Self-reported cognitive function in a large international cohort of people with multiple sclerosis: associations with lifestyle and other factors

Background and Purpose: We aimed to estimate the prevalence of perceived cognitive impairment (PCI) and explore its associations with lifestyle and disease characteristics in a large international cohort of people with multiple sclerosis (MS).Methods: This study was a cross-sectional analysis. Participants rated their cognitive function over the preceding 4 weeks using four questions in a subscale within the Multiple Sclerosis Quality of Life questionnaire (MSQOL-54). These questions assessed perceived concentration, attention and memory by the patient and family/friends. Four definitions of PCI were derived, ranging from lowest to highest specificity. Associations with PCI were assessed by log-binomial regression.Results: The prevalence of PCI in our sample ranged from 41.0% (95% confidence interval, 39.0-43.0) using the least-specific definition to 11.6% (95% confidence interval, 10.3-12.9) using the most specific definition. A number of factors were associated with PCI, increasing in magnitude as the definition specificity increased, including positive associations for smoking and body mass index, whereas physical activity, dietary quality and use of vitamin D/omega-3 supplements were inversely associated with PCI.Conclusions: Our study reports associations between healthy lifestyle behaviours and PCI in people with MS. Although reverse causality is a potential explanation for our findings, previous studies have shown comparable associations with healthy lifestyle and MS onset and progression. Subject to external validation, these results suggest benefits realized from a healthy lifestyle in people with MS

Proliferating Cell Nuclear Antigen (PCNA) Regulates Primordial Follicle Assembly by Promoting Apoptosis of Oocytes in Fetal and Neonatal Mouse Ovaries

Primordial follicles, providing all the oocytes available to a female throughout her reproductive life, assemble in perinatal ovaries with individual oocytes surrounded by granulosa cells. In mammals including the mouse, most oocytes die by apoptosis during primordial follicle assembly, but factors that regulate oocyte death remain largely unknown. Proliferating cell nuclear antigen (PCNA), a key regulator in many essential cellular processes, was shown to be differentially expressed during these processes in mouse ovaries using 2D-PAGE and MALDI-TOF/TOF methodology. A V-shaped expression pattern of PCNA in both oocytes and somatic cells was observed during the development of fetal and neonatal mouse ovaries, decreasing from 13.5 to 18.5 dpc and increasing from 18.5 dpc to 5 dpp. This was closely correlated with the meiotic prophase I progression from pre-leptotene to pachytene and from pachytene to diplotene when primordial follicles started to assemble. Inhibition of the increase of PCNA expression by RNA interference in cultured 18.5 dpc mouse ovaries strikingly reduced the apoptosis of oocytes, accompanied by down-regulation of known pro-apoptotic genes, e.g. Bax, caspase-3, and TNFα and TNFR2, and up-regulation of Bcl-2, a known anti-apoptotic gene. Moreover, reduced expression of PCNA was observed to significantly increase primordial follicle assembly, but these primordial follicles contained fewer guanulosa cells. Similar results were obtained after down-regulation by RNA interference of Ing1b, a PCNA-binding protein in the UV-induced apoptosis regulation. Thus, our results demonstrate that PCNA regulates primordial follicle assembly by promoting apoptosis of oocytes in fetal and neonatal mouse ovaries

NormalizeMets: assessing, selecting and implementing statistical methods for normalizing metabolomics data

INTRODUCTION: In metabolomics studies, unwanted variation inevitably arises from various sources. Normalization, that is the removal of unwanted variation, is an essential step in the statistical analysis of metabolomics data. However, metabolomics normalization is often considered an imprecise science due to the diverse sources of variation and the availability of a number of alternative strategies that may be implemented. OBJECTIVES: We highlight the need for comparative evaluation of different normalization methods and present software strategies to help ease this task for both data-oriented and biological researchers. METHODS: We present NormalizeMets-a joint graphical user interface within the familiar Microsoft Excel and freely-available R software for comparative evaluation of different normalization methods. The NormalizeMets R package along with the vignette describing the workflow can be downloaded from https://cran.r-project.org/web/packages/NormalizeMets/ . The Excel Interface and the Excel user guide are available on https://metabolomicstats.github.io/ExNormalizeMets . RESULTS: NormalizeMets allows for comparative evaluation of normalization methods using criteria that depend on the given dataset and the ultimate research question. Hence it guides researchers to assess, select and implement a suitable normalization method using either the familiar Microsoft Excel and/or freely-available R software. In addition, the package can be used for visualisation of metabolomics data using interactive graphical displays and to obtain end statistical results for clustering, classification, biomarker identification adjusting for confounding variables, and correlation analysis. CONCLUSION: NormalizeMets is designed for comparative evaluation of normalization methods, and can also be used to obtain end statistical results. The use of freely-available R software offers an attractive proposition for programming-oriented researchers, and the Excel interface offers a familiar alternative to most biological researchers. The package handles the data locally in the user's own computer allowing for reproducible code to be stored locally