BackgroundDepression and anxiety result in psychological distress, which can further affect mental status and quality of life in stroke patients. Exploring the associations between positive psychological variables and symptoms of psychological distress following stroke is of great significance for further psychological interventions.MethodsA total of 710 stroke patients from the five largest cities in Liaoning Province in China were enrolled into the present study in July 2014. All patients independently completed the questionnaires with respect to psychological distress and positive psychological variables. Depressive and anxiety symptoms were evaluated using Center for Epidemiologic Studies Depression Scale (CES-D) and Self-Rating Anxiety Scale, respectively. Positive psychological variables were evaluated using Perceived Social Support Scale, Adult Hope Scale (AHS), General Perceived Self-Efficacy Scale and Resilience Scale-14 (RS-14). Activities of Daily Living (ADL) was measured using Barthel Index. Factors associated with psychological variables and depressive and anxiety symptoms were identified using t-test, ANOVA, correlation and hierarchical linear regression analysis.ResultsDepressive and anxiety symptoms were present in 600 of 710 (84.51%) and 537 of 710 (75.63%) stroke patients enrolled, respectively.Social support (β = − 0.111, p < 0.001) and hope (β = − 0.120, p < 0.001) were negatively associated with both depressive and anxiety symptoms.Resilience (β = − 0.179, p < 0.001) was negatively associated with depressive symptoms.Self-efficacy (β = − 0.135, p < 0.001) was negatively associated with anxiety symptoms. Hierarchical regression analyses indicated that ADL accounted for 10.0 and 6.0% of the variance of depressive and anxiety symptoms, respectively.Social support, resilience, self-efficacy and hope as a whole accounted for 7.5 and 5.3% of the variance of depressive and anxiety symptoms.ConclusionsThe high frequency of depressive and anxiety symptoms among Chinese stroke survivors should receive attentions from all stakeholders. Findings suggested that intervention strategies on ADL, social support, hope, resilience and self-efficacy could be developed to improve psychosocial outcomes for stroke survivors.

BACKGROUND Pulmonary tuberculosis (TB) and lung cancer (LC) are common diseases with a high incidence and similar symptoms, which may be misdiagnosed by radiologists, thus delaying the best treatment opportunity for patients. AIM To develop and validate radiomics methods for distinguishing pulmonary TB from LC based on computed tomography (CT) images. METHODS We enrolled 478 patients (January 2012 to October 2018), who underwent preoperative CT screening. Radiomics features were extracted and selected from the CT data to establish a logistic regression model. A radiomics nomogram model was constructed, with the receiver operating characteristic, decision and calibration curves plotted to evaluate the discriminative performance. RESULTS Radiomics features extracted from lesions with 4 mm radial dilation distances outside the lesion showed the best discriminative performance. The radiomics nomogram model exhibited good discrimination, with an area under the curve of 0.914 (sensitivity = 0.890, specificity = 0.796) in the training cohort, and 0.900 (sensitivity = 0.788, specificity = 0.907) in the validation cohort. The decision curve analysis revealed that the constructed nomogram had clinical usefulness. CONCLUSION These proposed radiomic methods can be used as a noninvasive tool for differentiation of TB and LC based on preoperative CT data.

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Answer generation is one of the most important tasks in natural language processing, and deep learning-based methods have shown their strength over traditional machine learning based methods. However, most previous deep learning-based answer generation models were built on traditional recurrent neural networks or convolutional neural networks. The former model cannot well exploit contextual correlation preserved in paragraphs due to their inherent computation complexity. For the latter, since the size of the convolutional kernel is fixed, the model cannot extract complete semantic information features. In order to alleviate this problem, based on multi-layer Transformer aggregation coder, we propose an end-toend answer generation model (AG-MTA). AG-MTA consists of a multi-layer attention Transformer unit and a multi-layer attention Transformer aggregation encoder (MTA). It can focus on information representation at different positions and aggregate nodes at same layer to combine the context information. Thereby, it fuses semantic information from base layer to top layer, enhancing the information representation of the encoder. Furthermore, based on trigonometric function, a novel position encoding method is also proposed. Experiments are conducted on public datasets SQuAD. AG-MTA reaches the state-of-the-art performance, EM score achieves 71.1 and F1 score achieves 80.3. INDEX TERMS Question answering system, natural language processing, self-attention mechanism, transformer coding structure.