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Group A streptococcus (GAS) is a pathogen typically transmitted through respiratory droplets and skin contact, causing an estimated 700 million mild non-invasive infections worldwide each year. There are approximately 650 000 infections that progress to severe invasive infections, even resulting in death. Therefore, the ability to detect GAS rapidly, accurately and in real time is important. Herein, we developed a nanozyme linked multi-array gas driven sensor (NLMAGS) to point-of-care testing of GAS within 2 h. The NLMAGS demonstrated excellent performance as it combined the advantages of nanozyme techniques, immunoassay techniques, and 3D printing techniques. Platinum- and palladium-rich nanozyme particles (Au@Pt@PdNPs) were synthesized and used to label monocloning antibodies as detection probes. Magnetic beads were labeled with monocloning antibodies as capture probes to establish a double-antibody sandwich immunoassay for the detection of GAS. The sandwich immune complex can catalyze the H₂O₂ substrate and produce O₂. GAS quantification can be achieved by measuring the distance that the O₂ pushes the ink drops forward in the sensor. Under optimized conditions, the NLMAGS quantitatively detected 24 spiked samples with a limit of detection (LOD) of 62 CFU mL⁻¹, which was 5 times lower than that of ELISA (334 CFU mL⁻¹). A strong correlation with the conventional ELISA was found (r = 0.99, P < 0.001). In comparison, the traditional lateral flow immunoassay based on Au@Pt@PdNPs-mAb2 (Au@Pt@PdNPs-LFIA) had a LOD of 10⁴ CFU mL⁻¹, which was significantly higher than that of NLMAGS. The NLMAGS demonstrated excellent sensitivity to GAS. The intra- and inter-assay precisions of the sensor were below 15%. Overall, the established NLMAGS has promising potential as a rapid and quantitative method for detecting GAS and can also be used to detect various pathogens.

Spatially offset Raman spectroscopy (SORS) is a transformative method for probing subsurface chemical compositions in turbid media. This systematic study of Monte Carlo simulations provides closed-form characterizations of key SORS parameters, such as the distribution of spatial origins of collected Raman photons and optimal SORS geometry to selectively interrogate a subsurface region of interest. These results are unified across an extensive range of material properties by multiplying spatial dimensions by the medium's effective attenuation coefficient, which can be calculated when the absorption and reduced scattering coefficients are known from the literature or experimentation. This method of spatial nondimensionalization is validated via goodness-of-fit analysis on the aggregate models and by training a subsurface sample localization model on a heterogeneous population of materials. The findings reported here advance the understanding of SORS phenomena while providing a quantitative and widely applicable foundation for designing and interpreting SORS experiments, facilitating its application in disciplines such as biomedical, materials science, and cultural heritage fields.

A double-channel methane (CH₄) sensor was developed using a dual-pass multipass cell (DP-MPC) and a novel method that combines averaging dual-channel concentration signals with optimized detector gain configuration. This DP-MPC features two input/output coupling holes, resulting in absorption path lengths of approximately 95.8 m and 35.8 m, respectively. By optimizing the photodetector gain configuration and averaging the dual-channel concentration signals, the detection performance of the sensor was further enhanced. Allan deviation analysis indicated that after optimizing the detector gain, the measurement precision after dual-channel averaging reaches 21 ppb with an integration time of 1 s at a concentration of 2 ppm CH₄, which is approximately 1.4 times higher than the measurement precision of the long-path channel (31 ppb) and short-path channel (30 ppb). The time required to achieve a measurement precision of 21 ppb is 2.4 s for the long-path channel and 2.1 s for the short-path channel. The response speed of the dual-channel averaging is approximately 2 times that of any single channel. Meanwhile, the sensor demonstrated its stability and reliability through continuous outdoor atmospheric CH₄ measurements.

Scanning Electrochemical Microscopy (SECM) has been used as a non-invasive electrochemical technique for studying cellular processes. SECM enables the quantification of cellular metabolites in real-time providing a deeper understanding of cellular responses to external stimuli. SECM imaging of living cells requires maintaining an ideal physiological environment to ensure reliable data collection on cellular reactivity. The cellular response can be directly influenced by physicochemical parameters including cell media composition, temperature and light exposure. This research demonstrates the effect of media composition on the electrochemical current signal of adenocarcinoma cervical cancer (HeLa) cells during SECM measurements using ferrocenemethanol as a redox mediator. Investigated media that are commonly used as electrolyte, are phosphate buffered saline (PBS), and Dulbecco's modified Eagle's medium (DMEM) in the absence and presence of fetal bovine serum (FBS). In addition, this research demonstrates that fluctuating light illumination impacts the stability of the cellular electrochemical current response. Our findings reveal that media composition and illumination are important parameters that must be carefully considered and monitored during SECM live cell imaging.

