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Objective: Prognostic value of health-related quality of life (HRQoL) data may be important to inform patients in clinical practice and to guide clinical decision-making. Our study investigated the added prognostic value of HRQoL for overall survival (OS) and progression-free survival (PFS) in a large heterogeneous sample of glioma patients, besides known prognostic factors., Methods: We included individual baseline data from previously published randomised controlled trials (RCTs) in glioma patients in which HRQoL was assessed through the European Organisation for Research and Treatment of Cancer QLQ-C30 and QLQ-BN20 questionnaires. Multivariable Cox regression models (stratified for newly diagnosed versus recurrent disease) were constructed, first with clinical variables (age, sex, tumour type, performance status, allocated treatment and extent of resection) only and subsequently with HRQoL variables added, separately for OS and PFS. The added prognostic value of HRQoL was calculated using C-indices., Results: Baseline HRQoL and clinical data from 15 RCTs were included, comprising 5217 patients. In the model including both clinical and HRQoL variables, better cognitive and role functioning and less motor dysfunction were independently associated with longer OS, whereas better role and cognitive functioning, less nausea and vomiting and more appetite loss were independently associated with prolonged PFS. However, C-indices indicated only a small prognostic improvement of the models for OS and PFS when adding HRQoL to the clinical prognostic variables (+1.1% for OS and +.7% for PFS)., Conclusion: Our findings demonstrate that several baseline HRQoL variables are independently prognostic for OS and PFS, yet the added value of HRQoL to the known clinical prognostic variables was small., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Cells constantly adapt to environmental fluctuations. These physiological changes require time and therefore cause a lag phase during which the cells do not function optimally. Interestingly, past exposure to an environmental condition can shorten the time needed to adapt when the condition re-occurs, even in daughter cells that never directly encountered the initial condition. Here, we use the molecular toolbox of Saccharomyces cerevisiae to systematically unravel the molecular mechanism underlying such history-dependent behavior in transitions between glucose and maltose. In contrast to previous hypotheses, the behavior does not depend on persistence of proteins involved in metabolism of a specific sugar. Instead, presence of glucose induces a gradual decline in the cells' ability to activate respiration, which is needed to metabolize alternative carbon sources. These results reveal how trans-generational transitions in central carbon metabolism generate history-dependent behavior in yeast, and provide a mechanistic framework for similar phenomena in other cell types., Competing Interests: BC, AJ, GP, LV, JP, MC, AN, BG, MR, MD, SG, JH, EG, MC, PB, SV, PS No competing interests declared, (© 2018, Cerulus et al.)

One of the future issues in the forensic application of the haploid Y-chromosome (Y-chr) is surveying the distribution of the Y-chr variation on a micro-geographical scale. Studies on such a scale require observing Y-chr variation on a high resolution, high sampling efforts and reliable genealogical data of all DNA-donors. In the current study we optimised this framework by surveying the micro-geographical distribution of the Y-chr variation in the central-western European region named Brabant. The Duchy of Brabant was a historical region in the Low Countries containing three contemporary Belgian provinces and one Dutch province (Noord-Brabant). 477 males from five a priori defined regions within Brabant were selected based on their genealogical ancestry (known pedigree at least before 1800). The Y-haplotypes were determined based on 37 Y-STR loci and the finest possible level of substructuring was defined according to the latest published Y-chr phylogenetic tree. In total, eight Y-haplogroups and 32 different subhaplogroups were observed, whereby 70% of all participants belonged to only four subhaplogroups: R1b1b2a1 (R-U106), R1b1b2a2* (R-P312*), R1b1b2a2g (R-U152) and I1* (I-M253*). Significant micro-geographical differentiation within Brabant was detected between the Dutch (Noord-Brabant) vs. the Flemish regions based on the differences in (sub)haplogroup frequencies but not based on Y-STR variation within the main subhaplogroups. A clear gradient was found with higher frequencies of R1b1b2 (R-M269) chromosomes in the northern vs. southern regions, mainly related to a trend in the frequency of R1b1b2a1 (R-U106)., (Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.)