Opportunistic invasive mycoses in AIDS. An autopsy study of 211 cases.

The opportunistic mycoses are an important cause of morbidity-mortality among patients with severe immunosuppression provoked by HIV. We present a study of 211 serial autopsies of patients with HIV/AIDS infection carried out by our service in a period of 10 years, observing frequency of invasive mycoses of the 44.1%. Pneumocystis carinii infection was the most frequent (32%) with a prevalence of lung affection. Candidiasis follows it in order of frequency with 31.1%, predominantly the oropharyngeal manifestation. Systemic or cerebromeningeal cryptococcosis were serious and common disorder (29%). Diseminated histoplasmosis occurred in 9.6% and in three cases (3.2%) pulmonary aspergillosis was diagnosed as a postmortem discovery in cavity lesions. In our series, other less common HIV-associated were not identified.     

An accurate mathematical model predicting number of dengue cases in tropics

Dengue fever is a systemic viral infection of epidemic proportions in tropical countries. The incidence of dengue fever is ever increasing and has doubled over the last few decades. Estimated 50million new cases are detected each year and close to 10000 deaths occur each year. Epidemics are unpredictable and unprecedented. When epidemics occur, health services are over whelmed leading to overcrowding of hospitals. At present there is no evidence that dengue epidemics can be predicted. Since the breeding of the dengue mosquito is directly influenced by environmental factors, it is plausible that epidemics could be predicted using weather data. We hypothesized that there is a mathematical relationship between incidence of dengue fever and environmental factors and if such relationship exists, new cases of dengue fever in the succeeding months can be predicted using weather data of the current month. We developed a mathematical model using machine learning technique. We used Island wide dengue epidemiology data, weather data and population density in developing the model. We used incidence of dengue fever, average rain fall, humidity, wind speed, temperature and population density of each district in the model. We found that the model is able to predict the incidence of dengue fever of a given month in a given district with precision (RMSE between 18- 35.3). Further, using weather data of a given month, the number of cases of dengue in succeeding months too can be predicted with precision (RMSE 10.4—30). Health authorities can use existing weather data in predicting epidemics in the immediate future and therefore measures to prevent new cases can be taken and more importantly the authorities can prepare local authorities for outbreaks.