Biosensors-Basel最新文献

In this work, we present the introductory methodology for a graphene field-effect transistor (GFET)-based platform for probing the electrochemical fingerprints of amino acids, designed to enable stable and controlled surface chemistry and electrochemical measurements toward peptide and protein sequencing. We begin with a focused conceptual review that motivates electrochemical fingerprinting as a strategy for amino acid and peptide identification and contextualizes this approach within recent advances in protein manipulation relevant to sequencing. We then describe a graphene functionalization protocol that facilitates the directional attachment of amino acids onto the graphene surface. This surface chemistry is quantitatively characterized through surface plasmon resonance (SPR), yielding surface densities in the order of 10¹² molecules/cm². The same functionalization protocol enables in situ peptide synthesis directly on graphene, as demonstrated by the successful synthesis of a model tripeptide. To support electrochemical interrogation, we developed three complementary platforms for sensor preconditioning, surface functionalization, and titration-based electrochemical measurements, compatible with both aqueous and organic solutions. Preliminary stability measurements indicate a Dirac point drift below 10 mV over 45 min. Altogether, this work establishes the experimental foundations for electrochemical amino acid and peptide fingerprinting using GFET sensors and provides a framework for the future development of electrochemically enabled protein sequencing technologies.

The non-invasive recording of spinal cord neuronal activity, also known as electrospinography (ESG), using high-density surface electromyography (HD-sEMG) is a promising emerging biosensing modality. However, these recordings often contain electrocardiographic (ECG) artifacts that must be removed for accurate analysis. Given the emerging nature of ESG and the lack of dedicated signal processing methods, this study assesses the performance of seven established EMG denoising algorithms for their ability to preserve the broad spectral bandwidth needed for future ESG characterization: Template Subtraction (TS), Adaptive Template Subtraction (ATS), High-Pass Filtering at 200 Hz (HP200), ATS combined with HP200, Second-Order Extended Kalman Smoother (EKS2), Stationary Wavelet Transform (SWT), and Empirical Mode Decomposition (EMD). Performance was quantified using six metrics: Relative Error (RE), Signal-to-Noise Ratio (SNR), Cross-Correlation (CC), Spectral Distortion (SD), and Kurtosis Ratio (KR2) and its variation (ΔKR2). ESG data were recorded from nine healthy participants at brachial and lumbar plexus sites with various electrode configurations. ATS consistently outperformed all other methods in suppressing cardiac artifacts of varying shapes. Although it did not fully preserve low-frequency content, ATS achieved the best balance between artifact removal and signal integrity. Algorithm performance improved when ECG contamination was lower, especially in brachial plexus recordings with closer reference electrodes.

Hemostasis depends on the coordinated interaction between platelets, coagulation factors, endothelium, and shear forces. Current point-of-care (POC) assays evaluate isolated components of haemostasis or operate under nearly static conditions, limiting their ability to reproduce physiological thrombus formation. In this study, we performed the technical validation of Smart Clot, a fully automated, microfluidic POC platform that quantifies platelet aggregation, fibrin formation, and total thrombus growth under controlled arterial shear using unmodified whole blood. Recalcified citrated blood was perfused over collagen at γ˙_w = 300 s^-1. Dual-channel epifluorescence microscopy acquired platelet and fibrin(ogen) signals at 1 frame per second. Integrated Density time-series were fitted with a five-parameter logistic model; first derivatives and their integrals yielded standardized pseudo-volumes for platelets, fibrin(ogen), and total thrombus. Sixty-two healthy donors established reference distributions; one-hundred-thirteen patients on antithrombotic therapy assessed pharmacodynamic sensitivity. Platelet-derived parameters were approximately normally distributed, whereas fibrin(ogen) and total thrombus values followed log-normal patterns. Anticoagulants and antiplatelet agents produced graded, mechanism-consistent inhibition of all thrombus components. Cardiopulmonary bypass samples showed profound but transient suppression of platelet and fibrin activity. Across activity ranges, multiple statistical assessments supported high analytical repeatability. Smart Clot provides rapid, reproducible, flow-aware quantification of platelet-fibrin dynamics, capturing pharmacological modulation and peri-operative impairment with high sensitivity. These results support its potential as a next-generation POC assay for physiological hemostasis assessment.

