34 research outputs found
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Electric Scooter Injuries and Hospital Admissions in the United States, 2014-2018.
This study investigates trends of injury and hospital admission associated with electric scooter use
Quantitative characterization of viscoelastic behavior in tissue-mimicking phantoms and ex vivo animal tissues.
Viscoelasticity of soft tissue is often related to pathology, and therefore, has become an important diagnostic indicator in the clinical assessment of suspect tissue. Surgeons, particularly within head and neck subsites, typically use palpation techniques for intra-operative tumor detection. This detection method, however, is highly subjective and often fails to detect small or deep abnormalities. Vibroacoustography (VA) and similar methods have previously been used to distinguish tissue with high-contrast, but a firm understanding of the main contrast mechanism has yet to be verified. The contributions of tissue mechanical properties in VA images have been difficult to verify given the limited literature on viscoelastic properties of various normal and diseased tissue. This paper aims to investigate viscoelasticity theory and present a detailed description of viscoelastic experimental results obtained in tissue-mimicking phantoms (TMPs) and ex vivo tissues to verify the main contrast mechanism in VA and similar imaging modalities. A spherical-tip micro-indentation technique was employed with the Hertzian model to acquire absolute, quantitative, point measurements of the elastic modulus (E), long term shear modulus (η), and time constant (τ) in homogeneous TMPs and ex vivo tissue in rat liver and porcine liver and gallbladder. Viscoelastic differences observed between porcine liver and gallbladder tissue suggest that imaging modalities which utilize the mechanical properties of tissue as a primary contrast mechanism can potentially be used to quantitatively differentiate between proximate organs in a clinical setting. These results may facilitate more accurate tissue modeling and add information not currently available to the field of systems characterization and biomedical research
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Impact of alcohol and drug use on bicycle and electric scooter injuries and hospital admissions in the United States
Introduction Bicycles and electric scooters (e-scooters) are convenient and accessible means of transportation. Participant safety is contingent on available infrastructure and safe riding practices including not riding while intoxicated. Understanding national prevalence and injury characteristics of bicycle and e-scooter riders who ride while intoxicated may promote awareness campaigns for safe riding practices and decrease morbidity. Methods The National Electronic Injury Surveillance System (NEISS) provides national estimates of injuries that present to emergency departments across the United States. We obtained case information on admitting status, body part injured, diagnosis of injury, age, sex, alcohol usage, and drug usage. We then queried NEISS for injuries related to bicycles and e-scooters in 2019. Results A weighted total of 270,571 (95% confidence interval (CI): 204,517–336,625) bicycle injuries occurred in the United States during 2019; alcohol and drug use were associated with 7% (95% CI: 6–9) and 2% (95% CI: 2–3) of all injuries, respectively. Twenty-four percent (CI: 18--31) of alcohol- and 29% (95% CI: 20–41) of drug-related bicycle injuries resulted in hospital admissions, compared to 15% (95% CI: 12–17) of non–alcohol- and 15% (95% CI: 13–18) of non–drug-related injuries ( p < .001 and p = .002, respectively). A total of 28,702 (95% CI: 13,975–43,428) e-scooter injuries occurred in 2019; alcohol and drug use were associated with 8% (95% CI: 5–12) and 1% (95% CI: 1–2) of injuries, respectively. Sixty percent (95% CI: 47–72) of alcohol-related e-scooter injuries resulted in head trauma, compared to 28% (95% CI: 24–32) of non–alcohol-related injuries ( p < .001). Conclusions Intoxication is associated with increasingly severe injuries, hospital admissions, and head trauma in bicycle and e-scooter riders. The findings support awareness campaigns to educate riders about risky practices, improve non-auto infrastructure, and promote helmet usage
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Deep learning for large scale MRI-based morphological phenotyping of osteoarthritis.
Osteoarthritis (OA) develops through heterogenous pathophysiologic pathways. As a result, no regulatory agency approved disease modifying OA drugs are available to date. Stratifying knees into MRI-based morphological phenotypes may provide insight into predicting future OA incidence, leading to improved inclusion criteria and efficacy of therapeutics. We trained convolutional neural networks to classify bone, meniscus/cartilage, inflammatory, and hypertrophy phenotypes in knee MRIs from participants in the Osteoarthritis Initiative (n = 4791). We investigated cross-sectional association between baseline morphological phenotypes and baseline structural OA (Kellgren Lawrence grade > 1) and symptomatic OA. Among participants without baseline OA, we evaluated association of baseline phenotypes with 48-month incidence of structural OA and symptomatic OA. The area under the curve of bone, meniscus/cartilage, inflammatory, and hypertrophy phenotype neural network classifiers was 0.89 ± 0.01, 0.93 ± 0.03, 0.96 ± 0.02, and 0.93 ± 0.02, respectively (mean ± standard deviation). Among those with no baseline OA, bone phenotype (OR: 2.99 (95%CI: 1.59-5.62)) and hypertrophy phenotype (OR: 5.80 (95%CI: 1.82-18.5)) each respectively increased odds of developing incident structural OA and symptomatic OA at 48 months. All phenotypes except meniscus/cartilage increased odds of undergoing total knee replacement within 96 months. Artificial intelligence can rapidly stratify knees into structural phenotypes associated with incident OA and total knee replacement, which may aid in stratifying patients for clinical trials of targeted therapeutics