Sensors & diagnostics最新文献

With the prevalence of diabetes and its secondary complications, the effective monitoring of diabetic biomarkers is necessary. While portable analytical devices for blood glucose have been sophisticatedly developed, those for haemoglobin (Hb) and, especially haemoglobin A1c (HbA_1c), a glycated form of Hb, remain elusive. Here, we developed an aptamer-based duplexed electrochemical sensor for the simultaneous detection of Hb and HbA_1c. Ferrocene (Fc) and a thiol group were introduced to the 5′ and 3′-end of aptamers that bind Hb and HbA_1c, respectively. While the thiol group facilitates the formation of a self-assembled monolayer of the aptamers onto a customized, duplexed screen-printed gold electrode, the presence of Fc provides the electrodes an internal electrochemical signal. Upon analyte binding, the secondary conformation of the aptamers is changed, thus leading to a quenched current signal because of an increased distance between Fc and the electrode surface. Our duplexed electrochemical sensor showed a good linearity for both analytes over a wide concentration range, and has proved effective in simultaneously quantifying Hb and HbA_1c in calibration samples.

Spermine, a key biogenic amine indicating food freshness, is typically detected using costly and time-consuming chromatographic methods. This study introduces a more efficient, eco-friendly alternative—a label-free colorimetric sensing platform using gold nanoparticles (AuNPs) functionalized with 11-mercaptoundecanoic acid and capped with an N-hydroxysuccinimide (NHS)-ester. Transmission electron microscopy revealed monodisperse, spherical AuNPs (13 nm), with an increase in size upon functionalization. Fourier transform infrared spectra confirmed successful functionalization. The hydrodynamic size of the AuNPs increased from 17.26 nm to 2167 nm, and the zeta potential shifted from −48.86 mV to −35.59 mV. The platform takes advantage of the selective interaction between spermine and NHS-ester-functionalized AuNPs, inducing nanoparticle aggregation, as shown by a red shift in surface plasmon resonance (SPR). UV-vis spectroscopy demonstrated a robust linear correlation (R² = 0.958) between spermine concentration (1.0–4.0 μM) and nanoparticle aggregation index, with a limit of detection (LOD) of 0.77 μM. The sensor also exhibited high reproducibility in pork extract matrices (coefficient of variation <5%) and selectivity for spermine amid various interfering analytes. Its eco-friendly design and rapid response time position it as a viable tool for real-time spermine monitoring in food spoilage, offering comparable performance metrics to traditional chromatographic techniques while addressing sustainability concerns.

CRISPR-Cas-based lateral flow assays (LFAs) have emerged as a promising diagnostic tool for ultrasensitive detection of nucleic acids, offering improved speed, simplicity and cost-effectiveness compared to polymerase chain reaction (PCR)-based assays. However, visual interpretation of CRISPR-Cas-based LFA test results is prone to human error, potentially leading to false-positive or false-negative outcomes when analyzing test/control lines. To address this limitation, we have developed two neural network models: one based on a fully convolutional neural network and the other on a lightweight mobile-optimized neural network for automated interpretation of CRISPR-Cas-based LFA test results. To demonstrate proof of concept, these models were applied to interpret results from a CRISPR-Cas13-based LFA for the detection of the SARS-CoV-2 N gene, a key marker for COVID-19 infection. The models were trained, evaluated, and validated using smartphone-captured images of LFA devices in various orientations with different backgrounds, lighting conditions, and image qualities. A total of 3146 images (1569 negative, 1577 positive) captured using an iPhone 13 or Samsung Galaxy A52 Android smartphone were analyzed using the trained models, which classified the LFA results within 0.2 s with 96.5% accuracy compared to the ground truth. These results demonstrate the potential of machine learning to accurately interpret test results of CRISPR-Cas-based LFAs using smartphone-captured images in real-world settings, enabling the practical use of CRISPR-Cas-based diagnostic tools for self- and at-home testing.

Gold nanoparticles (AuNPs), which have been used as colorimetric biosensors, show strong light scattering, allowing individual AuNPs to be identified using a dark-field microscope (DFM). In this study, we developed a method of observing the target molecule-derived aggregation of AuNPs modified with DNA aptamers at the single-cluster level using the DFM. C-Reactive protein (CRP) is an important clinical biomarker of inflammatory and cardiovascular diseases, for which a simple, inexpensive, and sensitive detection method is needed. In this study, the CRP-mediated aggregate formation of CRP aptamer-modified AuNPs was evaluated with single-cluster analysis using the DFM, and the detection limit was 17 nM, which was sufficient as a diagnostic indicator for CRP. We also developed a portable DFM equipped with a smartphone and a stage adjustment system, which enables single-cluster observation of AuNPs, and showed that 50 nM of CRP could be detected, indicating that this approach is suitable for point-of-care diagnosis. With the selection of appropriate aptamers, this method can be applied for the detection of various molecules.