In this study, we describe a non-invasive approach to objectively assess fragrance-induced emotions using multiplex salivary biomarker profiling. Traditional self-reports, physiological monitoring, and neuroimaging remain limited by subjectivity, invasiveness, or poor temporal resolution. Saliva offers an advantageous alternative, reflecting rapid neuroendocrine changes linked to emotional states. We combined four key salivary biomarkers, cortisol, alpha-amylase, dehydroepiandrosterone, and oxytocin, to capture multidimensional emotional responses. Two clinical studies (n = 30, n = 63) and one user study (n = 80) exposed volunteers to six fragrances, with saliva collected before and 5 and 20 min after olfactory stimulation. Subjective emotional ratings were also obtained through questionnaires or an implicit approach. Rigorous analytical validation accounted for circadian variation and sample stability. Biomarker patterns revealed fragrance-induced emotional profiles, highlighting subgroups of participants whose biomarker dynamics correlated with particular emotional states. Increased oxytocin and decreased cortisol levels aligned with happiness and relaxation; in comparison, distinct biomarker combinations were associated with confidence or dynamism. Classification and Regression Trees (CART) analysis results demonstrated high sensitivity for detecting these profiles. Validation in an independent cohort using an implicit association test confirmed concordance between molecular profiles and behavioral measures, underscoring the robustness of this method. Our findings establish salivary biomarker profiling as an objective tool for decoding real-time emotional responses. Beyond advancing affective neuroscience, this approach holds translational potential in personalized fragrance design, sensory marketing, and therapeutic applications for stress-related disorders.

Current liquid-phase resonant biosensors, such as Quartz Crystal Microbalance, Surface Acoustic Wave, or Surface Plasmon Resonance, typically rely on specialized piezoelectric substrates or complex optical setups. These requirements often necessitate cleanroom fabrication, thereby limiting cost-effective scalability. This study presents a high-integration sensing platform based on standard Printed Circuit Board (PCB) technology, incorporating an embedded inductor within a fluidic system for real-time monitoring. This design leverages industrial manufacturing standards to achieve a compact, low-cost, and scalable architecture. Detection is governed by shifts in the resonance frequency of an LC tank circuit; specifically, increases in bulk ionic strength induce a frequency decrease, whereas biomolecular adsorption at the sensor surface leads to a frequency increase. This phenomenon can be explained by the modulation of the inter-turn capacitance, which is modeled as a combination of capacitive elements accounting for contributions from the bulk electrolyte and the surface-bound dielectric layer. Such divergent responses provide an intrinsic self-discriminating capability, allowing for the analytical differentiation between surface interactions and bulk effects. To the best of our knowledge, this is the first demonstration of an inductor-based resonant sensor fully embedded in a PCB fluidic architecture for continuous liquid-phase analyte monitoring. Validated through a protein-antibody model (Bovine Serum Albumin-anti-Bovine Serum Albumin), the sensor demonstrated a limit of detection of 1.7 ppm (0.026 mM) and a linear dynamic range of 31-211 ppm (0.47-3.2 mM). These performance metrics, combined with a reproducibility of 4 ± 3%, indicate that the platform meets the requirements for robust analytical applications. Its inherent simplicity and potential for miniaturization position this technology as a viable candidate for point-of-care diagnostics in diverse environments.

Foreign matter accumulating on catheters during ascites paracentesis in cancer patients can interfere with the procedure. The paracentesis site must be visually inspected by patients or medical staff. We propose a monitoring method using sensors, as they enable real-time, automatic status detection. The proposed design integrates a sensor into the drainage tube to detect liquid flow using the Lorentz force. The sensor consists of a permanent magnet, a yoke, and a signal processing circuit. Mu-metal shields the magnet to prevent interference with surrounding circuits. Physiological saline solution is used as a substitute for bodily fluids. Sensor performance was evaluated via finite element analysis. The Lorentz force generated an average voltage of 11.07 μV when liquid was present, enabling detection of the flow status. The proposed sensor is non-invasive and features a clip design, allowing attachment and detachment from the drainage tube for reuse. Non-invasiveness ensures safety from infection, and reusability can reduce medical costs. This study proposes a sensor for monitoring peritoneal puncture status. By detecting liquid flow using the Lorentz force, the system enables real-time monitoring during the procedure.

The integration of artificial intelligence (AI) with ultrasonic biosensing presents a transformative opportunity for enhancing diagnostic accuracy in agricultural and biomedical applications. This study develops a data-driven deep learning model to address the challenge of acoustic artifacts in B-mode ultrasound imaging, specifically for sow pregnancy diagnosis. We designed a biosensing system centered on a mechanical sector-scanning ultrasound probe (5.0 MHz) as the core biosensor for data acquisition. To overcome the limitations of traditional filtering methods, we introduced a lightweight Deep Neural Network (DNN) based on the YOLOv8 architecture, which was data-driven and trained on a purpose-built dataset of sow pregnancy ultrasound images featuring typical artifacts like reverberation and acoustic shadowing. The AI model functions as an intelligent detection layer that identifies and masks artifact regions while simultaneously detecting and annotating key anatomical features. This combined detection-masking approach enables artifact-aware visualization enhancement, where artifact regions are suppressed and diagnostic structures are highlighted for improved clinical interpretation. Experimental results demonstrate the superiority of our AI-enhanced approach, achieving a mean Intersection over Union (IOU) of 0.89, a Peak Signal-to-Noise Ratio (PSNR) of 34.2 dB, a Structural Similarity Index (SSIM) of 0.92, and clinically tested early gestation accuracy of 98.1%, significantly outperforming traditional methods (IoU: 0.65, PSNR: 28.5 dB, SSIM: 0.72, accuracy: 76.4). Crucially, the system maintains a single-image processing time of 22 ms, fulfilling the requirement for real-time clinical diagnosis. This research not only validates a robust AI-powered ultrasonic biosensing system for improving reproductive management in livestock but also establishes a reproducible, scalable framework for intelligent signal enhancement in broader biosensor applications.

