Role of prenatal imaging in the diagnosis and management of fetal facio-cervical masses

Abstract Congenital facio-cervical masses can be a developmental anomaly of cystic, solid, or vascular origin, and have an inseparable relationship with adverse prognosis. This retrospective cross-sectional study aimed at determining on the prenatal diagnosis of congenital facio-cervical masses, its management and outcome in a large tertiary referral center. We collected information on prenatal clinical data, pregnancy outcomes, survival information, and final diagnosis. Out of 130 cases of facio-cervical masses, a total of 119 cases of lymphatic malformations (LMs), 2 cases of teratoma, 2 cases of thyroglossal duct cyst, 4 cases of hemangioma, 1 case of congenital epulis, and 2 cases of dermoid cyst were reviewed. The accuracy of prenatal ultrasound was 93.85% (122/130). Observations of diameters using prenatal ultrasound revealed that the bigger the initial diameter is, the bigger the relative change during pregnancy. Magnetic resonance imaging (MRI) revealed that 2 cases of masses were associated with airway compression. In conclusion, ultrasound has a high overall diagnostic accuracy of fetal face and neck deformities. Prenatal US can enhance the management of ambulatory monitoring and classification. Furthermore, MRI provided a detailed assessment of fetal congenital malformations, as well as visualization of the trachea, presenting a multi-dimensional anatomical relationship

Rapid and Simple Detection of <i>Burkholderia gladioli</i> in Food Matrices Using RPA-CRISPR/Cas12a Method

Pathogenic variants of Burkholderia gladioli pose a serious threat to human health and food safety, but there is a lack of rapid and sensitive field detection methods for Burkholderia gladioli. In this study, the CRISPR/Cas12a system combined with recombinant enzyme polymerase amplification (RPA) was used to detect Burkholderia gladioli in food. The optimized RPA-CRISPR/Cas12a assay was able to specifically and stably detect Burkholderia gladioli at a constant 37 °C without the assistance of large equipment. The detection limit of the method was evaluated at two aspects, the genomic DNA (gDNA) level and bacterial quantity, of which there were 10−3 ng/μL and 101 CFU/mL, respectively. Three kinds of real food samples were tested. The detection limit for rice noodles, fresh white noodles, and glutinous rice flour samples was 101 CFU/mL, 102 CFU/mL, and 102 CFU/mL, respectively, without any enrichment steps. The whole detection process, including sample pretreatment and DNA extraction, did not exceed one hour. Compared with the qPCR method, the established RPA-CRISPR /Cas12a method was simpler and even more sensitive. Using this method, a visual detection of Burkholderia gladioli that is suitable for field detection can be achieved quickly and easily

EA-Net: Research on skin lesion segmentation method based on U-Net

Accurate segmentation of skin lesions is a challenging task because the task is highly influenced by factors such as location, shape and scale. In recent years, Convolutional Neural Networks (CNNs) have achieved advanced performance in automated medical image segmentation. However, existing CNNs have problems such as inability to highlight relevant features and preserve local features, which limit their application in clinical decision-making. This paper proposes a CNN with an added attention mechanism (EA-Net) for more accurate medical image segmentation.EA-Net is based on the U-Net network model framework. Specifically, we added a pixel-level attention module (PA) to the encoder section to preserve the local features of the image during downsampling, making the feature maps input to the decoder more relevant to the ground-truth. At the same time, we added a spatial multi-scale attention module (SA) after the decoding process to increase the spatial weight of the feature maps that are more relevant to the ground-truth, thereby reducing the gap between the output results and the ground-truth. We conducted extensive segmentation experiments on skin lesion images from the ISIC 2017 and ISIC 2018 datasets. The results demonstrate that, when compared to U-Net, our proposed EA-Net achieves an average Dice score improvement of 1.94% and 5.38% for skin lesion tissue segmentation on the ISIC 2017 and ISIC 2018 datasets, respectively. The IoU also increases by 2.69% and 8.31%, and the ASSD decreases by 0.3783 pix and 0.5432 pix, indicating superior segmentation performance. EA-Net can achieve better segmentation results when the original image of skin lesions has an obscure boundary and the segmentation area contains interference factors, which proves that the addition of attention mechanism in the encoder and the application of comprehensive attention mechanism can improve the performance of neural network in the field of skin lesions image segmentation