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"The dog is underrepresented large animal translational model for orthopedic cell-based tissue engineering," says the author. Although chondrogenic differentiation of canine multipotent stromal cells has been described using the classic micromass method has been reported, cMSCs respond in a inconsistent manner to this approach. The primary aim of this research was to develop a three-dimensional, serum-free Collagen Type I system to promote cMSC chondrogenesis and, once established, to determine the effect of chondrogenic growth factors on cMSC chondrogenesis. Both bone morphogenic protein-2 and basic fibroblast growth factor, u03b23, produced larger chondrogenic structures in the presence of dexamethasone and basic fibroblast growth factor, but BMP-2 was required to reach histological characteristics of chondrocytes. The 3D, serum-free Collagen Type-I assay described herein was useful in determining cMSC differentiation, and it will also function as a useful tool to characterize cMSCs or to produce tissue engineering constructs for clinical use.
Source link: https://doi.org/10.1371/journal.pone.0269571
"Feature selection is a useful machine learning tool to solve this problem. " BOSO stands for a new feature selection algorithm for linear regression. A comparison of BOSO using key algorithms from the literature was conducted, showing a high degree of feature selection in high-dimensional datasets. BOSO's proof-of-concept for predicting drug sensitivity in cancer is shown. Author review: We present BOSO, a novel approach to perform feature selection in linear regression models. paraphrasedoutput:Feature selection in machine learning consists of identifying the subset of input variables that are correctly associated with the response variable that is intended to be predicted. "Using RNA-seq results and drug screenings from the GDSC database, the relevance of BOSO is shown in the prediction of drug sensitivity of cancer cell lines.
Source link: https://doi.org/10.1371/journal.pcbi.1010180
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