The illicit use of recycled waste cooking oil poses a threat to food safety, yet there is currently a lack of on-site identification methods. This study targets a key component of recycled cooking oil, capsaicinoids, and establishes a rapid detection method for identifying illegally recycled waste cooking oil on-site. The method involves extracting capsaicinoids from the oil using a non-organic solvent extractant, and then detecting it using fluorescent lateral flow immunoassay (LFIA) strips. The preparation conditions of LFIA test strips were first optimized in this study. Subsequently, a 0.02 mol L⁻¹ solution of dimethyl-β-cyclodextrin was optimized as the extractant for capsaicin. The optimized sample extraction conditions involve a sample-to-extractant volume ratio of 1:2 and an extraction time of 1 minute, with a total extraction and detection time not exceeding 15 minutes. This method demonstrates a limit of detection (LOD) for natural capsaicin in oil samples of 0.14 μg kg⁻¹, with a detection range spanning 0.46 to 81 μg kg⁻¹. The method yields recovery rates between 88.76% and 115.79%, with coefficient of variation (CV) values ranging from 1.80% to 13.37%. Cross-reactivity rates for dihydrocapsaicin and synthetic capsaicin exceed 90%, while the impact of common contaminants or additives in other edible oils on detection is minimal. In conclusion, this approach fulfills the technical criteria mandated by the China Food and Drug Administration for distinguishing capsaicin compounds in recycled oil, offering advantages such as simplicity, rapidity, and “green” operation, making it suitable for rapid on-site identification of illegally recycled waste cooking oil.

Due to the rising prevalence of chronic diseases worldwide attributed to an aging population and the development of big data and artificial intelligence (AI), there is a significant demand for healthcare and long-term disease monitoring. As an emerging sensing technology, graphene field-effect transistor (GFET) sensors are anticipated to become the backbone of future large-scale electronic applications, owing to their rapid and accurate disease diagnosis capabilities, excellent biocompatibility, and ease of system integration. This review summarizes the recent advances in biosensing applications using GFETs. Initially, the working mechanism of GFETs for biosensing is briefly introduced, followed by an outline of various gate configuration strategies employed in GFETs and a discussion on methods for enhancing sensing performance. The multiplexing capabilities and flexible wearable applications of GFETs are then summarized and highlighted, aiming to increase the diversity and applicability of these sensors. Subsequently, a comprehensive survey of the advancements in the integration and miniaturization of multi-component GFET biosensors is discussed. Moreover, this review provides an outlook on the challenges and prospects associated with the commercialization of GFET technology in the biosensing field.

Giardia intestinalis, an intestinal protozoan parasite, is one of the potentially severe parasitic infections, especially in children. Rapid and simple diagnostic tools are highly desired to prevent the potential outbreak of G. intestinalis infection. The life cycle of Giardia species is quite simple and consists of trophozoite and cystic forms. This report presents the selection of ssDNA aptamers with high binding affinity to a G. intestinalis cyst recombinant protein using the SELEX process (systematic evolution of ligands by exponential enrichment). The process is based on incubating a random DNA library with the targeted protein, and the bound sequences are recovered and amplified by polymerase chain reaction (PCR). The generated pool of aptamer sequences is used in the subsequent selection round. After ten selection cycles, three sequences were isolated with low dissociation constants (K_d) of 7.98, 21.02, and 21.86 nM. Subsequently, the aptamer with the best affinity was integrated into a label-free electrochemical biosensor to detect G. intestinalis cyst protein. The developed aptasensor accurately detected the G. intestinalis recombinant cyst protein within the range of 0.1 pg mL⁻¹ to 1000 ng mL⁻¹, and a low detection limit of 0.0026 pg mL⁻¹. Furthermore, a selectivity study showed insignificant cross-reactivity against other proteins such as bovine serum albumin and globulin, and no reactivity against G. intestinalis trophozoite recombinant protein. Finally, the aptasensor was tested using G. intestinalis-spiked tap water samples and showed good recovery rates.

Continuous monitoring in healthcare could transform patient care by improving patient outcomes and reducing costs through rapid detection and timely intervention. The pH of body fluids is an essential indicator for detecting acid/base imbalances and subsequent diseases, as well as monitoring organ functions. However, current pH sensors experience signal drifts over continuous measurements, inhibiting their ability to provide longitudinal body's pH status. To enable real-time, continuous pH monitoring, we developed a polydopamine (PDA)-based pH sensor and validated its performance in PBS and simulated wound fluid exudate (WFE). To increase signal stability for continuous performance, heat treatment and agarose gel coating were applied to the sensor, and we observed that the agarose coating greatly improved the signal drift.

Expression of concern for ‘Sensing of COVID-19 spike protein in nasopharyngeal samples using a portable surface plasmon resonance diagnostic system’ by Hiba Saada et al., Sens. Diagn., 2022, 1, 1021–1031, https://doi.org/10.1039/D2SD00087C.

Signal amplification and noise reduction are very crucial for the sensitive determination of disease-related biomarkers. The development of rapid and laborious testing is urgently required due to extended incubation time and high mortality. Herein, we developed a Ce-based mesoporous metal–organic framework-based multi-enzyme encapsulation device for the realization of enzyme-linked immunosorbent assay (ELISA) for the dengue virus (DENV). Briefly, a UiO-66(Ce) framework structure with uniform mesopores was synthesized by a one-pot method, which allowed efficient encapsulation of natural enzymes with an encapsulation efficiency of 700%. The developed multi-enzyme reaction probes were able to maintain more than 90% of catalytic activity stable at room temperature for 60 days while ensuring efficient enzyme immobilization. Sensitive evaluation of DENV was achieved by encapsulating glucose oxidase and horseradish peroxidase, combined with the formation of an immune sandwich in the presence of DENV. The developed sensor enabled flexible detection of recombinant dengue virus serotype 2 NS1 protein (DENV2-NS1) from 0.05 to 100 ng mL⁻¹, with a low limit of detection of 39.7 pg mL⁻¹. In addition, there were no significant differences in the test results of the samples obtained through the developed multi-enzyme probes when compared to commercially available ELISA kits. This work provides new horizons for the development of efficient enzyme encapsulation systems.