Contact pressure-guided wearable dual-channel bioimpedance device for continuous hemodynamic monitoring.

Wearable devices for continuous monitoring of arterial pulse waves have the potential to improve the diagnosis, prognosis, and management of cardiovascular diseases. These pulse wave signals are often affected by the contact pressure between the wearable device and the skin, limiting the accuracy and reliability of hemodynamic parameter quantification. Here, we report a continuous hemodynamic monitoring device that enables the simultaneous recording of dual-channel bioimpedance and quantification of pulse wave velocity (PWV) used to calculate blood pressure (BP). Our investigations demonstrate the effect of contact pressure on bioimpedance and PWV. The pulsatile bioimpedance magnitude reached its maximum when the contact pressure approximated the mean arterial pressure of the subject. We employed PWV to continuously quantify BP while maintaining comfortable contact pressure for prolonged wear. The mean absolute error and standard deviation of the error compared to the reference value were determined to be 0.1 ± 3.3 mmHg for systolic BP, 1.3 ± 3.7 mmHg for diastolic BP, and -0.4 ± 3.0 mmHg for mean arterial pressure when measurements were conducted in the lying down position. This research demonstrates the potential of wearable dual-bioimpedance sensors with contact pressure guidance for reliable and continuous hemodynamic monitoring.

Freeze-Driven Synthesis of DNA Hairpin-Conjugated Gold Nanoparticle Biosensors for Dual-Mode Detection.

Freeze-based immobilization of deoxyribonucleic acid (DNA) oligonucleotides on gold nanoparticles (AuNPs) is highly efficient for single-stranded oligonucleotides but typically does not accommodate structures such as snap-cooled DNA hairpins (Sc-HPs) and snap-cooled molecular beacons (Sc-MBs) frequently used for biorecognition applications. Recognizing this limitation, we have developed a modified, freeze-based technique specifically designed to enable the adsorption of such hairpin oligonucleotides onto AuNP surfaces while ensuring that they retain their biosensing capabilities. Successful hairpin oligonucleotide conjugation of varying lengths to a wide range of AuNP diameters was corroborated by dynamic light scattering, ζ-potential, and UV-vis spectrophotometry. Moreover, we conducted a thorough evaluation of this modified method, confirming the retention of the sensing functions of Sc-HPs and Sc-MBs. This advancement not only offers a more efficient route for DNA hairpin conjugation but also elucidates the underlying biorecognition functions, with implications for broader applications in molecular diagnostics.

Arterial Pulse Wave Velocity Signal Reconstruction Using Low Sampling Rates.

Pulse Wave Velocity (PWV) analysis is valuable for assessing arterial stiffness and cardiovascular health and potentially for estimating blood pressure cufflessly. However, conventional PWV analysis from two transducers spaced closely poses challenges in data management, battery life, and developing the device for continuous real-time applications together along an artery, which typically need data to be recorded at high sampling rates. Specifically, although a pulse signal consists of low-frequency components when used for applications such as determining heart rate, the pulse transit time for transducers near each other along an artery takes place in the millisecond range, typically needing a high sampling rate. To overcome this issue, in this study, we present a novel approach that leverages the Nyquist-Shannon sampling theorem and reconstruction techniques for signals produced by bioimpedance transducers closely spaced along a radial artery. Specifically, we recorded bioimpedance artery pulse signals at a low sampling rate, reducing the data size and subsequently algorithmically reconstructing these signals at a higher sampling rate. We were able to retain vital transit time information and achieved enhanced precision that is comparable to the traditional high-rate sampling method. Our research demonstrates the viability of the algorithmic method for enabling PWV analysis from low-sampling-rate data, overcoming the constraints of conventional approaches. This technique has the potential to contribute to the development of cardiovascular health monitoring and diagnosis using closely spaced wearable devices for real-time and low-resource PWV assessment, enhancing patient care and cardiovascular disease management.

Add-on soft electronic interfaces for continuous cuffless blood pressure monitoring.

