TIMolol Nasal Spray as a Treatment for Epistaxis in Hereditary Hemorrhagic Telangiectasia (TIM-HHT)—A Prospective, Randomized, Double-Blind, Controlled, Cross-Over Trial

To date, there is no approved local therapeutic agent for the treatment of epistaxis due to hereditary hemorrhagic telangiectasia (HHT). Several case reports suggest the topical use of timolol. This monocentric, prospective, randomized, placebo-controlled, double-blinded, cross-over study investigated whether the effectiveness of the standard treatment with a pulsed diode laser can be increased by also using timolol nasal spray. The primary outcome was severity of epistaxis after three months, while the main secondary outcome was severity of epistaxis and subjective satisfaction after one month. Twenty patients were allocated and treated, of which 18 patients completed both 3-month treatment sequences. Timolol was well tolerated by all patients. Epistaxis Severity Score after three months, the primary outcome measure, showed a beneficial, but statistically nonsignificant (p = 0.084), effect of additional timolol application. Epistaxis Severity Score (p = 0.010) and patients’ satisfaction with their nosebleeds after one month (p = 0.050) showed statistically significant benefits. This placebo-controlled, randomized trial provides some evidence that timolol nasal spray positively impacts epistaxis severity and subjective satisfaction in HHT patients when additively applied to standard laser therapy after one month. However, the effect of timolol was observed to diminish over time. Trials with larger sample sizes are warranted to confirm these findings

The adipo‐fibrokine activin A is associated with metabolic abnormalities and left ventricular diastolic dysfunction in obese patients

Aims Left ventricular diastolic dysfunction (LVDD) is common in obese subjects, and a relationship between epicardial adipose tissue (EAT), increased adipocytokines, and cardiovascular diseases has been reported. This study sought to examine as to whether the adipo-fibrokine activin A is a link between increased EAT, the metabolic syndrome (MetS), and LVDD in severely obese subjects. Methods and results In 236 obese subjects (empty set body mass index 39.8 +/- 7.9 kg/m(2) ) with a variable degree of the MetS and in 60 healthy non-obese controls (empty set body mass index 24.8 +/- 3.4 kg/m(2)), serum activin A levels were measured and correlated with parameters of the MetS, epicardial fat thickness (EFT), and echocardiographic parameters of LVDD. Activin A levels were higher in obese than in non-obese subjects (362 +/- 124 vs. 301 +/- 94 pg/mL, P = 0.0004), increased with the number of MetS components (from 285 +/- 82 with no MetS component, 323 +/- 94 with one or two MetS components, to 403 +/- 131 pg/ml with >= 3 MetS components, P < 0.0001) and correlated with EFT (r = 0.41, P < 0.001). Furthermore, activin A levels were related to several parameters of LVDD [e.g. left atrial size (382 +/- 117 vs. 352 125 pg/ml, P = 0.024), E/e' (394 +/- 108 vs. 356 +/- 127 pg/mL, P = 0.005)]. LVDD was highest in MetS obese subjects with high EFT (44.3%) compared with MetS obese subjects with low EFT (27.0%), non-MetS obese subjects with high EFT (24.2%), and non-MetS obese subjects with low EFT (10.6%, P < 0.0001). Conclusions In severe obesity, activin A was significantly related to EFT, MetS, and LVDD, implicating MetS-related alterations in the secretory profile of EAT in the pathogenesis of obesity-related heart disease

“At ‘Amen Meals’ It’s Me and God” Religion and Gender: A New Jewish Women’s Ritual

New ritual practices performed by Jewish women can serve as test cases for an examination of the phenomenon of the creation of religious rituals by women. These food-related rituals, which have been termed ‘‘amen meals’’ were developed in Israel beginning in the year 2000 and subsequently spread to Jewish women in Europe and the United States. This study employs a qualitative-ethnographic methodology grounded in participant-observation and in-depth interviews to describe these nonobligatory, extra-halakhic rituals. What makes these rituals stand out is the women’s sense that through these rituals they experience a direct con- nection to God and, thus, can change reality, i.e., bring about jobs, marriages, children, health, and salvation for friends and loved ones. The ‘‘amen’’ rituals also create an open, inclusive woman’s space imbued with strong spiritual–emotional energies that counter the women’s religious marginality. Finally, the purposes and functions of these rituals, including identity building and displays of cultural capital, are considered within a theoretical framework that views ‘‘doing gender’’ and ‘‘doing religion’’ as an integrated experience

