Screening and Assessment of Cancer-Related Fatigue: A Clinical Practice Guideline for Health Care Providers

Cancer-related fatigue (CRF) is the most common side effect of cancer treatment. Regular surveillance is recommended, but few clinical practice guidelines transparently assess study bias, quality, and clinical utility in deriving recommendations of screening and assessment methods. The purpose of this clinical practice guideline (CPG) is to provide recommendations for the screening and assessment of CRF for health care professions treating individuals with cancer. Following best practices for development of a CPG using the Appraisal of Guidelines for Research and Evaluation (AGREE) Statement and Emergency Care Research Institute (ECRI) Guidelines Trust Scorecard, this CPG included a systematic search of the literature, quality assessment of included evidence, and stakeholder input from diverse health care fields to derive the final CPG. Ten screening and 15 assessment tools supported by 114 articles were reviewed. One screen (European Organisation for Research and Treatment of Cancer–Quality of Life Questionnaire–30 Core Questionnaire) and 3 assessments (Piper Fatigue Scale–Revised, Functional Assessment of Chronic Illness Therapy–Fatigue, and Patient Reported Outcome Measurement Information System [PROMIS] Fatigue-SF) received an A recommendation (“should be used in clinical practice”), and 1 screen and 5 assessments received a B recommendation (“may be used in clinical practice”). Health care providers have choice in determining appropriate screening and assessment tools to be used across the survivorship care continuum. The large number of tools available to screen for or assess CRF may result in a lack of comprehensive research evidence, leaving gaps in the body of evidence for measurement tools. More research into the responsiveness of these tools is needed in order to adopt their use as outcome measures. Impact: Health care providers should screen for and assess CRF using one of the tools recommended by this CPG

Screening and Assessment of Cancer-Related Fatigue: An Executive Summary and Road Map for Clinical Implementation

Background: Cancer-related fatigue (CRF) prevalence is reported as high as 90%. Cancer-related fatigue is multidimensional and associated with lower health-related quality of life. Effective screening and assessment are dependent upon use of valid, reliable, and clinically feasible measures. This Executive Summary of the Screening and Assessment of Cancer-related Fatigue Clinical Practice Guideline provides recommendations for best measures to screen and assess for CRF based on the quality and level of evidence, psychometric strength of the tools, and clinical utility. Methods: After a systematic review of the literature, studies evaluating CRF measurement tools were assessed for quality; data extraction included psychometrics and clinical utility. Measurement tools were categorized as either screens or assessments. Results: Four screens are recommended: European Organization of Research and Treatment of Cancer Quality of Life Questionnaire, the MD Anderson Symptom Inventory, the Distress Thermometer, and the One-Item Fatigue Scale. Eight assessments are recommended: Functional Assessment of Chronic Illness Therapy—Fatigue, Piper Fatigue Scale—Revised, Brief Fatigue Inventory, Cancer Fatigue Scale, Fatigue Symptom Inventory, Patient-Reported Outcome Measurement Information System (PROMIS) Fatigue Short Form and CAT, and Multidimensional Fatigue Inventory-20. Discussion: This Executive Summary is a synopsis of and road map for implementation of the Clinical Practice Guideline for Screening and Assessment of CRF. Review of the full Clinical Practice Guideline is recommended. Additional research focused on responsiveness of instruments is needed in order to consider them for use as outcome measures. Screening and assessing CRF will result in opportunities to improve the quality of life of individuals with cancer

Clinical impairment in premanifest and early Huntington's disease is associated with regionally specific atrophy.

TRACK-HD is a multicentre longitudinal observational study investigating the use of clinical assessments and 3-Tesla magnetic resonance imaging as potential biomarkers for future therapeutic trials in Huntington's disease (HD). The cross-sectional data from this large well-characterized dataset provide the opportunity to improve our knowledge of how the underlying neuropathology of HD may contribute to the clinical manifestations of the disease across the spectrum of premanifest (PreHD) and early HD. Two hundred and thirty nine gene-positive subjects (120 PreHD and 119 early HD) from the TRACK-HD study were included. Using voxel-based morphometry (VBM), grey and white matter volumes were correlated with performance in four domains: quantitative motor (tongue force, metronome tapping, and gait); oculomotor [anti-saccade error rate (ASE)]; cognition (negative emotion recognition, spot the change and the University of Pennsylvania smell identification test) and neuropsychiatric measures (apathy, affect and irritability). After adjusting for estimated disease severity, regionally specific associations between structural loss and task performance were found (familywise error corrected, P < 0.05); impairment in tongue force, metronome tapping and ASE were all associated with striatal loss. Additionally, tongue force deficits and ASE were associated with volume reduction in the occipital lobe. Impaired recognition of negative emotions was associated with volumetric reductions in the precuneus and cuneus. Our study reveals specific associations between atrophy and decline in a range of clinical modalities, demonstrating the utility of VBM correlation analysis for investigating these relationships in HD

Gene content evolution in the arthropods

Arthropods comprise the largest and most diverse phylum on Earth and play vital roles in nearly every ecosystem. Their diversity stems in part from variations on a conserved body plan, resulting from and recorded in adaptive changes in the genome. Dissection of the genomic record of sequence change enables broad questions regarding genome evolution to be addressed, even across hyper-diverse taxa within arthropods. Using 76 whole genome sequences representing 21 orders spanning more than 500 million years of arthropod evolution, we document changes in gene and protein domain content and provide temporal and phylogenetic context for interpreting these innovations. We identify many novel gene families that arose early in the evolution of arthropods and during the diversification of insects into modern orders. We reveal unexpected variation in patterns of DNA methylation across arthropods and examples of gene family and protein domain evolution coincident with the appearance of notable phenotypic and physiological adaptations such as flight, metamorphosis, sociality, and chemoperception. These analyses demonstrate how large-scale comparative genomics can provide broad new insights into the genotype to phenotype map and generate testable hypotheses about the evolution of animal diversity

How to identify essential genes from molecular networks?

