INTRODUCTION:Previously, in a murine model of blunt thoracic trauma, we provided evidence of primary pulmonary thrombosis associated with increased expression of the cell adhesion molecule, P-selectin. In this study, mice are treated with P-selectin blocking antibody after injury to investigate the clinical viability of this antibody for the prevention of pulmonary thrombosis. In addition, viscoelastic testing is performed to investigate if P-selectin inhibition has a detrimental impact on normal hemostasis. METHODS:A murine model of thoracic trauma was used. Mice were divided into sham control and experimental injury groups. Thirty minutes after trauma, mice were treated with the following: P-selectin blocking antibody, isotype control antibody, low-dose heparin, high-dose heparin, or normal saline. At 90 minutes, whole blood was collected for characterization of coagulation by viscoelastic coagulation monitor (VCM Vet; Entegrion, Durham, NC). Mean clotting time, clot formation time, clot kinetics (α angle), and maximum clot firmness were compared between each treatment group. RESULTS:Mice that received P-selectin antibody 30 minutes after blunt thoracic trauma had four-to fivefold less (p < 0.001) arterial fibrin accumulation than those that received the isotype control. In both sham and trauma groups, compared with vehicle (normal saline) alone, no statistical difference was noted in any coagulation parameters after injection with P-selectin antibody, isotype control, or low-dose heparin. In addition, blinded histopathological evaluation yielded no difference in hemorrhage scores between injured mice treated with P-selectin blocking antibody and those treated with isotype antibody control. CONCLUSION:This study supports the clinical use of P-selectin blocking antibody for the prevention of pulmonary thrombosis by confirming its efficacy when given after a blunt thoracic trauma. In addition, we demonstrated that the administration of P-selectin antibody does not adversely affect systemic coagulation as measured by viscoelastic testing, suggesting that P-selectin antibody can be safely given during the acute posttraumatic period.

Bone age assessment is challenging in clinical practice due to the complicated bone age assessment process. Current automatic bone age assessment methods were designed with rare consideration of the diagnostic logistics and thus may yield certain uninterpretable hidden states and outputs. Consequently, doctors can find it hard to cooperate with such models harmoniously because it is difficult to check the correctness of the model predictions. In this work, we propose a new graph-based deep learning framework for bone age assessment with hand radiographs, called Doctor Imitator (DI). The architecture of DI is designed to learn the diagnostic logistics of doctors using the scoring methods (e.g., the Tanner-Whitehouse method) for bone age assessment. Specifically, the convolutions of DI capture the local features of the anatomical regions of interest (ROIs) on hand radiographs and predict the ROI scores by our proposed Anatomy-based Group Convolution, summing up for bone age prediction. Besides, we develop a novel Dual Graph-based Attention module to compute patient-specific attention for ROI features and context attention for ROI scores. As far as we know, DI is the first automatic bone age assessment framework following the scoring methods without fully supervised hand radiographs. Experiments on hand radiographs with only bone age supervision verify that DI can achieve excellent performance with sparse parameters and provide more interpretability.