Increased shedding of HU177 correlates with worse prognosis in primary melanoma

<p>Abstract</p> <p>Background</p> <p>Increased levels of cryptic collagen epitope HU177 in the sera of melanoma patients have been shown to be associated with thicker primary melanomas and with the nodular histologic subtype. In this study, we investigate the association between HU177 shedding in the sera and clinical outcome in terms of disease-free survival (DFS) and overall survival (OS).</p> <p>Methods</p> <p>Serum samples from 209 patients with primary melanoma prospectively enrolled in the Interdisciplinary Melanoma Cooperative Group at the New York University Langone Medical Center (mean age = 58, mean thickness = 2.09 mm, stage I = 136, stage II = 41, stage III = 32, median follow-up = 54.9 months) were analyzed for HU177 concentration using a validated ELISA assay. HU177 serum levels at the time of diagnosis were used to divide the study cohort into two groups: low and high HU177. DFS and OS were estimated by Kaplan-Meier survival analysis, and the log-rank test was used to compare DFS and OS between the two HU177 groups. Multivariate Cox proportional hazards regression models were employed to examine the independent effect of HU177 category on DFS and OS.</p> <p>Results</p> <p>HU177 sera concentrations ranged from 0-139.8 ng/ml (mean and median of 6.2 ng/ml and 3.7 ng/ml, respectively). Thirty-eight of the 209 (18%) patients developed recurrences, and 34 of the 209 (16%) patients died during follow-up. Higher HU177 serum level was associated with an increased rate of melanoma recurrence (p = 0.04) and with increasing mortality (p = 0.01). The association with overall survival remained statistically significant after controlling for thickness and histologic subtype in a multivariate model (p = 0.035).</p> <p>Conclusions</p> <p>Increased shedding of HU177 in the serum of primary melanoma patients is associated with poor prognosis. Further studies are warranted to determine the clinical utility of HU177 in risk stratification compared to the current standard of care.</p

The impact of beliefs about face recognition ability on memory retrieval processes in young and older adults

This study examined whether beliefs about face recognition ability differentially influence memory retrieval in older compared to young adults. Participants evaluated their ability to recognise faces and were also given information about their ability to perceive and recognise faces. The information was ostensibly based on an objective measure of their ability, but in actuality, participants had been randomly assigned the information they received (high ability, low ability or no information control). Following this information, face recognition accuracy for a set of previously studied faces was measured using a remember– know memory paradigm. Older adults rated their ability to recognise faces as poorer compared to young adults. Additionally, negative information about face recognition ability improved only older adults’ ability to recognise a previously seen face. Older adults were also found to engage in more familiarity than item-specific processing than young adults, but information about their face recognition ability did not affect face processing style. The role that older adults’ memory beliefs have in the meta-cognitive strategies they employ is discussed

Designing Health Information Technology Tools to Prevent Gaps in Public Health Insurance

Background: Changes in health insurance policies have increased coverage opportunities, but enrollees are required to annually reapply for benefits which, if not managed appropriately, can lead to insurance gaps. Electronic health records (EHRs) can automate processes for assisting patients with health insurance enrollment and re-enrollment.Objective: We describe community health centers' (CHC) workflow, documentation, and tracking needs for assisting families with insurance application processes, and the health information technology (IT) tool components that were developed to meet those needs.Method: We conducted a qualitative study using semi-structured interviews and observation of clinic operations and insurance application assistance processes. Data were analyzed using a grounded theory approach. We diagramed workflows and shared information with a team of developers who built the EHR-based tools.Results: Four steps to the insurance assistance workflow were common among CHCs: 1) Identifying patients for public health insurance application assistance; 2) Completing and submitting the public health insurance application when clinic staff met with patients to collect requisite information and helped them apply for benefits; 3) Tracking public health insurance approval to monitor for decisions; and 4) assisting with annual health insurance reapplication. We developed EHR-based tools to support clinical staff with each of these steps.Conclusion: CHCs are uniquely positioned to help patients and families with public health insurance applications. CHCs have invested in staff to assist patients with insurance applications and help prevent coverage gaps. To best assist patients and to foster efficiency, EHR based insurance tools need comprehensive, timely, and accurate health insurance information

