Mesenchymal stem cells are widely used in many pre-clinical and clinical settings. Despite advances in molecular technology; the migration and homing activities of these cells in in vivo systems are not well understood. Labelling mesenchymal stem cells with gold nanoparticles has no cytotoxic effect and may offer suitable indications for stem cell tracking. Here, we report a simple protocol to label mesenchymal stem cells using 80 nm gold nanoparticles. Once the cells and particles were incubated together for 24 h, the labelled products were injected into the rat subretinal layer. Micro-computed tomography was then conducted on the 15th and 30th day post-injection to track the movement of these cells, as visualized by an area of hyperdensity from the coronal section images of the rat head. In addition, we confirmed the cellular uptake of the gold nanoparticles by the mesenchymal stem cells using transmission electron microscopy. As opposed to other methods, the current protocol provides a simple, less labour-intensive and more efficient labelling mechanism for real-time cell tracking. Finally, we discuss the potential manipulations of gold nanoparticles in stem cells for cell replacement and cancer therapy in ocular disorders or diseases.
Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms-Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine-in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.