Continuous monitoring of arterial blood pressure is clinically important for the diagnosis and management of cardiovascular diseases. Soft electronic devices with skin-like properties show promise in a wide range of applications, including the human-machine interface, the Internet of things, and health monitoring. Here, we report the use of add-on soft electronic interfaces to address the connection challenges between soft electrodes and rigid data acquisition circuitry for bioimpedance monitoring of cardiac signals, including heart rate and cuffless blood pressure. Nanocomposite films in add-on electrodes provide robust electrical and mechanical contact with the skin and the rigid circuitry. We demonstrate bioimpedance sensors composed of add-on electrodes for continuous blood pressure monitoring with high accuracy. Specifically, the bioimpedance collected with add-on nanocomposite electrodes shows a signal-to-noise ratio of 37.0 dB, higher than the ratio of 25.9 dB obtained with standard silver/silver chloride (Ag/AgCl gel) electrodes. Although the sample set is low, the continuously measured systolic and diastolic blood pressure offer accuracy of -2.0 ± 6.3 mmHg and -4.3 ± 3.9 mmHg, respectively, confirming the grade A performance based on the IEEE standard. These results show promise in bioimpedance measurements with add-on soft electrodes for cuffless blood pressure monitoring.

A Polarity-Sensitive Far-Red Fluorescent Probe for Glucose Sensing through Skin.

The field of glucose biosensors for diabetes management has been of great interest over the past 60 years. Continuous glucose monitoring (CGM) is important to continuously track the glucose level to provide better management of the disease. Concanavalin A (ConA) can reversibly bind to glucose and mannose molecules and form a glucose biosensor via competitive binding. Here, we developed a glucose biosensor using ConA and a fluorescent probe, which generated a fluorescent intensity change based on solvatochromism, the reversible change in the emission spectrum dependent on the polarity of the solvent. The direction in which the wavelength shifts as the solvent polarity increases can be defined as positive (red-shift), negative (blue-shift), or a combination of the two, referred to as reverse. To translate this biosensor to a subcutaneously implanted format, Cyanine 5.5 (Cy5.5)-labeled small mannose molecules were used, which allows for the far-red excitation wavelength range to increase the skin penetration depth of the light source and returned emission. Three Cy5.5-labeled small mannose molecules were synthesized and compared when used as the competing ligand in the competitive binding biosensor. We explored the polarity-sensitive nature of the competing ligands and examined the biosensor's glucose response. Cy5.5-mannotetraose performed best as a biosensor, allowing for the detection of glucose from 25 to 400 mg/dL. Thus, this assay is responsive to glucose within the physiologic range when its concentration is increased to levels needed for an implantable design. The biosensor response is not statistically different when placed under different skin pigmentations when comparing the percent increase in fluorescence intensity. This shows the ability of the biosensor to produce a repeatable signal across the physiologic range for subcutaneous glucose monitoring under various skin tones.

Exploring the Clinical Utility of Raman Spectroscopy for Point-of-Care Cardiovascular Disease Biomarker Detection.

A variety of innovative point-of-care (POC) solutions using Raman systems have been explored. However, the vast effort is in assay development, while studies of the characteristics required for Raman spectrometers to function in POC applications are lacking. In this study, we tested and compared the performance of eight commercial Raman spectrometers ranging in size from benchtop Raman microscopes to portable and handheld Raman spectrometers using paper fluidic cartridges, including their ability to detect cardiac troponin I and heart fatty acid binding protein, both of which are well-established biomarkers for evaluating cardiovascular health. Each spectrometer was evaluated in terms of excitation wavelength, laser characteristics, and ease of use to investigate POC utility. We found that the Raman spectrometers equipped with 780 and 785 nm laser sources exhibited a reduced background signal and provided higher sensitivity compared to those with 633 and 638 nm laser sources. Furthermore, the spectrometer equipped with the single acquisition line readout functionality showed improved performance when compared to the point scan spectrometers and allowed measurements to be made faster and easier. The portable and handheld spectrometers also showed similar detection sensitivity to the gold standard instrument. Lastly, we reduced the laser power for the spectrometer with single acquisition line readout capability to explore the system performance at a laser power that change the classification from a Class 3B laser device to a Class 3R device and found that it showed comparable performance. Overall, these findings show that portable Raman spectrometers have the potential to be used in POC settings with accuracy comparable to laboratory-grade instruments, are relatively low-cost, provide fast signal readout, are easy to use, and can facilitate access for underserved communities.

