A rhombohedral ferroelectric phase in epitaxially strained Hf0.5Zr0.5O2 thin films

Hafnia-based thin films are a favoured candidate for the integration of robust ferroelectricity at the nanoscale into next-generation memory and logic devices. This is because their ferroelectric polarization becomes more robust as the size is reduced, exposing a type of ferroelectricity whose mechanism still remains to be understood. Thin films with increased crystal quality are therefore needed. We report the epitaxial growth of Hf0.5Zr0.5O2 thin films on (001)-oriented La0.7Sr0.3MnO3/SrTiO3 substrates. The films, which are under epitaxial compressive strain and predominantly (111)-oriented, display large ferroelectric polarization values up to 34 mu C cm(-2) and do not need wake-up cycling. Structural characterization reveals a rhombohedral phase, different from the commonly reported polar orthorhombic phase. This finding, in conjunction with density functional theory calculations, allows us to propose a compelling model for the formation of the ferroelectric phase. In addition, these results point towards thin films of simple oxides as a vastly unexplored class of nanoscale ferroelectrics.</p

Comparison of Rx-defined morbidity groups and diagnosis- based risk adjusters for predicting healthcare costs in Taiwan

<p>Abstract</p> <p>Background</p> <p>Medication claims are commonly used to calculate the risk adjustment for measuring healthcare cost. The Rx-defined Morbidity Groups (Rx-MG) which combine the use of medication to indicate morbidity have been incorporated into the Adjusted Clinical Groups (ACG) Case Mix System, developed by the Johns Hopkins University. This study aims to verify that the Rx-MG can be used for adjusting risk and for explaining the variations in the healthcare cost in Taiwan.</p> <p>Methods</p> <p>The Longitudinal Health Insurance Database 2005 (LHID2005) was used in this study. The year 2006 was chosen as the baseline to predict healthcare cost (medication and total cost) in 2007. The final sample size amounted to 793 239 (81%) enrolees, and excluded any cases with discontinued enrolment. Two different kinds of models were built to predict cost: the concurrent model and the prospective model. The predictors used in the predictive models included age, gender, Aggregated Diagnosis Groups (ADG, diagnosis- defined morbidity groups), and Rx-defined Morbidity Groups. Multivariate OLS regression was used in the cost prediction modelling.</p> <p>Results</p> <p>The concurrent model adjusted for Rx-defined Morbidity Groups for total cost, and controlled for age and gender had a better predictive R-square = 0.618, compared to the model adjusted for ADGs (R²= 0.411). The model combined with Rx-MGs and ADGs performed the best for concurrently predicting total cost (R²= 0.650). For prospectively predicting total cost, the model combined Rx-MGs and ADGs (R²= 0.382) performed better than the models adjusted by Rx-MGs (R²= 0.360) or ADGs (R²= 0.252) only. Similarly, the concurrent model adjusted for Rx-MGs predicting pharmacy cost had a better performance (R-square = 0.615), than the model adjusted for ADGs (R²= 0.431). The model combined with Rx-MGs and ADGs performed the best in concurrently as well as prospectively predicting pharmacy cost (R²= 0.638 and 0.505, respectively). The prospective models showed a remarkable improvement when adjusted by prior cost.</p> <p>Conclusions</p> <p>The medication-based Rx-Defined Morbidity Groups was useful in predicting pharmacy cost as well as total cost in Taiwan. Combining the information on medication and diagnosis as adjusters could arguably be the best method for explaining variations in healthcare cost.</p

A mobile phone application for the assessment and management of youth mental health problems in primary care: a randomised controlled trial

<p>Abstract</p> <p>Background</p> <p>Over 75% of mental health problems begin in adolescence and primary care has been identified as the target setting for mental health intervention by the World Health Organisation. The <it>mobiletype </it>program is a mental health assessment and management mobile phone application which monitors mood, stress, coping strategies, activities, eating, sleeping, exercise patterns, and alcohol and cannabis use at least daily, and transmits this information to general practitioners (GPs) via a secure website in summary format for medical review.</p> <p>Methods</p> <p>We conducted a randomised controlled trial in primary care to examine the mental health benefits of the <it>mobiletype </it>program. Patients aged 14 to 24 years were recruited from rural and metropolitan general practices. GPs identified and referred eligible participants (those with mild or more mental health concerns) who were randomly assigned to either the intervention group (where mood, stress, and daily activities were monitored) or the attention comparison group (where only daily activities were monitored). Both groups self-monitored for 2 to 4 weeks and reviewed the monitoring data with their GP. GPs, participants, and researchers were blind to group allocation at randomisation. Participants completed pre-, post-, and 6-week post-test measures of the Depression, Anxiety, Stress Scale and an Emotional Self Awareness (ESA) Scale.</p> <p>Results</p> <p>Of the 163 participants assessed for eligibility, 118 were randomised and 114 participants were included in analyses (intervention group n = 68, comparison group n = 46). Mixed model analyses revealed a significant group by time interaction on ESA with a medium size of effect suggesting that the <it>mobiletype </it>program significantly increases ESA compared to an attention comparison. There was no significant group by time interaction for depression, anxiety, or stress, but a medium to large significant main effect for time for each of these mental health measures. Post-hoc analyses suggested that participation in the RCT lead to enhanced GP mental health care at pre-test and improved mental health outcomes.</p> <p>Conclusions</p> <p>Monitoring mental health symptoms appears to increase ESA and implementing a mental health program in primary care and providing frequent reminders, clinical resources, and support to GPs substantially improved mental health outcomes for the sample as a whole.</p> <p>Trial Registration</p> <p>ClinicalTrials.gov <a href="http://www.clinicaltrials.gov/ct2/show/NCT00794222">NCT00794222</a>.</p

