Women using a web‐based digital health coaching programme for stress management: stress sources, symptoms and soping strategies

Researchers have proposed and tested many theories to understand gender differences in stress experiences. However, little research has identified differences between subgroups of women in terms of stress sources, symptoms, coping strategies and help‐seeking behaviour. The purpose of this study was to examine these characteristics of women seeking help for stress management through a digital health coaching programme. We examined cross‐sectional data from 63,690 women between the ages of 18 and 59 years who participated in the stress management programme from 2001 to 2008. We divided the sample into age groups to identify developmental patterns in their stress characteristics. Work , time demands and psychological reactions to stress were consistent concerns, whereas between‐group comparisons indicated diverse stress characteristics by age group. Importantly, women at all ages reported being uncomfortable asking for help. The findings suggest that technology‐based solutions like digital health coaching may reach women who may not otherwise seek or receive help for stress management. The results also emphasize the importance of considering the unique characteristics of women when providing them stress management interventions. Copyright © 2011 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87059/1/smi1389.pd

Terminal Complement Inhibitor Eculizumab in Atypical Hemolytic-Uremic Syndrome

Background Atypical hemolytic-uremic syndrome is a genetic, life-threatening, chronic disease of complement-mediated thrombotic microangiopathy. Plasma exchange or infusion may transiently maintain normal levels of hematologic measures but does not treat the underlying systemic disease. Methods We conducted two prospective phase 2 trials in which patients with atypical hemolytic-uremic syndrome who were 12 years of age or older received eculizumab for 26 weeks and during long-term extension phases. Patients with low platelet counts and renal damage (in trial 1) and those with renal damage but no decrease in the platelet count of more than 25% for at least 8 weeks during plasma exchange or infusion (in trial 2) were recruited. The primary end points included a change in the platelet count (in trial 1) and thrombotic microangiopathy event-free status (no decrease in the platelet count of >25%, no plasma exchange or infusion, and no initiation of dialysis) (in trial 2). Results A total of 37 patients (17 in trial 1 and 20 in trial 2) received eculizumab for a median of 64 and 62 weeks, respectively. Eculizumab resulted in increases in the platelet count; in trial 1, the mean increase in the count from baseline to week 26 was 73x10(9) per liter (P<0.001). In trial 2, 80% of the patients had thrombotic microangiopathy event-free status. Eculizumab was associated with significant improvement in all secondary end points, with continuous, time-dependent increases in the estimated glomerular filtration rate (GFR). In trial 1, dialysis was discontinued in 4 of 5 patients. Earlier intervention with eculizumab was associated with significantly greater improvement in the estimated GFR. Eculizumab was also associated with improvement in health-related quality of life. No cumulative toxicity of therapy or serious infection-related adverse events, including meningococcal infections, were observed through the extension period. Conclusions Eculizumab inhibited complement-mediated thrombotic microangiopathy and was associated with significant time-dependent improvement in renal function in patients with atypical hemolytic-uremic syndrome

Additional file 10 of Molecular and spatial heterogeneity of microglia in Rasmussen encephalitis

Additional file 10. CD45+ ROI segment protein expression. Heatmap showing protein expression of 42 protein targets in each CD45+ ROI segment for each sample

Additional file 11 of Molecular and spatial heterogeneity of microglia in Rasmussen encephalitis

Additional file 11. DEP gene expression in microglia snRNA-Seq. a Volcano plot of significant abundantly expressed proteins detected in microglial nodules versus unaggregated microglia. Red shape and labels highlight DEPs selected for single-nucleus microglia analysis. b tSNE density plots displaying gene expression density in microglia of markers selected in a. c tSNE density plot showing the expression density in microglia where all selected markers are expressed

Additional file 3 of Molecular and spatial heterogeneity of microglia in Rasmussen encephalitis

Additional file 3. Microglial markers and DEGs in microglial Louvain clusters. a tSNE plot of microglia showing all eleven Louvain clusters. b Violin plots of top five most abundant differentially expressed genes in each microglial cluster, if applicable. c Violin plots illustrating the per-cluster expression of microglia-specific and homeostatic microglial markers

Additional file 5 of Molecular and spatial heterogeneity of microglia in Rasmussen encephalitis

Additional file 5. Immune modulation and inflammation marker expression differs by disease condition. Ridge plots showing the expression of immune and inflammation markers highlighted in Figure 3, separated by disease condition with accompanying tSNE of microglia for reference

Additional file 2 of Molecular and spatial heterogeneity of microglia in Rasmussen encephalitis

Additional file 2. Overlap of DEGs in RE relative to FCD and CTRL. a,b Venn diagrams showing the number of overlapping genes expressed differentially high (a) and differentially low (b) in RE vs CTRL and FCD. c Four-way plot showing FCD expression relative to RE vs CTRL expression relative to RE for each gene

Additional file 9 of Molecular and spatial heterogeneity of microglia in Rasmussen encephalitis

Additional file 9. IBA1+ ROI segment protein expression. Heatmap showing protein expression of 42 protein targets in each IBA1+ ROI segment for each sample

Additional file 6 of Molecular and spatial heterogeneity of microglia in Rasmussen encephalitis

Additional file 6. Pseudotime trajectory analysis. a UMAP plot of microglia showing all eleven Louvain clusters. b UMAP density plot showing the density of TMEM119 expression. Red star indicates root node for cell ordering. c UMAP plot showing pseudotime trajectories, where cells are colored by pseudotime score. Red star indicates root node for cell ordering. d Ridge plot displaying the pseudotime score of cells in each microglia cluster