Clinical classification of cardiac troponin I (cTnI) levels is essential for accurately diagnosing acute myocardial infarction. This study introduces an innovative point-of-care test that combines an engineered locked aptamer, magnetic nanoparticle and isothermal, non-enzymatic hybridization chain reaction with surface-enhanced resonance Raman scattering for the sensitive and specific detection of cTnI at clinically significant concentrations. This high sensitivity cTnI assay enabled accurate quantification of cTnI in 25 μL of cTnl doped serum, with a detection limit of 0.403 ng/L and a quantification limit of 1.22 ng/L with a dynamic range spanning from 0.5 ng/L to 50,000 ng/L. Further, optimized to classify cTnI levels beyond the established clinical threshold of 40 ng/L, the assay demonstrates its efficacy by accurately distinguishing between healthy and unhealthy individual clinical samples, achieving over 83 % accuracy and 86 % precision. This approach represents a significant advancement in the point-of-care diagnosis of myocardial infarction, offering a rapid, reliable, and accessible diagnostic tool for cardiac troponin testing.

Cardiovascular disease (CVD) is the leading cause of global mortality, with hypertension affecting over one billion people. Current noninvasive blood pressure (BP) systems, like cuffs, suffer from discomfort and placement errors and lack continuous monitoring. Wearable solutions promise improvements, but technologies like photoplethysmography (PPG) and bioimpedance (BIOZ) face usability and clinical accuracy limitations. PPG is sensitive to skin tone and body mass index (BMI) variability, while BIOZ struggles with electrode contact and reusability. We present a novel, strain gauge-based wearable BP device that directly quantifies pressure via a dual transducer system, compensating for tissue deformation and external forces to enable continuous, accurate BP measurement. The reusable, energy-efficient, and compact design suits long-term daily use. A novel leg press protocol across 10 subjects (systolic: 71.04–241.42 mmHg, diastolic: 53.46–123.84 mmHg) validated its performance under dynamic conditions, achieving mean absolute errors of 2.45 ± 3.99 mmHg (systolic) and 1.59 ± 2.08 mmHg (diastolic). The device showed enhanced robustness compared to the Finapres, with less motion-induced noise. This technology significantly advances current methods by delivering continuous, real-time BP monitoring without reliance on electrodes, independent of skin tone, while maintaining a high accuracy and user comfort.

Blood pressure is a critical risk factor for cardiovascular diseases (CVDs), yet most adults do not monitor it frequently enough to prevent serious complications. This is in part because the traditional cuff-based method is inconvenient, uncomfortable, and does not allow for continuous monitoring. To address these constraints, we developed a unique multi-modal wearable device and used a random forest regression (RFR) algorithm that resulted in a model capable of accurate cuffless blood pressure prediction. This multi-modal device features two photoplethysmography (PPG) sensors and two bioimpedance (BioZ) sensors to measure pulse wave propagation along the radial artery on the wrist. The redundancy in the design enhances prediction accuracy. To validate the device, a novel human subject study protocol was also developed that allows an individual's blood pressure to rise safely and repeatably by more than 40 mmHg (systolic pressure) from baseline measurements. In this study, using multiple pulsatile waveforms from the PPG and BioZ sensors as inputs into the machine learning prediction algorithm, showed that the model had higher accuracy than models using a single sensor. Specifically, the training, validation, and leaving one subject out of data sets all showed mean absolute errors of less than 3.3 mmHg for both systolic and diastolic blood pressures (BPs). While results from this test were promising, a subject-wise evaluation showed variability depending on how well an individual's BP distribution matched the training set. These findings demonstrate the potential for a universal model for cuffless BP estimation, with further validation needed in more diverse populations. Thus, the accompaniment of the RFR model with the multi-modal wearable device offers the potential for robust and continuous blood pressure monitoring, providing a unique and practical solution for long-term cardiovascular health management.

We review the published work on the optical properties of the epidermis. Considering three primary skin tone groups, we summarize the existing data on epidermal absorption and scattering coefficient across the 300- to 1000-nm range. We include both experimentally derived values and ones extrapolated through models of bulk skin. We note a paucity of experimental data across all skin tones, which highlights the need for further research on epidermal melanin and skin tone to improve the reliability of optical devices interfacing with the skin.

