Objective: To evaluate whether prophylactic chemotherapy (P-chem) increased the drug resistance rate of postmolar GTN and whether the first-line chemotherapy should be different from P-chem. Methods: Postmolar GTN received P-Chem was defined as P-Chem group. Postmolar GTN without P-chem was randomly selected as control group according to the ratio of 1:3 (P-chem:control) and matched by age for low risk and high risk GTN separately. Results: Totally 455 low-risk and 32 high-risk postmolar GTN patients were included. WHO risk score, chemotherapy cycles to achieve hCG normalization and resistant rate were similar between P-chem (27 cases) and control (81 cases) group. Among low-risk GTN patients, interval from hydatidiform mole to GTN was significantly longer in P-chem group than control (44 vs 69 days, P = 0.001). Total chemotherapy cycles and resistant rate were similar between low-risk GTN treated with same agent as P-chem (group A) and alternative agent (group B). But group A needed more chemotherapy cycles to achieve hCG normalization than group B. Conclusions: P-chem delayed the time to GTN diagnosis, but didn’t increase risk score or lead to drug resistance of postmolar GTN. Alternative agent different from P-chem had the potential of enhancing chemotherapy response in low- risk postmolar GTN.

During software development and maintenance, vulnerability detection is an essential part of software quality assurance. Even though many program-analysis-based and machine-learning-based approaches have been proposed to automatically detect vulnerabilities, they rely on explicit rules or patterns defined by security experts and suffer from either high false positives or high false negatives. Recently, an increasing number of studies leverage deep learning techniques, especially Graph Neural Network (GNN), to detect vulnerabilities. These approaches leverage program analysis to represent the program semantics as graphs and perform graph analysis to detect vulnerabilities. However, they suffer from two main problems: (i) Existing GNN-based techniques do not effectively learn the structural and semantic features from source code for vulnerability detection. (ii) These approaches tend to ignore fine-grained information in source code. To tackle these problems, in this paper, we propose a novel vulnerability detection approach, named MGVD ( M ULTIPLE- G RAPH-BASED V ULNERABILITY D ETECTION), to detect vulnerable functions. To effectively learn the structural and semantic features from source code, MGVD uses three different ways to represent each function into multiple forms, i.e., two statement graphs and a sequence of tokens. Then we encode such representations to a three-channel feature matrix. The feature matrix contains the structural feature and the semantic feature of the function. And we add a weight allocation layer to distribute the weights between structural and semantic features. To overcome the second problem, MGVD constructs each graph representation of the input function using multiple different graphs instead of a single graph. Each graph focuses on one statement in the function and its nodes denote the related statements and their fine-grained code elements. Finally, MGVD leverages CNN to identify whether this function is vulnerable based on such feature matrix. We conduct experiments on 3 vulnerability datasets with a total of 30,341 vulnerable functions and 127,931 non-vulnerable functions. The experimental results show that our method outperforms the state-of-the-art by 9.68% – 10.28% in terms of F1-score.

Background: Circular RNAs (circRNAs) have been reported to play key roles in the development of various cancers. However, the biological functions and clinical significance of most circRNAs are still elusive. The purpose of this study was to explore the function and mechanism of a certain circRNA named circCDKN2B-AS1 in cervical cancer development and its potential value in the clinic.Methods: qRT-PCR was used to verify the expression level of circCDKN2B-AS1. CCK-8, Transwell, and flow cytometry (FCM) assays were performed to detect cellular proliferation, migration, and apoptosis, respectively. A Seahorse XFe96 Analyzer was used to measure glycolysis metabolism level. RNA pull-down, RNA immunoprecipitation (RIP), actinomycin-D addition assays and Western blotting were used to screen and elucidate the potential mechanisms involved. BALB/c nude mice and zebrafish embryos (AB, WT) were used as animal models to investigate tumorigenesis capability.¹⁸FDG-microPET/CT imaging and lactic acid (LA) and pyruvic acid (PA) content detection assays were used to detect the level of glucose metabolism in subcutaneous tumors from nude mice.Results: CircCDKN2B-AS1, a circular isoform of the long noncoding RNA (lncRNA) CDKN2B-AS1, was upregulated in cervical cancer and precancerous tissues. We found that circCDKN2B-AS1 associated with the IMP3 protein depending on a specific binding site and regulated the stability of Hexokinase 2 (HK2) mRNA, the rate-limiting enzyme of the aerobic glycolysis pathway. The expression level of circCDKN2B-AS1 fated the binding of IMP3 to the 3′ untranslated region (UTR) of HK2 mRNA, consequently affecting the malignant cell phenotype and aerobic glycolysis in cervical cancer in vitro and in vivo. Mutant circCDKN2B-AS1, lacking the IMP3 binding site, did not have such effects. Utilization of an inhibitory peptide to block the interaction between circCDKN2B-AS1 and the IMP3 protein impeded the binding of IMP3 to the 3’UTR of HK2 mRNA and suppressed aerobic glycolysis in cervical cancer cells.Conclusions: Our findings demonstrate that circCDKN2B-AS1 facilitates aerobic glycolysis by sponging the IMP3 protein to stabilize HK2 mRNA, consequently promoting the malignant phenotype in cervical cancer, which may provide a potential approach for cervical cancer therapeutics.

Fraud detection has increasingly become a prominent research field due to the dramatically increased incidents of fraud. The complex connections involving thousands, or even millions of nodes, present challenges for fraud detection tasks. Many researchers have developed various graph-based methods to detect fraud from these intricate graphs. However, those methods neglect two distinct characteristics of the fraud graph: the non-additivity of certain attributes and the distinguishability of grouped messages from neighbor nodes. This paper introduces the Dynamic Grouping Aggregation Graph Neural Network (DGA-GNN) for fraud detection, which addresses these two characteristics by dynamically grouping attribute value ranges and neighbor nodes. In DGA-GNN, we initially propose the decision tree binning encoding to transform non-additive node attributes into bin vectors. This approach aligns well with the GNN’s aggregation operation and avoids nonsensical feature generation. Furthermore, we devise a feedback dynamic grouping strategy to classify graph nodes into two distinct groups and then employ a hierarchical aggregation. This method extracts more discriminative features for fraud detection tasks. Extensive experiments on five datasets suggest that our proposed method achieves a 3% ~ 16% improvement over existing SOTA methods. Code is available at https://github.com/AtwoodDuan/DGA-GNN.