Redefining neuromarketing as an integrated science of influence

Multiple transformative forces target marketing, many of which derive from new technologies that allow us to sample thinking in real time (i.e., brain imaging), or to look at large aggregations of decisions (i.e., big data). There has been an inclination to refer to the intersection of these technologies with the general topic of marketing as “neuromarketing”. There has not been a serious effort to frame neuromarketing, which is the goal of this paper. Neuromarketing can be compared to neuroeconomics, wherein neuroeconomics is generally focused on how individuals make “choices”, and represent distributions of choices. Neuromarketing, in contrast, focuses on how a distribution of choices can be shifted or “influenced”, which can occur at multiple “scales” of behavior (e.g., individual, group, or market/society). Given influence can affect choice through many cognitive modalities, and not just that of valuation of choice options, a science of influence also implies a need to develop a model of cognitive function integrating attention, memory, and reward/aversion function. The paper concludes with a brief description of three domains of neuromarketing application for studying influence, and their caveats

Identifying amyloid pathology–related cerebrospinal fluid biomarkers for Alzheimer\u27s disease in a multicohort study

Introduction: The dynamic range of cerebrospinal fluid (CSF) amyloid β (Aβ1–42) measurement does not parallel to cognitive changes in Alzheimer\u27s disease (AD) and cognitively normal (CN) subjects across different studies. Therefore, identifying novel proteins to characterize symptomatic AD samples is important. Methods: Proteins were profiled using a multianalyte platform by Rules Based Medicine (MAP-RBM). Due to underlying heterogeneity and unbalanced sample size, we combined subjects (344 AD and 325 CN) from three cohorts: Alzheimer\u27s Disease Neuroimaging Initiative, Penn Center for Neurodegenerative Disease Research of the University of Pennsylvania, and Knight Alzheimer\u27s Disease Research Center at Washington University in St. Louis. We focused on samples whose cognitive and amyloid status was consistent. We performed linear regression (accounted for age, gender, number of apolipoprotein E (APOE) e4 alleles, and cohort variable) to identify amyloid-related proteins for symptomatic AD subjects in this largest ever CSF–based MAP-RBM study. ANOVA and Tukey\u27s test were used to evaluate if these proteins were related to cognitive impairment changes as measured by mini-mental state examination (MMSE). Results: Seven proteins were significantly associated with Aβ1–42 levels in the combined cohort (false discovery rate adjusted P \u3c .05), of which lipoprotein a (Lp(a)), prolactin (PRL), resistin, and vascular endothelial growth factor (VEGF) have consistent direction of associations across every individual cohort. VEGF was strongly associated with MMSE scores, followed by pancreatic polypeptide and immunoglobulin A (IgA), suggesting they may be related to staging of AD. Discussion: Lp(a), PRL, IgA, and tissue factor/thromboplastin have never been reported for AD diagnosis in previous individual CSF–based MAP-RBM studies. Although some of our reported analytes are related to AD pathophysiology, other\u27s roles in symptomatic AD samples worth further explorations

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

Molecular Characterization of NRXN1 Deletions from 19,263 Clinical Microarray Cases Identifies Exons Important for Neurodevelopmental Disease Expression

PURPOSE: The purpose of the current study was to assess the penetrance of NRXN1 deletions. METHODS: We compared the prevalence and genomic extent of NRXN1 deletions identified among 19,263 clinically referred cases to that of 15,264 controls. The burden of additional clinically relevant copy-number variations (CNVs) was used as a proxy to estimate the relative penetrance of NRXN1 deletions. RESULTS: We identified 41 (0.21%) previously unreported exonic NRXN1 deletions ascertained for developmental delay/intellectual disability that were significantly greater than in controls (odds ratio (OR) = 8.14; 95% confidence interval (CI): 2.91-22.72; P \u3c 0.0001). Ten (22.7%) of these had a second clinically relevant CNV. Subjects with a deletion near the 3\u27 end of NRXN1 were significantly more likely to have a second rare CNV than subjects with a 5\u27 NRXN1 deletion (OR = 7.47; 95% CI: 2.36-23.61; P = 0.0006). The prevalence of intronic NRXN1 deletions was not statistically different between cases and controls (P = 0.618). The majority (63.2%) of intronic NRXN1 deletion cases had a second rare CNV at a prevalence twice as high as that for exonic NRXN1 deletion cases (P = 0.0035). CONCLUSIONS: The results support the importance of exons near the 5\u27 end of NRXN1 in the expression of neurodevelopmental disorders. Intronic NRXN1 deletions do not appear to substantially increase the risk for clinical phenotypes.Genet Med 19 1, 53-61

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

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

AAV-mediated intramuscular delivery of myotubularin corrects the myotubular myopathy phenotype in targeted murine muscle and suggests a function in plasma membrane homeostasis

Myotubular myopathy (XLMTM, OMIM 310400) is a severe congenital muscular disease due to mutations in the myotubularin gene (MTM1) and characterized by the presence of small myofibers with frequent occurrence of central nuclei. Myotubularin is a ubiquitously expressed phosphoinositide phosphatase with a muscle-specific role in man and mouse that is poorly understood. No specific treatment exists to date for patients with myotubular myopathy. We have constructed an adeno-associated virus (AAV) vector expressing myotubularin in order to test its therapeutic potential in a XLMTM mouse model. We show that a single intramuscular injection of this vector in symptomatic Mtm1-deficient mice ameliorates the pathological phenotype in the targeted muscle. Myotubularin replacement in mice largely corrects nuclei and mitochondria positioning in myofibers and leads to a strong increase in muscle volume and recovery of the contractile force. In addition, we used this AAV vector to overexpress myotubularin in wild-type skeletal muscle and get insight into its localization and function. We show that a substantial proportion of myotubularin associates with the sarcolemma and I band, including triads. Myotubularin overexpression in muscle induces the accumulation of packed membrane saccules and presence of vacuoles that contain markers of sarcolemma and T-tubules, suggesting that myotubularin is involved in plasma membrane homeostasis of myofibers. This study provides a proof-of-principle that local delivery of an AAV vector expressing myotubularin can improve the motor capacities of XLMTM muscle and represents a novel approach to study myotubularin function in skeletal muscle