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THC containing compounds have also been linked to vascular complications ranging from mild plaques to total arterial occlusion resulting in claudication, rest pain, ischemic ulceration and gangrene-recently termed cannabis arteritis. While this research remains in a nascent stage, marijuana consumption seems to be predisposing a youthful, traditionally low health risk cohort to a variety of major adverse cardiovascular events.

Magnetic resonance images (MRI) is the main diagnostic tool for risk stratification and treatment decision in nasopharyngeal carcinoma (NPC). However, the holistic feature information of multi-parametric MRIs has not been fully exploited by clinicians to accurately evaluate patients.

To help clinicians fully utilize the missed information to regroup patients, we built an end-to-end deep learning model to extract feature information from multi-parametric MRIs for predicting and stratifying the risk scores of NPC patients.

In this paper, we proposed an end-to-end multi-modality deep survival network (MDSN) to precisely predict the risk of disease progression of NPC patients. Extending from 3D dense net, this proposed MDSN extracted deep representation from multi-parametric MRIs (T1w, T2w, and T1c). Moreover, deep features and clinical stages were integrated through MDSN to more accurately predict the overall risk score (ORS) of individual NPC patient.

A total of 1,417 individuals treated between January 2012 and December 2014 were included for training and validating the end-to-end MDSN. Results were then tested in a retrospective cohort of 429 patients included in the same institution. The C-index of the proposed method with or without clinical stages was 0.672 and 0.651 on the test set, respectively, which was higher than the that of the stage grouping (0.610).

The C-index of the model which integrated clinical stages with deep features is 0.062 higher than that of stage grouping alone (0.672vs 0.610). We conclude that features extracted from multi-parametric MRIs based on MDSN can well assist the clinical stages in regrouping patients.

The C-index of the model which integrated clinical stages with deep features is 0.062 higher than that of stage grouping alone (0.672 vs 0.610). We conclude that features extracted from multi-parametric MRIs based on MDSN can well assist the clinical stages in regrouping patients.

Leptin is associated with cardiovascular risk. Some studies analyzed the potential association between leptin and arterial stiffness, an independent cardiovascular risk factor. However, the studies that investigated this association provided inconsistent and heterogeneous results.

We performed a systematic review and a meta-analysis of the available studies on the relationship between leptin and arterial stiffness to achieve definitive conclusions.

A systematic search of the on-line databases available (up to December 2019) was conducted including the observational studies that reported the evaluation of the relationship between non-invasively assessed arterial stiffness (expressed by carotid-femoral pulse wave velocity) and leptin. For each study, the effect size was standardized and pooled using a random effect model. Sensitivity analysis, heterogeneity, publication bias, meta-regression and sub-group analyses were also assessed.

Ten studies met the pre-defined inclusion criteria and provided 11 cohorts with 7,580 total participants. Leptin levels were positively and significantly associated with risk of increased arterial stiffness (odds ratio 1.04; p < 0.01), with no significant heterogeneity among studies. Likewise, pooled analysis of correlation showed a significant and positive association between leptin and pulse wave velocity (z = 0.27, p < 0.01), with significant heterogeneity among studies.

The results of this meta-analysis indicate that leptin is positively associated with arterial stiffness. This association significantly adds to the recognized value of leptin in cardiovascular disease.

The results of this meta-analysis indicate that leptin is positively associated with arterial stiffness. This association significantly adds to the recognized value of leptin in cardiovascular disease.

Metallic spinal implants undergo wear and corrosion which liberates ionic or particulate metal debris. The purpose of this study was to identify and review studies that report the concentration of metal ions following multi-level spinal fusion and to evaluate the impact on clinical outcomes.

Databases (PubMed, EBSCO MEDLINE) were searched up to August 2019 for studies in English-language assessing metal ion levels [chromium (Cr), titanium (Ti), nickel (Ni)] in whole blood, serum, or plasma after spinal fusion using a specific search string. Study, patient, and implant characteristics, method of analysis, metal ion concentration, as well as clinical and radiographic results was extracted.

The systematic search yielded 18 studies encompassing 653 patients. PTC-209 order 9 studies reported Ti ions, eight reported Cr, and six reported Ni. Ti levels were elevated compared to controls/reference range/preoperative baseline in seven studies with the other two reporting no difference. Cr levels were elevated compared to contr follow-up periods is indicated to evaluate the clinical impact and minimizing risk.

Retrospective cross-sectional study.

To analyze the patient demographic referred for scoliosis to the Hospital for Sick Children to determine the proportion of patients suitable for brace treatment, as per the Scoliosis Research Society guidelines. There is level 1 evidence that bracing in adolescent idiopathic scoliosis (AIS) decreases the risk of curve progression and need for surgery, but optimal brace treatment requires early curve detection.

We performed a retrospective review of 618 consecutive patients who underwent initial assessment in our Spine Clinic between Jan. 1 and Dec. 31, 2014. We included children 10-18years, with scoliosis greater than 10°, excluding those diagnosed with non-idiopathic curves. Primary outcomes were Cobb angle, menarchal status, and Risser score. We analyzed the effect of specific referral variables (family history, the person who first noticed the curve, and geographic location of residence) on presenting curve magnitude.

During the study period, 335 children met the inclusion criteria, with an average age of 14.