Sensors & diagnostics最新文献

Genetically-encoded, fluorescent protein (FP)-based biosensors are powerful tools for imaging dynamic cellular activities. Directed evolution is a highly effective method for developing enhanced versions of FP-based biosensors, but the screening process is laborious and time-consuming. Mammalian cell-based screening with electrical stimulation methods has been successful in accurately selecting variants of biosensors for imaging neuronal activities. We introduce an automated mammalian cell screening platform utilizing a fluorescence microscope and a liquid dispenser to enable the screening of biosensor responsiveness to chemical stimulation. We demonstrated the effectiveness of this platform in improving the response of a red fluorescent biosensor for Ca²⁺, K-GECO, for detection of histamine-induced changes in Ca²⁺ concentration. This method should be applicable to any FP-based biosensor that responds to pharmacological treatment or other exogenous chemical stimulation, simplifying efforts to develop biosensors tailored for specific applications in diverse biological contexts.

Accurately identifying tumor tissue is crucial during surgery, especially when removing head and neck squamous cell carcinomas (HNSCC). Our tumor-responsive probes are tailored for ex vivo diagnostics, streamlining today's complex surgical workflows and potentially enabling pathologists and surgeons to rapidly and objectively distinguish between healthy and tumor tissue. Designed based on insights from biological furin substrates and cleavage site screening, the probes detect HNSCC-associated protease activity. Within ten minutes of incubation, tumor tissue is differentiated from healthy tissue by visible fluorescence in biopsy supernatant.

Glycated hemoglobin (HbA1c) is a pivotal biomarker for the monitoring and early diagnosis of diabetes. The CRISPR-Cas system has fascinating application prospects in the next generation of biosensors due to its high specificity, efficiency, flexibility, and customization. Herein, a label-free electrochemical aptasensor was designed for the detection of HbA1c by combining the specific recognition ability of aptamers with the signal amplification effect of the CRISPR-Cas12a system. In the presence of HbA1c, the cis–trans cleavage ability of Cas12a protein was activated, causing the pre-formed probe DNA to be heavily cleaved and the electrochemical signal to increase. With CRISPR-assisted signal amplification, the developed electrochemical aptasensor can detect as low as 0.84 ng mL⁻¹ HbA1c. Moreover, this aptasensor can detect 10 ng mL⁻¹ HbA1c in 50% human serum due to its high selectivity, reproducibility, and long-term stability, which is lower than its physiological level in human blood samples. All results proved that the proposed aptasensor has a promising application in the early diagnosis and long-term monitoring of diabetes.

A novel alternative to cope with saliva-to-saliva variations and cross-interference while sensing delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD) is reported here using two voltammetric sensors coupled with machine learning. The screen-printed electrodes modified with the same analyte molecules (m-Z-THC and m-Z-CBD) were employed for sensing ultra-low concentrations of THC and CBD in the 0 to 5 ng mL⁻¹ range in real human saliva samples. Simultaneous detection of THC and CBD was carried out using m-Z-THC or m-Z-CBD to study the performance of each modified sensor. Also, CBD and THC have the same molecular structure; there is only a slight difference in how the atoms are arranged, and therefore both molecules will have similar electrochemical performance. Consequently, CBD can be a potential interference while detecting THC and THC can be an interference during CBD detection using electrochemical sensors. Therefore, machine learning was introduced to analyze the sensor analytical responses to overcome such issues. The data processing results provide suitable accuracies of 100% for training in the case of both sensors and 92 and 83% for m-Z-THC and m-Z-CBD, respectively, for dataset testing THC and CBD in saliva samples. Additionally, the saliva samples containing CBD and THC as cross-interference were accurately identified and classified.

MicroRNAs (miRNAs) are short (about 18–24 nucleotides) non-coding RNAs and have emerged as potential biomarkers for various diseases, including cancers. Due to their short lengths, the specificity often becomes an issue in conventional amplification-based methods. Next-generation sequencing techniques could be an alternative, but the long analysis time and expensive costs make them less suitable for routine clinical diagnosis. Therefore, it is essential to develop a rapid, selective, and accurate miRNA detection assay using a simple, affordable system. In this work, we report a CRISPR/Cas13a-based miRNA biosensing using point-of-care dark-field (DF) imaging. We utilized magnetic-gold nanoparticle (MGNPs) complexes as signal probes, which consist of 200 nm-sized magnetic beads and 60 nm-sized gold nanoparticles (AuNPs) linked by DNA hybridization. Once the CRISPR/Cas13a system recognized the target miRNAs (miR-21-5p), the activated Cas13a cleaved the bridge linker containing RNA sequences, releasing 60 nm-AuNPs detected and quantified by a portable DF imaging system. The combination of CRISPR/Cas13a, MGNPs, and DF imaging demonstrated amplification-free detection of miR-21-5p within 30 min at a detection limit of 500 attomoles (25 pM) and with single-base specificity. The CRISPR/Cas13a-assisted MGNP-DF assay achieved rapid, selective, and accurate detection of miRNAs with simple equipment, thus providing a potential application for cancer diagnosis.

