Estimates of global, regional, and national incidence, prevalence, and mortality of HIV, 1980-2015 : the Global Burden of Disease Study 2015

Background Timely assessment of the burden of HIV/AIDS is essential for policy setting and programme evaluation. In this report from the Global Burden of Disease Study 2015 (GBD 2015), we provide national estimates of levels and trends of HIV/AIDS incidence, prevalence, coverage of antiretroviral therapy (ART), and mortality for 195 countries and territories from 1980 to 2015. Methods For countries without high-quality vital registration data, we estimated prevalence and incidence with data from antenatal care clinics and population-based seroprevalence surveys, and with assumptions by age and sex on initial CD4 distribution at infection, CD4 progression rates (probability of progression from higher to lower CD4 cell-count category), on and off antiretroviral therapy (ART) mortality, and mortality from all other causes. Our estimation strategy links the GBD 2015 assessment of all-cause mortality and estimation of incidence and prevalence so that for each draw from the uncertainty distribution all assumptions used in each step are internally consistent. We estimated incidence, prevalence, and death with GBD versions of the Estimation and Projection Package (EPP) and Spectrum software originally developed by the Joint United Nations Programme on HIV/AIDS (UNAIDS). We used an open-source version of EPP and recoded Spectrum for speed, and used updated assumptions from systematic reviews of the literature and GBD demographic data. For countries with high-quality vital registration data, we developed the cohort incidence bias adjustment model to estimate HIV incidence and prevalence largely from the number of deaths caused by HIV recorded in cause-of-death statistics. We corrected these statistics for garbage coding and HIV misclassifi cation. Findings Global HIV incidence reached its peak in 1997, at 3·3 million new infections (95% uncertainty interval [UI] 3·1–3·4 million). Annual incidence has stayed relatively constant at about 2·6 million per year (range 2·5–2·8 million) since 2005, after a period of fast decline between 1997 and 2005. The number of people living with HIV/AIDS has been steadily increasing and reached 38·8 million (95% UI 37·6–40·4 million) in 2015. At the same time, HIV/AIDS mortality has been declining at a steady pace, from a peak of 1·8 million deaths (95% UI 1·7–1·9 million) in 2005, to 1·2 million deaths (1·1–1·3 million) in 2015. We recorded substantial heterogeneity in the levels and trends of HIV/AIDS across countries. Although many countries have experienced decreases in HIV/AIDS mortality and in annual new infections, other countries have had slowdowns or increases in rates of change in annual new infections. Interpretation Scale-up of ART and prevention of mother-to-child transmission has been one of the great successes of global health in the past two decades. However, in the past decade, progress in reducing new infections has been slow, development assistance for health devoted to HIV has stagnated, and resources for health in low-income countries have grown slowly. Achievement of the new ambitious goals for HIV enshrined in Sustainable Development Goal 3 and the 90-90-90 UNAIDS targets will be challenging, and will need continued eff orts from governments and international agencies in the next 15 years to end AIDS by 2030.

Molecular Mechanisms of Frontotemporal Lobar Degeneration

The aim of this thesis was to identify genetic factors involved in frontotemporal lobar degeneration (FTLD), a neurodegenerative disorder clinically characterised by a progressive change in personality, behaviour and language. FTLD is a genetically complex disorder and a positive family history is found in up to 40% of the cases. In 10-20% of the familial cases the disease can be explained by mutations in the gene encoding the microtubule associated protein tau (MAPT). In the first study we describe the clinical and neuropathological features of a Finnish family with FTLD caused by a mutation in MAPT. We also provide evidence that the pathogenic mechanism of this mutation is through altered splicing of MAPT transcripts. Recently, mutations in the gene encoding progranulin (PGRN) were identified as a major cause of FTLD. In the second study we describe a Swedish family with FTLD caused by a frameshift mutation in PGRN. We provide a clinical and neuropathological description of the family, as well as evidence that the pathogenicity of this mutation is through nonsense-mediated decay of the mutant mRNA transcripts and PGRN haploinsufficiency. In the third study we describe a novel PGRN splice site mutation and a previously described PGRN frameshift mutation, found in a mutation screen of 51 FTLD patients. We describe the clinical and neuropathological characteristics of the mutation carriers and demonstrate that haploinsufficiency is the pathogenic mechanism of the two mutations. In the fourth study we investigate the prevalence of PGRN and MAPT gene dosage alterations in 39 patients with FTLD. No gene dosage alterations were identified, indicating that variations in copy number of the PGRN and MAPT genes are not a common cause of disease, at least not in this FTLD patient collection.

Unsupervised learning to cluster the disease stages in parkinson's disease

Parkinson's disease (PD) is the second most common neurodegenerative disorder (after Alzheimer's disease) and directly affects upto 5 million people worldwide. The stages (Hoehn and Yaar) of disease has been predicted by many methods which will be helpful for the doctors to give the dosage according to it. So these methods were brought up based on the data set which includes about seventy patients at nine clinics in Sweden. The purpose of the work is to analyze unsupervised technique with supervised neural network techniques in order to make sure the collected data sets are reliable to make decisions. The data which is available was preprocessed before calculating the features of it. One of the complex and efficient feature called wavelets has been calculated to present the data set to the network. The dimension of the final feature set has been reduced using principle component analysis. For unsupervised learning k-means gives the closer result around 76% while comparing with supervised techniques. Back propagation and J4 has been used as supervised model to classify the stages of Parkinson's disease where back propagation gives the variance percentage of 76-82%. The results of both these models have been analyzed. This proves that the data which are collected are reliable to predict the disease stages in Parkinson's disease.