Diffusion of Precision Health Phenomenon into an Undergraduate Nursing Research Course

This 15-week course layout for NURS 4222 (Nursing Research & Evidence-Based Practice), illustrates the adoption of threads from an emerging phenomenon, Precision Health, into an undergraduate Nursing Research course

The Diffusion of Artificial Intelligence/Machine Learning Methods and Data Analytics into an Undergraduate Nursing Research Course

The project is the adoption of principles of Artificial Intelligence (AI)/Machine Learning and Data Analytics into a traditional nursing research (hypothesis testing) course. Necessitated by the recent entrance of “Big Data” use in health care, there is an urgent need to inject an understanding of the AI/ML techniques necessary to extract, organize and derive meaning from this huge amount of evidence in clinical decision-making. In fact, our department recently approved the adoption of Precision Health (PH) (the clinical use of the plethora of highly-individualized data in health & medical decisions) principles into six undergraduate courses, NURS 4222 (Nursing Research & Evidence-Based Practice) being one of them. The conversion to semesters affords a great opportunity to truly transform the course by blending in this additional necessary content that the graduate nurse is expected to apply. Faculty who are preparing to teach new matters related to “precise care” should avail themselves of gray literature searching, professional networking and incubation to replace, or at least supplement, traditional sources and sourcing such as texts and scholarly literature. These sources were applied creatively in this sample module entitled “Big Data & Evidence-Based Practice”. It is anticipated that our students, by extension and through sharing, our faculty, will develop the knowledge, skill and attitude (KSAs) desirable to adapt to this scientific renaissance known as Precision Health

Nursing-Relevant Patient Outcomes and Clinical Processes in Data Science Literature: 2019 Year in Review

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this paper, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (e.g., natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope the studies described in this paper help readers: (a) understand the breadth and depth of data science’s ability to improve clinical processes and patient outcomes that are relevant to nurses and (b) identify gaps in the literature that are in need of exploration

Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer's disease

Neurofilament light chain (NfL) is a promising fluid biomarker of disease progression for various cerebral proteopathies. Here we leverage the unique characteristics of the Dominantly Inherited Alzheimer Network and ultrasensitive immunoassay technology to demonstrate that NfL levels in the cerebrospinal fluid (n = 187) and serum (n = 405) are correlated with one another and are elevated at the presymptomatic stages of familial Alzheimer's disease. Longitudinal, within-person analysis of serum NfL dynamics (n = 196) confirmed this elevation and further revealed that the rate of change of serum NfL could discriminate mutation carriers from non-mutation carriers almost a decade earlier than cross-sectional absolute NfL levels (that is, 16.2 versus 6.8 years before the estimated symptom onset). Serum NfL rate of change peaked in participants converting from the presymptomatic to the symptomatic stage and was associated with cortical thinning assessed by magnetic resonance imaging, but less so with amyloid-β deposition or glucose metabolism (assessed by positron emission tomography). Serum NfL was predictive for both the rate of cortical thinning and cognitive changes assessed by the Mini-Mental State Examination and Logical Memory test. Thus, NfL dynamics in serum predict disease progression and brain neurodegeneration at the early presymptomatic stages of familial Alzheimer's disease, which supports its potential utility as a clinically useful biomarker

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

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

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

Cost-effectiveness of reducing salt intake in the Pacific Islands: protocol for a before and after intervention study

BackgroundThere is broad consensus that diets high in salt are bad for health and that reducing salt intake is a cost-effective strategy for preventing chronic diseases. The World Health Organization has been supporting the development of salt reduction strategies in the Pacific Islands where salt intakes are thought to be high. However, there are no accurate measures of salt intake in these countries. The aims of this project are to establish baseline levels of salt intake in two Pacific Island countries, implement multi-pronged, cross-sectoral salt reduction programs in both, and determine the effects and cost-effectiveness of the intervention strategies.Methods/DesignIntervention effectiveness will be assessed from cross-sectional surveys before and after population-based salt reduction interventions in Fiji and Samoa. Baseline surveys began in July 2012 and follow-up surveys will be completed by July 2015 after a 2-year intervention period.A three-stage stratified cluster random sampling strategy will be used for the population surveys, building on existing government surveys in each country. Data on salt intake, salt levels in foods and sources of dietary salt measured at baseline will be combined with an in-depth qualitative analysis of stakeholder views to develop and implement targeted interventions to reduce salt intake.DiscussionSalt reduction is a global priority and all Member States of the World Health Organization have agreed on a target to reduce salt intake by 30% by 2025, as part of the global action plan to reduce the burden of non-communicable diseases. The study described by this protocol will be the first to provide a robust assessment of salt intake and the impact of salt reduction interventions in the Pacific Islands. As such, it will inform the development of strategies for other Pacific Island countries and comparable low and middle-income settings around the world.<br /

High incidence of Noonan syndrome features including short stature and pulmonic stenosis in patients carrying NF1 missense mutations affecting p.Arg1809: genotype-phenotype correlation

Neurofibromatosis type 1 (NF1) is one of the most frequent genetic disorders, affecting 1:3,000 worldwide. Identification of genotype-phenotype correlations is challenging because of the wide range clinical variability, the progressive nature of the disorder, and extreme diversity of the mutational spectrum. We report 136 individuals with a distinct phenotype carrying one of five different NF1 missense mutations affecting p.Arg1809. Patients presented with multiple cafe-au-lait macules (CALM) with or without freckling and Lisch nodules, but no externally visible plexiform neurofibromas or clear cutaneous neurofibromas were found. About 25% of the individuals had Noonan-like features. Pulmonic stenosis and short stature were significantly more prevalent compared with classic cohorts (P<0.0001). Developmental delays and/or learning disabilities were reported in over 50% of patients. Melanocytes cultured from a CALM in a segmental NF1-patient showed two different somatic NF1 mutations, p.Arg1809Cys and a multi-exon deletion, providing genetic evidence that p.Arg1809Cys is a loss-of-function mutation in the melanocytes and causes a pigmentary phenotype. Constitutional missense mutations at p.Arg1809 affect 1.23% of unrelated NF1 probands in the UAB cohort, therefore this specific NF1 genotype-phenotype correlation will affect counseling and management of a significant number of patients