AlphaFold2: A versatile tool to predict the appearance of functional adaptations in evolution: Profilin interactions in uncultured Asgard archaea: Profilin interactions in uncultured Asgard archaea

The eukaryotic-like characteristics of small GTPase, roadblock and TRAPPC3 proteins from Asgard archaea

Membrane-enclosed organelles are defining features of eukaryotes in distinguishing these organisms from prokaryotes. Specification of distinct membranes is critical to assemble and maintain discrete compartments. Small GTPases and their regulators are the signaling molecules that drive membrane-modifying machineries to the desired location. These signaling molecules include Rab and Rag GTPases, roadblock and longin domain proteins, and TRAPPC3-like proteins. Here, we take a structural approach to assess the relatedness of these eukaryotic-like proteins in Asgard archaea, the closest known prokaryotic relatives to eukaryotes. We find that the Asgard archaea GTPase core domains closely resemble eukaryotic Rabs and Rags. Asgard archaea roadblock, longin and TRAPPC3 domain-containing proteins form dimers similar to those found in the eukaryotic TRAPP and Ragulator complexes. We conclude that the emergence of these protein architectures predated eukaryogenesis, however further adaptations occurred in proto-eukaryotes to allow these proteins to regulate distinct internal membranes.

Potential for artificial intelligence in medicine and its application to male infertility

Background

The third AI boom, which began in 2010, has been characterized by the rapid evolution and diversification of AI and marked by the development of key technologies such as machine learning and deep learning. AI is revolutionizing the medical field, enhancing diagnostic accuracy, surgical outcomes, and drug production.

Methods

This review includes explanations of digital transformation (DX), the history of AI, the difference between machine learning and deep learning, recent AI topics, medical AI, and AI research in male infertility.

Main Findings Results

In research on male infertility, I established an AI-based prediction model for Johnsen scores and an AI predictive model for sperm retrieval in non-obstructive azoospermia, both by no-code AI.

Conclusions

AI is making constant progress. It would be ideal for physicians to acquire a knowledge of AI and even create AI models. No-code AI tools have revolutionized model creation, allowing individuals to independently handle data preparation and model development. Previously a team effort, this shift empowers users to craft customized AI models solo, offering greater flexibility and control in the model creation process.