Monitoring gestational diabetes at the point-of-care via dual glycated albumin lateral flow assays in conjunction with a handheld reader.

Chronic conditions like diabetes require monitoring of vital biomarkers over extended periods of time. Monitoring gestational diabetes mellitus (GDM) is crucial to avoid short- and long-term adverse effects on both mother and infant. Providing monitoring systems to patients at the point-of-care (POC) has the potential to help mitigate these effects. In this manuscript, we propose the use of a sensing system combining lateral flow assays (LFAs) with a handheld colorimetric reader for use in tracking the glycemic status of a GDM patient at the POC. Current strategies of GDM monitoring include glucose and HbA1c measurements. These are often too frequent or not frequent enough for effective monitoring. Hence, we have developed a sensor for an intermediate interval biomarker - glycated albumin (GA). Based on the half-life of the protein, GA is measured once every 2-3 weeks. Here we first present two lateral flow assays, one for GA and another for total serum albumin used in conjunction with a handheld reader to read the colorimetric signals. Both assays have a sandwich aptamer format and measure the target proteins in their physiologically relevant ranges. The GA assay has a dynamic range of 3-20 mg ml-1 and the serum albumin assay has a range of 20-50 mg ml-1 without any sample dilution. Both LFAs were then incorporated into a single dual assay cartridge such that both assays could run simultaneously and provide the % glycated albumin value from a single test. Thus, the dual assay cartridge plus reader system has the potential to provide an effective platform for measuring GA for tracking GDM at the POC.

Geometry design for a fully insertable glucose biosensor with multimodal optical readout.

Significance: Insertable optical continuous glucose monitors (CGMs) with wearable readers are a strong option for monitoring individuals with diabetes. However, a fully insertable CGM requires a small form factor while still delivering sufficient signal to be read through tissue by an external device. Previous work has suggested that a multimodal repeating unit (barcode) approach may meet these requirements, but the biosensor geometry must be optimized to meet performance criteria. Aim: This work details in silico trials conducted to evaluate the geometry of a fully insertable multimodal optical biosensor with respect to both optical output and species diffusion in vivo. Approach: Monte Carlo modeling is used to evaluate the luminescent output of three presupposed biosensor designs based on size constraints for an injectable and logical placement of the bar code compartments. Specifically, the sensitivity of the luminescent output to displacement of the biosensor in the X and Y directions, overall size of the selected design, and size of an individual repeating unit are analyzed. Further, an experimentally validated multiphysics model is used to evaluate the diffusion and reaction of glucose and oxygen within the biosensor to estimate the occurrence of chemical crosstalk between the assay components. Results: A stacked cylinder multimodal biosensor 4.4 mm in length with repeating units 0.36 mm in length was found to yield a greater luminescent output than the current "barcode" biosensor design. In addition, it was found that a biosensor with enzymatic elements does not significantly deplete glucose locally and thus does not impact the diffusion profile of glucose in adjacent compartments containing nonenzymatic assays. Conclusions: Computational modeling was used to design the geometry of a multimodal, insertable, and optical CGM to ensure that the optical output and chemical diffusion profile are sufficient for this device to function in vivo.

Portable, multi-modal Raman and fluorescence spectroscopic platform for point-of-care applications.