Signal interface design advances have revolutionized electrochemical biosensing by greatly enhancing electron transfer efficiency, signal stability, and analyte selectivity. Recent innovations, including 3D-printed sensing platforms, soft and stretchable electrodes, and laser-induced graphene (LIG) interfaces, have enabled biosensors with heightened sensitivity, reduced interference, and improved durability. Merging high-performance surface engineering and embedded technologies like microfluidics has opened avenues toward improved functionality for convenient operation across wide physiological conditions. Such advances have been particularly impactful in biomarker detection, where signal-optimized interfaces allow ultrasensitive real-time monitoring of clinically relevant analytes in biofluids such as sweat and interstitial fluid (ISF). This development lays the groundwork for the next generations of non-invasive health monitoring tools by bridging the gap between traditional laboratory diagnostics and novel point-of-care systems. This review offers a snapshot of signal interface innovations, categorizing recent work from the past two years into key thematic areas. We emphasize emerging trends in electrode functionalization, transduction amplification, and interface engineering, with a critical analysis of how these translate into the enhancement of biosensor performance. Providing a snapshot of the state-of-the-art electrochemical biosensing, this review is a reference guide for researchers developing novel biosensing platforms.

The rate of oxygen consumption, which is measured as the volume of oxygen consumed per mass per minute (VO2) mL/kg/min, is a critical metric for evaluating cardiovascular health, metabolic status, and respiratory function. Specifically, VO2 is a powerful prognostic predictor of survival in patients with heart failure (HF) because it provides an indirect assessment of a patient’s ability to increase cardiac output (CO). In addition, VO2 measurements, particularly VO2 max, are significant because they provide a reliable indicator of your cardiovascular fitness and aerobic endurance. However, traditional VO2 assessment requires bulky, breath-by-breath gas analysis systems, limiting frequent and continuous monitoring to specialized settings. This study presents a novel wrist-worn multiwavelength photoplethysmography (PPG) device and machine learning algorithm designed to estimate VO2 continuously. Unlike conventional wearables that rely on static formulas for VO2 max estimation, our algorithm leverages the data from the PPG wearable and uses the Beer–Lambert Law with inputs from five wavelengths (670 nm, 770 nm, 810 nm, 850 nm, and 950 nm), incorporating the isosbestic point at 810 nm to differentiate oxy- and deoxy-hemoglobin. A validation study was conducted with eight subjects using a modified Bruce protocol, comparing the PPG-based estimates to the gold-standard Parvo Medics gas analysis system. The results demonstrated a mean absolute error of 1.66 mL/kg/min and an R2 of 0.94. By providing precise, individualized VO2 estimates using direct tissue oxygenation data, this wearable solution offers significant clinical and practical advantages over traditional methods, making continuous and accurate cardiovascular assessment readily available beyond clinical environments.

INTRODUCTION:Current methods to detect hypoglycemia in type 1 diabetes (T1D) require invasive sensors (ie, continuous glucose monitors, CGMs) that generally have low accuracy in the hypoglycemic range. A forward-looking alternative is to monitor physiological changes induced by hypoglycemia that can be measured non-invasively using, eg, electrocardiography (ECG). However, current methods require extraction of fiduciary points in the ECG signal (eg, to estimate QT interval), which is challenging in ambulatory settings. METHODS:To address this issue, we present a machine-learning model that uses (1) convolutional neural networks (CNNs) to extract morphological information from raw ECG signals without the need to identify fiduciary points and (2) ensemble learning to aggregate predictions from multiple ECG beats. We evaluate the model on an experimental data set that contains ECG and CGM recordings over a period of 14 days from ten participants with T1D. We consider two testing scenarios, one that divides ECG data according to CGM readings (CGM-split) and another that divides ECG data on a day-to-day basis (day-split)Results:We find that models trained using CGM-splits tend to produce overly optimistic estimates of hypoglycemia prediction, whereas day-splits provide more realistic estimates, which are consistent with the intrinsic accuracy of CGM devices. More importantly, we find that aggregating predictions from multiple ECG beats using ensemble learning significantly improves predictions at the beat level, though these improvements have large inter-individual differences. CONCLUSION:Deep learning models and ensemble learning can extract and aggregate morphological information in ECG signals that is predictive of hypoglycemia. Using two validation procedures, we estimate an upper bound on the accuracy of ECG hypoglycemia prediction of 81% equal error rate and a lower bound of 60%. Further improvements may be achieved using big-data approaches that require longitudinal data from a large cohort of participants.

