Notes

Early co-occurrence associated with fellow victimization as well as lack of control.
Age affects gross shoulder range of motion (ROM), but biomechanical changes over a lifetime are typically only characterized for the humerothoracic joint. Suitable age-related baselines for the scapulothoracic and glenohumeral contributions to humerothoracic motion are needed to advance understanding of shoulder injuries and pathology. Gefitinib manufacturer Notably, biomechanical comparisons between younger or older populations may obscure detected differences in underlying shoulder motion. Herein, biplane fluoroscopy and skin-marker motion analysis quantified humerothoracic, scapulothoracic, and glenohumeral motion during 3 static poses (resting neutral, internal rotation to L4-L5, and internal rotation to maximum reach) and 2 dynamic activities (scapular plane abduction and external rotation in adduction). Orientations during static poses and rotations during active ROM were compared between subjects 45 years of age (N = 10 subjects per group). Numerous age-related kinematic differences were measured, ranging 5-22°, where variations in scapular orientation and motion were consistently observed. These disparities are on par with or exceed mean clinically important differences and standard error of measurement of clinical ROM, which indicates that high resolution techniques and appropriately matched controls are required to avoid confounding results of studies that investigate shoulder kinematics. Understanding these dissimilarities will help clinicians manage expectations and treatment protocols where indications and prevalence between age groups tend to differ. Where possible, it is advised to select age-matched control cohorts when studying the kinematics of shoulder injury, pathology, or surgical/physical therapy interventions to ensure clinically important differences are not overlooked.Recently, with the significant developments in deep learning techniques, solving underdetermined inverse problems has become one of the major concerns in the medical imaging domain, where underdetermined problems are motivated by the willingness to provide high resolution medical images with as little data as possible, by optimizing data collection in terms of minimal acquisition time, cost-effectiveness, and low invasiveness. Typical examples include undersampled magnetic resonance imaging(MRI), interior tomography, and sparse-view computed tomography(CT), where deep learning techniques have achieved excellent performances. However, there is a lack of mathematical analysis of why the deep learning method is performing well. This study aims to explain about learning the causal relationship regarding the structure of the training data suitable for deep learning, to solve highly underdetermined problems. We present a particular low-dimensional solution model to highlight the advantage of deep learning methods over conventional methods, where two approaches use the prior information of the solution in a completely different way. We also analyze whether deep learning methods can learn the desired reconstruction map from training data in the three models (undersampled MRI, sparse-view CT, interior tomography). This paper also discusses the nonlinearity structure of underdetermined linear systems and conditions of learning (called M-RIP condition).Assessment of renal function and structure accurately remains essential in the diagnosis and prognosis of Chronic Kidney Disease (CKD). Advanced imaging, including Magnetic Resonance Imaging (MRI), Ultrasound Elastography (UE), Computed Tomography (CT) and scintigraphy (PET, SPECT) offers the opportunity to non-invasively retrieve structural, functional and molecular information that could detect changes in renal tissue properties and functionality. Currently, the ability of artificial intelligence to turn conventional medical imaging into a full-automated diagnostic tool is widely investigated. In addition to the qualitative analysis performed on renal medical imaging, texture analysis was integrated with machine learning techniques as a quantification of renal tissue heterogeneity, providing a promising complementary tool in renal function decline prediction. Interestingly, deep learning holds the ability to be a novel approach of renal function diagnosis. This paper proposes a survey that covers both qualitative and quantitative analysis applied to novel medical imaging techniques to monitor the decline of renal function. First, we summarize the use of different medical imaging modalities to monitor CKD and then, we show the ability of Artificial Intelligence (AI) to guide renal function evaluation from segmentation to disease prediction, discussing how texture analysis and machine learning techniques have emerged in recent clinical researches in order to improve renal dysfunction monitoring and prediction. The paper gives a summary about the role of AI in renal segmentation.
To determine safety, feasibility and patient satisfaction of an epilepsy nurse-based treatment course with frequent contacts and changes of anti-epileptic treatment provided by supervised anti-epileptic drug (AED) prescribing epilepsy nurses.

Regular prescheduled clinical contacts with a neurologist to adjust AED treatment were largely substituted by on-demand contacts with epilepsy nurses with the delegated right to adapt AED within predefined limits. To secure safety, electronic medical files of patients with 6 or more contacts with epilepsy nurses were retrospectively analysed for clinical characteristics, safety measures and seizure frequency before/after the intensive treatment course and patients were asked to complete a questionnaire about treatment satisfaction.

Between January 1
2016 and 31
December 2018, 2721 patients were treated by epilepsy nurses (2561 ambulatory controls, 8690 phone contacts). 617 patients received an intensive treatment course (six or more contacts in the observation period, range 6-65) with an average length of 24.3 months. The average number of AED tried was 3.4. In patients with ongoing seizures (n = 310), 165 (53.2 %) reported an improvement of seizure frequency by 50 % or more. Seizure frequency fell from 4.4 to 2.4 days with seizures/months (p < 0.001). The epilepsy-related hospitalization rate was 0.86/patient; 27 episodes with status epilepticus occurred in 21 patients, three hospitalizations were due to severe side effects. There were no fatal complications. No hospitalization was related to the intensive treatment course by prescribing epilepsy nurses. The overall patients' satisfaction was high.

Intensive epilepsy treatment facilitated by epilepsy nurses was safe and associated with high patient accept and improvement of seizure frequency.
Intensive epilepsy treatment facilitated by epilepsy nurses was safe and associated with high patient accept and improvement of seizure frequency.
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