Survival analysis mainly deals with the time to event, including death, onset of disease, and bankruptcy. The common characteristic of survival analysis is that it contains “censored” data, in which the time to event cannot be completely observed, but instead represents the lower bound of the time to event. Only the occurrence of either time to event or censoring time is observed. Many traditional statistical methods have been effectively used for analyzing survival data with censored observations. However, with the development of high-throughput technologies for producing “omics” data, more advanced statistical methods, such as regularization, should be required to construct the predictive survival model with high-dimensional genomic data. Furthermore, machine learning approaches have been adapted for survival analysis, to fit nonlinear and complex interaction effects between predictors, and achieve more accurate prediction of individual survival probability. Presently, since most clinicians and medical researchers can easily assess statistical programs for analyzing survival data, a review article is helpful for understanding statistical methods used in survival analysis. We review traditional survival methods and regularization methods, with various penalty functions, for the analysis of high-dimensional genomics, and describe machine learning techniques that have been adapted to survival analysis.
Bankruptcy ; Genomics ; Machine Learning ; Methods ; Survival Analysis

Survival analysis mainly deals with the time to event, including death, onset of disease, and bankruptcy. The common characteristic of survival analysis is that it contains “censored” data, in which the time to event cannot be completely observed, but instead represents the lower bound of the time to event. Only the occurrence of either time to event or censoring time is observed. Many traditional statistical methods have been effectively used for analyzing survival data with censored observations. However, with the development of high-throughput technologies for producing “omics” data, more advanced statistical methods, such as regularization, should be required to construct the predictive survival model with high-dimensional genomic data. Furthermore, machine learning approaches have been adapted for survival analysis, to fit nonlinear and complex interaction effects between predictors, and achieve more accurate prediction of individual survival probability. Presently, since most clinicians and medical researchers can easily assess statistical programs for analyzing survival data, a review article is helpful for understanding statistical methods used in survival analysis. We review traditional survival methods and regularization methods, with various penalty functions, for the analysis of high-dimensional genomics, and describe machine learning techniques that have been adapted to survival analysis.

Postconditioning has been shown to protect the mouse brain from ischemic injury. However, the neuroprotective mechanisms of postconditioning remain elusive. We have found that toll-like receptor 5 (TLR5) plays an integral role in postconditioning-induced neuroprotection through Akt/nuclear factor kappa B (NF-κB) activation in cerebral ischemia. Compared to animals that received 30 min of transient middle cerebral artery occlusion (tMCAO) group, animals that also underwent postconditioning showed a significant reduction of up to 60.51% in infarct volume. Postconditioning increased phospho-Akt (p-Akt) levels and NF-κB translocation to the nucleus as early as 1 h after tMCAO and oxygen-glucose deprivation. Furthermore, inhibition of Akt by Akt inhibitor IV decreased NF-κB promoter activity after postconditioning. Immunoprecipitation showed that interactions between TLR5, MyD88, and p-Akt were increased from postconditioning both in vivo and in vitro. Similar to postconditioning, flagellin, an agonist of TLR5, increased NF-κB nuclear translocation and Akt phosphorylation. Our results suggest that postconditioning has neuroprotective effects by activating NF-κB and Akt survival pathways via TLR5 after cerebral ischemia. Additionally, the TLR5 agonist flagellin can simulate the neuroprotective mechanism of postconditioning in cerebral ischemia.
Animals ; Brain ; Brain Ischemia* ; Flagellin ; Immunoprecipitation ; In Vitro Techniques ; Infarction, Middle Cerebral Artery ; Mice* ; Neuroprotection ; Neuroprotective Agents ; NF-kappa B ; Phosphorylation ; Toll-Like Receptor 5