Contemporary Challenges and Solutions

CA18131 CP16/00163 NIS-3317 NIS-3318 decision 295741 C18/BM/12585940The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 “ML4Microbiome” that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.publishersversionpublishe

Identification of Functional Networks of Estrogen- and c-Myc-Responsive Genes and Their Relationship to Response to Tamoxifen Therapy in Breast Cancer

BACKGROUND: Estrogen is a pivotal regulator of cell proliferation in the normal breast and breast cancer. Endocrine therapies targeting the estrogen receptor are effective in breast cancer, but their success is limited by intrinsic and acquired resistance. METHODOLOGY/PRINCIPAL FINDINGS: With the goal of gaining mechanistic insights into estrogen action and endocrine resistance, we classified estrogen-regulated genes by function, and determined the relationship between functionally-related genesets and the response to tamoxifen in breast cancer patients. Estrogen-responsive genes were identified by transcript profiling of MCF-7 breast cancer cells. Pathway analysis based on functional annotation of these estrogen-regulated genes identified gene signatures with known or predicted roles in cell cycle control, cell growth (i.e. ribosome biogenesis and protein synthesis), cell death/survival signaling and transcriptional regulation. Since inducible expression of c-Myc in antiestrogen-arrested cells can recapitulate many of the effects of estrogen on molecular endpoints related to cell cycle progression, the estrogen-regulated genes that were also targets of c-Myc were identified using cells inducibly expressing c-Myc. Selected genes classified as estrogen and c-Myc targets displayed similar levels of regulation by estrogen and c-Myc and were not estrogen-regulated in the presence of siMyc. Genes regulated by c-Myc accounted for 50% of all acutely estrogen-regulated genes but comprised 85% (110/129 genes) in the cell growth signature. siRNA-mediated inhibition of c-Myc induction impaired estrogen regulation of ribosome biogenesis and protein synthesis, consistent with the prediction that estrogen regulates cell growth principally via c-Myc. The 'cell cycle', 'cell growth' and 'cell death' gene signatures each identified patients with an attenuated response in a cohort of 246 tamoxifen-treated patients. In multivariate analysis the cell death signature was predictive independent of the cell cycle and cell growth signatures. CONCLUSIONS/SIGNIFICANCE: These functionally-based gene signatures can stratify patients treated with tamoxifen into groups with differing outcome, and potentially identify distinct mechanisms of tamoxifen resistance

DNA mismatch repair gene MSH6 implicated in determining age at natural menopause

notes: PMCID: PMC3976329This is a freely-available open access publication. Please cite the published version which is available via the DOI link in this record.The length of female reproductive lifespan is associated with multiple adverse outcomes, including breast cancer, cardiovascular disease and infertility. The biological processes that govern the timing of the beginning and end of reproductive life are not well understood. Genetic variants are known to contribute to ∼50% of the variation in both age at menarche and menopause, but to date the known genes explain <15% of the genetic component. We have used genome-wide association in a bivariate meta-analysis of both traits to identify genes involved in determining reproductive lifespan. We observed significant genetic correlation between the two traits using genome-wide complex trait analysis. However, we found no robust statistical evidence for individual variants with an effect on both traits. A novel association with age at menopause was detected for a variant rs1800932 in the mismatch repair gene MSH6 (P = 1.9 × 10(-9)), which was also associated with altered expression levels of MSH6 mRNA in multiple tissues. This study contributes to the growing evidence that DNA repair processes play a key role in ovarian ageing and could be an important therapeutic target for infertility.UK Medical Research CouncilWellcome Trus

Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk.

Blood pressure is a heritable trait influenced by several biological pathways and responsive to environmental stimuli. Over one billion people worldwide have hypertension (≥140 mm Hg systolic blood pressure or  ≥90 mm Hg diastolic blood pressure). Even small increments in blood pressure are associated with an increased risk of cardiovascular events. This genome-wide association study of systolic and diastolic blood pressure, which used a multi-stage design in 200,000 individuals of European descent, identified sixteen novel loci: six of these loci contain genes previously known or suspected to regulate blood pressure (GUCY1A3-GUCY1B3, NPR3-C5orf23, ADM, FURIN-FES, GOSR2, GNAS-EDN3); the other ten provide new clues to blood pressure physiology. A genetic risk score based on 29 genome-wide significant variants was associated with hypertension, left ventricular wall thickness, stroke and coronary artery disease, but not kidney disease or kidney function. We also observed associations with blood pressure in East Asian, South Asian and African ancestry individuals. Our findings provide new insights into the genetics and biology of blood pressure, and suggest potential novel therapeutic pathways for cardiovascular disease prevention

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies