A dual-functional module cellular electrochemical sensing platform for simultaneous detection guanine and xanthine.

The separation of the superimposed electrochemical signals of intracellular guanine (G) and xanthine (X) is difficult, which is great obstacle to the application of cell electrochemistry. In this paper, independent functional modules, G-functional module (G-FM) and X-functional module (X-FM), were constructed by molecular imprinting technology for sensitive detection of G and X without mutual interference, then integrated in dual-functional module cellular electrochemical sensing platform (DMCEP) as signal sensing units. DMCEP transmitted signals of G and X in cells synchronously to two windows by two signal sensing channels, and achieved the separation of superimposed signals of G and X in cells. DMCEP exhibited satisfactory reproducibility with relative standard deviation (RSD) of 3.10 and 2.22 %, repeatability with RSD of 3.72 and 3.05 % for G and X detection, and detection limit 0.05 µΜ for G and 0.06 µΜ for X. Good linear relationships between cell concentrations and the signals of G and X on DMCEP were shown in range of 0.75-85 × 106 and 3-85 × 106 cells/mL, respectively. The growth of MCF-7 cells was tracked by DMCEP, and showed consistent trend with the cell counting method, while the change of cell viability from lag to logarithmic phase captured by DMCEP was earlier than that of cell counting method. This strategy provided the foundation for the establishment of the cell viability electrochemical detection method, and new insights into the simultaneous recording of other analyses with superimposed peak positions and the simultaneous tracking of multiple biomarkers.

2019 Novel Coronavirus-Infected Pneumonia on CT: A Feasibility Study of Few-Shot Learning for Computerized Diagnosis of Emergency Diseases.

COVID-19 is an emerging disease with transmissibility and severity. So far, there are no effective therapeutic drugs or vaccines for COVID-19. The most serious complication of COVID-19 is a type of pneumonia called 2019 novel coronavirus-infected pneumonia (NCIP) with about 4.3% mortality rate. Comparing to chest Digital Radiography (DR), it is recently reported that chest Computed Tomography (CT) is more useful to serve as the early screening and diagnosis tool for NCIP. In this study, aimed to help physicians make the diagnostic decision, we develop a machine learning (ML) approach for automated diagnosis of NCIP on chest CT. Different from most ML approaches which often require training on thousands or millions of samples, we design a few-shot learning approach, in which we combine few-shot learning with weakly supervised model training, for computerized NCIP diagnosis. A total of 824 patients are retrospectively collected from two Hospitals with IRB approval. We first use 9 patients with clinically confirmed NCIP and 20 patients without known lung diseases for training a location detector which is a multitask deep convolutional neural network (DCNN) designed to output a probability of NCIP and the segmentation of targeted lesion area. An experienced radiologist manually localizes the potential locations of NCIPs on chest CTs of 9 COVID-19 patients and interactively segments the area of the NCIP lesions as the reference standard. Then, the multitask DCNN is furtherly fine-tuned by a weakly supervised learning scheme with 291 case-level labeled samples without lesion labels. A test set of 293 patients is independently collected for evaluation. With our NCIP-Net, the test AUC is 0.91. Our system has potential to serve as the NCIP screening and diagnosis tools for the fight of COVID-19's endemic and pandemic.