Therapeutic targeting potential of the protein lysine and arginine methyltransferases to reverse cancer chemoresistance.

Cancer treatments have continued to improve tremendously over the past decade, but therapy resistance is still a common, major factor encountered by patients diagnosed with cancer. Chemoresistance arises due to various circumstances and among these causes, increasing evidence has shown that enzymes referred to as protein methyltransferases (PMTs) play a significant role in the development of chemoresistance in various cancers. These enzymes are responsible for the methylation of different amino acids, particularly lysine and arginine, via protein lysine methyltransferases (PKMTs) and protein arginine methyltransferases (PRMTs), respectively.

5-Fluorouracil dose escalation generated desensitized colorectal cancer cells with reduced expression of protein methyltransferases and no epithelial-to-mesenchymal transition potential.

Background: Colorectal cancer (CRC) is one of the most frequently diagnosed cancers. In many cases, the poor prognosis of advanced CRC is associated with resistance to treatment with chemotherapeutic drugs such as 5-Fluorouracil (5-FU). The epithelial-to-mesenchymal transition (EMT) and dysregulation in protein methylation are two mechanisms associated with chemoresistance in many cancers.

BlinkLinMulT: Transformer-Based Eye Blink Detection.

This work presents BlinkLinMulT, a transformer-based framework for eye blink detection. While most existing approaches rely on frame-wise eye state classification, recent advancements in transformer-based sequence models have not been explored in the blink detection literature. Our approach effectively combines low- and high-level feature sequences with linear complexity cross-modal attention mechanisms and addresses challenges such as lighting changes and a wide range of head poses.

Statistical Approaches for Establishing Appropriate Immunogenicity Assay Cut Points: Impact of Sample Distribution, Sample Size, and Outlier Removal.

The statistical assessments needed to establish anti-drug antibody (ADA) assay cut points (CPs) can be challenging for bioanalytical scientists. Poorly established CPs that are too high could potentially miss treatment emergent ADA or, when set too low, result in detection of responses that may have no clinical relevance. We evaluated 16 validation CP datasets generated with ADA assays at Regeneron's bioanalytical laboratory and compared results obtained from different CP calculation tools.