Evolution of Complexity of Palliative Care Needs and Patient Profiles According to the PALCOM Scale (Part Two): Pooled Analysis of the Cohorts for the Development and Validation of the PALCOM Scale in Advanced Cancer Patients.

Introduction: Identifying the complexity of palliative care needs is a key aspect of referral to specialized multidisciplinary early palliative care (EPC) teams. The PALCOM scale is an instrument consisting of five multidimensional assessment domains developed in 2018 and validated in 2023 to identify the level of complexity in patients with advanced cancer. (1) Objectives: The main objective of this study was to determine the degree of instability (likelihood of level change or death), health resource consumption and the survival of patients according to the level of palliative complexity assigned at the baseline visit during a 6-month follow-up.

Maresin-1 promotes neuroprotection and prevents disease progression in experimental models of multiple sclerosis through metabolic reprogramming and shaping innate and adaptive disease-associated cell types.

Multiple sclerosis (MS) is one of the most common inflammatory neurodegenerative diseases in young adults and causes neurological abnormalities and disability. We studied the effect of maresin 1 (MaR1) on the progression of disease in a relapsing-remitting form of experimental allergic encephalomyelitis (RR-EAE). Treatment with MaR1 in RR-EAE accelerated inflammation resolution, protected against neurological deficits, and delayed disease progression by decreasing immune cell infiltration (CD4+IL17+ and CD4+IFNγ+) into the CNS.

Validation Study of the PALCOM Scale of Complexity of Palliative Care Needs: A Cohort Study in Advanced Cancer Patients.

Background: In a patient-centred model of care, referral to early palliative care (EPC) depends on both the prognosis and the complexity of care needs. The PALCOM scale is a 5-domain multidimensional assessment tool developed to identify the level of complexity of palliative care needs of cancer patients. The aim of this study was to validate the PALCOM scale.

Prediction of the Phase Composition Profile of Three-Compound Mixtures in Liquid-Liquid Equilibrium: A Chemoinformatics Approach.

Machine-learning models were developed to predict the composition profile of a three-compound mixture in liquid-liquid equilibrium (LLE), given the global composition at certain temperature and pressure. A chemoinformatics approach was explored, based on the MOLMAP technology to encode molecules and mixtures. The chemical systems involved an ionic liquid (IL) and two organic molecules.