搜索结果：Npj biosensing

Oral and dental health is an important indicator and determinant of an individual's overall well-being. Untreated oral diseases can lead to severe systemic complications. Monitoring the oral environment and identifying biochemical and physiological patterns associated with disease states, such as periodontitis, gingivitis, caries, and oral cancers, is essential for early diagnosis and effective intervention. This review evaluates the current clinical needs in biochemical and physiological monitoring for oral healthcare and state-of-the-art biosensors capable of continuous analyte measurement. We surveyed the relevant biomarkers for common oral and dental diseases in patients compared to healthy controls. The design and performance of recent biosensing devices for these target analytes are reviewed and evaluated. For biochemical sensing, we found intraoral biosensors for high-abundance small molecules, such as ions and metabolites, have advanced significantly in recent years. However, robust sensing technologies for low-abundance analytes, including cytokines and other inflammatory biomarkers, remain limited and require further development in sensing mechanisms, bio-interfaces, and device integration. For physiological sensing, particularly the measurement of forces in tooth movement, recent developments in force sensor technologies have substantially improved measurement accuracy over traditional techniques. Despite these advancements, current platforms still face limitations in achieving long-term, real-time monitoring of mechanical conditions within the oral cavity due to challenges related to biocompatibility and device miniaturization. In conclusion, while notable progress has been made in biosensing for oral applications, continued research in device integration with clinical practices is essential to realize robust and clinically deployable biosensor systems that can advance precision oral healthcare.

We present a perspective on an integrated workflow for GPCR drug discovery that combines computational modeling, functional cellular assays, and FLOWER, a label-free ultra-sensitive optical biosensing system for quantitating direct ligand receptor binding. We use bitter and sweet taste receptors (TAS2Rs and T1R2/T1R3) as examples of how FLOWER resolves receptor activation mechanisms left ambiguous after in silico screening and cell assays. This workflow offers a generalizable strategy for accelerating the discovery of selective and mechanism informed GPCR targeting therapeutics.

Von Willebrand Disease (VWD) is characterized by improper blood clotting, resulting from qualitative or quantitative changes in Von Willebrand factor (VWF). Diagnosis of VWD currently relies on measuring both the concentration of VWF and its activity by the binding ability to several different proteins, each of which are currently quantified separately. As such, the current diagnosis of VWD is complex and expensive, requiring multiple tests for a positive clinical determination. To address this challenge, we report a multiplexed biosensor that simultaneously measures VWF concentration and binding activity in plasma, enabling rapid diagnosis of VWD and discrimination among multiple subtypes. Using an 18-plex photonic ring resonator in a disposable, lateral flow assay-like format as the core technology, capture of VWF by an immobilized monoclonal antibody results in a red shift in resonance, which is referenced to a nonspecific binding control. Other ring resonators on the chip, functionalized with binding partners of VWF, allow simultaneous measurement of VWF binding to collagen, Factor VIII, and the GP1b receptor. Evaluation of a panel of 37 single-donor human plasma samples previously analyzed using FDA clinically approved assays demonstrated that the sensor has comparable concentration results and was able to accurately identify several categories of VWD (type 1, 2 A, and type 3).

Multi-enzyme electrocatalytic cascades often suffer from poor electron-transfer efficiency, limiting their utility. We overcome this critical challenge by integrating an interfacial metal-phenolic network (MPN) layer with tunable properties based on the metal and polyphenol employed. Upon electropolymerization, MPNs provide a stable matrix for co-immobilizing glucose oxidase and horseradish peroxidase, enhancing their tandem activity. Through systematic evaluation of the impact of MPN composition on electron transfer, we demonstrate the tunability of these materials for cascade-specific optimization. This simple material is expected to support diverse enzymatic reactions important for technologies ranging from bioenergy to biosensing.

Identifying plasma-based biomarkers that can accurately differentiate Lewy body disease (LBD) from Alzheimer's disease (AD) remains a major challenge. Extracellular vesicles (EVs), which carry molecular cargo from their parent cells and can cross the blood-brain barrier, offer a new path forward. We developed the multiplexed Track-Etch magnetic NanoPOre (mTENPO) platform, a highly parallelized microfluidic technology for cell-specific EV isolation, and demonstrated independent enrichment of GluR2+ (neuron-derived) and GLAST+ (astrocyte-derived) EVs from the antemortem plasma of 137 autopsy-confirmed LBD, AD, mixed pathology, and control subjects. By integrating miRNA sequencing of GluR2+ and GLAST + EV cargo with plasma measurements of Aβ40, Aβ42, tau, p-Tau181, and p-Tau231, we identified a multimodal 15-feature panel that more comprehensively reflects brain pathology than conventional biomarkers. Using tenfold cross-validation to mitigate overfitting, the panel achieved an accuracy of 0.95 and an area under the curve of 0.96 for distinguishing LBD versus AD.