Characterisation of paediatric brain tumours by their MRS metabolite profiles

1H‐magnetic resonance spectroscopy (MRS) has the potential to improve the noninvasive diagnostic accuracy for paediatric brain tumours. However, studies analysing large, comprehensive, multicentre datasets are lacking, hindering translation to widespread clinical practice. Single‐voxel MRS (point‐resolved single‐voxel spectroscopy sequence, 1.5 T: echo time [TE] 23–37 ms/135–144 ms, repetition time [TR] 1500 ms; 3 T: TE 37–41 ms/135–144 ms, TR 2000 ms) was performed from 2003 to 2012 during routine magnetic resonance imaging for a suspected brain tumour on 340 children from five hospitals with 464 spectra being available for analysis and 281 meeting quality control. Mean spectra were generated for 13 tumour types. Mann–Whitney U‐tests and Kruskal–Wallis tests were used to compare mean metabolite concentrations. Receiver operator characteristic curves were used to determine the potential for individual metabolites to discriminate between specific tumour types. Principal component analysis followed by linear discriminant analysis was used to construct a classifier to discriminate the three main central nervous system tumour types in paediatrics. Mean concentrations of metabolites were shown to differ significantly between tumour types. Large variability existed across each tumour type, but individual metabolites were able to aid discrimination between some tumour types of importance. Complete metabolite profiles were found to be strongly characteristic of tumour type and, when combined with the machine learning methods, demonstrated a diagnostic accuracy of 93% for distinguishing between the three main tumour groups (medulloblastoma, pilocytic astrocytoma and ependymoma). The accuracy of this approach was similar even when data of marginal quality were included, greatly reducing the proportion of MRS excluded for poor quality. Children's brain tumours are strongly characterised by MRS metabolite profiles readily acquired during routine clinical practice, and this information can be used to support noninvasive diagnosis. This study provides both key evidence and an important resource for the future use of MRS in the diagnosis of children's brain tumours

Added value of magnetic resonance spectroscopy for diagnosing childhood cerebellar tumours

1H‐magnetic resonance spectroscopy (MRS) provides noninvasive metabolite profiles with the potential to aid the diagnosis of brain tumours. Prospective studies of diagnostic accuracy and comparisons with conventional MRI are lacking. The aim of the current study was to evaluate, prospectively, the diagnostic accuracy of a previously established classifier for diagnosing the three major childhood cerebellar tumours, and to determine added value compared with standard reporting of conventional imaging. Single‐voxel MRS (1.5 T, PRESS, TE 30 ms, TR 1500 ms, spectral resolution 1 Hz/point) was acquired prospectively on 39 consecutive cerebellar tumours with histopathological diagnoses of pilocytic astrocytoma, ependymoma or medulloblastoma. Spectra were analysed with LCModel and predefined quality control criteria were applied, leaving 33 cases in the analysis. The MRS diagnostic classifier was applied to this dataset. A retrospective analysis was subsequently undertaken by three radiologists, blind to histopathological diagnosis, to determine the change in diagnostic certainty when sequentially viewing conventional imaging, MRS and a decision support tool, based on the classifier. The overall classifier accuracy, evaluated prospectively, was 91%. Incorrectly classified cases, two anaplastic ependymomas, and a rare histological variant of medulloblastoma, were not well represented in the original training set. On retrospective review of conventional MRI, MRS and the classifier result, all radiologists showed a significant increase (Wilcoxon signed rank test, p < 0.001) in their certainty of the correct diagnosis, between viewing the conventional imaging and MRS with the decision support system. It was concluded that MRS can aid the noninvasive diagnosis of posterior fossa tumours in children, and that a decision support classifier helps in MRS interpretation