Mass spectrometry is a powerful method to study protein complexes; however, biochemical reactions are typically beyond the scope of MS studies. Here, we have studied the gas-phase redox chemistry of the [copper(II) - amyloid β] complex and show that the sequence-dependence of this chemistry reflects key aspects of the known in vitro behaviour of different variants of the peptide.

Bacterial contamination is a serious issue for public health and food safety. In this work, a simple and label-free fluorescence detection nanoplatform for Escherichia coli (E. coli) was established on the basis of the competitive relationship for the reduction of Cu²⁺ in CuS-BSA between E. coli and O-phenylenediamine (OPD). OPD could be directly oxidized by Cu²⁺ to produce 2,3-diaminophenazine (ox OPD) with fluorescence properties. When OPD was introduced into an aqueous solution containing CuS-BSA and E. coli, the oxidation of OPD was inhibited owing to the reduction of Cu²⁺ to Cu⁺/Cu⁰ by NADH-2 dehydrogenase in the bacterial copper homeostasis mechanism, thus decreasing the fluorescence response signal of the system. Meanwhile, our strategy exhibited a satisfactory performance with a broad linear response to E. coli ranging from 12 to 1.2 × 10⁷ CFU mL⁻¹, and the limit of detection was 9 CFU mL⁻¹. The practicability of the developed fluorescence biosensing platform in real samples was evaluated by successful determination of E. coli in drinking water and orange juice. These findings provide a new sensing strategy for analyzing other foodborne bacteria and ensuring food safety assessment.

The increasing demand in healthcare for accessible and cost-effective analytical tools is driving the development of reliable platforms to the customization of therapy according to individual patient drug serum levels, e.g. of anti-psychotics in schizophrenia. A modifier-free microfluidic paper-based electroanalytical device (μPED) holds promise as a portable, sensitive, and affordable solution. While many studies focus on the working electrode catalysts, improvements by engineering aspects e.g. of the electrode arrangement are less reported. In our study, we demonstrate the enhanced capabilities of the 3D electrode layout of μPED compared to 2D μPED arrangements. We especially show that screen printing can be employed to prepare 3D μPEDs. We conducted a comparison of different 2D and 3D electrode arrangements utilizing cyclic voltammetry in [Fe(CN)₆]^3−/4−, along with square-wave voltammetry for clozapine (CLZ) sensing. Our findings reveal that the utilization of the 3D μPED leads to an increase in both the electrochemically active surface area and the electron transfer rate. Consequently, this enhancement contributes to improve sensitivity in the CLZ sensing. The 3D μPED clearly outperforms the 2D μPED arrangement in terms of signal strength. With the 3D μPED under the optimized conditions, a linear dose–response for a concentration range from 7.0 to 100 μM was achieved. The limit of detection and sensitivity was determined to be 1.47 μM and 1.69 μA μM⁻¹ cm⁻², respectively. This evaluation is conducted in the context of detection and determination of CLZ in a human blood serum sample. These findings underscore the potential of the 3D μPED for future applications in pharmacokinetic analyses and clinical tests to personalize the management of schizophrenia.

Metabolomics aims to study the downstream effects of variables like diet, environment, or disease on a given biological system. However, inconsistencies in sample preparation, data acquisition/processing protocols lead to reproducibility and accuracy concerns. A systematic study was conducted to assess how sample preparation methods and data analysis platforms affect metabolite susceptibility. A targeted panel of 25 metabolites was evaluated in 69 clinical metabolomics samples prepared following three different protocols: intact, ultrafiltration, and protein precipitation. The resulting metabolic profiles were characterized by 1D ¹H nuclear magnetic resonance (NMR) spectroscopy and analyzed with Chenomx v8.3 and SMolESY software packages. Greater than 90% of the metabolites were extracted more efficiently using protein precipitation than filtration, which aligns with previously reported results. Additionally, analysis of data processing software suggests that metabolite concentrations were overestimated by Chenomx batch-fitting, which only appears reliable for determining relative fold changes rather than absolute quantification. However, an assisted-fit method provided sufficient guidance to achieve accurate results while avoiding a time-consuming fully manual-fitting approach. By combining our results with previous studies, we can now provide a list of 5 common metabolites [2-hydroxybutyrate (2-HB), choline, dimethylamine (DMA), glutamate, lactate] with a high degree of variability in reported fold changes and standard deviations that need careful consideration before being annotated as potential biomarkers. Our results show that sample preparation and data processing package critically impact clinical metabolomics study success. There is a clear need for an increased degree of standardization and harmonization of methods across the metabolomics community to ensure reliable outcomes.