CD36 and Fyn kinase mediate malaria-induced lung endothelial barrier dysfunction in mice infected with Plasmodium berghei.

PMC3744507Severe malaria can trigger acute lung injury characterized by pulmonary edema resulting from increased endothelial permeability. However, the mechanism through which lung fluid conductance is altered during malaria remains unclear. To define the role that the scavenger receptor CD36 may play in mediating this response, C57BL/6J (WT) and CD36-/- mice were infected with P. berghei ANKA and monitored for changes in pulmonary endothelial barrier function employing an isolated perfused lung system. WT lungs demonstrated a >10-fold increase in two measures of paracellular fluid conductance and a decrease in the albumin reflection coefficient (σalb) compared to control lungs indicating a loss of barrier function. In contrast, malaria-infected CD36-/- mice had near normal fluid conductance but a similar reduction in σalb. In WT mice, lung sequestered iRBCs demonstrated production of reactive oxygen species (ROS). To determine whether knockout of CD36 could protect against ROS-induced endothelial barrier dysfunction, mouse lung microvascular endothelial monolayers (MLMVEC) from WT and CD36-/- mice were exposed to H2O2. Unlike WT monolayers, which showed dose-dependent decreases in transendothelial electrical resistance (TER) from H2O2 indicating loss of barrier function, CD36-/- MLMVEC demonstrated dose-dependent increases in TER. The differences between responses in WT and CD36-/- endothelial cells correlated with important differences in the intracellular compartmentalization of the CD36-associated Fyn kinase. Malaria infection increased total lung Fyn levels in CD36-/- lungs compared to WT, but this increase was due to elevated production of the inactive form of Fyn further suggesting a dysregulation of Fyn-mediated signaling. The importance of Fyn in CD36-dependent endothelial signaling was confirmed using in vitro Fyn knockdown as well as Fyn-/- mice, which were also protected from H2O2- and malaria-induced lung endothelial leak, respectively. Our results demonstrate that CD36 and Fyn kinase are critical mediators of the increased lung endothelial fluid conductance caused by malaria infection.JH Libraries Open Access Fun

Evaluating predictive modeling algorithms to assess patient eligibility for clinical trials from routine data

Background The necessity to translate eligibility criteria from free text into decision rules that are compatible with data from the electronic health record (EHR) constitutes the main challenge when developing and deploying clinical trial recruitment support systems. Recruitment decisions based on case-based reasoning, i.e. using past cases rather than explicit rules, could dispense with the need for translating eligibility criteria and could also be implemented largely independently from the terminology of the EHR’s database. We evaluated the feasibility of predictive modeling to assess the eligibility of patients for clinical trials and report on a prototype’s performance for different system configurations. Methods The prototype worked by using existing basic patient data of manually assessed eligible and ineligible patients to induce prediction models. Performance was measured retrospectively for three clinical trials by plotting receiver operating characteristic curves and comparing the area under the curve (ROC-AUC) for different prediction algorithms, different sizes of the learning set and different numbers and aggregation levels of the patient attributes. Results Random forests were generally among the best performing models with a maximum ROC-AUC of 0.81 (CI: 0.72-0.88) for trial A, 0.96 (CI: 0.95-0.97) for trial B and 0.99 (CI: 0.98-0.99) for trial C. The full potential of this algorithm was reached after learning from approximately 200 manually screened patients (eligible and ineligible). Neither block- nor category-level aggregation of diagnosis and procedure codes influenced the algorithms’ performance substantially. Conclusions Our results indicate that predictive modeling is a feasible approach to support patient recruitment into clinical trials. Its major advantages over the commonly applied rule-based systems are its independency from the concrete representation of eligibility criteria and EHR data and its potential for automation