<p>Abstract</p> <p>Background</p> <p>The prediction of essential genes from molecular networks is a way to test the understanding of essentiality in the context of what is known about the network. However, the current knowledge on molecular network structures is incomplete yet, and consequently the strategies aimed to predict essential genes are prone to uncertain predictions. We propose that simultaneously evaluating different network structures and different algorithms representing gene essentiality (centrality measures) may identify essential genes in networks in a reliable fashion.</p> <p>Results</p> <p>By simultaneously analyzing 16 different centrality measures on 18 different reconstructed metabolic networks for <it>Saccharomyces cerevisiae</it>, we show that no single centrality measure identifies essential genes from these networks in a statistically significant way; however, the combination of at least 2 centrality measures achieves a reliable prediction of most but not all of the essential genes. No improvement is achieved in the prediction of essential genes when 3 or 4 centrality measures were combined.</p> <p>Conclusion</p> <p>The method reported here describes a reliable procedure to predict essential genes from molecular networks. Our results show that essential genes may be predicted only by combining centrality measures, revealing the complex nature of the function of essential genes.</p

Myocardial production and release of MCP-1 and SDF-1 following myocardial infarction: differences between mice and man

<p>Abstract</p> <p>Background</p> <p>Stem cell homing to the heart is mediated by the release of chemo-attractant cytokines. Stromal derived factor -1 alpha (SDF-1a) and monocyte chemotactic factor 1(MCP-1) are detectable in peripheral blood after myocardial infarction (MI). It remains unknown if they are produced by, and released from, the heart in order to attract stem cells to repair the damaged myocardium.</p> <p>Methods</p> <p>Murine hearts were studied for expression of MCP-1 and SDF-1a at day 3 and day 28 following myocardial infarction to determine whether production is increased following MI. In addition, we studied the coronary artery and coronary sinus (venous) blood from patients with normal coronary arteries, stable coronary artery disease (CAD), unstable angina and MI to determine whether these cytokines are released from the heart into the systemic circulation following MI.</p> <p>Results</p> <p>Both MCP-1 and SDF-1a are constitutively produced and released by the heart. MCP-1 mRNA is upregulated following murine experimental MI, but SDF-1a is suppressed. There is less release of SDF-1a into the systemic circulation in patients with all stages of CAD including MI, mimicking the animal model. However MCP-1 release from the human heart following MI is also suppressed, which is the exact opposite of the animal model.</p> <p>Conclusions</p> <p>SDF-1a and MCP-1 release from the human heart are suppressed following MI. In the case of SDF-1a, the animal model appropriately reflects the human situation. However, for MCP-1 the animal model is the exact opposite of the human condition. Human observational studies like this one are paramount in guiding translation from experimental studies to clinical trials.</p

Plasmodium knowlesi: Reservoir Hosts and Tracking the Emergence in Humans and Macaques

Plasmodium knowlesi, a malaria parasite originally thought to be restricted to macaques in Southeast Asia, has recently been recognized as a significant cause of human malaria. Unlike the benign and morphologically similar P. malariae, these parasites can lead to fatal infections. Malaria parasites, including P. knowlesi, have not yet been detected in macaques of the Kapit Division of Malaysian Borneo, where the majority of human knowlesi malaria cases have been reported. In order to extend our understanding of the epidemiology and evolutionary history of P. knowlesi, we examined 108 wild macaques for malaria parasites and sequenced the circumsporozoite protein (csp) gene and mitochondrial (mt) DNA of P. knowlesi isolates derived from macaques and humans. We detected five species of Plasmodium (P. knowlesi, P. inui, P. cynomolgi, P. fieldi and P. coatneyi) in the long-tailed and pig-tailed macaques, and an extremely high prevalence of P. inui and P. knowlesi. Macaques had a higher number of P. knowlesi genotypes per infection than humans, and some diverse alleles of the P. knowlesi csp gene and certain mtDNA haplotypes were shared between both hosts. Analyses of DNA sequence data indicate that there are no mtDNA lineages associated exclusively with either host. Furthermore, our analyses of the mtDNA data reveal that P. knowlesi is derived from an ancestral parasite population that existed prior to human settlement in Southeast Asia, and underwent significant population expansion approximately 30,000–40,000 years ago. Our results indicate that human infections with P. knowlesi are not newly emergent in Southeast Asia and that knowlesi malaria is primarily a zoonosis with wild macaques as the reservoir hosts. However, ongoing ecological changes resulting from deforestation, with an associated increase in the human population, could enable this pathogenic species of Plasmodium to switch to humans as the preferred host