Objective To explore the association between manganese superoxide dismutase (MnSOD) Val-9Ala polymorphism and breast cancer risk and to investigate the interaction with menopausal status by meta-analysis. Methods Such databases as The Cochrane Libtary (Issue1, 2010), Pubmed, CBM, CNKI and WanFang Data were searched from the date of their establishment to October, 2010, and the case-control studies of MnSOD Val-9Ala polymorphism and breast cancer risk were collected according to the inclusion and exclusion criteria. Then the quality of the included trials was assessed and meta-analysis was performed by RevMan 4.2.10 software. Results A total of 14 studies involving 17 842 patients were included. The results of meta-analyses showed no significant relation between MnSOD Val-9Ala polymorphism and the breast cancer susceptibility (Val/Ala vs. Val/Val: OR=1.04, 95%CI 0.93 to 1.17; Ala/Ala vs. Val/Val: OR=1.12, 95%CI 0.95 to 1.33; Ala/Ala vs. Val/Ala+Val/Val: OR=1.06, 95%CI 0.93 to 1.20; Val/Ala+ Ala/Ala vs. Val/Val: OR=1.06, 95%CI 0.94 to 1.10). However, in the subgroup analysis, the breast cancer risk significantly increased for premenopausal women (Val/Ala+Ala/Ala vs. Val/Val: OR=1.15, 95%CI 1.01 to1.31). Conclusion This meta-analysis suggests that the MnSOD Val-9Ala polymorphism is not significantly associated with the breast cancer susceptibility, but it may increase the risk of breast cancer in the presence of menopausal state.
Accurate neurological outcome assessment after cardiac arrest is critical for clinical diagnosis and treatment. Existing electroencephalogram (EEG) prediction models suffer from high computational complexity, redundant full-channel data, and insufficient fusion of time-frequency domain features. This study proposes a lightweight deep learning model, WaveConv-SR-RepVGG, based on adaptive time-frequency feature fusion and reparameterized convolution. Taking the reparameterized visual convolutional network (RepVGG) as the baseline framework, the model introduces a shrinkage (SR) mechanism into the time-domain branch to suppress interference from low signal-to-noise ratio EEG signals. A multi-scale wavelet convolution (MS-Conv) module is constructed to extract multi-scale spectral features, and time-frequency features are fused to form joint representations for prognostic classification. Experiments were conducted on the PhysioNet 2023 dataset. Under the 18-channel setting, the model achieved an accuracy of 81.7%, an F1 score of 86.1%, and an AUC of 0.847. Under the 4-channel setting, the accuracy reached 81.2%, the F1 score reached 86.7%, and the AUC reached 0.877, with overall performance outperforming various mainstream deep learning algorithms. The proposed model enables neurological outcome prediction for patients after cardiac arrest and maintains stable and excellent classification performance with fewer EEG acquisition channels.