The COVID-19 pandemic has highlighted the importance and urgent need for rapid and accurate diagnostic tests for COVID-19 detection and screening. The objective of this work was to develop a simple immunosensor for rapid and high sensitivity measurements of SARS-CoV-2 nucleocapsid protein in serum. This assay is based on a unique sensing scheme utilizing duallylabeled magnetic nanobeads for immunomagnetic enrichment and signal amplification. This immunosensor is integrated onto a microfluidic chip, which offers the advantages of minimal sample and reagent consumption, simplified sample handling, and enhanced detection sensitivity. The functionality of this immunosensor was validated by using it to detect SARS-CoV-2 nucleocapsid protein, which could be detected at concentrations as low as 50 pg/mL in whole serum and 10 pg/mL in 5× diluted serum. We also adapted this assay onto a handheld smartphone-based diagnostic device that could detect SARS-CoV-2 nucleocapsid protein at concentrations as low as 230 pg/mL in whole serum and 100 pg/mL in 5× diluted serum. Lastly, we assessed the capability of this immunosensor to diagnose COVID-19 infection by testing clinical serum specimens, which revealed its ability to accurately distinguish PCR-positive COVID-19 patients from healthy, uninfected individuals based on SARS-CoV-2 nucleocapsid protein serum levels. To the best of our knowledge, this work is the first demonstration of rapid (<1 h) SARS-CoV-2 antigen quantification in whole serum samples. The ability to rapidly detect SARS-CoV-2 protein biomarkers with high sensitivity in very small (<50 μL) serum samples makes this platform a promising tool for point-of-care COVID-19 testing.

Current diagnostic tests for sensitive protein detection rely on immunological techniques, such as ELISA, which require sample purification, multiple washing steps and lengthy incubation, hindering their use for rapid testing. Here, we report a simple electrothermal flow-enhanced biosensor for ultrafast, high sensitivity measurements of protein biomarkers in whole blood. Magnetic nanobeads dually-labeled with a detection antibody and enzyme reporter are used to form immunocomplexes with the target protein, which are readily transported to the sensor via magnetic concentration. The incorporation of electrothermal flows enhances immunocomplex formation, allowing for rapid and sensitive detection without requiring blood purification or lengthy incubation. Proof of concept was carried out using Plasmodium falciparum histidine-rich protein 2 (PfHRP2), a malaria parasite biomarker, which could be detected at concentrations as low as 5.7 pg mL À 1 (95 fM) in whole blood in 7 min. The speed, sensitivity and simplicity of this device make it attractive for rapid diagnostic testing.

Current diagnostic tests for sensitive protein detection rely on immunological techniques, such as ELISA, which require sample purification, multiple washing steps and lengthy incubation, hindering their use for rapid testing. Here, we report a simple electrothermal flow-enhanced biosensor for ultrafast, high sensitivity measurements of protein biomarkers in whole blood. Magnetic nanobeads dually-labeled with a detection antibody and enzyme reporter are used to form immunocomplexes with the target protein, which are readily transported to the sensor via magnetic concentration. The incorporation of electrothermal flows enhances immunocomplex formation, allowing for rapid and sensitive detection without requiring blood purification or lengthy incubation. Proof of concept was carried out using Plasmodium falciparum histidine-rich protein 2 (PfHRP2), a malaria parasite biomarker, which could be detected at concentrations as low as 5.7 pg mL À 1 (95 fM) in whole blood in 7 min. The speed, sensitivity and simplicity of this device make it attractive for rapid diagnostic testing.

The detection and/or quantification of biomarkers in blood is important for the early detection, diagnosis, and treatment of a variety of diseases and medical conditions. Among the different types of sensors for detecting molecular biomarkers, such as proteins, nucleic acids, and small-molecule drugs, affinity-based electrochemical sensors offer the advantages of high analytical sensitivity and specificity, fast detection times, simple operation, and portability. However, biomolecular detection in whole blood is challenging due to its highly complex matrix, necessitating sample purification (i.e., centrifugation), which involves the use of bulky, expensive equipment and tedious sample-handling procedures. To address these challenges, various strategies have been employed, such as purifying the blood sample directly on the sensor, employing micro-/nanoparticles to enhance the detection signal, and coating the electrode surface with blocking agents to reduce nonspecific binding, to improve the analytical performance of affinity-based electrochemical sensors without requiring sample pre-processing steps or laboratory equipment. In this article, we present an overview of affinity-based electrochemical sensor technologies that employ these strategies for biomolecular detection in whole blood. Graphical abstract

Drilling string vibration data is a high-density ancillary data and it has the advantages of low-latency and low-cost which can be acquired in real time. In this study, vibration dataset is used as signal source, and the original vibration signal is filtered by Butterworth (BHPF). vibration time-frequency characteristics are extracted into time frequency images with the application of short-time Fourier transform (STFT). This paper develops lithology classification models using new data sources based on convolutional neural network (CNN) combining with Mobilenet and ResNet. This model is used for complex formation lithology including fine gravel sandstone, fine sandstone and mudstone. In order to improve the trustworthiness of decision-making results, the gradient-weighted class-activated thermal localization map is applied to interpret the results of the model. The final vertification test shows that the single-sample decision time of the model is 10ms, the test macro precision rate is 90.0%, and the macro recall rate is 89.3%. The lithology classification model is more efficiency and accessible. In conclusion, The CNN model using drill string vibration supplies a superior method of lithology classification. This study provides low-latency and low-cost lithology judgment methods to ensure safe and rapid drilling.