An overview of the contemporary diagnosis and management approaches for anaplastic thyroid carcinoma.

Thyroid carcinoma is a complex disease with several types, the most common being well-differentiated and undifferentiated. The latter, "undifferentiated carcinoma", also known as anaplastic thyroid carcinoma (ATC), is a highly aggressive malignant tumor accounting for less than 0.2% of all thyroid carcinomas and carries a poor prognosis with a median survival of 5 months. BRAF gene mutations are the most common molecular factor associated with this type of thyroid carcinoma. Recent advances in targeted biological agents, immunotherapy, stem cell therapy, nanotechnology, the dabrafenib/trametinib combination therapy, immune checkpoint inhibitors (ICI) and artificial intelligence offer novel treatment options. The combination therapy of dabrafenib and trametinib is the current standard treatment for patients with BRAF-V600E gene mutations. Besides, the dabrafenib/trametinib combination therapy, ICI, used alone or in combination with targeted therapies have raised some hopes for improving the prognosis of this deadly disease. Younger age, earlier tumor stage and radiotherapy are all prognostic factors for improved outcomes. Ultimately, therapeutic regimens should be tailored to the individual patient based on surveillance and epidemiological data, and a multidisciplinary approach is essential.

Nuclear miR-150 enhances hepatic lipid accumulation by targeting RNA transcripts overlapping the PLIN2 promoter.

Alcohol-associated liver disease is a prevalent chronic liver disease caused by excessive ethanol consumption. This study aims to investigate the role of miR-150 in regulating hepatic lipid homeostasis in alcoholic fatty liver (AFL). miR-150 was mainly distributed in the nucleus of hepatocytes and correlated with the degree of liver injury. The decreased expression of miR-150 observed in AFL was a compensatory response to ethanol-induced hepatic steatosis. Overexpression of miR-150 facilitated hepatic lipid accumulation in cellulo and exacerbated ethanol-induced liver steatosis in vivo. In silico analysis identified perilipin-2 (PLIN2) as a potential target gene of miR-150. miR-150 activated PLIN2 transcription by directly binding the RNA transcripts overlapping PLIN2 promoter and facilitating the recruitment of DNA helicase DHX9 and RNA polymeraseⅡ. Overall, our study provides fresh insights into the homeostasis regulation of hepatic steatosis induced by ethanol and identifies miR-150 as a pro-steatosis effector driving transcriptional PLIN2 gene activation.

Ferroptosis contributes to ethanol-induced hepatic cell death via labile iron accumulation and GPx4 inactivation.

Alcohol abuse is a significant cause of global morbidity and mortality, with alcoholic liver disease (ALD) being a common consequence. The pathogenesis of ALD involves various cellular processes, including oxidative stress, inflammation, and hepatic cell death. Recently, ferroptosis, an iron-dependent form of programmed cell death, has emerged as a potential mechanism in many diseases. However, the specific involvement and regulatory mechanisms of ferroptosis in ALD remain poorly understood. Here we aimed to investigate the presence and mechanism of alcohol-induced ferroptosis and the involvement of miRNAs in regulating ferroptosis sensitivity. Our findings revealed that long-term ethanol feeding induced ferroptosis in male mice, as evidenced by increased expression of ferroptosis-related genes, lipid peroxidation, and labile iron accumulation in the liver. Furthermore, we identified dysregulation of the methionine cycle and transsulfuration pathway, leading to severe glutathione (GSH) exhaustion and indirect deactivation of glutathione peroxidase 4 (GPx4), a critical enzyme in preventing ferroptosis. Additionally, we identified miR-214 as a ferroptosis regulator in ALD, enhancing hepatocyte ferroptosis by transcriptionally activating the expression of ferroptosis-driver genes. Our study provides novel insights into the involvement and regulatory mechanisms of ferroptosis in ALD, highlighting the potential therapeutic implications of targeting ferroptosis and miRNAs in ALD management.

Federated Visualization: A Privacy-Preserving Strategy for Aggregated Visual Query.

We present a novel privacy preservation strategy for aggregated visual query of decentralized data. The key idea is to imitate the flowchart of the federated learning framework, and reformulate the visualization process within a federated infrastructure. The federation of visualization is fulfilled by leveraging a shared global module that composes the encrypted externalizations of transformed visual features of data pieces in local modules. We design two implementations of federated visualization: a prediction-based scheme, and a query-based scheme. We demonstrate the effectiveness of our approach with a set of visual forms, and verify its robustness with evaluations. We report the value of federated visualization in real scenarios with an expert review.

Exemplar-based Layout Fine-tuning for Node-link Diagrams.

We design and evaluate a novel layout fine-tuning technique for node-link diagrams that facilitates exemplar-based adjustment of a group of substructures in batching mode. The key idea is to transfer user modifications on a local substructure to other substructures in the entire graph that are topologically similar to the exemplar. We first precompute a canonical representation for each substructure with node embedding techniques and then use it for on-the-fly substructure retrieval. We design and develop a light-weight interactive system to enable intuitive adjustment, modification transfer, and visual graph exploration. We also report some results of quantitative comparisons, three case studies, and a within-participant user study.

Evaluating Perceptual Bias During Geometric Scaling of Scatterplots.

Scatterplots are frequently scaled to fit display areas in multi-view and multi-device data analysis environments. A common method used for scaling is to enlarge or shrink the entire scatterplot together with the inside points synchronously and proportionally. This process is called geometric scaling. However, geometric scaling of scatterplots may cause a perceptual bias, that is, the perceived and physical values of visual features may be dissociated with respect to geometric scaling. For example, if a scatterplot is projected from a laptop to a large projector screen, then observers may feel that the scatterplot shown on the projector has fewer points than that viewed on the laptop. This paper presents an evaluation study on the perceptual bias of visual features in scatterplots caused by geometric scaling. The study focuses on three fundamental visual features (i.e., numerosity, correlation, and cluster separation) and three hypotheses that are formulated on the basis of our experience. We carefully design three controlled experiments by using well-prepared synthetic data and recruit participants to complete the experiments on the basis of their subjective experience. With a detailed analysis of the experimental results, we obtain a set of instructive findings. First, geometric scaling causes a bias that has a linear relationship with the scale ratio. Second, no significant difference exists between the biases measured from normally and uniformly distributed scatterplots. Third, changing the point radius can correct the bias to a certain extent. These findings can be used to inspire the design decisions of scatterplots in various scenarios.

RSATree: Distribution-Aware Data Representation of Large-Scale Tabular Datasets for Flexible Visual Query.

Analysts commonly investigate the data distributions derived from statistical aggregations of data that are represented by charts, such as histograms and binned scatterplots, to visualize and analyze a large-scale dataset. Aggregate queries are implicitly executed through such a process. Datasets are constantly extremely large; thus, the response time should be accelerated by calculating predefined data cubes. However, the queries are limited to the predefined binning schema of preprocessed data cubes. Such limitation hinders analysts' flexible adjustment of visual specifications to investigate the implicit patterns in the data effectively. Particularly, RSATree enables arbitrary queries and flexible binning strategies by leveraging three schemes, namely, an R-tree-based space partitioning scheme to catch the data distribution, a locality-sensitive hashing technique to achieve locality-preserving random access to data items, and a summed area table scheme to support interactive query of aggregated values with a linear computational complexity. This study presents and implements a web-based visual query system that supports visual specification, query, and exploration of large-scale tabular data with user-adjustable granularities. We demonstrate the efficiency and utility of our approach by performing various experiments on real-world datasets and analyzing time and space complexity.