Uric acid is closely related to diseases such as gout, kidney failure, and metabolic disorders. A conventional method for measuring uric acid over 24 h is time intensive and cumbersome for patients who have to take samples to the hospital. At present, hospitals use only laboratory instruments to determine 24-h uric acid concentrations in the urine. This study presents the proof-of-concept of a portable point-of-care tool called Uricia, designed to improve the quality of life of patients monitoring uric acid. Spectrophotometry was performed at a fixed wavelength of 295 nm. The urine sample contained within the cuvette absorbs ultraviolet light, with uric acid specifically responsible for this absorption, thereby allowing the device to measure its concentration. An internal calibration algorithm was used to accommodate the nonlinear optical response of Uricia and was calibrated to a benchtop GENESYS 10S UV-Vis spectrophotometer. The experiments further evaluated potential urinary interferences, revealing that while most constituents had minimal impact, ascorbic acid demonstrated the highest interference, contributing up to 15% of the total signal at high physiological concentrations. This device and the corresponding spectrophotometry method revealed that high concentrations of uric acid precipitated insoluble crystals. A dilution set to an alkali solution vial to be premixed and dissolve the uric acid crystals was added, increasing the detection window to 10 mg/dL, with an LOD of 0.0232 mg/dL and LOQ of 0.0702 mg/dL. Cloud-based data measurement enables spot analysis, which is meant to provide insight into patient status development. These results validated the technical architecture of a controlled matrix for measuring uric acid.

The interpretation and diagnosis of central sleep apnea in pediatrics by nocturnal polysomnography is challenging due to its technical complexity, which involves the simultaneous recording of multiple physiological parameters related to sleep and wakefulness. Furthermore, the unfamiliar environment of a sleep laboratory can hinder sleep evaluation, and diagnostic backlogs are common due to restricted capacity at specialist tertiary centers. The ability to undertake home sleep studies in a familiar environment using simple, robust, and low-cost technology is attractive. The potential to repurpose the PneumoWave biosensor, a UKCA Class 1 device, registered as an accelerometer-based monitoring device that is intended to capture and store chest motion data continuously over a period of time for retrospective analysis, was explored in an in vitro model of central sleep apnea. The PneumoWave system contains a biosensor (PW010), which was able to record simulated apnea episodes of 5 to 20 s across physiologically relevant pediatric breathing rates using an in vitro manikin model and manual annotation. The findings confirm that the PneumoWave biosensor could be a useful technology to support home sleep apnea testing and warrant further exploration.

Chronic kidney disease (CKD) afflicts 850 million people worldwide, with an estimate that it is the 5th highest cause of years of life lost (YLLs). Standard confirmatory procedures for disease are blood and urine analysis with ultrasound for confirmation. Neutrophil gelatinase-associated lipocalin (NGAL) has been established as a prognostic biomarker, especially for the pre-clinical stages of CKD, thus presenting itself as a dependable predictor of the progression. With the aim of designing diagnostics, fatty acids were explored as potential biorecognition elements for the selective capture of NGAL. Three fatty acids-linoleic acid, arachidonic acid, and retinoic acid-were shortlisted as plausible candidates based on their known affinity toward lipocalin family proteins. Docking followed by molecular dynamics simulations were employed to evaluate the binding affinity and stability of each complex. Among them, linoleic acid exhibited the most favorable interaction, as evidenced by the lowest binding free energy. Subsequently, fluorescence and electrochemical techniques-square-wave voltammetry, differential pulse voltammetry, cyclic voltammetry, and electrochemical impedance spectroscopy (EIS)-were systematically compared for qualitative and quantitative checking of the accuracy of NGAL detection. Amongst the electrochemical techniques, differential pulse voltammetry DPV demonstrated superior analytical performance with an LOD of 0.05 ng/mL with a sensitivity of 23.2 µA/cm²/pg. To the best of our knowledge, this is the first report of a fatty acid-based biosensor platform for NGAL detection, presenting a novel approach for CKD diagnostics. The sensitivity obtained is comparable with available NGAL detection methods yet cost-effective and robust.