An Evaluation of the Weibull and the Logistic Models for Cox's Proportional Hazards Model

Cox's proportional hazards model has been widely used in medical researches to evaluate the relationship between prognostic factors of a disease and the occurrence of outcome event. On a theoretical basis, regression coefficient estimated from Cox's proportional hazards model could be approximated by using the Weibull and the logistic model. Breast cancer cases (n=86) diagnosed at the Seoul National University Hospital were selected to evaluate the possibility of some accelerated models as an approximate model to Cox's proportional hazards model. Age at operation, tumor size and lymph node metastasis were the variables concerned in this study. Parameter estimates of two variables from the Weibull model, which seemed not to violate the proportionality assumption of Cox's model, showed almost identical values to those from Cox's proportional hazards model. However, there was a substantial degree of discrepancy in the parameter estimate of another variable, which showed an apparent unproportionality. This study confirmed that both the Weibull and the logistic models could be used as approximate methods to the estimates from Cox's proportional hazards model. Particularly noteworthy was the fact that the PC-SAS system could be successfully applied to survival analysis when the parameters were going to be estimated using Cox's model

Distinct patterns of Internet and smartphone-related problems among adolescents by gender: Latent class analysis

Background and objectives The ubiquitous Internet connections by smartphones weakened the traditional boundaries between computers and mobile phones. We sought to explore whether smartphone-related problems differ from those of computer use according to gender using latent class analysis (LCA). Methods After informed consents, 555 Korean middle-school students completed surveys on gaming, Internet use, and smartphone usage patterns. They also completed various psychosocial instruments. LCA was performed for the whole group and by gender. In addition to ANOVA and χ2 tests, post-hoc tests were conducted to examine differences among the LCA subgroups. Results In the whole group (n = 555), four subtypes were identified: dual-problem users (49.5%), problematic Internet users (7.7%), problematic smartphone users (32.1%), and “healthy” users (10.6%). Dual-problem users scored highest for addictive behaviors and other psychopathologies. The gender-stratified LCA revealed three subtypes for each gender. With dual-problem and healthy subgroup as common, problematic Internet subgroup was classified in the males, whereas problematic smartphone subgroup was classified in the females in the gender-stratified LCA. Thus, distinct patterns were observed according to gender with higher proportion of dual-problem present in males. While gaming was associated with problematic Internet use in males, aggression and impulsivity demonstrated associations with problematic smartphone use in females. Conclusions An increase in the number of digital media-related problems was associated with worse outcomes in various psychosocial scales. Gaming may play a crucial role in males solely displaying Internet-related problems. The heightened impulsivity and aggression seen in our female problematic smartphone users requires further research

Macrophage Lamin A/C Regulates Inflammation and the Development of Obesity-Induced Insulin Resistance

Obesity-induced chronic low-grade inflammation, in particular in adipose tissue, contributes to the development of insulin resistance and type 2 diabetes. However, the mechanism by which obesity induces adipose tissue inflammation has not been completely elucidated. Recent studies suggest that alteration of the nuclear lamina is associated with age-associated chronic inflammation in humans and fly. These findings led us to investigate whether the nuclear lamina regulates obesity-mediated chronic inflammation. In this study, we show that lamin A/C mediates inflammation in macrophages. The gene and protein expression levels of lamin A/C are significantly increased in epididymal adipose tissues from obese rodent models and omental fat from obese human subjects compared to their lean controls. Flow cytometry and gene expression analyses reveal that the protein and gene expression levels of lamin A/C are increased in adipose tissue macrophages (ATMs) by obesity. We further show that ectopic overexpression of lamin A/C in macrophages spontaneously activates NF-κB, and increases the gene expression levels of proinflammatory genes, such as Il6, Tnf, Ccl2, and Nos2. Conversely, deletion of lamin A/C in macrophages reduces LPS-induced expression of these proinflammatory genes. Importantly, we find that myeloid cell-specific lamin A/C deficiency ameliorates obesity-induced insulin resistance and adipose tissue inflammation. Thus, our data suggest that lamin A/C mediates the activation of ATM inflammation by regulating NF-κB, thereby contributing to the development of obesity-induced insulin resistance

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Standard error estimates in hierarchical generalized linear models

Hierarchical generalized linear models are often used to fit random effects models. However, attention is mostly paid to the estimation of fixed unknown parameters and inference for latent random effects. In contrast, standard error estimators receive less attention than they should be. Currently, the standard error estimators are based on various approximations, even when the mean parameters may be estimated from a higherorder approximation of the likelihood and the dispersion parameters are estimated by restricted maximum likelihood. Existing standard error estimation procedures are reviewed. A numerical illustration shows that the current standard errors are not necessarily accurate. Alternative standard errors are also proposed. In particular, a sandwich estimator that accounts for the dependence between the mean parameters and the dispersion parameters greatly improve the current standard errors. (c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons .org /licenses /by /4 .0/)

Effects of Internet and Smartphone Addictions on Depression and Anxiety Based on Propensity Score Matching Analysis

The associations of Internet addiction (IA) and smartphone addiction (SA) with mental health problems have been widely studied. We investigated the effects of IA and SA on depression and anxiety while adjusting for sociodemographic variables. In this study, 4854 participants completed a cross-sectional web-based survey including socio-demographic items, the Korean Scale for Internet Addiction, the Smartphone Addiction Proneness Scale, and the subscales of the Symptom Checklist 90 Items-Revised. The participants were classified into IA, SA, and normal use (NU) groups. To reduce sampling bias, we applied the propensity score matching method based on genetics matching. The IA group showed an increased risk of depression (relative risk 1.207; p < 0.001) and anxiety (relative risk 1.264; p < 0.001) compared to NUs. The SA group also showed an increased risk of depression (relative risk 1.337; p < 0.001) and anxiety (relative risk 1.402; p < 0.001) compared to NCs. These findings show that both, IA and SA, exerted significant effects on depression and anxiety. Moreover, our findings showed that SA has a stronger relationship with depression and anxiety, stronger than IA, and emphasized the need for prevention and management policy of the excessive smartphone use

A Van Der Waals Homojunction: Ideal p–n Diode Behavior in MoSe2

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Radiation-Induced Lung Fibrosis: Preclinical Animal Models and Therapeutic Strategies

Radiation-induced lung injury (RILI), including acute radiation pneumonitis and chronic radiation-induced lung fibrosis, is the most common side effect of radiation therapy. RILI is a complicated process that causes the accumulation, proliferation, and differentiation of fibroblasts and, finally, results in excessive extracellular matrix deposition. Currently, there are no approved treatment options for patients with radiation-induced pulmonary fibrosis (RIPF) partly due to the absence of effective targets. Current research advances include the development of small animal models reflecting modern radiotherapy, an understanding of the molecular basis of RIPF, and the identification of candidate drugs for prevention and treatment. Insights provided by this research have resulted in increased interest in disease progression and prognosis, the development of novel anti-fibrotic agents, and a more targeted approach to the treatment of RIPF