PUTransGCN: identification of piRNA-disease associations based on attention encoding graph convolutional network and positive unlabelled learning.

Piwi-interacting RNAs (piRNAs) play a crucial role in various biological processes and are implicated in disease. Consequently, there is an escalating demand for computational tools to predict piRNA-disease interactions. Although there have been computational methods proposed for the detection of piRNA-disease associations, the problem of imbalanced and sparse dataset has brought great challenges to capture the complex relationships between piRNAs and diseases. In response to this necessity, we have developed a novel computational architecture, denoted as PUTransGCN, which uses heterogeneous graph convolutional networks to uncover potential piRNA-disease associations. Additionally, the attention mechanism was used to adjust the weight parameters of aggregation heterogeneous node features automatically. For tackling the imbalanced dataset problem, the combined positive unlabelled learning (PUL) method comprising PU bagging, two-step and spy technique was applied to select reliable negative associations. The features of piRNAs and diseases were derived from three distinct biological sources by PUTransGCN, including information on piRNA sequences, semantic terms related to diseases and the existing network of piRNA-disease associations. In the experiment, PUTransGCN performs in 5-fold cross-validation with an AUC of 0.93 and 0.95 on two datasets, respectively, which outperforms the other six state-of-the-art models. We compared three different PUL methods, and the results of the ablation experiment indicate that the combined PUL method yields the best results. The PUTransGCN could serve as a valuable piRNA-disease prediction tool for upcoming studies in the biomedical field. The code for PUTransGCN is available at https://github.com/chenqiuhao/PUTransGCN.

The role of circular RNA in preeclampsia: From pathophysiological mechanism to clinical application.

Preeclampsia (PE) is a common pregnancy-induced hypertension disorder that poses a significant threat to the health of pregnant women and fetuses, and has become a leading cause of maternal, fetal, and neonatal mortality. Currently, the therapy strategy for PE is mainly prevention management and symptomatic treatment, and only delivery can completely terminate PE. Therefore, a deeper understanding of the pathogenesis of PE is needed to make treatment and prevention more effective and targeted. With the deepening of molecular etiology research, circular RNAs (circRNAs) have been found to be widely involved in various processes of PE pathogenesis. As a kind of RNA with a special "head to tail" loop structure, the characteristics of circRNAs enable them to play diverse roles in the pathophysiology of PE, and can also serve as ideal biomarkers for early prediction and monitoring progression of PE. In this review, we summarized the latest research on PE-related circRNAs, trying to elucidate the unique or shared roles of circRNAs in various pathophysiological mechanisms of PE, aiming to provide a whole picture of current research on PE-related circRNAs, and extend a new perspective for the precise screening and targeted therapy of PE.

Ultrathin MXene nanosheet-based TiO2/CdS heterostructure as a photoelectrochemical sensor for detection of CEA in human serum samples.

To develop highly accurate and ultrasensitive strategies is of great importance for the clinical measurement, in particular, the detection of cancer biomarkers. Herein, we synthesized an ultrasensitive TiO2/MXene/CdS QDs (TiO2/MX/CdS) heterostructure as a photoelectrochemical immunosensor, which favors energy levels matching and fast electron transfer from CdS to TiO2 in the help of ultrathin MXene nanosheet. Dramatic photocurrent quenching can be observed upon incubation of the TiO2/MX/CdS electrode by Cu2+ solution from 96-well microplate, which caused by the formation of CuS and subsequent CuxS (x = 1, 2), reducing the absorption of light and boosting the electron-hole recombination upon irradiation. As a result, the as-prepared biosensor demonstrates a linearly increased photocurrent quenching percentage (Q%) value with CEA concentration ranging from 1 fg/mL to 10 ng/mL, as well as a low detection limit of 0.24 fg/mL. Benefit from its excellent stability, high selectivity and good reproducibility of as-prepared PEC immunosensor, we believe that this proposed strategy might provide new opportunities for clinical diagnosis of CEA and other tumor markers.