The challenges of communicating research evidence in practice: perspectives from UK health visitors and practice nurses

<p>Background: Health practitioners play a pivotal role in providing patients with up-to-date evidence and health information. Evidence-based practice and patient-centred care are transforming the delivery of healthcare in the UK. Health practitioners are increasingly balancing the need to provide evidence-based information against that of facilitating patient choice, which may not always concur with the evidence base. There is limited research exploring how health practitioners working in the UK, and particularly those more autonomous practitioners such as health visitors and practice nurses working in community practice settings, negotiate this challenge. This research provides a descriptive account of how health visitors and practice nurses negotiate the challenges of communicating health information and research evidence in practice.</p> <p>Methods: A total of eighteen in-depth telephone interviews were conducted in the UK between September 2008 and May 2009. The participants comprised nine health visitors and nine practice nurses, recruited via adverts on a nursing website, posters at a practitioner conference and through recommendation. Thematic analysis, with a focus on constant comparative method, was used to analyse the data.</p> <p>Results: The data were grouped into three main themes: communicating evidence to the critically-minded patient; confidence in communicating evidence; and maintaining the integrity of the patient-practitioner relationship. These findings highlight some of the daily challenges that health visitors and practice nurses face with regard to the complex and dynamic nature of evidence and the changing attitudes and expectations of patients. The findings also highlight the tensions that exist between differing philosophies of evidence-based practice and patient-centred care, which can make communicating about evidence a daunting task.</p> <p>Conclusions: If health practitioners are to be effective at communicating research evidence, we suggest that more research and resources need to be focused on contextual factors, such as how research evidence is negotiated, appraised and communicated within the dynamic patient-practitioner relationship.</p&gt

Antigen-specific CD4 cells assist CD8 T-effector cells in eliminating keratinocytes

Keratinocytes expressing tumor or viral antigens can be eliminated by antigen-primed CD8 cytotoxic T cells. CD4 T-helper cells help induction of CD8 cytotoxic T cells from naive precursors and generation of CD8 T-cell memory. In this study, we show, unexpectedly, that CD4 cells are also required to assist primed CD8 effector T cells in rejection of skin expressing human growth hormone, a neo-self-antigen, in keratinocytes. The requirement for CD4 cells can be substituted by CD40 costimulation. Rejection of skin expressing ovalbumin (OVA), a non-self-antigen, by primed CD8 cytotoxic T cells can in contrast occur without help from antigen-specific CD4 T cells. However, rejection of OVA expressing keratinocytes is helped by antigen-specific CD4 T cells if only low numbers of primed or naive OVA-specific CD8 T cells are available. Effective immunotherapy directed at antigens expressed in squamous cancer may therefore be facilitated by induction of tumor antigen-specific CD4 helper T cells, as well as cytotoxic CD8 T cells

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

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

Lethal Antibody Enhancement of Dengue Disease in Mice Is Prevented by Fc Modification

Immunity to one of the four dengue virus (DV) serotypes can increase disease severity in humans upon subsequent infection with another DV serotype. Serotype cross-reactive antibodies facilitate DV infection of myeloid cells in vitro by promoting virus entry via Fcγ receptors (FcγR), a process known as antibody-dependent enhancement (ADE). However, despite decades of investigation, no in vivo model for antibody enhancement of dengue disease severity has been described. Analogous to human infants who receive anti-DV antibodies by transplacental transfer and develop severe dengue disease during primary infection, we show here that passive administration of anti-DV antibodies is sufficient to enhance DV infection and disease in mice using both mouse-adapted and clinical DV isolates. Antibody-enhanced lethal disease featured many of the hallmarks of severe dengue disease in humans, including thrombocytopenia, vascular leakage, elevated serum cytokine levels, and increased systemic viral burden in serum and tissue phagocytes. Passive transfer of a high dose of serotype-specific antibodies eliminated viremia, but lower doses of these antibodies or cross-reactive polyclonal or monoclonal antibodies all enhanced disease in vivo even when antibody levels were neutralizing in vitro. In contrast, a genetically engineered antibody variant (E60-N297Q) that cannot bind FcγR exhibited prophylactic and therapeutic efficacy against ADE-induced lethal challenge. These observations provide insight into the pathogenesis of antibody-enhanced dengue disease and identify a novel strategy for the design of therapeutic antibodies against dengue

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation

Molecular characterization and clinical relevance of metabolic expression subtypes in human cancers.

Metabolic reprogramming provides critical information for clinical oncology. Using molecular data of 9,125 patient samples from The Cancer Genome Atlas, we identified tumor subtypes in 33 cancer types based on mRNA expression patterns of seven major metabolic processes and assessed their clinical relevance. Our metabolic expression subtypes correlated extensively with clinical outcome: subtypes with upregulated carbohydrate, nucleotide, and vitamin/cofactor metabolism most consistently correlated with worse prognosis, whereas subtypes with upregulated lipid metabolism showed the opposite. Metabolic subtypes correlated with diverse somatic drivers but exhibited effects convergent on cancer hallmark pathways and were modulated by highly recurrent master regulators across cancer types. As a proof-of-concept example, we demonstrated that knockdown of SNAI1 or RUNX1—master regulators of carbohydrate metabolic subtypes-modulates metabolic activity and drug sensitivity. Our study provides a system-level view of metabolic heterogeneity within and across cancer types and identifies pathway cross-talk, suggesting related prognostic, therapeutic, and predictive utility