2001 Wild Blueberry CSREES Project Reports

The 2001 edition of the Wild Blueberry CSREES Progress Reports was prepared for the Maine Wild Blueberry Commission and the University of Maine Wild Blueberry Advisory Committee by researchers at the University of Maine, Orono. Projects in this report include: 1. Effect of Wild Blueberry Products on Oxidation in Meat Based Food Systems 2. Factors Affecting the Microbial and Pesticide Residues Levels on Wild Blueberries 3. Determination of Pesticide Residue Levels in Fresh and Processed Wild Blueberries 4. Separation of Maggot-Infested Wild Blueberries in the IQF Processing Line 5. Water Use of Wild Blueberries and the Impact of Plant Water Stress on Yields 6. Survey of Stem Blight and Leaf Spot Diseases in Wild Blueberry Fields 7. IPM Strategies 8. Control Tactics for Wild Blueberry Pest Insects, 2001 9. Biology and Ecology of Blueberry Pest Insects 10. Diurnal Bee Activity and Measurement of Honeybee Field Strength 11. Effect of Foliar-applied Iron (Fe) Chelate Concentration on Leaf Iron Concentration, Wild Blueberry Growth and Yield 12. Effect of Boron Application Methods on Boron Uptake in Wild Blueberries 13. Effect of Foliar Iron and Copper Application on Growth and Yield of Wild Blueberries 14. Effect of Fertilizer Timing on Wild Blueberry Growth and Productivity 15. Effect of Foliar Copper Application on Growth and Yield of Wild Blueberries 16. Effect of Prune-year Applications of Nutri-Phitetm P or Nutri-Phitetm P+K on Growth and Yield of Wild Blueberry (Vaccinium angustifolium Ait.) 17. Effect of Soil pH on Nutrient Uptake 18. Assessment of Azafenidin for Weed Control in Wild Blueberries 19. Assessment of Rimsulfuron for Weed Control in Wild Blueberries 20. Assessment of Pendimethalin for Weed Control in Wild Blueberries 21. Evaluation and Demonstration of Techniques for Filling in Bare Spots in Wild Blueberry Fields 22. Assessment of Sprout-less Weeder for Hardwood Control in Wild Blueberries 23. Wild Blueberry Extension Education Program in 2001 24. Evaluation of Fungicide Efficacy in Wild Blueberry Fields 25. 2001 Pesticide Groundwater Survey 26. Cultural Weed Management Using Sulfur to Lower the pH 27. Wild Blueberry Web Sit

Envisioning a World Beyond APCs/BPCs

This archival page includes documents and recordings related to the international symposium, “Envisioning a World Beyond APCs/BPCs,” held in Lawrence, Kansas, on Thursday and Friday, November 17-18. The presenters were a group of 18 internationally respected scholars, publishers, university librarians, and executives from foundations and organizations, who were asked to participate in a discussion about current models available for achieving an expansive, inclusive, and balanced worldwide open publishing ecosystem. The symposium was co-sponsored by the University of Kansas Libraries, Open Access Network (a project of K|N Consultants), Allen Press, SPARC, and ARL. The materials included here are the symposium schedule, recordings of Parts 1 and 2 of the Nov. 17 livestream, a transcript of the livestream, and team proposals originating from the Nov. 18 morning session.This symposium was sponsored by the University of Kansas Libraries, Open Access Network (a project of K|N Consultants), Allen Press, and SPARC

Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche.

