMC2875436 | biostudies-literature