LIM Homeobox-2 Suppresses Hallmarks of Adult and Pediatric Liver Cancers by Inactivating MAPK/ERK and Wnt/Beta-Catenin Pathways.

Introduction: Hepatocellular carcinoma and hepatoblastoma are two liver cancers characterized by gene deregulations, chromosomal rearrangements, and mutations in Wnt/beta-catenin (Wnt) pathway-related genes. LHX2, a transcriptional factor member of the LIM homeobox gene family, has important functions in embryogenesis and liver development. LHX2 is oncogenic in many solid tumors and leukemia, but its role in liver cancer is unknown. Methods: We analyzed the expression of LHX2 in hepatocellular carcinoma and hepatoblastoma samples using various transcriptomic datasets and biological samples. The role of LHX2 was studied using lentiviral transduction, in vitro cell-based assays (growth, migration, senescence, and apoptosis), molecular approaches (phosphokinase arrays and RNA-seq), bioinformatics, and two in vivo models in chicken and Xenopus embryos. Results: We found a strong connection between LHX2 downregulation and Wnt activation in these two liver cancers. In hepatoblastoma, LHX2 downregulation correlated with multiple poor outcome parameters including higher patient age, intermediate- and high-risk tumors, and low patient survival. Forced expression of LHX2 reduced the proliferation, migration, and survival of liver cancer cells in vitro through the inactivation of MAPK/ERK and Wnt signals. In vivo, LHX2 impeded the development of tumors in chick embryos and repressed the Wnt pathway in Xenopus embryos. RNA-sequencing data and bioinformatic analyses confirmed the deregulation of many biological functions and molecular processes associated with cell migration, cell survival, and liver carcinogenesis in LHX2-expressing hepatoma cells. At a mechanistic level, LHX2 mediated the disassembling of beta-catenin/T-cell factor 4 complex and induced expression of multiple inhibitors of Wnt (e.g., TLE/Groucho) and MAPK/ERK (e.g., DUSPs) pathways. Conclusion: Collectively, our findings demonstrate a tumor suppressive function of LHX2 in adult and pediatric liver cancers.

On electrophysiological signal complexity during biological neuronal network development and maturation.

Developing neuronal populations are assumed to increase their synaptic interactions and generate synchronized activity, such as bursting, during maturation. These effects may arise from increasing interactions of neuronal populations and increasing simultaneous intra-population activity in developing networks. In this paper, we investigated the neuronal network activity and its complexity by means of self-similarity during neuronal network development. We studied the phenomena using computational neuronal network models and actual in vitro microelectrode array data measured from a developing neuronal network of dissociated mouse cortical neurons. To achieve this, we assessed the spiking and bursting characteristics of the networks, and computed the signal complexity with Sample Entropy. The results show that we can relate increasing simultaneous activity in a neuronal population with decreasing entropy, and track the network development and maturation using this. We can conclude that the complexity of neuronal network signals decreases during the maturation. This can emerge from the fact that as networks mature, they exhibit more synchronous activity, thus decreasing the complexity of its signaling. However, increasing the number of interacting populations has lesser effect on the signal complexity. The entropy based measure provides a tool to assess the complexity of the neuronal network activity, and can be useful in the assessment of developing networks or the effects of drugs and toxins on their functioning.

SiMEA: a framework for simulating neurons on multi-electrode array.

A Multi-Electrode Array (MEA) is a practical device for recording the extracellular activity of in-vitro biological culture. Such culture - for instance neurons - is prone to mistakes leading to irrelevant recordings or no recording at all. Additionally, with the expenses generated by in-vitro culture, minimizing risks is a must. This paper proposes a framework designed and implemented for simulating the spatial positioning of neuronal cultures on a MEA. The framework serves as a sandbox for researchers to simulate the model of their MEA experiments before its eventual in-vitro implementation. The framework enables simulating the density of the plated culture, the death of cells over time, choosing diverse reconstructed morphologies of cells, and simulating their spiking activity in interaction with Brian2 simulator.

Data correction for seven activity trackers based on regression models.

Using an activity tracker for measuring activity-related parameters, e.g. steps and energy expenditure (EE), can be very helpful in assisting a person's fitness improvement. Unlike the measuring of number of steps, an accurate EE estimation requires additional personal information as well as accurate velocity of movement, which is hard to achieve due to inaccuracy of sensors. In this paper, we have evaluated regression-based models to improve the precision for both steps and EE estimation. For this purpose, data of seven activity trackers and two reference devices was collected from 20 young adult volunteers wearing all devices at once in three different tests, namely 60-minute office work, 6-hour overall activity and 60-minute walking. Reference data is used to create regression models for each device and relative percentage errors of adjusted values are then statistically compared to that of original values. The effectiveness of regression models are determined based on the result of a statistical test. During a walking period, EE measurement was improved in all devices. The step measurement was also improved in five of them. The results show that improvement of EE estimation is possible only with low-cost implementation of fitting model over the collected data e.g. in the app or in corresponding service back-end.