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.

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Purpose High residual stress caused by the high temperature gradient brings undesired effects such as shrinkage and cracking in selective laser melting (SLM). The purpose of this study is to predict the residual stress distribution and the effect of process parameters on the residual stress of selective laser melted (SLMed) Inconel 718 thin-walled part. Design/methodology/approach A three-dimensional (3D) indirect sequentially coupled thermal–mechanical finite element model was developed to predict the residual stress distribution of SLMed Inconel 718 thin-walled part. The material properties dependent on temperature were taken into account in both thermal and mechanical analyses, and the thermal elastic–plastic behavior of the material was also considered. Findings The residual stress changes from compressive stress to tensile stress along the deposition direction, and the residual stress increases with the deposition height. The maximum stress occurs at both ends of the interface between the part and substrate, while the second largest stress occurs near the top center of the part. The residual stress increases with the laser power, with the maximum equivalent stress increasing by 21.79 per cent as the laser power increases from 250 to 450 W. The residual stress decreases with an increase in scan speed with a reduction in the maximum equivalent stress of 13.67 per cent, as the scan speed increases from 500 to 1,000 mm/s. The residual stress decreases with an increase in layer thickness, and the maximum equivalent stress reduces by 33.12 per cent as the layer thickness increases from 20 to 60µm. Originality/value The residual stress distribution and effect of process parameters on the residual stress of SLMed Inconel 718 thin-walled part are investigated in detail. This study provides a better understanding of the residual stress in SLM and constructive guidance for process parameters optimization.

Mg alloys have attracted extensive attention in the biomedical fields owing to their great biocompatibility, suitable mechanical properties, and biodegradability, etc. However, the fast degradation rate restricts the application of Mg alloys. Thus, the surface treatment of Mg alloys is considered as one of the most effective ways to enhance the corrosion resistance of Mg alloys. Nevertheless, simple processing to improve the corrosion resistance can no longer meet the growing biofunctional clinical requirements. Therefore, functionalized processing has become one of the key development directions for surface treatment in the future, such as functionalized Mg alloys with antibacterial property and hydrophobicity. There are few papers that review the functionalized processing of surface treatment. This review summarized and compared the major advances of the surface treatment (anticorrosion processing and functionalized processing) of Mg alloys. Then, some potential research suggestions are proposed, which may provide a reference for the development of Mg alloys.

Purpose To non‐invasively evaluate the Ki‐67 level in digital breast tomosynthesis (DBT) images of breast cancer (BC) patients based on subregional radiomics. Methods A total of 266 patients who underwent DBT scans were consecutively enrolled at two centers, between September 2017 and September 2021. The whole tumor region was partitioned into various intratumoral subregions, based on individual‐ and population‐level clustering. Handcrafted radiomics and deep learning‐based features were extracted from the subregions and from the whole tumor region, and were selected by least absolute shrinkage and selection operator (LASSO) regression, yielding radiomics signatures (RSs). The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the developed RSs. Results Each breast tumor region was partitioned into an inner subregion (S1) and a marginal subregion (S2). The RSs derived from S1 always generated higher AUCs compared with those from S2 or from the whole tumor region (W), for the external validation cohort (AUCs, S1 vs. W, handcrafted RSs: 0.583 [95% CI, 0.429–0.727] vs. 0.559 [95% CI, 0.405–0.705], p‐value: 0.920; deep RSs: 0.670 [95% CI, 0.516–0.802] vs. 0.551 [95% CI, 0.397–0.698], p‐value: 0.776). The fusion RSs, combining handcrafted and deep learning‐based features derived from S1, yielded the highest AUCs of 0.820 (95% CI, 0.714–0.900) and 0.792 (95% CI, 0.647–0.897) for the internal and external validation cohorts, respectively. Conclusions The subregional radiomics approach can accurately predict the Ki‐67 level based on DBT data; thus, it may be used as a potential non‐invasive tool for preoperative treatment planning in BC.