Significance: Point-of-care (POC) platforms utilizing optical biosensing strategies can achieve on-site detection of biomarkers to improve the quality of care for patients in low-resource settings. Aim: We aimed to develop a portable, multi-modal spectroscopic platform capable of performing Raman and fluorescence measurements from a single sample site. Approach: We designed the spectroscopic platform in OpticStudio using commercial optical components and built the system on a portable optical breadboard. Two excitation and collection arms were utilized to detect the two optical signals. The multi-modal functionality was validated using ratiometric Raman/fluorescence samples, and the potential utility was demonstrated using a model bioassay for cardiac troponin I. Results: The designed spectroscopic platform achieved a spectral resolution of 0.67 ± 0.2 nm across the Raman detection range (660 to 770 nm). The ratiometric Raman/fluorescence samples demonstrated no crosstalk between the two detector arms across a gradient of high molar concentrations. Testing of the model bioassay response showed that the integrated approach improved the linearity of the calibration curve from (R2 = 0.977) for the Raman only and (R2 = 0.972) for the fluorescence only to (R2 = 0.988) for the multi-modal approach. Conclusion: These findings demonstrate the potential impact of a multi-modal POC spectroscopic platform to improve the sensitivity and robustness necessary for biomarker detection.

Detection of Hypoglycemia and Hyperglycemia Using Noninvasive Wearable Sensors: ECG and Accelerometry.

BACKGROUND: Monitoring glucose excursions is important in diabetes management. This can be achieved using continuous glucose monitors (CGMs). However, CGMs are expensive and invasive. Thus, alternative low-cost noninvasive wearable sensors capable of predicting glycemic excursions could be a game changer to manage diabetes. METHODS: In this article, we explore two noninvasive sensor modalities, electrocardiograms (ECGs) and accelerometers, collected on five healthy participants over two weeks, to predict both hypoglycemic and hyperglycemic excursions. We extract 29 features encompassing heart rate variability features from the ECG, and time- and frequency-domain features from the accelerometer. We evaluated two machine-learning approaches to predict glycemic excursions: a classification model and a regression model. RESULTS: The best model for both hypoglycemia and hyperglycemia detection was the regression model based on ECG and accelerometer data, yielding 76% sensitivity and specificity for hypoglycemia and 79% sensitivity and specificity for hyperglycemia. This had an improvement of 5% in sensitivity and specificity for both hypoglycemia and hyperglycemia when compared with using ECG data alone. CONCLUSIONS: Electrocardiogram is a promising alternative not only to detect hypoglycemia but also to predict hyperglycemia. Supplementing ECG data with contextual information from accelerometer data can improve glucose prediction.

Mid-Infrared Serial Microring Resonator Array for Real-Time Detection of Vapor-Phase Volatile Organic Compounds.

Chip-scale infrared spectrometers consisting of a microring resonator array (MRA) were developed for volatile organic compound (VOC) detection. The MRA is serially positioned to serve as a wavelength sorting element that enables wavelength demultiplexing. Unlike conventional devices operated by a single microring, our MRA can perform multiwavelength mid-infrared (mid-IR) sensing by routing the resonant wavelength light from a broadband mid-IR source into different sensing channels. Miniaturized spectrometer devices were fabricated on mid-IR transparent silicon-rich silicon nitride (SiNx) thin films through complementary metal-oxide-semiconductor (CMOS) processes, thus enabling wafer-level manufacturing and packaging. The spectral distribution of the resonance lines and the optimization of the microring structures were designed using finite-difference time-domain (FDTD) modeling and then verified by laser spectrum scanning. Using small microring structures, the spectrum showed a large free spectral range (FSR) of 100 nm and held four spectral channels without crosstalk. Unlike near-infrared microrings using refractive index sensing, our MRA can detect hexane and ethanol vapor pulses by monitoring the intensity variation at their characteristic mid-IR absorption bands, thus providing high specificity. Applying multiwavelength detection, the sensor module can discriminate among various VOC vapors. Hence, our mid-IR MRA could be an essential component to achieve a compact spectroscopic sensing module that has the potential for applications such as remote environmental monitoring and portable health care devices.

A Computational Modeling and Simulation Workflow to Investigate the Impact of Patient-Specific and Device Factors on Hemodynamic Measurements from Non-Invasive Photoplethysmography.

