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Early detection of cancers and their precise subtyping are essential to patient stratification and effective cancer management. Data-driven identification of expression biomarkers coupled with microfluidics-based detection shows promise to revolutionize cancer diagnosis and prognosis. MicroRNAs play key roles in cancers and afford detection in tissue and liquid biopsies. In this review, we focus on the microfluidics-based detection of miRNA biomarkers in AI-based models for early-stage cancer subtyping and prognosis. We describe various subclasses of miRNA biomarkers that could be useful in machine-based predictive modeling of cancer staging and progression. Strategies for optimizing the feature space of miRNA biomarkers are necessary to obtain a robust signature panel. This is followed by a discussion of the issues in model construction and validation towards producing Software-as-Medical-Devices (SaMDs). Microfluidic devices could facilitate the multiplexed detection of miRNA biomarker panels, and an overview of the different strategies for designing such microfluidic systems is presented here, with an outline of the detection principles used and the corresponding performance measures. Microfluidics-based profiling of miRNAs coupled with SaMD represent high-performance point-of-care solutions that would aid clinical decision-making and pave the way for accessible precision personalized medicine. [ABSTRACT FROM AUTHOR]

Miniaturization of electrochemical components has become less common in the last decade, with the focus predominantly being the design and development of state-of-the-art microelectrodes for achieving small volume analysis of samples. However, such microelectrodes involve cumbersome processing procedures to convert the base material for the required application. A potential paradigm shift in such miniaturization could be achieved by using cheaper alternatives such as plastics to build electrochemical components, such as micropipette tips made of polypropylene, which are commercially available at ease. Hence, this work presents the design of an electrochemical working electrode based upon a micropipette tip, involving minimal processing procedures. Furthermore, such a working electrode was realized by sputtering silver onto a bare micropipette tip using a radio-frequency sputtering technique, to obtain electrical contacts on the tip, followed by hydrothermal growth of ZnO, which acted as the active electrode material. The ZnO nanostructures grown on the micropipette tip were characterized for their morphology and surface properties using a scanning electron microscope (SEM), laser microscope, Raman spectrometer, and X-ray photoelectron spectrometer (XPS). The developed micropipette tip-based electrode was then used as the working electrode in a three-electrode system, wherein its electrochemical stability and properties were analyzed using cyclic voltammetry (CV). Furthermore, the above system was used to detect glucose concentrations of 10–200 µM, to evaluate its sensing properties using amperometry. The developed working electrode exhibited a sensitivity of 69.02 µA/µM cm−2 and limit of detection of 67.5 µM, indicating the potential for using such modified micropipette tips as low-cost miniaturized sensors to detect various bio-analytes in sample solutions. [ABSTRACT FROM AUTHOR]

Urea, being a nitrogen fertilizer, is crucial for plant growth but when excessively provided (above biuret 2% levels specified by the World Health Organization), plant characteristics are deeply affected. A real-time sensor to check the presence of excess urea in plants is therefore necessary. Towards this goal, a manganese oxide–reduced graphene oxide composite was synthesized by modified Hummer’s method and precipitation techniques, which was subsequently used as a nano-interface to immobilize urease enzyme for specific detection of urea. The synthesized nanocomposite helped in shuttling of electrons between the redox species and in enhanced electron transfer rate due to their high surface area, vindicated by their structural and morphological characterization using X-ray diffractometer (XRD), scanning electron microscope (SEM), and X-ray photoelectron spectrometer (XPS), and electrochemical characterization using cyclic voltammetry and amperometry, respectively. The fabricated biosensor for urea exhibited a linear range of 5–100 μM with a sensitivity of 9.7 × 10−3 μA μM−1, limit of detection of 14.693 μM, and a response time of 118 s. [ABSTRACT FROM AUTHOR]