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

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

Applying diagnosis and pharmacy-based risk models to predict pharmacy use in Aragon, Spain: The impact of a local calibration

<p>Abstract</p> <p>Background</p> <p>In the financing of a national health system, where pharmaceutical spending is one of the main cost containment targets, predicting pharmacy costs for individuals and populations is essential for budget planning and care management. Although most efforts have focused on risk adjustment applying diagnostic data, the reliability of this information source has been questioned in the primary care setting. We sought to assess the usefulness of incorporating pharmacy data into claims-based predictive models (PMs). Developed primarily for the U.S. health care setting, a secondary objective was to evaluate the benefit of a local calibration in order to adapt the PMs to the Spanish health care system.</p> <p>Methods</p> <p>The population was drawn from patients within the primary care setting of Aragon, Spain (n = 84,152). Diagnostic, medication and prior cost data were used to develop PMs based on the Johns Hopkins ACG methodology. Model performance was assessed through r-squared statistics and predictive ratios. The capacity to identify future high-cost patients was examined through c-statistic, sensitivity and specificity parameters.</p> <p>Results</p> <p>The PMs based on pharmacy data had a higher capacity to predict future pharmacy expenses and to identify potential high-cost patients than the models based on diagnostic data alone and a capacity almost as high as that of the combined diagnosis-pharmacy-based PM. PMs provided considerably better predictions when calibrated to Spanish data.</p> <p>Conclusion</p> <p>Understandably, pharmacy spending is more predictable using pharmacy-based risk markers compared with diagnosis-based risk markers. Pharmacy-based PMs can assist plan administrators and medical directors in planning the health budget and identifying high-cost-risk patients amenable to care management programs.</p

Insulin-like growth factor 2 mRNA binding protein 3 (IGF2BP3) overexpression in pancreatic ductal adenocarcinoma correlates with poor survival

<p>Abstract</p> <p>Background</p> <p>Pancreatic ductal adenocarcinoma is a lethal disease with a 5-year survival rate of 4% and typically presents in an advanced stage. In this setting, prognostic markers identifying the more agrressive tumors could aid in managment decisions. Insulin-like growth factor 2 mRNA binding protein 3 (IGF2BP3, also known as IMP3 or KOC) is an oncofetal RNA-binding protein that regulates targets such as insulin-like growth factor-2 (IGF-2) and ACTB (beta-actin).</p> <p>Methods</p> <p>We evaluated the expression of IGF2BP3 by immunohistochemistry using a tissue microarray of 127 pancreatic ductal adenocarcinomas with tumor grade 1, 2 and 3 according to WHO criteria, and the prognostic value of IGF2BP3 expression.</p> <p>Results</p> <p>IGF2BP3 was found to be selectively overexpressed in pancreatic ductal adenocarcinoma tissues but not in benign pancreatic tissues. Nine (38%) patient samples of tumor grade 1 (n = 24) and 27 (44%) of tumor grade 2 (n = 61) showed expression of IGF2BP3. The highest rate of expression was seen in poorly differentiated specimen (grade 3, n = 42) with 26 (62%) positive samples. Overall survival was found to be significantly shorter in patients with IGF2BP3 expressing tumors (P = 0.024; RR 2.3, 95% CI 1.2-4.8).</p> <p>Conclusions</p> <p>Our data suggest that IGF2BP3 overexpression identifies a subset of pancreatic ductal adenocarcinomas with an extremely poor outcome and supports the rationale for developing therapies to target the IGF pathway in this cancer.</p

Mesenchymal Stem Cells in a Transgenic Mouse Model of Multiple System Atrophy: Immunomodulation and Neuroprotection

Mesenchymal stem cells (MSC) are currently strong candidates for cell-based therapies. They are well known for their differentiation potential and immunoregulatory properties and have been proven to be potentially effective in the treatment of a large variety of diseases, including neurodegenerative disorders. Currently there is no treatment that provides consistent long-term benefits for patients with multiple system atrophy (MSA), a fatal late onset α-synucleinopathy. Principally neuroprotective or regenerative strategies, including cell-based therapies, represent a powerful approach for treating MSA. In this study we investigated the efficacy of intravenously applied MSCs in terms of behavioural improvement, neuroprotection and modulation of neuroinflammation in the (PLP)-αsynuclein (αSYN) MSA model.MSCs were intravenously applied in aged (PLP)-αSYN transgenic mice. Behavioural analyses, defining fine motor coordination and balance capabilities as well as stride length analysis, were performed to measure behavioural outcome. Neuroprotection was assessed by quantifying TH neurons in the substantia nigra pars compacta (SNc). MSC treatment on neuroinflammation was analysed by cytokine measurements (IL-1α, IL-2, IL-4, IL-5, IL-6, IL-10, IL-17, GM-CSF, INFγ, MCP-1, TGF-β1, TNF-α) in brain lysates together with immunohistochemistry for T-cells and microglia. Four weeks post MSC treatment we observed neuroprotection in the SNc, as well as downregulation of cytokines involved in neuroinflammation. However, there was no behavioural improvement after MSC application.To our knowledge this is the first experimental approach of MSC treatment in a transgenic MSA mouse model. Our data suggest that intravenously infused MSCs have a potent effect on immunomodulation and neuroprotection. Our data warrant further studies to elucidate the efficacy of systemically administered MSCs in transgenic MSA models