Evaluation and prediction of the physical properties and quality of Jatobá-do-Cerrado seeds processed and stored in different conditions using machine learning models.

The conservation of seed quality throughout storage depends on established conditions, monitoring, sampling and laboratory analysis, which are subject to errors and require technical and financial resources. Thus, machine learning techniques can help optimize processes and obtain more accurate results for decision-making regarding the processing and conservation of stored seeds. Therefore, the aim was to assess and predict the physical properties (moisture content, seed mass, length, thickness, width, volume, apparent specific mass, projected area, sphericity, mean diameter, circular area, circularity, drag coefficient), and physicochemical quality (crude protein, ash content, and acidity index) of Jatobá-do-Cerrado seeds under different processing conditions with pulp, without pulp (scarification), without pulp (fermented), and storage conditions at 10 and 23 °C over six months.

Characterization of phospholipid-modified lung surfactant in vitro and in a neonatal ARDS model reveals anti-inflammatory potential and surfactant lipidome signatures.

A strong inflammatory immune response drives the lung pathology in neonatal acute respiratory distress syndrome (nARDS). Anti-inflammatory therapy is therefore a promising strategy for improved treatment of nARDS. We demonstrate a new function of the anionic phospholipids POPG, DOPG, and PIP2 as inhibitors of IL-1β release by LPS and ATP-induced inflammasome activation in human monocyte-derived and lung macrophages.

Validation of a predictive model for identifying an increased risk for recurrence in adolescents and young adults with a first provoked thromboembolism.

Background: To develop and validate a predictive model to determinate patients at increased risk to suffer from recurrence following a first provoked deep vein thrombosis (VTE).

Methods: Predictive variables, i.e.