Quality of life in lung cancer patients: does socioeconomic status matter?

BACKGROUND: As part of a prospective study on quality of life in newly diagnosed lung cancer patients an investigation was carried out to examine whether there were differences among patients' quality of life scores and their socioeconomic status. METHODS: Quality of life was measured at two points in time (baseline and three months after initial treatment) using three standard instruments; the Nottingham Health Profile (NHP), the European Organization for Research and Cancer Treatment Quality of Life Questionnaire (EORTC QLQ-C30) and its lung cancer supplement (QLQ-LC13). Socioeconomic status for each individual patient was derived using Carstairs and Morris Deprivation Category ranging from 1 (least deprived) to 7 (most deprived) on the basis of the postcode sector of their address. RESULTS: In all, 129 lung cancer patients entered into the study. Of these data for 82 patients were complete (at baseline and follow-up). 57% of patients were of lower socioeconomic status and they had more health problems, less functioning, and more symptoms as compared to affluent patients. Of these, physical mobility (P = 0.05), energy (P = 0.01), role functioning (P = 0.04), physical functioning (P = 0.03), and breathlessness (P = 0.02) were significant at baseline. However, at follow-up assessment there was no significant difference between patient groups nor did any consistent pattern emerge. CONCLUSION: At baseline assessment patients of lower socioeconomic status showed lower health related quality of life. Since there was no clear trend at follow-up assessment this suggests that patients from different socioeconomic status responded to treatment similarly. In general, the findings suggest that quality of life is not only the outcome of the disease and its treatment, but is also highly dependent on each patients' socioeconomic characteristics

Does knowledge of cancer diagnosis affect quality of life? A methodological challenge

BACKGROUND: As part of an assessment of quality of life in lung cancer patients an investigation was carried out to examine whether the knowledge of their diagnosis affected their quality of life. METHODS: Every patient in a defined geographical area with a potential diagnosis of lung cancer was interviewed at first consultation and after a definitive treatment has been given. Quality of life was assessed using three standard measures: the Nottingham Health Profile (NHP), the EORTC quality of life questionnaire (QLQ-C30) and its lung cancer supplementary questionnaire (QLQ-LC13). Comparison was made in quality of life scores between patients who knew their cancer diagnosis and those who did not. RESULTS: In all, 129 lung cancer patients were interviewed. Of these, 30 patients (23%) knew and 99 (78%) did not know their cancer diagnosis at the time of baseline assessment. The patient groups were similar in their characteristics except for age (P = 0.04) and cell type (P < 0.0001). Overall, there were no significant differences between these two groups with regard to their scores on the three instruments used. A major finding was that both group scored almost the same on emotional reactions (P = 0.8) and social isolation (P = 1.0) as measured by the NHP, and emotional (P = 0.7) and social functioning (P = 1.0) as measured by the EORTC QLQ-C30. In addition there were no significant differences in patients' symptom scores between those who knew their diagnosis and those who did not, nor did any consistent pattern emerge. The only significant difference was for sleep difficulties (P = 0.02). CONCLUSION: The findings suggest that the knowledge of cancer diagnosis does not affect the way in which patients respond to quality of life questionnaires

Measuring the capability to raise revenue process and output dimensions and their application to the Zambia revenue authority

The worldwide diffusion of the good governance agenda and new public management has triggered a renewed focus on state capability and, more specifically, on the capability to raise revenue in developing countries. However, the analytical tools for a comprehensive understanding of the capability to raise revenue remain underdeveloped. This article aims at filling this gap and presents a model consisting of the three process dimensions ‘information collection and processing’, ‘merit orientation’ and ‘administrative accountability’. ‘Revenue performance’ constitutes the fourth capability dimension which assesses tax administration’s output. This model is applied to the case of the Zambia Revenue Authority. The dimensions prove to be valuable not only for assessing the how much but also the how of collecting taxes. They can be a useful tool for future comparative analyses of tax administrations’ capabilities in developing countries.Die weltweite Verbreitung der Good-Governance- und New-Public-Management-Konzepte hat zu einer zunehmenden Konzentration auf staatliche Leistungsfähigkeit und, im Besonderen, auf die Leistungsfähigkeit der Steuererhebung in Entwicklungsländern geführt. Allerdings bleiben die analytischen Werkzeuge für ein umfassendes Verständnis von Leistungsfähigkeit unterentwickelt. Dieser Artikel stellt hierfür ein Modell vor, das die drei Prozess-Dimensionen „Sammeln und Verarbeiten von Informationen“, „Leistungsorientierung der Mitarbeiter“ und „Verantwortlichkeit der Verwaltung“ beinhaltet. „Einnahmeperformanz“ ist die vierte Dimension und erfasst den Output der Steuerverwaltung. Das mehrdimensionale Modell wird für die Analyse der Leistungsfähigkeit der Steuerbehörde Zambias (Zambia Revenue Authority) genutzt. Es erweist sich nicht nur für die Untersuchung des Wieviel, sondern auch des Wie des Erhebens von Steuern als wertvoll. Die vier Dimensionen können in Zukunft zur umfassenden und vergleichenden Analyse der Leistungsfähigkeit verschiedener Steuerverwaltungen in Entwicklungsländern genutzt werden

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 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

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

Plasma Dynamics

Contains research objectives and summary of research on nineteen research projects split into five sections.National Science Foundation (Grant ENG75-06242-A01)U.S. Energy Research and Development Administration (Contract E(11-1)-2766)U.S. Air Force - Office of Scientific Research (Grant AFOSR-77-3143)U.S. Energy Research and Development Administration (Contract EY-76-C2-02-3070.*000