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This paper reports a rapid and sensitive sensor for the detection and quantification of the COVID-19 N-protein (N-PROT) via an electrochemical mechanism. Single-frequency electrochemical impedance spectroscopy was used as a transduction method for real-time measurement of the N-PROT in an immunosensor system based on gold-conjugate-modified carbon screen-printed electrodes (Cov-Ag-SPE). The system presents high selectivity attained through an optimal stimulation signal composed of a 0.0 V DC potential and 10 mV RMS−1 AC signal at 100 Hz over 300 s. The Cov-Ag-SPE showed a log response toward N-PROT detection at concentrations from 1.0 ng mL−1 to 10.0 μg mL−1, with a 0.977 correlation coefficient for the phase (θ) variation. An ML-based approach could be created using some aspects observed from the positive and negative samples; hence, it was possible to classify 252 samples, reaching 83.0, 96.2 and 91.3% sensitivity, specificity, and accuracy, respectively, with confidence intervals (CI) ranging from 73.0 to 100.0%. Because impedance spectroscopy measurements can be performed with low-cost portable instruments, the immunosensor proposed here can be applied in point-of-care diagnostics for mass testing, even in places with limited resources, as an alternative to the common diagnostics methods. [ABSTRACT FROM AUTHOR]

COVID-19 analysis from medical imaging is an important task that has been intensively studied in the last years due to the spread of the COVID-19 pandemic. In fact, medical imaging has often been used as a complementary or main tool to recognize the infected persons. On the other hand, medical imaging has the ability to provide more details about COVID-19 infection, including its severity and spread, which makes it possible to evaluate the infection and follow-up the patient's state. CT scans are the most informative tool for COVID-19 infection, where the evaluation of COVID-19 infection is usually performed through infection segmentation. However, segmentation is a tedious task that requires much effort and time from expert radiologists. To deal with this limitation, an efficient framework for estimating COVID-19 infection as a regression task is proposed. The goal of the Per-COVID-19 challenge is to test the efficiency of modern deep learning methods on COVID-19 infection percentage estimation (CIPE) from CT scans. Participants had to develop an efficient deep learning approach that can learn from noisy data. In addition, participants had to cope with many challenges, including those related to COVID-19 infection complexity and crossdataset scenarios. This paper provides an overview of the COVID-19 infection percentage estimation challenge (Per-COVID-19) held at MIA-COVID-2022. Details of the competition data, challenges, and evaluation metrics are presented. The best performing approaches and their results are described and discussed. [ABSTRACT FROM AUTHOR]