An Intelligent Graphene-Based Biosensing Device for Cytokine Storm Syndrome Biomarkers Detection in Human Biofluids.

Abnormal elevated levels of cytokines such as interferon (IFN), interleukin (IL), and tumor necrosis factor (TNF), are considered as one of the prognosis biomarkers for indicating the progression to severe or critical COVID-19. Hence, it is of great significance to develop devices for monitoring their levels in COVID-19 patients, and thus enabling detecting COVID-19 patients that are worsening and to treat them before they become critically ill. Here, an intelligent aptameric dual channel graphene-TWEEN 80 field effect transistor (DGTFET) biosensing device for on-site detection of IFN-γ, TNF-α, and IL-6 within 7 min with limits of detection (LODs) of 476 × 10-15 , 608 × 10-15 , or 611 × 10-15 m respectively in biofluids is presented. Using the customized Android App together with this intelligent device, asymptomatic or mild COVID-19 patients can have a preliminary self-detection of cytokines and get a warning reminder while the condition starts to deteriorate. Also, the device can be fabricated on flexible substrates toward wearable applications for moderate or even critical COVID-19 cases for consistently monitoring cytokines under different deformations. Hence, the intelligent aptameric DGTFET biosensing device is promising to be used for point-of-care applications for monitoring conditions of COVID-19 patients who are in different situations.

DUDA-Net: a double U-shaped dilated attention network for automatic infection area segmentation in COVID-19 lung CT images.

PURPOSE: The global health crisis caused by coronavirus disease 2019 (COVID-19) is a common threat facing all humankind. In the process of diagnosing COVID-19 and treating patients, automatic COVID-19 lesion segmentation from computed tomography images helps doctors and patients intuitively understand lung infection. To effectively quantify lung infections, a convolutional neural network for automatic lung infection segmentation based on deep learning is proposed. METHOD: This new type of COVID-19 lesion segmentation network is based on a U-Net backbone. First, a coarse segmentation network is constructed to extract the lung areas. Second, in the encoding and decoding process of the fine segmentation network, a new soft attention mechanism, namely the dilated convolutional attention (DCA) mechanism, is introduced to enable the network to focus on better quantitative information to strengthen the network's segmentation ability in the subtle areas of the lesions. RESULTS: The experimental results show that the average Dice similarity coefficient (DSC), sensitivity (SEN), specificity (SPE) and area under the curve of DUDA-Net are 87.06%, 90.85%, 99.59% and 0.965, respectively. In addition, the introduction of a cascade U-shaped network scheme and DCA mechanism can improve the DSC by 24.46% and 14.33%, respectively. CONCLUSION: The proposed DUDA-Net approach can automatically segment COVID-19 lesions with excellent performance, which indicates that the proposed method is of great clinical significance. In addition, the introduction of a coarse segmentation network and DCA mechanism can improve the COVID-19 segmentation performance.