The patients with low circFNDC3B expression had a shorter OS (P = 0.0014) than patients with high expression. Moreover, circFNDC3B inhibited the proliferation, invasion and migration of CC cells. Next, we identified that circFNDC3B could encode a novel protein circFNDC3B-218aa. Furthermore, circFNDC3B-218aa, not circFNDC3B, inhibited the proliferation, invasion and migration of CC. Additionally, the in vivo experiments implied that up-regulated circFNDC3B-218aa exhibited an inhibitory effect on CC progression. By RNA-sequencing, western blot and glucose metabolism-related assays, we found that circFNDC3B-218aa inhibited the expression of Snail, and subsequently promoted the tumor-suppressive effect of FBP1 in CC. CONCLUSIONS The novel circFNDC3B-218aa may serve as a tumor suppressive factor and potential biomarker which may supply the potential therapeutic target for CC.BACKGROUND Discovering a highly accurate and robust gene signature for the prediction of breast cancer metastasis from gene expression profiling of primary tumors is one of the most challenging tasks to reduce the number of deaths in women. Due to the limited success of gene-based features in achieving satisfactory prediction accuracy, many methodologies have been proposed in recent years to develop network-based features by integrating network information with gene expression. However, evaluation results are inconsistent to confirm the effectiveness of network-based features, because of many confounding factors involved in classification model learning process, such as data normalization, dimension reduction, and feature selection. An unbiased comparative evaluation is essential for uncovering the strength of network-based features. METHODS In this study, we compared several types of network-based features obtained using different mathematical operators (Mean, Maximum, Minimum, Median, Variance) on geneset (sed features. CONCLUSIONS Network-based features and their combination show promise for improving the prediction of breast cancer metastasis but may require a large amount of training data. Robustness claim of network-based features needs to be re-examined with network node degree and other confounding factors in consideration.BACKGROUND A large number of end-of-life decisions are made by a next-of-kin for a patient who has lost their decision-making capacity. This has given rise to investigations into how surrogates make these decisions. The experimental perspective has focused on examining how the decisions we make for others differ from our own, whereas the qualitative perspective has explored surrogate insights into making these decisions. METHODS We conducted a mixed methods study to bring these two perspectives together. This is crucial to comparing decision outcomes to the decision process. We asked older adult partners to make end-of-life decisions for each other.  https://www.selleckchem.com/products/bindarit.html  They then took part in a semi-structured interview about their decision process. Transcripts were analysed using thematic analysis. RESULTS 24 participants took part in the study. Surrogates were more likely to take a life-saving treatment at the risk of a diminished quality of life for their partner than for themselves. This was consistent with their transcripts which showed that they wanted to give their partner a better chance of living. Although there was evidence of surrogate inaccuracy in the decision task, participants overwhelmingly reported their intention to make a decision which aligns with the substituted judgment standard. However, uncertainty about their wishes pushed them to consider other factors. CONCLUSIONS Taking a mixed methods approach allowed us to make novel comparisons between decision outcome and process. We found that the intentions of surrogates broadly align with the expectations of the substituted judgment standard and that previous discussions with their partner helps them to make a decision.BACKGROUND Influenza vaccine manufacturers traditionally use egg-derived candidate vaccine viruses (CVVs) to produce high-yield influenza viruses for seasonal or pandemic vaccines; however, these egg-derived CVVs need an adaptation process for the virus to grow in mammalian cells. The low yields of cell-based manufacturing systems using egg-derived CVVs remain an unsolved issue. This study aimed to develop high-growth cell-derived CVVs for MDCK cell-based vaccine manufacturing platforms. METHODS Four H7N9 CVVs were generated in characterized Vero and adherent MDCK (aMDCK) cells. Furthermore, reassortant viruses were amplified in adherent MDCK (aMDCK) cells with certification, and their growth characteristics were detected in aMDCK cells and new suspension MDCK (sMDCK) cells. Finally, the plaque-forming ability, biosafety, and immunogenicity of H7N9 reassortant viruses were evaluated. RESULTS The HA titers of these CVVs produced in proprietary suspension MDCK (sMDCK) cells and chicken embryos were 2- to 8-fold higher than those in aMDCK cells. All H7N9 CVVs showed attenuated characteristics by trypsin-dependent plaque assay and chicken embryo lethality test. The alum-adjuvanted NHRI-RG5 (derived from the fifth wave H7N9 virus A/Guangdong/SP440/2017) vaccine had the highest immunogenicity and cross-reactivity among the four H7N9 CVVs. Finally, we found that AddaVax adjuvant improved the cross-reactivity of low pathogenic H7N9 virus against highly pathogenic H7N9 viruses. CONCLUSIONS Our study indicates that cell-derived H7N9 CVVs possessed high growth rate in new sMDCK cells and low pathogenicity in chicken embryo, and that CVVs generated by this platform are also suitable for both cell- and egg-based prepandemic vaccine production.BACKGROUND Alzheimer's disease (AD) is one of the leading causes of death in the US and there is no validated drugs to stop, slow or prevent AD. Despite tremendous effort on biomarker discovery, existing findings are mostly individual biomarkers and provide limited insights into the transcriptomic decoupling underlying AD. We propose to explore the gene co-expression patterns in multiple AD stages, including cognitively normal (CN), early mild cognitive impairment (EMCI), late MCI and AD. METHODS We modified traiditonal joint graphical lasso to model our asusmption that the co-expression networks in consecutive disease stages are largely similar with critical differences. In addition, we performed subsequent network comparison analysis for identification of stage specific transcriptomic decoupling. We focused our analysis on top AD-enriched pathways. RESULTS We observed that 419 edges in CN, 420 edges in EMCI, 381 edges in LMCI and 250 edges in AD were frequently estimated with non zero weights. With modified JGL, the weight of all estimated edges in CN, EMCI and LMCI are zero.