Analyst最新文献

Helicobacter pylori (H. pylori) is a globally widespread bacterial infection. Early diagnosis of this infection is vital for public and individual health. Prevalent diagnosis methods like the isotope ¹³C or ¹⁴C labelled urea breath test (UBT) are not convenient and may do harm to the human body. The use of cross-response gas sensor arrays (GSAs) is an alternative way for label-free detection of metabolite changes in exhaled breath (EB). However, conventional GSAs are complex to prepare, lack reliability, and fail to discriminate subtle changes in EB due to the use of numerous sensing elements and single dimensional signal. This work presents a dual-element multimodal GSA empowered with multimodal sensing signals including conductance (G), capacitance (C), and dissipation factor (DF) to improve the ability for gas recognition and H. pylori-infection diagnosis. Sensitized by poly(diallyldimethylammonium chloride) (PDDA) and the metal-organic framework material NH₂-UiO66, the dual-element graphene oxide (GO)-composite GSAs exhibited a high specific surface area and abundant adsorption sites, resulting in high sensitivity, repeatability, and fast response/recovery speed in all three signals. The multimodal sensing signals with rich sensing features allowed the GSA to detect various physicochemical properties of gas analytes, such as charge transfer and polarization ability, enhancing the sensing capabilities for gas discrimination. The dual-element GSA could differentiate different typical standard gases and non-dehumidified EB samples, demonstrating the advantages in EB analysis. In a case-control clinical study on 52 clinical EB samples, the diagnosis model based on the multimodal GSA achieved an accuracy of 94.1%, a sensitivity of 100%, and a specificity of 90.9% for diagnosing H. pylori infection, offering a promising strategy for developing an accurate, non-invasive and label-free method for disease diagnosis.

Aflatoxin B₁ (AFB₁), classified as a class I carcinogen, is a widespread mycotoxin that poses a serious threat to public health and economic development, and the food safety problems caused by AFB₁ have aroused worldwide concern. The development of accurate and sensitive methods for the detection of AFB₁ is significant for food safety monitoring. In this work, a sandwich-type photoelectrochemical (PEC) biosensor for AFB₁ detection was constructed on the basis of an aptamer-antibody structure. A good photocurrent response was obtained due to the sensitization of In₂S₃ by Ru(bpy)₃²⁺. In addition, this sandwich-type sensor constructed by modification with the antibody, target detector, and aptamer layer by layer attenuated the migration hindering effect of photogenerated carriers caused by the double antibody structure. The aptamer and antibody synergistically recognized and captured the target analyte, resulting in more reliable PEC response signals. CdSe@CdS QDs-Apt were modified as a signal-off probe onto the sensor platform to quantitatively detect AFB₁ with a "signal-off" response, which enhanced the sensitivity of the sensor. The PEC biosensor showed a linear response range from 10^-12 to 10^-6 g mL^-1 with a detection limit of 0.023 pg mL^-1, providing a feasible approach for the quantitative detection of AFB₁ in food samples.

Cannabigerol, cannabidiol, cannabinol and cannabichromene are non-psychoactive phytocannabinoids, highly present in Cannabis sativa, for which numerous therapeutical applications have been described. However, additional pre-clinical and clinical data, including toxicopharmacokinetic and pharmacodynamic studies, remain required to support their use in clinical practice and new therapeutic applications. To support these studies, a new high performance liquid chromatography technique (HPLC) with diode-array detection (DAD) was developed and validated to quantify these cannabinoids in human plasma and mouse matrices. Sample extraction was accomplished by protein precipitation and double liquid-liquid extraction. Simvastatin and perampanel were used as internal standards in human and mouse matrices, respectively. Chromatographic separation was achieved in 16 min on an InfinityLab Poroshell® 120 C₁₈ column (4.6 mm × 100 mm, 2.7 μm) at 40 °C. A mobile phase composed of water/acetonitrile was pumped with a gradient elution program at 1.0 mL min^-1. The technique revealed linearity in the defined concentration ranges with a determination coefficient of over 0.99. Intra and inter-day accuracy and precision values ranged from -14.83 to 13.97% and 1.08 to 13.74%, respectively. Sample stability was assessed to ensure that handling and storage conditions did not compromise analyte concentrations in different matrices. Carry-over was absent and recoveries were over 77.31%. This technique was successfully applied for the therapeutic monitoring of cannabidiol and preliminary pre-clinical studies with cannabigerol and cannabidiol. All samples were within calibration ranges, with the exception of cannabigerol after intraperitoneal administration. This is the first HPLC-DAD technique that simultaneously quantifies cannabinoids in these biological matrices, supporting future pre-clinical and clinical investigations.

Microelectromechanical systems (MEMSs) are microdevices fabricated using semiconductor-fabrication technology, especially those with moving components. This technology has become more widely used in daily life, e.g., in mobile phones, printers, and cars. In this review, MEMS sensors are largely classified as physical or chemical ones. Physical sensors include pressure, inertial force, acoustic, flow, temperature, optical, and magnetic ones. Chemical sensors include gas, odorant, ion, and biological ones. The fundamental principle of sensing is reading out either the movement or electrical-property change of microstructures caused by external stimuli. Here, sensing mechanisms of the sensors are explained using diagrams with equivalent circuits to show the similarity. Examples of multiple parameter measurement with single sensors (e.g. quantum sensors or resonant pressure and temperature sensors) and parallel sensor integration are also introduced.