Human dental pulp derived from exfoliated deciduous teeth has been described as a promising alternative source of multipotent stem cells. While these cells share certain similarities with mesenchymal stem-like cells (MSC) isolated from other tissues, basically they are still poorly characterized. In this study, for the first time, a proteomic map of abundantly expressed proteins in stromal cells derived from the dental pulp of human exfoliated deciduous teeth (SHED) was established. We also analyzed proteomic signatures of 2 clonal strains derived from SHEDs by single-cell cloning. The SHEDs were established from enzyme-disaggregated deciduous dental pulp from 6-year-old children. They had typical fibroblastoid morphology and high colony-forming efficiency index (16.4%). Cloning was performed at the second passage using limiting dilution in a 96-well plate (0.3 cell/well). Differentiation assessment revealed strong osteogenic but no adipogenic potential of the SHEDs in either clonal strain. The cells expressed characteristic antigens of MSC-like cells, including CD73, CD90, CD105, CD146, and did not express hematopoietic markers CD14, CD34, and CD45, as assessed with FACS analysis. For proteomic studies, cytosolic and nuclear proteins were analyzed with 2-dimensional gel electrophoresis (2-DE) and identified using matrix-assisted laser desorption/ionization (MALDI)-time of flight (TOF)-mass spectrometry (MS). All proteins were identified with high level of confidence (the lowest sequence coverage was 27%). Identification of highly expressed proteins in SHEDs revealed proteomic profiles very similar to that of MSC-like cells derived from other tissues. We also found a high degree of similarity between proteomic signatures of primary SHEDs and clonal cell strains. Thus, our data confirm a close resemblance between SHEDs and MSC-like cells from other tissues and may serve as starting point for creating-comprehensive proteomic maps.

Creation of head 3D models from videos or pictures of the head by using close-range photogrammetry techniques has many applications in clinical, commercial, industrial, artistic, and entertainment areas. This work aims to create a methodology for improving 3D head reconstruction, with a focus on using selfie videos as the data source. Then, using this methodology, we seek to propose changes for the general-purpose 3D reconstruction algorithm to improve the head reconstruction process. We define the improvement of the 3D head reconstruction as an increase of reconstruction quality (which is lowering reconstruction errors of the head and amount of semantic noise) and reduction of computational load. We proposed algorithm improvements that increase reconstruction quality by removing image backgrounds and by selecting diverse and high-quality frames. Algorithm modifications were evaluated on videos of the mannequin head. Evaluation results show that baseline reconstruction is improved 12 times due to the reduction of semantic noise and reconstruction errors of the head. The reduction of computational demand was achieved by reducing the frame number needed to process, reducing the number of image matches required to perform, reducing an average number of feature points in images, and still being able to provide the highest precision of the head reconstruction.

In the study, honey from oilseed rape Brassica napus L., and both hand-collected (winter rape Visby and Cult) and bee-collected pollen of oilseed rape were analyzed for their proteome content, in order to see if any plant proteins were present to allow the proteo-typing of the oilseed rape honey. Proteins were fractionated by two-dimensional gel electrophoresis (2DE), stained by Coomassie blue and then analyzed by mass spectrometry. All identified proteins were divided into few groups due to their biological function. In 2DE gels with separated proteins from blossom honey, only bee (Apis mellifera) main proteins (Major royal jelly protein 1-5 and Glucosidase) were found. So we analyzed all proteins using gel-free based analysis with the SYNAPT G2 high definition mass spectrometry. We identified proteins that were present in both oilseed rape pollen and honey (Bna, Polygalacturonase, Non-specific lipid-transfer protein, GAPDH and others). We believe that these proteins are important for the nutritional value of plant pollen-enriched honey and further research is required on honey and honeybee pollen protein.

Two-dimensional electrophoresis gels (2DE, 2DEG) are the result of the procedure of separating, based on two molecular properties, a protein mixture on gel. Separated similar proteins concentrate in groups, and these groups appear as dark spots in the captured gel image. Gel images are analyzed to detect distinct spots and determine their peak intensity, background, integrated intensity, and other attributes of interest. One of the approaches to parameterizing the protein spots is spot modeling. Spot parameters of interest are obtained after the spot is approximated by a mathematical model. The development of the modeling algorithm requires a rich, diverse, representative dataset. The primary goal of this research is to develop a method for generating a synthetic protein spot dataset that can be used to develop 2DEG image analysis algorithms. The secondary objective is to evaluate the usefulness of the created dataset by developing a neural-network-based protein spot reconstruction algorithm that provides parameterization and denoising functionalities. In this research, a spot modeling algorithm based on autoencoders is developed using only the created synthetic dataset. The algorithm is evaluated on real and synthetic data. Evaluation results show that the created synthetic dataset is effective for the development of protein spot models. The developed algorithm outperformed all baseline algorithms in all experimental cases.