The novel approach for deposition of iron oxide nanoparticles with narrow size distribution supported on different sized graphene oxide was reported. Two different samples with different size distributions of graphene oxide (0.5 to 7 mum and 1 to 3 mum) were selectively prepared, and the influence of the flake size distribution on the mitochondrial activity of L929 with WST1 assay in vitro study was also evaluated. Little reduction of mitochondrial activity of the GO-Fe3O4 samples with broader size distribution (0.5 to 7 mum) was observed. The pristine GO samples (0.5 to 7 mum) in the highest concentrations reduced the mitochondrial activity significantly. For GO-Fe3O4 samples with narrower size distribution, the best biocompatibility was noticed at concentration 12.5 mug/mL. The highest reduction of cell viability was noted at a dose 100 mug/mL for GO (1 to 3 mum). It is worth noting that the chemical functionalization of GO and Fe3O4 is a way to enhance the biocompatibility and makes the system independent of the size distribution of graphene oxide.

Hexagonal boron nitride is often referred to as white graphene. This is a 2D layered material, with a structure similar to graphene. It has gained many applications in cosmetics, dental cements, ceramics etc. Hexagonal boron nitride is also used in medicine, as a drug carrier similar as graphene or graphene oxide. Here we report that this material can be exfoliated in two steps: chemical treatment (via modified Hummers method) followed by the sonication treatment. Afterwards, the surface of the obtained material can be efficiently functionalized with gold nanoparticles. The mitochondrial activity was not affected in L929 and MCF-7 cell line cultures during 24-h incubation, whereas longer incubation (for 48, and 72 h) with this nanocomposite affected the cellular metabolism. Lysosome functionality, analyzed using the NR uptake assay, was also reduced in both cell lines. Interestingly, the rate of MCF-7 cell proliferation was reduced when exposed to h-BN loaded with gold nanoparticles. It is believed that h-BN nanocomposite with gold nanoparticles is an attractive material for cancer drug delivery and photodynamic therapy in cancer killing.

In the present study, the analysis of epithelial cells derived from various sources was undertaken, beginning from the mammary gland tissue through the primary cultures and their subsequent passages. The objective of the study was the comparative analysis of the stage in which the epithelial cells obtained from individuals in different lactation cycles and disparate phases of cell culture growth are the most suitable for morphological research and analysis of gene expression activity. The cultures of primary bovine mammary epithelial cells and passages were identified morphologically using immunocytochemical methods. After positive identification, real-time PCRs were performed for the analysis of the expression level of casein genes, whey protein genes, and butyrophilin gene. The most stable reference genes in real-time PCRs for the mammary gland tissue and cell cultures were also determined. Of the reference genes, the UXT and GAPDH genes appeared to be the most stable ones for the mammary gland tissue samples and epithelial cell cultures. The results obtained allowed concluding that the mammary gland samples collected from heifers constituted the most effective material for the initiation of primary cultures. The primary cultures formed characteristic for the mammary gland tissue dome structures, which images were obtained using confocal microscopy. The highest levels of expression of the CSN1S1, CSN1S2, CSN2, and CSN3 genes were detected in primary cultures. The levels of expression of whey protein genes (LALBA and BGL) were highest in the second passage. The most abundant expression of the BTN1A1 gene was observed in primary cultures and the third passage. On the basis of the whole experiment, it can be concluded that primary cultures and cells of the second passage derived from heifer individuals appeared to be the best materials for the analysis of mammary gland function and gene expression activity.

History of Cell Culture | InTechOpen, Published on: 2017-05-10. Authors: Magdalena Jedrzejczak-Silicka

The aim of this review was to present selected machine learning (ML) algorithms used in dairy cattle farming in recent years (2020–2024). A description of ML methods (linear and logistic regression, classification and regression trees, chi-squared automatic interaction detection, random forest, AdaBoost, support vector machines, k-nearest neighbors, naive Bayes classifier, multivariate adaptive regression splines, artificial neural networks, including deep neural networks and convolutional neural networks, as well as Gaussian mixture models and cluster analysis), with some examples of their application in various aspects of dairy cattle breeding and husbandry, is provided. In addition, the stages of model construction and implementation, as well as the performance indicators for regression and classification models, are described. Finally, time trends in the popularity of ML methods in dairy cattle farming are briefly discussed.