Cardiovascular disease is the leading cause of death globally. To provide continuous monitoring of blood pressure (BP), a parameter which has shown to improve health outcomes when monitored closely, many groups are trying to measure blood pressure via noninvasive photoplethysmography (PPG). However, the PPG waveform is subject to variation as a function of patient-specific and device factors and thus a platform to enable the evaluation of these factors on the PPG waveform and subsequent hemodynamic parameter prediction would enable device development. Here, we present a computational workflow that combines Monte Carlo modeling (MC), gaussian combination, and additive noise to create synthetic dataset of volar fingertip PPG waveforms representative of a diverse cohort. First, MC is used to determine PPG amplitude across age, skin tone, and device wavelength. Then, gaussian combination generates accurate PPG waveforms, and signal processing enables data filtration and feature extraction. We improve the limitations of current synthetic PPG frameworks by enabling inclusion of physiological and anatomical effects from body site, skin tone, and age. We then show how the datasets can be used to examine effects of device characteristics such as wavelength, analog to digital converter specifications, filtering method, and feature extraction. Lastly, we demonstrate the use of this framework to show the insensitivity of a support vector machine predictive algorithm compared to a neural network and bagged trees algorithm.

Monte Carlo method for assessment of a multimodal insertable biosensor.

SIGNIFICANCE: Continuous glucose monitors (CGMs) are increasingly utilized as a way to provide healthcare to the over 10% of Americans that have diabetes. Fully insertable and optically transduced biosensors are poised to further improve CGMs by extending the device lifetime and reducing cost. However, optical modeling of light propagation in tissue is necessary to ascertain device performance. AIM: Monte Carlo modeling of photon transport through tissue was used to assess the luminescent output of a fully insertable glucose biosensor that uses a multimodal Förster resonance energy transfer competitive binding assay and a phosphorescence lifetime decay enzymatic assay. APPROACH: A Monte Carlo simulation framework of biosensor luminescence and tissue autofluorescence was built using MCmatlab. Simulations were first validated against previous research and then applied to predict the response of a biosensor in development. RESULTS: Our results suggest that a diode within the safety standards for light illumination on the skin, with far-red excitation, allows the luminescent biosensor to yield emission strong enough to be detectable by a common photodiode. CONCLUSIONS: The computational model showed that the expected fluorescent power output of a near-infrared light actuated barcode was five orders of magnitude greater than a visible spectrum excited counterpart biosensor.

A portable brightfield and fluorescence microscope toward automated malarial parasitemia quantification in thin blood smears.

Malaria is often most endemic in remote regions where diagnostic microscopy services are unavailable. In such regions, the use of rapid diagnostic tests fails to quantify parasitemia measurements which reflect the concentration of Plasmodium parasites in the bloodstream. Thus, novel diagnostic and monitoring technologies capable of providing such information could improve the quality of treatment, monitoring, and eradication efforts. A low-cost, portable microscope for gathering quantitative parasitemia data from fluorescently stained thin blood smears is presented. The system employs bimodal imaging using components optimized for cost savings, system robustness, and optical performance. The microscope is novel for its use of monochromatic visible illumination paired with a long working distance singlet aspheric objective lens that can image both traditionally mounted and cartridge-based blood smears. Eight dilutions of red blood cells containing laboratory cultured wild-type P. falciparum were used to create thin smears which were stained with SYBR Green-1 fluorescent dye. Two subsequent images are captured for each field-of-view, with brightfield images providing cell counts and fluorescence images providing parasite localization data. Results indicate the successful resolution of sub-micron sized parasites, and parasitemia measurements from the prototype microscope display linear correlation with measurements from a benchtop microscope with a limit of detection of 0.18 parasites per 100 red blood cells.

Detection of volatile organic compounds using mid-infrared silicon nitride waveguide sensors.

