Using novel micropore technology combined with artificial intelligence to differentiate Staphylococcus aureus and Staphylococcus epidermidis.

Methods for identifying bacterial pathogens are broadly categorised into conventional culture-based microbiology, nucleic acid-based tests, and mass spectrometry. The conventional method requires several days to isolate and identify bacteria. Nucleic acid-based tests and mass spectrometry are relatively rapid and reliable, but they require trained technicians. Moreover, mass spectrometry requires expensive equipment. The development of a novel, inexpensive, and simple technique for identifying bacterial pathogens is needed. Through combining micropore technology and assembly machine learning, we developed a novel classifier whose receiver operating characteristic (ROC) curve showed an area under the ROC curve of 0.94, which rapidly differentiated between Staphylococcus aureus and Staphylococcus epidermidis in this proof-of-concept study. Morphologically similar bacteria belonging to an identical genus can be distinguished using our method, which requires no specific training, and may facilitate the diagnosis and treatment of patients with bacterial infections in remote areas and in developing countries.

High-precision rapid testing of omicron SARS-CoV-2 variants in clinical samples using AI-nanopore.

A digital platform that can rapidly and accurately diagnose pathogenic viral variants, including SARS-CoV-2, will minimize pandemics, public anxiety, and economic losses. We recently reported an artificial intelligence (AI)-nanopore platform that enables testing for Wuhan SARS-CoV-2 with high sensitivity and specificity within five minutes. However, which parts of the virus are recognized by the platform are unknown. Similarly, whether the platform can detect SARS-CoV-2 variants or the presence of the virus in clinical samples needs further study. Here, we demonstrated the platform can distinguish SARS-CoV-2 variants. Further, it identified mutated Wuhan SARS-CoV-2 expressing spike proteins of the delta and omicron variants, indicating it discriminates spike proteins. Finally, we used the platform to identify omicron variants with a sensitivity and specificity of 100% and 94%, respectively, in saliva specimens from COVID-19 patients. Thus, our results demonstrate the AI-nanopore platform is an effective diagnostic tool for SARS-CoV-2 variants.

Methodology to Detect Biological Particles Using a Biosensing Surface Integrated in Resistive Pulse Sensing.

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.

Combining machine learning and nanopore construction creates an artificial intelligence nanopore for coronavirus detection.

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.