Phospholipases in Gliomas: Current Knowledge and Future Perspectives from Bench to Bedside.

Phospholipases are essential intermediaries that work as hydrolyzing enzymes of phospholipids (PLs), which represent the most abundant species contributing to the biological membranes of nervous cells of the healthy human brain. They generate different lipid mediators, such as diacylglycerol, phosphatidic acid, lysophosphatidic acid, and arachidonic acid, representing key elements of intra- and inter-cellular signaling and being involved in the regulation of several cellular mechanisms that can promote tumor progression and aggressiveness. In this review, it is summarized the current knowledge about the role of phospholipases in brain tumor progression, focusing on low- and high-grade gliomas, representing promising prognostic or therapeutic targets in cancer therapies due to their influential roles in cell proliferation, migration, growth, and survival. A deeper understanding of the phospholipases-related signaling pathways could be necessary to pave the way for new targeted therapeutic strategies.

Learning intraoperative organ manipulation with context-based reinforcement learning.

PURPOSE: Automation of sub-tasks during robotic surgery is challenging due to the high variability of the surgical scenes intra- and inter-patients. For example, the pick and place task can be executed different times during the same operation and for distinct purposes. Hence, designing automation solutions that can generalise a skill over different contexts becomes hard. All the experiments are conducted using the Pneumatic Attachable Flexible (PAF) rail, a novel surgical tool designed for robotic-assisted intraoperative organ manipulation. METHODS: We build upon previous open-source surgical Reinforcement Learning (RL) training environment to develop a new RL framework for manipulation skills, rlman. In rlman, contextual RL agents are trained to solve different aspects of the pick and place task using the PAF rail system. rlman is implemented to support both low- and high-dimensional state information to solve surgical sub-tasks in a simulation environment. RESULTS: We use rlman to train state of the art RL agents to solve four different surgical sub-tasks involving manipulation skills using the PAF rail. We compare the results with state-of-the-art benchmarks found in the literature. We evaluate the ability of the agent to be able to generalise over different aspects of the targeted surgical environment. CONCLUSION: We have shown that the rlman framework can support the training of different RL algorithms for solving surgical sub-task, analysing the importance of context information for generalisation capabilities. We are aiming to deploy the trained policy on the real da Vinci using the dVRK and show that the generalisation of the trained policy can be transferred to the real world.