Acetaldehyde dehydrogenase 2 (ALDH2) is the second enzyme involved in the breakdown of acetaldehyde into acetic acid during the process of alcohol metabolism. Roughly 40% of East Asians carry one or two ALDH2*2 alleles, and the presence of ALDH2 genetic mutations in individuals may affect the bone remodeling cycle owing to accumulation of acetaldehyde in the body. In this study, we investigated the effects of ALDH2 mutations on bone remodeling. In this study, we examined the effects of ALDH2 polymorphisms on in vitro osteogensis using human induced pluripotent stem cells (hiPSCs). We differentiated wild-type (ALDH2*1/*1-) and ALDH2*1/*2-genotyped hiPSCs into osteoblasts (OBs) and confirmed their OB characteristics. Acetaldehyde was administered to confirm the impact caused by the mutation during OB differentiation. Calcium deposits formed during osteogenesis were significantly decreased in ALDH2*1/*2 OBs. The expression of osteogenic markers were also decreased in acetaldehyde-treated OBs differentiated from the ALDH2*1/*2 hiPSCs. Furthermore, the impact of ALDH2 polymorphism and acetaldehyde-induced stress on inflammatory factors such as 4-hydroxynonenal and tumor necrosis factor α was confirmed. Our findings suggest that individuals with ALDH2 deficiency may face challenges in acetaldehyde breakdown, rendering them susceptible to disturbances in normal bone remodeling therefore, caution should be exercised regarding alcohol consumption. In this proof-of-concept study, we were able to suggest these findings as a result of a disease-in-a-dish concept using hiPSCs derived from individuals bearing a certain mutation. This study also shows the potential of patient-derived hiPSCs for disease modeling with a specific condition.

Acetaldehyde dehydrogenase 2 (ALDH2) is the second enzyme involved in the breakdown of acetaldehyde into acetic acid during the process of alcohol metabolism. Roughly 40% of East Asians carry one or two ALDH2*2 alleles, and the presence of ALDH2 genetic mutations in individuals may affect the bone remodeling cycle owing to accumulation of acetaldehyde in the body. In this study, we investigated the effects of ALDH2 mutations on bone remodeling. In this study, we examined the effects of ALDH2 polymorphisms on in vitro osteogensis using human induced pluripotent stem cells (hiPSCs). We differentiated wild-type (ALDH2*1/*1-) and ALDH2*1/*2-genotyped hiPSCs into osteoblasts (OBs) and confirmed their OB characteristics. Acetaldehyde was administered to confirm the impact caused by the mutation during OB differentiation. Calcium deposits formed during osteogenesis were significantly decreased in ALDH2*1/*2 OBs. The expression of osteogenic markers were also decreased in acetaldehyde-treated OBs differentiated from the ALDH2*1/*2 hiPSCs. Furthermore, the impact of ALDH2 polymorphism and acetaldehyde-induced stress on inflammatory factors such as 4-hydroxynonenal and tumor necrosis factor α was confirmed. Our findings suggest that individuals with ALDH2 deficiency may face challenges in acetaldehyde breakdown, rendering them susceptible to disturbances in normal bone remodeling therefore, caution should be exercised regarding alcohol consumption. In this proof-of-concept study, we were able to suggest these findings as a result of a disease-in-a-dish concept using hiPSCs derived from individuals bearing a certain mutation. This study also shows the potential of patient-derived hiPSCs for disease modeling with a specific condition.

Acetaldehyde dehydrogenase 2 (ALDH2) is the second enzyme involved in the breakdown of acetaldehyde into acetic acid during the process of alcohol metabolism. Roughly 40% of East Asians carry one or two ALDH2*2 alleles, and the presence of ALDH2 genetic mutations in individuals may affect the bone remodeling cycle owing to accumulation of acetaldehyde in the body. In this study, we investigated the effects of ALDH2 mutations on bone remodeling. In this study, we examined the effects of ALDH2 polymorphisms on in vitro osteogensis using human induced pluripotent stem cells (hiPSCs). We differentiated wild-type (ALDH2*1/*1-) and ALDH2*1/*2-genotyped hiPSCs into osteoblasts (OBs) and confirmed their OB characteristics. Acetaldehyde was administered to confirm the impact caused by the mutation during OB differentiation. Calcium deposits formed during osteogenesis were significantly decreased in ALDH2*1/*2 OBs. The expression of osteogenic markers were also decreased in acetaldehyde-treated OBs differentiated from the ALDH2*1/*2 hiPSCs. Furthermore, the impact of ALDH2 polymorphism and acetaldehyde-induced stress on inflammatory factors such as 4-hydroxynonenal and tumor necrosis factor α was confirmed. Our findings suggest that individuals with ALDH2 deficiency may face challenges in acetaldehyde breakdown, rendering them susceptible to disturbances in normal bone remodeling therefore, caution should be exercised regarding alcohol consumption. In this proof-of-concept study, we were able to suggest these findings as a result of a disease-in-a-dish concept using hiPSCs derived from individuals bearing a certain mutation. This study also shows the potential of patient-derived hiPSCs for disease modeling with a specific condition.