Early diagnosis by detecting ultralow concentrations of disease biomarkers is critical for timely treatment of the two most common neurodegenerative diseases, Alzheimer's and Parkinson's diseases. Innovative biosensors technologies can provide accurate, faster, and cheaper diagnostic pathways. In this review, the most recent electrochemical and optical sensing and biosensing platforms for diagnosing these diseases are critically selected and reviewed. Diagnostic targets (generally biomarkers) related to each disease and novel technologies, such as nanomaterials and biomolecular techniques to optimize the detection process and enhance signals, are discussed. In particular, multiplex detection and detection of multiple analytes by a (bio) sensing platform, to improve clinical sensitivity and selectivity are considered. This review is intended to open new approaches in the field and advance future research by identifying those strategies that optimize real-world performance and minimize present shortcomings.

Many commercial wearable sensor systems typically rely on a single continuous cardiorespiratory sensing modality, photoplethysmography (PPG), which suffers from inherent biases (i.e., differences in skin tone) and noise (e.g., motion and pressure artifacts). In this research, we present a wearable device that provides robust estimates of cardiorespiratory variables by combining three physiological signals from the upper arm: multiwavelength PPG, single-sided electrocardiography (SS-ECG), and bioimpedance plethysmography (BioZ), along with an inertial measurement unit (IMU) providing 3-axis accelerometry and gyroscope information. We evaluated the multimodal device on 16 subjects by its ability to estimate heart rate (HR) and breathing rate (BR) in the presence of various static and dynamic noise sources (e.g., skin tone and motion). We proposed a hierarchical approach that considers the subject’s skin tone and signal quality to select the optimal sensing modality for estimating HR and BR. Our results indicate that, when estimating HR, there is a trade-off between accuracy and robustness, with SS-ECG providing the highest accuracy (low mean absolute error; MAE) but low reliability (higher rates of sensor failure), and PPG/BioZ having lower accuracy but higher reliability. When estimating BR, we find that fusing estimates from multiple modalities via ensemble bagged tree regression outperforms single-modality estimates. These results indicate that multimodal approaches to cardiorespiratory monitoring can overcome the accuracy–robustness trade-off that occurs when using single-modality approaches.

Despite its ubiquity as a clinical sample, whole blood remains inherently challenging to use in diagnostics, leading to routine reliance on serum and plasma in clinical testing. Whole blood is a complex mixture of cells, lipids, proteins, and metabolites, many of which can interfere with assays designed for plasma or serum if used directly, thereby reducing the sensitivity and accuracy. While traditional laboratory workflows use multistep preparations and benchtop instruments such as centrifuges to overcome these challenges, such methods are often impractical for point-of-care applications, particularly in decentralized or resource-limited settings. This review systematically examines whole blood-based point-of-care testing (POCT) platforms that either operate directly on blood samples or integrate current or emerging separation techniques, including centrifugation, microfluidics, membrane filtration, capillary-driven flow, electrophoresis, and acoustofluidics. We highlight the operational principles, performance metrics (yield, purity, turnaround time), and feasibility of these approaches for a reliable integrated POCT deployment. By evaluating these technologies against the functional demands of modern diagnostics, we outline trade-offs among efficiency, cost, complexity, and integration potential. Our goal is to provide a framework for selecting or engineering appropriate blood separation strategies tailored to specific diagnostic needs, ultimately supporting the broader deployment of POCT across community clinics, emergency medicine, and at-home settings.

Urinary tract infections (UTIs) are common and costly healthcare issues, particularly among catheterized patients. Current diagnostic methods, such as urine cultures and dipstick tests, suffer from delayed results and limited accuracy, highlighting the need for more reliable, real-time approaches. This letter presents a proof-of-concept point-of-care device for multiplex detection of key urine parameters-pH and nitrite levels-and urine volume in catheter bags used for intermittent or long-term urinary drainage. The system integrates commercial pH and nitrite probes with an ultrasonic liquid level sensor, all of which are managed by a control unit that processes and transmits data wirelessly via Bluetooth or near-field communication. The sensors were calibrated via standard solutions and tested for stability over four days, which revealed minimal drift. The system was further validated using human urine spiked with varying concentrations of nitrites and pH. The results demonstrated reliable sensor performance and accurate detection of urinary biomarkers in real time. While the system shows promise for the early detection of catheter-associated urinary tract infections, further validation is needed to confirm its validity over standard diagnostic methods. This device offers a feasible solution for real-time, noninvasive UTI monitoring at the point of care.

