IMPORTANCE Reducing levels of low-density lipoprotein cholesterol (LDL-C) with intensive statin therapy reduces progression of coronary atherosclerosis in proportion to achieved LDL-C levels. Proprotein convertase subtilisin kexin type 9 (PCSK9) inhibitors produce incremental LDL-C lowering in statin-treated patients; however, the effects of these drugs on coronary atherosclerosis have not been evaluated. OBJECTIVE To determine the effects of PCSK9 inhibition with evolocumab on progression of coronary atherosclerosis in statin-treated patients. DESIGN, SETTING, AND PARTICIPANTS The GLAGOV multicenter, double-blind, placebo-controlled, randomized clinical trial (enrollment May 3, 2013, to January 12, 2015) conducted at 197 academic and community hospitals in North America, Europe, South America, Asia, Australia, and South Africa and enrolling 968 patients presenting for coronary angiography. INTERVENTIONS Participants with angiographic coronary disease were randomized to receive monthly evolocumab (420 mg) (n = 484) or placebo (n = 484) via subcutaneous injection for 76 weeks, in addition to statins. MAIN OUTCOMES AND MEASURES The primary efficacy measure was the nominal change in percent atheroma volume (PAV) from baseline to week 78, measured by serial intravascular ultrasonography (IVUS) imaging. Secondary efficacy measures were nominal change in normalized total atheroma volume (TAV) and percentage of patients demonstrating plaque regression. Safety and tolerability were also evaluated. RESULTS Among the 968 treated patients (mean age, 59.8 years [SD, 9.2]; 269 [27.8%] women; mean LDL-C level, 92.5 mg/dL [SD, 27.2]), 846 had evaluable imaging at follow-up. Compared with placebo, the evolocumab group achieved lower mean, time-weighted LDL-C levels (93.

In the largest monotherapy trial using a PCSK9 inhibitor to date, evolocumab yielded significant LDL-C reductions compared with placebo or ezetimibe and was well tolerated in patients with hypercholesterolemia. (Monoclonal Antibody Against PCSK9 to Reduce Elevated LDL-C in Subjects Currently Not Receiving Drug Therapy for Easing Lipid Levels-2 [MENDEL-2]; NCT01763827).

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Aim: Multiple sclerosis is a severe brain and/or spinal cord disease. It may lead to a wide range of symptoms. Hence, the early diagnosis and treatment is quite important.Method: This study proposed a 14-layer convolutional neural network, combined with three advanced techniques: batch normalization, dropout, and stochastic pooling. The output of the stochastic pooling was obtained via sampling from a multinomial distribution formed from the activations of each pooling region. In addition, we used data augmentation method to enhance the training set. In total 10 runs were implemented with the hold-out randomly set for each run.Results: The results showed that our 14-layer CNN secured a sensitivity of 98.77 ± 0.35%, a specificity of 98.76 ± 0.58%, and an accuracy of 98.77 ± 0.39%.Conclusion: Our results were compared with CNN using maximum pooling and average pooling. The comparison shows stochastic pooling gives better performance than other two pooling methods. Furthermore, we compared our proposed method with six state-of-the-art approaches, including five traditional artificial intelligence methods and one deep learning method. The comparison shows our method is superior to all other six state-of-the-art approaches.

In addition to LDL-C reduction, evolocumab, dosed either Q2W or Q4W, demonstrated significant and favourable changes in other atherogenic and anti-atherogenic lipoproteins, and was well tolerated over the 12-week treatment period.

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AimsTo investigate the efficacy of a bi-modality deep convolutional neural network (DCNN) framework to categorise age-related macular degeneration (AMD) and polypoidal choroidal vasculopathy (PCV) from colour fundus images and optical coherence tomography (OCT) images.MethodsA retrospective cross-sectional study was proposed of patients with AMD or PCV who came to Peking Union Medical College Hospital. Diagnoses of all patients were confirmed by two retinal experts based on diagnostic gold standard for AMD and PCV. Patients with concurrent retinal vascular diseases were excluded. Colour fundus images and spectral domain OCT images were taken from dilated eyes of patients and healthy controls, and anonymised. All images were pre-labelled into normal, dry or wet AMD or PCV. ResNet-50 models were used as the backbone and alternate machine learning models including random forest classifiers were constructed for further comparison. For human-machine comparison, the same testing data set was diagnosed by three retinal experts independently. All images from the same participant were presented only within a single partition subset.ResultsOn a test set of 143 fundus and OCT image pairs from 80 eyes (20 eyes per-group), the bi-modal DCNN demonstrated the best performance, with accuracy 87.4%, sensitivity 88.8% and specificity 95.6%, and a perfect agreement with diagnostic gold standard (Cohen’s κ 0.828), exceeds slightly over the best expert (Human1, Cohen’s κ 0.810). For recognising PCV, the model outperformed the best expert as well.ConclusionA bi-modal DCNN for automated classification of AMD and PCV is accurate and promising in the realm of public health.