Sex differences in the outcomes after transcatheter aortic valve replacement with newer generation devices: A meta-analysis.

BACKGROUND: Transcatheter aortic valve replacement (TAVR) is a reasonable therapeutic approach among patients with symptomatic severe aortic stenosis irrespective of surgical risk. Data regarding sex-specific differences in the outcomes with newer generation valves are limited. METHODS: Electronic databases were searched for studies assessing sex differences in the outcomes of patients undergoing TAVR with newer generation valves (SAPIEN 3 or Evolut). Random effects model was constructed for summary estimates. RESULTS: Four observational studies with 4522 patients (44.8% women) were included in the meta-analysis. Women were older and had a lower prevalence of coronary artery disease and mean EuroScore. Women had a higher incidence of short-term mortality (up to 30 days) (risk ratio [RR]: 1.60, 95% confidence interval [CI]: 1.14-2.25), but no difference in 1-year mortality (RR: 0.92, 95% CI: 0.72-1.17). There was no significant difference in the incidence of major bleeding (RR: 1.16, 95% CI: 0.86-1.57), permanent pacemaker (PPM) (RR: 0.80, 95% CI: 0.62-1.04), or disabling stroke (RR: 1.16, 95% CI: 0.54-2.45). CONCLUSION: In this meta-analysis, we found that women undergoing TAVR with newer-generation devices were older but had a lower prevalence of comorbidities. Women had a higher incidence of short-term mortality but no difference in the 1-year mortality, bleeding, PPM, or stroke compared with men. Future studies are required to confirm these findings.

YWHAZ amplification/overexpression defines aggressive bladder cancer and contributes to chemo-/radio-resistance by suppressing caspase-mediated apoptosis.

The objective of this study was to characterize the oncogenic actions of a recently identified cancer-associated gene YWHAZ (also named as 14-3-3 ζ/δ) in urothelial carcinomas of the urinary bladder (UCUB). A genome-wide study revealed YWHAZ to be involved in the amplicon at 8q22.3, and its genetic amplification was detected predominantly in muscle-invasive bladder cancer (MIBC). Immunohistochemical staining confirmed the association of YWHAZ overexpression with higher tumor stages, lymph node/vascular invasion, and mitotic activity. Univariate and multivariate analyses further indicated the prognostic potential of YWHAZ for more aggressive cancer types. Both gene set enrichment analysis and STRING network studies suggested involvement of YWHAZ in regulating caspase-mediated apoptosis. Ectopic expression of YWHAZ in bladder cells with low endogenous YWHAZ levels boosted cell resistance to doxorubicin and cisplatin, as well as to ionizing radiation. Conversely, YWHAZ-knockdown using specific shRNA in cells with high endogenous YWHAZ levels diminished survival activity, suppressing cell growth and increasing cell death. Our findings confirm the essential role played by YWHAZ in sustaining cell proliferation during chemo/radiotherapy. Treatments based on anti-YWHAZ strategies may thus be beneficial for UCUB patients overexpressing YWHAZ. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Unveiling How Examples Shape Visualization Design Outcomes.

Visualization designers (e.g., journalists or data analysts) often rely on examples to explore the space of possible designs, yet we have little insight into how examples shape data visualization design outcomes. While the effects of examples have been studied in other disciplines, such as web design or engineering, the results are not readily applicable to visualization due to inconsistencies in findings and challenges unique to visualization design. Towards bridging this gap, we conduct an exploratory experiment involving 32 data visualization designers focusing on the influence of five factors (timing, quantity, diversity, data topic similarity, and data schema similarity) on objectively measurable design outcomes (e.g., numbers of designs and idea transfers). Our quantitative analysis shows that when examples are introduced after initial brainstorming, designers curate examples with topics less similar to the dataset they are working on and produce more designs with a high variation in visualization components. Also, designers copy more ideas from examples with higher data schema similarities. Our qualitative analysis of participants' thought processes provides insights into why designers incorporate examples into their designs, revealing potential factors that have not been previously investigated. Finally, we discuss how our results inform how designers may use examples during design ideation as well as future research on quantifying designs and supporting example-based visualization design. All supplemental materials are available in our OSF repo.

A machine learning approach using conditional normalizing flow to address extreme class imbalance problems in personal health records.

BACKGROUND: Supervised machine learning models have been widely used to predict and get insight into diseases by classifying patients based on personal health records. However, a class imbalance is an obstacle that disrupts the training of the models. In this study, we aimed to address class imbalance with a conditional normalizing flow model, one of the deep-learning-based semi-supervised models for anomaly detection. It is the first introduction of the normalizing flow algorithm for tabular biomedical data. METHODS: We collected personal health records from South Korean citizens (n = 706), featuring genetic data obtained from direct-to-customer service (microarray chip), medical health check-ups, and lifestyle log data. Based on the health check-up data, six chronic diseases were labeled (obesity, diabetes, hypertriglyceridemia, dyslipidemia, liver dysfunction, and hypertension). After preprocessing, supervised classification models and semi-supervised anomaly detection models, including conditional normalizing flow, were evaluated for the classification of diabetes, which had extreme target imbalance (about 2%), based on AUROC and AUPRC. In addition, we evaluated their performance under the assumption of insufficient collection for patients with other chronic diseases by undersampling disease-affected samples. RESULTS: While LightGBM (the best-performing model among supervised classification models) showed AUPRC 0.16 and AUROC 0.82, conditional normalizing flow achieved AUPRC 0.34 and AUROC 0.83 during fifty evaluations of the classification of diabetes, whose base rate was very low, at 0.02. Moreover, conditional normalizing flow performed better than the supervised model under a few disease-affected data numbers for the other five chronic diseases - obesity, hypertriglyceridemia, dyslipidemia, liver dysfunction, and hypertension. For example, while LightGBM performed AUPRC 0.20 and AUROC 0.75, conditional normalizing flow showed AUPRC 0.30 and AUROC 0.74 when predicting obesity, while undersampling disease-affected samples (positive undersampling) lowered the base rate to 0.02. CONCLUSIONS: Our research suggests the utility of conditional normalizing flow, particularly when the available cases are limited, for predicting chronic diseases using personal health records. This approach offers an effective solution to deal with sparse data and extreme class imbalances commonly encountered in the biomedical context.