Point-of-care (POC) biosensors have enormous potential to help guide and inform clinical decisions at a patient's location. They are particularly relevant to underserved populations, and people living in remote locations where healthcare infrastructure and resources are often limited. The translation of effective POC biosensors into commercial products is rapidly growing across many research fields. A significant quantity of scientific articles focused on the fundamental, applied, and proof-of-concept aspects of biosensing are reported each year. However, this extensive body of work is not reflected in the comparatively small number of commercial biosensors available on the market. Here, we discuss key aspects of the biosensor translation process including the selection of analytical biomarkers in various body fluids, clinical trials, regulatory approval, consumer engagement, manufacturing and scale-up strategies, health economics, and legal and ethical considerations.

This present work aimed to craft copper (Cu²⁺)-doped carbon dots (CuCDs) for the selective and sensitive detection of a guanine nucleobase. By employing a hydrothermal method, we synthesized blue-emitting CuCDs having emission maxima at 423 nm. CuCDs were used as a fluorescence turn-on ratiometric probe to detect guanine, a critical purine base in DNA involved in energy transduction, cell signalling, and metabolic processes. In the presence of guanine, the fluorescence intensity of CuCDs significantly increased due to the stable non-covalent interaction between Cu²⁺ and guanine. CuCDs achieved a very low limit of detection (LOD) of 0.59 nM for guanine as a highly sensitive probe. CuCDs demonstrated selectivity for guanine with no interference from other nucleobases (adenine, thymine, and cytosine) and various biomolecules and metal ions commonly found in the cellular environment. In addition, CuCDs demonstrated a higher affinity for guanine-enriched oligonucleotide cMYC G 27-mer over dsDNA 26-mer devoid of a large guanine population. Furthermore, the fluorescence intensity of CuCDs increased in guanine-treated mammalian cells and G-quadruplex-enriched cancer cells compared with that in non-cancerous cells. Hence, we developed a highly sensitive ratiometric fluorescence probe, CuCDs, for the selective detection of guanine both in vitro and within mammalian cells via a “fluorescence turn-on mechanism”.

In large-scale radiation exposure events, the ability to triage potential victims by the received radiation dosage is crucial. This can be evaluated by radiation-induced biological changes. Radiation-responsive mRNA is a class of biomarkers that has been explored for dose-dependency with methods such as RT-qPCR. However, these methods are challenging to implement for point-of-care devices. We have designed and used molecular beacons as probes for the measurement of radiation-induced changes of intracellular mRNA in a microfluidic device towards determining radiation dosage. Our experiments, in which fixed TK6 cells labeled with a molecular beacon specific to BAX mRNA exhibited dose-dependent fluorescence in a manner consistent with RT-qPCR analysis, demonstrate that such intracellular molecular probes can potentially be used in point-of-care radiation biodosimetry. This proof of concept could readily be extended to any RNA-based test to provide direct measurements at the bedside.

Fast and reliable identification of pathogenic bacteria is of upmost importance to human health and safety. Methods that are currently used in clinical practice are often time consuming, require expensive equipment, trained personnel, and therefore have limited applications in low resource environments. Molecular identification methods address some of these shortcomings. At the same time, they often use antibodies, their fragments, or other biomolecules as recognition units, which makes such tests specific to a particular target. In contrast, array-based methods use a combination of reporters that are not specific to a single pathogen. These methods provide a more data-rich and universal response that can be used for identification of a variety of bacteria of interest. In this report, we demonstrate the application of the excitation–emission spectroscopy of an environmentally sensitive fluorescent dye for identification of pathogenic bacterial species. 2-(4′-Dimethylamino)-3-hydroxyflavone (DMAF) interacts with the bacterial cell envelope resulting in a distinct spectral response that is unique to each bacterial species. The dynamics of dye–bacteria interaction were thoroughly investigated, and the limits of detection and identification were determined. Neural network classification algorithm was used for pattern recognition analysis and classification of spectral data. The sensor successfully discriminated between eight representative pathogenic bacteria, achieving a classification accuracy of 85.8% at the species level and 98.3% at the Gram status level. The proposed method based on excitation–emission spectroscopy of an environmentally sensitive fluorescent dye is a powerful and versatile diagnostic tool with high accuracy in identification of bacterial pathogens.

The contamination of per- and polyfluoroalkyl substances (PFAS) in drinking water presents a significant concern and requires a simple, portable detection method. This study aims to demonstrate the effectiveness of Raman and surface-enhanced Raman scattering (SERS) spectroscopies for identifying and quantifying various PFASs in water. Experimental Raman spectra of different PFASs reveal unique characteristic peaks that enable their classification. While direct SERS measurements from silver nanorod (AgNR) substrates may not exhibit distinct PFAS characteristic peaks, the presence of PFAS on SERS substrates induces noticeable spectral changes. By integration with machine learning (ML) techniques, these SERS spectra can be used to successfully differentiate and quantify PFOA in water, achieving a limit of detection (LOD) of 1 ppt. Modifying the AgNR substrates with cysteine and 6-mercapto-1-hexanol enhances the differentiation and quantification capabilities of SERS-ML. Despite alkanethiol molecules affecting spectral features, PFAS and PFOS concentrations produce observable spectral variations. A support vector machine model achieves 93% accuracy in differentiating PFOA, PFOS, and references, independent of concentration. A support vector regression model further establishes LODs of 1 ppt for PFOA and 4.28 ppt for PFOS. By removing spectra with concentrations lower than LODs, the classification accuracy is improved to 95%.