No recommendations can be made from this review, regarding overall low quality of evidence as a result of high risk of bias, low sample size in most of the studies, and notable heterogeneity in type of intervention, outcome measurement, and duration of treatment. Therefore, future high-quality RCT studies with higher sample sizes and more homogeneity are strongly recommended to provide high-quality evidence and make applicable recommendations for prevention and treatment of SCI-related bone loss.

MVC is a common cause of SCI in children; therefore, paying attention to risk factors and modes of prevention is important. As MVC-related SCI can lead to permanent disability, prevention and education play an important role in decreasing childrens' morbidity and mortality. Making behavior, roads and vehicles safer can significantly reduce MVC-related SCI in children.

Purpose: To evaluate the association of five different polymorphisms from a genomewide- associated study with susceptibility to glaucoma in the northeast Iranian population. Methods: Hundred and thirty patients with primary angle closure glaucoma (PACG) and 130 healthy controls were genotyped for the polymorphic regions with the aid of tetraamplification refractory mutation system-polymerase chain reaction. The association of these variants with the disease susceptibility was measured statistically with the logistic regression method. Results: Hundred and thirty patients with PACG (53 males, 77 females) with a mean age of 64.5 ± 6.2 years and 130 healthy control subjects (51 males, 79 females) with a mean age of 64.0 ± 5.7 years were selected for evaluation. There was a significant association between rs3816415 (P = 0.005), rs736893 (P < 0.001), rs7494379 (P < 0.001), and rs1258267 (P = 0.02) with PACG susceptibility. This association could not be shown for rs3739821. Conclusion: It was revealed that studied variants in GLIS3, EPDR1, FERMT2, and CHAT genes can contribute to the incidence of PACG. Additional studies in other populations are needed to evaluate DPM2-FAM102A.

PurposeTo find a possible association between patients’ cooperation, perceived pain, and ocular dominance in patients who undergo photorefractive keratectomy (PRK).MethodsOne hundred-one eligible candidates for PRK refractive surgery were recruited. Preoperative exams were performed for all patients, and the dominant eye was specified. The surgeon was unaware about which eye was dominant. After surgery, the surgeon completed a cooperation score form for each patient. Ocular cyclotorsion, cooperation, and perceived pain scores were compared between the first-second eye surgeries and between dominant-non-dominant eyes surgeries.ResultsThe dominant eye was the right eye in 68 patients and the left eye in 33 patients. First, eye surgery was performed on the dominant eye in 56 patients and on the non-dominant eye in 45 patients. Cooperation score and perceived pain were not significantly different between the first and second eye surgeries (P = 0.902 and P = 0.223, respectively), but cyclotorsion was more in the second eye (P = 0.031). Cooperation score, pain score, and cyclotorsion were not significantly different between dominant and non-dominant eye surgeries (P = 0.538, P = 0.581, and P = 0.193, respectively). Also, there was no correlation between cooperation score and duration of the surgery for the first or second eye (P = 0.12 and P = 0.78).ConclusionDuring PRK surgery, the patients’ cooperation and perceived pain did not seem to be associated with eye laterality or dominancy.

IntroductionCan we apply graph representation learning algorithms to identify autism spectrum disorder (ASD) patients within a large brain imaging dataset? ASD is mainly identified by brain functional connectivity patterns. Attempts to unveil the common neural patterns emerged in ASD are the essence of ASD classification. We claim that graph representation learning methods can appropriately extract the connectivity patterns of the brain, in such a way that the method can be generalized to every recording condition, and phenotypical information of subjects. These methods can capture the whole structure of the brain, both local and global properties.MethodsThe investigation is done for the worldwide brain imaging multi-site database known as ABIDE I and II (Autism Brain Imaging Data Exchange). Among different graph representation techniques, we used AWE, Node2vec, Struct2vec, multi node2vec, and Graph2Img. The best approach was Graph2Img, in which after extracting the feature vectors representative of the brain nodes, the PCA algorithm is applied to the matrix of feature vectors. The classifier adapted to the features embedded in graphs is an LeNet deep neural network.Results and discussionAlthough we could not outperform the previous accuracy of 10-fold cross-validation in the identification of ASD versus control patients in this dataset, for leave-one-site-out cross-validation, we could obtain better results (our accuracy: 80%). The result is that graph embedding methods can prepare the connectivity matrix more suitable for applying to a deep network.