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Background and aims: Patients with gastric cancer often show signs of malnutrition. We sought to evaluate the influence of sarcopenia in patients with locally advanced, not metastasized, gastric or gastro-esophageal junction (GEJ) cancer undergoing curative treatment (perioperative chemotherapy and surgery) on morbidity and mortality in order to identify patients in need for nutritional intervention.
Patients and methods: Two-centre study, conducted in the Frankfurt University Clinic and Krankenhaus Nordwest (Frankfurt) as part of the University Cancer Center Frankfurt (UCT). 47/83 patients were treated in the FLOT trial (NCT01216644). Patients´ charts were reviewed for clinical data. Two consecutive CT scans were retrospectively analyzed to determine the degree of sarcopenia. Survival was calculated using the Kaplan-Meier method, multivariate analysis was performed using the Cox regression.
Results: 60 patients (72.3%) were male and 23 (27.7%) female. 45 patients (54.2%) had GEJ type 1–3 and 38 (45.8%) gastric tumors, respectively. Sarcopenic patients were significantly older than non-sarcopenic patients (mean age 65.1 years vs. 59.5 years, p = 0.042), terminated the chemotherapy significantly earlier (50% vs. 22.6%, p = 0.037) and showed higher Clavien-Dindo scores, indicating more severe perioperative complications (score ≥3 43.3 vs. 17.0%, p = 0.019). Sarcopenic patients had a significantly shorter survival than non-sarcopenic patients (139.6 ± 19.5 [95% CI, 101.3–177.9] vs. 206.7 ± 13.8 [95% CI, 179.5–233.8] weeks, p = 0.004). Multivariate Cox regression analysis showed that, besides UICC stage, sarcopenia significantly influenced survival.
Conclusion: Sarcopenia is present in a large proportion of patients with locally advanced gastric or GEJ cancer and significantly influences tolerability of chemotherapy, surgical complications and survival.
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.