Palliative Care in Advanced Liver Disease: Similar or Different Palliative Care Needs in Patients with a Prospect of Transplantation? Prospective Study from a Portuguese University Hospital and Transplantation Center

Background and Aims: End-stage liver disease (ESLD) is an important cause of morbidity and mortality, comparable to a large extent to other organ insufficiencies. The need for palliative care (PC) in patients with ESLD is high. In Portugal, in the only identified study, more than 80% of patients hospitalized with ESLD had criteria for PC. No results specified which needs they identified or their transplantation prospect status. Methods: Prospective observational study including 54 ESLD patients who presented to a university hospital and transplantation center, between November 2019 and September 2020. Assessment of their PC needs through the application of NECPAL CCOMS-ICO© and IPOS, considering their transplantation perspective status. Results: Of the 54 patients, 5 (9.3%) were on active waiting list for transplantation and 8 (14.8%) under evaluation. NECPAL CCOMS-ICO© identified 23 patients (n = 42.6%) that would benefit from PC. Assessment of PC needs by clinicians, functional markers and significant comorbidities were the most frequent criteria (47.8%, n = 11). IPOS also revealed a different sort of needs: on average, each patient identified about 9 needs (8.9 ±2.8). Among the symptoms identified, weakness (77.8%), reduced mobility (70.3%), and pain (48.1%) stood out, as well as the psychoemotional symptoms of depression (66.7%) and anxiety (77.8%). There were no significant differences between the subgroups of patients analyzed. Only 4 patients (7.4%) were followed by the PC team. Conclusion: All the ESLD patients included, independently of the group they belonged to, presented with PC needs. No significant differences between the subgroups of patients were identified, confirming that even patients with a transplantation prospect have important needs for PC

Deep reinforcement learning for drone navigation using sensor data

Mobile robots such as unmanned aerial vehicles (drones) can be used for surveillance, monitoring and data collection in buildings, infrastructure and environments. The importance of accurate and multifaceted monitoring is well known to identify problems early and prevent them escalating. This motivates the need for flexible, autonomous and powerful decision-making mobile robots. These systems need to be able to learn through fusing data from multiple sources. Until very recently, they have been task specific. In this paper, we describe a generic navigation algorithm that uses data from sensors on-board the drone to guide the drone to the site of the problem. In hazardous and safety-critical situations, locating problems accurately and rapidly is vital. We use the proximal policy optimisation deep reinforcement learning algorithm coupled with incremental curriculum learning and long short-term memory neural networks to implement our generic and adaptable navigation algorithm. We evaluate different configurations against a heuristic technique to demonstrate its accuracy and efficiency. Finally, we consider how safety of the drone could be assured by assessing how safely the drone would perform using our navigation algorithm in real-world scenarios

PESCADOR, a web-based tool to assist text-mining of biointeractions extracted from PubMed queries

BACKGROUND: Biological function is greatly dependent on the interactions of proteins with other proteins and genes. Abstracts from the biomedical literature stored in the NCBI's PubMed database can be used for the derivation of interactions between genes and proteins by identifying the co-occurrences of their terms. Often, the amount of interactions obtained through such an approach is large and may mix processes occurring in different contexts. Current tools do not allow studying these data with a focus on concepts of relevance to a user, for example, interactions related to a disease or to a biological mechanism such as protein aggregation. RESULTS: To help the concept-oriented exploration of such data we developed PESCADOR, a web tool that extracts a network of interactions from a set of PubMed abstracts given by a user, and allows filtering the interaction network according to user-defined concepts. We illustrate its use in exploring protein aggregation in neurodegenerative disease and in the expansion of pathways associated to colon cancer. CONCLUSIONS: PESCADOR is a platform independent web resource available at: http://cbdm.mdc-berlin.de/tools/pescador