Lung cancer risk discrimination of prediagnostic proteomics measurements compared with existing prediction tools

Background We sought to develop a proteomics-based risk model for lung cancer and evaluate its risk-discriminatory performance in comparison with a smoking-based risk model (PLCOm2012) and a commercially available autoantibody biomarker test. Methods We designed a case-control study nested in 6 prospective cohorts, including 624 lung cancer participants who donated blood samples at most 3 years prior to lung cancer diagnosis and 624 smoking-matched cancer free participants who were assayed for 302 proteins. We used 470 case-control pairs from 4 cohorts to select proteins and train a protein-based risk model. We subsequently used 154 case-control pairs from 2 cohorts to compare the risk-discriminatory performance of the protein-based model with that of the Early Cancer Detection Test (EarlyCDT)-Lung and the PLCOm2012 model using receiver operating characteristics analysis and by estimating models’ sensitivity. All tests were 2-sided. Results The area under the curve for the protein-based risk model in the validation sample was 0.75 (95% confidence interval [CI] = 0.70 to 0.81) compared with 0.64 (95% CI = 0.57 to 0.70) for the PLCOm2012 model (Pdifference = .001). The EarlyCDT-Lung had a sensitivity of 14% (95% CI = 8.2% to 19%) and a specificity of 86% (95% CI = 81% to 92%) for incident lung cancer. At the same specificity of 86%, the sensitivity for the protein-based risk model was estimated at 49% (95% CI = 41% to 57%) and 30% (95% CI = 23% to 37%) for the PLCOm2012 model. Conclusion Circulating proteins showed promise in predicting incident lung cancer and outperformed a standard risk prediction model and the commercialized EarlyCDT-Lung.US NCI (INTEGRAL program U19 CA203654R03 CA245979), l’Institut National Du Cancer (2019-1-TABAC-01Foundation of Northern Sweden (AMP19-962), an early detection of cancer development grantSwedish Department of Health ministry, and Cancer Research UK [C18281/A29019]. RJH is supported by the Canada Research Chair of the Canadian Institute of Health Research

Tuning electrochemical catalytic activity of defective 2D terrace MoSe2 heterogeneous catalyst via Co doping

This study presents successful growth of defective 2D terrace MoSe2/CoMoSe lateral heterostructures (LH), bilayer and multilayer MoSe2/CoMoSe LH, and vertical heterostructures (VH) nanolayers by doping metal Co (cobalt) element into MoSe2 atomic layers to form a CoMoSe alloy at the high temperature (~900 °C). After the successful introduction of metal Co heterogeneity in the MoSe2 thin layers, more active sites can be created to enhance hydrogen evolution reaction (HER) activities combining with metal Co catalysis, through the mechanisms including (1) atomic arrangement distortion in CoMoSe alloy nanolayers, (2) atomic level coarsening in LH interfaces and terrace edge layer architecture in VH, (3) formation of defective 2D terrace MoSe2 nanolayers heterogeneous catalyst via metal Co doping. The HER investigations indicated that the obtained products with LH and VH exhibited an improved HER activity in comparison with those from the pristine 2D MoSe2 electrocatalyst and LH type MoSe2/CoMoSe. The present work shows a facile yet reliable route to introduce metal ions into ultrathin 2D transition metal dichalcogenides (TMDCS) and produce defective 2D alloy atomic layers for exposing active sites, and thus eventually improve their electrocatalytic performance

GCF2-Net: global-aware cross-modal feature fusion network for speech emotion recognition

Emotion recognition plays an essential role in interpersonal communication. However, existing recognition systems use only features of a single modality for emotion recognition, ignoring the interaction of information from the different modalities. Therefore, in our study, we propose a global-aware Cross-modal feature Fusion Network (GCF2-Net) for recognizing emotion. We construct a residual cross-modal fusion attention module (ResCMFA) to fuse information from multiple modalities and design a global-aware module to capture global details. More specifically, we first use transfer learning to extract wav2vec 2.0 features and text features fused by the ResCMFA module. Then, cross-modal fusion features are fed into the global-aware module to capture the most essential emotional information globally. Finally, the experiment results have shown that our proposed method has significant advantages than state-of-the-art methods on the IEMOCAP and MELD datasets, respectively

Investigating and Mitigating the Side Effects of Noisy Views in Multi-view Clustering in Practical Scenarios

Multi-view clustering (MvC) aims at exploring category structures among multi-view data without label supervision. Multiple views provide more information than single views and thus existing MvC methods can achieve satisfactory performance. However, their performance might seriously degenerate when the views are noisy in practical scenarios. In this paper, we first formally investigate the drawback of noisy views and then propose a theoretically grounded deep MvC method (namely MvCAN) to address this issue. Specifically, we propose a novel MvC objective that enables un-shared parameters and inconsistent clustering predictions across multiple views to reduce the side effects of noisy views. Furthermore, a non-parametric iterative process is designed to generate a robust learning target for mining multiple views' useful information. Theoretical analysis reveals that MvCAN works by achieving the multi-view consistency, complementarity, and noise robustness. Finally, experiments on extensive public datasets demonstrate that MvCAN outperforms state-of-the-art methods and is robust against the existence of noisy views

SKP2 Promotes Hepatocellular Carcinoma Progression Through Nuclear AMPK-SKP2-CARM1 Signaling Transcriptionally Regulating Nutrient-Deprived Autophagy Induction

Background/Aims: SKP2 overexpression has been associated with poor prognosis in numerous cancers. The mechanisms of autophagy in the tumor pathogenesis have been a research focus recently. How the SKP2 involved in autophagy expresses oncogenic characteristics, especially in HCC, are largely unclear. Methods: The expression of SKP2 was detected by qPCR, Western blot, Immunohistochemical (IHC) and Immunofluorescence (IF) techniques. SKP2 was knocked down or overexpressed by lentivirus transfection in HCC cells. Functional assays such as CCK8 assays, transwell migration and invasion assays, and colony formation assays were performed to determine the role of SKP2 in HCC. Furthermore, autophagy was induced by glucose deprivation in HCC cells followed by monitoring of the levels and distributions of SKP2, CARM1 and AMPK. Results: Our data showed that SKP2 levels were significantly increased in HCC cell lines and HCC tissues rather than corresponding normal liver tissues, and augmented SKP2 levels were statistically correlated with tumor grade, size and metastases. By up-regulation or down-regulation of SKP2 in HCC cells, we confirmed that SKP2 encourages proliferation, migration, invasion, and colony formation. We then found that SKP2 was inhibited, CARM1 increased and AMPKα2 became activated in the nucleus under glucose deprivation induced autophagy. Moreover, we discovered that SKP2 was repressing CARM1 in the nucleus under nutrient-sufficient conditions in HCC. Conclusions: We show that SKP2 promotes HCC progression and its nuclear functions of autophagy induction with CARM1 and AMPK, which may provide a potential target for HCC therapy