Have medical specimens ever found nuclear microtubules?10 answersYes, medical specimens have indeed found nuclear microtubules, as evidenced by various studies across different cell types and conditions. For instance, liver biopsy specimens from a patient with delta-positive hepatitis revealed filamentous or microtubular structures within the nuclei of hepatocytes, suggesting a possible association with the delta agent. Similarly, research on damaged DNA has uncovered DNA damage-inducible intranuclear microtubule filaments (DIMs) that mobilize damaged DNA to promote repair, indicating the presence and functional significance of intranuclear microtubules in the DNA repair process.
Further investigations have identified nuclear tubules in the nucleus of HeLa cells, although their function was not fully ascertained. Studies on various cell lines, including rat C6 glioma cells and human MCF-7 breast carcinoma cells, have detected nuclear beta(II)-tubulin, suggesting that microtubules or their components can exist within the nucleus and may be involved in processes such as apoptosis. Additionally, research has shown that microtubules play a role in the structural integrity and positioning of nuclei within cells, as demonstrated in Spirogyra crassa.
The presence of the βII isotype of tubulin in the nuclei of certain cultured cells and its potential involvement in nuclear reassembly and cancer progression further supports the existence of nuclear microtubules. Variable aggregates of tubules have also been observed within polyploid nuclei in various tissues, indicating their widespread occurrence. Studies on nuclear protrusions in cytologic specimens suggest that microtubules are involved in aberrant cell division processes. The βII isotype of tubulin has been found in the nuclei of several cancer cell lines, hinting at a transformation-related localization of microtubules to the nucleus. Lastly, microtubules have been observed in close association with nuclear pores, suggesting their involvement in nucleus-cytoplasm transport and other nuclear activities.
What causes nuclear blebbing in cancer?4 answersNuclear blebbing in cancer is primarily caused by abnormalities in the nuclear structure, particularly involving lamin proteins. The nucleoskeleton, composed of lamin proteins, plays a crucial role in maintaining nuclear shape and stability. Studies suggest that nuclear blebs, protruding structures on the nucleus, are associated with pathological gene expression and are enriched in A-type lamins. These blebs can form due to the opening of the lamin meshwork, leading to structural distortions in the nucleus. Additionally, cytoplasmic membrane dynamics, such as blebbing, can pull chromatin out of the nucleus through lamina breaks, contributing to the formation of nuclear blebs. Overall, disruptions in the lamin meshwork and chromatin extrusion through lamina breaks are key factors contributing to nuclear blebbing in cancer.
What is based paper about LLMs?5 answersLarge language models (LLMs) have been the focus of several papers. One paper explores how to enable cost-effective interactions between an agent and an LLM by using a reinforcement learning based mediator model. Another paper investigates the intention to use generative tools based on LLMs in the context of work, finding that acceptability is influenced by perceived usefulness and frequency of use. Additionally, there are papers that address the issue of verifying the accuracy of LLM-generated answers. These papers propose methods to automatically verify generated answers against external sources, highlighting the need to detect and address hallucinations. Overall, these papers contribute to understanding the capabilities, limitations, and ethical concerns associated with LLMs in various applications.
Which model system is best to study nuclear proteins?4 answersThe yeast Saccharomyces cerevisiae is a popular model system for studying nuclear proteins due to its biochemical and genetic amenability, as well as the availability of procedures and reagents for isolating protein complexes and studying macromolecules in vivo. Additionally, advances in microscopy and data analysis have enabled quantitative nuclear import studies in yeast, providing new insights into the mechanisms of nucleo-cytoplasmic transport. Furthermore, a system of vectors has been developed that allows for the screening and visualization of nuclear proteins in planta, making it a convenient experimental system for identifying and studying nuclear proteins in plants. Overall, the yeast Saccharomyces cerevisiae and plant systems offer valuable tools for investigating nuclear proteins and their functions.
What is the latest research on LLMs?5 answersLarge Language Models (LLMs) have been the subject of recent research in various domains, including medicine, science, law, and reasoning tasks. One line of research focuses on LLM-driven causal discovery, proposing a framework that combines knowledge-based LLM causal analysis with data-driven causal structure learning. Another research direction investigates the reasoning abilities of LLMs, particularly in analogical, spatial, and moral reasoning tasks. Additionally, LLMs have been examined in the context of user rating prediction, comparing their performance with traditional recommender models. These studies highlight the potential of LLMs in advancing causal analysis, understanding diverse reasoning proficiencies, and exploring user preferences based on previous behavior.
What are the latest discoveries in LLM?5 answersLarge Language Models (LLMs) have made significant advancements in causal analysis and discovery in various domains such as medicine, science, and law. Recent research has shown that LLMs can combine knowledge-based analysis with data-driven causal structure learning, resulting in a novel framework for causal discovery. By integrating LLM expertise on existing causal mechanisms into statistical analysis of objective data, LLMs can enhance the quality of recovered causal structures from data. This pioneering study emphasizes the new frontier that LLMs are opening for classical causal discovery and inference, encouraging the widespread adoption of LLM capabilities in data-driven causal analysis. Additionally, LLMs have been used to study effective field theory around classical background geometries with a gauge theory dual, providing insights into nearby states and the topology of these states beyond the strict infinite N limit.
