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The translation inhibitor and tumor suppressor Pdcd4 was reported to be lost in various tumors and put forward as prognostic marker in tumorigenesis. Decreased Pdcd4 protein stability due to PI3K-mTOR-p70S6K1 dependent phosphorylation of Pdcd4 followed by β-TrCP1-mediated ubiquitination, and proteasomal destruction of the protein was characterized as a major mechanism contributing to the loss of Pdcd4 expression in tumors. In an attempt to identify stabilizers of Pdcd4, we used a luciferase-based high-throughput compatible cellular assay to monitor phosphorylation-dependent proteasomal degradation of Pdcd4 in response to mitogen stimulation. Following a screen of approximately 2000 compounds, we identified 1,2-bis(4-chlorophenyl)disulfide as a novel Pdcd4 stabilizer. To determine an initial structure-activity relationship, we used 3 additional compounds, synthesized according to previous reports, and 2 commercially available compounds for further testing, in which either the linker between the aryls was modified (compounds 2–4) or the chlorine residues were replaced by groups with different electronic properties (compounds 5 and 6). We observed that those compounds with alterations in the sulfide linker completely lost the Pdcd4 stabilizing potential. In contrast, modifications in the chlorine residues showed only minor effects on the Pdcd4 stabilizing activity. A reporter with a mutated phospho-degron verified the specificity of the compounds for stabilizing the Pdcd4 reporter. Interestingly, the active diaryl disulfides inhibited proliferation and viability at concentrations where they stabilized Pdcd4, suggesting that Pdcd4 stabilization might contribute to the anti-proliferative properties. Finally, computational modelling indicated that the flexibility of the disulfide linker might be necessary to exert the biological functions of the compounds, as the inactive compound appeared to be energetically more restricted.
Humanized mouse models have become increasingly valuable tools to study human hematopoiesis and infectious diseases. However, human T cell differentiation remains inefficient. We generated mice expressing human interleukin (IL-7), a critical growth and survival factor for T cells, under the control of murine IL-7 regulatory elements. After transfer of human cord blood-derived hematopoietic stem and progenitor cells, transgenic mice on the NSGW41 background, termed NSGW41hIL7, showed elevated and prolonged human cellularity in the thymus while maintaining physiological ratios of thymocyte subsets. As a consequence, numbers of functional human T cells in the periphery were increased without evidence for pathological lymphoproliferation or aberrant expansion of effector or memory-like T cells. We conclude that the novel NSGW41hIL7 strain represents an optimized mouse model for humanization to better understand human T cell differentiation in vivo and to generate a human immune system with a better approximation of human lymphocyte ratios.
umanized mouse models have become increasingly valuable tools to study human hematopoiesis and infectious diseases. However, human T-cell differentiation remains inefficient. We generated mice expressing human interleukin-7 (IL-7), a critical growth and survival factor for T cells, under the control of murine IL-7 regulatory elements. After transfer of human cord blood-derived hematopoietic stem and progenitor cells, transgenic mice on the NSGW41 background, termed NSGW41hIL7, showed elevated and prolonged human cellularity in the thymus while maintaining physiological ratios of thymocyte subsets. As a consequence, numbers of functional human T cells in the periphery were increased without evidence for pathological lymphoproliferation or aberrant expansion of effector or memory-like T cells. We conclude that the novel NSGW41hIL7 strain represents an optimized mouse model for humanization to better understand human T-cell differentiation in vivo and to generate a human immune system with a better approximation of human lymphocyte ratios.
Importance: The entry of artificial intelligence into medicine is pending. Several methods have been used for the predictions of structured neuroimaging data, yet nobody compared them in this context.
Objective: Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging.
Design, setting, and participants: Atlas-based volumetry was performed on multi-centric T1-weighted MRI data from 940 subjects, i.e., 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes.
Interventions: N.A.
Main outcomes and measures: Cohen’s kappa, accuracy, and F1-score to assess model performance.
Results: Overall, the neural network produced both the best performance measures and the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with widespread and rather weak atrophy.
Conclusions and relevance: Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best.
In ethnographic research and analysis, reflexivity is vital to achieving constant coordination between field and concept work. However, it has been conceptualized predominantly as an ethnographer’s individual mental capacity. In this article, we draw on ten years of experience in conducting research together with partners from social psychiatry and mental health care across different research projects. We unfold three modes of achieving reflexivity co-laboratively: contrasting and discussing disciplinary concepts in interdisciplinary working groups and feedback workshops; joint data interpretation and writing; and participating in political agenda setting. Engaging these modes reveals reflexivity as a distributed process able to strengthen the ethnographer’s interpretative authority, and also able to constantly push the conceptual boundaries of the participating disciplines and professions.
