S2DIA: a diagnostic system for Diabetes mellitus using SANA platform.

Currently, Diabetes is a very common disease around the world, and with an increase in sedentary lifestyles, obesity and an aging population the number of people with Diabetes worldwide will increase by more than 50%. In this context, the MIT (Massachusetts Institute of Technology) developed the SANA platform, which brings the benefits of information technology to the field of healthcare. It offers healthcare delivery in remote areas, improves patient access to medical specialists for faster, higher quality, and more cost effective diagnosis and intervention. For these reasons, we developed a system for diagnosis of Diabetes using the SANA platform, called S2DIA. It is the first step towards knowing the risks for type 2 Diabetes, and it will be evaluated, especially, in remote/poor areas of Brazil.

Counting pollen grains using readily available, free image processing and analysis software.

BACKGROUND AND AIMS: Although many methods exist for quantifying the number of pollen grains in a sample, there are few standard methods that are user-friendly, inexpensive and reliable. The present contribution describes a new method of counting pollen using readily available, free image processing and analysis software. METHODS: Pollen was collected from anthers of two species, Carduus acanthoides and C. nutans (Asteraceae), then illuminated on slides and digitally photographed through a stereomicroscope. Using ImageJ (NIH), these digital images were processed to remove noise and sharpen individual pollen grains, then analysed to obtain a reliable total count of the number of grains present in the image. A macro was developed to analyse multiple images together. To assess the accuracy and consistency of pollen counting by ImageJ analysis, counts were compared with those made by the human eye. KEY RESULTS AND CONCLUSIONS: Image analysis produced pollen counts in 60 s or less per image, considerably faster than counting with the human eye (5-68 min). In addition, counts produced with the ImageJ procedure were similar to those obtained by eye. Because count parameters are adjustable, this image analysis protocol may be used for many other plant species. Thus, the method provides a quick, inexpensive and reliable solution to counting pollen from digital images, not only reducing the chance of error but also substantially lowering labour requirements.