Sensors & diagnostics最新文献

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.

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.

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.

Dengue is one of the world's fastest-growing health issues, affecting primarily tropical and sub-tropical countries. Dengue infection is spread via mosquitoes, i.e., Aedes, and caused by the dengue virus (DENV), a single-stranded RNA virus having 4 serotypes, i.e., DENV 1–4, and any one of the 4-serotypes can cause dengue fever. Dengue symptoms can range from asymptomatic primary infection to fatal secondary infections, such as dengue shock syndrome (DSS) and dengue hemorrhagic fever (DHF). Since there are no dengue vaccinations or antiviral treatments available, total bed rest, proper hydration, and the use of analgesics for symptomatic pain relief are usually prescribed, which make recovery both difficult and time-consuming. This mandates the development of early detection methods for DENV so that the disease can be stopped before it spreads. For example, traditional laboratory-based diagnostic approaches for detecting dengue infection have one or more flaws. This includes cross-reactivity with other flaviviruses, the need for several samples for serological assays, and the high cost and complexity of PCR processes. Thus, biosensors have garnered considerably greater attention because of their simple fabrication, ease of use, ultra-sensitivity, selectivity, and low cost of production. This review starts with an introduction and a discussion on the conventional methods used for dengue virus detection, along with their limitations. Later, recently developed and futuristic biosensors are summarized before and finally closing this review with a brief conclusion.

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Impairment of protein quality control is a critical factor in the development of neurodegenerative disorders like Alzheimer's, Parkinson's and Huntington's disease, characterized by the accumulation of protein aggregates. As such, detection and monitoring of protein aggregates remains a crucial area of study. In this work, we synthesize a series of bithiophene derivatives based on a red emitting amyloid fluorophore NIAD-4. By molecular engineering, widened Stokes shifts and spectral tuning can be achieved in these derivatives. Through molecular docking and aggregation assays, we demonstrate the specificity of these derivatives towards protein fibrils over monomers and amorphous aggregates. Utilizing unbiased flow cytometry together with a cell viability indicator, we show that derivative NIAD-CNOET facilitates the discrimination of cells treated with and without preformed fibrils of α-synuclein, a model of the pathological hallmark of Parkinson's disease.

Abnormal DNA methylation mediated by DNA methyltransferases is one of the most common epigenetic modifications, and abnormal DNA methyltransferase activity is often responsible for serious diseases such as cancer. For rapid, sensitive and efficient detection of DNA methyltransferase activity, a bioassay system using gold nanoclusters (DNA-AuNCs) as output has been developed. In this study, dumbbell DNA substrate is recognized and methylated by methyltransferase followed by cleavage by endonuclease (GlaI). In the presence of terminal deoxynucleotidyl transferase (TdT), the poly-A DNA product eventually becomes the template for the reduction of gold nanoclusters and then generated with a strong fluorescence signal. The assay is highly sensitive and requires no amplification or complex material synthesis steps. The detection limit is 0.077 U mL⁻¹. The bioassay showed good detection efficiency in both human serum, cell lysates and human-derived cells. Moreover, it can be used for screening and evaluation of M.SssI MTase inhibitors and hence has great potential use in disease diagnosis and drug discovery. The method was universal and allowed for other biological target detection by simply replacing the sequence of the substrate DNA recognition site; thus the proposed assay has a broad scope of application in both bioassay and drug screening.

In this study, we present a sensor array for precise pH monitoring, based on carbon dots (CDs) synthesised from fructose and p-phenylenediamine through a one-step hydrothermal method. The CDs exhibited significant photostability and different fluorescence emissions in different pH conditions, making them suitable for real-time pH sensing across a wide pH range of 3 to 10. Our mechanistic studies revealed how surface functionalisation affects the pH response. For statistical analysis of our array data, we employed Gaussian process regression to create a predictive model for determining pH levels of test samples based on the array spectral data, and linear discriminant analysis for high-precision classification of pH values. This combined approach enabled a comprehensive analysis of the CDs' pH-sensitive fluorescence, demonstrating a significant methodological advancement in pH sensor technology. Our research advances the understanding of the fluorescence mechanisms of CDs in response to pH variations. Furthermore, our study demonstrates the power of integrating machine learning techniques to improve the performance and application scope of fluorescent nanomaterials. These findings have important implications for chemical sensing across a range of fields, including environmental monitoring and biomedical diagnostics.

Correction for ‘Modulation of the binding sites for an adaptable DNA interactive probe: efficient chromo-fluorogenic recognition of Al³⁺ and live cell bioimaging’ by Atanu Maji et al., Sens. Diagn., 2024, https://doi.org/10.1039/D4SD00242C