Age at menarche is a marker of timing of puberty in females. It varies widely between individuals, is a heritable trait and is associated with risks for obesity, type 2 diabetes, cardiovascular disease, breast cancer and all-cause mortality. Studies of rare human disorders of puberty and animal models point to a complex hypothalamic-pituitary-hormonal regulation, but the mechanisms that determine pubertal timing and underlie its links to disease risk remain unclear. Here, using genome-wide and custom-genotyping arrays in up to 182,416 women of European descent from 57 studies, we found robust evidence (P < 5 × 10(-8)) for 123 signals at 106 genomic loci associated with age at menarche. Many loci were associated with other pubertal traits in both sexes, and there was substantial overlap with genes implicated in body mass index and various diseases, including rare disorders of puberty. Menarche signals were enriched in imprinted regions, with three loci (DLK1-WDR25, MKRN3-MAGEL2 and KCNK9) demonstrating parent-of-origin-specific associations concordant with known parental expression patterns. Pathway analyses implicated nuclear hormone receptors, particularly retinoic acid and γ-aminobutyric acid-B2 receptor signalling, among novel mechanisms that regulate pubertal timing in humans. Our findings suggest a genetic architecture involving at least hundreds of common variants in the coordinated timing of the pubertal transition

Noise suppression of proton magnetic resonance spectroscopy improves paediatric brain tumour classification

Proton magnetic resonance spectroscopy (1H‐MRS) is increasingly used for clinical brain tumour diagnosis, but suffers from limited spectral quality. This retrospective and comparative study aims at improving paediatric brain tumour classification by performing noise suppression on clinical 1H‐MRS. Eighty‐three/forty‐two children with either an ependymoma (ages 4.6 ± $$\pm$$ 5.3/9.3 ± $$\pm$$ 5.4), a medulloblastoma (ages 6.9 ± $$\pm$$ 3.5/6.5 ± $$\pm$$ 4.4), or a pilocytic astrocytoma (8.0 ± $$\pm$$ 3.6/6.3 ± $$\pm$$ 5.0), recruited from four centres across England, were scanned with 1.5T/3T short‐echo‐time point‐resolved spectroscopy. The acquired raw 1H‐MRS was quantified by using Totally Automatic Robust Quantitation in NMR (TARQUIN), assessed by experienced spectroscopists, and processed with adaptive wavelet noise suppression (AWNS). Metabolite concentrations were extracted as features, selected based on multiclass receiver operating characteristics, and finally used for identifying brain tumour types with supervised machine learning. The minority class was oversampled through the synthetic minority oversampling technique for comparison purposes. Post‐noise‐suppression 1H‐MRS showed significantly elevated signal‐to‐noise ratios (P .05, Wilcoxon signed‐rank test), and significantly higher classification accuracy (P < .05, Wilcoxon signed‐rank test). Specifically, the cross‐validated overall and balanced classification accuracies can be improved from 81% to 88% overall and 76% to 86% balanced for the 1.5T cohort, whilst for the 3T cohort they can be improved from 62% to 76% overall and 46% to 56%, by applying Naïve Bayes on the oversampled 1H‐MRS. The study shows that fitting‐based signal‐to‐noise ratios of clinical 1H‐MRS can be significantly improved by using AWNS with insignificantly altered line width, and the post‐noise‐suppression 1H‐MRS may have better diagnostic performance for paediatric brain tumours

Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors

Brain tumors represent the highest cause of mortality in the pediatric oncological population. Diagnosis is commonly performed with magnetic resonance imaging. Survival biomarkers are challenging to identify due to the relatively low numbers of individual tumor types. 69 children with biopsy-confirmed brain tumors were recruited into this study. All participants had perfusion and diffusion weighted imaging performed at diagnosis. Imaging data were processed using conventional methods, and a Bayesian survival analysis performed. Unsupervised and supervised machine learning were performed with the survival features, to determine novel sub-groups related to survival. Sub-group analysis was undertaken to understand differences in imaging features. Survival analysis showed that a combination of diffusion and perfusion imaging were able to determine two novel sub-groups of brain tumors with different survival characteristics (p < 0.01), which were subsequently classified with high accuracy (98%) by a neural network. Analysis of high-grade tumors showed a marked difference in survival (p = 0.029) between the two clusters with high risk and low risk imaging features. This study has developed a novel model of survival for pediatric brain tumors. Tumor perfusion plays a key role in determining survival and should be considered as a high priority for future imaging protocols