The analysis of boric acid effect on epithelial-mesenchymal transition of CD133 + CD117 + lung cancer stem cells.

Targeting lung cancer stem cells (LC-SCs) for metastasis may be an effective strategy against lung cancer. This study is the first on epithelial-mesenchymal transition (EMT) properties of boric acid (BA) in LC-SCs. LC-SCs were isolated using the magnetic cell sorting (MACS) method. Tumor-sphere formation and flow cytometry confirmed CSC phenotype. The cytotoxic effect of BA was measured by MTT analysis, and the effect of BA on EMT was examined by migration analysis. The expression levels of ZEB1, SNAIL1, ITGA5, CDH1, ITGB1, VIM, COL1A1, and LAMA5 genes were analyzed by RT-qPCR. E-cadherin, Collagen-1, MMP-3, and Vimentin expressions were analyzed immunohistochemically. Boric acid slightly reduced the migration of cancer cells. Increased expression of transcription factor SNAIL (p < 0.001), but not ZEB1, was observed in LC-SCs. mRNA expression levels of ITGB1 (p < 0.01), ITGA5 (p < 0.001), COL1A1 (p < 0.001), and LAMA5 (p < 0.001) increased; CDH1 and VIM decreased in LC-SCs. Moreover, while E-cadherin (p < 0.001) and Collagen-1 (p < 0.01) immunoreactivities significantly increased, MMP-3 (p < 0.001) and Vimentin (p < 0.01) immunoreactivities decreased in BA-treated LC-SCs. To conclude, the current study provided insights into the efficacy and effects of BA against LC-SCs regarding proliferation, EMT, and cell death for future studies.

Comparison of exosomes secreted by synovial fluid-derived mesenchymal stem cells and adipose tissue-derived mesenchymal stem cells in culture for microRNA-127-5p expression during chondrogenesis.

This study aimed to investigate the differences between the exosomal microRNA-127-5p expression profiles of human adipose tissue-derived mesenchymal stem cells (hAT-MSCs) and human synovial fluid-derived mesenchymal stem cells (hSF-MSCs) during chondrogenesis in terms of regenerative treatment of cartilage. Synovial fluid-derived mesenchymal stem cells, adipose tissue-derived mesenchymal stem cells, and human fetal chondroblast cells (hfCCs) were directed to chondrogenic differentiation. Alcian Blue and Safranin O stainings were performed to detect chondrogenic differentiation histochemically. Exosomes derived from chondrogenic differentiated cells and their exosomes were isolated and characterized. microRNA-127-5p expressions were measured by Quantitative reverse transcription PCR (qRT-PCR). Significantly higher levels of microRNA-127-5p expression in differentiated hAT-MSCs exosomes, similar to human fetal chondroblast cells, which are the control group in the chondrogenic differentiation process, were observed. hAT-MSCs are better sources of microRNA-127-5p than hSF-MSCs for stimulating chondrogenesis or in the regenerative therapy of cartilage-related pathologies. hAT-MSCs exosomes are rich sources of microRNA-127-5p and can be an essential candidate for cartilage regeneration treatments.

Application of Machine Learning Techniques for Enuresis Prediction in Children.

INTRODUCTION: As a subset of artificial intelligence, machine learning techniques (MLTs) may evaluate very large and raw datasets. In this study, the aim is to establish a model by MLT for the prediction of enuresis in children. MATERIALS AND METHODS: The study included 8,071 elementary school students. A total of 704 children had enuresis. For analysis of data with MLT, another group including 704 nonenuretic children was structured with stratified sampling. Out of 34 independent variables, 14 with high feature values significantly affecting enuresis were selected. A model of estimation was created by training the data. RESULTS: Fourteen independent variables in order of feature importance value were starting age of toilet training, having urinary urgency, holding maneuvers to prevent voiding, frequency of defecation, history of enuresis in mother and father, having child's own room, parent's education level, history of enuresis in siblings, consanguineous marriage, incomplete bladder emptying, frequent voiding, gender, history of urinary tract infection, and surgery in the past. The best MLT algorithm for the prediction of enuresis was determined as logistic regression algorithm. The total accuracy rate of the model in prediction was 81.3%. CONCLUSION: MLT might provide a faster and easier evaluation process for studies on enuresis with a large dataset. The model in this study may suggest that selected variables with high feature values could be preferred with priority in any screening studies for enuresis. MLT may prevent clinical errors due to human cognitive biases and may help the physicians to be proactive in diagnosis and treatment of enuresis.