We present SalivaPRINT, which serves as a patient characterization tool to identify molecular weights related with particular conditions and, from there, find proteins, which may be involved in the underlying dysregulated cellular mechanisms. The proposed analysis strategy has the potential to boost personalized diagnosis. To our knowledge this is the first independent tool for electrophoretic protein profile evaluation and is crucial when a large number of complex electrophoretic profiles needs to be compared and classified.
Currently, the molecular diagnosis is based on the quantification of RNA, proteins and metabolites because they present changes in their quantity related to clinical situations. The same molecules are not generally suitable for early diagnosis or to follow clinical evolution, making necessary strategies to evaluate the complete molecular scenario. There are already experimental strategies that allow the determination of total protein profiles from saliva samples (the SalivaPrint). The goal of this work is to identify a profile of saliva proteins (similar to a fingerprint) and, using computational methods, identify how this profiles changes with age and gender. So far it has been possible to collect 79 samples as well as the metadata associated with each sample using an electronic questionnaire developed by us. A total protein profile was obtained and their association with gender was verified using statistical methods. Currently we are developing the Python scripts for automatic data acquiring and normalization. Total protein profiles annotation on a database (SalivaPrintDB) and their integration with the factors that affects them using machine learning strategies can empower the use of the approach proposed on this work as a tool for monitoring the individual's health status.
The aim of this study was to determine whether ultrasound imaging is an efficient method to assess subcutaneous fat in cats, the anatomical sites where more significant fat deposition occurs, and if there is a correlation between subcutaneous fat thickness and serum levels of cholesterol, triglycerides, and fructosamine. A total of 30 healthy adult cats were used and divided into three groups of 10 animals each, based on the estimated body condition score (BCS). The ideal group (IG) included cats with BCS 3; the overweight group (OWG), with BCS 4; and the obese group (OG), with BCS 5. Ultrasonographic measurement of subcutaneous fat was conducted in five anatomical regions: lumbar, abdominal, thoracic, femoral, and pectoral. We observed that obese cats had greater fat deposition in the abdominal and thoracic regions when compared to those with ideal weight, and that cholesterol and triglyceride levels were higher with the increase in subcutaneous fat thickness in the thoracic region. Nonetheless, there were no differences in fat deposition in the OWG compared to cats from the IG and OG. Ultrasonography allows to differentiate obese cats from those with ideal weight through the analysis of subcutaneous fat thickness in the abdominal and thoracic regions, rendering this method efficient and less subjective.
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