From 2009 to 2016, >21,000 children died and an estimated 118,000 suffered non-fatal injuries from firearms in the United States. Limited data is available on resource utilization by injury intent. We use hospital charges as a proxy for resource use and sought to 1) estimate mean charges for initial ED and inpatient care for acute firearm injuries among children in the U.S.; 2) compare differences in charges by firearm injury intent among children; and 3) evaluate trends in charges for pediatric firearm injuries over time, including within intent subgroups.

In this repeated cross-sectional analysis of the 2009-2016 Nationwide Emergency Department Sample, we identified firearm injury cases among children aged ≤19years using ICD-9-CM and ICD-10-CM external cause of injury codes (e-codes). Injury intent was categorized using e-codes as unintentional, assault-related, self-inflicted, or undetermined. Linear regressions utilizing survey weighting were used to examine associations between injury intent and hreased over time among unintentional injuries (p-trend=0.002), but not among cases with assault-related or self-inflicted injuries.

Self-inflicted and assault-related firearm injuries are associated with higher mean healthcare charges than unintentional firearm injuries among children. Mean charges for pediatric firearm injuries have also increased over time. These findings can help guide prevention interventions aimed at reducing the substantial burden of firearm injuries among children.

Self-inflicted and assault-related firearm injuries are associated with higher mean healthcare charges than unintentional firearm injuries among children. Mean charges for pediatric firearm injuries have also increased over time. These findings can help guide prevention interventions aimed at reducing the substantial burden of firearm injuries among children.In the U.S., approximately 14 million tendon and ligament injuries are reported each year. Dry needling (DN) is a conservative treatment introduced to alleviate pain and restore function; however, it is invasive and has mixed success. Focused ultrasound (fUS) is a non-invasive technology that directs ultrasound energy into a well-defined focal volume. fUS induces thermal and/or mechanical bioeffects which can be controlled by the choice of ultrasound parameters. fUS could be an alternative to DN for treatment of tendon injuries, but the bioeffects must be established. Thus, the purpose of this pilot study was to compare the effect of DN and fUS on the mechanical properties and cell morphology of 30 ex vivo rat Achilles tendons. Tendons were randomly assigned to sham, DN, or fUS, with 10 tendons per group. Within each group, 5 tendons were evaluated mechanically, and 5 tendons were analyzed histologically. Elastic modulus in the DN (74.05 ± 15.0 MPa) group was significantly lower than sham (149.84 ± 59.1 MPa; p = 0.0094) and fUS (128.84 ± 28.3 MPa; p = 0.0453) groups. Stiffness in DN (329.05 ± 236.8 N/mm; p = 0.0034) and fUS (315.26 ± 68.9 N/mm; p = 0.0027) groups were significantly lower than sham (786.10 ± 238.7 N/mm) group. Histologically, localized necrosis was observed in 3 out of 5 tendons exposed to fUS, with surrounding tissue unharmed; no evidence of cellular injury was observed in DN or sham groups. These results suggest that fUS preserves the mechanical properties of tendon better than DN. Further studies are needed to evaluate fUS as an alternative, noninvasive treatment modality for tendon injuries.The VT-Lowe’s exoskeleton was designed to help support the back during repetitive lifting tasks. This study focused on the kinematic differences between lifting with and without the exoskeleton (With-Exo and Without-Exo) over three different lifting styles (Freestyle, Squat, and Stoop) and two different box weights (0% and 20% of bodyweight). Twelve young and healthy males (Age 23.5 +/- 4.42 years; Height 179.33 +/- 6.37 cm; Weight 80.4 +/- 5.59 kg) participated in this study. Variables analyzed include the ankle and knee angles and angle between the Shoulder-Hip-Knee (SHK); the shoulder, elbow, and wrist heights; and the lifting speed and acceleration. The relationships between the torso angle, SHK angle, center of mass of the torso, torso torque, box height, as well as electromyography (EMG) data from a related study were also analyzed. On average, wearing the exoskeleton resulted in a 1.5 degree increase in ankle dorsiflexion, a 2.6 degree decrease in knee flexion, and a decrease of 2.3 degrees in SHK angle. Subjects’ shoulder, elbow, and wrist heights were slightly higher while wearing the exoskeleton, and they lifted slightly more slowly while wearing the exoskeleton. Subjects moved more quickly while bending down as compared to standing up, and with the 0% bodyweight box as compared to the 20% bodyweight box. The values for Freestyle lifts generally fell in between Squat and Stoop lift styles or were not significantly different from Squat. EMG data from the leg muscles had relationships with torso torque while the back and stomach muscles showed no significant relationships.Feature vectors provided by pre-trained deep artificial neural networks have become a dominant source for image representation in recent literature. Their contribution to the performance of image analysis can be improved through fine-tuning. As an ultimate solution, one might even train a deep network from scratch with the domain-relevant images, a highly desirable option which is generally impeded in pathology by lack of labeled images and the computational expense. Hygromycin B In this study, we propose a new network, namely KimiaNet, that employs the topology of the DenseNet with four dense blocks, fine-tuned and trained with histopathology images in different configurations. We used more than 240,000 image patches with 1000×1000 pixels acquired at 20× magnification through our proposed “high-cellularity mosaic” approach to enable the usage of weak labels of 7126 whole slide images of formalin-fixed paraffin-embedded human pathology samples publicly available through The Cancer Genome Atlas (TCGA) repository. We tested KimiaNet using three public datasets, namely TCGA, endometrial cancer images, and colorectal cancer images by evaluating the performance of search and classification when corresponding features of different networks are used for image representation.