Juxiang Zhang

BACKGROUND: Long non-coding RNAs (lncRNAs) are crucial in the invasion, angiogenesis, progression, and metastasis of hepatocellular carcinoma (HCC). The lncRNA MYLK-AS1 promotes the growth and invasion of HCC through the EGFR/HER2-ERK1/2 signaling pathway. However, the clinical significance of MYLK-AS1 in HCC still needs to be further determined. METHODS: Bioinformatic analysis was performed to determine the potential relationship among MYLK-AS1, miRNAs and mRNAs. A total of 156 samples of normal liver and paired HCC tissues from HCC patients were used to evaluate MYLK-AS1 expression by qRT-PCR. Human HCC cell lines were used to evaluate the colony formation, cell proliferation, migration, invasion, cell cycle and apoptosis after transfection of lentiviral short-hairpin RNAs (shRNAs) targeting MYLK-AS1 or MYLK-AS1 vectors. The competitive endogenous RNA (ceRNA) mechanism was clarified using fluorescence in situ hybridization (FISH), Western blotting, qPCR, RNA binding protein immunoprecipitation (RIP), and dual luciferase reporter analysis. RESULTS: MYLK-AS1 up-regulation was detected in the HCC tumor tissues and cell lines associated with the enhancement of the angiogenesis and tumor progression. The down-regulation of MYLK-AS1 reversed the effects on angiogenesis, proliferation, invasion and metastasis in the HCC cells and in vivo. MYLK-AS1 acted as ceRNA, capable of regulating the angiogenesis in HCC, while the microRNA miR-424-5p was the direct target of MYLK-AS1. Promoting the angiogenesis and the tumor proliferation, the complex MYLK-AS1/miR-424-5p activated the VEGFR-2 signaling through E2F7, whereas the specific targeting of E2F transcription factor 7 (E2F7) by miR-424-5p, was indicated by the mechanism studies. CONCLUSIONS: MYLK-AS1 and E2F7 are closely related to some malignant clinicopathological features and prognosis of HCC, thus the MYLK-AS1/ miR-424-5p/E2F7 signaling pathway might represent a promising treatment strategy to combat HCC.

OBJECTIVE: Metabolic biomarkers are expected to decode the phenotype of gastric cancer (GC) and lead to high-performance blood tests towards GC diagnosis and prognosis. We attempted to develop diagnostic and prognostic models for GC based on plasma metabolic information. DESIGN: We conducted a large-scale, multicentre study comprising 1944 participants from 7 centres in retrospective cohort and 264 participants in prospective cohort. Discovery and verification phases of diagnostic and prognostic models were conducted in retrospective cohort through machine learning and Cox regression of plasma metabolic fingerprints (PMFs) obtained by nanoparticle-enhanced laser desorption/ionisation-mass spectrometry (NPELDI-MS). Furthermore, the developed diagnostic model was validated in prospective cohort by both NPELDI-MS and ultra-performance liquid chromatography-MS (UPLC-MS). RESULTS: We demonstrated the high throughput, desirable reproducibility and limited centre-specific effects of PMFs obtained through NPELDI-MS. In retrospective cohort, we achieved diagnostic performance with areas under curves (AUCs) of 0.862-0.988 in the discovery (n=1157 from 5 centres) and independent external verification dataset (n=787 from another 2 centres), through 5 different machine learning of PMFs, including neural network, ridge regression, lasso regression, support vector machine and random forest. Further, a metabolic panel consisting of 21 metabolites was constructed and identified for GC diagnosis with AUCs of 0.921-0.971 and 0.907-0.940 in the discovery and verification dataset, respectively. In the prospective study (n=264 from lead centre), both NPELDI-MS and UPLC-MS were applied to detect and validate the metabolic panel, and the diagnostic AUCs were 0.855-0.918 and 0.856-0.916, respectively. Moreover, we constructed a prognosis scoring system for GC in retrospective cohort, which can effectively predict the survival of GC patients. CONCLUSION: We developed and validated diagnostic and prognostic models for GC, which also contribute to advanced metabolic analysis towards diseases, including but not limited to GC.

This letter presents an adaptive frequency-locked loop (FLL) with fixed-length transfer delay units for single-phase systems. By analyzing the relationship between the grid voltage and its transfer delay signals, a linear regression model of the grid voltage is established. Accordingly, a transfer delay based adaptive FLL (TD-AFLL) is proposed. A mathematic proof indicates that the proposed TD-AFLL can reject both phase offset errors and double-frequency oscillatory errors. Thus, the grid voltage parameters can be estimated accurately, even when the frequency drifts away from its nominal value. Moreover, fast dynamics of the TD-AFLL are achieved due to the transfer delay structure. Experiments verify the effectiveness of the proposed method.