Nanopore technology holds great promise for a wide range of applications such as biomedical sensing, chemical detection, desalination, and energy conversion. For sensing performed in electrolytes in particular, abundant information about the translocating analytes is hidden in the fluctuating monitoring ionic current contributed from interactions between the analytes and the nanopore. Such ionic currents are inevitably affected by noise; hence, signal processing is an inseparable component of sensing in order to identify the hidden features in the signals and to analyze them. This Guide starts from untangling the signal processing flow and categorizing the various algorithms developed to extracting the useful information. By sorting the algorithms under Machine Learning (ML)-based versus non-ML-based, their underlying architectures and properties are systematically evaluated. For each category, the development tactics and features of the algorithms with implementation examples are discussed by referring to their common signal processing flow graphically summarized in a chart and by highlighting their key issues tabulated for clear comparison. How to get started with building up an ML-based algorithm is subsequently presented. The specific properties of the ML-based algorithms are then discussed in terms of learning strategy, performance evaluation, experimental repeatability and reliability, data preparation, and data utilization strategy. This Guide is concluded by outlining strategies and considerations for prospect algorithms.

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Congenital cytomegalovirus (CMV) infection is the most common infectious cause of sensorineural hearing loss in children. While the importance of CMV-induced SNHL has been described, the mechanisms underlying its pathogenesis and the role of inflammatory responses remain elusive. The present study established an experimental model of hearing loss after systemic infection with murine CMV (MCMV) in newborn mice. Auditory brainstem responses were tested to evaluate hearing at 3 weeks, expression of inflammasome-associated factors was assessed by immunofluorescence, western blot analysis, reverse transcription-quantitative polymerase chain reaction and ELISA. MCMV sequentially induced inflammasome-associated factors. Furthermore, the inflammasome-associated factors were also increased in cultured spiral ganglion neurons infected with MCMV for 24 h. In addition, MCMV increased the content of reactive oxygen species (ROS). These results suggest that hearing loss caused by MCMV infection may be associated with ROS-induced inflammation.

Temporal changes in electrical resistance of a nanopore sensor caused by translocating target analytes are recorded as a sequence of pulses on current traces. Prevalent algorithms for feature extraction in pulse-like signals lack objectivity because empirical amplitude thresholds are user-defined to single out the pulses from the noisy background. Here, we use deep learning for feature extraction based on a bi-path network (B-Net). After training, the B-Net acquires the prototypical pulses and the ability of both pulse recognition and feature extraction without a priori assigned parameters. The B-Net is evaluated on simulated data sets and further applied to experimental data of DNA and protein translocation. The B-Net results are characterized by small relative errors and stable trends. The B-Net is further shown capable of processing data with a signal-to-noise ratio equal to 1, an impossibility for threshold-based algorithms. The B-Net presents a generic architecture applicable to pulse-like signals beyond nanopore currents.