Metabolomic Fingerprint Assay in Zebrafish Embryos.

In order for new drugs to enter the market, extensive studies are needed to examine toxic effects. Among others, teratogenicity studies are of paramount importance. Of even higher importance is to gain knowledge on the biological responses that take place upon drug exposure, so as to have a better understanding of the molecular mechanisms that govern developmental changes. Metabolomics is the research field that studies the changes in the chemical composition of metabolites contained within cells. Conducting metabolomics studies results in valuable information. Zebrafish is a vertebrate model organism that bridges in vivo assays and in vivo studies. In this chapter, we propose a metabolomic fingerprint assay for the study of metabolic changes in zebrafish embryos upon exposure to various drugs. The metabolome of zebrafish is extracted, and the 1H-NMR spectrum is recorded. Using open-access metabolomic databases, a list of tentative metabolites is retrieved. The presence of the tentative metabolites is further confirmed by UHPLC-HRMS. Ultimately, after a metabolic pathway analysis, the metabolic network is revealed and useful conclusions can be drawn.

Mammography Datasets for Neural Networks-Survey.

Deep neural networks have gained popularity in the field of mammography. Data play an integral role in training these models, as training algorithms requires a large amount of data to capture the general relationship between the model's input and output. Open-access databases are the most accessible source of mammography data for training neural networks. Our work focuses on conducting a comprehensive survey of mammography databases that contain images with defined abnormal areas of interest. The survey includes databases such as INbreast, the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM), the OPTIMAM Medical Image Database (OMI-DB), and The Mammographic Image Analysis Society Digital Mammogram Database (MIAS). Additionally, we surveyed recent studies that have utilized these databases in conjunction with neural networks and the results they have achieved. From these databases, it is possible to obtain at least 3801 unique images with 4125 described findings from approximately 1842 patients. The number of patients with important findings can be increased to approximately 14,474, depending on the type of agreement with the OPTIMAM team. Furthermore, we provide a description of the annotation process for mammography images to enhance the understanding of the information gained from these datasets.