Porous hydroxyapatite (HA) artificial bone scaffolds were prepared via the freeze-gel casting process in order to improve their mechanical strengths. As a porogen, various volumes of poly (methyl methacrylate) (PMMA) powders were added to obtain high porosity, such as in cancellous bone. After fabrication, the porous and mechanical properties of the scaffolds were examined. The HA60 scaffold, with a porosity over 80%, had proper compressive strength and modulus and satisfied the range of properties of cancellous bone. Moreover, it was found that the investigated mechanical properties were affected by the scaffolds’ porosity. However, a section was found where the compressive strength was high despite the increase in the porosity. Specifically, HA30 had a porosity of 62.9% and a compressive strength of 1.73 MPa, whereas the values for HA60 were 81.9% and 3.23 MPa, respectively. The results indicate that there are factors that can preserve the mechanical properties even if the porosity of the scaffold increases. Therefore, in this study, various parameters affecting the porous and mechanical properties of the scaffolds during the manufacturing process were analyzed. It is expected that the improvement in the mechanical properties of the artificial bone scaffold having a high porosity can be applied to tissue engineering.

Background: Prognostic genes or gene signatures have been widely used to predict patient survival and aid in making decisions pertaining to therapeutic actions. Although some web-based survival analysis tools have been developed, they have several limitations. Objective: Taking these limitations into account, we developed ESurv (Easy, Effective, and Excellent Survival analysis tool), a web-based tool that can perform advanced survival analyses using user-derived data or data from The Cancer Genome Atlas (TCGA). Users can conduct univariate analyses and grouped variable selections using multiomics data from TCGA. Methods: We used R to code survival analyses based on multiomics data from TCGA. To perform these analyses, we excluded patients and genes that had insufficient information. Clinical variables were classified as 0 and 1 when there were two categories (for example, chemotherapy: no or yes), and dummy variables were used where features had 3 or more outcomes (for example, with respect to laterality: right, left, or bilateral). Results: Through univariate analyses, ESurv can identify the prognostic significance for single genes using the survival curve (median or optimal cutoff), area under the curve (AUC) with C statistics, and receiver operating characteristics (ROC). Users can obtain prognostic variable signatures based on multiomics data from clinical variables or grouped variable selections (lasso, elastic net regularization, and network-regularized high-dimensional Cox-regression) and select the same outputs as above. In addition, users can create custom gene signatures for specific cancers using various genes of interest. One of the most important functions of ESurv is that users can perform all survival analyses using their own data. Conclusions: Using advanced statistical techniques suitable for high-dimensional data, including genetic data, and integrated survival analysis, ESurv overcomes the limitations of previous web-based tools and will help biomedical researchers easily perform complex survival analyses.