Hemoglobin (Hb) concentration is a fundamental physiological marker widely used in the diagnosis of anemia and the assessment of cardiovascular health. Although invasive blood testing provides high accuracy, its reliance on laboratory infrastructure limits scalability and real-time applicability. Here, we present Hb-PPG, a four-wavelength photoplethysmography (PPG) dataset designed to support research on non-invasive hemoglobin assessment and cardiovascular monitoring. The dataset comprises 1008 PPG signal segments acquired at 660, 730, 850, and 940 nm from 252 adult subjects, alongside reference measurements of hemoglobin, fasting blood glucose, and brachial artery systolic and diastolic blood pressure. Hb-PPG enables systematic investigation of wavelength-dependent PPG signal characteristics and their relationships with hematological and hemodynamic parameters. By providing high-quality, multi-wavelength optical signals with clinically grounded reference data, this dataset facilitates the development, validation, and benchmarking of non-invasive approaches for hemoglobin estimation and related vascular health applications. The dataset is intended to support algorithm development, benchmarking, and methodological studies in non-invasive hemoglobin estimation, rather than direct clinical diagnosis.

Ensuring fairness in clinical machine learning is a major concern, yet the dominant driver of unequal performance across sex groups remains unclear: is it the dataset or the algorithm. We conducted a systematic fairness evaluation across three healthcare domains-wearable physiology (MHEALTH), cardiac risk prediction (UCI Heart Disease), and stroke assessment-using ten widely used classifiers and three controlled sex-ratio sampling scenarios (50/50, 90/10, 10/90) under an identical analytical pipeline. Gender accuracy gaps varied markedly across datasets and exhibited dataset-specific patterns that did not generalize across clinical domains. Mixed-effects interaction modelling showed that the same algorithm could display negligible bias in one dataset and substantial bias in another. Variance contribution decomposition of the absolute Gender Accuracy Gap (∣GAG∣) indicated that dataset identity accounted for most of the observed variability (63.4%), with additional contribution from dataset-algorithm interactions (17.2%); algorithm choice alone explained 9.7%, whereas sampling scenario contributed negligibly (0.2%). Balanced sampling reduced disparities but did not eliminate them, consistent with residual sex-associated signal/feature structure beyond representation imbalance. These findings demonstrate that fairness in healthcare machine learning is primarily dataset-dependent, motivating dataset- and context-specific auditing before clinical deployment.

Proximity assays have emerged as a leading technology for protein detection, yet broad adoption is currently limited by complex workflows that include enzymatic amplification, thermocycling, and expensive laboratory equipment for signal readout. In this report, we introduce a Proximity Initiated Nucleic Acid Target Amplification (PINATA) assay that is performed at room temperature with a simple two-step, 90-min protocol using a low-cost detection instrument. In contrast to alternative proximity assays, PINATA applies toehold-mediated strand displacement of nucleic acids for enzyme-free reactions that occur at room temperature to uniquely combine linear amplification and digital detection using Photonic Resonator Absorption Microscopy (PRAM). Utilizing human interleukin-6, we demonstrate a detection limit of 37 fg/ml with 6-log dynamic range, high selectivity against non-target cytokines, and assays were performed with complex sample matrices without significant loss of efficacy. We envision that PINATA can address a range of protein quantitation applications for life science research and diagnostics.

Contactless heart rate (HR) monitoring demonstrates significant potential for mobile health and telemedicine, but current remote photoplethysmography (rPPG) approaches remain vulnerable to various noise sources. While existing research has emphasized signal-level enhancement, correcting erroneous HR estimates remains underexplored. We present a plug-and-play adaptive correction algorithm that leverages cardiac dynamics constraints, adjusting HR estimates based on physiological priors of HR elevation and recovery. By mapping HR frequencies to indices and applying adaptive corrections, our method significantly reduces measurement errors with minimal computational load, even under challenging conditions. Across three public datasets, the algorithm increased the proportion of accurate measurements (mean absolute error ≤ 10 beats per minute) from 46.26% to 84.14% (LGI-PPGI), 48.03% to 69.21% (BUAA-MIHR), and 92.22% to 96.67% (UBFC-rPPG), outperforming existing correction techniques. The lightweight design facilitates seamless edge-side integration, providing a scalable solution for enhancing the reliability of contactless HR monitoring in mobile and remote healthcare settings.

