Notes

Global effort throughout many studies for kids along with adolescents with cancer-A systematic assessment by ACCELERATE.
Regarding the oxidative status, the species increased its SOD activity while MDA production slightly declined to the increase of temperature, protecting the organism from cellular damage. These results indicate that S. plana has a great capacity to adapt to environmental temperature changes, however, the expected higher frequency/duration of heatwaves with climate change trends can cause some debility of the species face to other stressors, which can compromise its success in the future.
Fingolimod, an oral drug, has been reported to reduce relapse rate in multiple sclerosis (MS). However disease progression may still occur in spite of control of inflammation. Functional imbalances within and between cerebral networks associated with disruption of structural and functional network integrity, have been reported in MS. An effective therapy is expected to stabilize such functional network integrity.

The purpose of this study was to investigate changes in structural and resting-state functional connectivity of motor and cognitive networks, and associated changes in neurologic scores in MS, during 2years of fingolimod therapy.

Twenty five subjects with MS were recruited for this study. Subjects were scanned with diffusion tensor imaging (DTI) and resting-state functional connectivity MRI (fcMRI) scan protocol at 3T with 6-month interval over a period of 2years. Neurologic performance scores of motor and cognitive performances were also obtained.

DTI measures worsened during the 1st year and then stabilized; any trend of stabilization of fcMRI was delayed until the 2nd year. While motor performance did not change, cognitive performance showed improvement. Several baseline DTI measures correlated with relevant neurologic scores.

Initial worsening of motor and cognitive network was reported after 1year of treatment, but seems DTI and fcMRI measures seem to stabilize after around one year fingolimod therapy.
Initial worsening of motor and cognitive network was reported after 1 year of treatment, but seems DTI and fcMRI measures seem to stabilize after around one year fingolimod therapy.Versatile soft tissue contrast in magnetic resonance imaging is a unique advantage of the imaging modality. However, the versatility is not fully exploited. In this study, we propose a deep learning-based strategy to derive more soft tissue contrasts from conventional MR images obtained in standard clinical MRI. read more Two types of experiments are performed. First, MR images corresponding to different pulse sequences are predicted from one or more images already acquired. As an example, we predict T1ρ weighted knee image from T2 weighted image and/or T1 weighted image. Furthermore, we estimate images corresponding to alternative imaging parameter values. In a representative case, variable flip angle images are predicted from a single T1 weighted image, whose accuracy is further validated in quantitative T1 map subsequently derived. To accomplish these tasks, images are retrospectively collected from 56 subjects, and self-attention convolutional neural network models are trained using 1104 knee images from 46 subjects and tested using 240 images from 10 other subjects. High accuracy has been achieved in resultant qualitative images as well as quantitative T1 maps. The proposed deep learning method can be broadly applied to obtain more versatile soft tissue contrasts without additional scans or used to normalize MR data that were inconsistently acquired for quantitative analysis.
To (1) compare the Altmetric Attention Score (AAS) and citation rates between orthopaedic and nonorthopaedic randomized controlled trials (RCTs) from 5 high-impact medical journals and (2) identify general characteristics of these articles associated with greater exposure on social media platforms.

Articles published in The New England Journal of Medicine (NEJM), Lancet, The Journal of the American Medical Association (JAMA), Annals of Internal Medicine, and Archives of Internal Medicine between January 2011 and December 2016 were analyzed. These journals were selected based on retaining high impact factors with rigorous publication standards and availability of the AAS for their publications. The queried time frame was chosen to balance the inception of the AAS with an optimal period for citation accrual. A total of 14 article characteristics, in addition to number of Tweets, Facebook shares, news mentions, and the AAS, were extracted. Inclusion criteria were orthopaedic RCTs reporting on outcomes after on between the AAS and citation rate of orthopaedic RCTs. Orthopaedic RCTs had greater social media exposure on both Twitter and Facebook.

Orthopaedic surgeons, researchers, and providers who publish RCTs in high-impact medical journals can anticipate extensive social media attention for their articles relative to other nonorthopaedic RCTs in the same journals. Social media attention may be related to operative versus nonoperative management topics. This study provides further evidence for the increasing use of the AAS and its association with citation accrual.
Orthopaedic surgeons, researchers, and providers who publish RCTs in high-impact medical journals can anticipate extensive social media attention for their articles relative to other nonorthopaedic RCTs in the same journals. Social media attention may be related to operative versus nonoperative management topics. This study provides further evidence for the increasing use of the AAS and its association with citation accrual.
To (1) determine the diagnostic efficacy of artificial intelligence (AI) methods for detecting anterior cruciate ligament (ACL) and meniscus tears and to (2) compare the efficacy to human clinical experts.

PubMed, OVID/Medline, and Cochrane libraries were queried in November 2019 for research articles pertaining to AI use for detection of ACL and meniscus tears. Information regarding AI model, prediction accuracy/area under the curve (AUC), sample sizes of testing/training sets, and imaging modalities were recorded.

A total of 11 AI studies were identified 5 investigated ACL tears, 5 investigated meniscal tears, and 1 investigated both. The AUC of AI models for detecting ACL tears ranged from 0.895 to 0.980, and the prediction accuracy ranged from 86.7% to 100%. Of these studies, 3 compared AI models to clinical experts. Two found no significant differences in diagnostic capability, whereas one found that radiologists had a significantly greater sensitivity for detecting ACL tears (P= .002) and statistically similar specificity and accuracy.
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