Whether infantile hemangiomas (IHs) need to be treated and which treatment should be preferred are still controversial. We aimed to compare and rank the treatments and identify the optimal treatment for IHs. We searched PubMed, EMBASE, the Cochrane Library, Web of Science, and other sources for randomized controlled trials up to August 2019. We included trials comparingdifferent treatments and reported response or adverse events rate in IH patients. Two reviewers independently evaluated studies by specific criteria and extracted data. We assessed the risk of bias with the Cochrane risk of bias tool. Random-effects were performed for pair-to-pair and Bayesian framework network meta-analyses. The primary outcomes were efficacy and safety. We deemed 20 studies eligible, including 1149 participants and eight interventions. For efficacy, oral propranolol and topical propranolol/timolol were better than observation/placebo (OR, 95% CrI: 17.05, 4.02–94.94; 9.72, 1.91–59.08). For safety, topical propranolol/timolol was significantly better tolerated than oral propranolol (0.05, 0.001–0.66). Cluster analysis demonstrated oral propranolol was the most effective treatment for IHs, while topical propranolol/timolol showed high efficacy and the highest safety. Laser, intralesional propranolol or glucocorticoid, oral glucocorticoid, or captopril had significantly lower priority than oral propranolol or topical propranolol/timolol considering both efficacy and safety. The quality of evidence was rated as moderate or low in most comparisons. This network meta-analysis found topical beta-blockers had the potential to be the most preferable and beneficial option for IHs in consideration of both efficacy and safety. 相似文献
Journal of NeuroVirology - Listeria rhombencephalitis (L. rhombencephalitis) is an uncommon form of central nervous system infection caused by Listeria monocytogenes (LM). It often occurs to... 相似文献
In recent years, iris recognition has been widely used in various fields. As the first step of iris recognition, segmentation accuracy is of great significance to the final recognition. However, iris images exhibit a variety of noise in the real world, which leads to lower segmentation accuracy than the ideal case. To address this problem, this paper proposes an iris segmentation method using feature channel optimization for noisy images. The method for non-ideal environments with noise is more suitable for practical applications. We add dense blocks and dilated convolutional layers to the encoder so that the information gradient flow obtained by different layers can be reused, and the receptive field can be expanded. In the decoder, based on Jensen-Shannon (JS) divergence, we first recalculate the weight of the feature channels obtained from each layer, which enhances the useful information and suppresses the interference information in the noisy environments to boost the segmentation accuracy. The proposed architecture is validated in the CASIA v4.0 interval (CASIA) and IIT Delhi v1.0 datasets (IITD). For CASIA, the mean error rate is 0.78%, and the F-measure value is 98.21%. For IITD, the mean error rate is 0.97%, and the F-measure value is 97.87%. Experimental results show that the proposed method outperforms other state-of-art methods under noisy environments, such as Gaussian blur, Gaussian noise, and salt and pepper noise.
Neurotoxicity Research - The brain is one of organs vulnerable to aluminum insult. Aluminum toxicity is involved in neurobehavioral deficit, neuronal cell dysfunction, and death. The aim of this... 相似文献