Diabetes is a major health challenge that affects millions of people worldwide. Managing diabetes effectively requires monitoring blood glucose levels continuously, typically through invasive sensing devices such as continuous glucose monitors (CGMs). Blood glucose excursions have been shown to induce changes in several physiological signals such as electrocardiography (ECG), photoplethysmography (PPG) and electrodermal activity (EDA) that can be measured non-invasively with consumer-grade wearable sensors. These physiological changes can be mapped into glucose levels using machine-learning models trained on comprehensive multimodal datasets of physiological signals. However, most existing datasets lack ground-truth measurements from CGMs and often only include aggregated physiological data (e.g. heart rate) at low sampling rates. To address these gaps, we introduce PhysioCGM, an open-source dataset that contains raw physiological recordings from multiple sensors including ECG, PPG, EDA, skin temperature, accelerometry and ground-truth CGM data collected for up to 17 days from 10 participants with Type 1 Diabetes in ambulatory settings. This dataset aims to promote the development of non-invasive methods for glucose monitoring and improve diabetes management.

OBJECTIVE:Rapid advances in cuffless blood pressure (BP) monitoring have the potential to radically transform clinical care for cardiovascular health. However, due to the large heterogeneity in device design and evaluation, it is difficult to critically and quantitatively evaluate research progress. In this two-part manuscript, we provide a principled way of describing and accounting for heterogeneity in device and study design. METHODS:We first provide an overview of foundational elements and design principles of three critical aspects: 1) sensors and systems, 2) pre-processing and feature extraction, and 3) BP estimation algorithms. Then, we critically analyze the state-of-the-art methods via a systematic review. RESULTS:First, we find large heterogeneity in study designs, making fair comparisons extremely challenging. Moreover, many study designs have data leakage and are underpowered. We suggest a first open-contribution BP estimation benchmark for standardization. Next, we observe that BP distribution in the study sample and the time between calibration and test in emerging personalized devices confound BP estimation error. We suggest accounting for these using a convenient metric coined "explained deviation". Finally, we complement this manuscript with a website, https://wearablebp.github.io, containing a bibliography, meta-analysis results, datasets, and benchmarks, providing a timely plaWorm to understand state-of-the-art devices. CONCLUSION:There is large heterogeneity in device and study design, which should be carefully accounted for when designing, comparing, and contrasting studies. SIGNIFICANCE:Our findings will allow readers to parse out the heterogeneous literature and move toward promising directions for safer and more reliable devices in clinical practice and beyond.

Rapid and early identification of emergent infections is essential for delivering prompt clinical care. To advance the development of algorithms for the clinical management of infection identification, we performed a vaccination clinical trial to investigate the potential of using vaccination as a model for studying mild inflammation responses associated with different infections (NCT05346302). We collected data at various time points over 4 weeks from blood samples, wearable devices, and questionnaires. Following a 2-week baseline period, 210 healthy participants, aged 18-40 years, were administered either a Pneumococcal Polysaccharide vaccine (PPSV23), Typhoid Vi Polysaccharide vaccine (Typhim Vi), or placebo. In longitudinal analyses of blood biomarkers, we found that CRP was significantly higher at 2 days postvaccination, whereas basophils, IL-10, IL-12p40, and MIG were significantly higher at 7 days post-vaccination in the PPSV23 group compared to both other groups (all p < 0.05). MIP-1 beta was significantly lower in the PPSV23 group than in the placebo group, while monocytes and MPV were significantly lower in the Typhim Vi group than in the placebo group at 7 days post-vaccination (all p < 0.05). The PPSV3 group showed a higher inflammatory profile, suggesting that PPSV23 induces a stronger immune response compared to Typhim Vi. The distinct immune responses induced by the two vaccines indicate the potential for utilizing vaccines as models for studying inflammation responses associated with different infectious pathogens.

