Augmentation of nitric oxide (NO) production in vivo decreases lesions in a variety of models of arterial injury, and inhibition of NO synthase exacerbates experimental intimal lesions. Both vascular smooth muscle cell (VSMC) proliferation and migration contribute to lesion formation. Although NO inhibits VSMC proliferation, its effects on VSMC migration are unknown. To test the hypothesis that NO inhibits VSMC migration independent of inhibition of proliferation, we examined migration of rat aortic VSMCs after wounding of a confluent culture in the presence of chemical donors of NO. Hydroxyurea was used to eliminate any confounding effect of NO on proliferation. Three NO donors, diethylamine NONOate, spermine NONOate, and S-nitrosoglutathione, exhibited concentration-dependent inhibition of both number of migrating VSMCs and maximal distance migrated. Inhibition of migration was also seen with 8-Br-cGMP, suggesting that activation of guanylate cyclase may play a role in mediating the antimigratory effects of NO. Migration resumed after removal of NO donors, as evidenced by an increase in distance migrated. Measurement of VSMC protein synthesis and mitochondrial respiration indicated that inhibition of migration by NO donors was not due to metabolic cytostasis. These findings indicate that NO reversibly inhibits VSMC migration independent of proliferation or cytotoxicity, a novel mechanism by which both endogenous and pharmacological NO may alter vascular pathology.

Purpose: Hip fractures are a common cause of morbidity and mortality. Automatic identification and classification of hip fractures using deep learning may improve outcomes by reducing diagnostic errors and decreasing time to operation. Methods: Hip and pelvic radiographs from 1118 studies were reviewed and 3034 hips were labeled via bounding boxes and classified as normal, displaced femoral neck fracture, nondisplaced femoral neck fracture, intertrochanteric fracture, previous ORIF, or previous arthroplasty. A deep learning-based object detection model was trained to automate the placement of the bounding boxes. A Densely Connected Convolutional Neural Network (DenseNet) was trained on a subset of the bounding box images, and its performance evaluated on a held out test set and by comparison on a 100-image subset to two groups of human observers: fellowshiptrained radiologists and orthopaedists, and senior residents in emergency medicine, radiology, and orthopaedics. Results: The binary accuracy for fracture of our model was 93.8% (95% CI, 91.3-95.8%), with sensitivity of 92.7% (95% CI, 88.7-95.6%), and specificity 95.0% (95% CI, 91.5-97.3%). Multiclass classification accuracy was 90.4% (95% CI, 87.4-92.9%). When compared to human observers, our model achieved at least expert-level classification under all conditions. Additionally, when the model was used as an aid, human performance improved, with aided resident performance approximating unaided fellowship-trained expert performance. Conclusions: Our deep learning model identified and classified hip fractures with at least expert-level accuracy, and when used as an aid improved human performance, with aided resident performance approximating that of unaided fellowship-trained attendings.

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Fractures in the elderly represent a significant and rising socioeconomic problem. Although aging has been associated with delays in healing, there is little direct clinical data isolating the effects of aging on bone healing from the associated comorbidities that are frequently present in elderly populations. Basic research has demonstrated that all of the components of fracture repair -cells, extracellular matrix, blood supply, and molecules and their receptors-are negatively impacted by the aging process, which likely explains poorer clinical outcomes. Improved understanding of agerelated fracture healing should aid in the development of novel treatment strategies, technologies, and therapies to improve bone repair in elderly patients.