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Background: Alzheimer's disease is a common debilitating dementia with known heritability, for which 20 late onset susceptibility loci have been identified, but more remain to be discovered. This study sought to identify new susceptibility genes, using an alternative gene-wide analytical approach which tests for patterns of association within genes, in the powerful genome-wide association dataset of the International Genomics of Alzheimer's Project Consortium, comprising over 7 m genotypes from 25,580 Alzheimer's cases and 48,466 controls.
Principal findings: In addition to earlier reported genes, we detected genome-wide significant loci on chromosomes 8 (TP53INP1, p = 1.4×10−6) and 14 (IGHV1-67 p = 7.9×10−8) which indexed novel susceptibility loci.
Significance: The additional genes identified in this study, have an array of functions previously implicated in Alzheimer's disease, including aspects of energy metabolism, protein degradation and the immune system and add further weight to these pathways as potential therapeutic targets in Alzheimer's disease.
The KASCADE-Grande experiment has significantly contributed to the current knowledge about the energy spectrum and composition of cosmic rays for energies between the knee and the ankle. Meanwhile, post-LHC versions of the hadronic interaction models are available and used to interpret the entire data set of KASCADE-Grande. In addition, a new, combined analysis of both arrays, KASCADE and Grande, was developed significantly increasing the accuracy of the shower observables. First results of the new analysis with the entire data set of the KASCADE-Grande experiment will be the focus of this contribution.
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.
Background and Aims. Systemic treatment with sorafenib has been the standard of care (SOC) in patients with advanced Barcelona Clinic Liver Cancer (BCLC) stage C hepatocellular carcinoma (HCC) for more than a decade. TACE has been reported to allow better local tumor control in selected patients with BCLC stage C HCC. Methods. A retrospective analysis of patients with BCLC stage C HCC that were treated with sorafenib and TACE was conducted; they were compared to BCLC stage C patients treated either with TACE or sorafenib in the same period of time outside a clinical trial. Results. A total of 201 patients with BCLC stage C were identified, who were treated with either sorafenib and TACE (group A; n = 54), sorafenib (group B; n = 82) or TACE (group C; n = 65). No significant difference in baseline characteristics was observed. Time to progression was 7.0 months (95% CI: 4.3–9.7), 4.1 months (95% CI: 3.6–4.7) and 5.0 months (95% CI: 2.9–7.1) in groups A, B and C, respectively, and overall survival was 16.5 months (95% CI: 15.0–18.1), 8.4 months (95% CI: 6.0–10.8) and 10.5 months (95% CI: 7.5–13.6), respectively (group A vs. group B: p < 0.001; group A vs. group C: p = 0.0023). Adverse events of grade 3/4 occurred in 34% of patients in group A. Conclusions. Although sorafenib is a SOC in patients with BCLC stage C HCC, TACE is frequently used as an additional locoregional treatment in selected patients. This combined approach resulted in a significant overall survival benefit in selected patients, although randomized trials have not yet proven this benefit.