Acetaldehyde dehydrogenase 2 (ALDH2) is the second enzyme involved in the breakdown of acetaldehyde into acetic acid during the process of alcohol metabolism. Roughly 40% of East Asians carry one or two ALDH2*2 alleles, and the presence of ALDH2 genetic mutations in individuals may affect the bone remodeling cycle owing to accumulation of acetaldehyde in the body. In this study, we investigated the effects of ALDH2 mutations on bone remodeling. In this study, we examined the effects of ALDH2 polymorphisms on in vitro osteogensis using human induced pluripotent stem cells (hiPSCs). We differentiated wild-type (ALDH2*1/*1-) and ALDH2*1/*2-genotyped hiPSCs into osteoblasts (OBs) and confirmed their OB characteristics. Acetaldehyde was administered to confirm the impact caused by the mutation during OB differentiation. Calcium deposits formed during osteogenesis were significantly decreased in ALDH2*1/*2 OBs. The expression of osteogenic markers were also decreased in acetaldehyde-treated OBs differentiated from the ALDH2*1/*2 hiPSCs. Furthermore, the impact of ALDH2 polymorphism and acetaldehyde-induced stress on inflammatory factors such as 4-hydroxynonenal and tumor necrosis factor α was confirmed. Our findings suggest that individuals with ALDH2 deficiency may face challenges in acetaldehyde breakdown, rendering them susceptible to disturbances in normal bone remodeling therefore, caution should be exercised regarding alcohol consumption. In this proof-of-concept study, we were able to suggest these findings as a result of a disease-in-a-dish concept using hiPSCs derived from individuals bearing a certain mutation. This study also shows the potential of patient-derived hiPSCs for disease modeling with a specific condition.

PURPOSE: The 2017 international consensus guidelines (ICG) for intraductal papillary mucinous neoplasm (IPMN) of the pancreas were recently released. Important changes included the addition of worrisome features such as elevated serum CA 19-9 and rapid cyst growth (>5 mm over 2 years). We aimed to clinically validate the 2017 ICG and compare the diagnostic performance between the 2017 and 2012 ICG. METHODS: This was a retrospective cohort study. During January 2000–January 2017, patients who underwent complete surgical resection and had pathologic confirmation of branch-duct or mixed-type IPMN were included. To evaluate diagnostic performance, the areas under the receiver operating curves (AUCs) were evaluated. RESULTS: A total of 448 patients were included. The presence of mural nodule (hazard ratio [HR], 9.12; 95% confidence interval [CI], 4.60–18.09; P = 0.001), main pancreatic duct dilatation (>5 mm) (HR, 5.32; 95% CI, 2.67–10.60; P = 0.001), thickened cystic wall (HR, 3.40; 95% CI, 1.51–7.63; P = 0.003), and elevated CA 19-9 level (>37 unit/mL) (HR, 5.25; 95% CI, 2.05–13.42; P = 0.001) were significantly associated with malignant IPMN. Malignant lesions showed a cyst growth rate >5 mm over 2 years more frequently than benign lesions (60.9% vs. 29.7%, P = 0.012). The AUC was higher for the 2017 ICG than the 2012 ICG (0.784 vs. 0.746). CONCLUSION: The new 2017 ICG for IPMN is clinically valid, with a superior diagnostic performance to the 2012 ICG. The inclusion of elevated serum CA 19-9 level and cyst growth rate to the 2017 ICG is appropriate.
Area Under Curve ; Carcinoma, Pancreatic Ductal ; Cohort Studies ; Consensus ; Dilatation ; Humans ; Mucins ; Pancreas ; Pancreatic Ducts ; Retrospective Studies