Mid-infrared (mid-IR) sensors consisting of silicon nitride (SiN) waveguides were designed and tested to detect volatile organic compounds (VOCs). SiN thin films, prepared by low-pressure chemical vapor deposition (LPCVD), have a broad mid-IR transparent region and a lower refractive index (nSiN = 2.0) than conventional materials such as Si (nSi = 3.4), which leads to a stronger evanescent wave and therefore higher sensitivity, as confirmed by a finite-difference eigenmode (FDE) calculation. Further, in-situ monitoring of three VOCs (acetone, ethanol, and isoprene) was experimentally demonstrated through characteristic absorption measurements at wavelengths λ = 3.0-3.6 µm. The SiN waveguide showed a five-fold sensitivity improvement over the Si waveguide due to its stronger evanescent field. To our knowledge, this is the first time SiN waveguides are used to perform on-chip mid-IR spectral measurements for VOC detection. Thus, the developed waveguide sensor has the potential to be used as a compact device module capable of monitoring multiple gaseous analytes for health, agricultural and environmental applications.

Skin optical properties in the obese and their relation to body mass index: a review.

SIGNIFICANCE: Obesity is a worldwide epidemic contributing directly to several cardiovascular risk factors including hypertension and type 2 diabetes. Wearable devices are becoming better at quantifying biomarkers relevant for the management of health and fitness. Unfortunately, both anecdotal evidence and recent studies indicate that some wearables have higher levels of error when utilized by populations with darker skin tones and high body mass index (BMI). There is an urgent need for a better evaluation of the limits of wearable health technologies when used by obese individuals. AIMS: (1) To review the current know-how on changes due to obesity in the skin epidermis, dermis, and subcutis that could affect the skin optical properties; (2) for the green wavelength range, to evaluate the difference in absorption and scattering coefficients from the abdominal skin between individuals with and without elevated BMI. The changes include alterations in layer thickness and cell size, as well as significant differences in chromophores and scatterer content, e.g., water, hemoglobin, collagen, and lipids. APPROACH: We have summarized literature pertaining to changes in skin and its components in obesity and report the results of our search using articles published between years 1971 and 2020. A linear model was used to demonstrate the absorption and reduced scattering coefficient of the abdominal skin of individuals with and without elevated BMI in the green wavelength range (530 to 550 nm) that is typically found in most wearables. RESULTS: The general trends indicate a decrease in absorption for both dermis and subcutis and an increase in reduced scattering for both epidermis and dermis. At 544-nm wavelength, a typical wavelength used for photoplethysmography (PPG), the absorption coefficient's relative percentage difference between high and low BMI skin, was 49% in the subcutis, 19% in the dermis, and negligible in the epidermis, whereas the reduced scattering coefficient relative difference was 21%, 29%, and 165% respectively. CONCLUSIONS: These findings suggest that there could be significant errors in the output of optical devices used for monitoring health and fitness if changes due to obesity are not accounted for in their design.

Spectrally multiplexed assay using gap enhanced nanoparticle for detection of a myocardial infarction biomarker panel.

Multiplexed assays are essential for the detection of biomarker panels. Differentiating signals from different biomarkers in a single test zone makes the detection more efficient. In this paper, a new method is designed for the synthesis of gap-enhanced nanoparticles (GeNPs) using Raman reporter molecules (RRM) and 6-amino-1-hexanethiol (6-AHT) as the spacer. The GeNPs show a nanometer-size gap, generate strong surface-enhanced Raman scattering (SERS) attributed to the gap, and exhibit discriminative spectral peaks. The strong Au-S bonds on both core and shell sides and the covalent bond between RRM and 6-AHT led to a stable structure, which ensured the stable SERS signal generation from the GeNPs. Using the GeNPs, a spectrally multiplexed assay for the detection of a biomarker panel is developed. The biomarker panel is composed of cardiac troponin I (cTnI), copeptin, and heart-type fatty acid-binding protein (h-FABP), which improves myocardial infarction (MI) diagnostic performance. A paper-based platform that is more amenable to point-of-care diagnostic analysis is used. The developed single biomarker assay achieves limits of detection of 0.01 ng mL-1, 0.86 ng mL-1, 0.004 ng mL-1 for cTnI, h-FABP, and copeptin in buffer solutions. The dynamic range of the assay in human serum samples also covers the clinically relevant range of the biomarkers. The cross interference in the multiplexed assay is low. These results show the strong potential of the developed GeNPs in multiplexed detection of biomarkers and the developed simple-to-use multiplexed assay in the diagnosis of MI at the point of care.

