Nidogen 1 regulates proliferation and migration/invasion in murine claudin-low mammary tumor cells

Nidogen 1 (NID1) is a glycoprotein found in basement membranes involved in cross-linking collagen IV and laminin. The role of NID in breast cancer has only been evaluated in a small number of studies and the findings of these studies have been inconsistent. Our previous work revealed that highly tumorigenic murine mammary tumor cells express high levels of Nid1 while weakly tumorigenic mammary tumor cells express low levels of Nid1. To investigate Nid1, two stable knockdown lines were created, and Nid1 knockdown was confirmed at both the mRNA and protein level. Nid1 knockdown significantly reduced cell proliferation and migration/invasion and these reductions in proliferation and migration/invasion could be rescued by conditioned media containing NID1 protein. The reduced migration/invasion observed in the Nid1 knockdown cells was not associated with significant alterations in the epithelial gene Cdh1 or the mesenchymal genes Snai1, Snai2, Twist1, Twist2, Zeb1 and Zeb2. Therefore, suppression of Nid1 expression reduces proliferation and migration/invasion in claudin-low murine mammary tumor cells.     

Effects of consumption of contaminated feed with 2,4-dichlorophenoxyacetic acid (2,4-D) on the rat tibia: analysis by Raman spectroscopy and mechanical properties

Lasers in Medical Science - Studies reported the harmful effects of 2,4-D on body tissues, provoking changes in the anatomy and physiology of the kidneys, liver, and testicles. Thus, the objective...     

Optimal tracking control for non-zero-sum games of linear discrete-time systems via off-policy reinforcement learning

In this article, a model-free off-policy reinforcement learning algorithm is applied to address the optimal tracking problem based on multiplayer non-zero-sum games for discrete-time linear systems. In contrast to the traditional method and the policy iteration method for solving the optimal tracking problems, the proposed algorithm operates with the system data rather than the knowledge of the system dynamics. For performing the proposed algorithm, an auxiliary augmented system is constructed via assembling the original system and the reference trajectory while a discount factor is introduced into the performance indexes. It is analyzed that the solutions of the proposed algorithm converge to the Nash equilibrium and the result is not influenced by the probing noise. Two simulations are presented to verify the feasibility and effectiveness of the proposed algorithm.