Treatment Options for Seasonal Affective Disorder

Many patients who have undiagnosed Seasonal Affective Disorder (SAD) may come into the pharmacy to try to self-treat their symptoms with over-the-counter and herbal drugs. Often, patients don\u27t recognize their symptoms as a true depressive disorder since they are not constant. The pharmacist has the opportunity to talk to these patients, educate them on the disease state and explain that they do have options, both pharmacologic and non-pharmacologic. It also is important for pharmacists to point out any interactions that the herbal or over-the-counter medications may have with other medications and to refer patients to their physician for further treatment. Currently, the Diagnostic and Statistical Manual for Mental Disorders (DSM) IV does not recognize SAD as a separate disorder but rather a specifier of Major Depressive Disorder (MDD). However, there are currently recommendations to include SAD as a distinct disorder in DSM V, which is to be released in May 2013

Fecal Contamination of Drinking Water Was Associated with Diarrheal Pathogen Carriage among Children Younger than 5 Years in Three Peruvian Rural Communities.

Drinking water contamination is a frequent problem in developing countries and could be associated with bacterial pathogen carriage in feces. We evaluated the association between the risk of drinking water and bacterial carrier status in children younger than 5 years in a cross-sectional study conducted in 199 households from three Peruvian rural communities. Fecal samples from children were screened for pathogenic Aeromonas, Campylobacter, and Vibrio species, as well as for Enterobacteriaceae, including pathogenic Escherichia coli. The drinking water risk was determined using E. coli as an indicator of contamination. Nineteen (9.5%) children were colonized with pathogens and classified as carriers, all without diarrhea symptoms. Of 199 drinking water samples, 38 (19.1%) were classified as very high risk because of high fecal contamination (> 100 E. coli/100 mL). Shared-use water sources, daily washing of containers, and washing using only water were associated with higher prevalence of bacterial carriage, whereas there was no association between households reporting boiling and chlorination of water and carrier status. The prevalence of carriage in children exposed to very high-risk water was 2.82 (95% CI: 1.21-6.59) times the prevalence of those who consumed less contaminated water, adjusted by the water source and daily washing. Our results suggest that household drinking water plays an important role in the generation of carriers with diarrheal pathogens. Our findings also highlight the importance of interventions to ensure the safety of drinking water. Further studies are needed to validate the observed association and determine its significance with respect to diarrhea in the community

Deep Brain Stimulation Targeting the Fornix for Mild Alzheimer Dementia: Design of the ADvance Randomized Controlled Trial

Background: There are currently few available treatments and no cure for Alzheimer disease (AD), a growing public health burden. Animal models and an open-label human trial have indicated that deep brain stimulation (DBS) of memory circuits may improve symptoms and possibly slow disease progression. The ADvance trial was designed to examine DBS of the fornix as a treatment for mild AD. Methods: ADvance is a randomized, double-blind, placebo-controlled, delayed-start, multicenter clinical trial conducted at six sites in the US and one site in Canada. Eighty-five subjects initially consented to be screened for the trial. Of these, 42 subjects who met inclusion and exclusion criteria were implanted with DBS leads anterior to the columns of the fornix bilaterally. They were randomized 1:1 to DBS off or DBS on groups for the initial 12 months of follow-up. After 1 year, all subjects will have their devices turned on for the remainder of the study. Postimplantation, subjects will return for 13 follow-up visits over 48 months for cognitive and psychiatric assessments, brain imaging (up to 12 months), and safety monitoring. The primary outcome measures include Alzheimer\u27s Disease Assessment Scale -- cognitive component (ADAS-cog-13), Clinical Dementia Rating sum of boxes (CDR-SB), and cerebral glucose metabolism measured with positron emission tomography. This report details the study methods, baseline subject characteristics of screened and implanted participants, and screen-to-baseline test€“retest reliability of the cognitive outcomes. Results: Implanted subjects had a mean age of 68.2 years, were mostly male (55%), and had baseline mean ADAS-cog-13 and CDR-SB scores of 28.9 (SD, 5.2) and 3.9 (SD, 1.6), respectively. There were no significant differences between screened and implanted or nonimplanted subjects on most demographic or clinical assessments. Implanted subjects had significantly lower (better) ADAS-cog-11 (17.5 vs 21.1) scores, but did not differ on CDR-SB. Scores on the major outcome measures for the trial were consistent at screening and baseline. Conclusion: ADvance was successful in enrolling a substantial group of patients for this novel application of DBS, and the study design is strengthened by rigorous subject selection from seven sites, a double-blind placebo-controlled design, and extensive open-label follow-up

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks