High-throughput, high-accuracy detection of emerging viruses allows for the control of disease outbreaks. Currently, reverse transcription-polymerase chain reaction (RT-PCR) is currently the most-widely used technology to diagnose the presence of SARS-CoV-2. However, RT-PCR requires the extraction of viral RNA from clinical specimens to obtain high sensitivity. Here, we report a method for detecting novel coronaviruses with high sensitivity by using nanopores together with artificial intelligence, a relatively simple procedure that does not require RNA extraction. Our final platform, which we call the artificially intelligent nanopore, consists of machine learning software on a server, a portable high-speed and high-precision current measuring instrument, and scalable, cost-effective semiconducting nanopore modules. We show that artificially intelligent nanopores are successful in accurately identifying four types of coronaviruses similar in size, HCoV-229E, SARS-CoV, MERS-CoV, and SARS-CoV-2. Detection of SARS-CoV-2 in saliva specimen is achieved with a sensitivity of 90% and specificity of 96% with a 5-minute measurement.

Solid-state nanopores with a through-hole diameter of less than a few hundred nanometers can detect single nanoparticles owing to the ionic current flowing through the nanopore. Improvements in our understanding of the electrical properties of nanopores and the flow dynamics of a single nanoparticle passing through a nanopore have facilitated the use of machine learning methods for single nanoparticle detection. However, the sensing performance of solid-state nanopores integrated with machine learning methods has not been investigated to date. In this work, we reveal the sensing performance of artificially intelligent nanopores (AINs) comprising solid-state nanopores combined with a machine learning approach. An AIN with a diameter of 295 nm provided single nanoparticle identification with an accuracy of >91% based on single ionic current−time waveforms of 2−7 types of polystyrene nanoparticles with nominal diameters ranging from 90 to 300 nm. A 98% sample identification accuracy was achieved for the samples involving nanoparticles with diameters of 200 and 220 nm. Additionally, AINs can be used to develop multiplex diagnostics for infectious diseases owing to the capability of AINs to identify several viruses.

Resistive pulse sensing (RPS) is an analytical method that can be used to individually count particles from a small sample. RPS simply monitors the physical characteristics of particles, such as size, shape, and charge density, and the integration of RPS with biosensing is an attractive theme to detect biological particles such as virus and bacteria. In this report, a methodology of biosensing on RPS was investigated. Polydopamine (PD), an adhesive component of mussels, was used as the base material to create a sensing surface. PD adheres to most materials, such as noble metals, metal oxides, semiconductors, and polymers; as a result, PD is a versatile intermediate layer for the fabrication of a biosensing surface. As an example of a biological particle, human influenza A virus (H1N1 subtype) was used to monitor translocation of particles through the pore membrane. When virus-specific ligands (6′-sialyllactose) were immobilized on the pore surface, the translocation time of the virus particles was considerably extended. The detailed translocation data suggest that the viral particles were trapped on the sensing surface by specific interactions. In addition, virus translocation processes on different pore surfaces were distinguished using machine learning. The result shows that the simple and versatile PD-based biosensor surface design was effective. This advanced RPS measurement system could be a promising analytical technique.

High-throughput, high-accuracy detection of emerging viruses allows for pandemic prevention and control. Currently, reverse transcription-polymerase chain reaction (RT-PCR) is used to diagnose the presence of SARS-CoV-2. The principle of the test is to detect RNA in the virus using a pair of primers that specifically binds to the base sequence of the viral RNA. However, RT-PCR is a sophisticated technique requiring a time-consuming pretreatment procedure for extracting viral RNA from clinical specimens and to obtain high sensitivity. Here, we report a method for detecting novel coronaviruses with high sensitivity using artificial intelligent nanopores utilizing a simple procedure that does not require RNA extraction. Artificial intelligent nanopore platform consists of machine learning software on the servers, portable high-speed and high-precision current measuring instrument, and scalable, cost-effective semiconducting nanopore modules. Here we show that the artificial intelligent nanopores are successful in accurate identification of four types of coronaviruses, HCoV-229E, SARS-CoV, MERS-CoV, and SARS-CoV-2, which are usually extremely difficult to detect. The positive/negative diagnostics of the new coronavirus is achieved with a sensitivity of 95 % and specificity of 92 % with a 5-minute diagnosis. The platform enables high throughput diagnostics with low false negatives for the novel coronavirus.