Introduction: An error grid compares measured versus reference glucose concentrations to assign clinical risk values to observed errors. Widely used error grids for blood glucose monitors (BGMs) have limited value because they do not also reflect clinical accuracy of continuous glucose monitors (CGMs). Methods: Diabetes Technology Society (DTS) convened 89 international experts in glucose monitoring to (1) smooth the borders of the Surveillance Error Grid (SEG) zones and create a user-friendly tool—the DTS Error Grid; (2) define five risk zones of clinical point accuracy (A-E) to be identical for BGMs and CGMs; (3) determine a relationship between DTS Error Grid percent in Zone A and mean absolute relative difference (MARD) from analyzing 22 BGM and nine CGM accuracy studies; and (4) create trend risk categories (1-5) for CGM trend accuracy. Results: The DTS Error Grid for point accuracy contains five risk zones (A-E) with straight-line borders that can be applied to both BGM and CGM accuracy data. In a data set combining point accuracy data from 18 BGMs, 2.6% of total data pairs equally moved from Zones A to B and vice versa (SEG compared with DTS Error Grid). For every 1% increase in percent data in Zone A, the MARD decreased by approximately 0.33%. We also created a DTS Trend Accuracy Matrix with five trend risk categories (1-5) for CGM-reported trend indicators compared with reference trends calculated from reference glucose. Conclusion: The DTS Error Grid combines contemporary clinician input regarding clinical point accuracy for BGMs and CGMs. The DTS Trend Accuracy Matrix assesses accuracy of CGM trend indicators.

Preeclampsia (PE) is a serious complication that poses risks to both mothers and their children. This condition is typically asymptomatic until the second or even third trimester, which can lead to poor outcomes and can be costly. Detection is particularly challenging in low- and middle-income countries, where a lack of centralized testing facilities coincides with high rates of PE-related maternal mortality. Variations in the levels of hsa-miR-17-5p have been identified as constituting a potential early indicator for distinguishing between individuals with PE and those without PE during the first trimester. Thus, developing a screening test to measure hsa-miR-17-5p levels would not only facilitate rapid detection in the early stages of pregnancy but also help democratize testing globally. Here, we present a proof-of-principle lateral-flow assay (LFA) designed to measure hsa-miR-17-5p levels using DNA-hairpin recognition elements for enhanced specificity and nanoprobes for sensitive surface-enhanced resonance Raman scattering (SERS) signal transduction. The theoretical limit of detection for hsa-miR-17-5p was 3.84 × 10−4 pg/µL using SERS.

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.

Chip-scale fluorescence-based biomolecule sensing was demonstrated using polymer-based micro-photonic circuits. The device components, including the optical waveguides and micro-ring resonators, were fabricated using a rapid laser writing method. The polymer resin material is transparent in the visible regime and has a lower refractive index than the conventional dielectric waveguide materials, such as Si3N4 or TiO2. The lower index enabled a strong evanescent sensing field, which was simulated using a finite difference eigenmode (FDE) method. In addition, the free spectral range (FSR) of the polymer micro-ring was calculated using a finite difference time domain (FDTD) method. The devices were then experimentally characterized, where a Q-factor of 3 x 104 was obtained from the resin based micro-ring. Fluorescence emission from a Cy3-labeled genomic target was detected using the micro-ring sensor chip. Overall, the micro-ring showed a 12x fluorescence enhancement compared to a high refractive index TiO2 strip waveguide. The emission enhancement was attributed to the micro-ring resonance and a stronger evanescent field. The fabrication method used to create the polymer microphotonic device is flexible. It offered rapid design optimization and manufacturing processes capable of creating various 3-D photonic structures without the need for intermediate masks and post lithography. Our miniaturized photonic devices have the potential to be applied for compact and remote sensing, and are compatible with various biomolecule reporters and assays.

In this research, we examine the potential of measuring physiological variables, including heart rate (HR) and respiration rate (RR) on the upper arm using a wireless multimodal sensing system consisting of an accelerometer, a gyroscope, a three-wavelength photoplethysmography (PPG), single-sided electrocardiography (SS-ECG), and bioimpedance (BioZ). The study included collecting HR data when the subject was at rest and typing, and RR data when the subject was at rest. The data from three wavelengths of PPG and BioZ were collected and compared to the SS-ECG as the standard. The accelerometer and gyro signals were used to exclude data with excessive noise due to motion. The results showed that when the subject remained sedentary, the mean absolute error (MAE) for the HR calculation for all three wavelengths of the PPG modality was less than two bpm, while the BioZ was 3.5 bpm compared with SS-ECG HR. The MAE for typing increased for both modalities and was less than three bpm for all three wavelengths of the PPG but increased to 7.5 bpm for the BioZ. Regarding RR, both modalities resulted in RR within one breath per minute of the SS-ECG modality for the one breathing rate. Overall, all modalities on this upper arm wearable worked well when the subject was sedentary. Still, the SS-ECG and PPG showed less variability for the HR signal in the presence of motion during micro-motions such as typing.