Non-AIDS defining cancers in the D:A:D Study-time trends and predictors of survival : a cohort study

BACKGROUND:Non-AIDS defining cancers (NADC) are an important cause of morbidity and mortality in HIV-positive individuals. Using data from a large international cohort of HIV-positive individuals, we described the incidence of NADC from 2004-2010, and described subsequent mortality and predictors of these.METHODS:Individuals were followed from 1st January 2004/enrolment in study, until the earliest of a new NADC, 1st February 2010, death or six months after the patient's last visit. Incidence rates were estimated for each year of follow-up, overall and stratified by gender, age and mode of HIV acquisition. Cumulative risk of mortality following NADC diagnosis was summarised using Kaplan-Meier methods, with follow-up for these analyses from the date of NADC diagnosis until the patient's death, 1st February 2010 or 6 months after the patient's last visit. Factors associated with mortality following NADC diagnosis were identified using multivariable Cox proportional hazards regression.RESULTS:Over 176,775 person-years (PY), 880 (2.1%) patients developed a new NADC (incidence: 4.98/1000PY [95% confidence interval 4.65, 5.31]). Over a third of these patients (327, 37.2%) had died by 1st February 2010. Time trends for lung cancer, anal cancer and Hodgkin's lymphoma were broadly consistent. Kaplan-Meier cumulative mortality estimates at 1, 3 and 5 years after NADC diagnosis were 28.2% [95% CI 25.1-31.2], 42.0% [38.2-45.8] and 47.3% [42.4-52.2], respectively. Significant predictors of poorer survival after diagnosis of NADC were lung cancer (compared to other cancer types), male gender, non-white ethnicity, and smoking status. Later year of diagnosis and higher CD4 count at NADC diagnosis were associated with improved survival. The incidence of NADC remained stable over the period 2004-2010 in this large observational cohort.CONCLUSIONS:The prognosis after diagnosis of NADC, in particular lung cancer and disseminated cancer, is poor but has improved somewhat over time. Modifiable risk factors, such as smoking and low CD4 counts, were associated with mortality following a diagnosis of NADC

Global nutrient profiling by Phenotype MicroArrays: a tool complementing genomic and proteomic studies in conidial fungi*

Conidial fungi or molds and mildews are widely used in modern biotechnology as producers of antibiotics and other secondary metabolites, industrially important enzymes, chemicals and food. They are also important pathogens of animals including humans and agricultural crops. These various applications and extremely versatile natural phenotypes have led to the constantly growing list of complete genomes which are now available. Functional genomics and proteomics widely exploit the genomic information to study the cell-wide impact of altered genes on the phenotype of an organism and its function. This allows for global analysis of the information flow from DNA to RNA to protein, but it is usually not sufficient for the description of the global phenotype of an organism. More recently, Phenotype MicroArray (PM) technology has been introduced as a tool to characterize the metabolism of a (wild) fungal strain or a mutant. In this article, we review the background of PM applications for fungi and the methodic requirements to obtain reliable results. We also report examples of the versatility of this tool

Opportunistic infections and AIDS malignancies early after initiating combination antiretroviral therapy in high-income countries

Background: There is little information on the incidence of AIDS-defining events which have been reported in the literature to be associated with immune reconstitution inflammatory syndrome (IRIS) after combined antiretroviral therapy (cART) initiation. These events include tuberculosis, mycobacterium avium complex (MAC), cytomegalovirus (CMV) retinitis, progressive multifocal leukoencephalopathy (PML), herpes simplex virus (HSV), Kaposi sarcoma, non-Hodgkin lymphoma (NHL), cryptococcosis and candidiasis. Methods: We identified individuals in the HIV-CAUSAL Collaboration, which includes data from six European countries and the US, who were HIV-positive between 1996 and 2013, antiretroviral therapy naive, aged at least 18 years, hadCD4+ cell count and HIV-RNA measurements and had been AIDS-free for at least 1 month between those measurements and the start of follow-up. For each AIDS-defining event, we estimated the hazard ratio for no cART versus less than 3 and at least 3 months since cART initiation, adjusting for time-varying CD4+ cell count and HIV-RNA via inverse probability weighting. Results: Out of 96 562 eligible individuals (78% men) with median (interquantile range) follow-up of 31 [13,65] months, 55 144 initiated cART. The number of cases varied between 898 for tuberculosis and 113 for PML. Compared with non-cART initiation, the hazard ratio (95% confidence intervals) up to 3 months after cART initiation were 1.21 (0.90-1.63) for tuberculosis, 2.61 (1.05-6.49) for MAC, 1.17 (0.34-4.08) for CMV retinitis, 1.18 (0.62-2.26) for PML, 1.21 (0.83-1.75) for HSV, 1.18 (0.87-1.58) for Kaposi sarcoma, 1.56 (0.82-2.95) for NHL, 1.11 (0.56-2.18) for cryptococcosis and 0.77 (0.40-1.49) for candidiasis. Conclusion: With the potential exception of mycobacterial infections, unmasking IRIS does not appear to be a common complication of cART initiation in high-income countries