c_Jun (Phospho_Thr91) Antibody
- Known as:
- c_Jun (Phospho_Thr91) Antibody
- Catalog number:
- E011021-2
- Product Quantity:
- 100ug
- Category:
- Antibodies
- Supplier:
- EnoGene
- Gene target:
- c_Jun (Phospho_Thr91) Antibody
Related products to: c_Jun (Phospho_Thr91) Antibody
Related articles to: c_Jun (Phospho_Thr91) Antibody
- - Source: PubMed
Lander PetrinaHarris Benjamin H LKoizia Louis J - Despite growing racial and ethnic diversity in North America, interracial romantic relationships remain rare among youth. This study tested whether forming an interracial friendship by the end of middle school predicted interracial romantic relationships by the end of high school, beyond opportunities provided by school demographics. Data were from a longitudinal study of 2418 students followed from grades 8 to 12 from 26 racially diverse California public schools. Of those in relationships, 32.6% had an interracial partner. Greater availability of cross-race peers in high school increased the odds of interracial romantic relationships, OR = 2.23, p < .001. Having at least one earlier interracial friendship 4 years earlier nearly doubled this likelihood, OR = 1.86, p < .001. Findings underscore the role of school diversity and continuity across close relationship types. - Source: PubMed
Dryburgh Nicole S JCho BrandonKline Naomi GJuvonen Jaana - Myocardial infarction (MI) is one of the leading causes of death worldwide. MI is associated with cardiac structural and functional alterations. Among these, cardiac fibrosis may be significantly influenced by mitochondrial dysfunction. We sought to evaluate whether the injection of functional mitochondria from healthy muscle could improve the detrimental consequences of MI. - Source: PubMed
Cuesta-Corral MaríaMontoro-Garrido AlejandroRomero-Miranda AnaIslas FabiánRamchandani BuntyGredilla RicardoFernández-Irigoyen JoaquínSantamaría EnriqueDelgado-Valero BeatrizJiménez-González SaraRodrigues Díez RaquelNieto María LuisaCachofeiro VictoriaMartínez-Martínez Ernesto - High mammographic density is a well-known risk factor for breast cancer and reduces the sensitivity of mammography-based screening. While automated machine and deep learning-based methods provide more consistent and precise measurements compared to subjective Breast Imaging Reporting and Data System (BI-RADS) assessments, they often fail to account for the longitudinal evolution of density. Many of these methods assess mammographic density in a cross-sectional manner, overlooking correlations in repeated measures, irregular visit intervals, missing data, and informative dropouts. Joint models address these limitations by simultaneously modeling the relationship between longitudinal biomarkers and time-to-event outcomes. We introduce the DeepJoint algorithm, an open-source method combining deep learning-based mammographic density estimation with joint modeling to assess its longitudinal relationship with breast cancer risk. Our approach adequately analyzes processed mammograms from various manufacturers, estimating both dense area and percent density, two established risk factors for breast cancer. We utilize a joint model to explore their association with breast cancer risk and provide individualized risk predictions. Bayesian inference and the consensus Monte Carlo algorithm make the approach reliable for large screening datasets. By integrating deep learning with joint modeling, our new method provides a robust, comprehensive framework for evaluating breast cancer risk based on longitudinal density profiles. The complete pipeline is publicly available, promoting broader application and comparison with other methods. - Source: PubMed
Rakez ManelGuillaumin JulienChick AurelienCoureau GaelleChamming's FoucauldFillard PierreAmadeo BriceRondeau Virginie - Seamless phase II/III design aims to integrate a phase II trial for treatment selection and a phase III confirmatory trial. It offers valuable flexibility through mid-trial modifications, potentially optimizing resource utilization and reducing patient burden. In the first stage, multiple experimental treatments are evaluated to select promising ones, which enter the second stage for the confirmatory analysis against the control using data from both stages. Proper statistical methods are needed for the final analysis to control the overall Type I error rate at a prespecified level, regardless of the treatment selection rule used at the interim analysis. In this article, we provide a comprehensive evaluation of four classes of statistical approaches in the literature, which use different ways to integrate combination functions (or conditional error functions) and multiple testing methods (including Bonferroni, Simes, and Dunnett adjustments). Extensive simulation studies are performed to evaluate both the Type I error control and power. In addition, we illustrate the practical implementation of these approaches in real clinical trial settings. - Source: PubMed
Liu JialuoWang LuluXi Dong