Effectiveness of Low-Frequency Electrical Stimulation for Radical Hysterectomy Women: Systematic Review and Meta-Analysis.

INTRODUCTION: The aim of the study was to explore the effects of low-frequency electrical stimulation (LFES) in preventing urinary retention after radical hysterectomy (RH) in women with cervical cancer. METHODS: Seven electronic bibliographic databases were searched from inception to December 25, 2021. The mean difference (MD) or risk ratio (RR) with its corresponding 95% CI was selected as effect size. The meta-analysis of all data was conducted using RevMan 5.4 and the evidence was summarized according to GRADE (the grading of recommendation, assessment, development, and evaluation). RESULTS: Twelve randomized control trials consisting of 1,033 women with cervical cancer who had undergone RH were included. Compared with women in the control group, women receiving LFES had improved therapeutic effect (RR = 0.22, 95% CI: 0.16-0.29) and reduced residual urine volume (MD = -32.27, 95% CI: -34.10 to -30.43) and catheter retention time (MD = -4.46, 95% CI: -5.17 to -3.76) following treatment. Muscle strength scores of pelvic floor type I and type II muscle fibers in the LFES group were also higher than in the control group (MD = 1.07, 95% CI: 0.91-1.24). CONCLUSION: LFES may be an effective auxiliary treatment for women with cervical cancer after hysterectomy, which can help reduce the duration of indwelling urethral catheter and residual urine volume.

Prediction of the Drug-Drug Interaction Types with the Unified Embedding Features from Drug Similarity Networks.

Drug combination therapies are a promising strategy to overcome drug resistance and improve the efficacy of monotherapy in cancer, and it has been shown to lead to a decrease in dose-related toxicities. Except the synergistic reaction between drugs, some antagonistic drug-drug interactions (DDIs) exist, which is the main cause of adverse drug events. Precisely predicting the type of DDI is important for both drug development and more effective drug combination therapy applications. Recently, numerous text mining- and machine learning-based methods have been developed for predicting DDIs. All these methods implicitly utilize the feature of drugs from diverse drug-related properties. However, how to integrate these features more efficiently and improve the accuracy of classification is still a challenge. In this paper, we proposed a novel method (called NMDADNN) to predict the DDI types by integrating five drug-related heterogeneous information sources to extract the unified drug mapping features. NMDADNN first constructs the similarity networks by using the Jaccard coefficient and then implements random walk with restart algorithm and positive pointwise mutual information for extracting the topological similarities. After that, five network-based similarities are unified by using a multimodel deep autoencoder. Finally, NMDADNN implements the deep neural network (DNN) on the unified drug feature to infer the types of DDIs. In comparison with other recent state-of-the-art DNN-based methods, NMDADNN achieves the best results in terms of accuracy, area under the precision-recall curve, area under the ROC curve, F1 score, precision and recall. In addition, many of the promising types of drug-drug pairs predicted by NMDADNN are also confirmed by using the interactions checker tool. These results demonstrate the effectiveness of our NMDADNN method, indicating that NMDADNN has the great potential for predicting DDI types.

Prediction of drug-target interaction by integrating diverse heterogeneous information source with multiple kernel learning and clustering methods.

BACKGROUND: Identification of potential drug-target interaction pairs is very important for pharmaceutical innovation and drug discovery. Numerous machine learning-based and network-based algorithms have been developed for predicting drug-target interactions. However, large-scale pharmacological, genomic and chemical datum emerged recently provide new opportunity for further heightening the accuracy of drug-target interactions prediction. RESULTS: In this work, based on the assumption that similar drugs tend to interact with similar proteins and vice versa, we developed a novel computational method (namely MKLC-BiRW) to predict new drug-target interactions. MKLC-BiRW integrates diverse drug-related and target-related heterogeneous information source by using the multiple kernel learning and clustering methods to generate the drug and target similarity matrices, in which the low similarity elements are set to zero to build the drug and target similarity correction networks. By incorporating these drug and target similarity correction networks with known drug-target interaction bipartite graph, MKLC-BiRW constructs the heterogeneous network on which Bi-random walk algorithm is adopted to infer the potential drug-target interactions. CONCLUSIONS: Compared with other existing state-of-the-art methods, MKLC-BiRW achieves the best performance in terms of AUC and AUPR. MKLC-BiRW can effectively predict the potential drug-target interactions.