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Background: Diabetic retinopathy is the most common microvascular complication of diabetes; however, early changes in retinal microvessels are difficult to detect clinically, and a patient's vision may have begun to deteriorate by the time a problem is identified. Optical coherence tomography angiography (OCTA) is an innovative tool for observing capillaries in vivo. The aim of this study was to analyze retinal vessel density and thickness changes in patients with diabetes.Methods: This was a retrospective, observational cross-sectional study. Between August 2018 and February 2019, we collected OCTA data from healthy participants and diabetics from the First Affiliated Hospital of Harbin Medical University. Analyzed their retinal vessel density and thickness changes.Results: A total of 97 diabetic patients with diabetes at different severity stages of diabetic retinopathy and 85 controls were involved in the experiment. Diabetic patients exhibited significantly lower retinal VD (particularly in the deep vascular complexes), thickening of the neurosensory retina, and thinning of the retinal pigment epithelium compared with controls. In the control group, nondiabetic retinopathy group and mild diabetic retinopathy group, superficial VD was significantly correlated with retinal thickness (r = 0.3886, P < 0.0001; r = 0.3276, P = 0.0019; r = 0.4614, P = 0.0024, respectively).Conclusions: Patients with diabetes exhibit ischemia of the retinal capillaries and morphologic changes in vivo prior to vision loss. Therefore, OCTA may be useful as a quantitative method for the early detection of diabetic retinopathy. [ABSTRACT FROM AUTHOR]

Background: The treatment of open tibial shaft fractures is challenging. External fixation (EF) is comparatively safe in treating these open injuries, meanwhile it has the advantages of easy application, minimal additional disruption, and convenient subsequent soft tissue repair. Nevertheless, its application is accompanied by a series of problems in alignment and bone healing. Therefore, limited internal fixation (LIF), such as cortical screws, has been used based on the external fixator for better therapeutic effect. The aim of this study is to compare the outcomes of EF combined with LIF and simple EF in the management of open tibial shaft fractures, evaluating the efficacy and safety of using the combined technique in treating such fractures.Methods: From January 2012 to December 2016, patients with open tibial shaft fractures treated with EF with or without LIF augmentation were identified. A total of 152 patients were included in the analysis, and there were 85 patients in the simple external fixation group and 67 patients in the EF-LIF group. General assessment indicators included the direct cost of hospitalization and the times of first surgery, full weight bearing, and complete union. Infections and complications in union or limb alignment were compared as primary outcomes. Additionally, the number of patients who changed the fixation system for various reasons were analysed.Results: Effective follow-up of all participants for statistical analysis was obtained. The follow-up time averaged 17.15 months (range: 12.00 to 24.00 months) in the EF group and 16.20 months (range: 12.00 to 19.00 months) in the EF-LIF group. Combined fixation provided shortened time to bear full weight and achieve complete bone union, while requiring additional first surgery time. No significant difference was found in infection rates or direct cost of hospitalization. Delayed union and non-union in the EF-LIF group were significantly decreased (20.9% versus 40.0, 1.5% versus 14.1%, p < 0.05). In limb alignment, patients with combined fixation exhibited reduced malreduction, loss of reduction, and malunion. In terms of secondary fixation, the EF-LIF group showed a markedly lower incidence (5.8% versus 34.1%, p < 0.001).Conclusion: Compared with simple EF, combined fixation is an effective and safe alternative for management of open tibial diaphyseal fractures. It provides superior initial reduction, better stability and decreases the risk of inferior alignment and delayed union without increasing the risk of infection. [ABSTRACT FROM AUTHOR]

The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of different approaches that detect chemicals in documents. We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines. Each of the chemical entity mentions was manually labeled according to its structure-associated chemical entity mention (SACEM) class: abbreviation, family, formula, identifier, multiple, systematic and trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study between annotators, obtaining a percentage agreement of 91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts) we provide not only the Gold Standard manual annotations, but also mentions automatically detected by the 26 teams that participated in the BioCreative IV CHEMDNER chemical mention recognition task. In addition, we release the CHEMDNER silver standard corpus of automatically extracted mentions from 17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus in the BioC format has been generated as well. We propose a standard for required minimum information about entity annotations for the construction of domain specific corpora on chemical and drug entities. The CHEMDNER corpus and annotation guidelines are available at: http://www.biocreative.org/resources/biocreative-iv/chemdner-corpus/.

Background: In order to improve information access on chemical compounds and drugs (chemical entities) described in text repositories, it is very crucial to be able to identify chemical entity mentions (CEMs) automatically within text. The CHEMDNER challenge in BioCreative IV was specially designed to promote the implementation of corresponding systems that are able to detect mentions of chemical compounds and drugs, which has two subtasks: CDI (Chemical Document Indexing) and CEM., Results: Our system processing pipeline consists of three major components: pre-processing (sentence detection, tokenization), recognition (CRF-based approach), and post-processing (rule-based approach and format conversion). In our post-challenge system, the cost parameter in CRF model was optimized by 10-fold cross validation with grid search, and word representations feature induced by Brown clustering method was introduced. For the CEM subtask, our official runs were ranked in top position by obtaining maximum 88.79% precision, 69.08% recall and 77.70% balanced F-measure, which were improved further to 88.43% precision, 76.48% recall and 82.02% balanced F-measure in our post-challenge system., Conclusions: In our system, instead of extracting a CEM as a whole, we regarded it as a sequence labeling problem. Though our current system has much room for improvement, our system is valuable in showing that the performance in term of balanced F-measure can be improved largely by utilizing large amounts of relatively inexpensive un-annotated PubMed abstracts and optimizing the cost parameter in CRF model. From our practice and lessons, if one directly utilizes some open-source natural language processing (NLP) toolkits, such as OpenNLP, Standford CoreNLP, false positive (FP) rate may be very high. It is better to develop some additional rules to minimize the FP rate if one does not want to re-train the related models. Our CEM recognition system is available at: http://www.SciTeMiner.org/XuShuo/Demo/CEM.

Background: Identification of phosphorylation sites by computational methods is becoming increasingly important because it reduces labor-intensive and costly experiments and can improve our understanding of the common properties and underlying mechanisms of protein phosphorylation., Methods: A multitask learning framework for learning four kinase families simultaneously, instead of studying each kinase family of phosphorylation sites separately, is presented in the study. The framework includes two multitask classification methods: the Multi-Task Least Squares Support Vector Machines (MTLS-SVMs) and the Multi-Task Feature Selection (MT-Feat3)., Results: Using the multitask learning framework, we successfully identify 18 common features shared by four kinase families of phosphorylation sites. The reliability of selected features is demonstrated by the consistent performance in two multi-task learning methods., Conclusions: The selected features can be used to build efficient multitask classifiers with good performance, suggesting they are important to protein phosphorylation across 4 kinase families.