Predicting the Macronutrient Composition of Mixed Meals From Dietary Biomarkers in Blood.

Diet monitoring is an essential intervention component for a number of diseases, from type 2 diabetes to cardiovascular diseases. However, current methods for diet monitoring are burdensome and often inaccurate. In prior work, we showed that continuous glucose monitors (CGMs) may be used to predict meal macronutrients (e.g., carbohydrates, protein, fat) by analyzing the shape of the post-prandial glucose response. In this study, we examine a number of additional dietary biomarkers in blood by their ability to improve macronutrient prediction, compared to using CGMs alone. For this purpose, we conducted a nutritional study where (n = 10) participants consumed nine different mixed meals with varied but known macronutrient amounts, and we analyzed the concentration of 33 dietary biomarkers (including amino acids, insulin, triglycerides, and glucose) at various times post-prandially. Then, we built machine learning models to predict macronutrient amounts from (1) individual biomarkers and (2) their combinations. We find that the additional blood biomarkers provide complementary information, and more importantly, achieve lower normalized root mean squared error (NRMSE) for the three macronutrients (carbohydrates: 22.9%; protein: 23.4%; fat: 32.3%) than CGMs alone (carbohydrates: 28.9%, t(18) =1.64, p =0.060; protein: 46.4%, t(18) =5.38, p 0.001; fat: 40.0%, t(18) =2.09, p =0.025). Our main conclusion is that augmenting CGMs to measure these additional dietary biomarkers improves macronutrient prediction performance, and may ultimately lead to the development of automated methods to monitor nutritional intake. This work is significant to biomedical research as it provides a potential solution to the long-standing problem of diet monitoring, facilitating new interventions for a number of diseases.

Postprandial concentration of circulating branched chain amino acids are able to predict the carbohydrate content of the ingested mixed meal.

BACKGROUND: The amount of the macronutrients protein and carbohydrate (CHO) in a mixed meal is known to affect each other's digestion, absorption, and subsequent metabolism. While the effect of the amount of dietary protein and fat on the glycemic response is well studied, the ability of postprandial plasma amino acid patterns to predict the meal composition is unknown. OBJECTIVE: To study the postprandial plasma amino acid patterns in relation to the protein, CHO, and fat content of different mixed meals and to investigate if these patterns can predict the macronutrient meal composition. DESIGN: Ten older adults were given 9 meals with 3 different levels (low, medium, and high) of protein, CHO, and fat in different combinations, taking the medium content as that of a standardized western meal. We monitored the postprandial plasma response for amino acids, glucose, insulin, and triglycerides for 8 h and the areas under the curve (AUC) were subsequently calculated. Multiple regression analysis was performed to determine if amino acid patterns could predict the meal composition. RESULTS: Increasing meal CHO content reduced the postprandial plasma response of several amino acids including all branched chain amino acids (BCAA) (leucine; q < 0.0001, isoleucine; q = 0.0035, valine; q = 0.0022). The plasma BCAA patterns after the meal significantly predicted the meal's CHO content (leucine; p < 0.0001, isoleucine; p = 0.0003, valine; p = 0.0008) along with aspartate (p < 0.0001), tyrosine (p < 0.0001), methionine (p = 0.0159) and phenylalanine (p = 0.0332). Plasma citrulline predicted best the fat content of the meal (p = 0.0024). CONCLUSIONS: The postprandial plasma BCAA patterns are lower with increasing meal CHO content and are strong predictors of a mixed meal protein and CHO composition, as are plasma citrulline for the fat content. We hypothesize that postprandial plasma amino acid concentrations can be used to predict the meal's macronutrient composition.

Diabetes Technology Meeting 2020.

Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 12 to November 14, 2020. This meeting brought together speakers to cover various perspectives about the field of diabetes technology. The meeting topics included artificial intelligence, digital health, telemedicine, glucose monitoring, regulatory trends, metrics for expressing glycemia, pharmaceuticals, automated insulin delivery systems, novel insulins, metrics for diabetes monitoring, and discriminatory aspects of diabetes technology. A live demonstration was presented.