Remote photoplethysmography (rPPG) enables non-contact heart rate (HR) and waveform measurement from facial video, offering advantages over contact methods while contending with motion, illumination changes, compression, and frame-rate variability. This roadmap integrates a systematic review, technical synthesis, and a clinic-acquired demonstration to accelerate rPPG research and translation. We outline the measurement pipeline and synthesize progress across eight domains: datasets, device factors, data scarcity, skin/ROI detection, model- and data-driven algorithms, filtering, and evaluation metrics. Strategies for improved robustness include targeted dataset expansion, augmentation, optimized ROI policies, motion and photometric normalization, and hardware configuration. A proof-of-feasibility using data collected in a routine clinical setting alongside reference monitors demonstrates high signal-level agreement under a multi-metric framework (correlation/concordance, absolute errors, and Bland-Altman bias/dispersion). We conclude with recommendations for accuracy, efficiency, and deployment in real-world and clinical contexts, providing a consolidated resource for researchers and clinicians and a pragmatic path toward reliable clinical adoption.

Anxiety disorders affect hundreds of millions of people worldwide, yet objective and continuous assessment remains limited in clinical practice. To our knowledge, this is the first modality-specific, translational synthesis focusing on wearable ECG and PPG for anxiety detection. Wearable electrocardiography (ECG) and photoplethysmography (PPG), combined with data-driven analytics, have emerged as promising tools for anxiety monitoring, but translation into routine care has been slow. Here, we present a PRISMA-guided systematic review of 38 studies (2015-2025) investigating wearable ECG- and PPG-based anxiety detection. We analyze anxiety induction paradigms, sensor configurations, signal acquisition strategies, and analytical approaches, including statistical, machine learning, and hybrid methods. While autonomic markers derived from ECG and PPG consistently reflect anxiety-related physiological changes, substantial heterogeneity in study design, limited population diversity, and laboratory-centric validation constrain clinical generalizability. Critically, most studies lack evaluation in real-world settings and do not demonstrate clinical utility or impact on patient outcomes. We identify key translational barriers and propose a digital medicine roadmap emphasizing standardized protocols, robust validation across diverse populations, workflow integration, and outcome-driven evaluation to enable clinically actionable, real-world anxiety monitoring.

There is growing interest in using biosignals from wearable devices to assess anxiety disorders. Among these, electrocardiography is the most widely used due to its ability to monitor cardiovascular activity. Other signals, such as respiratory, electrodermal activity, and photoplethysmography, also show promise. This review aims to evaluate how these signals, individually and in combination, have been used for anxiety detection. We systematically reviewed 26 studies published between 2014 and 2024 that used wearable devices to collect signals for anxiety detection. Extracted information included study design, signal types, features, classification methods, and accuracy outcomes. Pooled accuracies were calculated to compare single-signal and multi-signal approaches. Here we show that approaches combining multiple signals outperform those using a single signal, with a pooled accuracy of 81.94% compared to 76.85%. Electrocardiography was the most reliable individual signal, with a pooled accuracy of 80.34% across 12 studies. However, the limited number of single-sensor studies and methodological variability limit conclusions about the superiority of any one modality. The most common features included mean heart rate and heart rate variability for electrocardiography, the mean inspiratory-to-expiratory time ratio for respiratory signals, mean skin conductance for electrodermal activity, and the mean heart rate for photoplethysmography. Support vector machine was the predominant classifier. This review underscores the clinical potential of wearable devices for anxiety detection, emphasizing the value of multimodal approaches. Future research should focus on refining algorithms, expanding sample sizes, and exploring diverse contexts to improve the accuracy and generalizability of these methods. Anxiety disorders are common, but detecting them in everyday life can be difficult. Wearable devices, such as smartwatches, can record body signals that change with anxiety, including heart activity, breathing, skin responses, and blood flow. We reviewed 26 studies from the past decade that tested these signals for detecting anxiety. We found that combining several signals gives more accurate results than using a single signal, while heart activity was the most reliable single signal. However, most studies were small, used different devices and methods to trigger and measure anxiety, making it difficult to draw firm conclusions. Overall, wearable devices show strong potential for anxiety detection, but future research needs larger, standardized studies before they can be widely used in daily life and healthcare.

Circulating tumor cells (CTCs) are cancer cells found in the bloodstream that serve as biomarkers for early cancer detection, prognostication, and disease monitoring. However, CTC detection remains challenging due to low cell abundance and heterogeneity. Digital holographic microscopy (DHM) offers a promising, label-free method for high-throughput CTC identification by capturing superior morphological information compared to traditional imaging methods, while remaining compatible with in-flow data acquisition. We present a streamlined DHM-based system that integrates microfluidic enrichment with deep learning-driven image analysis, supplemented by immunofluorescent profiling, to improve sensitivity and specificity of CTC enumeration. Specifically, our platform combines inertial microfluidic preprocessing with dual-modality imaging, integrating holography with fluorescence sensing of up to two markers. A deep learning model, trained on a diverse set of healthy blood samples and cancer cell lines, and executed in real-time, provides a morphological confidence on a cell-by-cell basis that may then be combined with immunofluorescence criteria for enumeration. In a pilot study, we demonstrate higher CTC counts in patients with late-stage prostate cancer (n = 13) compared to healthy controls (n = 8), with a patient-level false positive rate of 1 cell/mL. Notably, nearly two-thirds of identified CTCs were EpCAM-negative but PSMA positive (a prostate specific epithelial marker), suggesting that traditional use of EpCAM as an epithelial marker for CTCs may lead to false negatives. These findings highlight the potential of DHM for applications including but not limited to screening, diagnostics, and precision oncology.

