Longitudinal Results With Intratympanic Dexamethasone in the Treatment of Ménière’s Disease

To assess patient satisfaction with vertigo control using intratympanic (IT) dexamethasone (12 mg/mL) for medically refractory unilateral Meniere's disease. STUDY DESIGN: Retrospective study. SETTING: Tertiary referral neurotology clinic. PATIENTS: One hundred twenty-nine subjects diagnosed with unilateral Meniere's disease still having vertigo despite medical therapy. INTERVENTION: IT dexamethasone injections as needed to control vertigo attacks. MAIN OUTCOME MEASURE: A Kaplan-Meier time-to-event method was used to determine the rate of "survival," meaning sufficient satisfaction with vertigo control that the subject did not wish to have subsequent ablative treatment. "Failure" was defined as poor control and the choice to proceed to ablative treatment. RESULTS: Acceptable vertigo control ("survival") was achieved in 117 (91%) of 129 subjects. Vertigo control required only one dexamethasone injection in 48 (37%), 2 injections in 26 (20%), 3 injections in 18 (14%), and 4 injections in 10 (8%). More than 4 injections were needed in 15 subjects (21%). Of 12 failures (9%), 9 occurred within 6 months of the first IT dexamethasone injection. Follow-up data for 2 years were available for 96 subjects. Of these, 87 (91%) had vertigo control with IT dexamethasone, of whom 61 (70)% required no further injections after 2 years, 23 (26%) continued to receive IT dexamethasone injections, and 3 (3%) chose IT gentamicin treatment. CONCLUSION: IT dexamethasone injection therapy on an as-needed outpatient basis can provide vertigo control that is satisfactory in patients with Meniere's disease. The Kaplan-Meier method addresses the need for an outcome measure suited to repeated treatments and variable lengths of follow-up. However, due to the retrospective nature of this study, the presence of bias caused by loss of subjects from follow-up cannot be ruled out

Perspective: Advancing the research agenda for improving understanding of cyanobacteria in a future of global change

Harmful cyanobacterial blooms (=cyanoHABs) are an increasing feature of many waterbodies throughout the world. Many bloom-forming species produce toxins, making them of particular concern for drinking water supplies, recreation and fisheries in waterbodies along the freshwater to marine continuum. Global changes resulting from human impacts, such as climate change, over-enrichment and hydrological alterations of waterways, are major drivers of cyanoHAB proliferation and persistence. This review advocates that to better predict and manage cyanoHABs in a changing world, researchers need to leverage studies undertaken to date, but adopt a more complex and definitive suite of experiments, observations, and models which can effectively capture the temporal scales of processes driven by eutrophication and a changing climate. Better integration of laboratory culture and field experiments, as well as whole system and multiple-system studies are needed to improve confidence in models predicting impacts of climate change and anthropogenic over-enrichment and hydrological modifications. Recent studies examining adaptation of species and strains to long-term perturbations, e.g. temperature and carbon dioxide (CO2) levels, as well as incorporating multi-species and multi-stressor approaches emphasize the limitations of approaches focused on single stressors and individual species. There are also emerging species of concern, such as toxic benthic cyanobacteria, for which the effects of global change are less well understood, and require more detailed study. This review provides approaches and examples of studies tackling the challenging issue of understanding how global changes will affect cyanoHABs, and identifies critical information needs for effective prediction and management

Predicting the resilience and recovery of aquatic systems: A framework for model evolution within environmental observatories

Maintaining the health of aquatic systems is an essential component of sustainable catchment management, however, degradation of water quality and aquatic habitat continues to challenge scientists and policy-makers. To support management and restoration efforts aquatic system models are required that are able to capture the often complex trajectories that these systems display in response to multiple stressors. This paper explores the abilities and limitations of current model approaches in meeting this challenge, and outlines a strategy based on integration of flexible model libraries and data from observation networks, within a learning framework, as a means to improve the accuracy and scope of model predictions. The framework is comprised of a data assimilation component that utilizes diverse data streams from sensor networks, and a second component whereby model structural evolution can occur once the model is assessed against theoretically relevant metrics of system function. Given the scale and transdisciplinary nature of the prediction challenge, network science initiatives are identified as a means to develop and integrate diverse model libraries and workflows, and to obtain consensus on diagnostic approaches to model assessment that can guide model adaptation. We outline how such a framework can help us explore the theory of how aquatic systems respond to change by bridging bottom-up and top-down lines of enquiry, and, in doing so, also advance the role of prediction in aquatic ecosystem management

Endocrine Therapy Nonadherence and Discontinuation in Black and White Women

Background: Differential use of endocrine therapy (ET) by race may contribute to breast cancer outcome disparities, but racial differences in ET behaviors are poorly understood. Methods: Women aged 20-74 years with a first primary, stage I-III, hormone receptor-positive (HR+) breast cancer were included. At 2 years postdiagnosis, we assessed nonadherence, defined as not taking ET every day or missing more than two pills in the past 14 days, discontinuation, and a composite measure of underuse, defined as either missing pills or discontinuing completely. Using logistic regression, we evaluated the relationship between race and nonadherence, discontinuation, and overall underuse in unadjusted, clinically adjusted, and socioeconomically adjusted models. Results: A total of 1280 women were included; 43.2% self-identified as black. Compared to white women, black women more often reported nonadherence (13.7% vs 5.2%) but not discontinuation (10.0% vs 10.7%). Black women also more often reported the following: hot flashes, night sweats, breast sensitivity, and joint pain; believing that their recurrence risk would not change if they stopped ET; forgetting to take ET; and cost-related barriers. In multivariable analysis, black race remained statistically significantly associated with nonadherence after adjusting for clinical characteristics (adjusted odds ratio = 2.72, 95% confidence interval = 1.75 to 4.24) and after adding socioeconomic to clinical characteristics (adjusted odds ratio = 2.44, 95% confidence interval = 1.50 to 3.97) but was not independently associated with discontinuation after adjustment. Low recurrence risk perception and lack of a shared decision making were strongly predictive of ET underuse across races. Conclusions: Our results highlight important racial differences in ET-Adherence behaviors, perceptions of benefits/harms, and shared decision making that may be targeted with culturally tailored interventions

Measurement of the Negative Muon Anomalous Magnetic Moment to 0.7 ppm

The anomalous magnetic moment of the negative muon has been measured to a precision of 0.7 parts per million (ppm) at the Brookhaven Alternating Gradient Synchrotron. This result is based on data collected in 2001, and is over an order of magnitude more precise than the previous measurement of the negative muon. The result a_mu= 11 659 214(8)(3) \times 10^{-10} (0.7 ppm), where the first uncertainty is statistical and the second is sytematic, is consistend with previous measurements of the anomaly for the positive and negative muon. The average for the muon anomaly a_{mu}(exp) = 11 659 208(6) \times 10^{-10} (0.5ppm).Comment: 4 pages, 4 figures, submitted to Physical Review Letters, revised to reflect referee comments. Text further revised to reflect additional referee comments and a corrected Fig. 3 replaces the older versio

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure

Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies

Driver Fusions and Their Implications in the Development and Treatment of Human Cancers.

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy