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Hypochlorous acid (HClO/ClO^-) is a common ROS that exhibits elevated activity levels in cancer cells. In this study, an ClO^--triggered TADF probe, PTZ-MNI, was designed based on a naphthalimide core. PTZ-MNI self-assemble in aqueous environments, exhibiting significantly enhanced fluorescence that demonstrated typical aggregation-induced delayed fluorescence (AIDF) characteristics. The probe not only showed high sensitivity to ClO^- but also exhibited remarkable selectivity over other reactive oxygen species and disturbance. PTZ-MNI displayed TADF characteristic, including sensitivity to oxygen in toluene, insensitivity to oxygen in aggregated states that maintain long fluorescence lifetimes, a vertical conformation, and a minimal ΔE_ST of 0.01 eV. Cell imaging studies showed the probe could trace ClO^- by red to green fluorescence in HeLa cell. The colocalization analysis indicated its excellent lysosome-targeting specificity. In addition, PTZ-MNI-O, the compound after oxidation, exhibited effective ROS generation ability and significant PDT effect after irradiation. This work provides guidance for the rational design of responsive TADF luminescent materials used in cell imaging and activatable-PDT.

Spatial metabolomics offers the combination of molecular identification and localization. As a tool for spatial metabolomics, mass spectrometry imaging (MSI) can provide detailed information on localization. However, molecular annotation with MSI is challenging due to the lack of separation prior to mass spectrometric analysis. Contrarily, surface sampling capillary electrophoresis mass spectrometry (SS-CE-MS) provides detailed molecular information, although the size of the sampling sites is modest. Here, we describe a platform for spatial metabolomics where MSI using pneumatically assisted nanospray desorption electrospray ionization (PA-nano-DESI) is combined with SS-CE-MS to gain both in-depth chemical information and spatial localization from thin tissue sections. We present the workflow, including the user-friendly setup and switching between the techniques, compare the obtained data, and demonstrate a quantitative approach when using the platform for spatial metabolomics of ischemic stroke.

Bacterial bloodstream infections cause high morbidity and mortality. Although bacteria can be detected by various methods, culture methods are often used for the detection of live, accurate, reproducible, and selective bacterial identification. However, the culture method is time-consuming, and clinicians often start treatment immediately due to the long determination time. This reduces the bacterial density detectable by culture, and in some cases, makes determination difficult. To overcome this challenge, we propose a method that directly combines bacteriophage-based lysis with quantitative PCR (qPCR). This method enables the simple and rapid detection of bacteria without the need for pre-concentration or DNA extraction steps. Escherichia coli K12 (E. coli K12) was used as the model bacterium, and bacteria lysed by the E. coli K12-specific bacteriophage were detected using qPCR. The total analysis time was less than 3 h, and only live bacterial cells were selectively lysed. The method was also used to detect bacteria spiked into reference plasma samples, and bacterial DNA was detected via qPCR. The results obtained from the calibration graph created with cultured bacteria and the one created by spiking bacteria into reference plasma were consistent. The similarities between the calibration graphs from both methods were found to be in the range of 92-102.7 %. The LOD and LOQ values for bacteria spiked into reference plasma were calculated as 14.80 and 3.5x10³ CFU/mL, respectively.

Near-infrared (NIR) spectroscopy analysis technology has become a widely utilized analytical tool in various fields due to its convenience and efficiency. However, with the promotion of instrument precision, the spectral dimension can now be expanded to include hundreds of dimensions. This expansion results in time-consuming modeling processes and a decrease in model performance. Hence, it is crucial to carefully choose representative features before constructing models. This paper focuses on the limitations of filter algorithms, which can only sort features and cannot directly determine the best subset of features. A hybrid method of combination of the Max-Relevance Min-Redundancy (mRMR) algorithm and the Genetic Algorithm (GA), as well as filter and wrapper feature selection methods, are combined to select appropriate features automatically. This hybrid algorithm retains the features in each individual that are considered to have a strong correlation and low redundancy by the mRMR algorithms during each iteration of the GA. On the other hand, it deletes the features that are regarded as having little correlation or high redundancy. Through the process of iteration, the feature subset is continuously optimized. We use the proposed hybrid method to select features on two datasets and establish various models to verify our proposed method in this paper. The experimental results indicate the feature selection approach, which combines mRMR with the GA, covers the advantages of both feature selection methods. This approach can select features that show good predictive performance. When compared with other common feature selection methods, such as the Uninformative Variable Elimination algorithm (UVE), Competitive Adaptive Reweighted Sampling algorithm (CARS), Successive Projections Algorithm (SPA), Iteratively Retains Informative Variables (IRIV), and GA, the hybrid algorithm can select a larger number of feature variables that are both representative and informative, additionally, it significantly enhances the predictive performance of the model.

This study introduces an innovative electrochemical biosensor, engineered through the functionalization screen-printed electrode (SPE) with a coordination complex comprised of 4-mercaptobenzoic acid (4-MBA) and copper ions (Cu²⁺), achieving precise quantitative determination of glyphosate. Electrodepositing gold nanoparticles (AuNPs) onto the electrode surface, forming a self-assembled monolayer (SAM) of 4-MBA via thiol-gold interactions, and immobilizing Cu²⁺ via coordination bonding with the monolayer, finalizing the electrochemical biosensor construction as Cu²⁺/4-MBA/AuNPs/SPE. The successful modification of the biosensor interface is confirmed through scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and electrochemical characterization. Through parameter optimization, critical metrics for the biosensor preparation process have been determined. Using square wave voltammetry (SWV), a linear relationship between the glyphosate concentration and the peak current inhibition ratio at the electrode surface is established. Additionally, the repeatability and anti-interference capabilities of the fabricated biosensors are evaluated. The experimental outcomes affirm the biosensor's capability for quantitative glyphosate detection across a 5-100 nM range, boasting a 1.65 nM limit of detection (LOD). Testing on tap water samples verifies a robust recovery rate for glyphosate residues, spanning 89.84 %-107.48 %. The proposed biosensor holds significant promise for glyphosate detection, offering substantial applicability and this study provides a valuable reference for the advancement of biosensors geared toward the quantitative assessment of organophosphate pesticides (OPs).

Pursuing nanomaterials with high fluorescence quantum yields is of great significance in the fields of bioimaging, medical diagnosis, and food safety monitoring. This work reports on orange-emitting aggregation-induced emission (AIE) copper nanoclusters (Cu NCs) integrated with blue-emitting nitrogen-doped carbon dots (N-CDs), which enables highly sensitive detection of S^2- and Zn²⁺ ions through an off-on ratiometric fluorescence method. The highly emissive Cu NCs was doped by Ce³⁺ with a high quantum yield of 51.30 % in aqueous solution. The S^2- can induce fluorescence quenching of AIE Cu NCs/N-CDs from orange to blue, while Zn²⁺ can restore the orange fluorescence. The probe provided linear detection ranges of 0.5-170 μM for S^2- and 0.05-200 μM for Zn²⁺, with detection limits of 0.17 μM and 0.02 μM, respectively. Moreover, a smartphone assistant ratiometric fluorescent test strips were developed for the rapid and visual detection of S^2- and Zn²⁺. The AIE Cu NCs/N-CDs probe exhibited diverse fluorescence color responses, high fluorescence stability, and low cytotoxicity. The ratiometric system was successfully applied to the detection of S^2- and Zn²⁺ in real water samples as well as in cellular and living imaging, demonstrating its potential in biochemical analysis and food safety monitoring.

CYFRA21-1 is a tumor marker for lung cancer, and its rapid and accurate detection can provide evidence for the early diagnosis of lung cancer. In this work, Bi-Fe turnbull blue analogues (Bi-Fe-TBA) were synthesized by the self-templating method. Bi₂O₃-SFNs was prepared by simple oxidation in air using Bi-Fe-TBA as a template. Bi₂O₃ Star-like Flower Nanoclusters (Bi₂O₃-SFNs) and CdS Hollow Nanorods (CdS-HNRs) were used to form a unique type II heterojunction for the first time. The arrangement of energy levels between CdS-HNRs and Bi₂O₃-SFNs, along with their hollow structure and star shape, effectively suppressed the recombination of photogenerated electrons and holes while shortening carrier transport distance. An ultra-sensitive PEC biosensor was developed to detect the lung cancer marker CYFRA21-1, leveraging the superior photoelectric conversion capabilities of Bi₂O₃-SFNs/CdS-HNRs. The sensor demonstrates outstanding stability, specificity, reproducibility as well as a wide linear range (10^-4 - 10 ng mL^-1) and low detection limit (4.23 × 10^-5 ng mL^-1). This study is valuable for the preparation of other functional materials using TBA as a template.

Mercury (II) ions (Hg²⁺) are a significant source of heavy metal contamination in groundwater, posing a serious threat to human health and the environment. Therefore, there is an urgent need for the development of a new detection technique with high sensitivity for monitoring Hg²⁺ in contaminated groundwater. Here, we developed a signal amplifying MOF-based probe (NXS@ZIF-8) for on-site and ultrasensitive dual-channel portable detection of Hg²⁺ in groundwater. The successful grafting of the fluorescent probe (NXS) onto ZIF-8 effectively enhanced the enrichment of the NXS probe, thereby amplifying the detection signal for Hg²⁺. Upon exposure to Hg²⁺, NXS@ZIF-8 quickly emits fluorescent signals, which can be easily detected using portable laser-induced fluorescence spectrometers (LIFs) with a low detection limit of 0.30 ppb. Importantly, the platform enables on-site detection of Hg²⁺ in groundwater samples and direct on-site and in-situ detection of Hg²⁺ in contaminated groundwater, achieving acceptable results. Furthermore, NXS@ZIF-8 was fabricated as a paper-based sensor and integrated into a portable smartphone device for visual detection of Hg²⁺ in contaminated groundwater. This work presents an approach for on-site, in-situ and highly sensitive portable detection of heavy metals in contaminated groundwater, eliminating the need for access to specialized laboratory equipment.

Enzyme immobilization techniques are crucial for enhancing enzyme stability and catalytic efficiency. Traditional methods such as physical adsorption and simple covalent binding often fail to maintain enzyme activity and stability. In this study, an innovative multi-level immobilization strategy was proposed to achieve efficient targeted immobilization of nuclease P1 (NP1) by fine-tuning the surface microenvironment. Molecular simulation results revealed that the distinctive electrostatic distribution and the specific placement of basic amino acids, such as lysine, on the NP1 surface caused dopamine to preferentially adsorb on areas away from NP1's active site. This selective adsorption facilitated the directed immobilization of NP1, while the positively charged environment generated by the co-deposited surface further enhanced NP1's adsorption capacity. This multilevel modification was found to significantly optimize the physicochemical environment of the immobilized surface through surface characterization and enzymatic testing. This strategy greatly improves enzyme activity (3590.0 U/mg), stability, and reusability (70 % after 10 cycles). In particular, NP1 on this surface exhibited an optimal Michaelis constant (K_m) of 34.0 mM and a maximum reaction rate of 5.5 mM min^-1, demonstrating the remarkable effect of the modification strategy in enhancing the enzyme catalytic performance. The present study provides an efficient and stable immobilization platform for enzyme catalytic applications by precisely modulating the surface microenvironment and the oriented immobilization strategy, which has an important potential for practical applications. This stable and reusable NP1 platform allows for efficient DNA/RNA cleavage, facilitating its application in industrial biocatalysis, biomedical enzyme-based processes, and biosensors.

Exosomes, crucial for intercellular communication, hold potential as noninvasive liquid biopsy biomarkers especially in early breast cancer detection benefitted from the distinctive "cancer signature" on their membrane surface. Yet, the present methodologies of exosomes for breast cancer detection have involved the implementation of only a single member from the tetraspanin protein group as a biomarker. Moreso, due to the high concentration of exosomes in complex body fluids, there is a compelling need to measure a small concentration of cancer-derived exosomes with a low background noise signal. In this study, we designed and characterized magnetic core-gold shell nanohybrids (mAuNHs) that function as detection and isolator probes, which were integrated in a simple colorimetric sandwich magneto-immunoassay (mLISA). The magnetic core of mAuNHs facilitates the separation of exosomes from complex samples of biological origin whereby amorphous structures were effectively removed, decreasing background signal. Meanwhile, the coalescence effect of pairing biologically abundance exosomal marker (CD9 antibody) with the cancer specific (CD24 antibody) offers a highly selective and sensitive detection of our target model, MCF7 exosomes. As a result, using our mLISA system, exosomes derived from MCF7 can be selectively recognized from other tested cancer cell lines, BT474 and PC3. Besides, as low as 37 particles/μL of limit of detection (LOD) was achieved using mLISA sensor, exhibiting a good sensitivity as compared to conventional ELISA. Overall, our proposed dual-target biosensor offers a great reduction on background noise from samples, simplicity for users as in exosome's lengthy preparation is reduced as well as good sensitivity.