The circadian rhythm regulates physiological and behavioral processes, with disruptions linked to metabolic and neuropsychiatric disorders. Circadian genes play a crucial role in the regulation of dopaminergic signaling, yet the underlying molecular mechanisms remain unclear. This study investigates how the Clock gene modulates dopamine (DA) dynamics using in vivo electrochemical DA sensing and molecular profiling. Utilizing carbon fiber electrodes (CFEs) with poly(3,4-ethylenedioxythiophene)/carbon nanotube (PEDOT/CNT) coatings, we measured extracellular DA levels in the striatum of wild-type (WT) and ClockΔ19 mutant mice via square wave voltammetry (SWV). Pharmacological perturbation with raclopride (D2/D3 receptor antagonist) and nomifensine (DA reuptake inhibitor) revealed an increased DA receptor sensitivity in ClockΔ19 mice, with a significantly faster DA response to raclopride. Molecular profiling via qRT-PCR showed elevated tyrosine hydroxylase (TH) expression in the ventral tegmental area (VTA) of ClockΔ19 mice, suggesting increased DA synthesis. Additionally, ClockΔ19 mice exhibited higher expression of D2 DA receptors and glutamate decarboxylase 67 (Gad67) in the VTA and of D3 DA receptors in the nucleus accumbens (NAc), implicating altered dopaminergic and γ-aminobutyric acid (GABA)ergic regulation. These findings highlight the Clock gene's role in DA homeostasis, revealing its impact on neurotransmission.

Rapid and sensitive detection of mycotoxins in foodstuffs is of great significance. As a powerful detection tool, biosensing technologies and microfluidic devices have shown a great potential in rapid and on-site detection of mycotoxins. This review comprehensively summarized the latest advances on the construction of microfluidic biosensors and their promising applications in on-site detection of mycotoxins. Finally, future challenges and chances in this significant and promising field were proposed.

This work introduces a realization of a proportional-integral-derivative-acceleration control scheme as a chemical reaction network governed by mass action kinetics. A central feature of this architecture is a speed and acceleration biosensing mechanism integrated into a feedback configuration. Our control scheme provides enhanced dynamic performance and robust steady-state tracking. In addition to our theoretical analysis, this is practically highlighted in-silico in both the deterministic and stochastic settings.

Nanoplasmonic optical antennas function as sensors and actuators, facilitating rapid and selective on-site molecular diagnostics for personalized precision medicine. Here, we highlight advancements in plasmonic biosensors and actuators within point-of-care diagnostics platforms, including optical trapping, cell lysis and ultrafast photonic polymerase chain reaction. Furthermore, we discuss nanoplasmonic optical sensing technologies, and commercial optical diagnostic systems. Nanoplasmonic optical antennas are essential to photonic sample-to-answer systems, significantly enhancing advancing preventive, personalized, and precision medicine.

Parkinson's Disease (PD) is an age-progressive disorder caused by misfolding of alpha-synuclein (α-Syn) that can begin years before clinical symptoms appear, making early diagnosis crucial for timely intervention. In this study, a novel antibody-functionalized Organic Electrolyte-Gated Field-Effect-Transistor (Ab-OEGFET) biosensor was implemented to detect α-Syn levels in blood serum samples from an A53T Transgenic (TG) mouse line. PD-like pathology was examined in blood serum using Ab-OEGFET devices and in brain tissue samples using Western Blot and immunohistochemistry. Different forms (monomeric, phosphorylated, oligomeric) of α-Syn were identified in low volumes of blood serum samples collected from TG and Wild Type (WT) populations of mice at ages 2, 5 and 8 months, and the biosensor response was correlated to Blot and immunohistochemistry results. The Ab-OEGFETs performance in this study is a promising result towards a minimally invasive blood biomarker-based multianalyte testing strategy for early screening of PD and similar neurodegenerative disease pathologies.

Remote photoplethysmography (rPPG) is gaining traction for non-contact heart rate estimation, yet most publicly available datasets are demographically biased. In this study, we analyze 100 rPPG studies, providing the first quantitative cross-model audit of demographic bias in rPPG and demonstrating significant underrepresentation of darker skin tones and gender imbalance. Our findings reveal how this bias limits model fairness and accuracy and propose steps to improve dataset inclusivity and algorithmic robustness.