Automatic lymphoma detection and accurate lymphoma boundary delineation from whole body Positron Emission Tomography/Computed Tomography (PET/CT) scans are essential for surgical navigation and radiation therapy. Besides, labeling the data, which means contouring the lymphoma contour in images is time-consuming, operator intensive and subjective. Hence, this paper integrates the supervised learning and unsupervised learning to propose an end-to-end segmentation network, namely DenseX-Net, for both lymphoma detection and segmentation. There are two important flows in the proposed DenseX-Net. One is a reconstruction flow (based on the convolutional encoder-decoder form) that can be used for learning semantic representations of different lymphomas by minimizing the discrepancy between each input and its output in an unsupervised learning form. The other one is a segmentation flow (based on DenseU-Net) that performs the lymphoma segmentation task. Note that, the encoders in both flows are trained jointly with the same weights, which can facilitate DenseX-Net obtaining the accurate segmentation using a little labeled data. We evaluate our proposed DenseX-Net for lymphoma segmentation on 80 real PET/CT cases (from General Hospital of Northern Military Area) with a Dice coefficient of 72.84%. Experimentations and comparisons demonstrate the accuracy and robustness of DenseX-Net as well as its performance advantages as compared with related segmentation networks.

An increasing number of studies reported that exogenous miRNAs (xenomiRs) can be detected in animal bodies, however, some others reported negative results. Some attributed this divergence to the selective absorption of plant-derived xenomiRs by animals. Here, we analyzed 166 plant-derived xenomiRs reported in our previous study and 942 non-xenomiRs extracted from miRNA expression profiles of four species of commonly consumed plants. Employing statistics analysis and cluster analysis, our study revealed the potential sequence specificity of plant-derived xenomiRs. Furthermore, a random forest model and a one-dimensional convolutional neural network model were trained using miRNA sequence features and raw miRNA sequences respectively and then employed to predict unlabeled plant miRNAs in miRBase. A total of 241 possible plant-derived xenomiRs were predicted by both models. Finally, the potential functions of these possible plant-derived xenomiRs along with our previously reported ones in human body were analyzed. Our study, for the first time, presents the systematic plant-derived xenomiR sequences analysis and provides evidence for selective absorption of plant miRNA by human body, which could facilitate the future investigation about the mechanisms underlying the transference of plant-derived xenomiR.

Diabetic retinopathy (DR) and age-related macular degeneration (AMD) are forms of degenerative retinal disorders that may result in vision impairment or even permanent blindness. Early detection of these conditions is essential to maintaining a patient’s quality of life. The fundus photography technique is non-invasive, safe, and rapid way of assessing the function of the retina. It is widely used as a diagnostic tool for patients who suffer from fundus-related diseases. Using fundus images to analyze these two diseases is a challenging exercise, since there are rarely obvious features in the images during the incipient stages of the disease. In order to deal with these issues, we have proposed a deep learning method called FunSwin. The Swin Transformer constitutes the main framework for this method. Additionally, due to the characteristics of medical images, such as their small number and relatively fixed structure, transfer learning strategy that are able to increase the low-level characteristics of the model as well as data enhancement strategy to balance the data are integrated. Experiments have demonstrated that the proposed method outperforms other state-of-the-art approaches in both binary and multiclass classification tasks on the benchmark dataset.